DE112018004336T5 - Building management system with intelligent entities, time series, generation of a nested stream, cloud control and ID management and security services - Google Patents

Abstract

One or more non-volatile computer readable storage media contain program instructions that, when executed, cause one or more processors to: receive first raw data, including one or more data points, generated by a first object of several objects associated with one or more buildings; Generating a first input time series according to the one or more data points; Access a database of interconnected intelligent entities, the intelligent entities including object entities each representing the plurality of objects and data entities representing data stored, the intelligent entities being connected by relational objects, the relationships between the intelligent entities specify; Identifying a first object entity representing the first object from a first identifier in the first input time series; Identifying a first data entity from a first relational object that indicates a relationship between the first object entity and the first data entity; and storing the first input time series in the first data entity.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the utility and priority of the preliminary U.S. Patent Application No. 62 / 564,247 , filed on September 27, 2017, the provisional U.S. Patent Application No. 62 / 611,974 , filed on December 29, 2017, the provisional U.S. Patent Application No. 62 / 611,984 , filed on December 29, 2017, the provisional U.S. Patent Application No. 62 / 612,228 , filed on December 29, 2017, the provisional U.S. Patent Application No. 62 / 612,167 , submitted on December 29- 2017 and the preliminary U.S. Patent Application No. 62 / 580,867 , filed on November 2, 2017. The entire disclosure of each of these patent applications is hereby incorporated by reference.

BACKGROUND

The present disclosure relates generally to the field of a building management platform that is communicatively connected to one or more building management systems in an intelligent building environment. A building management system (BMS) is generally a system of devices configured to control, monitor, and manage equipment in or around a building or building area. For example, a BMS may include an HVAC system, a security system, a lighting system, a fire alarm system, or any other system capable of managing building functions or devices, or any combination thereof.

A BMS can collect data from objects that are assigned to a building, e.g. B. other BMSs, building subsystems, devices, sensors and other types of building equipment. Building management platforms are used to register and manage the objects, collect and analyze data generated by the objects, and provide recommendations or results based on the data collected. With the increasing number of buildings moving to an intelligent building environment, the amount of data produced and collected has increased exponentially. Accordingly, an effective analysis of a variety of data collected is desired.

SUMMARY

Smart entity

According to one aspect of an exemplary embodiment, a building management cloud computing system for managing data relating to a plurality of objects which are assigned to one or more buildings, the plurality of objects each representing an area, a person, a building subsystem and / or a device, connected to one or more electronic communication networks, the following: one or more processors; and one or more computer readable storage media communicatively coupled to the one or more processors and having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: create a database of each other connected intelligent entities, the intelligent entities including object entities each representing the plurality of objects and data entities representing data generated by the objects, the intelligent entities connected by relational objects indicating the relationships between the object entities and the data entities; Receiving data from a first object of the plurality of objects; Determining a second object from a relational object for the first object based on the received data; and modifying a data entity associated with an object entity of the second object within the intelligent entity database based on the received data for the first object.

In one embodiment, the first object can be a sensor and the second object can be an area within the building in which the sensor is located.

In one embodiment, the sensor may be a temperature sensor and the data entity associated with the object entity representing the area may be configured to store an ambient temperature value of the area based on the data received from the temperature sensor.

In one embodiment, the instructions may further cause the one or more processors to create a shadow entity to store historical values of the data entity associated with the object entity representing the area.

In one embodiment, the instructions may further cause the one or more processors to calculate an average ambient temperature value from the historical values stored in the shadow entity.

In one embodiment, the instructions may further cause the one or more processors to calculate an abnormal ambient temperature value from the historical values stored in the shadow entity.

In one embodiment, each of the object entities may include a static attribute for identifying the object entity, a dynamic attribute for storing a data point associated with the object entity that changes over time, and a behavior attribute that provides an expected response from the object entity in response defined on an input.

In one embodiment, the data entity associated with the object entity of the second object may be configured to store the dynamic attribute of the object entity.

In one embodiment, a relational object can semantically define the connection between the data entity and the object entity of the second object.

In one embodiment, modifying the data entity associated with the object entity of the second object may include: identifying a dynamic attribute in the data associated with the object entity of the second object; Determining a relational object that connects the data entity to the object entity of the second object; and storing a value of the data corresponding to the dynamic attribute in the data entity.

In one embodiment, the first object may be an access control device and the second object may be a person associated with the building in which the access control device is located.

In one embodiment, the data entity associated with the object entity representing the person may be configured to store a location attribute of the person based on the data received from the access control device.

In one embodiment, the instructions may further cause the one or more processors to generate a shadow entity to store historical values of the data received from the access control device and calculate an average time of arrival of the person from the historical values stored in the shadow entity.

According to one aspect of an exemplary embodiment, a method for managing data relating to a plurality of objects which are assigned to one or more buildings, the plurality of objects each representing an area, a person, a building subsystem and / or a device which is associated with an or connected to multiple electronic communication networks, the following: creating, by one or more processors, a database of interconnected intelligent entities, the intelligent entities including object entities each representing the multiple objects and data entities representing data generated by the objects, wherein the intelligent entities are linked by relational objects that indicate relationships between the object entities and the data entities; Receiving, by the one or more processors, data from a first object of the plurality of objects; Determining, by the one or more processors, a second object from a relational object for the first object based on the received data; and modifying, by the one or more processors, a data entity associated with an object entity of the second object within the intelligent entity database based on the received data for the first object.

In one embodiment, the first object can be a sensor and the second object can be an area within the building in which the sensor is located.

In one embodiment, the sensor may be a temperature sensor and the data entity associated with the object entity representing the area may be configured to store an ambient temperature value of the area based on the data received from the temperature sensor.

In one embodiment, the method may further generate, by the one or more processors, a shadow entity to store historical values of the data entity associated with the object entity representing the area.

In one embodiment, the method may further calculate, by the one or more processors, an average ambient temperature value from the historical values stored in the shadow entity.

In one embodiment, the method may further calculate, by the one or more processors, an abnormal ambient temperature value from the historical values stored in the shadow entity.

In one embodiment, each of the object entities may include a static attribute for identifying the object entity, a dynamic attribute for storing a data point associated with the object entity that changes over time, and a behavior attribute that provides an expected response from the object entity in response defined on an input.

In one embodiment, the data entity associated with the object entity of the second object may be configured to store the dynamic attribute of the object entity.

In one embodiment, a relational object can semantically define the connection between the data entity and the object entity of the second object.

In one embodiment, modifying the data entity associated with the object entity of the second object may include: identifying, by the one or more processors, a dynamic attribute in the data associated with the object entity of the second object; Determining, by the one or more processors, a relational object that connects the data entity to the object entity of the second object; and storing, by the one or more processors, a value of the data corresponding to the dynamic attribute in the data entity.

In one embodiment, the first object may be an access control device and the second object may be a person associated with the building in which the access control device is located.

In one embodiment, the data entity associated with the object entity representing the person may be configured to store a location attribute of the person based on the data received from the access control device.

In one embodiment, the method may further include: generating, by the one or more processors, a shadow entity to store historical values of the data received from the access control device, and calculating, by the one or more processors, an average arrival time of the Person from the historical values stored in the shadow entity.

In one aspect of an embodiment, a non-transitory computer-readable medium is provided that contains program instructions to cause a computer to perform the method of: creating a database of interconnected intelligent entities, the intelligent entities each representing multiple objects are assigned to one or more buildings, and wherein the plurality of objects each represent an area, a person, a building subsystem and / or a device, and contain data entities which represent data generated by the objects, the intelligent entities being connected to one another by relational objects that display relationships between the object entities and the data entities; Receiving data from a first object of the plurality of objects; Determining a second object from a relational object for the first object based on the received data; and modifying a data entity associated with an object entity of the second object within the intelligent entity database based on the received data for the first object.

In one embodiment, the first object can be a sensor and the second object can be an area within the building in which the sensor is located.

In one embodiment, the sensor may be a temperature sensor and the data entity associated with the object entity representing the area may be configured to store an ambient temperature value of the area based on the data received from the temperature sensor.

In one embodiment, the program instructions may further cause the one or more processors to perform operations that include creating a shadow entity to store historical values of the data entity associated with the object entity that represents the region.

In one embodiment, the program instructions may further cause the one or more processors to perform operations that include computing an average ambient temperature value from the historical values stored in the shadow entity.

In one embodiment, the program instructions may further cause the one or more processors to perform operations that include computing an abnormal ambient temperature value from the historical values stored in the shadow entity.

In one embodiment, each of the object entities may include a static attribute for identifying the object entity, a dynamic attribute for storing a data point associated with the object entity that changes over time, and a behavior attribute that provides an expected response from the object entity in response defined on an input.

In one embodiment, the data entity associated with the object entity of the second object may be configured to store the dynamic attribute of the object entity.

In one embodiment, a relational object can semantically define the connection between the data entity and the object entity of the second object.

In one embodiment, modifying the data entity associated with the object entity of the second object may include: identifying a dynamic attribute in the data associated with the object entity of the second object; Determining a relational object that connects the data entity to the object entity of the second object; and storing a value of the data corresponding to the dynamic attribute in the data entity.

In one embodiment, the first object may be an access control device and the second object may be a person associated with the building in which the access control device is located.

In one embodiment, the data entity associated with the object entity representing the person may be configured to store a location attribute of the person based on the data received from the access control device.

In one embodiment, the program instructions may further cause the one or more processors to perform operations, creating a shadow entity to store historical values of the data received from the access control device, and computing an average time of arrival of the person from the historical ones stored in the shadow entity Include values.

Another implementation of the present disclosure is a building management cloud computing system for managing data related to multiple objects associated with one or more buildings, the multiple objects each representing an area, person, building subsystem, and / or device connected to one or more electronic communication networks. The system includes one or more processors and one or more computer readable storage media communicatively coupled to the one or more processors with instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to have one Create a database of interconnected intelligent entities. The intelligent entities include object entities, each the multiple Represent objects, and data entities representing data generated by the objects, the intelligent entities being connected by relational objects that indicate the relationships between the object entities and the data entities. The instructions cause the one or more processors to: receive new data from a first object of the plurality of objects; Determining whether the database includes a first object entity that represents the first object; in response to a determination that the database includes the first object entity, determining whether the database includes a first data entity that represents data received from the first object; and in response to a determination that the database includes the first data entity, determining an attribute of the first data entity using the new data received from the first object.

In one embodiment, the instructions, in response to a determination that the database does not contain the first data entity, further cause the one or more processors to create the first data entity, create a first relational object that has a relationship between the first object entity and the first Data entity defined, and generating an attribute of the first data entity and generating a value for the attribute of the first data entity using the new data received from the first object.

In one embodiment, the instructions, in response to a determination that the database does not contain the first object entity, further cause the one or more processors to create the first object entity, create the first data entity, create a first relational object that has a relationship between the first object entity and the first data entity, and generating an attribute of the first data entity and generating a value for the attribute of the first data entity using the new data received from the first object.

In one embodiment, determining whether the database contains the first object entity includes reading one or more static attributes of the object entities and determining whether any of the static attributes identify the first object.

In one embodiment, determining whether the database contains the first data entity includes reading a relational attribute of the first object entity and determining whether the relational attribute identifies the first data entity.

In one embodiment, determining whether the database contains the first data entity includes identifying a first relational object that defines a relationship between the first data entity and one or more of the data entities, and determining whether the first relational object identifies the first data entity.

In one embodiment, the first data entity includes a static attribute that identifies the first data entity and a dynamic attribute that includes one or more dynamic values of the first data entity. Updating the attribute of the first data entity may include updating the one or more dynamic values of the dynamic attribute using the new data received from the first object.

In one embodiment, one or more of the object entities include a static attribute for identifying the object entity, a dynamic attribute for storing a data point associated with the object entity that changes over time, and a behavioral attribute that is an expected response of the object entity as Response to an input defined.

In one embodiment, the first object can be a temperature sensor located in the building.

In one embodiment, the instructions may further cause the one or more processors to create a shadow entity to store historical values of the first data entity using historical data received from the temperature sensor.

In one embodiment, the instructions may further cause the one or more processors to calculate an average temperature value from the historical values stored in the shadow entity.

In one embodiment, the instructions may further cause the one or more processors to calculate an abnormal temperature value from the historical values stored in the shadow entity.

In one embodiment, the first object may be an access control device located in the building, and the first data entity may be associated with an access key card object related to a person object.

In one embodiment, the instructions may further cause the one or more processors to update a location attribute of the person object according to the new data received from the access control device.

Another implementation of the present disclosure is a method of managing data related to multiple objects associated with one or more buildings, the multiple objects each representing an area, person, building subsystem, and / or device associated with one or several electronic communication networks are connected. The method involves creating a database of interconnected intelligent entities. The intelligent entities include object entities each representing the plurality of objects and data entities representing data generated by the objects, the intelligent entities being connected by relational objects that indicate the relationships between the object entities and the data entities. The method includes: receiving new data from a first object of the plurality of objects; Determining whether the database includes a first object entity that represents the first object; in response to a determination that the database includes the first object entity, determining whether the database includes a first data entity that represents data received from the first object; and in response to a determination that the database includes the first data entity, determining an attribute of the first data entity using the new data received from the first object.

In one embodiment, in response to a determination that the database does not contain the first data entity, the method includes creating the first data entity, creating a first relational object that defines a relationship between the first object entity and the first data entity, and generating an attribute of the first data entity and generating a value for the attribute of the first data entity using the new data received from the first object.

In one embodiment, in response to a determination that the database does not contain the first object entity, the method includes creating the first object entity, creating the first data entity, creating a first relational object that defines a relationship between the first object entity and the first data entity, and generating an attribute of the first data entity and generating a value for the attribute of the first data entity using the new data received from the first object.

In one embodiment, determining whether the database contains the first object entity includes reading one or more static attributes of the object entities and determining whether any of the static attributes identify the first object.

In one embodiment, determining whether the database contains the first data entity includes reading a relational attribute of the first object entity and determining whether the relational attribute identifies the first data entity.

In one embodiment, determining whether the database contains the first data entity includes identifying a first relational object that defines a relationship between the first data entity and one or more of the data entities, and determining whether the first relational object identifies the first data entity.

In one embodiment, the first data entity includes a static attribute that identifies the first data entity and a dynamic attribute that includes one or more dynamic values of the first data entity. Updating the attribute of the first data entity may include updating the one or more dynamic values of the dynamic attribute using the new data received from the first object.

In one embodiment, one or more of the object entities include a static attribute for identifying the object entity, a dynamic attribute for storing a data point associated with the object entity that changes over time, and a behavioral attribute that is an expected response of the object entity as Response to an input defined.

In one embodiment, the first object can be a temperature sensor located in the building.

In one embodiment, the method may further include generating a shadow entity to store historical values of the first data entity using historical data received from the temperature sensor.

In one embodiment, the method may further include computing an average temperature value from the historical values stored in the shadow entity.

In one embodiment, the method may further include computing an abnormal temperature value from the historical values stored in the shadow entity.

In one embodiment, the first object may be an access control device located in the building, and the first data entity may be associated with an access key card object related to a person object.

In one embodiment, the method may further include updating a location attribute of the person object according to the new data received from the access control device.

Another implementation of the present disclosure is one or more non-volatile computer-readable media that contain program instructions that, when executed by one or more processors, cause the one or more processors to perform operations that create a database of interconnected smarter Include entities. The intelligent entities include object entities, each representing multiple objects associated with one or more buildings, the multiple objects each representing an area, a person, a building subsystem, and / or a device, and data entities created by the objects Represent data with the intelligent entities connected by relational objects that indicate relationships between the object entities and the data entities. The operations include: receiving new data from a first object of the plurality of objects; Determining whether the database includes a first object entity that represents the first object; in response to a determination that the database includes the first object entity, determining whether the database includes a first data entity that represents data received from the first object; and in response to a determination that the database includes the first data entity, determining an attribute of the first data entity using the new data received from the first object.

In one embodiment, the instructions, in response to a determination that the database does not contain the first data entity, further cause the one or more processors to create the first data entity, create a first relational object that has a relationship between the first object entity and the first Data entity defined, and generating an attribute of the first data entity and generating a value for the attribute of the first data entity using the new data received from the first object.

In one embodiment, the instructions, in response to a determination that the database does not contain the first object entity, further cause the one or more processors to create the first object entity, create the first data entity, create a first relational object that has a relationship between the first object entity and the first data entity, and generating an attribute of the first data entity and generating a value for the attribute of the first data entity using the new data received from the first object.

In one embodiment, determining whether the database contains the first object entity includes reading one or more static attributes of the object entities and determining whether any of the static attributes identify the first object.

In one embodiment, determining whether the database contains the first data entity includes reading a relational attribute of the first object entity and determining whether the relational attribute identifies the first data entity.

In one embodiment, determining whether the database contains the first data entity includes identifying a first relational object that defines a relationship between the first data entity and one or more of the data entities, and determining whether the first relational object identifies the first data entity.

In one embodiment, the first data entity includes a static attribute that identifies the first data entity and a dynamic attribute that includes one or more dynamic values of the first data entity. Updating the attribute of the first data entity may include updating the one or more dynamic values of the dynamic attribute using the new data received from the first object.

In one embodiment, one or more of the object entities include a static attribute for identifying the object entity, a dynamic attribute for storing a data point associated with the object entity that changes over time, and a behavioral attribute that is an expected response of the object entity as Response to an input defined.

In one embodiment, the first object can be a temperature sensor located in the building.

In one embodiment, the instructions may further cause the one or more processors to create a shadow entity to store historical values of the first data entity using historical data received from the temperature sensor.

In one embodiment, the instructions may further cause the one or more processors to calculate an average temperature value from the historical values stored in the shadow entity.

In one embodiment, the instructions may further cause the one or more processors to calculate an abnormal temperature value from the historical values stored in the shadow entity.

In one embodiment, the first object may be an access control device located in the building, and the first data entity may be associated with an access key card object related to a person object.

In one embodiment, the instructions may further cause the one or more processors to update a location attribute of the person object according to the new data received from the access control device.

Smart entity with time series

An implementation of the present disclosure is a building management cloud computing system for managing time series data related to multiple objects associated with one or more buildings. The multiple objects each represent an area, a person, a building subsystem and / or a device. The multiple objects are connected to one or more electronic communication networks. The system includes one or more processors and one or more computer readable storage media. The one or more processors are communicatively connected to a database of interconnected intelligent entities, the intelligent entities including object entities each representing the multiple objects and data entities representing stored data, the intelligent entities being interconnected by relational objects that specify relationships between the object entities and the data entities. The one or more computer-readable storage media are communicatively connected to the one or more processors and have instructions stored thereon. When executed by the one or more processors, the instructions cause the one or more processors to receive first raw data from a first object of the plurality of objects. The first raw data contain one or more first data points that are generated by the first object. Get the instructions done when executed the one or more processors do the following: generate a first input time series according to the one or more data points, identify a first object entity representing the first object from a first identifier in the first input time series, identify a first data entity from a first relational object indicating a relationship between the first object entity and the first data entity and storing the first input time series in the first data entity.

In some embodiments, the relational objects can semantically define the relationships between the object entities and the data entities.

In some embodiments, one or more of the object entities may include a static attribute to identify the object entity, a dynamic attribute to store data associated with the object entity that changes over time, and a behavior attribute that is an expected response from the object entity defined in response to an input.

In some embodiments, the first input time series may correspond to the dynamic attribute of the first object entity.

In some embodiments, at least one of the first data points in the first input time series may be stored in the dynamic attribute of the first object entity.

In some embodiments, the input time series may include the first identifier, a timestamp that indicates a generation time of the one or more first data points, and a value of the one or more first data points.

In some embodiments, the instructions may further cause the one or more processors to: identify a second data entity. representing a second object from a second relational object indicating a relationship between the first object entity and the second object entity, and identifying a second data entity from a third relational object indicating a relationship between the second object entity and the second data entity. The second data entity can store a second input time series that corresponds to one or more second data points associated with the second object.

In some embodiments, the instructions may further cause the one or more processors to: identify one or more processing workflows that define one or more processing operations to generate derived time series using the first and second input time series, execute the one or more processing workflows to generate the derived time series, identifying a third data entity from a fourth relational object indicating a relationship between the second object entity and the third data entity, and storing the derived input time series in the third data entity.

In some embodiments, the first object may be an access control device and the second object may be an access key card associated with a person.

In some embodiments, the derived time series may include one or more virtual data points calculated according to the first and second input time series.

In some embodiments, the one or more virtual data points may include one or more location attributes of the person.

In some embodiments, the first object may be a temperature sensor and the second object may be a variable air volume unit (VAV).

In some embodiments, the derived time series may include an abnormal temperature attribute that corresponds to an area in which the temperature sensor is located and for which the VAV is configured to be supplied.

In some embodiments, the first and / or second object can be a temperature sensor.

In some embodiments, the instructions may further include the one or more processors to periodically receive temperature measurements from the temperature sensor and Have at least the derived time series update in the third data entity every time a new temperature measurement is received by the temperature sensor.

In some embodiments, the derived time series may include an average ambient temperature of an area in which the temperature sensor is located.

In some embodiments, the instructions may further cause the one or more processors to create a shadow entity to store historical values of the first raw data.

In some embodiments, the instructions may further cause the one or more processors to compute a virtual data point from the historical values and generate a fourth data entity to store the virtual data point.

Another implementation of the present disclosure is a method for managing time series data related to multiple objects associated with one or more buildings. The multiple objects each represent an area, a person, a building system and / or a device. The multiple objects are connected to one or more electronic communication networks. The method includes receiving first raw data from a first object of the plurality of objects. The first raw data contain one or more first data points that are generated by the first object. The method includes generating a first input time series according to the one or more data points and accessing a database of interconnected intelligent entities. The intelligent entities include object entities each representing the plurality of objects and data entities representing stored data, the intelligent entities being connected by relational objects that indicate relationships between the object entities and the data entities. The method includes: identifying a first object entity that represents the first object from a first identifier in the first input time series, identifying a first data entity from a first relational object that indicates a relationship between the first object entity and the first data entity, and storing the first input time series in the first data entity.

In some embodiments, the relational objects can semantically define the relationships between the object entities and the data entities.

In some embodiments, one or more of the object entities may include a static attribute to identify the object entity, a dynamic attribute to store data associated with the object entity that changes over time, and a behavior attribute that is an expected response from the object entity defined in response to an input.

In some embodiments, the first input time series may correspond to the dynamic attribute of the first object entity.

In some embodiments, at least one of the first data points in the first input time series may be stored in the dynamic attribute of the first object entity.

In some embodiments, the input time series may include the first identifier, a timestamp that indicates a generation time of the one or more first data points, and a value of the one or more first data points.

In some embodiments, the method may further include: identifying a second data entity. representing a second object from a second relational object indicating a relationship between the first object entity and the second object entity, and identifying a second data entity from a third relational object indicating a relationship between the second object entity and the second data entity. The second data entity can store a second input time series that corresponds to one or more second data points associated with the second object.

In some embodiments, the method may further include: identifying one or more processing workflows that define one or more processing operations to generate derived time series using the first and second input time series, executing the one or more processing workflows to generate the derived time series , Identify a third data entity from a fourth relational object that has a relationship between the second Object entity and the third data entity, and storing the derived input time series in the third data entity.

In some embodiments, the first object may be an access control device and the second object may be an access key card associated with a person.

In some embodiments, the derived time series may include one or more virtual data points calculated according to the first and second input time series.

In some embodiments, the one or more virtual data points may include one or more location attributes of the person.

In some embodiments, the first object may be a temperature sensor and the second object may be a variable air volume unit (VAV).

In some embodiments, the derived time series may include an abnormal temperature attribute that corresponds to an area in which the temperature sensor is located and for which the VAV is configured to be supplied.

In some embodiments, the first and / or second object can be a temperature sensor.

In some embodiments, the method may further include periodically receiving temperature measurements from the temperature sensor and updating at least the derived time series in the third data entity each time a new temperature measurement is received from the temperature sensor.

In some embodiments, the derived time series may include an average ambient temperature of an area in which the temperature sensor is located.

In some embodiments, the method may further include creating a shadow entity to store historical values of the first raw data.

In some embodiments, the method may further include computing a virtual data point from the historical values and generating a fourth data entity to store the virtual data point.

Another implementation of the present disclosure is one or more non-volatile computer readable media that contain program instructions. When executed by one or more processors, the instructions cause the one or more processors to perform operations that include receiving first raw data from a first object of the plurality of objects associated with the one or more buildings. The multiple objects each represent an area, a person, a building subsystem and / or a device. The first raw data contain one or more first data points that are generated by the first object. The method includes generating a first input time series according to the one or more data points and accessing a database of interconnected intelligent entities. The intelligent entities include object entities each representing the multiple objects and data entities representing stored data, the intelligent entities being connected by relational objects that indicate relationships between the object entities and the data entities. The method includes: identifying a first object entity representing the first object from a first identifier in the first input time series, identifying a first data entity from a first relational object indicating a relationship between the first object entity and the first data entity, and storing the first input time series in the first data entity.

In some embodiments, the relational objects can semantically define the relationships between the object entities and the data entities.

In some embodiments, one or more of the object entities may include a static attribute to identify the object entity, a dynamic attribute to store data associated with the object entity that changes over time, and a behavior attribute that is an expected response from the object entity defined in response to an input.

In some embodiments, the first input time series may correspond to the dynamic attribute of the first object entity.

In some embodiments, at least one of the first data points in the first input time series may be stored in the dynamic attribute of the first object entity.

In some embodiments, the input time series may include the first identifier, a timestamp that indicates a generation time of the one or more first data points, and a value of the one or more first data points.

In some embodiments, the instructions may further cause the one or more processors to: identify a second data entity. representing a second object from a second relational object indicating a relationship between the first object entity and the second object entity, and identifying a second data entity from a third relational object indicating a relationship between the second object entity and the second data entity. The second data entity can store a second input time series that corresponds to one or more second data points associated with the second object.

In some embodiments, the program instructions may further cause the one or more processors to: identify one or more processing workflows that define one or more processing operations to generate derived time series using the first and second input time series, execute the one or more processing workflows to generate the derived time series, identifying a third data entity from a fourth relational object indicating a relationship between the second object entity and the third data entity, and storing the derived input time series in the third data entity.

In some embodiments, the first object may be an access control device and the second object may be an access key card associated with a person.

In some embodiments, the derived time series may include one or more virtual data points calculated according to the first and second input time series.

In some embodiments, the one or more virtual data points may include one or more location attributes of the person.

In some embodiments, the first object may be a temperature sensor and the second object may be a variable air volume unit (VAV).

In some embodiments, the derived time series may include an abnormal temperature attribute that corresponds to an area in which the temperature sensor is located and for which the VAV is configured to be supplied.

In some embodiments, the first and / or second object can be a temperature sensor.

In some embodiments, the instructions may cause the one or more processors to periodically receive temperature measurements from the temperature sensor and update at least the derived time series in the third data entity each time a new temperature measurement is received from the temperature sensor.

In some embodiments, the derived time series may include an average ambient temperature of an area in which the temperature sensor is located.

In some embodiments, the instructions may further cause the one or more processors to create a shadow entity to store historical values of the first raw data.

In some embodiments, the instructions may further cause the one or more processors to compute a virtual data point from the historical values and generate a fourth data entity to store the virtual data point.

Creation of a nested stream

An implementation of the present disclosure is a building management system (BMS). The BMS includes building equipment, a data collector, a time series service and a time series storage interface. The building equipment is configured to provide samples of one or more data points in the building management system. The data collector is configured to collect the samples from the building equipment and generate one or more input time series including multiple samples. The time series service is configured to: identify a first time series processing workflow that uses the input time series as an input and defines one or more processing operations to be applied to the samples of the input time series, performing the one or more processing operations defined by the first time series processing workflow to generate a first derived time series that includes a first set of derived time series samples, identifying a second time series processing workflow that uses the first derived time series as an input and defines one or more processing operations to be applied to the samples of the first derived time series , and perform the one or more processing operations defined by the second time series processing workflow by a second generate a time series that includes a second set of derived time series samples. The time series storage interface is configured to store the input time series and the first and second derived time series in a time series database.

In some embodiments, building equipment includes sensors, HVAC equipment, lighting equipment, access control equipment, and / or security equipment.

In some embodiments, generating the first derived time series includes transforming one or more samples of the input time series into one or more samples of the first set of derived time series samples by applying the one or more samples of the input time series as an input to the first time series processing workflow and assembling the first set of derived ones Time series samples to form the first derived time series.

In some embodiments, generating the second derived time series includes transforming one or more samples of the first set of derived time series samples into one or more samples of the second set of derived time series samples by applying the one or more samples of the first set of derived time series samples as an input to the second Time series processing workflow and assembling the second set of derived time series samples to form the second derived time series.

In some embodiments, the time series service is configured to identify one or more other time series to be used as inputs to the first time series processing workflow and generate an enriched time series processing workflow that includes the first time series processing workflow, the input time series, and the one or more other time series.

In some embodiments, the BMS contains the time series database. The time series database can store several time series. In some embodiments, the time series service includes a time series identifier configured to identify the input time series or the first derived time series from the plurality of time series stored in the time series database.

In some embodiments, the BMS includes a Directed Acyclic Graph (DAG) database that stores multiple DAGs, each of the DAGs defining a time series processing workflow. In some embodiments, the time series service includes a DAG identifier configured to determine whether any of the DAGs stored in the DAG database uses the input time series or the first derived time series as an input.

In some embodiments, the time series service in generating a derived time series is configured to: (a) determine whether the derived time series is used as an input to any of a number of stored time series processing workflows, and (b) in response to a determination that the derived time series is as an input is used for at least one of the stored time series processing workflows, performing one or more processing operations defined by the time series processing workflows that use the derived time series as an input to generate another derived time series.

In some embodiments, the time series service is configured to iteratively repeat steps (a) and (b) until the derived time series generated in step (b) is not used as an input to any of the plurality of stored time series processing workflows.

In some embodiments, the time series database includes a local time series database within the building management system and / or a hosted time series database at a remote location relative to the building management system.

In some embodiments, the input time series is a time series of measurements obtained from a sensor, and the first derived time series and / or the second derived time series is an abnormal event time series indicating whether each of the measurements is normal or abnormal.

In some embodiments, the input time series is a time series of building access events obtained from a physical access control device associated with a building area, and the first derived time series and / or the second derived time series is an abnormal event time series indicating whether each Access events is normal or abnormal.

Another implementation of the present disclosure is a building management system (BMS) for managing time series data provided by building equipment. The BMS contains one or more computer readable storage media with instructions stored thereon. When executed by one or more processors, the instructions cause the one or more processors to do the following: collect samples of the time series data from the building equipment and generate one or more input time series including multiple of the samples, identify a first time series processing workflow that does the Using input time series as an input and defining one or more processing operations to be applied to the samples of the input time series, performing the one or more processing operations defined by the first time series processing workflow to produce a first derived time series that is a first set derived time series samples includes identifying a second time series processing workflow that uses the first derived time series as an input and defines one or more processing operations that are to be applied to the samples of the first derived time series, performing the one or more processing operations defined by the second time series processing workflow to produce a second derived time series that includes a second set of derived time series samples, and storing the input time series and the first and second derived time series in a time series database.

In some embodiments, building equipment includes sensors, HVAC equipment, lighting equipment, access control equipment, and / or security equipment.

In some embodiments, generating the first derived time series includes transforming one or more samples of the input time series into one or more samples of the first set of derived time series samples by applying the one or more samples of the input time series as an input to the first time series processing workflow and assembling the first set of derived ones Time series samples to form the first derived time series.

In some embodiments, generating the second derived time series includes transforming one or more samples of the first set of derived time series samples into one or more samples of the second set of derived time series samples by applying the one or more samples of the first set of derived time series samples as an input to the second Time series processing workflow and assembling the second set of derived time series samples to form the second derived time series.

In some embodiments, the instructions cause one or the plurality of processors for identifying one or more other time series to be used as inputs to the first time series processing workflow and generating an enriched time series processing workflow that includes the first time series processing workflow, the input time series, and the one or more other time series.

In some embodiments, the BMS contains the time series database. The time series database can store several time series. In some embodiments, the instructions cause the one or more processors to identify the input time series or the first derived time series from the plurality of time series stored in the time series database.

In some embodiments, the BMS includes a Directed Acyclic Graph (DAG) database that stores multiple DAGs, each of the DAGs defining a time series processing workflow. In some embodiments, the instructions cause the one or more processors to determine whether any of the DAGs stored in the DAG database use the input time series or the first derived time series as an input.

In some embodiments, the instructions cause the one or more processors when generating a derived time series to: (a) determine whether the derived time series is used as an input to any of a number of stored time series processing workflows, and (b) in response to a determination, that the derived time series be used as an input to at least one of the stored time series processing workflows, performing one or more processing operations defined by the time series processing workflows that use the derived time series as an input to generate another derived time series.

In some embodiments, the instructions cause the one or more processors to iteratively repeat steps (a) and (b) until the derived time series generated in step (b) is not used as an input to any of the plurality of stored time series processing workflows.

In some embodiments, the time series database includes a local time series database within the building management system and / or a hosted time series database at a remote location relative to the building management system.

In some embodiments, the input time series is a time series of measurements obtained from a sensor, and the first derived time series and / or the second derived time series is an abnormal event time series indicating whether each of the measurements is normal or abnormal.

In some embodiments, the input time series is a time series of building access events obtained from a physical access control device associated with a building area, and the first derived time series and / or the second derived time series is an abnormal event time series indicating whether each Access events is normal or abnormal.

Another implementation of the present disclosure is a method for managing time series data provided by building equipment. The method includes: collecting samples of the time series data from the building equipment and generating one or more input time series comprising multiple of the samples, identifying a first time series processing workflow that uses the input time series as an input and defining one or more processing operations that are based on the samples of the Input time series to be applied, performing the one or more processing operations defined by the first time series processing workflow to generate a first derived time series comprising a first set of derived time series samples, identifying a second time series processing workflow that takes the first derived time series as an input used and defining one or more processing operations to be applied to the samples of the first derived time series, performing the one or the plurality of processing operations defined by the second time series processing workflow to generate a second derived time series comprising a second set of derived time series samples and storing the input time series and the first and second derived time series in a time series database.

In some embodiments, building equipment includes sensors, HVAC equipment, lighting equipment, access control equipment, and / or security equipment.

In some embodiments, generating the first derived time series includes transforming one or more samples of the input time series into one or more samples of the first set of derived time series samples by applying the one or more samples of the input time series as an input to the first time series processing workflow and assembling the first set of derived ones Time series samples to form the first derived time series.

In some embodiments, generating the second derived time series includes transforming one or more samples of the first set of derived time series samples into one or more samples of the second set of derived time series samples by applying the one or more samples of the first set of derived time series samples as an input to the second Time series processing workflow and assembling the second set of derived time series samples to form the second derived time series.

In some embodiments, the method includes identifying one or more other time series to be used as inputs to the first time series processing workflow and generating an enriched time series processing workflow that includes the first time series processing workflow, the input time series, and the one or more other time series.

In some embodiments, the time series database stores multiple time series. The method may include identifying the input time series or the first derived time series from the plurality of time series stored in the time series database.

In some embodiments, the method includes storing multiple directed acyclic graphs in a DAG database, each of the DAGs defining a time series processing workflow. The method may include determining whether any of the DAGs stored in the DAG database uses the input time series or the first derived time series as an input.

In some embodiments, the method of generating a derived time series includes: (a) determining whether the derived time series is used as an input for any of a number of stored time series processing workflows, and (b) in response to a determination that the derived time series is used as an input for at least one of the stored time series processing workflows, performing one or more processing operations defined by the time series processing workflows that use the derived time series as an input to generate another derived time series.

In some embodiments, the method includes iteratively repeating steps (a) and (b) until the derived time series generated in step (b) is not used as an input to any of the plurality of stored time series processing workflows.

In some embodiments, the time series database includes a local time series database within the building management system and / or a hosted time series database at a remote location relative to the building management system.

In some embodiments, the input time series is a time series of measurements obtained from a sensor, and the first derived time series and / or the second derived time series is an abnormal event time series indicating whether each of the measurements is normal or abnormal.

In some embodiments, the input time series is a time series of building access events obtained from a physical access control device associated with a building area, and the first derived time series and / or the second derived time series is an abnormal event time series indicating whether each Access event is normal or abnormal.

Cloud-based feedback control

An implementation of the present disclosure is a building management platform that monitors and controls equipment of a building management system. The building management platform includes a data collector and a time series service. The data collector is configured to collect feedback samples provided by one or more sensors of a building management system and to generate one or more feedback time series including several of the feedback samples. The time series service is configured to: identify a first feedback control workflow that uses the feedback time series as an input and defines one or more processing operations to be applied to the feedback time series feedback samples, performing the one or more processing operations defined by the feedback control workflow to generate a control signal time series that includes a set of control signal samples and provide a control signal that corresponds to the Control signal samples and / or the control signal time series includes, as an output to controllable building equipment of the building management system that operates using the control signal as an input.

In some embodiments, the sensors include a temperature sensor, a humidity sensor, an illumination sensor, an air quality sensor, and / or a presence sensor configured to detect an environmental condition within a building area. In some embodiments, the sensors include a temperature sensor, a flow rate sensor, an enthalpy sensor, and / or a voltage sensor configured to detect an operating condition or condition of central plant equipment within a central plant. In some embodiments, the data sources include Internet of Things (IoT) devices. In some embodiments, the controllable building equipment includes HVAC equipment, security equipment, lighting equipment, or access control equipment installed within a building.

In some embodiments, generating the control signal time series includes transforming one or more samples of the feedback time series into one or more samples of the control signal samples by applying the one or more samples of the feedback time series as an input to the feedback control workflow and assembling the control samples to form the control signal time series.

In some embodiments, the time series service is configured to identify one or more other time series required as inputs to the feedback control workflow and generate an enriched feedback control workflow that includes the feedback control workflow, the feedback time series, and the one or more other time series. In some embodiments, the one or more other time series include multiple setpoint time series that include multiple setpoint samples, each of the setpoint samples defining a setpoint that corresponds to one of the feedback samples.

In some embodiments, the building management platform includes a time series database that stores multiple time series. The time series service may include a time series identifier configured to identify the feedback time series from the plurality of time series stored in the time series database.

In some embodiments, the building management platform includes a directional acyclic graph (DAG) database in which multiple feedback DAGs are stored. Each of the DAGs can define a feedback control workflow. In some embodiments, the time series service includes a DAG identifier configured to determine whether any of the feedback control DAGs stored in the DAG database use the feedback time series as an input.

In some embodiments, time series service is distributed across multiple systems or devices.

In some embodiments, the feedback control workflow includes a state-based control workflow, an extremum seeking control (ESC) workflow, a proportional-integral (PI) control workflow, a proportional-integral-derivative (PID ) Control workflow and / or an MPC (Model Predictive Control) workflow that causes the time series service to transform the feedback time series into the control signal time series using a feedback control technique.

In some embodiments, the feedback control workflow includes a proportional integral derivative (PID) control workflow that causes the time series service to generate an error time series that includes multiple error samples. Each of the error samples can indicate a difference between one or more of the feedback samples and a corresponding target value. The PID control workflow can cause the time series service to generate the control signal time series by applying a set of PID control operations to the error time series.

In some embodiments, applying the set of PID control operations to the error time series includes generating an integrated error time series based on multiple error samples and generating a derivative error time series based on a change in value between successive samples of the error time series.

In some embodiments, applying the set of PID control operations to the error time series includes: calculating a proportional gain component by multiplying the error time series by a proportional gain parameter, calculating an integral gain component by multiplying the integrated error time series by an integral gain parameter, calculating a derivative gain parameter by a derivative gain parameter by multiplying the derivative time series , and combining the proportional gain component, the integral gain component and the derivative gain component to generate the control signal time series.

Another implementation of the present disclosure is a building management platform for monitoring and controlling equipment of a building management system. The building management platform includes one or more computer-readable storage media with instructions stored thereon that, when executed by one or more processors, cause the one or more processors to: collect feedback samples provided by one or more sensors of the building management system and generate one or more a plurality of feedback time series that include more of the feedback samples, identifying a first feedback control workflow that uses the feedback time series as an input and defines one or more processing operations to be applied to the feedback samples of the feedback time series, performing the one or more processing operations performed by the feedback control work to generate a control signal time series that includes a set of control signal samples, i nd providing a control signal, including the control signal samples and / or the control signal time series, as an output to controllable building equipment of the building management system that operates using the control signal as an input.

In some embodiments, the sensors include a temperature sensor, a humidity sensor, an illumination sensor, an air quality sensor, and / or a presence sensor configured to detect an environmental condition within a building area. In some embodiments, the sensors include a temperature sensor, a flow rate sensor, an enthalpy sensor, and / or a voltage sensor configured to detect an operating condition or condition of central plant equipment within a central plant. In some embodiments, the data sources include Internet of Things (IoT) devices. In some embodiments, the controllable building equipment includes HVAC equipment, security equipment, lighting equipment, or access control equipment installed within a building.

In some embodiments, generating the control signal time series includes transforming one or more samples of the feedback time series into one or more samples of the control signal samples by applying the one or more samples of the feedback time series as an input to the feedback control workflow and assembling the control samples to form the control signal time series.

In some embodiments, the instructions cause the one or more processors to identify one or more other time series required as inputs to the feedback control workflow and generate an enriched feedback control workflow that includes the feedback control workflow, the feedback time series, and the one or more other time series . In some embodiments, the one or more other time series include multiple setpoint time series that include multiple setpoint samples, each of the setpoint samples defining a setpoint that corresponds to one of the feedback samples.

In some embodiments, the building management platform includes a time series database that stores multiple time series. In some embodiments, the instructions cause the one or more processors to identify the feedback time series from the plurality of time series stored in the time series database.

In some embodiments, the building management platform includes a directed acyclic graph (DAG) database that stores multiple DAGs, each of the DAGs defining a feedback control workflow. In some embodiments, the instructions cause the one or more processors to determine whether any of the feedback control DAGs stored in the DAG database use the feedback time series as an input.

In some embodiments, the one or more processors are distributed across multiple systems or devices.

In some embodiments, the feedback control workflow includes a state-based control workflow, an extremum seeking control (ESC) workflow, a proportional-integral (PI) control workflow, a proportional-integral-derivative (PID ) Control workflow and / or an MPC (Model Predictive Control) workflow that causes the one or more processors to transform the feedback time series into the control signal time series using a feedback control technique.

In some embodiments, the feedback control workflow includes a proportional integral derivative (PID) control workflow that causes the one or more processors to generate an error time series that includes multiple error samples. Each of the error samples can indicate a difference between one or more of the feedback samples and a corresponding target value. The PID control workflow may cause the one or more processors to generate the control signal time series by applying a set of PID control operations to the error time series.

In some embodiments, applying the set of PID control operations to the error time series includes generating an integrated error time series based on multiple error samples and generating a derivative error time series based on a change in value between successive samples of the error time series.

In some embodiments, applying the set of PID control operations to the error time series includes: calculating a proportional gain component by multiplying the error time series by a proportional gain parameter, calculating an integral gain component by multiplying the integrated error time series by an integral gain parameter, calculating a derivative gain parameter by a derivative gain parameter by multiplying the derivative time series , and combining the proportional gain component, the integral gain component and the derivative gain component to generate the control signal time series.

Another implementation of the present disclosure is a method for monitoring and controlling equipment of a building management system. The method includes: collecting feedback samples provided by one or more sensors of the building management system and generating one or more feedback time series including multiple of the feedback samples, identifying a first feedback control workflow that uses the feedback time series as an input, and one or more processing operations defined to be applied to the feedback samples of the feedback time series, performing the one or more processing operations defined by the feedback control workflow to generate a control signal time series that includes a set of control signal samples, and providing a control signal that includes the control signal samples and / or the control signal time series includes, as an output to a controllable building equipment using the building management system of the control signal works as an input.

In some embodiments, the sensors include a temperature sensor, a humidity sensor, an illumination sensor, an air quality sensor, and / or a presence sensor configured to detect an environmental condition within a building area. In some embodiments, the sensors include a temperature sensor, a flow rate sensor, an enthalpy sensor, and / or a voltage sensor configured to detect an operating condition or condition of central plant equipment within a central plant. In some embodiments, the data sources include Internet of Things (IoT) devices. In some embodiments, the controllable building equipment includes HVAC equipment, security equipment, lighting equipment, or access control equipment installed within a building.

In some embodiments, generating the control signal time series includes transforming one or more samples of the feedback time series into one or more samples of the control signal samples by applying the one or more samples of the feedback time series as an input to the feedback control workflow and assembling the control samples form the control signal time series, assemble the control samples to form the control signal time series.

In some embodiments, the method includes identifying one or more other time series required as inputs to the feedback control workflow and generating an enriched feedback control workflow that includes the feedback control workflow, the feedback time series, and the one or more other time series. In some embodiments, the one or more other time series include multiple setpoint time series that include multiple setpoint samples, each of the setpoint samples defining a setpoint that corresponds to one of the feedback samples.

In some embodiments, the method includes accessing a time series database that stores multiple time series and identifying the feedback time series from the plurality of time series stored in the time series database.

In some embodiments, the method includes accessing a directed acyclic graph (DAG) database that stores multiple feedback DAGs, each of the DAGs defining a feedback control workflow, and determining whether any of the feedback control controls stored in the DAG database DAGs used the feedback time series as an input.

In some embodiments, the one or more processing operations defined by the feedback control workflow are distributed across multiple systems or devices.

In some embodiments, the feedback control workflow includes a state-based control workflow, an extremum seeking control (ESC) workflow, a proportional-integral (PI) control workflow, a proportional-integral-derivative (PID ) Control workflow and / or an MPC (Model Predictive Control) workflow that cause the feedback time series to be transformed into the control signal time series using a feedback control technique.

In some embodiments, the feedback control workflow includes a proportional-integral-derivative (PID) control workflow. Performing the one or more processing operations defined by the feedback control workflow may include generating an error time series that includes multiple error samples. Each of the error samples can indicate a difference between one or more of the feedback samples and a corresponding target value. Performing the one or more processing operations defined by the feedback control workflow may include generating the control signal time series by applying the set of PID control operations to the error time series.

In some embodiments, applying the set of PID control operations to the error time series includes generating an integrated error time series based on multiple error samples and generating a derivative error time series based on a change in value between successive samples of the error time series.

In some embodiments, applying the set of PID control operations to the error time series includes: calculating a proportional gain component by multiplying the error time series by a proportional gain parameter, calculating an integral gain component by multiplying the integrated error time series by an integral gain parameter; Computing a derivative gain component by multiplying the derivative error time series by a derivative gain parameter; and combining the proportional gain component, the integral gain component and the derivative gain component to produce the control signal time series.

Identity management and security services

An implementation of the present disclosure is a building management system that includes an entity database and an identity management service. The entity database stores several interconnected intelligent entities. The intelligent entities include object entities that represent multiple people or physical devices and data entities that represent data associated with the people or physical devices. The intelligent entities are through relational Objects connected to each other that indicate relationships between the object entities and the data entities. Each of the object entities contains several stored identity attributes. The identity management service is configured to: receive a first identity attribute from a first device within a building, receive a second identity attribute from a second device within the building, compare the first and second identity attributes with the stored identity attributes of an object entity from the plurality of intelligently connected Entities, and granting access to a building area, building equipment device, and / or a computer system in response to the first and second identity attributes matching the stored identity attributes of the object entity.

In some embodiments, the first device is an access card reader and the first identity attribute is a card ID attribute recorded by the access card reader. In some embodiments, the second device is a security camera and the second identity attribute is an image of a person that is captured by the security camera.

In some embodiments, the first device is an access card reader and the first identity attribute is a card ID attribute recorded by the access card reader. In some embodiments, the second device is a mobile device worn by a person and the second identity attribute is a mobile device ID attribute associated with the mobile device.

In some embodiments, the first device is a user interface device and the first identity attribute is a user ID received from a user via the user interface device. In some embodiments, the second device is a security camera and the second identity attribute is an image of a person that is captured by the security camera.

In some embodiments, the first device is a user interface device and the first identity attribute is a user ID received from a user via the user interface device. In some embodiments, the second device is a mobile device worn by a person and the second identity attribute is a mobile device ID attribute associated with the mobile device.

In some embodiments, the first device is a mobile device, an information technology (IT) device, an Internet of Things (IoT) sensor, a building equipment device, and / or a security device. In some embodiments, the second device is another one of the mobile device, the IT device, the IoT device, the building equipment device, or the security device.

In some embodiments, the identity management service is configured to: determine a location associated with the first device in response to the first device providing the first identity attribute, identify a building area in which the first device is located, and select the second device from one Set of devices located in the same building area as the first device.

In some embodiments, the identity management service is configured to deny access to the building area, building equipment device, and / or the computer system in response to the first and / or second identity attribute not matching the stored identity attributes of the object entity.

Another implementation of the present disclosure is a method of controlling access to a building area, building equipment device, or computer system in a building management system. The method includes storing multiple interconnected intelligent entities in an entity database. The intelligent entities include object entities that represent multiple people or physical devices and data entities that represent data associated with the people or physical devices. The intelligent entities are linked by relational objects that indicate relationships between the object entities and the data entities. Each of the object entities contains several stored identity attributes. The method further includes: receiving a first identity attribute from a first device within a building, receiving a second identity attribute from a second device within the building, comparing the first and second identity attributes with the stored identity attributes of an object entity from the plurality of interconnected intelligent entities , and granting the Access to a building area, building equipment apparatus, and / or a computer system in response to the first and second identity attributes matching the stored identity attributes of the object entity.

In some embodiments, the first device is an access card reader and the first identity attribute is a card ID attribute recorded by the access card reader. In some embodiments, the second device is a security camera and / or a mobile device carried by a person and the second identity attribute is an image of a person captured by the security camera and / or a mobile device associated with the mobile device. ID attribute.

In some embodiments, the first device is a user interface device and the first identity attribute is a user ID received from a user via the user interface device. In some embodiments, the second device is a security camera and / or a mobile device carried by a person and the second identity attribute is an image of a person captured by the security camera and / or a mobile device associated with the mobile device. ID attribute.

In some embodiments, the method includes: determining a location associated with the first device in response to the first device providing the first identity attribute, identifying a building area in which the first device is located, and selecting the second device from a set of Devices that are in the same building area as the first device.

Another implementation of the present disclosure is a building management system that includes multiple building equipment devices, an entity database, and a security service. The entity database stores several interconnected intelligent entities. The intelligent entities include object entities that represent multiple building equipment devices and data entities that represent data associated with the multiple building equipment devices. The intelligent entities are linked by relational objects that indicate relationships between the object entities and the data entities. Each object entity contains a stored attribute that indicates a version of software installed on a building equipment device represented by the object entity. The security service is configured to: automatically detect a version of software installed on each of the building equipment devices by reading the stored attributes of the object entities, and automatically update the software installed on one or more of the building equipment devices, in response to a determination that the version of the software installed on the one or more building equipment devices is not a newest version of the software.

In some embodiments, the security service includes an identity and security service configured to ensure that each building equipment device is capable of accessing configuration backups.

In some embodiments, the backup service includes a device management service configured to create an intelligent entity for each building equipment device and register each building equipment device with the corresponding intelligent entity.

In some embodiments, the security service includes a transport and notification service that is configured to enable bidirectional communication between the security service and the building equipment.

In some embodiments, the backup service includes a device shadow / manifest service configured to synchronize configuration settings, parameters, and / or device-specific information between the building equipment and the backup service.

In some embodiments, the backup service includes a parcel service configured to create a compressed data object including a configuration of the building equipment and store the compressed data object as a backup of the configuration.

In some embodiments, the backup service includes an asset and backup service configured to create and present a user interface that each device of the Building equipment lists and indicates whether a backup configuration of each device has been stored in the backup service.

In some embodiments, the backup service includes a manual upload service configured to upload a backup configuration in response to a user request for the backup configuration.

One skilled in the art will detect that the summary is illustrative only and is not intended to be limiting in any way. Other aspects, inventive features, and advantages of the devices and / or methods described herein, as defined solely by the claims, will become apparent in the detailed description set forth herein and to be understood in conjunction with the accompanying drawings.

Figure list

• 1 ist ein Blockdiagramm einer intelligenten Gebäudeumgebung gemäß manchen Ausführungsbeispielen.
• 2 ist eine perspektivische Ansicht eines intelligenten Gebäudes gemäß manchen Ausführungsbeispielen.
• 3 ist ein Blockdiagramm eines Wasserseitensystems gemäß manchen Ausführungsbeispielen.
• 4 ist ein Blockdiagramm eines Luftseitensystems gemäß manchen Ausführungsbeispielen.
• 5 ist ein Blockdiagramm eines Gebäudeverwaltungssystems gemäß manchen Ausführungsbeispielen.
• 6 ist ein Blockdiagramm eines anderen Gebäudeverwaltungssystems gemäß manchen Ausführungsbeispielen.
• 7 ist ein Blockdiagramm, das einen Entitätsdienst aus 6 ausführlicher veranschaulicht, gemäß manchen Ausführungsbeispielen.
• 8 ist ein beispielhafter Entitätsgraph von Entitätsdaten gemäß manchen Ausführungsbeispielen.
• 9 ist ein Blockdiagramm, das einen Zeitreihendienst aus 6 ausführlicher veranschaulicht, gemäß manchen Ausführungsbeispielen.
• 10 ist ein Flussdiagramm eines Prozesses oder eines Verfahrens zum Aktualisieren/Erzeugen eines Attributs einer in Zusammenhang stehenden Entität basierend auf Daten, die von einer Vorrichtung eines Gebäudeverwaltungssubsystems empfangen werden, gemäß manchen Ausführungsbeispielen.
• 11 ist ein beispielhafter Entitätsgraph von Entitätsdaten gemäß manchen Ausführungsbeispielen.
• 12 ist ein Flussdiagramm eines Prozesses oder eines Verfahrens zum Analysieren von Daten von einer zweiten in Zusammenhang stehenden Vorrichtung basierend auf Daten von einer ersten Vorrichtung gemäß manchen Ausführungsbeispielen.
• 13 ist ein Flussdiagramm eines Prozesses oder eines Verfahrens zum Erzeugen einer abgeleiteten Zeitreihe aus Daten, die durch eine erste Vorrichtung oder eine zweite Vorrichtung erzeugt werden, gemäß manchen Ausführungsbeispielen.
• 14 ist ein Blockdiagramm, das eine Aggregationstechnik veranschaulicht, die durch den in 13 gezeigten Abtastwertaggregator verwendet werden kann, um Rohdatenabtastwerte zu aggregieren, gemäß manchen Ausführungsformen.
• 15 ist eine Datentabelle, die zum Speichern einer Rohdatenzeitreihe und einer Vielzahl abgeleiteter Datenzeitreihen verwendet werden kann, die durch den Zeitreihendienst aus 9 erzeugt werden können, gemäß manchen Ausführungsformen.
• 16 ist eine Zeichnung einiger Zeitreihen, die die Synchronisation von Datenabtastwerten veranschaulichen, die durch den in 9 gezeigten Abtastwertaggregator durchgeführt werden kann, gemäß manchen Ausführungsformen.
• 17 ist ein Flussdiagramm, das die Erzeugung und Speicherung einer Fehlerdetektionszeitreihe veranschaulicht, die durch den in 9 gezeigten Fehlerdetektor durchgeführt werden kann, gemäß manchen Ausführungsformen.
• 18 ist eine Datentabelle, die zum Speichern der Rohdatenzeitreihe und der Fehlerdetektionszeitreihe verwendet werden kann, gemäß manchen Ausführungsformen.
• 19A ist eine Datentabelle, die zum Speichern von Zuständen, die Abtastwerten einer Datenzeitreihe zugewiesen sind, verwendet werden kann, gemäß manchen Ausführungsformen.
• 19B ist eine Tabelle, die verschiedene Ereignisse beinhaltet, die basierend auf den in der Tabelle aus 19A gezeigten zugewiesenen Zuständen erzeugt werden, gemäß manchen Ausführungsformen.
• 19C ist ein Flussdiagramm eines Prozesses zum Erzeugen und Aktualisieren von Ereignissen und Ereignisreihen gemäß manchen Ausführungsformen.
• 20A ist ein gerichteter azyklischer Graph (DAG), der durch den DAG-Generator aus 9 erzeugt werden kann, gemäß manchen Ausführungsformen.
• 20B ist ein Codeausschnitt, der durch den DAG-Generator aus 9 automatisch basierend auf dem DAG erzeugt werden kann, gemäß manchen Ausführungsformen.
• 21A ist ein Entitätsgraph, der Beziehungen zwischen einer Organisation, einem Bereich, einem System, einem Punkt und einer Zeitreihe veranschaulicht und der durch den Datenkollektor aus 6 verwendet werden kann, gemäß manchen Ausführungsformen.
• 21B ist ein Beispiel für einen Entitätsgraphen für ein spezielles System von Vorrichtungen gemäß manchen Ausführungsformen.
• 22 ist ein Objektbeziehungsdiagramm, das Beziehungen zwischen einer Entitätsvorlage, einem Punkt, einer Zeitreihe und einem Datenabtastwert veranschaulicht und das durch den Datenkollektor aus 6 und den Zeitreihendienst aus 9 verwendet werden kann, gemäß manchen Ausführungsformen.
• 23A ist ein Blockdiagramm, das einen Zeitreihenverarbeitungsarbeitsfluss veranschaulicht, der durch den Zeitreihendienst aus 9 durchgeführt werden kann, gemäß manchen Ausführungsformen.
• 23B ist ein Flussdiagramm eines Prozesses, der durch den Arbeitsflussverwalter aus 23A durchgeführt werden kann, gemäß manchen Ausführungsformen.
• 24 ist ein Blockdiagramm eines Systems zum Verarbeiten von Streaming-Daten, das als Teil der Gebäudeverwaltungsplattform aus 1 implementiert werden kann, gemäß manchen Ausführungsformen.
• 25A ist ein Blockdiagramm, das eine iterative Zeitreihenverarbeitungstechnik veranschaulicht, die durch das System aus 24 verwendet wird, gemäß manchen Ausführungsformen.
• 25B ist ein Flussdiagramm eines iterativen Zeitreihenverarbeitungsprozesses, der durch das aus 24 durchgeführt werden kann, gemäß manchen Ausführungsformen.
• 26 ist ein Blockdiagramm eines Cloud-basierten Rückkopplungssteuersystems einschließlich der Gebäudeverwaltungsplattform aus 1 gemäß manchen Ausführungsformen.
• 27 ist ein Blockdiagramm eines Identitätsverwaltungssystems eischließlich eine Identitätsverwaltungsdienstes und eines Entitätsdienstes gemäß manchen Ausführungsformen.
• 28 ist ein Blockdiagramm eines Sicherstellungsdienstes gemäß manchen Ausführungsformen.

The above and other aspects and features of the present disclosure will become more apparent to those skilled in the art from the following detailed description of the embodiments with reference to the accompanying drawings.

• 1 10 is a block diagram of an intelligent building environment in accordance with some embodiments.
• 2nd 10 is a perspective view of an intelligent building in accordance with some embodiments.
• 3rd 10 is a block diagram of a water side system in accordance with some embodiments.
• 4th FIG. 4 is a block diagram of an airside system according to some embodiments.
• 5 10 is a block diagram of a building management system in accordance with some embodiments.
• 6 10 is a block diagram of another building management system in accordance with some embodiments.
• 7 is a block diagram that shows an entity service 6 Illustrated in more detail, according to some embodiments.
• 8th 10 is an example entity graph of entity data in accordance with some embodiments.
• 9 Figure 3 is a block diagram showing a time series service 6 Illustrated in more detail, according to some embodiments.
• 10th 10 is a flowchart of a process or method for updating / generating a related entity attribute based on data received from a building management subsystem device, according to some embodiments.
• 11 10 is an example entity graph of entity data in accordance with some embodiments.
• 12th 10 is a flowchart of a process or method for analyzing data from a second related device based on data from a first device, in accordance with some embodiments.
• 13 10 is a flowchart of a process or method for generating a derived time series from data generated by a first device or a second device, according to some embodiments.
• 14 FIG. 4 is a block diagram illustrating an aggregation technique performed by the method shown in FIG 13 The sample aggregator shown can be used to aggregate raw data samples, according to some embodiments.
• 15 is a data table that can be used to store a raw data time series and a plurality of derived data time series that are performed by the time series service 9 can be generated, according to some embodiments.
• 16 FIG. 4 is a drawing of some time series that illustrate the synchronization of data samples obtained by the method shown in FIG 9 Sample aggregator shown can be performed, according to some embodiments.
• 17th FIG. 10 is a flowchart illustrating the generation and storage of an error detection time series by the method shown in FIG 9 The error detector shown can be performed, according to some embodiments.
• 18th FIG. 10 is a data table that can be used to store the raw data time series and the error detection time series, according to some embodiments.
• 19A 10 is a data table that can be used to store states associated with samples of a data time series, in accordance with some embodiments.
• 19B is a table that includes various events based on those in the table 19A assigned states shown are generated, according to some embodiments.
• 19C 10 is a flowchart of a process for creating and updating events and series of events, in accordance with some embodiments.
• 20A is a directed acyclic graph (DAG) made by the DAG generator 9 can be generated, according to some embodiments.
• 20B is a snippet of code made by the DAG generator 9 can be generated automatically based on the DAG, according to some embodiments.
• 21A is an entity graph that illustrates relationships between an organization, an area, a system, a point, and a time series and is characterized by the data collector 6 may be used in accordance with some embodiments.
• 21B FIG. 10 is an example of an entity graph for a particular system of devices in accordance with some embodiments.
• 22 FIG. 10 is an object relationship diagram illustrating relationships between an entity template, a point, a time series, and a data sample, and that by the data collector 6 and the time series service 9 may be used in accordance with some embodiments.
• 23A FIG. 12 is a block diagram illustrating a time series processing workflow performed by the time series service 9 can be performed, according to some embodiments.
• 23B is a flowchart of a process performed by the workflow manager 23A can be performed, according to some embodiments.
• 24th is a block diagram of a system for processing streaming data that is part of the building management platform 1 can be implemented, according to some embodiments.
• 25A Figure 12 is a block diagram illustrating an iterative time series processing technique implemented by the system 24th is used, according to some embodiments.
• 25B Figure 11 is a flowchart of an iterative time series processing process characterized by the 24th can be performed, according to some embodiments.
• 26 Figure 12 is a block diagram of a cloud-based feedback control system including the building management platform 1 according to some embodiments.
• 27th 10 is a block diagram of an identity management system including an identity management service and an entity service, in accordance with some embodiments.
• 28 FIG. 10 is a block diagram of a security service in accordance with some embodiments.

DETAILED DESCRIPTION

Exemplary embodiments are described in more detail below with reference to the accompanying drawings.

1 is a block diagram of an intelligent building environment 100 according to some embodiments. It is shown that the intelligent building environment 100 a building management platform 102 includes. The building management platform 102 can be configured to collect data from a variety of different data sources. For example, it is shown that the building management platform 102 Building data 110 , 120 , 130 and 140 collects. For example, the buildings can be a school 110 , a hospital 120 , a factory 130 , an office building 140 and / or the like. However, the present disclosure is not based on the number or types of buildings 110 , 120 , 130 and 140 limited that in 1 are shown. For example, in some embodiments, the Building management platform 102 configured to collect data from one or more buildings, and the one or more buildings can be of the same building type or can include one or more of those in 1 include different types of buildings shown.

The building management platform 102 can be configured to receive data from a variety of devices 112-116 , 122-126 , 132-136 and 142-146 either directly (e.g. directly via the network 104 ) or indirectly (e.g. via systems or applications in the buildings 110 , 120 , 130 , 140 ) to collect. In some embodiments, the devices are 112-116 , 122-126 , 132-136 and 142-146 Internet of Things (IoT) devices. IoT devices can include any of a variety of physical devices, sensors, actuators, electronic devices, vehicles, household appliances, and / or other network connectivity items that enable IoT devices with the building management platform 102 to communicate. For example, loT devices can include intelligent home hub devices, intelligent home devices, doorbell cameras, air quality sensors, intelligent switches, intelligent lights, intelligent devices, garage door openers, smoke detectors, heart monitoring implants, biochip transponders, cameras that stream live feeds, automobiles with built-in sensors, DNA -Analysis devices, field operating devices, locating devices for people / vehicles / equipment, networked sensors, wireless sensors, wearable sensors, environmental sensors, RFID gateways and reading devices, IoT gateway devices, robots and other robotic devices, GPS devices, smartwatches, Include virtual / augmented reality devices and / or other networked or networkable devices. While the devices described herein are generally referred to as IoT devices, it is understood that, in various embodiments, the devices referred to in the present disclosure may be any type of devices that are used to communicate data via an electronic device Network are able.

In some embodiments, IoT devices may include sensors or sensor systems. For example, IoT devices can include acoustic sensors, sound sensors, vibration sensors, automotive or transport sensors, chemical sensors, electrical current sensors, electrical voltage sensors, magnetic sensors, radio sensors, environmental sensors, weather sensors, moisture sensors, air humidity sensors, flow sensors, fluid speed sensors, sensors for ionizing radiation, sensors for subatomic sensors Particles, navigation instruments, position sensors, angle sensors, displacement sensors, distance sensors, speed sensors, acceleration sensors, optical sensors, light sensors, imaging devices, photon sensors, pressure sensors, force sensors, density sensors, level sensors, thermal sensors, heat sensors, temperature sensors, proximity sensors, any kind of presence sensors and / or Include sensors or detection systems.

Examples of acoustic, sound or vibration sensors include geophones, hydrophones, lace sensors, guitar pickups, microphones and seismometers. Examples of automotive or transport sensors include air flow meters, air-fuel ratio measuring devices, AFR sensors Totwinkelmonitore, crankshaft position sensors, defect detection equipment, engine coolant temperature sensors, Hall effect sensors, knock sensors, map sensors, mass flow sensors, oxygen sensors, parking sensors, radar guns, speedometer, speed sensors, throttle position sensors , Tire pressure monitoring sensors, torque sensors, transmission fluid temperature sensors, turbine speed sensors, variable reluctance sensors, vehicle speed sensors, water sensors and wheel speed sensors.

Examples of chemical sensors include alcohol testers, carbon dioxide sensors, carbon monoxide detectors, catalytic pearl sensors, chemical field effect transistors, chemical resistors, electrochemical gas sensors, electronic noses, electrolyte isolator semiconductor sensors, fluorescent chloride sensors, holographic sensors, hydrocarbon condensation point analyzers, infrared sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, hydrogen sensors, non-dispersive infrared sensors, microwave chemistry sensors, nitrogen oxide sensors, olfactometers, optrodes, oxygen sensors, ozone monitoring devices, pellistors, pH glass electrodes, potentiometric sensors, redox electrodes, smoke detectors and zinc oxide nanorod sensors.

Examples of electromagnetic sensors include current sensors, daly detectors, electroscopes, electron multipliers, Faraday beakers, galvanometers, Hall effect sensors, Hall probes, magnetic anomaly detectors, magnetometers, magnetoresistors, MEMS magnetic field sensors, metal detectors, planar Hall sensors, radio direction finders and Voltage detectors.

Examples of environmental sensors include actinometers, air pollution sensors, bed wetness alarms, cloud altimeters, thaw warnings, electrochemical gas sensors, fish counters, frequency domain sensors, gas detectors, hook evaporators, humistors, hygrometers, leaf sensors, lysimeters, pyranometers, pyrgeometers, psychometers, rain sensors, OTEL sensors, rain sensors , Snow meters, soil moisture sensors, flow meters and tide meters. Examples of flow and fluid velocity sensors include air flow meters, anemometers, flow sensors, gas meters, mass flow sensors, and water meters.

Examples of radiation and particle sensors include cloud chambers, Geiger counters, Geiger-Müller tubes, ionization chambers, neutron detectors, proportional counters, scintillation counters, semiconductor detectors and thermoluminescence dosimeters. Examples of navigation instruments include air speed indicators, altimeters, position indicators, depth gauges, fluxgate compasses, gyroscopes, inertial navigation systems, inertial reference units, magnetic compasses, MHD sensors, ring laser gyroscopes, turning indicators, tialinx sensors, variometers and vibrational structure gyroscopes and yaw rate sensors.

Examples of position, angle, displacement, distance, speed and acceleration sensors include auxanometers, capacitive displacement sensors, capacitive sensing devices, bend sensors, free fall sensors, gravimeters, gyroscopic sensors, impact sensors, inclinometers, piezoelectric sensors with integrated circuits, laser rangefinders, laser ID AR speed -Sensors, linear encoders, linear variable differential transformers (LVDT), liquid capacitive inclinometers, hodometers, photoelectric sensors, piezoelectric accelerometers, position sensors, position-sensitive devices, angular velocity sensors, rotary encoders, rotating variable differential transformers, rotary detectors, shock detectors, shock data loggers, inclination sensors, tachometers, tachometers with variable reluctance and speed receiver.

Examples of optical sensors, light, imaging and photon sensors include charge coupled devices, CMOS sensors, color measurement devices, contact image sensors, electro-optical sensors, flame detectors, infrared sensors, kinetic inductance detectors, which are led as light sensors, light-addressable potentiometric sensors, Nichols radiometers, fiber optic sensors Sensors, optical position sensors, thermopile laser sensors, photodetectors, photodiodes, photomultiplier tubes, phototransistors, photoelectric sensors, photo ionization detectors, photomultipliers, photoresistors, photo switches, phototubes, scintillometers, Shack-Hartmann sensors, single-photon avalanche diode detectors, photodiode sensors, transition diodes, photodiode detectors, supervisory diode detectors, photodiode detectors, transitional diode detectors, photodiode detectors, transitional diode detectors, photodiode detectors, transitional diode detectors, photodiode detectors, transitional diode detectors, photodiode detectors, transitional diode detectors, photodiode detectors, transitional diode detectors, photodiode detectors, transponder diodes, supernatant detectors, photodiode transmitters, photodiode detectors, transponder sensors, photodiode detectors, transponder diodes, supernatant detectors, photodiode detectors, photodiode detectors, photodiode detectors, photodiode transmitters, transponder sensors for transponders Wavefront sensors.

Examples of pressure sensors include barographs, barometers, boost gauges, Bourdon gauges, hot filament ionization gauges, ionization gauges, McLeod gauges, oscillating U-tubes, permanent borehole gauges, piezometers, Pirani gauges, pressure sensors, pressure gauges, tactile sensors and time pressure gauges. Examples of force, density and level sensors include hang meters, hydrometers, force meters and force sensors, level sensors, load cells, magnetic level meters, core density meters, piezocapacitive pressure sensors, piezoelectric sensors, strain gauges, torque sensors and viscometers.

Examples of thermal sensors, heat and temperature sensors include bolometers, bimetallic strips, calorimeters, exhaust gas temperature measuring devices, flame detectors, Gardon measuring devices, Golay cells, heat flow sensors, infrared thermometers, microbolometers, microwave radiometers, network radiometers, quartz thermometers, resistance thermometers, silicon band gap sensor temperature sensors, temperature sensors, silicon band gap sensor temperature sensors , Thermistors, thermocouples, thermometers and pyrometers. Examples of proximity and presence sensors include alarm sensors, Doppler radars, motion detectors, presence sensors, proximity sensors, passive infrared sensors, reed switches, bar detectors, triangulation sensors, touch switches and data gloves.

In some embodiments, different sensors send measurements or other data to the building management platform using a variety of different communication protocols or data formats 102 . The building management platform 102 can be configured to record sensor data received in any protocol or data format and to translate the incoming sensor data into a common data format. The building management platform 102 can be used for any sensor connected to the building management platform 102 communicates, create an intelligent sensor object entity. Each intelligent sensor object entity can have one or more static attributes that represent the describe the corresponding sensor, one or more dynamic attributes that indicate the last values collected by the sensor, and / or contain one or more relational attributes that combine the intelligent sensor object entities with one another and / or other types of intelligent entities (e.g. area entities, System entities, data entities, etc.).

In some embodiments, the building management platform stores 102 Sensor data using data entities. Each data unit can correspond to a specific sensor and can contain a time series of data values which are received by the corresponding sensor. In some embodiments, the building management platform stores 102 relational objects that define relationships between sensor object entities and the corresponding data entity. For example, each relational object can identify a specific sensor object entity, a specific data entity, and can define a link between such entities.

The building management platform 102 can collect data from a variety of external systems or services. For example, it is shown that the building management platform 102 Weather data from a weather service 152 , News data from a news service 154 , Documents and other document-related data from a document service 156 and media (e.g., video, images, audio, social media, etc.) from a media service 158 receives. In some embodiments, the building management platform creates 102 Internal data. For example, the building management platform 102 include a web advertising system, website traffic monitoring system, web sales system, or other types of platform services that generate data. From the building management platform 102 generated data can be collected, stored and processed together with the data received from other data sources. The building management platform 102 can transfer data directly from external systems or devices or over a network 104 (e.g., a WAN, the Internet, a cellular network, etc.). The building management platform 102 can process and transform collected data to generate time series data and entity data. Some features of the building management platform 102 are described in more detail below.

Building HVAC systems and building management systems

Now referring to 2-5 Some building management systems (BMS) and HVAC systems are shown in accordance with some embodiments in which the systems and methods of the present disclosure can be implemented. In a short overview shows 2nd shows a building 10th that, for example, with an HVAC system 200 Is provided. The building 10th can be any of the buildings 210 , 220 , 230 and 140 be like in 1 shown, or can be any other suitable building that communicates with the building management platform 202 connected is. 3rd Figure 3 is a block diagram of a water side system 300 that is used to supply the building 10th can be used. 4th Figure 3 is a block diagram of an airside system 400 that is used to supply the building 10th can be used. 5 is a block diagram of a building management system (BMS) used to monitor and control the building 10th can be used.

Building and HVAC system

With special reference to 2nd is a perspective view of an intelligent building 10th shown. The building 10th is supplied by a BMS. A BMS is generally a system of devices configured to control, monitor and manage equipment in or around a building or building area. For example, a BMS may include an HVAC system, a security system, a lighting system, a fire alarm system, or any other system capable of managing building functions or devices, or any combination thereof. Furthermore, each of the systems may include multiple sensors and other devices (e.g., IoT devices) for the proper operation, maintenance, monitoring, and the like of the respective systems.

The BMS that the building 10th supplied, includes an HVAC system 200 . The HVAC system 200 may include multiple HVAC devices (e.g., heaters, chillers, ventilation units, pumps, fans, thermal energy storage, etc.) that provide heating, cooling, ventilation, or other services to buildings 10th are configured. For example, the HVAC system is shown 200 a water side system 220 and an air side system 230 includes. The water side system 220 can be a heated or cooled fluid to the ventilation unit of the air side system 230 deliver. The airside system 230 can use the heated or cooled fluid to heat or cool an air stream, of the building 10th is delivered. An exemplary water side system and an air side system included in the HVAC system 200 can be used with reference to 3rd and 4th described in more detail.

It is shown that the HVAC system 200 a cooler 202 , a boiler 204 and a roof ventilation unit (AHU) 206 includes. The water side system 220 can the boiler 204 and the cooler 202 can be used to heat or cool a working fluid (e.g. water, glycol, etc.) and can transfer the working fluid to the AHU 206 circulate. In various embodiments, the HVAC devices of the water side system 220 in or around the building 10th around (as in 2nd shown) or in an external location such as a central system (e.g. a refrigeration system, a steam system, a heating system, etc.). The working fluid can vary depending on whether in the building 10th Heating or cooling is required in the boiler 204 heated or in the cooler 202 be cooled. The boiler 204 can add heat to the circulating fluid, such as by burning a combustible material (e.g., natural gas) or using an electrical heating element. The cooler 202 may place the circulated fluid in a heat exchange relationship with another fluid (e.g., a coolant) in a heat exchanger (e.g., an evaporator) to absorb heat from the circulating fluid. The working fluid from the cooler 202 and / or boiler 204 can via a pipeline 208 to the AHU 206 be transported.

The AHU 206 can place the working fluid in a heat exchange relationship with an air flow through the AHU 206 passes through (e.g. over one or more stages of cooling coils and / or heating coils). The air flow can, for example, outside air, return air from inside the building 10th or a combination of both. The AHU 206 can transfer heat between the airflow and working fluid to provide heating or cooling for the airflow. For example, the AHU 206 include one or more fans or blowers configured to direct the airflow over or through a heat exchanger that contains the working fluid. The working fluid can then be piped 210 to the cooler 202 or the boiler 204 to return.

The airside system 230 can through the AHU 206 provided air flow (ie the supply flow) via air supply ducts 212 to the building 10th deliver and can via air return ducts 214 Return air from the building 10th to the AHU 206 deliver. In some embodiments, the air side system includes 230 several VAV units 216 (VAV: Variable Air Volume - variable air volume). For example, it is shown that the air side system 230 a separate VAV unit 216 on every floor or zone of the building 10th includes. The VAV units 216 can include air dampers or other flow control elements that can be operated to control a quantity of the supply current that is delivered to individual zones of the building 10th is delivered. In other embodiments, the air side system provides 230 the supply air flow into one or more zones of the building 10th (e.g. via supply channels 212 ), without intermediate VAV units 216 or use other flow controls. The AHU 206 may include various sensors (e.g., temperature sensors, pressure sensors, etc.) that are configured to measure attributes of the supply air flow. The AHU 206 can receive inputs from sensors located within the AHU 206 and / or located within the building zone, and may be the flow rate, temperature, or other attributes of the supply air flow through the AHU 206 adjust to achieve setpoint conditions for the building zone.

Water side system

Now referring to 3rd Figure 3 is a block diagram of a water side system 300 shown in accordance with some embodiments. In various embodiments, the water side system 300 the water side system 220 in the HVAC system 200 supplement or replace or can be separate from the HVAC system 200 be implemented. When implemented in the HVAC system 200 can the water side system 300 a subset of the HVAC devices in the HVAC system 200 (e.g. the boiler 204 , Cooler 202 , Pumps, valves, etc.) and can act as a heated or cooled fluid 206 feed. The HVAC devices of the water side system 300 can be inside the building 10th (e.g. as components of the water system 220 ) or in an external location, such as a central system.

In 3rd is the water side system 300 as a central system with several sub-systems 302-312 shown. It is shown that the sub-plants 302-312 a heating subsystem 302 , a heat recovery cooler sub-system 304 , a cooler subsystem 306 , a cooling tower sub-system 308 , a warm thermal energy storage (TES) subsystem 310 and a cold heat energy storage (TES) subsystem 312 include. The substations 302-312 consume resources (e.g. water, natural gas, electricity, etc.) from utilities to supply thermal energy loads (e.g., hot water, cold water, heating, cooling, etc.) to a building or campus. For example, the heating subsystem 302 be configured to water in a hot water circuit 314 to heat up the hot water between the heating sub-system 302 and the building 10th circulates. The radiator sub-system 306 can be configured to water in a cold water cycle 316 to cool the cold water between the radiator sub-system 306 and the building 10th circulates. The heat recovery cooler sub-system 304 can be configured to heat from the cold water circuit 316 to the hot water circuit 314 transfer to provide additional heating for the hot water and additional cooling for the cold water. The condenser water circuit 318 can heat from the cold water in the cooler sub-system 306 absorb and the absorbed heat in the cooling tower sub-system 308 reject or the absorbed heat to the hot water circuit 314 transfer. The warm TES sub-system 310 and the cold TES sub-system 312 can store warm or cold heat energy for later use.

The hot water circuit 314 and the cold water circuit 316 the heated and / or cooled water can be ventilated on the roof of the building 10th (e.g. the AHU 206 ) or to individual floors or zones of the building 10th (e.g. VAV units 216 ) deliver. The ventilation devices push air past heat exchangers (e.g. heating coils or cooling coils) through which the water flows in order to heat or cool the air. The heated or cooled air can go to individual zones of the building 10th to be delivered to the thermal energy loads of the building 10th to supply. The water then returns to the sub-systems 302-312 back to be further heated or cooled.

Although the substations 302-312 As shown and described as heating and cooling water for circulation to a building, it is understood that any other type of working fluid (e.g., glycol, CO2, etc.) can be used in place of or in addition to water to handle thermal energy loads to supply. In other embodiments, the substations 302-312 provide heating and / or cooling directly to the building or campus without the need for an intermediate heat transfer fluid. These and other variations of the water side system 300 are within the teachings of the present disclosure.

Each of the sub-systems 302-312 may include a variety of equipment configured to promote the functions of the substation. For example, it is shown that the heating subsystem 302 several heating elements 320 (e.g. boilers, electric heaters, etc.) that are configured to the hot water in the hot water circuit 314 Add heat. It is also shown that the heating sub-system 302 some pumps 322 and 324 includes, which are configured to the hot water in the hot water circuit 314 to circulate and the flow rate of hot water through individual heating elements 320 to control. It is shown that the radiator sub-system 306 several coolers 332 contains, which are configured to the cold water in the cold water circuit 316 To withdraw heat. It is also shown that the radiator sub-system 306 some pumps 334 and 336 includes, which are configured to the cold water in the cold water circuit 316 to circulate and the flow rate of cold water through individual coolers 332 to control.

It is shown that the heat recovery cooler sub-system 304 several heat recovery heat exchangers 326 (e.g., refrigeration circuits) configured to use heat from the cold water circuit 316 to the hot water circuit 314 to transfer. It is also shown that the heat recovery cooler system 304 some pumps 328 and 330 includes, which are configured to the hot water and / or cold water through the heat recovery heat exchanger 326 to circulate and the flow rate of water through individual heat recovery heat exchangers 326 to control. It is shown that the cooling tower sub-system 308 several cooling towers 338 contains, which are configured to the condenser water in the condenser water circuit 318 To withdraw heat. It is also shown that the cooling tower sub-system 308 some pumps 340 includes, which are configured to the condenser water in the condenser water circuit 318 to circulate and the flow rate of the condenser water through individual cooling towers 338 to control.

It is shown that the warm TES sub-system 310 a warm TES tank 342 contains, which is configured to store the hot water for later use. The warm TES sub-system 310 can also have one or more pumps or contain valves configured to measure the flow rate of hot water into the warm TES tank 342 to steer in or out of it. It is shown that the cold TES sub-system 312 Cold TES tanks 344 contains, which are configured to store the cold water for later use. The cold TES sub-system 312 may also include one or more pumps or valves configured to measure the flow rate of hot water into the cold TES tanks 344 to steer in or out of them.

In some embodiments, one or more of the pumps include in the water side system 300 (e.g. pumps 322 , 324 , 328 , 330 , 334 , 336 and or 340 ) or pipes in the water side system 300 an associated shut-off valve. Shut-off valves can be integrated with the pumps or positioned upstream or downstream of the pumps to limit the fluid flows in the water side system 300 to control. In various embodiments, the water side system 300 based on the special configuration of the water side system 300 and the types of loads from the water side system 300 be operated, include more, less or different types of devices and / or sub-systems.

Airside system

Now referring to 4th Figure 3 is a block diagram of an airside system 400 shown in accordance with some embodiments. In various embodiments, the air side system 400 the airside system 230 in the HVAC system 200 supplement or replace or can be separate from the HVAC system 200 be implemented. When implemented in the HVAC system 200 can the air side system 400 a subset of the HVAC devices in the HVAC system 200 include (e.g. AHU 206 , VAV units 216 , Channels 212-214 , Fans, air dampers etc.) and can be in the or building 10th or around it. The airside system 400 can act to keep an airflow flowing to the building 10th is supplied using a heated or cooled fluid from the water side system 300 is provided to heat or cool.

In 4th is shown that the air side system 400 an economizer ventilation unit (AHU) 402 contains. Economizer AHUs vary the amount of outside air and return air used by the ventilation unit for heating or cooling. For example, the AHU 402 the return air 404 from the building zone 406 via the return air duct 408 receive and can supply air 410 via the supply air duct 412 to the building zone 406 deliver. In some embodiments, the AHU 402 a roof unit, located on the roof of the building 10th (e.g. the AHU 206 , as in 2nd shown) or otherwise positioned to both the return air 404 as well as the outside air 414 to recieve. The AHU 402 can be configured to an exhaust air damper 416 , a mixing flap 418 and an outside air damper 420 operate to a lot of outside air 414 and the return air 404 to control, which are combined to supply air 410 to build. Any return air 404 that are not through the mixing flap 418 can go through from the AHU 402 through the exhaust air flap 416 as exhaust air 422 be blown out.

Each of the air flaps 416-420 can be operated by an actuator. For example, the exhaust air flap 416 by the actuator 424 can be operated, the mixing flap 418 by the actuator 426 can be operated and the outside air damper 420 by the actuator 428 operate. The actuators 424-428 can with an AHU control 430 via a communication link 432 communicate. The actuators 424-428 can control signals from the AHU control 430 received and feedback signals to the AHU control 430 deliver. Feedback signals can include, for example, an indication of a current actuator or air damper position, an amount of torque or force exerted by the actuator, diagnostic information (e.g. results of diagnostic tests carried out by the actuators 424-428 be performed), status information, commissioning information, configuration settings, calibration data and / or other types of information or data provided by the actuators 424-428 collected, stored or used. The AHU control 430 can be an economizer controller configured to use one or more control algorithms (e.g., state-based algorithms, ESC algorithms (controller searching for extremes), proportional-integral (PI) control algorithms, and proportional-integral-derivative (PID ) Control algorithms, MPC (model prediction control) algorithms, feedback control algorithms, etc.) for controlling the actuators 424-428 to use.

Still referring to 4th is shown that the AHU 304 a cooling coil 434 , a heating coil 436 and a fan 438 contains that within the supply air duct 412 are positioned. The ventilator 438 can be configured to supply air 410 through the cooling coil 434 and / or the heating coil 436 to force and the supply air 410 to the building zone 406 to deliver. The AHU control 430 can be via the communication link 440 with the fan 438 communicate to a flow rate of the supply air 410 to control. In some embodiments, the AHU controller controls 430 a heating or cooling quantity based on the supply air 410 is applied by a fan speed 438 is modulated.

The cooling coil 434 can be a cooled fluid from the water side system 300 (e.g. from the cold water circuit 316 ) via the pipeline 442 received and the cooled fluid through the pipeline 444 to the water side system 300 lead back. A valve 446 can along the pipeline 442 or the pipeline 444 be positioned at a flow rate of the cooled fluid through the cooling coil 434 to control. In some embodiments, the cooling coil includes 434 Several stages of cooling coils that can be activated and deactivated independently of each other (e.g. by the AHU control 430 , by the BMS control 466 etc.) to one on the supply air 410 to modulate the cooling quantity applied.

The heating coil 436 can be a heated fluid from the water side system 300 (e.g. from the hot water circuit 314 ) via the pipeline 448 received and the heated fluid through the pipeline 450 to the water side system 300 lead back. A valve 452 can along the pipeline 448 or the pipeline 450 be positioned at a flow rate of the heated fluid through the heating coil 436 to control. In some embodiments, the heating coil includes 436 Several levels of heating coils that can be activated and deactivated independently of one another (e.g. by the AHU control 430 , by the BMS control 466 etc.) to one on the supply air 410 modulate the amount of heating applied.

Each of the valves 446 and 452 can be controlled by an actuator. For example, the valve 446 by the actuator 454 can be controlled and the valve 452 by the actuator 456 to be controlled. The actuators 454-456 can with the AHU control 430 via the communication links 458-460 communicate. The actuators 454-456 can control signals from the AHU control 430 received and feedback signals to the controller 430 deliver. In some embodiments, the AHU controller receives 430 a measurement of the supply air temperature from a temperature sensor 462 that in the supply air duct 412 positioned (e.g. downstream of the cooling coil 434 and / or the heating coil 436 ). The AHU control 430 can also measure the temperature of the building zone 406 from a temperature sensor 464 received who is in the building zone 406 located.

In some embodiments, the AHU control operates 430 the valves 446 and 452 about actuators 454-456 to modulate a heating or cooling quantity that corresponds to the supply air 410 is supplied (e.g. around a setpoint temperature for the supply air 410 to reach or to the temperature of the supply air 410 to keep within a setpoint temperature range). The positions of the valves 446 and 452 influence the amount of heating or cooling that of the supply air 410 through the cooling coil 434 or the heating coil 436 is supplied, and can correlate with the amount of energy used to reach a desired supply air temperature. The AHU 430 can be the temperature of the supply air 410 and / or the building zone 406 by activating or deactivating the coils 434-436 , Adjusting a fan speed 438 or control a combination of both.

Still referring to FIG. In 4th is shown that the air side system 400 a building management system (BMS) controller 466 and a client device 468 contains. The BMS control 466 may include one or more computer systems (e.g., servers, monitoring controls, subsystem controls, etc.) acting as system level controls, application or data servers, head nodes, or master controls for the airside system 400 , Water system 300 , HVAC system 200 and / or other controllable systems that the building 10th provide, serve. The BMS control 466 can be used with several downstream building systems or subsystems (e.g. the HVAC system 200 , a security system, a lighting system, the water side system 300 etc.) via a communication link 470 communicate according to the same or different protocols (e.g. LON, BACnet, etc.). In various embodiments, the AHU control 430 and the BMS control 466 separated (as in 4th shown) or be integrated. With an integrated implementation, the AHU control can 430 be a software module that is to be executed by a processor of the BMS controller 466 is configured.

In some embodiments, the AHU controller receives 430 Information from the BMS control 466 (e.g. commands, setpoints, operating limits, etc.) and provides information to the BMS controller 466 (e.g. temperature measurements, valve or actuator positions, operating states, diagnostics, etc.). For example, the AHU control 430 the BMS control 466 with temperature measurements from temperature sensors 462-464 , Equipment on / off states, equipment operating capacities and / or any other information supplied by the BMS controller 466 to monitor or control a variable condition or condition within the building zone 406 can be used.

The client device 468 can control one or more human-machine interfaces or client interfaces (e.g. graphical user interfaces, report interfaces, text-based computer interfaces, web services with client orientation, web servers that provide pages for web clients etc.) for controlling, viewing or otherwise Interact with the HVAC system 200 , its subsystems and / or devices. The client device 468 can be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. The client device 468 can be a stationary terminal or a mobile device. For example, the client device 468 a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA or any other type of mobile or non-mobile device. the client device 468 can use the BMS controller 466 and / or the AHU control 430 via the communication link 472 communicate.

Building management system

Now referring to 5 is a block diagram of a building management system (BMS) 500 shown in accordance with some embodiments. The BMS 500 can in the building 10th be implemented to automatically monitor and control various building functions. It is shown that the BMS 500 the BMS control 466 and several building subsystems 528 contains. It is shown that the building subsystems 528 a building electrical subsystem 534 , an information communication technology (ICT) subsystem 536, a security subsystem 538 , an HVAC subsystem 540 , a lighting subsystem 542 , an elevator / escalator subsystem 532 and a fire protection subsystem 530 include. In various embodiments, the building subsystems 528 include fewer, additional or alternative subsystems. For example, the building subsystems 528 also or alternatively, include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a sales subsystem, a printer or copy service subsystem, or other type of building subsystem that includes controllable equipment and / or uses sensors to monitor or control the building 10th used. In some embodiments, the building subsystems include 528 the water side system 300 and / or the air side system 400 as referring to 3-4 described.

Each of the building subsystems 528 can include any number of devices (e.g., IoT devices), sensors, controls, and connections to complete its individual functions and control activities. The HVAC subsystem 540 can do many of the same components as the HVAC system 200 included that with reference to 2-4 is described. For example, the HVAC subsystem 540 a cooler, a boiler, any number of ventilation units, economizers, field controls, monitoring controls, actuators, temperature sensors and other devices for controlling the temperature, humidity, air flow or other variable conditions within the building 10th include. The lighting subsystem 542 can include any number of lighting fixtures, ballasts, light sensors, dimmers, or other devices that are configured to controllably adjust the amount of light provided for a building area. The security subsystem 538 may include presence sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices.

Still referring to 5 is shown that the BMS control 466 a communication interface 507 and a BMS interface 509 includes. the interface 507 can control the communications between the BMS 466 and external applications (e.g. monitoring and reporting applications 522 , Corporate tax applications 526 , Remote systems and applications 544 , Applications that relate to client devices 548 located, etc.) facilitate user control, monitoring and adjustment of the BMS control 466 and / or the subsystems 528 to enable. the interface 507 can also control communications between the BMS controller 466 and the client devices 548 facilitate. The BMS interface 509 can control the communications between the BMS 466 and the building subsystems 528 (e.g. HVAC, lighting security, elevators, power distribution, business, etc.).

The interfaces 507 , 509 can use wired or wireless communication interfaces (e.g. sockets, antennas, transmitters, receivers, transceivers, cable connections, etc.) to carry out data communication with the building subsystems 528 or other external systems or devices. In various embodiments, communication can take place via the interfaces 507 , 509 directly (e.g. local wired or wireless communications) or over a communications network 546 (e.g. a WAN, the Internet, a cellular network, etc.). For example, the Interfaces 507 , 509 contain an Ethernet card and a port for sending and receiving data via an Ethernet-based communication link or an Ethernet-based network. In another example, the interfaces 507 , 509 a Wi-Fi transceiver for communicating over a wireless communication network. In another example, one or both of the interfaces can be used 507 , 509 cellular or mobile phone communication transceivers included. In one embodiment, the communication interface 507 a power line communication interface and is the BMS interface 509 an ethernet interface. In other embodiments, both are the communication interface 507 as well as the BMS interface 509 Ethernet interfaces or are the same Ethernet interface.

Still referring to 5 is shown that the BMS control 466 a processing circuit 504 including a processor 506 and a memory 508 includes. The processing circuit 504 can communicate with the BMS interface 509 and / or the communication interface 507 be connected so that the processing circuit 504 and the various components of which data through the interfaces 507 , 509 can send and receive. The processor 506 may be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components or other suitable electronic processing components.

The memory 508 (e.g., memory, storage device, storage device, etc.) may include one or more devices (e.g., RAM, ROM, flash memory, hard disk storage, etc.) for storing data and / or computer code for completion or facilitation of the various processes, layers and modules described in the present application. The memory 508 can be or include volatile memory or non-volatile memory. The memory 508 may include database components, object code components, script components, or any other type of information structure to support the various activities and information structures described in the present application. According to some embodiments, the memory 508 via the processing circuit 504 communicative with the processor 506 connected and contains computer code for execution (e.g. by the processing circuitry 504 and / or the processor 506 ) one or more processes described here.

In some embodiments, the BMS control is 466 implemented within a single computer (e.g., a server, chassis, etc.). In various other embodiments, the BMS controller can 466 be distributed across multiple servers or computers (e.g. that may exist in distributed locations). While 4th Applications 522 and 526 than outside the BMS control 466 shows the applications in some embodiments 522 and 526 within the BMS control 466 (e.g. within the memory 508 ) are hosted.

Still referring to 5 is shown that the memory 508 a business integration layer 510 , a layer 512 for automated measurement and validation (AM&V), a demand response (DR) layer 514 , a layer 516 for fault detection and diagnosis (FDD), an integrated control layer 518 contains and a building subsystem integration layer 520 includes. The layers 510-520 can be configured to input from the building subsystems 528 and other data sources, optimal control actions for the building subsystems 528 determine based on the inputs, generate control signals based on the optimal control actions and the generated control signals to the building subsystems 528 to deliver. The following sections describe some of the general functions performed by each of the layers 510-520 in the BMS 500 be performed.

The corporate integration layer 510 can be configured to provide clients or local applications with information and services to support a wide variety of enterprise-level applications. For example, corporate tax applications 526 configured to provide cross-system control for a graphical user interface (GUI) or for any number of business applications at the enterprise level (e.g. accounting systems, user identification systems, etc.). Corporate tax applications 526 can also or alternatively be configured, configuration GUIs for configuring the BMS controller 466 to provide. In still other embodiments, the corporate tax applications 526 with layers 510-520 work to optimize building performance (e.g. efficiency, energy use, comfort or security) based on inputs made at the interface 507 and / or the BMS interface 509 be received.

The building subsystem integration layer 520 can be configured to communicate between the BMS controller 466 and the building subsystems 528 manage. For example, the Building subsystem integration layer 520 Sensor data and input signals from the building subsystems 528 receive and output data and control signals to the building subsystems 528 deliver. The building subsystem integration layer 520 can also be configured to communicate between building subsystems 528 manage. The building subsystem integration layer 520 translates communications (e.g. sensor data, input signals, output signals etc.) across multiple systems with multiple providers / multiple protocols.

The demand response layer 514 may be configured to determine (e.g., optimize) resource consumption (e.g., electricity consumption, natural gas consumption, water consumption, etc.) and / or the monetary cost of such resource consumption to meet the needs of the building 10th cover up. Resource usage determination can be based on usage time prices, cutback signals, energy availability, or other data provided by utilities, distributed power generation systems 524 , Energy storage 527 (e.g. warm TES 342 , Cold-TES 344 etc.) or received from other sources. The demand response layer 514 can input from other layers of BMS control 466 received (e.g. the building subsystem integration layer 520 , the integrated control layer 518 etc.). The inputs received from other layers may include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, presence sensor outputs, room plans, and the like. The inputs may also include inputs such as power consumption (e.g., expressed in kWh), thermal load measurements, price information, forecast prices, smoothed prices, utility cutback signals, and the like.

In some embodiments, the demand response layer includes 514 control logic to respond to the data and signals it receives. These responses can be used to communicate with the control algorithms in the integrated control layer 518 , changing control strategies, changing setpoints, or enabling / disabling building equipment or subsystems in a controlled manner. The demand response layer 514 may also include control logic configured to determine when to use stored energy. For example, the demand response layer 514 determine immediately before the start of a peak usage hour with the use of energy from the energy storage 527 to start.

In some embodiments, the demand response layer includes 514 a control module configured to actively initiate control actions (e.g., automatically changing setpoints) that base energy costs on one or more inputs that are representative or based on demand (e.g., price, a cut signal, a demand level, etc.), reduce (e.g. minimize). In some embodiments, the demand response layer uses 514 Equipment models to determine an optimal set of tax acts. The equipment models may include, for example, thermodynamic models that describe the inputs, outputs, and / or functions performed by different sets of building equipment. Equipment models can represent collections of building equipment (e.g. sub-systems, cooler arrangements, etc.) or individual devices (e.g. individual coolers, heaters, pumps, etc.).

The demand response layer 514 may also contain or use one or more definitions of demand response policies (e.g. databases, XML files, etc.). The policy definitions can be edited or adapted by a user (e.g. via a graphical user interface) so that the control actions initiated in response to demand inputs are tailored to the user's application, the desired level of comfort, the specific building equipment or based on other concerns can be. For example, the definitions of the demand response guidelines can specify which equipment can be switched on or off in response to special demand inputs, how long a system or equipment element should be switched off, which setpoints can be changed, what is the permissible setpoint adjustment range and how long a setpoint for high demand should be maintained before returning to a normally planned set point, how close to the capacity limits are to be reached, which equipment modes to use, the energy transfer rates (e.g. the maximum rate, an alarm rate, other rate limit information, etc.) in the and from the energy storage devices (e.g. heat storage tanks, battery banks, etc.) and when the energy generation is to take place on site (e.g. via fuel cells, an engine generator set, etc.).

The integrated control layer 518 can be configured to input or output the building subsystem integration layer 520 and / or the demand response layer 514 to use to make tax decisions. Due to subsystem integration through the building subsystem integration layer 520 is provided, the integrated control layer 518 Tax activities of the Subsystems 528 integrate so that the subsystems 528 behave like a single integrated super system. In some embodiments, the integrated control layer includes 518 control logic that uses inputs and outputs from multiple building subsystems to provide greater convenience and energy savings compared to the convenience and energy savings that separate subsystems alone could provide. For example, the integrated control layer 518 configured to use input from a first subsystem to make an energy saving control decision for a second subsystem. Results of these decisions can be sent to the building subsystem integration layer 520 be reported back.

It is shown that the integrated control layer 518 logically below the demand response layer 514 lies. The integrated control layer 518 can be configured to affect the effectiveness of the demand response layer 514 to improve by allowing the building subsystems 528 and their respective control loops in coordination with the demand response layer 514 to be controlled. This configuration can advantageously reduce disruptive demand response behavior compared to conventional systems. For example, the integrated control layer 518 configured to ensure that a demand response driven upward adjustment to the chilled water temperature set point (or other component that directly or indirectly affects the temperature) does not increase the fan energy (or other energy used to cool an area is used), which would lead to a higher total energy consumption of the building than was saved with the cooler.

The integrated control layer 518 can be configured to provide feedback to the demand response layer 514 to deliver, so the demand response layer 514 checks whether restrictions (e.g. temperature, lighting levels, etc.) are properly observed, even while the required load shedding is being carried out. The restrictions may also include setpoints or sensed limits related to safety, equipment operating limits and performance, comfort, fire regulations, electrical regulations, energy regulations, and the like. The integrated control layer 518 is also logically below the layer 516 for fault detection and diagnosis and the shift 512 for automated measurement and validation. The integrated control layer 518 may be configured to provide calculated inputs (e.g., aggregations) for these higher levels based on outputs from more than one building subsystem.

The layer 512 for automated measurement and validation (AM&V) can be configured to verify whether that of the integrated control layer 518 or the demand response layer 514 ordered control strategies work properly (e.g. using data through the AM&V layer 512 , the integrated control layer 518 , the building subsystem integration layer 520 who have favourited FDD layer 516 or others have been aggregated). That through the AM&V layer 512 Calculations performed can be based on building system energy models and / or equipment models for individual BMS devices or subsystems. For example, the AM&V layer 512 an output predicted by a model with an actual output from the building subsystems 528 compare to determine the accuracy of the model.

The layer 516 for Fault Detection and Diagnostics (FDD) can be configured to provide continuous fault detection for building subsystems 528 To provide building subsystem devices (ie, building equipment) and control algorithms by the demand response layer 514 and the integrated control layer 518 be used. The FDD layer 516 can input data from the integrated control layer 518 received directly from one or more building subsystems or devices or from another data source. The FDD layer 516 can automatically diagnose detected errors and react to them. Responses to detected or diagnosed errors can include providing a warning message to a user, a maintenance planning system, or a control algorithm configured to attempt to correct the error or to bypass the error.

The FDD layer 516 can be configured to output a specific identification of the faulty component or the cause of the fault (e.g. loose air damper connection) using detailed subsystem inputs in the building subsystem integration layer 520 Are available. In other embodiments, the FDD layer is 516 configured to "error" events for the integrated control layer 518 To provide control strategies and policies in response to received error events. According to some embodiments, the FDD layer 516 (or a policy executed by an integrated control engine or a business rules engine) Shut down systems or direct control activities around faulty devices or systems to one Reduce energy wastage, extend equipment life, or ensure a proper control response.

The FDD layer 516 can be configured to store or access a variety of different system data stores (or data points for live data). The FDD layer 516 may use some of the content of the data stores to identify faults at the equipment level (e.g., a special cooler, a special AHU, a special terminal unit, etc.) and other content to identify faults at the component or subsystem level . For example, building subsystems 528 Generate temporal (ie time series) data that the performance of the BMS 500 and the various components thereof. The through the building subsystems 528 Data generated may include measured or calculated values that have statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) behaves with respect to the error of its setpoint. These processes can be done through the FDD layer 516 are examined to reveal when the performance of the system begins to deteriorate and to warn a user to repair the error before it becomes more serious.

Building management system with cloud building management platform

Now referring to 6 is a block diagram of another building management system (BMS) 600 shown in accordance with some embodiments. The BMS 600 can be configured to sample data from the building subsystems 528 collect and the data samples to the cloud building management platform 620 to provide raw time series data, derived time series data and / or entity data from the data samples. In some embodiments, the cloud building management platform 620 in the 1 shown building management platform 102 supplement or replace or can be separate from the building management platform 102 be implemented. The cloud building management platform 620 can process and transform the raw time series data to produce derived time series data. Throughout this disclosure, the term "derived time series data" is used to describe the result or output of a transformation or other time series processing operation performed by various services of the building management platform 620 is carried out (e.g. data aggregation, data cleansing, calculation of virtual points, etc.). The term “entity data” is used to describe the attributes of different intelligent entities (e.g. IoT systems, devices, components, sensors and the like) and the relationships between the intelligent entities. The derived time series data can be used in various applications 630 delivered and / or in the store 614 saved (e.g. as materialized views of the raw time series data). In some embodiments, the cloud building management platform separates 620 Data collection; Data storage, retrieval and analysis; and data visualization in three different layers. This enables the cloud building management platform 620 , a variety of applications 630 supports that use the derived time series data and enables new applications 630 the existing infrastructure through the cloud building management platform 620 is provided, reuse.

It should be noted that the components of the BMS 600 and / or the cloud building management platform 620 integrated into a single device (e.g., a monitoring controller, a BMS controller, etc.) or distributed across multiple separate systems or devices. In other embodiments, some or all of the components of the BMS 600 and / or the cloud building management platform 620 be implemented as part of a cloud-based computing system configured to receive and process data from one or more building management systems. In other embodiments, some or all of the components of the BMS 600 and / or the cloud building management platform 620 Components of a controller at the subsystem level (e.g. an HVAC controller), a subsystem controller, a device controller (e.g. the AHU controller 330 , cooler controller, etc.), a field controller, a computer workstation, a client device, or any other system or device that receives and processes data from building systems and equipment.

The BMS 600 can do many of the same components as the BMS 500 included that with reference to 5 is described. For example, it is shown that the BMS 600 a BMS interface 602 and a communication interface 604 includes. The interfaces 602-604 can use wired or wireless communication interfaces (e.g. sockets, antennas, transmitters, receivers, transceivers, cable connections, etc.) to carry out data communication with the building subsystems 528 or other external systems or devices. Communication through the interfaces 602-604 can be performed directly (e.g., local wired or wireless communications) or over a communications network 546 (e.g. a WAN, the Internet, a cellular network, etc.).

The communication interface 604 can the communication between the BMS 600 and external applications (e.g. remote systems and applications 544 ) facilitate user control, monitoring and adaptation for the BMS 600 to enable. The communication interface 604 can also establish communications between the BMS 600 and the client devices 548 facilitate. The BMS interface 602 can the communication between the BMS 600 and the building subsystems 528 facilitate. The BMS 600 can be configured with the building subsystems 528 communicate using a variety of building automation system protocols (e.g., BACnet, Modbus, ADX, etc.). In some embodiments, the BMS receives 600 Data samples from building subsystems 528 and delivers control signals to building subsystems 528 via the BMS interface 602 .

The building subsystems 528 can the building electrical subsystem 534 , the Information Communication Technology (ICT) subsystem 536, the security subsystem 538 , the HVAC subsystem 540 , the lighting subsystem 542 , the elevator / escalator subsystem 532 and the fire protection subsystem 530 as referring to 5 described. In various embodiments, the building subsystems 528 include fewer, additional or alternative subsystems. For example, the building subsystems 528 also or alternatively, include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a sales subsystem, a printer or copy service subsystem, or other type of building subsystem that includes controllable equipment and / or uses sensors to monitor or control the building 10th used. In some embodiments, the building subsystems include 528 the water side system 300 and / or the air side system 400 as referring to 3-4 described. Each of the building subsystems 528 can include any number of devices, controls, and connections to complete its individual functions and control activities. The building subsystems 528 may include building equipment (e.g. sensors, ventilation units, coolers, pumps, valves, etc.) configured to monitor and control a building's condition, such as temperature, humidity, airflow, etc.

Still referring to 6 is shown that the BMS 600 a processing circuit 606 including a processor 608 and a memory 610 includes. The cloud building management platform may include one or more processing circuitry including one or more processors and memory. Each of the processors may be a general purpose or special purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Each of the processors is configured to execute computer code or instructions stored in memory or received by other computer readable media (e.g., CD-ROM, network storage, remote server, etc.).

The memory may include one or more devices (e.g., storage devices, storage devices, storage devices, etc.) for storing data and / or computer code to complete and / or facilitate the various processes described in the present disclosure. The memory may be random access memory (RAM), read-only memory (ROM), hard disk storage, temporary storage, non-volatile memory, flash memory, optical memory or any other suitable memory for storing software objects and / or include computer instructions. The memory may include database components, object code components, script components, or any other type of information structure to support the various activities and information structures described in the present disclosure. The memory can be communicably connected to the processors via the processing circuitry and can be computer code for execution (e.g. by the processor 508 ) contain one or more processes described here.

Still referring to 6 is shown that the cloud building management platform 620 a data collector 612 contains. The data collector 612 receives data samples from building subsystems 528 via the BMS interface 602 . However, the present disclosure is not limited to this and the data collector 612 can get the data samples directly from the building subsystems 528 received (e.g. via the network 546 or using a suitable method). In some embodiments, the data samples include data values for different data points. The data values can be measured or calculated depending on the type of data point. For example, a data point received by a temperature sensor can contain a measured data value, which is a value obtained by the temperature sensor indicates measured temperature. A data point received by a cooler controller can contain a calculated data value that indicates a calculated efficiency of the cooler. The data collector 612 can acquire data samples from several different devices (e.g., IoT devices, sensors, etc.) within the building subsystems 528 receive.

The data samples can contain one or more attributes that describe or characterize the corresponding data points. For example, the data samples may be a name attribute that defines a point name or ID (e.g., "B1F4R2.TZ"), a device attribute that indicates a type of device from which the data samples are received (e.g., temperature sensor , Humidity sensor, cooler, etc.), a unit attribute that defines a unit of measurement associated with the data value (e.g., ° F, ° C, kPA, etc.), and / or any other attribute that describes the corresponding data point or provides contextual information regarding the data point. The attribute types contained in each data point may depend on the communication protocol used to send the data samples to the BMS 600 and / or the cloud building management platform 620 is used. For example, data samples received via the ADX protocol or the BACnet protocol may contain a plurality of descriptive attributes along with the data value, whereas data samples received via the Modbus protocol may contain a smaller number of attributes (e.g. E.g. only the data value without any corresponding attributes).

wobei Schlüssel eine Kennung der Quelle der Rohdatenabtastwerte (z. B. Zeitreihen-ID, Sensor-ID, Vorrichtung-ID usw.) ist, Zeitstempel_i den Zeitpunkt identifiziert, zu dem der i-te Abtastwert gesammelt wurde, und Wert_i den Wert von des i-ten Abtastwerts angibt.In some embodiments, the data sample is received with a timestamp that indicates a point in time at which the corresponding data value was measured or calculated. In other embodiments, the data collector inserts 612 add timestamps to the data samples based on the times when the data samples are received. The data collector 612 can generate raw time series data for each of the data points for which data samples are received. Each time series can contain a series of data values for the same data point and a time stamp for each of the data values. For example, a time series for a data point provided by a temperature sensor can include a series of temperature values that are measured by the temperature sensor and the corresponding points in time at which the temperature values were measured. An example of a time series created by the data collector 612 can be generated is as follows: $$\begin{array}{l}[<ScHl\ddot{u}ssel,Zeitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1,W\mathrm{<figure-callout\; id="1"\; label="e\; r\; t"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">e\; </figure-callout>}r{t}_{}{}_1>,<ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{2nd}},Wer{t}_{\mathrm{2nd}}>,\\ <ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{3rd}},Wer{t}_{\mathrm{3rd}}>]\end{array}$$

where key is an identifier of the source of the raw data samples (e.g., time series ID, sensor ID, device ID, etc.), time stamp _i identifies the time at which the ith sample was collected, and value _i the value of the ith sample.

The data collector 612 can add timestamps to the data samples or change existing timestamps so that each data sample contains a local timestamp. Each local timestamp indicates the local time at which the corresponding data sample was measured or collected and can include an offset relative to universal time. The local timestamp indicates the local time at the location at which the data point was measured at the time of the measurement. The offset indicates the difference between the local time and a universal time (e.g. the time at the international date limit). For example, a data sample collected in a time zone six hours behind universal time may include a local timestamp (e.g. Timestamp = 18 - 03 - 2016Z14: 10: 02) and an offset that indicates that the local time stamp is six hours behind universal time (e.g. offset = -6: 00). The offset can be adjusted depending on whether the time zone is in daylight saving time when the data sample is being measured or collected (e.g. +1: 00 or -1: 00).

The combination of the local timestamp and the offset provides a unique timestamp beyond the summer time limits. This enables an application that uses the time series data to display the time series data in local time without first converting from universal time. The combination of the local time stamp and the offset also provides enough information to convert the local time stamp to universal time without having to look up a daylight saving time schedule. For example, the offset can be subtracted from the local timestamp to produce a universal time value that corresponds to the local timestamp without referring to an external database and without requiring any other information.

In some embodiments, the data collector organizes 612 the raw time series data. The data collector 612 can identify a system or device associated with each of the data points. For example, the data collector 612 assign a data point to a temperature sensor, a ventilation unit, a cooler or any other type of system or device. In some embodiments, a data entity can be created for the data point, in which case the data collector 612 (e.g. via an entity service) can assign the data point to the data entity. In various embodiments, the data collector uses the name of the data point, a range of values of the data point, statistical characteristics of the data point, or other attributes of the data point to identify a particular system or device associated with the data point. The data collector 612 can then determine from entity data how that system or device is related to the other systems or device at the building location. For example, the data collector 612 determine from the entity data that the identified system or device is part of a larger system (e.g. an HVAC system) or a specific area (e.g. a specific building, room or zone of the building, etc .) provided. In some embodiments, the data collector uses 512 an entity graph (e.g. via the entity service 626 ) or retrieve it when it organizes the time series data.

The data collector 612 can transfer the raw time series data to the services of the cloud building management platform 620 deliver and / or the raw time series data in storage 614 to save. The storage 614 can be internal storage or external storage. For example, the storage 614 internal storage related to the cloud building management platform 620 and / or the BMS 600 and / or may include a remote database, cloud-based data hosting, or other remote data storage. The storage 614 can be configured by the data collector 612 raw time series data obtained through the cloud building management platform 620 generated derived time series data and / or that generated by the cloud building management platform 620 store directional acyclic graphs (DAGs) used to process the time series data.

Still referring to 5 can the cloud building management platform 620 the raw time series data from the data collector 612 received and / or the raw time series data from storage 614 recall. The cloud building management platform 620 can include a variety of services configured to analyze, process, and transform raw series data. For example, the cloud building management platform is shown 620 a security service 622 , an analysis service 624 , an entity service 626 and a time series service 628 contains. The security service 622 can assign security attributes to the raw time series data to ensure that the time series data is only accessible to authorized persons, systems or applications. The security service 622 can create a notification layer to exchange secure messages with the entity service 626 contain. In some embodiments, the security service can 622 Entitlement service credentials 626 deliver so that the entity service 626 can determine the types of entity data that a particular entity or device can access. The entity service 624 may assign entity information (or entity data) to the time series data to associate data points with a particular system, device, or area. The time series service 628 and the analytics service 624 can apply various transformations, operations, or other functions to the raw time series data to generate derived time series data.

In some embodiments, the time series service aggregates 628 predefined intervals of raw time series data (e.g. quarterly intervals, hourly intervals, daily intervals, monthly intervals, etc.) to generate new derived time series of the aggregated values. These derived time series can be called "data rollups" because they are condensed versions of the raw time series data. The through the time series service 628 generated data rollups represent an efficient mechanism for applications 630 ready to query the time series data. For example, applications 630 Generate visualizations of the time series data (e.g. charts, graphs, etc.) using the pre-aggregated data rollups instead of the raw time series data. This enables the applications 630 Simply retrieve and present the pre-aggregated data rollups without the applications 630 need to perform an aggregation in response to the query. Because the data rollups are pre-aggregated, the applications can 630 Present the data rollups quickly and efficiently without additional processing at query time to generate aggregate time series values.

In some embodiments, the time series service computes 628 virtual points based on the raw time series data and / or the derived time series data. Virtual points can be calculated using a variety of mathematical operations (e.g. addition, subtraction, multiplication, division etc.) or functions (e.g. average value, maximum value, minimum value, thermodynamic functions, linear functions, non-linear functions, etc.) are applied to the actual data points represented by the time series data. For example, the time series service 628 calculate a virtual data point (point ID ₃ ) by adding two or more actual data points (point ID ₁ and point ID ₂ ) (e.g. pointID ₃ = pointID ₁ + point ID ₂ ). As another example, the time series service 628 calculate an enthalpy data point (point ID ₄ ) based on a measured temperature data point (point ID ₅ ) and a measured pressure data point (point ID ₆ ) (e.g. point ID ₄ = enthalpy (point ID ₅ , point ID ₆ )). The virtual data points can be saved as derived time series data.

The applications 630 can access and use the virtual data points in the same way as the actual data points. The applications 630 may not need to know if a data point is an actual data point or a virtual data point since both types of data points can be stored as derived time series data and by the applications 630 can be treated in the same way. In some embodiments, the derived time series are stored with attributes that designate each data point as either a virtual data point or an actual data point. Such attributes allow the applications 630 identify whether a given time series represents a virtual data point or an actual data point, although both types of data points by the applications 630 can be treated in the same way. These and other characteristics of time series service 628 are with reference to 9 described in more detail.

In some embodiments, the analytics service analyzes 624 the raw time series data and / or the derived time series data to detect errors. The analysis service 624 may apply a set of mis-detection rules to the time series data to determine whether an error is detected in each interval of the time series. The error detections can be saved as derived time series data. For example, the analysis service 624 generate a new error detection time series with data values that indicate whether an error has been detected in each interval of the time series. The error detection time series can be stored as derived time series data together with the raw time series data 614 get saved.

Still referring to 6 is shown that the BMS 600 some applications 630 contains, including a power management application 632 , Monitoring and reporting applications 634 and corporate tax applications 636 . Although only a few uses 630 are shown, it is intended that the applications 630 may include any of a variety of suitable applications configured through the cloud building management platform 620 generated raw time series or derived time series. In some embodiments, the applications exist 630 as a separate layer of the BMS 600 (e.g. as part of the cloud building management platform 620 and / or the data collector 612 ). In other embodiments, the applications 630 exist as remote applications that run on remote systems or devices (e.g. remote systems and applications 544 , Client devices 548 and / or the like).

The applications 630 can use the derived time series data to perform a variety of data visualization, monitoring, and / or control activities. For example, the power management application 632 and the monitoring and reporting application 634 use the derived time series data to create user interfaces (e.g., graphs, graphs, etc.) that present the derived time series data to a user. In some embodiments, the user interfaces present the raw time series data and the derived data rollups in a single diagram or graph. For example, a drop-down selector can be provided to allow a user to select the raw time series data or any of the data rollups for a particular data point.

The corporate tax application 636 can use the derived time series data to perform various tax activities. For example, the corporate tax application 636 the derived time series data as input to a control algorithm (e.g. a state based algorithm, an ESC algorithm (controller looking for extremes), proportional integral (PI) control algorithm, a proportional integral derived (PID) control algorithm, one MPC (Model Prediction Control) algorithm, a feedback control algorithm, etc.) for generating control signals for the building subsystems 528 use. In some embodiments, the building subsystems use 528 the control signals to operate building equipment. Operating building equipment can affect the measured or calculated values of the data samples sent to the BMS 600 and / or the Cloud building management platform 620 to be delivered. Accordingly, the corporate tax application 636 use the derived time series data as feedback to the systems and devices of the building subsystems 528 to control.

Cloud building management platform entity service

With reference to 7 A block diagram is shown, according to some embodiments, that the entity service 626 illustrated in more detail. The entity service 626 registers and manages various buildings (e.g. 110-140 ), Areas, people, subsystems (e.g. 428 ), Devices (e.g. 112-146 ) and other entities in the cloud building management platform 620 . According to various embodiments, an entity can be any person, location or physical object, which is referred to below as an object entity. Furthermore, an entity can be any event, any data point or a recording structure, which is referred to below as a data entity. In addition, relationships between entities can be defined by relational objects.

In some embodiments, an object entity can be defined as having at least three types of attributes. For example, an object entity can have a static attribute, a dynamic attribute, and a behavior attribute. The static attribute can include any unique identifier of the object entity or any feature of the object entity that either does not change over time or changes only rarely (e.g., a device ID, a person's name, or social security number , the address or room number of a place and the like). The dynamic attribute may include a property of the object entity that changes over time (e.g., location, age, measurement, data point, and the like). In some embodiments, the dynamic attribute of an object entity may be linked to a data entity. In this case, the dynamic attribute of the object entity can simply refer to a location (e.g. data / network address) or a static attribute (e.g. identifier) of the linked data entity that the data (e.g. the value or stores the information) of the dynamic attribute. Accordingly, in some such embodiments, when a new data point (e.g., time series data) is received for the object entity, only the linked data entity may be updated while the object entity remains unchanged. Therefore, resources that would have been spent updating the object entity can be reduced.

However, the present disclosure is not so limited. For example, in some embodiments, there may also be some data that is updated in the dynamic attribute of the object entity itself (e.g., during predetermined intervals). For example, the linked data entity may be configured to be updated each time a new data point is received, whereas the corresponding dynamic attribute of the object entity may be configured to be updated less frequently (e.g., at predetermined intervals, the lower than the intervals during which the new data points are received). In some implementations, the dynamic attribute of the object entity can include both a link to the data entity and either part of the data from the data entity or data derived from the data of the data entity. For example, in one embodiment where periodic temperature readings are received by a thermostat, an object entity corresponding to the thermostat could include the last temperature readout and a link to a data unit that stores a series of the last ten temperature readings received by the thermostat.

The behavioral attribute can define a function of the object entity, for example based on inputs, skills and / or permissions. For example, behavioral attributes can define the types of inputs that the object entity is configured to accept, how the object entity should respond under certain conditions, what types of functions the object entity can perform, and the like. As a non-limiting example, if the object entity represents a person, the person's behavioral attribute may be their job title or job tasks, user permissions to access certain systems or locations, an expected location or behavior at a particular time of day, tendencies or preferences based on related Activity data provided by the entity service 626 (e.g., social media activity), and the like. As a further non-limiting example, if the object entity represents a device, the behavioral attributes may include the types of inputs that the device can receive, the types of outputs that the device can produce, the types of controls to which the device is connected It is capable of the types of software or versions that the device currently has, known device responses to certain types of inputs (e.g., behavior of the device defined by its programming) and the like.

In some embodiments, the data entity can be defined as having at least one static attribute and one dynamic attribute. The static attribute of the data entity can contain a unique identifier or description of the data entity. For example, if the data entity is linked to a dynamic attribute of an object entity, the static attribute of the data entity can include an identifier that is used to link to the dynamic attribute of the object entity. In some embodiments, the dynamic attribute of the data entity represents the data for the dynamic attribute of the linked object entity. In some embodiments, the dynamic attribute of the data entity may represent some other data that is derived, analyzed, inferred, calculated, or determined based on data from multiple data sources.

 hatTeil {Entität A, Entität B}

wobei das statische Attribut hatTeil definiert, was die Beziehung der aufgelisteten Entitäten ist, und die Reihenfolge der aufgelisteten Entitäten oder Datenfelder des relationalen Objekts gibt an, welche Entität der Teil der anderen ist (z. B. Entität A → hatTeil → Entität B).In some embodiments, the relational object can be defined as having at least one static attribute. The relational object's static attribute can semantically define the type of relationship between two or more entities. For example, in one non-limiting embodiment, a relational object for a relationship that semantically defines that entity A is part of entity B or that entity B is part of entity A may include:

 hatpart {entity A, entity B} 

where the static attribute has part defines what the relationship of the listed entities is, and the order of the listed entities or data fields of the relational object indicates which entity is part of the other (e.g. entity A → has part → entity B).

In various embodiments, the relational object is an object-oriented construct with predefined fields that define the relationship between two or more entities regardless of the type of entities. For example, the cloud building management platform 620 provide a comprehensive set of pre-made entity models with standardized relational objects that can be used to describe how two or more entities are semantically related and how data is exchanged and / or processed between the entities. Accordingly, a global change in the definition or relationship of a relational object at the system level at the object level can be achieved without having to manually change the entity relationships for each object or entity. Furthermore, in some embodiments, a global change at the system level can be propagated to third-party applications that are in the cloud building management platform 620 are integrated so that the global change can be implemented across all of the third-party applications without the need to manually implement the change in each different application.

For example, referring to 8th an exemplary entity graph of entity data according to some embodiments is shown. The term “entity data” is used to describe the attributes of different entities and the relationships between the entities. For example, entity data can be represented in the form of an entity graph. In some embodiments, entity data includes any suitable predefined data models (e.g., as a table, JSON data, and / or the like), such as entity type or object, and further includes one or more relational objects that semantically define the relationships between the entities . The relational objects can help, for example, semantically define hierarchical or directional relationships between the entities (e.g., entity X controls entity Y, entity A feeds entity B, entity 1 resides in entity 2, and the like ). For example, an object entity (e.g., an IoT device) can be represented by an entity type or an object, which generally describes how data corresponding to the entity is structured and stored.

       Thermostat {
               Typ,
               Modell-Nr.,
               Vorrichtungsname,
               Herstellungsdatum,
               Seriennummer,
               MAC-Adresse,
               Standort,
               Aktuelle Luftqualität,
               Aktuelle Innentemperatur,
               Aktuelle Außentemperatur,
               Zielinnentemperatur,
       }Punktzeitplan (z. B. BACnet-Zeitplanobjekt)

wobei verschiedene Attribute statische Attribute (z. B. „Typ“, „Modellnummer“, „Vorrichtungsname“ usw.), dynamische Attribute (z. B. „Aktuelle Luftqualität“, „Aktuelle Außentemperatur“ usw.) oder Verhaltensattribute (z. B. „Innentemperatur“ usw.) für die Objektentität „Thermostat“ sind. In einer relationalen Datenbank ist das Objekt „Thermostat“ ein Tabellenname und repräsentieren die Attribute Spaltennamen.For example, an entity type (or object) "thermostat" can be represented using the following scheme:

       Thermostat {
               Type,
               Model No.,
               Device name,
               Manufacturing date,
                Serial number,
               MAC address,
               Location,
               Current air quality,
               Current inside temperature,
               Current outside temperature,
               Target internal temperature,
       } Point schedule (e.g. BACnet schedule object) 

where various attributes are static attributes (e.g. "type", "model number", "device name" etc.), dynamic attributes (e.g. "current air quality", "current outside temperature" etc.) or behavioral attributes (e.g. "Indoor temperature" etc.) for the object entity "Thermostat". In a relational database, the "Thermostat" object is a table name and the attributes represent column names.

An example of an object entity data model for a person named John Smith in a relational database can be represented by the following table: First name Surname Telephone number. Age Location Job title John Smith (213)220-XXXX 36 At home engineer where various attributes are static attributes (e.g. "first name", "last name" etc.), dynamic attributes (e.g. "age", "location" etc.) or behavioral attributes (e.g. "engineer" for the Object entity "John Smith" are.

An example data entity for the data point "Current inside temperature" for the "Thermostat" from John Smith in a relational database can be represented by the following table: Current value description Device type Unit of measurement 68 "Current inside temperature of John's house" thermostat Degree-F where various attributes are static attributes (e.g. "Description" and "Device type") and dynamic attributes (e.g. "Current value").

While structuring entities through the entity type or object can help define the data representation of the entities, these data models do not provide information about how the entities are related. For example, a BMS, building subsystem, or device may require data from multiple sources, as well as information about how the sources are related, to provide a proper decision, action, or recommendation. Accordingly, in various embodiments, the entity data further includes the relational objects to semantically define the relationships between the entities, which can help increase data parsing speeds and provide easy navigation and browsing.

Still referring to 8th includes, for example, an entity graph 800 for the thermostat object entity 802 Different class entities (e.g. user, address, setpoint command and temperature object), object entities (e.g. John and thermostat), relational objects (e.g. is a type of, has, is linked, has storage and has operation) and data entities (AI- 201-01, TS-ID 1 , Daily average 1 , Abnormal indoor temperature 1 , AO 101-1 and geo 301-01 ). The relational objects describe the relationships between the various class, object, and data entities in a semantic and syntactic way, so that an application or a user can see the entity graph 800 quickly looks at the relationships and data process flow of the thermostat object entity 802 can determine without having to rely on a database analyst or engineer to create, index, and / or manage the entities (e.g., using SQL or NoSQL). In some embodiments, each of the entities (e.g., class entity, object entity, and data entity) represent a node in the entity graph 800 and the relational objects define the relationships or connections between the entities (or nodes).

For example, the entity graph shows 800 that a person named john (object entity) 804 a kind of (relational object) 806 User (class entity) 808 is. John 804 Possessed (relational object) 810 the thermostat (object entity) 802 . The thermostat 802 assigns a location attribute (dynamic attribute) 812 on, for which is linked (relational object) 814 with geo 301-01 (Data entity) 816 applies to which is a kind of (relational object) 818 an address (class entity) 820 applies. Accordingly, Geo 301-01 316 have a data point that corresponds to an address.

The thermostat 802 also contains an attribute "Current indoor temperature" (dynamic attribute) 822 , for which is linked (relational object) 824 with AI 201-01 (Data entity) 826 applies. AI 201-01 826 is a kind of (relational object) 828 Temperature object (class entity) 830 . Therefore AI 201-01 826 contain some kind of temperature related data. AI 201-01 826 has storage (relational object) 832 at TS-ID 1 (Data entity) 834 what can be raw time series data or derived time series data for the temperature readings. AI 201-01 826 HatOperation (relational object) 836 of daily average 1 (Data entity) 838 what is a kind of (relational object) for 840 Analysis operator (class entity) 842 applies. This results in the daily average 1 from an analysis operation that calculates the daily average of the inside temperature. AI also applies 201-01 826 hatOperation (relational object) 854 of abnormal internal temperature (data entity) 856 what is a kind of (relational object) for 858 Analysis operator (class entity) 860 applies. Accordingly, an abnormal indoor temperature results from an analysis operation to determine an abnormal temperature (e.g., exceeding or falling below a threshold).

       Punkt {
               Name: „AI 201-01“;
               Typ: „analoger Eingang“;
               Wert: 72;
               Einheit: „Grad-F“;
       } Quelle: „Temperatursensor 1“

wobei „Punkt“ ein Beispiel für eine Datenentität ist, die durch die Cloud-Gebäudeverwaltungsplattform 620 erzeugt werden kann, um den Wert für das verknüpfte dynamische Attribut „Aktuelle Innentemperatur“ 822 der Thermostatentität 802 zu halten, und Quelle der Sensor oder die Vorrichtung in der Thermostatvorrichtung ist, der/die die Daten für das verknüpfte dynamische Attribut „Aktuelle Innentemperatur“ 822 bereitstellt.In this example, the data entity AI 201-01 526 can be represented by the following data model:

       Point {
               Name: "AI 201-01";
               Type: "analog input";
               Value: 72;
               Unit: "Grade-F";
       } Source: "Temperature sensor 1" 

where "dot" is an example of a data entity created by the cloud building management platform 620 can be generated to the value for the linked dynamic attribute "Current internal temperature" 822 the thermostat entity 802 and the source is the sensor or device in the thermostat device that contains the data for the linked dynamic attribute "current internal temperature" 822 provides.

       Zeitreihe {
               Name: „TS-Id 1“;
               Typ: „Täglicher Durchschnitt“;
               Werte: [68, 20666, 70, 69, 71];
               Einheit: „Grad-F“;
               Punkt: „AI 201-01“;
       } Quelle: „Täglicher Durchschnitt 1“

wobei die Datenentität Täglicher Durchschnitt 1 838 einen speziellen Analyseoperator repräsentiert, der zum Erzeugen der Datenentität für die durchschnittliche tägliche Zeitreihe TS-Id 1 834 basierend auf den Werten der entsprechenden Datenentität für den Punkt AI 201-01 826 verwendet wird. Das relationale Objekt hatOperation zeigt, dass die AI-201-01-Datenentität 826 als eine Eingabe für die spezielle Logik/Mathematik-Operation verwendet wird, die durch Täglicher Durschnitt 1 838 repräsentiert wird. Die TS-ID 1 834 kann auch ein Attribut enthalten, das den Analyseoperator Täglicher Durchschnitt 1 838 als die Quelle für die Datenabtastwerte in den Zeitreihen identifiziert.The data entity TS-Id 1 534 can be represented by the following data model, for example:

       Time series {
               Name: "TS-Id 1";
               Type: "Daily average";
               Values: [68, 20666, 70, 69, 71];
               Unit: "Grade-F";
               Point: "AI 201-01";
       } Source: "Daily average 1" 

where the daily average data entity 1 838 represents a special analysis operator that is used to generate the data entity for the average daily time series TS-Id 1 834 based on the values of the corresponding data entity for the point AI 201-01 826. The relational object has Operation shows that the AI-201-01 data entity 826 is used as an input to the special logic / math operation represented by Daily Average 1,838. TS ID 1 834 may also include an attribute that identifies the Daily Average Analysis Operator 1 838 as the source for the data samples in the time series.

Still referring to 8th shows the entity graph 800 for thermostat 802 that for the attribute "target internal temperature" (dynamic attribute) 844 is linked (relational attribute) 846 with the data entity AO 101-01 (Data entity) 848 applies. The AO-101-O1 data entity 848 is a kind of (relational attribute) 850 Setpoint command (class entity) 852 . The data in the data entity AO 101-01 848 can be set via a command by the user or another entity and can be used to control the thermostat object 802 to control. Accordingly, in various embodiments, the entity graph 800 a user-friendly view of the various relationships between the entities and a data processing flow, which enables easy navigation, simple browsing and analysis of data.

In some embodiments, any two entities (or nodes) can be linked together via one or more relational objects that define different relationships between the two entities (or nodes). For example, FIG. In 8th shown that the object entity John 804 owns a relational object 810 with the object entity thermostat 802 connected is. In another embodiment, the object entity can be John 804 however, more than one relational object with the thermostat object entity 802 connected so that in addition to owning the relational object 810 another relational object a different relationship between the John object entity 804 and the activity tracker object entity 802 can define. For example, another relational object, such as istInZone or istNichtInZone, can define whether John (or the entity object is for John 804 ) currently within that of the thermostat 802 zone supplied (e.g. via the relational object is InZone) or is currently not within the range of the thermostat 802 supplied zone (e.g. via the relational object is not in zone).

In this case, the relational object is InZone if that of the thermostat object entity 802 associated data entities indicate that John is within the zone served by the thermostat (e.g. from the location attribute 812 and the location data for John 810 can be determined), between the object entity for John 610 and the object entity for thermostat 802 be generated. If, on the other hand, that of the thermostat object entity 802 associated data entities indicate that John is not within the zone served by the thermostat (which can be determined, for example, if the location attribute 812 location other than a known John location), the relational object is not in the zone between the object entity for John 810 and the object entity for thermostat 802 be shown. For example, the relational object isNotInZone can be created by modifying the relational object istInZone or by deleting the relational object istInZone and creating the relational object isNotInZone. Thus, in some embodiments, the relational objects can be dynamically created, modified, or deleted as needed or desired.

Referring again to 7 can the entity service 626 Transform raw data samples and / or raw time series data into data corresponding to entity data. For example, as above with reference to 8th , the entity service 626 Generate data entities that use and / or represent data points in the time series data. The entity service 626 includes a web service 702 , a registration service 704 , an administrative service 706 , a transformation service 708 , a search service 710 and storage 712 . In some embodiments, storage 712 internal storage or external storage. For example, the storage 712 a storage 614 (please refer 6 ), internal storage related to entity service 626 and / or may include a remote database, cloud-based data hosting, or other remote data storage.

The web service 702 may be configured to interact with web-based applications to send and / or receive entity data (e.g., data samples, time series data, and the like). For example, the web service 702 provide an interface (e.g., API, UI / UX, and the like) to manage an entity (e.g., class entity, object entity, data entity, and / or the like) and the relational objects that define the relationships between the entities (e.g. to register, create, edit, delete and / or update). In some embodiments, the web service delivers 702 Entity data to web-based applications. For example, if one or more of the applications 630 are web-based applications, the web service can 702 provide the web-based applications with entity data. In some embodiments, the web service receives 702 Raw data samples and / or raw time series data, including device information, from a web-based data collector or web-based security service to identify authorized entities and exchange secured messages. For example, if the data collector 612 is a web-based application, the web service can 702 receive the raw data samples and / or time series data, including a device attribute indicating a type of device (e.g., an IoT device) from which the data samples and / or time series data are received from the data collector 612 be received. In some embodiments, the web service can 702 the security service 622 Notify to request authorization information and / or authorization information from a particular user, building, BMS, building subsystem, device, application, or other entity. With some The web service receives embodiments 702 derived time series data from the time series service 628 and / or can provide entity data to the time series service 628 deliver. In some embodiments, the entity service processes and transforms 626 the collected data to generate the entity data.

The registration service 704 can register devices and entities. For example, the registration service 704 with the building subsystems 528 and the client devices 548 (e.g. via the web service 702 ) communicate with each entity (e.g., buildings, BMS, building subsystems, devices, and the like) with the cloud building management platform 620 to register. In some embodiments, the registration service registers 704 a special building subsystem 528 (or the devices therein) with a specific user and / or a specific set of permissions and / or powers. For example, a user can send a device key and / or a device ID assigned to the device via a web portal (e.g. the web service 702 ) to register. In some embodiments, the device ID and device key may be unique to the device. The device ID can be a unique number associated with the device, e.g. B. a unique alphanumeric string, a serial number of the device and / or any other static identifier. In various embodiments, the device is provided by a manufacturer and / or another entity. In various embodiments, the device key and / or the device ID are associated with the device or building subsystem 528 based on whether the device contains a trusted platform module (TPM). If the device contains a TPM, the device or the building subsystem 528 store the device key and / or device ID according to the protocols of the TPM. If the device does not contain a TPM, the device or the building subsystem can 528 store the device key and / or device ID in a file and / or file field that can be stored in a secure location. Furthermore, in some embodiments, the device ID can be stored with the device's BIOS software. For example, a serial number of the BIOS software can become and / or be updated with the device ID.

In various embodiments, the device key and / or the device ID are in the registration service 704 (e.g. an IoT hub such as AZURE® IoT Hub). In some embodiments, the registration service is 704 configured to store the device key and device ID in secure permanent storage and / or can be used by the security service 622 (e.g. from a security API). In some embodiments, a manufacturer and / or any other person may use the registration service to provide the device key and device ID 704 (e.g. via the web service 702 ) to register. In various embodiments, the device key and device ID are with a special profile that matches the building subsystem 528 or assigned to the device, and / or linked to a special user profile (e.g. a special user). In this regard, a device (or building subsystem 528 ) assigned to a special user. In various embodiments, the device key and device ID form the profile for the device. The profile can be registered as a device that has been manufactured and / or provided but has not yet been purchased by an end user.

In various embodiments, the registration service adds 704 add and / or update a device in a building hub device registration. In various embodiments, the registration service 704 determine whether the device is already registered, can set various authentication values (e.g., device ID, device key) and can update the building hub device registration. Similarly, the registration service 704 update a document database with the various device registration information.

In some embodiments, the registration service can 704 configured to have a virtual representation (e.g., "digital twins" or "shadow records") of each object entity (e.g., person, room, building subsystem, device, and the like) in the building within the cloud building management platform 620 to create. In some embodiments, the virtual representations are intelligent entities that contain attributes that define or characterize the corresponding object and are associated with the corresponding object entity via relational objects that define the relationship of the object and its representation of the intelligent entity. In some embodiments, the virtual representations maintain shadow copies of the object entities with version information so that the entity service 626 not only save the last update of an attribute (e.g. a dynamic attribute) associated with the object, but also records of previous states of the attributes (e.g. the dynamic attributes) and / or entities. For example, the shadow record can be created as a type of data entity that is related to a linked data entity that is associated with the dynamic attribute of the object entity (e.g. person, room, building subsystem, device and the like). For example, the shadow entity can be associated with the linked data entity through a relational object (e.g., is linked, has storage, has operation, and the like). In this case, the shadow entity can be used to determine additional analyzes for the data point of the dynamic attribute. For example, the shadow entity can be used to determine an average value, an expected value, or an abnormal value of the data point from the dynamic attribute.

The administrative service 706 can have various attributes, data entities and / or relational objects provided by the entity service 626 Create, modify, or update managed objects for each entity rather than per class or entity type. This enables separate processing / analysis for each individual entity instead of just for one class or entity type. For example, some attributes (or data entities) may correspond to the latest value of a data point, which is sent to the BMS via the raw data samples and / or time series data 600 or the cloud building management platform 620 is delivered. For example, the dynamic attribute "Current inside temperature" of the object entity 802 “Thermostat” in the example discussed above may be the latest indoor temperature value provided by the thermostat device. The administrative service 706 can use the entity data relational objects for the thermostat to determine where to update the attribute data.

For example, the administration service 706 determine that a data entity (e.g. AI 201-01 ) is linked to the dynamic attribute "Actual internal temperature" of the thermostat via a relational istKinkt-object. In this case, the administrative service 706 automatically update the attribute data in the linked data entity. If there is no linked data entity, the administration service can 706 a data entity (e.g. AI 201-01 ) and an instance of the relational is linked object 824 generate in order to save and link the dynamic attribute "Current internal temperature" of the thermostat. Accordingly, the processing / analysis for the thermostat 802 be automated. As another example, a "last view" attribute (or a linked data entity) of a web page object entity can indicate the last time the web page was viewed. The administrative service 706 can use the entity data from an associated click tracking system object entity or a web server object entity to determine when the last view occurred and can automatically update the last view attribute (or the associated data entity) of the web page entity accordingly.

Other data entities and / or attributes can be generated and / or updated as a result of an analysis, transformation, calculation or other processing operation based on the raw data and / or entity data. For example, the administration service 706 use the relational objects in entity data to identify an associated access control device (e.g., a card reader, keypad, etc.) at the entrance / exit of a building object entity. The administrative service 706 may use raw data received from the identified access control device to keep track of the number of users entering and exiting the building object entity (e.g., related card entities used by users to enter and exit the building). The administrative service 706 can update a "number of users" attribute (or a corresponding data entity) of the building object entity each time a person enters or leaves the building using an associated card object entity, so that the "number of users" attribute (or the data entity) updates the current one Number of users within the building (or the associated building object entity). As another example, an "overall sales" attribute associated with a product line object entity can be the sum of all revenues generated by associated sales entity entities. The administrative service 706 can use the raw data received from the associated point of sale entities to determine when a sale of the product takes place and can identify the amount of sales generated by the sales. The administrative service 706 can then update the total sales attribute (or associated data entity) of the line item entity by adding the last sales revenue from each of the associated sales entity entities to the previous value of the attribute.

In some embodiments, the management service can 706 Use derived time series data from the time series service 628 generated to a data entity (e.g. Daily Average 1 ) update or generate that uses or stores the data points in the derived time series data. For example, the derived time series data may include a virtual data point that is from the time series service 628 calculated calculated daily average steps, and the administrative service 706 can update the data entity or entities that store or use the data that corresponds to the virtual data point as determined by the relational objects. In some embodiments, the management service can 706 , if one corresponding to the virtual data point Data entity does not exist, automatically create a corresponding data entity and one or more relational objects that describe the relationship between the corresponding data entity and other entities.

In some embodiments, the administration service uses 706 Entity data and / or raw data from several different data sources to update the attributes (or corresponding data entities) of different object entities. For example, an object entity that represents a person (e.g., a person's cellular device or other related object entity) may include a "risk" attribute that quantifies the person's level of risk, attributable to various physical, environmental, or other conditions is. The administrative service 706 may use person object entity relational objects to identify an associated card device and / or card reader from an associated building object entity (e.g., the building in which the person works) to determine the person's physical location at any given time determine. The administrative service 706 can determine from raw data (e.g., the time the card device was scanned by the card reader) or derived time series data (e.g., the average arrival time) whether the person object is in the building or on the way to that Building. The administrative service 706 may use weather data from a weather service in the area where the building object entity is located to determine if any storm is approaching the person's location. Similarly, the administrative service 706 Use building data from related building entities of the building object entity to determine whether the building in which the person is located has any emergency situations (e.g. fire, building lock etc.) or environmental hazards (e.g. detected air contamination, pollutants, extreme temperatures etc.), which could increase the risk level of the person. The administrative service 706 can use this and other data types as inputs to a risk function that calculates the value of the risk attribute of the person object, and can update the person object (or the associated device entity of the person object) accordingly.

In some embodiments, the management service can 706 configured to include configuration settings, parameters, and other device-specific or object-specific information between the entities and the cloud building management platform 620 to synchronize. In some embodiments, synchronization is asynchronous. The administrative service 706 can be configured to dynamically manage device properties. The device properties, configuration settings, parameters and other device-specific information can be synchronized between the intelligent entities provided by the cloud building management platform 620 generated and stored in this.

In some embodiments, the management service is 706 configured to have a manifest for each of the building subsystems 528 (or devices therein) to manage. The manifesto can be a set of relationships between the building subsystems 528 and various entities. Furthermore, the manifesto can have a set of powers for the building subsystems 528 and / or specify powers of the various entities and / or other entities. The set of powers can allow a BMS 600 , a building subsystem 528 and / or a user performs certain actions within the building (e.g. controlling, configuring, monitoring and / or the like).

Still referring to 7 can the transformation service 708 provide data virtualization and can transform different predefined standard data models for entities in the same class or type, so that they have the same entity data structure, regardless of the object, device, or item that represents the entity. For example, each object entity under an object class can contain a location attribute regardless of whether the location attribute is used or even generated. Therefore, if an application is later developed that requires that each object entity contain a location attribute, manual assignment of heterogeneous data from different entities in the same class can be avoided. Accordingly, the interoperability and scalability of applications can be improved.

In some embodiments, the transformation service 708 provide entity matching, cleansing, and correlation so that a unified, clean view of the entity data, including the entity-related information (e.g., relational objects), can be provided. The transformation service 708 can support a semantic and syntactic relationship description in the form of standardized relational objects between the different entities. This can simplify machine learning because the relational objects themselves provide the entire relationship description between the entities. Accordingly, the extensive set of ready-made entity models and standardized relational objects can provide for rapid application development and data analysis.

Still referring to 7 provides the search service 710 a unified view of product-related information in the form of the entity graph, which correlates entity relationships (via relational objects) between several data sources (e.g. CRM, ERP, MRP and the like). In some embodiments, the search service is based 710 on a schematic and graph-based indexing architecture. For example, in some embodiments, the search service provides 710 the entity graph, in which the entities are represented as nodes with relational objects that define the relationship between the entities (or nodes). The search service 710 facilitates simple queries without having to search through several levels of the hierarchical tree of the entity graph. For example, the search service 710 Return results based on the search for entity type, individual entities, attributes or even relational objects without having to search other levels or entities in the hierarchy.

Time series data platform service

With reference to 9 14 is a block diagram shown, according to some embodiments, that the time series service 628 illustrated in more detail. It is shown that the time series service 628 a time series web service 902 , an event service 903 , a time series processing engine 904 and a time series storage interface 916 contains. The time series web service 902 can be configured to interact with web-based applications to send and / or receive time series data. In some embodiments, the time series web service delivers 902 Time series data to web-based applications. For example, if one or more of the applications 630 are web-based applications, the time series web service can 902 deliver the derived time series data and / or raw time series data to web-based applications. In some embodiments, the time series web service receives 902 Raw time series data from a web-based data collector. For example, if the data collector 612 is a web-based application, the time series web service can 902 Raw data samples or raw time series data from the data collector 612 receive. In some embodiments, the time series web service 902 and the entity service web service 702 be integrated as parts of the same web service.

The time series storage interface 916 may be configured to store and read samples of different time series (e.g., raw time series data and derived time series data) and event series (described in more detail below). The time series storage interface 916 can with the storage 614 to interact. For example, the time series storage interface 916 Time series data from a time series database 928 within storage 614 recall. In some embodiments, the time series storage interface reads 916 Samples from a specified start time or start position in the time series to a specified end time or stop position in the time series. Likewise, the time series storage interface 916 Event series data from an event series database 929 within storage 614 recall. The time series storage interface 916 can also hold time series data in the time series database 928 store and event series data in the event series database 929 to save. The time series storage interface advantageously provides 916 a consistent interface that enables logical data independence.

wobei Schlüssel eine Kennung der Quelle der Datenabtastwerte (z. B. Zeitreihen-ID, Sensor-ID, Vorrichtung-ID usw.) ist, Zeitstempel_i einen Zeitpunkt identifiziert, der dem i-ten Abtastwert zugeordnet ist, und Wert_i den Wert von des i-ten Abtastwerts angibt.In some embodiments, the time series storage interface stores 916 Time series as lists of data samples organized by time. For example, the time series storage interface 916 Save time series in the following format: $$\begin{array}{l}[<ScHl\ddot{u}ssel,Zeitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1,W\mathrm{<figure-callout\; id="1"\; label="e\; r\; t"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">e\; </figure-callout>}r{t}_{}{}_1>,<ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{2nd}},Wer{t}_{\mathrm{2nd}}>,\\ <ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{3rd}},Wer{t}_{\mathrm{3rd}}>]\end{array}$$

where key is an identifier of the source of the data samples (e.g. time series ID, sensor ID, device ID, etc.), time stamp _i identifies a time associated with the i th sample, and value _i the value of of the ith sample.

wobei Ereignis - ID_i eine Kennung des i-ten Ereignisses ist, Startzeitstempel_i der Zeitpunkt ist, zu dem das i-te Ereignis begonnen hat, Endzeitstempel_i der Zeitpunkt ist, zu dem das i-te Ereignis endete, Zustand_i einen Zustand oder eine Bedingung beschreibt, die/der mit dem i-ten Ereignis zugeordnet ist(z. B. kalt, heiß, warm usw.), und N die Gesamtzahl der Ereignisse in der Ereignisreihe ist.In some embodiments, the time series storage interface stores 916 Event series as lists of events with a start time, an end time and a state. For example, the time series storage interface 916 Save series of events in the following format: $$\begin{array}{l}[<EreiGnis-\mathrm{<figure-callout\; id="1"\; label="I.\; D"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">I.\; </figure-callout>}{D}_{}{}_1,Start\mathrm{e.g.}eitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1,End\mathrm{e.g.}eitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1,Zusta\mathrm{<figure-callout\; id="1"\; label="n\; d"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">n\; </figure-callout>}{d}_{}{}_1>,...,\\ <EreiGnis-\mathrm{I.}{D}_N,Start\mathrm{e.g.}eitstempe{l}_N,End\mathrm{e.g.}eitstempe{l}_N,Zustan{d}_N>]\end{array}$$

where event ID _{i is} an identifier of the i th event, start time stamp _{i is} the point in time at which the i th event started, end time stamp _{i is} the point in time at which the i th event ended, state _i a state or describes a condition associated with the ith event (e.g., cold, hot, warm, etc.) and N is the total number of events in the series of events.

In some embodiments, the time series storage interface stores 916 Time series and event series in a table format. The time series storage interface 916 can store time series and event series in different tables with one column for each attribute of the time series / event series samples (e.g. key, time stamp, value). The time series tables can be found in the time series database 928 stored while the event series tables are in the event series database 929 can be saved. In some embodiments, the time series storage interface stores 916 older data in storage 614 in between, but stores newer data in RAM. This can improve read performance when the newer data is requested for processing.

In some embodiments, the time series storage interface leaves 916 one or more of the attributes away when the time series samples are stored. For example, the time series storage interface 916 may not repeatedly store the key or time series ID for each sample in the time series. In some embodiments, the time series storage interface leaves 916 Timestamps away from one or more of the samples. If samples of a particular time series have time stamps at regular intervals (e.g. one sample per minute), the time series storage interface can 916 organize the samples according to time stamps and store the values of the samples in one line. The time stamp of the first sample can be stored along with the interval between the time stamps. The time series storage interface 916 can determine the timestamp of any sample in the row based on the timestamp of the first sample and the position of the sample in the row.

In some embodiments, the time series storage interface stores 916 one or more samples with an attribute indicating a change in value relative to the previous sample. The change in value can replace the actual value of the sample if the sample is in the time series database 928 is saved. This enables the time series storage interface 916 fewer bits are used in storing samples and their corresponding values. The time series storage interface 916 can determine the value of any sample based on the value of the first sample and the change in value in each subsequent sample.

In some embodiments, the time series storage interface calls 916 the entity service 626 to generate data entities in which samples of time series data and / or event series data can be stored. The data entities can include JSON objects or other types of data objects to store one or more time series samples and / or event series samples. The time series storage interface 916 can be configured to add samples to the data entities and read samples from the data entities. For example, the time series storage interface 916 a set of samples from the data collector 612 , the entity service 626 , the time series web service 902 , the event service 903 and / or the time series processing engine 904 receive. The time series storage interface 916 can add the set of samples to a data entity, for example, to the entity service 626 may be sent, for example, to be stored in the data entity, or may be directly coupled to the data entity to add / modify the samples to the data entity.

The time series storage interface 916 can use data entities when samples from storage 614 to be read. For example, the time series storage interface 916 a set of samples from storage 614 or from entity service 626 and can add the samples to a data entity (e.g. directly or through the entity service 626 ). In some embodiments, the set of samples includes all samples within a specified time range (e.g., samples with timestamps in the specified time range) or event series samples with a specified state. The time series storage interface 916 can send the samples in the data entity to the time series web service 902 , the event service 903 , the time series processing engine 904 , the applications 630 and / or provide other components configured to use the time series / event series samples.

Still referring to 9 is shown that the time series processing engine 904 some time series operators 906 contains. The time series operators 906 can be configured apply various operations, transformations, or functions to one or more input time series to generate output time series and / or event series. The input time series can contain raw time series data and / or derived time series data. The time series operators 906 can be configured to calculate aggregate values and averages, or to apply other mathematical operations to the input time series. In some embodiments, the time series operators generate 906 Virtual point time series by combining two or more input time series (e.g. adding the time series), generating multiple output time series from a single input time series, or performing mathematical operations on the input time series. In some embodiments, the time series operators perform 906 Data cleansing operations or deduplication operations on an input time series. In some embodiments, the time series operators use 906 the input time series to generate event series based on the values of the time series samples. The output time series can be stored as derived time series data 614 stored as one or more time series data entities. Similarly, the event series can be stored as event series data entities 614 get saved.

In some embodiments, the time series operators change or replace 906 The raw time series data does not, but creates different "views" of the raw time series data (e.g. as separate data entities) with corresponding relational objects that define the relationships between the raw time series data entity and the different view data entities. The views can be queried in the same way as the raw time series data. For example, samples can be read from the raw time series data entity, transformed to generate the view entity, and then provided as an output. Because the transformations used to generate the views can be computationally intensive, the views can be viewed as data entities with a "materialized view" in the time series database 928 get saved. Instances of relational objects can be created to define the relationship between the raw time series data entity and the materialization view data entities. These materialized views are referred to as derived data time series throughout the present disclosure.

The time series operators 906 can be configured to be at the polling time (e.g. when a derived data time series request is received) or before the polling time (e.g. when new raw data samples are received in response to a defined event or trigger, etc.) be carried out. This flexibility allows the time series operators 906 perform some or all of their operations in advance and / or in response to a request for special derived data time series. For example, the time series operators 906 configured to preprocess one or more time series that are read frequently to ensure that the time series are updated whenever new data samples are received, and the preprocessed time series can be stored in a corresponding data entity for retrieval. The time series operators 906 however, may be configured to wait until the polling time to process one or more time series that are rarely read to avoid unnecessary processing.

In some embodiments, the time series operators 906 triggered in a special sequence defined by a directed acyclic graph (DAG). The DAG can define a workflow or a sequence of operations or transformations to be applied to one or more input time series. For example, for a raw data time series, the DAG can include a data clean up operation, an aggregation operation, and a summation operation (e.g., adding two raw data time series to create a virtual point time series). The DAGs can be stored in a DAG database 930 within storage 614 or internally within the time series processing engine 904 get saved. DAGs can be managed by the workflow manager 922 are retrieved and used to determine how and when to process input data samples. Exemplary systems and methods for creating and using DAGs are described in more detail below.

Time series operators 906 can aggregate for dashboards, cleanup operations, logic operations for rules and error detection, machine learning predictions or classifications, invocation for external services, or any of a variety of other operations that can be applied to time series data. That through the time series operators 906 operations performed are not limited to time series data. The time series operators 906 can also act on event data or act as a billing engine for a consumption or tariff-based billing system. It is shown that the time series operators 906 a sample aggregator 908 , a virtual point calculator 910 , a weather point calculator 912 , an error detector 914 and an event series generator 915 contain.

Still referring to 9 is shown that the time series processing engine 904 a DAG optimizer 918 contains. The DAG optimizer 918 can be configured to combine multiple DAGs or multiple stages of a DAG to increase the efficiency of the time series operators 906 performed operations to improve. For example, it is assumed that a DAG has a function block that adds “time series A” and “time series B” to produce “time series C” (ie A + B = C) and another function block, the “time series C” and "time series D" added to produce "time series E" (ie C + D = E). The DAG optimizer 918 can combine these two function blocks into a single function block that calculates "time series E" directly from "time series A", "time series B" and "time series D" (ie E = A + B + D). Alternatively, both "Time Series C" and "Time Series E" can be calculated in the same function block to reduce the number of independent operations required to process the DAG.

In some embodiments, the DAG optimizer combines 918 DAGs or levels of a DAG in response to a determination that multiple DAGs or levels of a DAG will use similar or shared inputs (e.g., one or more of the same input time series). This allows the inputs to be retrieved and loaded once instead of performing two separate operations, both of which load the same inputs. In some embodiments, the DAG optimizer plans 918 Time series operators 906 at nodes on which data is in memory, the amount of data that comes from the time series database 928 needs to be further reduced.

It is shown that the time series processing engine 904 a generator 920 for directed acyclic graphs (DAG) contains. The DAG generator 920 can be configured to generate one or more DAGs for each raw data time series. Each DAG can define a workflow or a sequence of operations performed by the time series operators 906 can be executed on the raw data time series. When new samples of the raw data time series are received, the workflow manager can 922 retrieve the appropriate DAG and use the DAG to determine how the raw data time series should be processed. In some embodiments, the DAGs are declarative views that represent the sequence of operations that are applied to each raw data time series. The DAGs can be designed for time series instead of SQL (Structured Query Language).

In some embodiments, DAGs apply over time windows. For example, the time series processing operations defined by a DAG may include a data aggregation operation that aggregates multiple raw data samples with timestamps within a given time window. The start time and end time of the time window can be defined by the DAG and the time series to which the DAG is applied. The DAG can define the duration of the time window over which the data aggregation operation will be performed. For example, the DAG may perform the aggregation operation as an hourly aggregation (i.e., to generate an hourly data rollup time series), a daily aggregation (i.e., to generate a daily data rollup time series), a weekly aggregation (i.e., to generate a) weekly data Rollup time series) or any other aggregation duration. The location of the time window (e.g., a particular day, week, etc.) over which the aggregation is performed can be defined by the time stamps of the data samples from time series provided as an input to the DAG.

In operation, the sample aggregator can 908 Use the DAG to identify the length of the time window (e.g., an hour, a day, a week, etc.) over which the data aggregation operation is performed. The sample aggregator 908 can use the time stamps of the data samples in the time series provided as an input to the DAG to identify the location of the time window (ie, the start time and the end time). The sample aggregator 908 can set the start time and end time of the time window so that the time window has the identified duration and contains the time stamps of the data samples. In some embodiments, the time windows are fixed and have predefined start times and end times (e.g., the beginning and end of every hour, every day, every week, etc.). In other embodiments, the time slots can be sliding time slots that have start times and end times that depend on the time stamps of the data samples in the input time series.

10th FIG. 14 shows a flow diagram of a process or method for updating / generating a data entity based on data received from a device of a building subsystem, according to some embodiments. With reference to 10th the process begins, and when time series data (e.g., raw or input time series data) generated for a building subsystem device (e.g., by the data collector) is received, the transformation service can 708 at block 1005 determine an identifier of the device from the received time series data. At block 1010 can the transformation service 708 a static identity attribute from the data with static Compare identity attributes of registered object entities to locate a data container for the device. If there is no match from the comparison at block 1015 is available, the transformation service 708 Call the registration service to block the device at 1020 to register. If there is a match from the comparison at block 1015 is available, the transformation service 708 generate an entity graph or entity data for the device at block 1025 recall. The transformation service can use the entity graph or the entity data 708 based on the relational objects (e.g., Linked) for the device determine whether there is a corresponding data entity to block at 1025 update a dynamic attribute from the data. If not, the administration service can 706 create a data entity for the dynamic attribute and an instance of a corresponding relational object (e.g., linked) to block 1040 define the relationship between the dynamic attribute and the data entity created. If the corresponding data entity is available, the administration service can 706 the data entity corresponding to the dynamic attribute from the data at block 1045 To update. Then the transformation service 708 the entity graph or the entity data at block 1050 update or recreate and the process can end.

11 10 is an exemplary entity graph of entity data according to an embodiment of the present disclosure. The example from 11 assumes that an HVAC fault detection application is an abnormal temperature measurement with respect to the temperature sensor 1112 has detected. The temperature sensor 1112 however, itself can function properly, but may depend on various factors, conditions, and other systems and devices to properly measure temperature. Accordingly, for example, the HVAC fault detection application may need the room 1114 in which the temperature sensor 1112 the corresponding temperature setpoint, the state of the VAV 1104 who the room 1114 supplied with conditioned air, the condition of the AHU 1102 who the VAV 1104 feeds the condition of the ventilation openings in the HVAC zone 1110 etc. to find the cause of the abnormal measurement. Therefore, the HVAC fault detection application may need additional information from various related subsystems and devices (e.g., entity objects) and the zones and rooms (e.g., entity objects) that the subsystems and devices are configured to serve to address the cause of the abnormal To correctly determine or infer measurement.

With reference to 11 represents the entity graph 1100 each of the entities (e.g. the temperature sensor 1112 and associated entities) as nodes in the entity graph 1100 and shows the relationship between the nodes (e.g. the temperature sensor 1112 and related entities) via relational objects (e.g. feed, has point, has part, controls, etc.). For example, the entity graph shows 1100 that the temperature sensor 1112 Temperature readings (e.g. has point) to the VAV 1104 and the HVAC zone 1110 delivers. An AHU 1102 supplies (e.g. feeds) the VAV 1104 with cooled and / or heated air. The AHU 1102 receives / delivers power readings (e.g. hatPoint) from / to an electricity meter 1108 . The VAV 1104 delivers (e.g. feeds) air to the HVAC zone 1110 using (e.g. part) an air damper 1106 . The HVAC zone 1110 delivers the air to the room 1114 . Furthermore, the rooms are located (e.g. has part) 1114 and 1120 in the lighting zone 1118 by the lighting control 1116 is controlled (e.g. controls).

Accordingly, in the example 11 in response to receiving the erroneous measurement from the temperature sensor 1112 the HVAC fault detection application and / or the analysis service 624 determine from the entity graph that the failure could be caused by some malfunction in one or more of the other related entities and not necessarily a malfunction of the temperature sensor 1112 is. Thus, the HVAC fault detection application and / or the analysis service 624 further examine the other related entities to determine or infer the most likely cause of the failure.

For example 12th 5 is a flowchart of a process or method for analyzing data from a second related device based on data from a first device, in accordance with some embodiments. With reference to 12th the process starts and blocks time series data (e.g., raw or input time series data generated by the data collector), including an abnormal measurement from a first device 1205 receive. The transformation service 708 determines an identifier of the first device from the received time series data at block 1210 . The transformation service 708 identified at block 1215 a second device associated with the first device through relational objects associated with the first device. The second device may be part of the same building subsystem as that of the first device or another building subsystem and may be for the same building as that of the first device or another building. The transformation service 708 calls the web service 702 on to at block 1220 Retrieve measurement data from the second device. The analysis service 624 analyzes the data from the first device and the second device at block 1225 . The analysis service 624 puts at block 1230 a recommendation of analyzing the data from each of the first device and the second device is ready and the process ends.

13 10 is a flowchart of a process or method for generating a derived time series from data generated by a first device or a second device, according to some embodiments. With reference to 13 the process starts and are at block 1305 Receive raw data from a first device of a building management subsystem. The raw data can contain one or more data points that are generated by the first device. For example, the data points can be measured values that are generated by the first device. The data collector 612 generated at block 1310 Raw (or input -) - time series from the raw data. The raw time series may include an identifier of the first device, a time stamp (e.g., a local time stamp) of when the one or more data points were generated by the first device, and an offset value and a value of the one or more data points.

The transformation service 708 determined at block 1315 an identifier of the first device from the raw time series data and identifies (e.g. using an entity graph or data) a first object entity representing the first device. The raw time series data are at block 1320 stored in a corresponding data entity that is related to the first object entity. For example, the transformation service 708 identify the corresponding data entity from a relational object that defines the relationship between the first object entity and the corresponding data entity.

The time series processing engine 904 identifies a processing workflow (e.g., a DAG processing workflow) to block 1325 to process the raw time series data. In the example 13 The processing workflow takes the raw time series data for the first device and data from a second device as an input. The second device may be part of the same building subsystem as that of the first device or another building subsystem and may be for the same building as that of the first device or another building. A second object entity for the second device is at block 1330 identified. For example, the second object entity can be identified from a relational object that indicates a relationship between the first object entity and the second object entity. A corresponding data entity that stores raw time series data or derived time series data for the second device is stored in block 1335 identified. For example, the corresponding data entity can be determined from a relational descriptor that indicates a relationship between the second object entity and the corresponding data entity.

The processing workflow is carried out at block 1340 to generate the derived time series. For example, the derived time series may include a virtual data point that is calculated using data in front of and from the second device. For example, an arithmetic operation can be performed on the data of the first and second devices to calculate the virtual data point. A corresponding data entity is identified to save the derived time series. For example, the corresponding data entity can be identified by one or more relational objects that indicate a relationship between the corresponding data entity and the first device and / or the corresponding data entity and the second device. The derived time series is at block 1345 stored in the appropriate data entity and the process ends.

Sample aggregation

With reference to 14-15 are a block diagram 1400 and a data table 1450 which illustrate an aggregation technique used by the sample aggregator 908 may be used, according to some embodiments. In 14 is a data point 1402 shown. The data point 1402 is an example of a measured data point for which time series values can be obtained. For example, the data point 1402 shown as an outside air temperature point and has values that can be measured by a temperature sensor. Although a special type of data point 1402 in 14 is shown, it is understood that the data point 1402 can be any type of measured or calculated data point. Time series values of the data point 1402 can from the data collector 612 collected and a raw data time series 1404 be put together.

As in 15 shown contains the raw data time series 1404 a time series of data samples, each as a separate line in the data table 1450 are shown. It is shown that each sample of the Raw data time series 1404 contains a timestamp and a data value. The timestamps of the raw data time series 1404 are ten minutes and one second apart, indicating that the sampling interval of the raw data time series 1404 is ten minutes and one second. For example, the time stamp of the first data sample is shown as 31-12-2015Z23: 10: 00, indicating that the first data sample of the raw data time series 1404 on December 31, 2015 at 11:10 p.m. The time stamp of the second data sample is shown as 31-12-2015Z23: 20: 01, indicating that the second data sample is the raw data time series 1404 on December 31, 2015 at 11:20:01 p.m. In some embodiments, the timestamps become the raw data time series 1404 stored along with an offset relative to universal time, as previously described. The values of the raw data time series 1404 start with a value of 10 and increase by 10 with each sample. For example, the value of the second sample is the raw data time series 1404 20th , is the value of the third sample of the raw data time series 1404 30th etc.

In 14 are multiple data rollup time series 1406-1414 shown. The data rollup time series 1406-1414 can by the sample aggregator 908 generated and saved as derived time series data. The data rollup time series 1406-1414 include an average quarter-hour series 1406 , an average hourly time series 1408 , an average daily time series 1410 , an average monthly time series 1412 and an average annual time series 1414 . Each of the data rollup time series 1406-1414 is dependent on a parental leave series. In some embodiments, the parent time series is for each of the data rollup time series 1406-1414 the time series with the next shorter duration between successive time series values. For example, the parental time series is for the average quarter-hour series 1406 the raw data time series 1404 . Similarly, the parental time series is for the average hourly time series 1408 the average quarter-hour series 1406 ; the parental time series for the average daily time series 1410 is the average hourly time series 1408 ; the parental time series for the average monthly time series 1412 is the average daily time series 1410 ; and the parental time series for the average annual time series 1414 is the average monthly time series 1412 .

The sample aggregator 908 can each of the data rollup time series 1406-1414 generate from the time series values of the corresponding parental time series. For example, the sample aggregator 908 the average quarter-hour series 1406 generate by taking all samples of the data point 1402 in the raw data time series 1404 are aggregated that have timestamps within every quarter of an hour. Similarly, the sample aggregator can 908 the average hourly time series 1408 generate all of the time series values of the average quarter hour time series 1406 are aggregated with timestamps within every hour. The sample aggregator 908 can be the average daily time series 1410 generate all of the time series values of the average hourly time series 1408 be aggregated that have timestamps within each day. The sample aggregator 908 can be the average monthly time series 1412 generate all of the time series values of the average daily time series 1410 aggregated that have timestamps within each month. The sample aggregator 908 can be the average annual time series 1414 generate all of the time series values of the average monthly time series 1412 be aggregated that have timestamps within each year.

In some embodiments, the timestamps are for each sample in the data rollup time series 1406-1414 the beginnings of the aggregation interval used to calculate the value of the sample. For example, it is shown that the first data sample of the average quarter hour time series 1406 contains the time stamp 31 - 12 - 2015Z23: 00: 00. This timestamp indicates that the first data sample of the average quarter hour time series 1406 corresponds to an aggregation interval that begins on December 31, 2015 at 11:00 p.m. Since during this interval only one data sample of the raw data time series 1404 occurs is the value of the first data sample of the average quarter hour time series 1406 the average of a single data value (ie average (10) = 10). The same applies to the second data sample of the average quarter-hour time series 1406 (ie average (20) = 20).

The third data sample is shown to be of the average quarter hour time series 1406 contains the time stamp 31-12 - 2015Z23: 30: 00. This timestamp indicates that the third data sample of the average quarter hour time series 1406 corresponds to an aggregation interval that begins on December 31, 2015 at 11:30 p.m. Because each aggregation interval of the average quarter-hour series 1406 lasts a quarter of an hour, the aggregation interval ends on December 31, 2015 at 11:45 p.m. This aggregation interval contains two data samples of the raw data time series 1404 (ie the third Raw data sample with a value of 30 and the fourth raw data sample with a value of 40). The sample aggregator 908 can be the value of the third sample of the average quarter hour series 1406 calculate by averaging the values of the third raw data sample and the fourth raw data sample (ie average (30; 40) = 35). Accordingly, the third sample has the average quarter hour series 1406 a value of 35. The sample aggregator 908 can be the remaining values of the average quarter-hour series 1406 calculate in a similar way.

Still referring to 15 it is shown that the first data sample of the average hourly time series 1408 contains the timestamp 31-12 - 2015Z23: 00: 00. This timestamp indicates that the first data sample of the average hourly time series 1408 corresponds to an aggregation interval that begins on December 31, 2015 at 11:00 p.m. Because each aggregation interval of the average hourly time series 1408 lasts one hour, the aggregation interval ends on January 1st, 2016 at 00:00:00. This aggregation interval contains the first four samples of the average quarter-hour series 1406 . The sample aggregator 908 can be the value of the first sample of the average hourly time series 1408 calculate the values of the first four values of the average quarter-hour series 1406 be averaged (i.e. average (10; 20; 35; 50) = 28.8). Accordingly, the first sample has the average hourly time series 1408 a value of 28.8. The sample aggregator 908 can be the remaining values of the average hourly time series 1408 calculate in a similar way.

It is shown that the first data sample of the average daily time series 1410 contains the time stamp 31-12 - 2015Z00: 00: 00. This timestamp indicates that the first data sample of the average daily time series 1410 corresponds to an aggregation interval that begins at 00:00:00 on December 31, 2015. Because each aggregation interval of the average daily time series 1410 lasts for one day, the aggregation interval ends on January 1st, 2016 at 00:00:00. Only a data sample of the average hourly time series occurs during this interval 1408 on. Accordingly, the value of the first data sample is the average daily time series 1410 the average of a single data value (ie average (28.8) = 28.8). The same applies to the second data sample of the average daily time series 1410 (ie average (87.5) = 87.5).

In some embodiments, the sample aggregator stores 908 each of the data rollup time series 1406-1414 in a single data table (e.g. the data table 1450 ) together with raw data time series 1404 . This enables the applications 630 all of the time series 1404-1414 retrieved quickly and efficiently by accessing a single data table. In other embodiments, the sample aggregator 908 the different time series 1404-1414 Store in separate data tables that can be stored in the same data storage device (e.g., the same database) or distributed across multiple data storage devices. In some embodiments, the sample aggregator stores 908 the data time series 1404-1414 in a format other than a data table. For example, the sample aggregator 908 the time series 1404-1414 Save as vectors, as a matrix, as a list, or using a variety of other data storage formats.

In some embodiments, the sample aggregator updates 908 the data rollup time series 1406-1414 automatically every time a new raw data sample is received. Refreshing the data rollup time series 1406-1414 may include recalculating the aggregated values based on the value and timestamp of the new raw data sample. When a new raw data sample is received, the sample aggregator can 908 determine whether the timestamp of the new raw data sample is within any of the aggregation intervals for the samples of the data rollup time series 1406-1414 lies. For example, if a new raw data sample is received with a timestamp from 01-01 - 2016Z00: 52: 00, the Sample aggregator 908 determine that the new raw data sample occurs within the aggregation interval that timestamped 01-01 - 2016Z00: 45: 00 for the average quarter-hour time series 1406 begins. The sample aggregator 908 can determine the value of the new raw data point (e.g. value = 120 ) to get the aggregate value of the final data sample of the average quarter hour time series 1406 to be updated (ie average (110; 120) = 115).

If the new raw data sample has a time stamp that does not occur in any of the previous aggregation intervals, the sample aggregator can 908 a new data sample in the average quarter-hour series 1406 produce. The new data sample in the average quarter-hour series 1406 may have a new data timestamp that defines the beginning of an aggregation interval that contains the timestamp of the new raw data sample. For example, if the new raw data sample has a timestamp from 01-01 - 2016Z01: 00: 11, the sample aggregator can 908 determine that the new raw data sample does not occur within any of the aggregation intervals previously for the average quarter hour time series 1406 were set up. The sample aggregator 908 can get a new data sample in the average quarter hour time series 1406 with the time stamp 01-01 - 2016Z01: 00: 00 and can generate the value of the new data sample in the average quarter-hour time series 1406 based on the value of the new raw data sample as previously described.

The sample aggregator 908 can read the values of the remaining data rollup time series 1408-414 update in a similar way. For example, the sample aggregator determines 908 whether the timestamp of the updated data sample in the average quarter-hour time series within any of the aggregation intervals for the samples of the average hourly time series 1408 lies. The sample aggregator 908 may determine that the time stamp 01-01 - 2016Z00: 45:00 occurs within the aggregation interval that the time stamp 01-01 - 2016Z00: 00: 00 for the average hourly time series 1408 begins. The sample aggregator 908 can be the value of the updated value of the final data sample of the quarter hour time series 1406 (e.g. value = 115 ) to the value of the second sample of the average hourly time series 1408 to update (i.e. average (65; 80; 95; 115) = 88.75). The sample aggregator 908 can be the updated value of the final data sample of the average hourly time series 1408 use the final sample of the average daily time series 1410 update using the same technique.

In some embodiments, the sample aggregator updates 908 the aggregate data values of the data rollup time series 1406-1414 every time a new raw data sample is received. Updating every time a new raw data sample is received ensures that the data rollup time series 1406-1414 always display the latest data samples. In other embodiments, the sample aggregator updates 908 the aggregate data values of the data rollup time series 1406-1414 periodically at predetermined update intervals (e.g., hourly, daily, etc.) using a batch update technique. Periodic updates can be more efficient and require less data processing than updating each time a new data sample is received, but can result in aggregate data values that are not always updated to reflect the latest data samples.

In some embodiments, the sample aggregator is 908 configured to run raw data time series 1404 to clean. Clean up the raw data time series 1404 can include discarding exceptionally high or low data. For example, the sample aggregator 908 a minimum expected data value and a maximum expected data value for the raw data time series 1404 identify. The sample aggregator 908 can discard data values outside this range as bad data. In some embodiments, the minimum and maximum expected values are based on attributes of the data point represented by the time series. For example, the data point represents 1402 a measured outside air temperature and therefore has an expected value within a range of reasonable outside air temperature values for a given geographic location (e.g. between -20 ° F and 110 ° F). The sample aggregator 908 can have a data value of 330 for the data point 1402 discard as a temperature value of 330 ° F is not realistic for a measured outside air temperature.

In some embodiments, the sample aggregator identifies 908 a maximum rate at which a data point can change between successive data samples. The maximum rate of change can be based on physical principles (e.g., heat transfer principles), weather patterns, or other parameters that limit the maximum rate of change of a particular data point. For example, the data point represents 1402 is a measured outside air temperature and can therefore be limited to having a rate of change that is less than a maximum realistic rate of change for the outside temperature (e.g. five degrees per minute). If two consecutive data samples of the raw data time series 1404 The sample aggregator can have values that would require the outside air temperature to change at a rate that is above the maximum expected rate of change 908 discard one or both data samples as bad data.

The sample aggregator 908 can perform a variety of data cleansing operations to identify and discard bad data samples. In some embodiments, the sample aggregator performs 908 the data cleansing operations for the raw data time series 1404 through before the data rollup time series 1406-1414 be generated. This ensures that the raw data time series 1404 that are used to generate the data rollup time series 1406-1414 does not contain bad data samples. Accordingly, the data rollup time series 1406-1414 not be cleaned up again after the aggregation has been performed.

Virtual points

Referring again to 9 is shown that the time series operators 906 a virtual point calculator 910 contain. The virtual point calculator 910 is configured to generate virtual data points and to calculate time series values for the virtual data points. A virtual data point is a type of calculated data point that is derived from one or more actual data points. In some embodiments, actual data points are measured data points, while virtual data points are calculated data points. Virtual data points can be used as a replacement for actual sensor data if the sensor data required for a specific application are not available, but can be calculated from one or more actual data points. For example, a virtual data point that represents the enthalpy of a coolant can be calculated using actual data points that measure the temperature and pressure of the coolant. Virtual data points can also be used to provide time series values for calculated quantities such as efficiency, performance coefficient and other variables that cannot be measured directly.

The virtual point calculator 910 can calculate virtual data points by applying any of a variety of mathematical operations or functions to actual data points or other virtual data points. For example, the virtual point calculator 910 calculate a virtual data point (point ID ₃ ) by adding two or more actual data points (point ID ₁ and point ID ₂ ) (e.g. point ID ₃ = point ID ₁ + point ID ₂ ). As another example, the virtual point calculator 910 calculate an enthalpy data point (point ID ₄ ) based on a measured temperature data point (point ID ₅ ) and a measured pressure data point (point ID ₆ ) (e.g. point ID ₄ = enthalpy (point ID ₅ , point ID ₆ )). In some cases, a virtual data point can be derived from a single actual data point. For example, the virtual point calculator 910 calculate a saturation temperature (point ID ₇ ) of a known coolant based on a measured coolant pressure (point ID ₈ ) (e.g. point ID ₇ = T _sat (point ID ₈ )). In general, the virtual point calculator 910 calculate the time series values of a virtual data point using the time series values of one or more actual data points and / or the time series values of one or more other virtual data points.

In some embodiments, the virtual point calculator uses 910 a set of rules for virtual points to calculate the virtual data points. The rules for virtual points can define one or more input data points (e.g., actual or virtual data points) and the mathematical operations that should be applied to the input data point (s) to calculate each virtual data point. The rules for virtual points can be provided by a user, received by an external system or device, and / or in memory 610 get saved. The virtual point calculator 910 can apply the set of rules for virtual points to the time series values of the input data points to calculate time series values for the virtual data points. The time series values for the virtual data points can be derived time series data in the time series database 928 get saved.

The virtual point calculator 910 can create virtual data points using the values of the raw data time series 1404 and / or the aggregated values of the data rollup time series 1406-1414 to calculate. In some embodiments, the input data points used to calculate a virtual data point are collected at different sampling times and / or at different sampling rates. Accordingly, the samples of the input data points may not be synchronized with one another, which can lead to an ambiguity as to which samples of the input data points should be used to calculate the virtual data point. The use of the data rollup time series 1406-1414 for computing the virtual data points ensures that the timestamps of the input data points are synchronized and removes any ambiguity as to which data samples should be used.

With reference to 16 are multiple time series 1600 , 1620 , 1640 and 1660 10, which illustrate the synchronization of data samples resulting from the aggregation of the raw time series data, shown in accordance with some embodiments. The time series 1600 and 1620 are raw data time series. The raw data time series 1600 has multiple raw data samples 1602-1610 on. The raw data sample 1602 will at the time t ₁ collected; the raw data sample 1604 will at the time t ₂ collected; the raw data sample 1606 will at the time t ₃ collected; the raw data sample 1608 will at the time t ₄ collected; the raw data sample 1610 will at the time t ₅ collected; and the raw data sample 1612 will at the time t ₆ collected.

The raw data time series 1620 also has multiple raw data samples 1622 , 1624 , 1626 , 1628 and 1630 on. Raw data samples 1622-1630 but are not with raw data samples 1602-1612 synchronized. For example, the raw data sample 1622 before the time t ₁ collected; the raw data sample 1624 will between times t ₂ and t ₃ collected; the raw data sample 1626 will between times t ₃ and t ₄ collected; the raw data sample 1628 will between times t ₄ and t ₅ collected; and the raw data sample 1630 will between times t ₅ and t ₆ collected. The lack of synchronization between the data samples 1602-1612 and the raw data samples 1622-1630 can lead to ambiguity as to which of the data samples should be used together to calculate a virtual data point.

The time series 1640 and 1660 are data rollup time series. The data rollup time series 1640 can by the sample aggregator 908 by aggregating the raw data time series 1600 be generated. Similarly, the data rollup time series 1660 by the sample aggregator 908 by aggregating the raw data time series 1620 be generated. The two raw data time series 1600 and 1620 can be aggregated using the same aggregation interval. Accordingly, the resulting data rollup time series 1640 and 1660 synchronized data samples. For example, the aggregate data sample 1642 with the aggregated data sample 1662 synchronized at time t _{1 '} . Similarly, the aggregate data sample 1644 at time t _{2 '} with the aggregated data sample 1664 synchronized; becomes the aggregated data sample 1646 at time t _{3 '} with the aggregated data sample 1666 synchronized; and becomes the aggregate data sample 1648 at time t _{4 '} with the aggregated data sample 1668 synchronized.

The synchronization of data samples in the data rollup time series 1640 and 1660 allows the virtual point calculator 910 easy to identify which of the data samples should be used together to calculate a virtual point. For example, the virtual point calculator 910 identify which of the samples of the data rollup time series 1640 and 1660 have the same time stamp (e.g. the data samples 1642 and 1662 , the data samples 1644 and 1664 etc.). The virtual point calculator 910 can use two or more aggregated data samples with the same time stamp to compute a time series value of the virtual data point. In some embodiments, the virtual point calculator 910 the common time stamp of the input data samples to the time series value of the virtual data point calculated from the input data samples.

Weather points

Referring again to 9 is shown that the time series operators 906 a weather point calculator 912 contain. The weather point calculator 912 is configured to perform weather-based calculations using the time series data. In some embodiments, the weather point calculator generates 912 Virtual data points for weather-related variables such as cooling degree days (CDD), heating degree days (HDD), cooling energy days (CED), heating energy days (HED) and normalized energy consumption. The time series values of the virtual data points by the weather point calculator 912 can be calculated as derived time series data in the time series database 928 get saved.

wobei Periode die Integrationsperiode ist. Bei manchen Ausführungsformen ist die Außenlufttemperatur T_OA ist ein gemessener Datenpunkt, während der Kühlungsgleichgewichtspunkt T_bC ein gespeicherter Parameter ist. Um die CDD für jeden Abtastwert der Außenlufttemperatur T_OA zu berechnen, kann der Wetterpunktrechner 912 die Quantität max{0, (T_OA - T_bC)} mit der Abtastperiode Δt der Außenlufttemperatur T_OA multiplizieren. Der Wetterpunktrechner 912 kann die CED auf ähnliche Weise unter Verwendung der Außenluftenthalpie E_OA anstelle der Außenlufttemperatur T_OA berechnen. Die Außenluftenthalpie E_OA kann ein gemessener oder virtueller Datenpunkt sein.The weather point calculator 912 can calculate the CDD by integrating the positive temperature difference between the time-varying outside air temperature T _OA and the cooling _{equilibrium point} T _bC , as shown in the following equation: $$\mathrm{C.}DD={\displaystyle {\int }_{}^{PeriOde}\text{Max}\left\{\mathrm{0,}\left(T_{OA}-T_{b\mathrm{C.}}\right)\right\}}dt$$

where period is the integration period. In some embodiments, the outside air _temperature T _OA is a measured data point, while the cooling _equilibrium point T _{bC is} a stored parameter. To calculate the CDD for each sample of the outside air temperature T _OA , the weather point calculator 912 _multiply the quantity max {0, (T _OA - T _bC )} by the sampling period Δt of the outside air _temperature T _OA . The weather point calculator 912 can similarly calculate the CED using the outside air enthalpy E _OA instead of the outside air temperature T _OA . The outside air enthalpy E _OA can be a measured or virtual data point.

wobei Periode die Integrationsperiode ist. In manchen Ausführungsformen ist die Außenlufttemperatur T_OA ist ein gemessener Datenpunkt, während der Heizungsgleichgewichtspunkt T_bH ein gespeicherter Parameter ist. Um die HDD für jeden Abtastwert der Außenlufttemperatur T_OA zu berechnen, kann der Wetterpunktrechner 912 die Quantität max{0, (T_bH - T_OA)} mit der Abtastperiode Δt der Außenlufttemperatur T_OA multiplizieren. Der Wetterpunktrechner 912 kann die HED auf ähnliche Weise unter Verwendung der Außenluftenthalpie E_OA anstelle der Außenlufttemperatur T_OA berechnen. The weather point calculator 912 can calculate the HDD by integrating the positive temperature difference between a heating _{equilibrium point} T _bH and the time-varying outside air temperature T _OA , as shown in the following equation: $$HDD={\displaystyle {\int }_{}^{PeriOde}\text{Max}\left\{\mathrm{0,}\left(T_{bH}-T_{OA}\right)\right\}}dt$$

where period is the integration period. In some embodiments, the outside air _temperature T _OA is a measured data point, while the heating _equilibrium point T _{bH is} a stored parameter. The weather point calculator can calculate the HDD for each sample of the outside air temperature T _OA 912 _multiply the quantity max {0, (T _bH - T _OA )} by the sampling period Δt of the outside air _temperature T _OA . The weather point calculator 912 can similarly calculate the HED using the outside air enthalpy E _OA instead of the outside air temperature T _OA .

In some embodiments, both the virtual point calculator compute 910 as well as the weather point calculator 912 Time series values of virtual data points. The weather point calculator 912 can calculate time series values of virtual data points that depend on weather-related variables (e.g. outside air temperature, outside air enthalpy, outside air humidity, outside light intensity, precipitation, wind speed, etc.). The virtual point calculator 910 can calculate time series values of virtual data points that depend on other types of variables (e.g. non-weather related variables). Although only a few weather-related variables are described in detail here, it is provided that the weather point calculator 912 can calculate virtual data points for any weather related variable. That from the weather point calculator 912 Weather-related data points used can be received as time series data from various weather sensors and / or from a weather service.

Fault detection

Still referring to 9 is shown that the time series operators 906 an error detector 914 contain. The fault detector 914 can be configured to detect errors in time series data. In some embodiments, the fault detector performs 914 error detection for time series data representing counter data (e.g. measurements from a sensor) and / or for other types of time series data. The fault detector 914 can detect errors in the raw time series data and / or the derived time series data. In some embodiments, the error detector receives 914 Error detection rules from the analysis service 624 . The error detection rules can be defined by a user (e.g. via a rule editor) or received by an external system or device. In various embodiments, the error detection rules can be in the time series database 928 get saved. The fault detector 914 can get the error detection rules from the time series database 928 and can use the error detection rules to analyze the time series data.

In some embodiments, the error detection rules provide criteria set by the error detector 914 can be evaluated in order to detect errors in the time series data. For example, the error detection rules can define an error as a data value above or below a threshold value. As another example, the error detection rules may define an error as a data value outside of a predetermined range of values. The threshold and the predefined range of values can be based on the type of time series data (e.g. counter data, calculated data etc.), the type of variable represented by the time series data (e.g. temperature, humidity, energy consumption etc.), the system or device that measures or provides the time series data (e.g., a sensor, an IoT device, etc.) and / or other attributes of the time series data.

The fault detector 914 may apply the error detection rules to the time series data to determine whether each sample of the time series data qualifies as an error. In some embodiments, the error detector generates 914 an error detection time series that contains the results of the error detection. The error detection time series may include a set of time series values, each of which corresponds to a data sample of the time series data provided by the error detector 914 be evaluated. In some embodiments, each time series value in the error detection time series includes a time stamp and an error detection value. The time stamp can be the same as the time stamp of the corresponding data sample of the data time series. The error detection value can indicate whether the corresponding data sample of the data time series is qualified as an error. For example, the error detection value can have the value “error” if an error is detected and a value “no error” if no error is detected in the corresponding data sample of the data time series. The fault detection time series can be together with the raw time series data and the derived time series data in the time series database 928 get saved.

With reference to 17-18 are a block diagram and a data table 1800 10 that illustrate the error detection time series shown in accordance with some embodiments. In 17th is shown that the error detector 914 a data time series 1702 from the time series database 928 receives. The data time series 1702 can be a raw data time series or a derived data time series. In some embodiments, the data time series is 1702 a time series of values of an actual data point (e.g. a measured temperature). In other embodiments, the data time series is 1702 a time series of values of a virtual data point (e.g. a calculated efficiency). As shown in Table 1800, the data time series contains 1702 a set of data samples. Each data sample contains a time stamp and a value. Most data samples have values within the range of 65-66. However, three of the data samples have values of 42.

The fault detector 914 can the data time series 1702 using a set of error detection rules to evaluate errors in the data time series 1702 to detect. In some embodiments, the error detector determines 914 that the data samples with values of 42 are qualified as errors according to the error detection rules. The fault detector 914 generates an error detection time series 1704 that contains the results of the error detection. As shown in Table 1800, the error detection time series includes 1704 a set of data samples. Like the data time series 1702 contains each data sample of the error detection time series 1704 a timestamp and a value. Most values of the fault detection time series 1704 are shown as "No Error", indicating that for the corresponding sample of the data time series 1702 (ie the data sample with the same time stamp) no error was detected. Three of the data samples in the error detection time series 1704 however, have the value "error", which indicates that the corresponding sample of the data time series 1702 is qualified as an error. As in 17th shown, the error detector 914 the fault detection time series 1704 together with the raw time series data and the derived time series data in the time series database 928 to save.

The error detection time series 1704 can from the building management platform 102 used to perform various fault detection, diagnostic and / or control processes. In some embodiments, the error detection time series 1704 from the time series processing engine 904 processed further to generate a new time series from the error detection time series 1704 is derived. For example, the sample aggregator 908 the fault detection time series 1704 use to generate a fault duration time series. The sample aggregator 908 can multiple successive data samples of the error detection time series 1704 aggregate into a single data sample with the same data value. For example, the sample aggregator 908 the first two "No Error" data samples of the error detection time series 1704 aggregate into a single data sample that represents a time range in which no error was detected. Similarly, the sample aggregator can 908 the last two "error" data samples of the error detection time series 1704 aggregate into a single data sample representing a time range in which an error was detected.

In some embodiments, each data sample in the error duration time series has an error occurrence time and an error duration. The error occurrence time can be specified by the time stamp of the data sample in the error duration time series. The sample aggregator 908 can make the time stamp of each data sample in the error duration time series equal to the time stamp of the first data sample in the series of data samples in the error detection time series 1704 set that have been aggregated to form the aggregated data sample. For example, if the sample aggregator 908 the first two "No Error" data samples of the error detection time series 1704 aggregated, the sample aggregator can 908 set the time stamp of the aggregated data sample to 31-12 - 2015Z23: 10: 00. Similarly, the sample aggregator can 908 set the time stamp of the aggregated data sample to 31-12 - 2015Z23: 50: 00 if the sample aggregator 908 the last two "error" data samples of the error detection time series 1704 aggregated.

The error duration can be specified by the value of the data sample in the error duration time series. The sample aggregator 908 The value of each data sample in the error duration time series can be equal to the duration by successive data samples in the error detection time series 1704 spanned, set that have been aggregated to form the aggregated data sample. The sample aggregator 908 can calculate the duration spanned by several successive data samples by taking the time stamp of the first data sample from that contained in the aggregation Fault detection time series 1704 from the time stamp of the next data sample of the error detection time series 1704 is subtracted from the data samples contained in the aggregation.

For example, if the sample aggregator 908 the first two "No Error" data samples of the error detection time series 1704 aggregated, the sample aggregator can 908 the duration of the aggregated data sample by subtracting the timestamp 31-12 - 2015Z23: 10: 00 (ie the timestamp of the first "no error" sample) from the timestamp 31-12 - 2015Z23: 30: 00 (ie the timestamp of the first " Calculate Error ”sample after the consecutive“ No Error ”sample) for an aggregate time of twenty minutes. Likewise, if the sample aggregator 908 the last two "error" data samples of the error detection time series 1704 aggregated, the sample aggregator 908 the duration of the aggregate data sample by subtracting the time stamp 31-12 - 2015Z23: 50: 00 (ie the time stamp of the first "error" sample contained in the aggregation) from the time stamp 01-01 - 2016Z00: 10: 00 (ie the time stamp of the first "no error" sample after the successive "error" samples) for an aggregate time of twenty minutes.

Series of events

Referring again to 9 is shown that the time series operators 906 an event series generator 915 contain. The event series generator 915 can be configured to generate event series based on the raw data time series and / or the derived data time series. Each series of events can contain multiple event samples that characterize different events and define the start and end times of the events. In the context of series of events, an "event" can be defined as a state or condition that occurs over a certain time period. In other words, an event is not an instantaneous occurrence, but is a non-instantaneous condition or condition that is observed over a non-zero time period (ie, with both a start time and a subsequent end time). The state or condition of the event can be based on the values of the time series samples used to generate the event series. In some embodiments, the event series generator 915 assign a state to each time series sample based on the value of the time series sample, and then aggregate several consecutive samples with the same state to define the time range over which that state is observed.

The event series generator 915 may be configured to assign a state to each sample of an input time series (e.g., a raw data time series or a derived time series) by applying a set of rules to each sample. The process of assigning a state to each sample of the input time series can be described as an event condition action process (ECA process). ECA refers to the structure of active rules in an event-driven architecture and active database systems. For example, each rule in the set of rules can include an event, condition, and action. The event part of the rule can specify a signal that triggers the call of the rule. The condition part of the rule can be a logical test (or a series of logical tests) which, if fulfilled or judged to be true, causes the action to be performed. The action part of the rule can specify one or more actions to be performed if the corresponding logical test is fulfilled (e.g. assigning a special state to a sample of the input time series).

In some embodiments, the event portion is the arrival of a new sample of an input time series. Different rules can apply to different input time series. For example, the arrival of a new sample of a first input time series can be qualified as a first event, while the arrival of a new sample of a second input time series can be qualified as a second event. The event series generator 915 can use the input time series identity to determine which event occurred when a new sample of a particular input time series is received. In other words, the event series generator 915 select a special rule that should be evaluated based on the identity of the input time series.

wobei Wert der Wert des Abtastwerts der Eingabezeitreihe ist, θ₁ - θ₄ Schwellen für den Wert sind und Handlung₁ - Handlung₄ spezielle Handlungen sind, die durchgeführt werden, falls die entsprechende logische Aussage erfüllt wird. Beispielsweise kann Handlung₁ durchgeführt werden, falls der Wert des Abtastwerts größer als θ₁ ist.In some embodiments, the condition includes one or more mathematical checks or logic statements made by the event series generator 915 be assessed. For example, assessing the condition of a particular rule may include comparing the value of the sample of the input time series to a threshold. The condition may be met depending on the particular logic statement specified by the condition if the value of the sample is less than the threshold, equal to or greater than the threshold. With some Embodiments, the condition includes a series of mathematical checks performed by the event series generator 915 be performed in a predetermined order. Each math check can correspond to a different action to be performed if that math check is met. For example, the conditions and corresponding actions can be specified as follows: $$\begin{array}{c}\mathrm{I.}fWert>{\mathrm{<figure-callout\; id="1"\; label="\theta "\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">\theta </figure-callout>}}_1,HandlunG=Handlun{G}_1\\ Else\mathrm{<figure-callout\; id="1"\; label="I.\; f\; \theta "\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">I.\; </figure-callout>}f{\theta }_{}{}_1\ge Wert>{\theta }_{\mathrm{2nd}},HandlunG=Handlun{G}_{\mathrm{2nd}}\\ Else\mathrm{I.}f{\theta }_{\mathrm{2nd}}\ge Wert>{\theta }_{\mathrm{3rd}},HandlunG=Handlun{G}_{\mathrm{3rd}}\\ Else\mathrm{I.}f{\theta }_{\mathrm{3rd}}\ge Wert,HandlunG=Handlun{G}_{\mathrm{4th}}\end{array}$$

where value is the value of the sample of the input time series, θ ₁ - θ _{4 are} thresholds for the value and action ₁ - action _{4 are} special actions that are performed if the corresponding logical statement is fulfilled. For example, action ₁ can be performed if the value of the sample is greater than θ ₁ .

In some embodiments, the actions involve assigning different states to the sample of the input time series. For example, action ₁ may involve assigning a first state to the sample of the input time series, whereas action _{2 may} include assigning a second state to the sample of the input time series. Accordingly, different states can be assigned to the sample based on the value of the sample relative to the threshold values. Each time a new sample of an input time series is received, the event series generator 915 traversing the set of rules, selecting rules that apply to that particular input time series, applying them in a predetermined order, determining which condition is met, and assigning a particular state of the sample based on which condition is met.

$$ElseIf\text{ 10}0\ge OAT>\mathrm{80,}Zustand=Warm$$

$$ElseIf\text{ 8}0\ge OAT>\mathrm{50,}Zustand=K\ddot{u}hl$$

$$ElseIf\text{ 5}0\ge OAT,Zustand=Kalt$$

wobei OAT der Wert eines speziellen Zeitreihendatenabtastwerts ist. Wenn die OAT oberhalb von 100 liegt, kann der Ereignisreihengenerator 915 den Zeitreihenabtastwert dem Temperaturzustand „Heiß“ zuweisen. Falls die OAT kleiner oder gleich 100 und größer als 80 ist, kann der Ereignisreihengenerator 915 den Zeitreihenabtastwert dem Temperaturzustand „Warm“ zuweisen. Falls die OAT kleiner oder gleich 80 und größer als 50 ist, kann der Ereignisreihengenerator 915 den Zeitreihenabtastwert dem Temperaturzustand „Kühl“ zuweisen. Falls die OAT kleiner oder gleich 50 ist, kann der Ereignisreihengenerator 915 den Zeitreihenabtastwert dem Temperaturzustand „Kalt“ zuweisen.An example of an event series created by the event series generator 915 can be generated is an outside air temperature (OAT) event series. The OAT event series can define one or more temperature states and can specify the time ranges during which each of the temperature states is observed. In some embodiments, the OAT event series is based on a time series of measurements of the OAT received as a raw data time series. The event series generator 915 can use a set of rules to assign a specific temperature condition (e.g., hot, warm, cool, cold) to each of the time series OAT samples. For example, the event series generator 915 Apply the following set of rules to the samples of an OAT time series: $$\mathrm{I.}fOAT>\mathrm{100,}Zustand=Heiß$$

$$Else\mathrm{I.}f\text{10th}0\ge OAT>\mathrm{80,}Zustand=Warm$$

$$Else\mathrm{<figure-callout\; id="0"\; label="I.\; f\; 8th"\; filenames="DE112018004336T5\_0057.png"\; state="\{\{state\}\}">I.\; </figure-callout>}f\text{8th}0\ge OAT>\mathrm{50,}Zustand=K\ddot{u}Hl$$

$$Else\mathrm{<figure-callout\; id="5"\; label="I.\; f"\; filenames="DE112018004336T5\_0052.png"\; state="\{\{state\}\}">I.\; </figure-callout>}f\text{5}0\ge OAT,Zustand=Kalt$$

where OAT is the value of a special time series data sample. If the OAT is above 100, the event series generator can 915 assign the time series sample to the "hot" temperature state. If the OAT is less than or equal to 100 and greater than 80, the event series generator can 915 assign the time series sample to the "warm" temperature state. If the OAT is less than or equal to 80 and greater than 50, the event series generator can 915 assign the time series sample to the temperature status "cooling". If the OAT is less than or equal to 50, the event series generator can 915 assign the time series sample to the "cold" temperature state.

wobei Zustand_i der dem i-ten Abtastwert der Eingabezeitreihe zugewiesene Zustand ist, Zeitstempelp_i der Zeitstempel des i-ten Abtastwerts der Eingabezeitreihen ist und N die Gesamtzahl der Abtastwerte in den Eingabezeitreihen ist. In manchen Fällen können zwei oder mehr der Zustandswerte gleich sein, falls derselbe Zustand mehreren Abtastwerten der Eingabezeitreihe zugewiesen ist.In some embodiments, the event series generator generates 915 a new time series that contains the assigned states for each sample of the original input time series. The new time series can be referred to as a "state time series" because it indicates the state to be assigned to each time series sample. The state time series can be generated by applying the set of rules to an input time series as previously described. In some embodiments, the State time series a state value and a time stamp for each sample of the state time series. An example of a state time series is as follows: $$\left[〈Zusta\mathrm{<figure-callout\; id="1"\; label="n\; d"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">n\; </figure-callout>}{d}_{}{}_1,Zeitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1〉,〈Zustan{d}_{\mathrm{2nd}},Zeitstempe{l}_{\mathrm{2nd}}〉,...〈Zustan{d}_N,Zeitstempe{l}_N〉\right]$$

where state _{i is} the state assigned to the i th sample of the input time series, time stamp p _{i is} the time stamp of the i th sample of the input time series and N is the total number of samples in the input time series. In some cases, two or more of the state values may be the same if the same state is assigned to multiple samples of the input time series.

wobei Wert_i der ursprüngliche Wert des i-ten Abtastwerts der Eingabezeitreihe ist. Die Zustandszeitreihe ist eine Art einer abgeleiteten Zeitreihe, die von dem Zeitreihendienst 628 gespeichert und verarbeitet werden kann.In some embodiments, the state time series also includes the original value of each sample of the input time series. For example, each sample of the state time series can include a state value, a timestamp, and an input data value, as shown in the following equation: $$\left[〈Zusta\mathrm{<figure-callout\; id="1"\; label="n\; d"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">n\; </figure-callout>}{d}_{}{}_1,Zeitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1,W\mathrm{<figure-callout\; id="1"\; label="e\; r\; t"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">e\; </figure-callout>}r{t}_{}{}_1〉,...〈Zustan{d}_N,Zeitstempe{l}_N,Wer{t}_N〉\right]$$

where value _{i is} the original value of the ith sample of the input time series. The state time series is a kind of a derived time series that is derived from the time series service 628 can be saved and processed.

Now referring to 19A 14 is a table 1910 that illustrates the result of assigning a temperature state to each time series sample, in accordance with some embodiments. Each time series sample is shown as a separate row of table 1910. The “Time” column of table 1910 indicates the time stamp assigned to each sample, while the “OAT” column of table 1910 indicates the value of each time series sample. The "State" column of table 1910 indicates the state of each time series sample by the event series generator 915 is assigned.

Now referring to 19B is a table 1920 that contains a set of by the event series generator 915 generated events, shown in accordance with some embodiments. Each event is shown as a separate row from table 1920. The "Event ID" column in Table 1920 specifies the unique identifier for each event (e.g. event 1 , Event 2nd etc.). The "Start time" column of table 1920 indicates the time at which each event begins, and the "End time" column of table 1920 indicates the time at which the event ends. The "State" column of table 1920 indicates the state that is assigned to each event.

The event series generator 915 may generate any event shown in table 1920 by identifying consecutive time series samples with the same assigned state and determining a time range that contains the identified samples. In some embodiments, the time range begins with the time stamp of the first sample with a given state and ends immediately before the time stamp of the next sample with another state. For example, the first two time series samples shown in Table 1910 are in the "Cold" state, while the third sample in Table 1910 is in the "Cool" state. The event series generator 915 can identify the first two samples as having the same state and can generate the time range 00:00 - 01:59 that contains the two identified samples. This time range begins with the time stamp of the first sample (ie at 00:00) and ends immediately before the time stamp of the third sample (ie at 02:00). The event series generator 915 can generate an event for each group of consecutive samples with the same state.

The event series generator 915 may perform a similar analysis for the remaining time series samples in table 1910 to generate each of the events shown in table 1920. In some cases, multiple events can have the same assigned state. For example, both event 1 event as well 7 in the table 1920 the state "cold". Likewise, both event 2nd event as well 6 the state "cool" and both show event 3rd event as well 5 the "warm" state. Note that an event not only defines a particular state, but also a time range (ie, a series of consecutive time samples) during which that state is observed. If the same condition for several non-consecutive When time ranges are observed, multiple events with the same state can be generated to represent each of the non-consecutive time ranges.

wobei jeder Gegenstand innerhalb der spitzen Klammern 〈 〉 ein Ereignis mit den Attributen ID, Zustand, Startzeit, und Endzeit ist. Ereignisse können in einem tabellarischen Format (wie in 9B gezeigt), als Textzeichenfolge (wie oben gezeigt), als ein Datenobjekt (z. B. ein JSON-Objekt), in einem Containerformat oder in beliebigen einer Vielzahl von Formaten gespeichert werden.In some embodiments, the event series generator generates 915 a series of events for a set of events. A series of events is conceptually similar to a time series in that both represent a series of events. However, the samples of a time series correspond to current events with a single time stamp, while the samples of an event series correspond to non-current events that have both a start time and an end time. For example, the event series generator 915 generate the following series of events for the set of events shown in Table 1920: $$\begin{array}{l}[〈\mathrm{I.}D=\mathrm{1,}Zustand=Kalt,Start\mathrm{e.g.}eit=00:\mathrm{00,}End\mathrm{e.g.}eit=01:59〉,\\ 〈\mathrm{I.}D=\mathrm{2,}Zustand=K\ddot{u}Hl,Start\mathrm{e.g.}eit=02:\mathrm{00,}End\mathrm{e.g.}eit=08:59〉,\\ 〈\mathrm{I.}D=\mathrm{3,}Zustand=Warm,Start\mathrm{e.g.}eit=09:\mathrm{00,}End\mathrm{e.g.}eit=11:59〉,\\ 〈\mathrm{I.}D=\mathrm{4,}Zustand=Heiß,Start\mathrm{e.g.}eit=\mathrm{12th}:\mathrm{00,}End\mathrm{e.g.}eit=15:59〉,\\ 〈\mathrm{I.}D=\mathrm{5,}Zustand=Warm,Start\mathrm{e.g.}eit=16:\mathrm{00,}End\mathrm{e.g.}eit=\mathrm{18th}:59〉,\\ 〈\mathrm{I.}D=\mathrm{6,}Zustand=K\ddot{u}Hl,Start\mathrm{e.g.}eit=\mathrm{19th}:\mathrm{00,}End\mathrm{e.g.}eit=\mathrm{21st}:59〉,\\ 〈\mathrm{I.}D=\mathrm{7,}Zustand=Kalt,Start\mathrm{e.g.}eit=22:\mathrm{00,}End\mathrm{e.g.}eit=23:59〉,]\end{array}$$

where each item within the angle brackets 〈〉 is an event with the attributes ID, state, start time, and end time. Events can be presented in a tabular format (as in 9B ), as a text string (as shown above), as a data object (e.g., a JSON object), in a container format, or in any of a variety of formats.

Event series process

Now referring to 19C is a flowchart of a process 1960 shown for generating and updating series of events according to some embodiments. The process 1960 can by the event series generator 915 be carried out as with reference to FIG 9 and 17-18 described. In some embodiments, the process 1960 performed to generate an event series based on the samples of a data time series. The process 1960 can be performed after all of the samples of the data time series have been collected, or can be performed each time a new sample of the data time series is collected.

It is shown that the process 1960 obtaining a new sample of a data time series (step 1962 ) and assigning a state to the sample using a set of rules (step 1964 ) includes. In some embodiments, the sample is obtained from a sensor configured to measure a variable of interest. For example, the sample can be a sample of a raw data time series. In other embodiments, the sample is a sample of a derived data time series generated by the sample aggregator 908 , the virtual point calculator 910 , the weather point calculator 912 or other time series operators 906 is produced. The sample can be obtained from a set of samples of a complete time series or can be received as the latest sample of an input data stream.

$$ElseIf100\ge OAT>\mathrm{80,}Zustand=Warm$$

$$ElseIf80\ge OAT>\mathrm{50,}Zustand=K\ddot{u}hl$$

$$ElseIf\text{ 5}0\ge OAT,Zustand=Kalt$$

wobei OAT der Wert eines speziellen Zeitreihendatenabtastwerts ist. Wenn die OAT oberhalb von 100 liegt, kann dem Abtastwert der Temperaturzustand „Heiß“ zuwiesen werden. Falls die OAT kleiner oder gleich 100 und größer als 80 ist, kann dem Abtastwert der Temperaturzustand „Warm“ zugewiesen werden. Falls die OAT kleiner oder gleich 80 und größer als 50 ist, kann dem Abtastwert der Temperaturzustand „Kühl“ zugewiesen werden. Falls die OAT kleiner als 50 ist, kann dem Abtastwert der Temperaturzustand „Kalt“ zugewiesen werden.In some embodiments, step includes 1964 Applying a set of rules to the sample of the data time series to determine which state to assign. The set of rules can define different value ranges and a corresponding state for each value range. step 1964 may include assigning the sample to a particular state if the value of the value of the sample is within the corresponding range of values. For example, if the sample is an outside air temperature (OAT) sample, the set of rules may define different temperature ranges and a temperature condition for each of the temperature ranges. An example of such a set of rules is as follows: $$\mathrm{I.}fOAT>\mathrm{100,}Zustand=Heiß$$

$$Else\mathrm{<figure-callout\; id="100"\; label="I.\; f"\; filenames="DE112018004336T5\_0037.png,DE112018004336T5\_0050.png"\; state="\{\{state\}\}">I.\; </figure-callout>}f100\ge OAT>\mathrm{80,}Zustand=Warm$$

$$Else\mathrm{<figure-callout\; id="80"\; label="I.\; f"\; filenames="DE112018004336T5\_0050.png"\; state="\{\{state\}\}">I.\; </figure-callout>}f80\ge OAT>\mathrm{50,}Zustand=K\ddot{u}Hl$$

$$Else\mathrm{<figure-callout\; id="5"\; label="I.\; f"\; filenames="DE112018004336T5\_0052.png"\; state="\{\{state\}\}">I.\; </figure-callout>}f\text{5}0\ge OAT,Zustand=Kalt$$

where OAT is the value of a special time series data sample. If the OAT is above 100, the temperature can be assigned to the sample temperature "hot". If the OAT is less than or equal to 100 and greater than 80, the temperature can be assigned to the sample value "Warm". If the OAT is less than or equal to 80 and greater than 50, the temperature value "Cool" can be assigned to the sample. If the OAT is less than 50, the temperature value "cold" can be assigned to the sample.

Still referring to 19C it is shown that the process 1960 Determining includes whether the sample is part of an existing event (step 1966 ). step 1966 may include identifying all of the events in an existing series of events and determining whether the sample belongs to any of the identified events. Each event can be defined by combining a special state and a time range that has both a start time and an end time. step 1966 may include determining that the sample is part of an existing event if the sample is both (1) assigned the same state as the existing event and (2) has a timestamp that is either (a) within the time range associated with the existing event or (b) is consecutive to the time range associated with the existing event. step 1966 however, may include determining that the sample is not part of an existing event if the sample is not in the same state as the existing event or does not have a timestamp that is either within the time range associated with the existing event or continuous to that associated with the existing event Time range is.

In step 1966 For example, a timestamp can be considered to be within the time range associated with an existing event if the timestamp is between the start time of the event and the end time of the event. A timestamp can be considered to be continuous to the time range assigned to an existing event if the timestamp is immediately before the start time or immediately after the end time of the event. For example, if a new sample has a timestamp prior to the start time of an event and there are no other samples between the timestamp of the new sample and the start time of the event, the timestamp can be considered to be continuous to the time range associated with the existing event. If a new sample has a time stamp after the end time of an event and there are no other samples between the end time of the event and the time stamp of the new sample, the time stamp can be considered to be continuous to the time range assigned to the existing event.

If the new sample is part of an existing event (ie the result of step 1966 is "Yes"), the process can 1960 proceed to determine whether the new sample extends the existing event (step 1968 ). step 1968 may include determining whether the timestamp of the new sample is consecutive to the time range associated with the existing event (ie, immediately before the event start time or immediately after the event end time). If the time stamp of the new sample is continuous to the time range assigned to the existing event, step can 1968 determining that the sample extends the existing event. If, however, the time stamp of the new sample is not continuous with the time range assigned to the existing event, step can 1968 determining that the sample does not expand the existing event.

If the sample does not expand the existing event (ie the result of step 1968 is "No"), the process can 1960 Determining includes that no update of the existing event is required (step 1970 ). This situation can occur if the time stamp of the new sample lies between the start time of the existing event and the end time of the existing event (ie within the time range assigned to the existing event). Because the time range associated with the existing event already covers the time stamp of the new sample, the existing event may not need to be updated to include the time stamp of the new sample.

However, if the sample expands the existing event (ie the result of step 1968 is "Yes"), the process can 1960 Proceed to update the start or end time of the existing event based on the timestamp of the sample (step 1972 ). step 1972 can the temporal Moving back the start time of the event or advancing the end time of the event in time so that the time range between the start time and the end time contains the time stamp of the new sample. For example, if the timestamp of the sample is before the start time of the event, step 1972 include replacing the start time of the existing event with the timestamp of the sample.

Likewise, if the timestamp of the sample is after the end time of the event, step can 1972 Replace the end time of the existing event with a new end time that occurs after the timestamp of the sample. For example, if the existing event has an original end time of 04:59 and the new sample has a time stamp of 05:00, step can 1972 Update the end time of the event to 05:59 (or any other time that occurs after 05:00) so that the adjusted time range associated with the event contains the timestamp of the new sample. If the original end time of the existing event is "zero" and the new sample extends the end time of the existing event, step can 1972 keep the original end time of "zero".

Back to step 1966 can if the sample is not part of an existing event (ie the result of step 1966 is "No"), the process 1960 Proceed to generate a new event based on the state and time stamp of the new sample (step 1974 ). The new event may have a state that corresponds to the state that corresponds to the new sample in step 1964 is assigned. The new event can have a start time equal to the time stamp of the sample and an end time that occurs after the time stamp of the sample, so that the time range assigned to the new event contains the time stamp of the sample. The end time can have a value of zero if the new event is the last event in the series of events, or a non-zero value if the new event is not the last event in the series of events. For example, if the next event in the time series is at a timestamp 06 : 00 starts, can step 1974 Set the end time of the new event to include 05:59.

After creating the new event in step 1974 can the process 1960 the steps 976-988 perform to update other events in the series of events based on the new information provided by the new event. For example, if the new event is the last event in the series of events (that is, the result of step 1976 is "Yes"), the process can 1960 Update the end time of the previous event (that is, the event that occurs immediately before the new event) (step 1978 ). The one in step 1978 the update performed may include setting the end time of the previous event to be immediately before the time stamp of the new sample. For example, if the next sample has a time stamp of 05:00, step 1978 Update the end time of the previous event to include 04:59. If the new event is not the last event in the series of events (ie the result of step 1976 is "No"), the process can 1960 to step 1980 Continue.

If the new event occurs between existing events in the series of events (ie the result of step 1980 is "Yes"), the process can 1960 Update the end time of the previous event (step 1982 ). The one in step 1982 The update performed can be the same as that in step 1978 carried out update. The one in step 1982 For example, the update performed may include setting the end time of the previous event to be immediately before the time stamp of the new sample. If the new event does not occur between existing events in the series of events (ie the result of step 1980 is "No"), the process can 1960 to step 1984 Continue.

If the new event splits an existing event in the series of events (ie the result of step 1984 is "Yes"), the process can 1960 that split the existing event into two events with the new event in between. In some embodiments, splitting the existing event into two events includes updating the end time of the existing event to end before the new event (step 1986 ), and creating a second new event that begins after the first new event and ends at the previous end time of the existing event (step 1988 ). For example, consider a situation where the existing event has a start time of 4:00 AM, an end time of 11:59 AM, and a “warm” state. That in step 1974 added new event can have a start time of 8:00 am, an end time of 8:59 am and a “hot” state. Accordingly, step 1986 Include changing the end time of the existing event to 07:59 so that the existing event corresponds to a first "warm" event and covers the time range from 04:00 to 07:59. The "hot" event in between can cover the time range from 08:00 to 08:59. The second new event in step 1988 was generated (ie the second "warm" event), a start time of 09:00 and have an end time of 11:59. The state of the second new event can be the same as the state of the existing event.

Properties of events and series of events

Similar to the time series, an event series can be used in two ways. In some embodiments, a series of events is used only for storage. For example, events can be generated by an external application and saved in an event series. In this scenario, the series of events is used only as a storage container. In other embodiments, a series of events can be used for both storage and processing. For example, events can be generated by the event series generator 915 based on a raw time series or derived time series by applying a set of rules as previously described. In this scenario, the series of events is both the storage container and the mechanism for generating the events.

In some embodiments, each event series contains the following properties or attributes: event series ID, org ID, input time series ID, state time series ID, rules and states. The event series ID property can be a unique ID that is provided by the event series generator 915 is generated when a new series of events is created. The series of events ID property can be used to uniquely identify the series of events and to distinguish the series of events from other series of events. The Org ID property can identify the organization (for example, "ABC Corporation") to which the series of events belongs. Similar to time series, each series of events can belong to a specific organization, customer, facility, or other entity (which is explained in more detail with reference to 1 1A-11B).

wobei Schlüsseleine Kennung der Quelle der Datenabtastwerte ist (z. B. Zeitreihe-ID, Sensor-ID, usw.) ist, Zeitstempel_i einen Zeitpunkt identifiziert, der dem i-ten Abtastwert zugeordnet ist, und Wert_i den Wert von des i-ten Abtastwerts angibt.The input time series ID property can identify the time series used to generate the event series. For example, if the event series are a series of outside air temperature (OAT) events, the input time series ID property can identify the OAT time series from which the OAT event series is generated. In some embodiments, the input time series has the following format: $$\begin{array}{l}[<ScHl\ddot{u}ssel,Zeitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1,W\mathrm{<figure-callout\; id="1"\; label="e\; r\; t"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">e\; </figure-callout>}r{t}_{}{}_1>,<ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{2nd}},Wer{t}_{\mathrm{2nd}}>,\\ <ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{3rd}},Wer{t}_{\mathrm{3rd}}>]\end{array}$$

where key is an identifier of the source of the data samples (e.g., time series ID, sensor ID, etc.), time stamp _i identifies a time associated with the i th sample, and value _{i is} the value of the i- specifies th sample.

$$ElseIf\text{ 10}0\ge OAT>\mathrm{80,}Zustand=Warm$$

$$ElseIf\text{ 8}0\ge OAT>\mathrm{80,}Zustand=K\ddot{u}hl$$

$$ElseIf\text{ 5}0\ge OAT,Zustand=Kalt$$

The rules property can identify a list of rules that are applied to the input time series to assign a particular state to each sample of the input time series. In some embodiments, the list of rules includes multiple rules that are applied in a particular order. The order can be defined by the logical structure of the rules. For example, the rules can include a set of "If" and "ElseIf" statements that are evaluated in the order in which the statements appear in the set of rules. An example of a set of rules is as follows: $$\mathrm{I.}fOAT>\mathrm{100,}Zustand=Heiß$$

$$Else\mathrm{I.}f\text{10th}0\ge OAT>\mathrm{80,}Zustand=Warm$$

$$Else\mathrm{<figure-callout\; id="0"\; label="I.\; f\; 8th"\; filenames="DE112018004336T5\_0057.png"\; state="\{\{state\}\}">I.\; </figure-callout>}f\text{8th}0\ge OAT>\mathrm{80,}Zustand=K\ddot{u}Hl$$

$$Else\mathrm{<figure-callout\; id="5"\; label="I.\; f"\; filenames="DE112018004336T5\_0052.png"\; state="\{\{state\}\}">I.\; </figure-callout>}f\text{5}0\ge OAT,Zustand=Kalt$$

wobei Zustand_i der dem i-ten Abtastwert der Eingabezeitreihe zugewiesene Zustand ist, Zeitstempel_i der Zeitstempel des i-ten Abtastwerts der Eingabezeitreihen ist und N die Gesamtzahl der Abtastwerte in den Eingabezeitreihen ist.The state time series ID property can identify the state time series in which the assigned states are stored. The state time series can be generated by applying the set of rules to an input time series as previously described. In some embodiments, the state time series includes a state value and a timestamp for each sample of the state time series. An example of a state time series is as follows: $$\left[〈Zusta\mathrm{<figure-callout\; id="1"\; label="n\; d"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">n\; </figure-callout>}{d}_{}{}_1,Zeitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1〉,〈Zustan{d}_{\mathrm{2nd}},Zeitstempe{l}_{\mathrm{2nd}}〉,...〈Zustan{d}_N,Zeitstempe{l}_N〉\right]$$

where state _{i is} the state assigned to the i th sample of the input time series, time stamp _{i is} the time stamp of the i th sample of the input time series, and N is the total number of samples in the input time series.

The state property can indicate whether the series of events is active (ie state = active) or inactive (ie state = inactive). In some embodiments, a series of events is active by default when the series of events is generated. A series of events can be done by the event service 903 be deactivated. The event service 903 can change the state property from active to inactive when a series of events is deactivated.

Each series of events can contain a set of events. Each event can include the following properties: event ID, state, start timestamp, end timestamp, and event series ID. The event ID property can be a unique ID that is provided by the event series generator 915 is generated when a new event is created. The event ID property can be used to uniquely identify a specific event and to distinguish the event from other events in the series of events. The state property can be a text string that defines the state associated with the event. Each event can be uniquely assigned to a state. The start timestamp property can specify the start time of the event, while the end timestamp property specifies the end time of the event. The start timestamp and end timestamp property can be timestamps in a variety of formats (e.g. 01-01-2017Z00: 00: 00). The event series ID property can identify the event series that contains the event. The event series ID property can be the same unique identifier that is used to identify and differentiate event series.

Event service

Referring again to 9 is shown that the time series service 628 an event service 903 contains. In some embodiments, the event service is 903 part of the time series service 628 . In other embodiments, the event service is 903 a separate service (ie separate from the time series service 628 ) within the cloud building management platform 620 . The event service 903 can be configured to receive and process requests for information related to various events and series of events. The event service 903 can also create and update events and series of events in response to a request from an application or user. Below are some examples of how the event service works 903 Can process requests. The following table identifies the types of actions that the Event Service does 903 with regard to events and series of events: resource GET (reading) POST PUT (update) / Series of events Get the list of series of events Create one or more new series of events - / Event Series / {Event Series ID} Read a special series of events Generate a special series of events Update the special series of events / Events Get a list of events Create one or more new events - / Events / {Event ID} Read a special event Generate a special event Update the special event

 Post {Zeitreihe V2}/Ereignisreihe/Neu 

 {
       „Org-Id“: „Abc Inc“,
 } „Verarbeitungstyp“: „keiner“
 }

wobei „Abc Inc“ die ID der Organisation ist, zu der die neue Ereignisreihe gehören wird, und kein Verarbeitungstyp spezifiziert ist.The event service 903 can be configured to generate a new series of events in response to a request that includes an Org ID attribute and a processing type attribute. For example, the event service 903 received the following request:

 Post {time series V2} / event series / new 

  {
       "Org-Id": "Abc Inc",
 } "Processing type": "none"
 } 

where "Abc Inc" is the ID of the organization to which the new series of events will belong and no processing type is specified.

 {
       „Ereignisreihe-ID“: „C7c157e4-303f-4b25-b182-ce7b0f8291d8“,
       „Org-Id“: „Abc Inc“,
       „Eingabezeitreihe-ID“: leer,
       „Zustandszeitreihe-ID“: leer,
       „Regeln“: leer,
       „Zustand“: „aktiv“,
       „Verarbeitungstyp“: „Stream“

In response to this request, the event service can 903 create a new series of events (ie an empty series of events container) and assign an event series ID to the series of events. For example, the event service 903 respond to the request as follows:

 {
       "Event series ID": "C7c157e4-303f-4b25-b182-ce7b0f8291d8",
       "Org-Id": "Abc Inc",
       "Entry time series ID": empty,
       "State time series ID": empty,
       "Rules": empty,
       "State": "active",
       "Processing type": "Stream" 

 {
       „Org-Id“: „Abc Inc“,
       „Eingabezeitreihe-ID“: „793c156e4-303f-4b2e-bt82-ce7b0f829uj3“, 





       „Zustandszeitreihe-ID“: „uic157e4-6r2f-4b25-b682-ct7b0f82917u“,
       „Regeln“: [
          {„VergleicheOP“: „Gt“, „Skalar“: 100, „Zustand“: „Heiß“},
          {„VergleicheOP“: „Gt“, „Skalar“: 80, „Zustand“: „Warm“},
          {„VergleicheOP“: „Gt“, „Skalar“: 50, „Zustand“: „Kühl“},
          {„VergleicheOP“: „Lte“, „Skalar“: 50, „Zustand“: „Kalt“} 
      ]
 }

wobei „793c156e4-303f-4b2e-bt82-ce7b0f829uj3“ die ID der Eingabezeitreihe ist, die zum Erzeugen der Ereignisreihe verwendet wird, „uic157e4-6r2f-4b25-b682-ct7b0f82917u“ die ID der Zustandszeitreihe ist, die die jedem Abtastwert zugewiesenen Zustände enthält, und das Attribut „Regeln“ einen Satz von Regeln enthält, die verwendet werden, um jedem Abtastwert der Eingabezeitreihe einen Zustand zuzuweisen.In some embodiments, the event service is 903 configured to generate a new series of events in response to a request that includes an Org ID attribute, an input time series ID attribute, a state time series ID attribute, and a rules attribute. For example, the event service 903 received the following request:

 {
       "Org-Id": "Abc Inc",
       "Input time series ID": "793c156e4-303f-4b2e-bt82-ce7b0f829uj3", 





       "State time series ID": "uic157e4-6r2f-4b25-b682-ct7b0f82917u",
       "Regulate": [
          {"Compare OP": "Gt", "Skalar": 100, "State": "Hot"},
          {"Compare OP": "Gt", "Skalar": 80, "State": "Warm"},
          {"Compare OP": "Gt", "Skalar": 50, "State": "Cool"},
          {"Compare OP": "Lte", "Skalar": 50, "State": "Cold"} 
      ]
 } 

where "793c156e4-303f-4b2e-bt82-ce7b0f829uj3" is the ID of the input time series that is used to generate the event series, "uic157e4-6r2f-4b25-b682-ct7b0f82917u" is the ID of the state time series that assigned to each sample , and the "rules" attribute contains a set of rules that are used to assign a state to each sample in the input time series.

In response to this request, the event service can 903 create a new series of events (ie an empty series of events container) and assign an event series ID to the series of events. For example, the event service 903 respond to the request as follows:

Post{ZeitreiheV2}/Ereignisreihe/c7c157e4-303f-4b25-b182-ce7b0f8291d8/Ereignisse
 [
    {
       „Ereignisreihe-ID“: „c7c157e4-303f-4b25-b182-ce7b0f8291d8“,
       „Startzeitstempel“: „01-04-2017 13:48:23-05:00“,
       „Endzeitstempel“: „01-04-2017 13:54:11-05:00“,
       „Zustand“: „Hochdruckalarm“ 
    }
 ]

In some embodiments, the event service is 903 configured to add new events to an existing series of events. For example, the event service 903 received a request to add a new event to a series of events. The request can specify the series of events ID, the start time of the event, the end time of the event, and the state assigned to the event, as shown in the following request:

Post {time series V2} / event series / c7c157e4-303f-4b25-b182-ce7b0f8291d8 / events
 [
    {
       "Event series ID": "c7c157e4-303f-4b25-b182-ce7b0f8291d8",
       "Start time stamp": "01-04-2017 13: 48: 23-05: 00",
       "End time stamp": "01-04-2017 13: 54: 11-05: 00",
       "State": "High pressure alarm" 
    }
 ] 

In response to this request, the event service can 903 generate a new event ID for the new event and add the new event to the event series designated by the event series ID "c7c157e4-303f-4b25-b182-ce7b0f8291d8". The new event can have the start time "01-04-2017 13: 48: 23-05: 00", the end time "01-04-2017 13: 54: 11-05: 00" and the state "high pressure alarm", such as is specified in the request. In some embodiments, the event service responds 903 at the request by confirming that the new event has been added to the series of events.

In some embodiments, the event service is 903 configured to update existing events in a series of events. For example, the event service 903 received a request to update to add one or more properties of an existing event in an event series. The request may specify the series of events ID, the start time of the updated event, the end time of the updated event, and / or the updated state assigned to the event, as shown in the following request:

In response to this request, the event service can 903 update the specified properties of the event designated by the event series ID "c7c157e4-303f-4b25-b182-ce7b0f8291d8". The updated event can have the start time "01-04-2017 13: 48: 23-05: 00", the end time "01-04-2017 13: 54: 11-05: 00" and the state "High pressure alarm", such as is specified in the request. In some embodiments, the event service responds 903 to the request by confirming that the event has been updated.

In some embodiments, the event service is 903 configured to read the events of a series of events. For example, the event service 903 received a request to identify all of the events associated with a series of events. The request can be specified as a get request as follows:

wobei „g9c197e4-003f-4u25-b182-se7b0f81945y“ die Ereignis-ID eines identifizierten Ereignisses ist, das den Suchparametern entspricht. Die Antwort kann die Ereignisreihe-ID-, Startzeitstempel-, Endzeitstempel und Zustand-Eigenschaften jedes identifizierten Ereignisses spezifizieren.In response to this request, the event service can 903 search for all events in the specified series of events and can return a list of the identified events. A sample response provided by the event service 903 can be provided is as follows:

where "g9c197e4-003f-4u25-b182-se7b0f81945y" is the event ID of an identified event that matches the search parameters. The response can specify the event series ID, start timestamp, end timestamp, and state properties of each identified event.

 Get {ZeitreiheV2}/Ereignisreihe/c7c157e4-303f-4b25-b182-
 ce7b0f8291d8/Ereignisse?Zustand=Heiß

wobei „c7c157e4-303f-4b25-b182-ce7b0f8291d8“ die Ereignisreihe-ID einer bestimmten Ereignisreihe ist und „Zustand=Heiß“ spezifiziert, dass die Suche nur Ereignisse der Ereignisreihe zurückgeben soll, die den Zustand „Heiß“ aufweisen. Als Reaktion auf diese Anforderung kann der Ereignisdienst 903 nach allen entsprechenden Ereignissen suchen (d. h. nach Ereignissen der spezifizierten Ereignisreihe, die den spezifizierten Zustand aufweisen) und kann eine Liste von Ereignissen zurückgeben, die den Suchparametern entsprechen.In some embodiments, the event service is 903 configured to search for the events in a series of events that have a specific state. For example, the event service 903 received a request to identify all of the events associated with a particular series of events and having a particular state. The request can be specified as a get request as follows:

 Get {time series V2} / event series / c7c157e4-303f-4b25-b182-
 ce7b0f8291d8 / Events? Condition = Hot 

where "c7c157e4-303f-4b25-b182-ce7b0f8291d8" is the event series ID of a specific event series and "State = Hot" specifies that the search should only return events of the event series that have the state "Hot". In response to this request, the event service can 903 search for all corresponding events (ie, events of the specified series of events that have the specified state) and can return a list of events that match the search parameters.

 Get {ZeitreiheV2}/Ereignisreihe/c7c157e4-303f-4b25-b182-
 ce7b0f8291 d8/EReignisse?Starrzeit=01 -04-2017%2010:00:00-05:00&Endzeit=01 -04-
 2017%2010:00:00-05:00

wobei „c7c157e4-303f-4b25-b182-ce7b0f8291d8“ die Ereignisreihe-ID einer speziellen Ereignisreihe ist und die Parameter „Startzeit“ und „Endzeit“ die Startzeit und Endzeit des Ereignisses spezifizieren. Als Reaktion auf diese Anforderung kann der Ereignisdienst 903 nach allen entsprechenden Ereignissen suchen (d. h. (Startzeitstempel des Ereignisses < Startzeit und Endzeitstempel des Ereignisses > Endzeit) und kann eine Liste von Ereignissen zurückgeben, die den Suchparametern entsprechen. In some embodiments, the event service is 903 configured to search for the events in a series of events that have a start time or an end time that corresponds to a given value. For example, the event service 903 receive a request to identify all of the events of a particular series of events that have a start time or end time that corresponds to a specified time stamp. The request can be specified as a get request as follows:

 Get {time series V2} / event series / c7c157e4-303f-4b25-b182-
 ce7b0f8291 d8 / E Events? Stare time = 01 -04-2017% 2010: 00: 00-05: 00 & end time = 01 -04-
 2017% 2010: 00: 00-05: 00 

where “c7c157e4-303f-4b25-b182-ce7b0f8291d8” is the event series ID of a special event series and the parameters “start time” and “end time” specify the start time and end time of the event. In response to this request, the event service can 903 search for all corresponding events (ie (event start time stamp <start time and event end time stamp> end time) and can return a list of events that match the search parameters.

 Get {ZeitreiheV2}/Ereignisreihe/c7c157e4-303f-4b25-b182-
 ce7b0f8291 d8/EReignisse?Starrzeit=01 -04-2017%2010:00:00-05:00&Endzeit=01 -04-
 2017%2011:59:00-05:00

wobei „c7c157e4-303f-4b25-b182-ce7b0f8291d8“ die Ereignisreihe-ID einer speziellen Ereignisreihe ist und die Parameter „Startzeit“ und „Endzeit“ die Startzeit und Endzeit des Ereignisses spezifizieren. Als Reaktion auf diese Anforderung kann der Ereignisdienst 903 nach allen Ereignissen suchen, die den folgenden Kriterien entsprechen:

und kann eine Liste von Ereignissen zurückgeben, die diesen Kriterien entsprechen.In some embodiments, the event service is 903 configured to search for the events of a series of events that have a time range that overlaps (at least partially) with a specified time range. For example, the event service 903 receive a request to identify all of the events of a particular series of events that have (1) an event start time before a specified start time and an event end time after the specified start time, or (2) an event start time before a specified end time and event end time after the specified end time. The request can be specified as a get request as follows:

 Get {time series V2} / event series / c7c157e4-303f-4b25-b182-
 ce7b0f8291 d8 / E Events? Stare time = 01 -04-2017% 2010: 00: 00-05: 00 & end time = 01 -04-
 2017% 2011: 59: 00-05: 00 

where “c7c157e4-303f-4b25-b182-ce7b0f8291d8” is the event series ID of a special event series and the parameters “start time” and “end time” specify the start time and end time of the event. In response to this request, the event service can 903 search for all events that meet the following criteria:

and can return a list of events that meet these criteria.

wobei „c7c157e4-303f-4b25-b182-ce7b0f8291d8“ die Ereignisreihe-ID einer speziellen Ereignisreihe ist, der Parameter „Zustand“ einen speziellen Zustand spezifiziert und die Parameter „Startzeit“ und „Endzeit“ die Startzeit und Endzeit des Ereignisses spezifizieren. Als Reaktion auf diese Anforderung kann der Ereignisdienst 903 nach allen Ereignissen suchen, die den folgenden Kriterien entsprechen:

und kann eine Liste von Ereignissen zurückgeben, die diesen Kriterien entsprechen.In some embodiments, the event service is 903 configured to search for events in a series of events that have a particular state and a time range that overlaps (at least partially) with a given time range. For example, the event service 903 receive a request to identify all of the events of a particular series of events that have a particular state and either (1) an event start time before a specified start time and an event end time after the specified start time, or (2) an event start time before a specified end time and event end time after the specified Show end time. The request can be specified as a get request as follows:

where “c7c157e4-303f-4b25-b182-ce7b0f8291d8” is the event series ID of a special event series, the parameter “state” specifies a special state and the parameters “start time” and “end time” specify the start time and end time of the event. In response to this request, the event service can 903 search for all events that meet the following criteria:

and can return a list of events that meet these criteria.

Directed acyclic graphs

Now referring to 20A is an example of a DAG 2000 by the DAG generator 920 can be generated, shown according to an embodiment. The DAG 2000 is shown as a structured tree that represents a graph of data flow instead of a formal scripting language. blocks 2002 and 2004 represent the input time series identified by a time series ID (e.g. ID 123 , ID 456 etc.) can be specified. The blocks 2006 and 2008 are function blocks that Represent data cleansing operations. Likewise, block 2010 a function block representing a weekly rollup aggregation, and block 2012 is a function block representing an addition operation. The blocks 2014 and 2016 represent store operations that indicate where the output of the DAG 2000 should be saved (e.g. local storage, hosted storage, etc.).

In the DAG 2000 represent the arrows representing the blocks 2002-2016 connect, the data flow and specify the sequence in which the operations defined by the function blocks are to be carried out. For example, the block 2006 represented cleanup operation be the first operation performed on the by block 2002 represented time series is carried out. The output of the cleanup operation at block 2006 is then used as an input to both the block 2010 represented aggregation operation as well as that by block 2012 represented addition operation. Likewise, the block 2008 represented cleanup operation be the first operation performed on the by block 2004 represented time series is carried out. The output of the cleanup operation at block 2008 is then used as an input to the through block 2012 represented addition operation.

In some embodiments, the DAG 2000 reference other DAGs as inputs. The time series processing engine 904 can combine the DAGs into larger groups. The DAG 2000 can support scalar operators (e.g. execute this function on this sample at this time stamp) as well as aggregate window operators (e.g. use this function over all values in the time series from this time window). The time windows can be arbitrary and are not limited to fixed aggregation windows. Logical operators can be used to express rules and to implement error detection algorithms. In some embodiments, the DAG supports 2000 custom functions and custom aggregates.

In some embodiments, the DAG 2000 generated based on user input. A user can use different input blocks 2002-2004 , Function blocks 2006-2012 and output blocks 2014-2016 via drag & drop in the DAG 2000 drag and link them with arrows to define a sequence of operations. The user can edit the operations to define various parameters of the operations. For example, the user can set parameters such as upper and lower limits for the data cleansing operations in the blocks 2006-2008 and an aggregation interval for the aggregation operation in block 2010 , define. The DAG 2000 can be created and edited in a graphical drag and drop flow editor without the user having to write or edit any formal code. In some embodiments, the DAG generator is 920 to automatically configure the formal code used by the time series operators 906 is used based on the DAG 2000 to create.

Now referring to 20B is an example of a code 2050 by the DAG generator 920 can be generated, shown according to an embodiment. The code 2050 is a collection of JSON objects 2052-2056 shown the different by the DAG 2000 represent defined operations. Each JSON object corresponds to one of the function blocks in the DAG 2000 and specifies the inputs / sources, calculation and outputs of each block. For example, the object matches 2052 the by block 2006 represented cleanup operation and defines the input time series (ie "123_Raw"), the special cleanup operation to be performed (ie "BoundsLimitingCleanseOP"), the parameters of the cleanup operation (ie "Upperbound" and "Lowerbound") and the output of the cleansing operation (ie "123_Cleanse" and " BLCleanseFlag “).

The object corresponds equally 2054 the by block 2010 represents the aggregation operation and defines the input time series (ie "123_Cleanse"), the aggregation operation to be performed (ie "AggregateOP"), the parameter of the aggregation operation (ie "interval": "week") and the output of the aggregation operation (ie "123_WeeklyRollup"). The object 2056 corresponds to that by block 2012 represented addition operation and defines the input time series (ie "123_Cleanse" and "456_Cleanse"), the addition to be performed (ie "AddOP") and the output of the addition operation (ie "123 + 456"). Although not specifically in 20B can shown the code 2050 an object for each function block in the DAG 2000 contain.

Advantageously, the declarative views defined by the DAGs provide a comprehensive view of the operations that are applied to different input time series. This provides flexibility to define the workflow defined by a DAG at the polling time (e.g. when a request for derived time series data is received) or before the polling time (e.g. when new raw data samples respond to a defined event or trigger, etc . are received). This flexibility allows the time series processing engine 904 some or all of them Perform operations in advance and / or in response to a request for special derived data time series.

Entity graph

Now referring to 21A is an entity graph 2100 shown in accordance with some embodiments. In some embodiments, the entity graph 2100 from the data collector 612 created or used as referring to FIG 6 is described. The entity graph 2100 describes how the collection of devices and areas is organized and how the various devices and areas are related. For example, it is shown that the entity graph 2100 an organisation 2102 , an area 2104 , a system 2106 , one point 2108 and a time series 2109 contains. The arrows representing the organization 2102 , the area 2104 , the system 2106 , the point 2108 and the time series 2109 connect with each other, identify the relationships between such entities. In some embodiments, the relationships are stored as attributes of the entity described by the attribute.

It is shown that the organization 2102 a "contains descendants" attribute 2110 , a “Parent Ancestor” attribute 2122112, a “Contains” attribute 2142114, a “Located in” attribute 2162116, a “Occupied by” attribute 2182118 and a “Occupied by” attribute 2122 contains. The "contains descendants" attribute 2110 identifies any descendant entities that exist in the organization 2102 are included. The "parent ancestor" attribute 2122112 identifies any elementalities for the organization 2102 . The "Contains" attribute 2142114 identifies any other organization that is within the organization 2102 are included. The asterisk next to the "Contains" attribute 2142114 indicates that the organization 2102 can contain any number of other organizations. The "Located in" attribute 2162116 identifies another organization within which the organization is located 2102 located. The number 1 next to the "Located in" attribute 2162116 indicates that the organization 2102 can be in exactly one other organization. The "busy" attribute 2122 identifies any areas affected by the organization 2102 be occupied. The asterisk next to the "busy" attribute 2122 indicates that the organization 2102 can occupy any number of areas.

It is shown that the area 2104 a "Used by" attribute 2202120, a "Used by ancestor" attribute 2182118, a "Contains area descendant" attribute 2124 , an "is in ancestor" attribute 2126 , a "Contains areas" attribute 2128 , a "is in" attribute 2130 , a "powered by systems" attribute 2138 and a "powered by system descendants" attribute 2134 contains. The "Occupied By" attribute 2202120 identifies an organization by the area 2104 is occupied. The number 1 next to the "Occupied by" attribute 2202120 indicates that the range 2104 can be documented by exactly one organization. The "Occupied by Ancestors" attribute 2182118 identifies one or more ancestors of the organization 2102 going through the area 2104 be occupied. The asterisk next to the "Used by ancestor" attribute 2182118 indicates that the range 2104 any number of ancestors can be occupied.

The "Contain area descendants" attribute 2124 identifies any descendants for the area 2104 that are within the range 2104 are included. The "is in ancestors" attribute 2126 identifies any ancestors of the area 2104 within which the area 2104 located. The "Contains Areas" attribute 2128 identifies any other areas that are within the area 2104 are included. The asterisk next to the "Contains areas" attribute 2128 indicates that the area 2104 can contain any number of other areas. The "is in" attribute 2130 identifies another area within which the area is located 2104 located. The number 1 next to the "is in" attribute 2130 indicates that the area 2104 can be in exactly another area. The "Provided by Systems" attribute 2138 identifies any systems that span the area 2104 supply. The asterisk next to the "Provided by systems" attribute 2138 indicates that the area 2104 can be supplied by any number of systems. The "Provided by System Descendants" attribute 2134 identifies any descendant systems that the area 2104 supply. The asterisk next to the "Provided by Descendant Systems" attribute 2134 indicates that the area 2104 can be supplied by any number of descendant systems.

It is shown that the system 2106 a "Supplies Areas" attribute 2136 , a "Provided Area Ancestor" attribute 2132 , a "subsystem descendant" attribute 2140 , a "part of the ancestor" attribute 2142 , a "subsystem" attribute 2144 , a "part of" attribute 2146 and a "points" attribute 2150 contains. The "Supplies Areas" attribute 2136 identifies any areas through the system 2106 be supplied. The asterisk next to the "Supplies Areas" attribute 2136 indicates that the system 2106 can supply any number of areas. The “Area Ancestor” attribute 2132 identifies any ancestors of the area 2104 by the system 2106 be supplied. The asterisk next to the "Supplied Ancestor Areas Attribute 2132 indicates that the system 2106 can supply any number of ancestor areas.

The "Subsystem descendant" attribute 2140 identifies any subsystem descendants of other systems that are within the system 2106 are included. The "part of ancestors" attribute 2142 identifies any ancestors of the system 2106 , of which the system 2106 is part. The "subsystem" attribute 2144 identifies any subsystem within the system 2106 are included. The asterisk next to the "subsystem" attribute 2144 indicates that the system 2106 can contain any number of subsystems. The "part of" attribute 2146 identifies any other system from which the system 2106 is part. The number 1 next to the "part of" attribute 2146 indicates that the system 2106 Can be part of exactly another system. The "points" attribute 2150 identifies any data points associated with the system 2106 are associated. The asterisk next to the "points" attribute 2150 indicates that any number of data points the system 2106 can be assigned.

It is shown that the point 2108 a "Used by system" attribute 2148 contains. The asterisk next to the "Used by system" attribute 2148 indicates that the point 2108 can be used by any number of systems. It is also shown that point 2108 a "Used by time series" attribute 2154 contains. The asterisk next to the "Used by time series" attribute 2154 indicates that the point 2108 any number of time series (e.g. raw data time series, virtual point time series, data rollup time series, etc.) can be used. For example, multiple virtual point time series on the same actual data point 2108 based. In some embodiments, the "Used by time series" attribute is used 2154 treated as a list of time series, the changes in value of the data point 2108 subscribe. If the value of the point 2108 Changes can be made in the "Used by Time Series" attribute 2154 listed time series can be identified and automatically updated to reflect the changed value of the point 2108 play.

It is shown that the time series 2109 a "use point" attribute 2152 contains. The asterisk next to the "Use point" attribute 2152 indicates that the time series 2109 can use any number of actual data points. For example, a virtual point time series can be based on several actual data points. In some embodiments, the "uses point" attribute 2152 treated as a list of points for monitoring value changes. If any of the ones used by the "Uses Point" attribute 2152 identified points is updated, the time series 2109 are automatically updated to reflect the changed value of the time series 2109 play points used.

It is also shown that the time series 2109 a "Used by time series" attribute 2156 and a "uses time series" attribute 2158 contains. The asterisks next to the "Used by time series" attribute 2156 and the "Uses time series" attribute 2158 indicates that the time series 2109 can be used by any number of other time series and can use any number of other time series. For example, both a data rollup time series and a virtual point time series can be based on the same raw data time series. As another example, a single virtual point time series can be based on several other time series (e.g. several raw data time series). In some embodiments, the "Used by time series" attribute is used 2156 treated as a list of time series, the updates of the time series 2109 subscribe. If the value of the time series 2109 can be updated in the "Used by time series" attribute 2156 listed time series are identified and updated automatically to reflect the change in the time series 2109 play. Likewise, the "Uses Time Series" attribute 2158 are considered as a list of time series for monitoring updates. If any of the by the "Uses Time Series" attribute 2158 identified time series is updated, the time series 2109 are automatically updated to reflect the updates of the other time series on which the time series 2109 based.

Now referring to 21B is an example of an entity graph 2160 shown for a particular system of devices according to some embodiments. It is shown that the entity graph 2160 an organisation 2161 ("ACME Corp") contains. The organization 2161 can be a collection of people, a legal entity, a company, an agency or another type of organization. The organization 2161 occupies the area 2163 ("Milwaukee Campus"), as by the "busy" attribute 2164 is specified. The area 2163 is from the organization 2161 as evidenced by the "Occupied By" attribute 2162 is specified.

In some embodiments, the range is 2163 a top-level area in a hierarchy of areas. For example, the range 2163 represent an entire campus (ie a collection of buildings). The area 2163 can contain different sub-areas (e.g. individual buildings), such as an area 2165 ("Building 1") and an area 2173 ("Building 2"), as through the "Contains" attributes 2168 and 2180 is specified. The areas 2165 and 2180 are in the area 2163 as by the "is in" attribute 2166 is specified. Each of the areas 2165 and 2173 may contain lower-level sub-areas such as individual floors, zones, or rooms in each building. However, such sub-areas are from the entity graph for simplicity 2160 omitted.

The area 2165 is through the system 2167 supplied ("Elek.-Hauptzählerl"), as by the "supplied by" attribute 2172 is specified. The system 2167 can be any system covering the area 2165 supplied (e.g. an HVAC system, a lighting system, an electrical system, a security system, etc.). The "Provided" attribute 2170 indicates that the system 2167 the area 2165 provided. In the entity graph 2160 is the system 2167 as an electrical system with a subsystem 2169 ("Lighting sub-counter 1") and a subsystem 2171 ("Connector load counter2") as shown by the subsystem attributes 2176 and 2178 specified. The subsystems 2169 and 2171 are part of the system 2167 as by the part of the attribute 2174 specified.

The area 2173 is through the system 2175 supplied ("Elect. main counter2"), as by the "supplied by" attribute 2184 is specified. The system 2175 can be any system covering the area 2173 supplied (e.g. an HVAC system, a lighting system, an electrical system, a security system, etc.). The "Provided" attribute 2182 indicates that the system 2175 the area 2173 provided. In the entity graph 2160 is the system 2175 as an electrical system with a subsystem 2177 ("Lighting Sub-Counter3") as shown by the subsystem attribute 2188 specified. The subsystems 2177 is part of the system 2175 as by the part of the attribute 2186 specified.

In addition to the in 21B shown attributes can be the entity graph 2160 "Ancestor" and "Descendant" attributes included for each entity in the hierarchy. The "ancestor" attribute can identify all of the entities that are ancestors of a given entity (e.g., a flat list). For example, the "ancestor" attribute for the area 2165 both the area 2163 as well as the organization 2161 identify as ancestor. Similarly, the "descendant" attribute can identify all (e.g., in a flat list) of the entities that are descendants of a given entity. For example, the "descend" attribute for the area 2165 the system 2167 , the subsystem 2169 and the subsystem 2171 identify as descendants. This provides each entity with a complete list of its ancestors and descendants, regardless of how many levels are in the hierarchical tree. This is a form of transitive closure.

               /Systems?$filter=(systemType eq Jci.Be.Data.SystemType'Meter')
               and ancestorSpaces/any(a:a/name eq ‚Milwaukee Campus‘)

und kann unter Verwendung nur des „Nachfahren“-Attributs des Milwaukee-Campus-Bereich 2163 beantwortet werden. Beispielsweise kann das „Nachfahren“-Attribut des Bereichs 2163 alle Zähler identifizieren, die hierarchisch unter dem Bereich 2163 liegen. Das „Nachfahren“-Attribut kann als flache Liste organisiert und als ein Attribut des Bereichs 2163 gespeichert werden. Dies ermöglicht, dass die Abfrage bedient wird, indem nur das „Nachfahren“-Attribut des Bereichs 2163 durchsucht wird, ohne dass andere Ebenen oder Entitäten der Hierarchie durchsucht werden müssen.In some embodiments, the transitive closure provided by the "Descendant" and "Ancestor" attributes enables the entity graph 2160 simple queries made easier without having to search through several levels of the hierarchical tree. For example, the following query can be used to view all counters under the Milwaukee campus area 2163 to find:

               / Systems? $ Filter = (systemType eq Jci.Be.Data.SystemType'Meter ')
               and ancestorSpaces / any (a: a / name eq 'Milwaukee Campus') 

and can be made using only the "descendant" attribute of the Milwaukee campus area 2163 get answered. For example, the "descend" attribute of the area 2163 identify all counters that are hierarchical under the area 2163 lie. The "descendant" attribute can be organized as a flat list and as an attribute of the area 2163 get saved. This enables the query to be served by just the "descend" attribute of the area 2163 is searched without having to search other levels or entities of the hierarchy.

Now referring to 22 is an object relationship diagram 2200 shown in accordance with some embodiments. It is shown that the relationship diagram 2200 an entity template 2202 , one point 2204 , a time series 2206 and contains an example 2208. In some embodiments, the entity template 2202 , the point 2204 , the time series 2206 and the sample 2208 as data objects within the memory 610 or the time series database 928 saved. The relationship diagram 2200 illustrates the relationships between the entity template 2202 , the point 2204 and the time series 2206 .

The entity template 2202 can contain various attributes, such as an ID attribute, a name attribute, a property attribute, and a relationship attribute. The ID attribute can be provided as a text string and identifies a unique ID for the entity template 2202 . The name attribute can also be provided as a text string and identifies the name of the entity template 2202 . The property attribute can be provided as a vector and identifies one or more properties of the entity template 2202 . The relationship attribute can also be provided as a vector and identifies one or more relationships of the entity template 2202 .

The point 2204 may include various attributes such as an ID attribute, an entity template ID attribute, a time series attribute, and a device ID attribute. The ID attribute can be provided as a text string and identifies a unique ID for the point 2204 . The entity template ID attribute can also be provided as a text string and identifies the entity template 2202 that the point 2204 is assigned (e.g. by listing the ID attribute of the entity template 2202 ). Any number of points 2204 can the entity template 2202 be assigned. However, in some embodiments, each point is 2204 a single entity template 2202 assigned. The time series attribute can be provided as a text string and identifies any one with a period 2204 linked time series (e.g. by listing the ID string of any point 2204 assigned time series 2206 ). The unit ID attribute can also be provided as a text string and identifies the units by the point 2204 quantified variable.

The time series 2206 may include various attributes such as an ID attribute, a sample attribute, a transformation type attribute, and a unit ID attribute. The ID attribute can be provided as a text string and identifies a unique ID for the time series 2206 . The unique ID of the time series 2206 can in the point's time series attribute 2204 be listed to the time series 2206 the point 2204 assign. Any number of time series 2206 can the point 2204 be assigned. Every time series 2206 is a single point 2204 assigned. The sample attribute can be provided as a vector and identifies one or more of the time series 2206 assigned samples. The transformation type attribute identifies the transformation type that is used to generate the time series 2206 is used (e.g. hourly on average, daily average, monthly average, etc.). The unit ID attribute can also be provided as a text string and identifies the units through the time series 2206 quantified variable.

The sample 2208 can contain a timestamp attribute and a value attribute. The timestamp attribute can be provided in local time and can include an offset relative to universal time. The value attribute can be a data value of the sample 2208 contain. In some cases, the value attribute is a numerical value (e.g. for measured variables). In other cases, the value attribute can be a text string, such as "error", if the sample 2208 Is part of a fault detection time series.

Creation of a nested stream

With reference to 23A-25B can the building management platform 102 configured to generate nested streams of time series data. Nested streams can contain various types of derived time series that are generated by processing DAGs. For example, nested streams may include data rollup time series, virtual point time series, weather point time series, error detection time series, assigned state time series, abnormal event time series, and / or any other type of derived time series described above. In some embodiments, the nested streams are generated from input time series from the time series database 928 retrieved (as with reference to 13A-13B is described). In other embodiments, the interleaved streams are generated from streaming data generated in real time by IoT devices 2614 and / or other data sources (as with reference to 14 described). In some embodiments, the nested streams are used as an intermediate time series in a time series processing workflow. For example, a first derived time series may be generated by processing a first DAG and used as an input to a second DAG to generate a second derived time series (as with reference to FIG 25A-25B is described).

Time series processing workflow

With special reference to 23A Fig. 4 is a block diagram showing a time series processing workflow 2300 illustrated, shown according to an embodiment. The workflow 2300 can from the workflow manager 922 in combination with other components of the time series service 628 be performed. It is shown that the workflow 2300 Performing reading of the time series data includes (step 2302 ). step 2302 may include reading raw data samples and / or the derived data samples through the time series storage interface 916 to be provided. The time series data can be found in the time series database 928 get saved. In some embodiments, the time series database includes 928 on-site data storage (e.g. Redis, PostgreSQL etc.) and / or cloud data storage (e.g. Azure Redis, DocDB, HBase etc.).

The time series storage interface 916 may be configured to read and write a time series collection, a sample collection, and a PSR (Post Sample Request) collection. Each of these collections can be found in the time series database 928 get saved. The time series collection can all be in the workflow manager 922 registered time series included. The time series collection can also contain the DAG for each time series. The time series collection can be done by the workflow manager 922 can be used to accept only PSRs related to valid time series stored in the workflow manager 922 are registered. The time series collection can also be done in steps 2314-2316 can be used to search the DAG for a special time series ID.

In some embodiments, the entire time series collection is loaded into local storage. The time series collection can be a regular collection or a partitioned collection (e.g., a partition for approximately every 100 time series). In some embodiments, the time series collection contains approximately 200,000 to 250,000 time series. The ID for each document in the time series collection can be the time series ID. The DAG for each time series may include a set of operations and / or transformations that must be performed to generate the derived time series data based on the time series. When registering a new time series, the DAG can be selected for the time series from DAG templates. The DAG template can contain a set of standard operations that apply to the time series. When defining a new metric for a time series, the new metric and the list of operations to generate that metric can be added to the DAG.

The sample collection may include all of the time series samples (e.g. raw samples, derived time series samples). The sample collection can be used for all GET requests for a specific time series ID. Part of the sample collection can be stored in local memory (e.g., after 48 hours), while the rest of the sample collection can be stored in the time series database 928 can be saved. The sample collection can act as a partitioned collection instead of a regular collection to improve efficiency and performance. In some embodiments, the sample collection is stored in a JSON format and partitioned with respect to the time series ID. The ID field can be unique for each partition and can have the form "Metric: Time stamp".

The PSR collection can include all of the PSRs and can be used to provide status updates for a PSR to the user related to a particular time series ID. Part of the PSR collection can be stored in local memory (e.g. after 48 hours), while the rest of the PSR collection can be stored in the time series database 928 can be saved. The PSR collection can be partitioned according to the time series ID. In some embodiments, the ID for each document in the PSR collection takes the form of "time series ID: time stamp".

Still referring to 23A is shown that the workflow 2300 includes accepting a PSR (step 2304 ). step 2304 can be done by performing a PSR process. In some embodiments, the PSR process receives a PSR and determines whether the PSR contains more than one time series ID. In response to a determination that the PSR contains more than one time series ID, the PSR process may split the PSR into multiple PSRs, each of which is limited to a single time series ID. The PSRs can be sent to the PSR event hub 2306 to be delivered. The PSR event hub 2306 can be configured to store PSR events. Each PSR event can contain a PSR for a time series ID. In some embodiments, each PSR event is stored in the form of "time series ID: time stamp".

It is shown that the workflow 2300 includes deduplicating raw samples (step 2308 ). step 2308 can be done by performing a deduplication process. In some embodiments, the deduplication process includes accepting PSR events from the PSR event hub 2306 and dividing each PSR into a list of samples. step 2308 may include marking each sample as a new sample, an updated sample, or a duplicated sample. New samples and updated samples can be added to the raw sample event hub 2310 sent while duplicate samples can be discarded. With some Embodiments will be the step 2308 deployed in Azure using Azure Worker Roles. step 2308 can check in the time series database 928 and the samples that are currently in the raw sample event hub 2310 on duplicate samples.

In some embodiments, the deduplication process removes in step 2308 all duplicate data samples, leaving only a unique copy of each data sample. Removing all duplicate samples can ensure that aggregate operations produce accurate aggregate values. In other embodiments, the deduplication process is in step 2308 configured to remove most, but not all, of the duplicate samples. For example, the deduplication process can be implemented using a Bloom filter that allows the possibility of false positive, but not false negative results. In step 2308 a false positive can be defined as a non-duplicated new or updated sample. Accordingly, some duplicates may be marked as not duplicated, introducing the possibility that some duplicate samples may not be identified and removed properly. The deduplicated raw samples can be sent to the raw sample event hub 2310 be sent.

).It is shown that the workflow 2300 includes storing the raw samples (step 2312 ). step 2312 can accept the raw samples from the raw sample event hub 2310 and shifting the raw samples to permanent storage. In some embodiments, the step 2312 deployed in Azure using Azure Worker Roles. The worker role can generate requests at a rate based on X% of the storage capacity. For example, if the storage capacity is 10,000 storage units and X% is 20% (e.g., 20% of the storage throughput is reserved for sample writes) and each write operation requires 5 storage units, step 2312 generate a total of 400 writes per second (i.e. $\frac{\mathrm{10,000}\mathrm{\ast }\mathrm{<figure-callout\; id="5"\; label="20th\; \%"\; filenames="DE112018004336T5\_0052.png"\; state="\{\{state\}\}">20th\; </figure-callout>}\%}{5}=400$

).

It is shown that the workflow 2300 Creating an event trigger DAG includes (step 2314 ). step 2314 can be done by performing an event trigger DAG process. step 2314 can accept events (samples) from the raw sample event hub 2310 include. For each sample event, step can 2314 Identify the time series ID of the sample and access the time series collection to obtain the DAG for the corresponding time series. step 2314 may include identifying each derived data time series generated by the DAG and each operation contained in the DAG. In some embodiments, step marks 2314 each operation to indicate whether the operation is being executed on the C # engine 2332 or the Python engine 2334 should be sent. step 2314 may include identifying and retrieving any additional data (e.g., samples, time series, parameters, etc.) that may be required to perform the operations defined by the DAG. step 2314 can create an enriched DAG that contains the original DAG along with all of the data required to perform the operations defined by the DAG. The enriched DAG can be sent to the DAG event hub 2318 be sent.

In some embodiments, the workflow includes 2300 Generate a clock trigger DAG (step 2316 ). step 2316 can be done by performing a clock trigger DAG process. step 2316 can be similar to step 2314 be. step 2316 however, can be done in response to a clock trigger rather than in response to receiving a raw sample event. The clock trigger can step 2316 Trigger periodically to perform batch queries (e.g. every hour). step 2316 may include identifying a time series ID specific to the clock trigger and accessing the time series collection to obtain the DAG for the corresponding time series. step 2316 may include identifying each derived data time series generated by the DAG and each operation contained in the DAG. In some embodiments, step marks 2316 each operation to indicate whether the operation is being executed on the C # engine 2332 or the Python engine 2334 should be sent. step 2316 may include identifying and retrieving any additional data (e.g., samples, time series, parameters, etc.) that may be required to perform the operations defined by the DAG. step 2316 can create an enriched DAG that contains the original DAG along with all of the data required to perform the operations defined by the DAG. The enriched DAG can be sent to the DAG event hub 2318 be sent.

The DAG event hub 2318 can be configured to store enriched DAG events. Each enriched DAG event can contain an enriched DAG. The enriched DAG may include a DAG for a specific time series along with all of the data required to perform the operations defined by the DAG. The DAG event hub 2318 can step up the enriched DAG events 2320 provide.

Still referring to 23A is shown that the workflow 2300 Executing the DAG includes (step 2320 ). step 2320 can accept enriched DAG events from the DAG event hub 2318 and execute through the sequence of operations defined by the DAG. The workflow manager 922 can perform any operation in series to the execution engines 2330 submit and wait for results before submitting the next operation. The execution engines 2330 can be a C # engine 2332 , a python engine 2334 or include any other engine configured to perform the operations defined by the DAG. In some embodiments, the execution engines include 2330 Time series operators 906 . When a given operation is complete, the execution engines can 2330 the results of the operation to the workflow manager 922 deliver. The workflow manager 922 can use the results of one or more operations as inputs for the next operation, along with any other inputs required to perform the operation. In some embodiments, the results of the operations are the derived time series samples. The derived time series samples can be sent to the derived time series event hub 2322 to be delivered.

The Derived Time Series Event Hub 2322 can be configured to store derived time series samples. Each derived time series sample may include a derived time series sample. The derived time series can include the results of the operations performed by the execution engines 2330 be performed. The Derived Time Series Event Hub 2322 can derive the derived time series samples for step 2324 provide.

It is shown that the workflow 2300 including storing the derived time series samples (step 2324 ). step 2324 may accept derived time series samples from the derived time series event hub 2322 and storing the derived time series samples in permanent storage (e.g., the time series database 928 ) include. In some embodiments, the step 2324 deployed in Azure using Azure Worker Roles. The worker role can generate requests at a rate based on Y% of the storage capacity. For example, if the storage capacity 10,000 Storage units and Y% is 50% (e.g., 50% of storage throughput is reserved for sample writes) and each write 5 Storage units needed can step 2324 generate a total of 1,000 writes per second $\left(\text{d}.\text{H}.\frac{\mathrm{10,000}\ast 50\%}{5}=\mathrm{1,000}\right).$

Now referring to 23B is a flowchart of a process 2350 shown for obtaining and processing time series data according to an embodiment. The process 2350 can from the workflow manager 922 in combination with other components of the time series service 628 be performed. It is shown that the process 2350 Obtaining samples of a time series from the time series storage includes (step 2352 ). step 2352 can read raw data samples and / or derived data samples via the time series storage interface 916 include. The samples of the time series can be taken from the time series database 928 received or received in real time by a sensor or other device that generates the samples. step 2352 may include loading all or a subset of the time series samples into local memory. For example, some of the time series samples may be stored in local memory (e.g., after 48 hours), while the rest of the time series samples may be stored in the time series database 928 can be saved.

It is shown that the process 2350 Editing a PSR (Post-Sample-Request) includes that is assigned to the time series (step 2354 ). The PSR can be collected from a PSR via the time series storage interface 916 be preserved. The PSR can be used to provide status updates to the user for a specific time series ID. In some embodiments, step includes 2354 Receive a PSR and determine if the PSR contains more than one time series ID. In response to a determination that the PSR contains more than one time series ID, step 2354 Split the PSR into multiple PSRs, each limited to a single time series ID. The PSRs can be sent to the PSR event hub 2306 delivered and saved as PSR events. Each PSR event can contain a PSR for a time series ID. In some embodiments, each PSR event is stored in the form of "time series ID: time stamp".

It is shown that the process 2350 Deduplicate samples of the time series includes (step 2356 ). step 2356 can be done by performing a deduplication process. In some embodiments, the deduplication process includes accepting PSR events from the PSR event hub 2306 and dividing each PSR into a list of samples. step 2356 may include marking each sample as a new sample, an updated sample, or a duplicated sample. New samples and updated samples can be added to the raw sample event hub 2310 sent while duplicate samples can be discarded. In some embodiments, the step 2356 deployed in Azure using Azure Worker Roles. step 2356 can check in the time series database 928 and the samples that are currently in the raw sample event hub 2310 on duplicate samples.

In some embodiments, the deduplication process removes in step 2356 all duplicate data samples, leaving only a unique copy of each data sample. Removing all duplicate samples can ensure that aggregate operations produce accurate aggregate values. In other embodiments, the deduplication process is in step 2356 configured to remove most, but not all, of the duplicate samples. For example, the deduplication process can be implemented using a Bloom filter that allows the possibility of false positive, but not false negative results. In step 2356 a false positive can be defined as a non-duplicated new or updated sample. Accordingly, some duplicates may be marked as not duplicated, introducing the possibility that some duplicate samples may not be identified and removed properly. The deduplicated samples can be sent to the raw sample event hub 2310 be sent.

Still referring to 23B is shown that the process 2350 Identifying one or more stored DAGs that use the time series as an input (step 2358 ). step 2358 can get the stored DAGs over time series via the time series storage interface 916 and identify the required time series inputs of each DAG. For each DAG that uses the time series as an input, the process can 2350 identify the time series processing operations defined by the DAG (step 2360 ). The time series processing operations may include data cleansing operations, data aggregation operations, time series addition operations, virtual point calculation operations, or any other type of operation that can be applied to one or more input time series.

It is shown that the process 2350 Identifying and obtaining samples of any time series required to perform the time series processing operations (step 2362 ). The time series can be identified by inspecting the inputs for each of the steps 2360 identified time series processing operations are required. For example, it is shown that the DAG 2000 in 20A Both the "Time series ID: 123" and the "Time series ID 456" contain the necessary entries. Assuming that in step 2352 Samples of the time series ID 123 can be obtained, the DAG 2000 in step 2358 be identified as a DAG that has the time series ID 123 used as an input. The one in step 2362 identified time series can use the time series ID 123 , Time series ID 456 or contain any other time series as an input to the DAG 2000 is used. step 2362 may include identifying and retrieving any additional data (e.g., samples, time series, parameters, etc.) that may be required to perform the operations defined by the DAG.

In some embodiments, these are based on step 2362 obtained sample values on the time series processing operations defined by the DAG as well as on the time stamps of the in step 2352 original samples obtained. For example, the DAG can include a data aggregation operation that aggregates multiple data samples with timestamps within a given time window. The start time and end time of the time window can be defined by the DAG and the time series to which the DAG is applied. The DAG can define the duration of the time window over which the data aggregation operation will be performed. For example, the DAG may perform the aggregation operation as an hourly aggregation (i.e., to generate an hourly data rollup time series), a daily aggregation (i.e., to generate a daily data rollup time series), a weekly aggregation (i.e., to generate a) weekly data Rollup time series) or any other aggregation duration. The position of the time window (e.g. a special day, a special week etc.) over which the aggregation is carried out can be determined by the time stamp in step 2352 obtained samples can be defined.

step 2362 may include using the DAG to identify the length of time window (e.g., an hour, a day, a week, etc.) over which the data aggregation operation is performed. step 2362 can use the timestamp in step 2352 obtained data samples to identify the location of the time window (ie, the start time and the end time). step 2362 can include setting the start time and end time of the time window in such a way that the time window has the identified duration and the time stamps in step 2352 data samples obtained. In some embodiments, the time windows are fixed and have predefined start times and end times (e.g., the beginning and end of every hour, every day, every week, etc.). In other embodiments, the time slots can be sliding time slots that have start times and end times that depend on the time stamps of the data samples in the input time series. Once the appropriate time window has been determined and the other input time series have been identified, step can 2362 Obtain samples of any input time series for the DAG that have time stamps within the corresponding time window. The time series can be entered in step 2352 contain identified original time series and any other time series that are used as input into the DAG.

It is shown that the process 2350 Generating an enriched DAG including the original DAG and all the time series samples required to perform the time series processing operations (step 2364 ). The original DAG can be the one in step 2358 identified DAG. The time series samples required to perform the time series processing operations can be any of those in step 2362 obtained time series samples. In some embodiments, step includes 2364 Identify each derived data time series generated by the DAG and each operation contained in the DAG. In some embodiments, step marks 2364 each operation to a special execution engine (e.g. C # engine 2332 , Python engine 2334 etc.) to which the processing operation is to be sent for execution.

It is shown that the process 2350 Executing the enriched DAG includes to generate one or more derived time series (step 2366 ). step 2366 can dispatch each time series processing operation in series to the execution engines 2330 and waiting for results before submitting the next operation. When a given operation is complete, the execution engines can 2330 the results of the operation to the workflow manager 922 deliver. process 2350 can use the results of one or more operations as inputs for the next operation, along with any other inputs required to perform the operation. In some embodiments, the results of the operations are the derived time series samples.

It is shown that process 2350 Storing the derived time series in the time series storage contains (step 2368 ). The derived time series can contain the results of the operations performed in step 2366 be performed. step 2368 may accept derived time series samples from the derived time series event hub 2322 and storing the derived time series samples in permanent storage (e.g., the time series database 928 ) include. In some embodiments, the step 2368 deployed in Azure using Azure Worker Roles. The worker role can generate requests at a rate based on Y% of the storage capacity. For example, if the storage capacity 10,000 Storage units and Y% is 50% (e.g., 50% of storage throughput is reserved for sample writes) and each write 5 Storage units needed can step 2368 generate a total of 1,000 writes per second $\left(\text{d}\text{.H}\text{.}\frac{\mathrm{10,000}\mathrm{\ast }50\%}{5}=\mathrm{1,000}\right).$

Streaming data processing

Now referring to 24th is a system 2400 shown for streaming data according to an embodiment. The system 2400 can be part of the building management platform 102 may be implemented and may include various systems or devices configured to collect and process time series data. For example, it is shown that the system 2400 the IoT devices 2614 , the time series service 628 , a weather service 152 , the time series database 928 contains.

The IoT devices 2614 can be any of a variety of sensors 2404 , physical devices or equipment 2406 (e.g. actuators, electronic devices, vehicles, household appliances, etc.) and / or other items with network connectivity that include IoT devices 2614 enable with the building management platform 102 to communicate. IoT devices 2614 For example, intelligent home hub devices, intelligent home devices, doorbell cameras, air quality sensors, intelligent switches, intelligent lights, intelligent devices, garage door openers, smoke detectors, heart monitoring implants, biochip transponders, cameras that stream live feeds, automobiles with built-in sensors, DNA Analysis devices, field operating devices, location devices for people / Vehicles / equipment, networked sensors, wireless sensors, wearable sensors, environmental sensors, RFID gateways and readers, IoT gateway devices, robots and other robotic devices, GPS devices, smartwatches, virtual / augmented reality devices and / or include other networked or networkable devices. In some embodiments, the IoT devices include 2614 some or all of the devices 112-116 , 122-126 , 132-136 and 142-146 as referring to 1 is described.

It is shown that the IoT devices 2614 Time series samples and event data to the time series service 628 deliver. Time series samples can include measurements taken by the sensors 2404 be preserved. For example, the sensors 2404 collect different types of measurements and take the measurements to the time series service 628 send. In some embodiments, each measurement includes a measured value that indicates a value of the measured variable and a timestamp that indicates a time when the variable was measured. Time series samples can also be monitored variables or equipment states 2406 contain. For example, IoT devices 2614 Store internal variables that represent equipment states (e.g. equipment on / off, door open / closed, equipment works with a capacity of 50%, etc.). Each time series sample may contain values of a particular variable or state and a time stamp that indicates a point in time at which the variable or state was observed.

Event data can include any type of data that describes various events performed by the IoT devices 2614 to be watched. Each sample of event data can include a description or indication of the event and a timestamp that indicates when the event occurred. For example, event data may include badge access events that occur when a person has an ID badge on a card reader of the equipment 2406 scanned (e.g. a special ID card was scanned on a special card reader at a specific time). Event data can include security events generated by security equipment (e.g., an intruder was detected at the south entrance). Event data can include alarms or errors caused by the equipment 2406 and / or other types of IoT devices 2614 can be detected (e.g. a special fault occurring within a special device of the equipment 2406 occurred at a specific time).

It is shown that the weather service 152 Weather data to the time series service 628 delivers. Weather data can include samples of various weather related variables provided by the weather service 152 to be watched. For example, weather data may include temperature data, humidity data, precipitation data, wind speed data, cloud position data, atmospheric pressure data and / or other types of weather related variables. Weather data can include current values of the weather related variables, past values of the weather related variables (e.g. historical values) and / or future values of the weather related variables (e.g. predicted or estimated values). Each sample of the weather data may include a value of a particular weather related variable and a timestamp that includes a time at which the corresponding value was observed or a time at which the corresponding value is predicted.

wobei Schlüsseleine Kennung der Quelle der Datenabtastwerte ist (z. B. Zeitreihe-ID, Sensor-ID, usw.) ist, Zeitstemepel_i einen Zeitpunkt identifiziert, der dem i-ten Abtastwert zugeordnet ist, und Wert_i den Wert von des i-ten Abtastwerts angibt. Der Zeitreihenidentifikator 2414 kann Attribute der eingehenden Datenabtastwerte (z. B. Datenquelle, Sensor-ID, Variablen-ID usw.) verwenden, um eine spezielle Zeitreihe zu identifizieren, die jedem Abtastwert zugeordnet ist, und kann die identifizierte Zeitreihen-ID an den DAG-Identifikator 2416 liefern.It is shown that the time series service 628 a time series identifier 2414 , a DAG identifier 2416 , Execution engines 2330 and a time series generator 2418 contains. The time series identifier 2414 can collect the time series samples and event data from IoT devices 2614 and the weather data from the weather service 152 receive. The time series identifier 2414 can identify a time series associated with each incoming data sample. The identified time series for a particular data sample can be a raw data time series stored in the time series database 928 stored and which contains a series of values for the same variable or data source. For example, the time series can be saved in the following format: $$\begin{array}{l}[<ScHl\ddot{u}ssel,Zeitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1,W\mathrm{<figure-callout\; id="1"\; label="e\; r\; t"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">e\; </figure-callout>}r{t}_{}{}_1>,<ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{2nd}},Wer{t}_{\mathrm{2nd}}>,\\ <ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{3rd}},Wer{t}_{\mathrm{3rd}}>]\end{array}$$

where key is an identifier of the source of the data samples (e.g. time series ID, sensor ID, etc.), time stamp level _i identifies a time associated with the i th sample, and value _{i is} the value of the i- specifies th sample. The time series identifier 2414 may use attributes of the incoming data samples (e.g., data source, sensor ID, variable ID, etc.) to identify a particular time series associated with each sample, and may pass the identified time series ID to the DAG identifier 2416 deliver.

The DAG identifier 2416 can be that of the time series identifier 2414 Use received time series ID to identify one or more DAGs that use the identified time series as an input. As described above, a DAG can be a predefined sequence of processing operations that transform one or more input time series into one or more output time series. Accordingly, each DAG can have one or more assigned input time series. In some embodiments, the input time series for each DAG are considered attributes of the DAG in the DAG database 930 saved. The DAG identifier 2416 can get such information from the DAG database 930 Read to determine which of the stored DAGs use the identified time series as an input. The DAG identifier 2416 may provide the execution engines with the identified DAGs in the form of one or more DAG IDs 2330 deliver.

As described above, the execution engines 2330 a C # engine 2332 , a python engine 2334 or include any other engine configured to perform the operations defined by a DAG. In some embodiments, the execution engines include 2330 Time series operators 906 . The execution engines 2330 can receive the incoming data from the IoT devices 2614 and the weather service 152 (ie the time series samples, event data and weather data) as well as the DAG IDs from the DAG identifier 2416 receive. The execution engines 2330 can execute the DAGs using the incoming data as an input to produce derived time series samples. Each derived time series sample may be the result of a time series processing operation that uses an incoming data sample as an input. In some embodiments, each derived time series sample includes a key (e.g., a time series ID), a time stamp, and a value.

One type of derived time series sample is a virtual point sample. The execution engine 2330 can calculate virtual data points by applying any of a variety of mathematical operations or functions to actual data points or other virtual data points. For example, the execution engines 2330 calculate a virtual data point (point ID ₃ ) by adding two or more actual data points (point ID ₁ and point ID ₂ ) (e.g. point ID ₃ = point ID ₁ + point ID ₂ ). As another example, the execution engines 2330 calculate an enthalpy data point (point ID ₄ ) based on a measured temperature data point (point ID ₅ ) and a measured pressure data point (point ID ₆ ) (e.g. point ID ₄ = enthalpy (point ID ₅ , point ID ₆ )). In some cases, a virtual data point can be derived from a single actual data point. For example, the execution engines 2330 calculate a saturation temperature (point ID ₇ ) of a known coolant based on a measured coolant pressure (point ID ₈ ) (e.g. point ID ₇ = T _sat (point ID ₈ )).

Another type of derived time series sample is a virtual weather point sample. The execution engines 2330 can calculate values from virtual weather point samples by applying the time series processing operations defined by a DAG to the incoming weather data. For example, the execution engines 2330 Perform weather-based calculations using the incoming weather data to generate values for weather-related variables such as cooling degree days (CDD), heating degree days (HDD), cooling energy days (CED), generating heating energy days (HED) and normalized energy consumption. These and other examples of weather related derived time series samples are described with reference to the weather point calculator 912 described in detail.

Another type of derived time series sample is a sample of an error detection time series. The execution engines 2330 can evaluate error detection rules defined by a DAG in order to detect errors in the incoming data. For example, the execution engines 2330 apply the error detection rules to the input time series samples to determine whether each sample of the input time series qualifies as an error. In some embodiments, each derived time series sample includes a time stamp and an error detection value. The time stamp can be the same as the time stamp of the corresponding data sample of the input time series. The error detection value can indicate whether the corresponding data sample of the data time series is qualified as an error. For example, the error detection value can have the value “error” if an error is detected and a value “no error” if no error is detected in the corresponding data sample of the data time series.

Another type of derived time series sample is an abnormal event time series sample. The execution engines 2330 can evaluate abnormal event detection rules defined by a DAG to detect abnormal events in the event data. For example, the execution engines 2330 apply the abnormal event detection rules to the event data to determine whether each sample of the event data qualifies as an abnormal event. In some embodiments, each derived time series sample of an abnormal event time series includes a time stamp and an abnormal event value. The timestamp can be the same as the timestamp of the corresponding sample of the event data. The abnormal event value can indicate whether the corresponding sample of the event data is normal or abnormal. For example, the abnormal event value may have a value of “abnormal” if the event meets the criteria for abnormal events and a value of “normal” if the event does not meet the criteria for abnormal events.

In some embodiments, an event is considered abnormal if it differs significantly from other similar events (e.g., events associated with the same person, area, equipment, etc.). For example, if the event data indicates that a particular person typically enters a building using a badge between 8:30 a.m. and 9 a.m., an event indicating that the person is entering the building at 3 a.m. is considered abnormal. Similarly, if the weather data indicates that outside air temperatures typically range between 30 ° F and 40 ° F in a particular building in a particular month, a temperature of 60 F that month may be considered abnormal.

The time series generator 2418 can use the derived time series samples to generate various derived time series. The derived time series may include data rollup time series, virtual point time series, weather point time series, error detection time series, assigned state time series, abnormal event time series and / or any other type of derived time series generated by execution of the identified DAGs. The time series generator 2418 can the derived time series in the time series database 928 or other permanent storage.

Iterative time series processing

Now referring to 25A can the system 2400 configured to perform iterative time series processing. For example, it is shown that the time series identifier 2414 receives an input time series. The input time series can be raw data time series, derived time series, or a collection of time series samples obtained from the IoT devices 2614 or the weather service 152 be received in real time (e.g. incoming streaming data). The time series identifier 2414 identifies a time series ID that is assigned to the input time series and can pass the time series ID to the DAG identifier 2416 deliver.

The DAG identifier 2416 can be that of the time series identifier 2414 Use received time series ID to identify one or more DAGs that use the input time series as an input. As described above, a DAG can be a predefined sequence of processing operations that transform one or more input time series into one or more output time series. Accordingly, each DAG can have one or more assigned input time series. In some embodiments, the input time series for each DAG are considered attributes of the DAG in the DAG database 930 saved. The DAG identifier 2416 can get such information from the DAG database 930 Read to determine which of the stored DAGs use the input time series as an input. The DAG identifier 2416 may provide the execution engines with the identified DAGs in the form of one or more DAG IDs 2330 deliver.

The execution engines 2330 can execute the identified DAGs using the input time series as an input to produce derived time series samples. The time series generator 2418 can then compile the derived time series samples into a first derived time series. The first derived time series can be found in the time series database 928 get saved. The first derived time series can also be used as an input to the time series identifier 2414 to be delivered.

The time series identifier 2414 can treat the first derived time series as an input and the whole process can be repeated. For example, the time series identifier identifies 2414 a time series ID associated with the first derived time series, and can pass the time series ID to the DAG identifier 2416 deliver. The DAG identifier 2416 can be that of the time series identifier 2414 Use received time series ID to identify one or more DAGs that use the first derived time series as an input. The DAG identifier 2416 may provide the execution engines with the identified DAGs in the form of one or more DAG IDs 2330 deliver. The execution engines 2330 can execute the identified DAGs using the first derived time series as an input to generate derived time series samples. The time series generator 2418 can then Assemble derived time series samples into a second derived time series. The second derived time series can be found in the time series database 928 get saved.

Now referring to 25B Fig. 4 is a flowchart of an iterative time series processing work process 2500 shown according to an embodiment. The process 2500 can be from one or more components of the building management platform 102 or the system 2400 as described above. It is shown that the process 2500 Get an input time series (step 2502 ) and identifying a first DAG that uses the input time series as an input (step 2504 ). It is shown that the process 2500 Performing time series processing operations defined by the DAG to generate a derived time series (step 2506 ). The derived time series can be found in the time series database 928 get saved.

The derived time series can then be input to the time series service 628 be treated. For example, it is shown that the process 2500 Identify another DAG that uses the derived time series as an input (step 2508 ), and performing time series processing operations defined by the other DAG to generate another derived time series (step 2510 ). In some embodiments, the DAG that uses the derived time series as an input differs from the first DAG that uses the input time series as an input. The one in step 2510 Derived time series generated can be in the time series database 928 get saved.

The one in step 2510 Derived time series generated can also be used as another input for time series service 628 be treated. The steps 2508-2510 can be repeated iteratively until the last iteration of step 2510 generated time series is not used as input for any of the DAGs. Every iteration of the steps 2508-2510 can create another derived time series that is in the time series database 928 can be saved.

Cloud-based feedback control

Now referring to 26 Figure 3 is a block diagram of a cloud-based feedback control system 2600 shown according to an embodiment. Conventional feedback control systems typically include on-site feedback control that is in the vicinity of the controlled system or device. For example, control systems for building equipment typically include a controller that is within the same building or facility as the building equipment. The building equipment provides measurements or other feedback to the controller via a wired or wireless communication link (e.g. Ethernet, Wi-Fi etc.) or a local area network (LAN). The controller uses feedback from the building equipment to generate an appropriate control signal that is provided to the building equipment as a control input via the wired or wireless communication link or the LAN.

The cloud-based feedback control system 2600 uses the building management platform 102 to provide feedback control as a cloud-based platform service. For example, it is shown that the control system 2600 the building management platform 102 , the network 104 and the campus 2602 includes. The campus provides a brief overview 2602 the network via feedback samples (e.g. measurements, samples of monitored variables, system states, values of points, etc.) 104 to the building management platform 102 . The building management platform 102 uses the feedback samples as an input to a cloud-based feedback control algorithm (e.g., PID, MPC, etc.) that uses the feedback samples to generate control signal samples. In some embodiments, the building management platform handles 102 the feedback samples as samples of an input time series and processes the input time series using a feedback control DAG. The feedback control DAG converts the feedback samples into control signal samples, which are a type of derived time series samples. The control signal samples are then sent as a control signal over the network 104 back to campus 102 delivered.

As described above, the building management platform 102 be distributed across multiple processing devices and can therefore use multiple processing devices to generate the control signals. The use of multiple processing devices offers several advantages over conventional on-site controls that use only a single processing device. For example, using multiple remote processing devices reduces the need for built-in processing devices or other on-site resources, e.g. B. physical controls that are in the Building site. The use of multiple remote processing devices also reduces processing latency compared to a single processing device by allowing parallel processing across multiple devices. For example, some parts of the feedback control DAG can be processed by a first processing device, while other parts of the feedback control DAG can be processed by other processing devices. This enables faster execution and more responsive feedback control compared to conventional on-site controls with a single processing device.

It is shown that the campus 2602 a building management system (BMS) 2604 , a central system 2606 and IoT devices 2614 includes. A BMS is generally a system of devices configured to control, monitor and manage equipment in or around a building or building area. For example, the BMS 2604 an HVAC system, a security system, a lighting system, a fire alarm system, or any other system capable of managing building functions or devices, or any combination thereof. An example of a BMS that is called the BMS 2604 can be used is detailed in the U.S. Patent Application No. 14 / 717,593 , entitled "Building Management System for Forecasting Time Series Values of Building Variables" and filed on May 20, 2015, the entire disclosure of which is hereby incorporated by reference.

The BMS 2604 may include a variety of building subsystems, including, for example, an electrical building subsystem, an information communication technology (ICT) subsystem, a security subsystem, an HVAC subsystem, a lighting subsystem, an elevator / escalator subsystem, and a fire protection subsystem. In various embodiments, the building subsystems may include fewer, additional, or alternative subsystems. For example, the building subsystems may also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a sales subsystem, a printer or copier service subsystem, or another type of building subsystem that uses controllable equipment and / or uses sensors to monitor or control a variable condition or a variable condition of a building.

Each of the building subsystems can have any number of devices, controls, and connections (collectively, as building equipment 2608 be included) to complete its individual functions and control activities. The building equipment 2608 may include HVAC equipment such as coolers, boilers, ventilation units, economizers, field controls, monitoring controls, actuators, sensors (e.g. temperature, humidity, flow rate, etc.) and other devices for controlling temperature, humidity, air flow or other variable Include conditions within a building. In some embodiments, the building equipment includes 2608 Lighting equipment, such as lighting fixtures, ballasts, light sensors, dimmers, or other devices that are configured to controllably adjust the amount of light provided for a building area. In some embodiments, the building equipment includes 2608 Security equipment such as presence sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices.

The central system 2606 may include one or more substations that consume utilities' resources (e.g. water, natural gas, electricity, etc.) to support the campus 2602 to supply. For example, the central system 2606 a heating subsystem, a heat recovery cooler subsystem, a cooler subsystem, a cooling tower subsystem, a warm TES subsystem (TES: thermal energy storage) and a cold TES subsystem (TES: thermal energy storage), a steam subsystem and / or any other type of subsystem that include to supply the campus 2602 is configured. Each of the substations can carry a variety of central plant equipment 2610 included (e.g. boilers, coolers, heat recovery coolers, cooling towers, thermal energy storage tanks, batteries, etc.). The substations can be configured to convert input resources (e.g., electricity, water, natural gas, etc.) to output resources (e.g., cold water, hot water, cooled air, heated air, etc.) on campus buildings 2602 to be delivered. An exemplary central system called the central system 2606 can be used is in the U.S. Patent Application No. 14 / 634,609 , entitled "High Level Central Plant Optimization" and filed on February 27, 2015, the entire disclosure of which is hereby incorporated by reference.

The IoT devices 2614 may include any of a variety of sensors, physical devices, or equipment (e.g., actuators, electronic devices, vehicles, household appliances, etc.) and / or other network connectivity items that are IoT devices 2614 enable with the building management platform 102 to communicate. IoT devices 2614 For example, intelligent home hub devices, intelligent home devices, doorbell cameras, air quality sensors, intelligent switches, intelligent lights, intelligent devices, garage door openers, smoke detectors, heart monitoring implants, biochip transponders, cameras that stream live feeds, automobiles with built-in sensors, DNA Analysis devices, field operating devices, locating devices for people / vehicles / equipment, networked sensors, wireless sensors, wearable sensors, environmental sensors, RFID gateways and reading devices, IoT gateway devices, robots and other robotic devices, GPS devices, smartwatches, virtual machines / Include augmented reality devices and / or other networked or networkable devices. In some embodiments, the IoT devices include 2614 some or all of the devices 112-116 , 122-126 , 132-136 and 142-146 as referring to 1 is described.

It is shown that the campus 2602 Feedback samples for the building management platform 102 , especially for the time series service 628 , provides. The feedback samples may include measurements by building equipment sensors 2608 , the central plant equipment 2610 and / or the IoT devices 2614 be preserved. For example, the sensors can collect different types of measurements and the measurements to the time series service 628 send. In some embodiments, each measurement includes a measured value that indicates a value of the measured variable and a timestamp that indicates a time when the variable was measured. The feedback samples can also be monitored variables or building equipment states 2608 , the central plant equipment 2610 and / or the IoT devices 2614 include. For example, the building equipment 2608 who have favourited Central System Equipment 2610 and / or the IoT devices 2614 Store internal variables that represent equipment states (e.g. equipment on / off, door open / closed, equipment works with a capacity of 50%, etc.). Each feedback sample may include a value of a particular variable or state and a timestamp indicating a time when the variable or state was observed.

wobei Schlüsseleine Kennung der Quelle der Rückkopplungsabtastwerte ist (z. B. Zeitreihe-ID, Sensor-ID, usw.) ist, Zeitstempel_i einen Zeitpunkt identifiziert, der dem i-ten Abtastwert zugeordnet ist, und Wert_i den Wert von des i-ten Abtastwerts angibt. Der Zeitreihenidentifikator 2414 kann Attribute der eingehenden Rückkopplungsabtastwerte (z. B. Datenquelle, Sensor-ID, Variablen-ID usw.) verwenden, um eine spezielle Rückkopplungszeitreihe zu identifizieren, die jedem Abtastwert zugeordnet ist, und kann die identifizierte Zeitreihen-ID an den DAG-Identifikator 2416 liefern.It is shown that the time series service 628 a time series identifier 2414 , a DAG identifier 2416 , Execution engines 2330 and a time series generator 2418 contains. The time series identifier 2414 can get the feedback samples from campus 2602 received and can identify a time series associated with each incoming data sample. The identified time series for a particular feedback sample may be a feedback time series (ie, a type of raw data time series) that is in the time series database 928 stored and which contains a series of values for the same variable or data source. For example, the feedback time series can be saved in the following format: $$\begin{array}{l}[<ScHl\ddot{u}ssel,Zeitstem\mathrm{<figure-callout\; id="1"\; label="p\; e\; l"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">p\; </figure-callout>}e{l}_{}{}_1,W\mathrm{<figure-callout\; id="1"\; label="e\; r\; t"\; filenames="DE112018004336T5\_0044.png,DE112018004336T5\_0048.png"\; state="\{\{state\}\}">e\; </figure-callout>}r{t}_{}{}_1>,<ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{2nd}},Wer{t}_{\mathrm{2nd}}>,\\ <ScHl\ddot{u}ssel,Zeitstempe{l}_{\mathrm{3rd}},Wer{t}_{\mathrm{3rd}}>]\end{array}$$

where key is an identifier of the source of the feedback samples (e.g., time series ID, sensor ID, etc.), time stamp _i identifies a time associated with the i th sample, and value _{i is} the value of the i- specifies th sample. The time series identifier 2414 may use attributes of the incoming feedback samples (e.g., data source, sensor ID, variable ID, etc.) to identify a particular feedback time series associated with each sample, and may pass the identified time series ID to the DAG identifier 2416 deliver.

The DAG identifier 2416 can be that of the time series identifier 2414 Use received time series ID to identify one or more feedback control DAGs that use the identified feedback time series as an input. As described above, a DAG can be a predefined sequence of processing operations that transform one or more input time series into one or more output time series. Accordingly, each DAG can have one or more assigned input time series. A feedback control DAG is a type of DAG that defines a feedback control algorithm. For example, a feedback control DAG may accept the feedback samples as an input and define a sequence of processing operations that transform the feedback samples into control signal samples using a feedback control technique. The processing operations defined by a feedback control DAG can be any implement a variety of feedback control techniques that include, for example, state-based control, ESC (extreme looking control), proportional integral (PI) control, proportional integral derivative (PID) control, MPC (model prediction control), or include any other type of feedback control technique.

An example of a feedback control DAG is a PID control DAG. A PID control DAG can run the execution engines 2330 cause a set of processing operations to be performed that are typically performed by a PID controller. For example, the PID control DAG can be the execution engines 2330 cause a difference between the feedback time series and a setpoint time series to be calculated. The calculated difference can be saved as an error time series. The PID control DAG can run the execution engines 2330 cause a proportional gain to apply to the error time series (e.g., multiplying the error time series by a proportional gain parameter) to produce a proportional gain component of the control signal.

The PID control DAG can run the execution engines 2330 cause the error time series to integrate over time to calculate an integrated error value for each sample of the error time series. The integrated error values can be summations (ie numerical integrals) of the last error sample and a predetermined number of previous error samples. In some embodiments, the integrated error values are stored as another derived time series (e.g., an integrated error time series). The PID control DAG can run the execution engines 2330 cause an integral gain to be applied to the integrated error time series (e.g., multiplying the integrated error time series by an integral gain parameter) to generate an integral gain component of the control signal.

The PID control DAG can run the execution engines 2330 cause a derivative error value to be calculated for each sample of the error time series. The derivative error values can be the slope or rate of change of the error time series relative to the previous sample. In some embodiments, the derivative error values are stored as another derived time series (e.g., a derivative error time series). The PID control DAG can run the execution engines 2330 cause a derivative gain to be applied to the derivative error time series (e.g., multiplying the derivative error time series by a derivative gain parameter) to produce a derivative gain component of the control signal. The PID control DAG can run the execution engines 2330 cause the proportional gain component, the integral gain component, and the derivative gain component to be combined (e.g., sum) to produce a value for the next sample of the control signal time series.

In some embodiments, the feedback time series for each feedback control DAG are stored as attributes of the feedback control DAG in the DAG database 930 saved. The DAG identifier 2416 can get such information from the DAG database 930 Read to determine which of the stored feedback control DAGs use the identified feedback time series as an input. The DAG identifier 2416 may provide an indication of the identified feedback control DAGs to the execution engines in the form of one or more DAG IDs 2330 deliver.

In some embodiments, the DAG identifier identifies 2416 also any other input time series that are required as inputs for the identified feedback control DAGs. For example, a feedback control DAG for controlling the temperature of a building area can have two inputs. The first input can be a feedback time series that contains temperature measurements that were detected by a temperature sensor within the building area. The second input can be a setpoint time series that defines the temperature setpoint for the building area at each of several times. The DAG identifier 2416 can identify all of the time series required as inputs to the identified feedback control DAGs, and can provide the execution engines with the identified time series in the form of one or more time series IDs 2330 deliver.

As described above, the execution engines 2330 a C # engine 2332 , a python engine 2334 or include any other engine configured to perform the operations defined by a DAG. In some embodiments, the execution engines include 2330 Time series operators 906 . The execution engines 2330 can get the incoming feedback samples from campus 2602 as well as the DAG IDs and time series IDs from the DAG identifier 2416 receive. The execution engines 2330 can the identified feedback control DAGs from the DAG database 930 retrieve and can identify the identified time series from the time series database 928 retrieve and the Execute feedback control DAGs using the feedback samples as an input to generate control signal samples. Each control signal sample may be the result of a time series processing operation that uses the feedback samples (and possibly other time series samples) as an input. In some embodiments, each control signal sample includes a key (e.g., a time series ID), a time stamp, and a value.

The one from the time series database 928 retrieved time series may include samples of any of the input time series required by one or more of the feedback control DAGs, including (in some cases) previous samples of the feedback time series. For example, some types of feedback control, such as PI control or PID control, can include both the current value of a feedback signal (ie, the last feedback sample) and one or more past values of the feedback signal (ie, one or more previous samples of the same feedback time series) require to generate a control signal sample (e.g., to evaluate an integrated error over time). Accordingly, the execution engines 2330 one or more previous samples of the feedback time series from the time series database 928 recall. The execution engines 2330 may use the previous feedback time series samples in combination with the current feedback time series sample and any other input time series to perform the feedback control DAG, thereby generating control signal samples.

The time series generator 2418 can use the control signal samples to generate a control signal time series. The control signal time series can be in the time series database 928 stored and / or as an edition of the building management platform 102 to be provided. It is shown that the time series generator 2418 a control signal to the network 104 and the campus 2602 delivers. The control signal may be the value of the last control signal sample generated by executing the feedback control DAG and / or the control signal time series generated by the time series generator 2418 is generated. The campus 2602 can use the control signal to the building equipment 2608 who have favourited Central System Equipment 2610 and / or the IoT devices 2614 to operate. For example, the control signal can be input to the building equipment 2608 (e.g. via the BMS 2604 ), the central plant equipment 2610 (e.g. via the central system 2606 ) and / or the IoT devices 2614 to be delivered.

Advantageously, the through the building management platform 102 The cloud-based feedback control provided replaces local (e.g., on-site or in-building) control loops typically used by conventional feedback control systems. Rather than requiring local feedback control to receive feedback data and generate control signals, the feedback data is sent as an input to the building management platform 102 delivered. The building management platform 102 identifies and executes feedback control DAGs that provide feedback control functionality. The outputs of the feedback control DAGs are control signal samples that are sent back to campus 2602 delivered and used to control campus equipment 2602 can be used.

Identity management using intelligent entities

Now referring to 27th Figure 3 is a block diagram of an identity management system 2700 shown according to an embodiment. It is shown that the identity management system 2700 an identity management service 2702 and an entity service 2730 includes. In some embodiments, the identity management service 2702 and the entity service 2730 Types of cloud building management platform 620 within the cloud building management platform 620 . In some embodiments, the identity management service includes 2702 and the entity service 2730 some or all of the features and / or functions of the data platform described in the preliminary U.S. Patent Application No. 62 / 564,247 , filed on September 27, 2017, the entire disclosure of which is hereby incorporated by reference. The identity management service 2702 can be configured to perform various identity management functions. For example, the identity management service is shown 2702 an identity correlation module 2704 , an identity recognition module 2706 , an identity verification module 2708 , an identity syndication module 2710 , an identity consolidation module 2712 , a real-time decision module 2714 , an identity analysis module 2716 and an identity learning module 2718 includes. In various implementations, the identity management service can 2702 fewer, additional or different modules than in 27th illustrated included.

The entity service 2730 can be the same or similar to that in 6 entity service shown 626 be. The entity service 2730 can be configured to create and manage multiple interconnected smart entities and the smart entities in the entity database 2734 save. The intelligent entities may include object entities that represent multiple people or physical devices and data entities that represent data associated with the people or physical devices. The intelligent entities can be linked by relational objects that indicate relationships between the object entities and the data entities. In some embodiments, each of the object entities includes multiple stored identity attributes. In some embodiments, the entity service runs 2730 some or all of the functions for creating and managing intelligent entities are detailed in the preliminary U.S. Patent Application No. 62 / 611,974 , filed on December 29, 2017, and preliminary U.S. Patent Application No. 62 / 611,984 , filed on December 29, 2017, the entire disclosures of which are hereby incorporated by reference.

The modules 2704-2718 may be configured to receive and consolidate identity attributes to create an entity that represents a person (ie, a person entity 2732 ). In some embodiments, the person entity 2732 a kind of object entity that is in the entity database 2734 is saved. The identity attributes can be received by various systems or devices, including, for example, a mobile device 2720 , an information technology (IT) system 2722 , Internet of Things (IoT) sensors 2724 , Building equipment 2726 and a security system 2728 . Identity attributes from the mobile device 2720 may be a mobile device ID (e.g., a MAC address, a Wi-Fi address, a device serial number, etc.) by the mobile device 2720 collected biometric attributes (e.g., fingerprint, voiceprint, iris scan, face scan, etc.), data from the mobile device 2720 that uniquely identify a user (e.g., a login identity that the user has authenticated on the mobile device, such as a login identity from an application or website / web portal), or other attributes that include a particular mobile device or uniquely identify a particular user associated with a mobile device. Identity attributes from the IT system 2722 may include a person’s username, password, access rights, staff ID, directory ID, phone number, office location, role, authorized areas, or other attributes associated with a particular user profile that the IT system 2722 is managed. Identity attributes of sensors, such as the IoT sensors 2724 , may include images / videos from cameras, data from biometric sensors, data collected by wearable devices (e.g., skin temperature, heartbeat, movement, etc.), or other attributes associated with a user, alone or in Combination with other attributes can be used to confirm the identity of the user. Identity attributes from building equipment 2726 and the security system 2728 may include a card ID, fingerprint, face scan, iris scan, images / videos from cameras, or other types of identification information. In some embodiments, some or all of the attributes are stored in an encrypted form to prevent access to private attributes by unauthorized parties / systems. In some such embodiments, the system does not allow the attributes to be accessed by external systems, but instead receives requests including data to be compared against the stored attributes and responds to the requests with response messages (e.g., indicating whether those with the Requests received data correspond to the stored data or not, whether access should be granted or denied, etc.).

The identity management service 2702 can consolidate identity attributes and into the person entity 2732 write to create a single entity (e.g., a data object or an object entity) that contains all identity attributes associated with a particular person. The person entity 2732 can be in an entity database 2734 be stored and by the identity management service 2702 be invoked to perform various identity management functions such as identity recognition, real-time decision making (e.g., access control), identity analysis, and identity learning. The personal entity is advantageous 2732 an intelligent entity that contains all of the identity attributes associated with a person, regardless of the system or device from which the identity attributes were collected. This enables the identity management service 2702 performs a variety of different identity management functions, only those in the person entity 2732 contained information can be used. Some examples of the functions provided by the identity management service 2702 are described in detail below.

In some embodiments, the identity management service uses 2702 the person entity 2732 and the identity attributes contained therein to recognize, verify and / or authenticate a person's identity. For example, the security system 2728 an authorization from that Identity management service 2702 in response to a person requesting an access card on a card reader of the security system 2728 scans. The security system 2728 can read a card ID from the access card and the card ID to the identity management service 2702 deliver. The identity management service 2702 can use the card ID to identify a specific person entity 2732 to identify which contains a card ID attribute that matches that of the security system 2728 received card ID matches. The identity management service 2702 can have other attributes of the person entity 2732 Read to identify the person's authorization, access rights, role, and the like. If the person is authorized to enter a special area associated with the card reader, the identity management service can 2702 an "access permission" signal to the security system 2728 send to give the person access to the building area. Conversely, if the person is not authorized to enter the area assigned to the card reader, the identity management service can 2702 a signal "deny access" to the security system 2728 to deny the person access to the building area.

In some embodiments, the identity management service uses 2702 multiple identity attributes of the person entity 2732 to perform multi-factor authentication or identity verification. For example, the identity management service 2702 a first identity attribute from the mobile device 2720 , the IT system 2722 , the IoT sensors 2724 , the building equipment 2726 and / or the security system 2728 receive. The first identity attribute can be one of those in the person entity 2732 saved identity attributes (e.g. name, role, employee ID, card ID, user name, password, etc.). The identity management service 2702 can use the first identity attribute to identify a specific person entity 2732 to identify which contains the first identity attribute. The identity management service 2702 can then create a second identity attribute from the person entity 2732 read. The second identity attribute can be any of the identity attributes included in the person entity 2732 are saved (except for the first identity attribute).

The identity management service 2702 may compare the second identity attribute with data from the mobile device 2720 , the IT system 2722 , the IoT sensors 2724 , the building equipment 2726 and / or the security system 2728 were obtained to determine whether the second identity attribute is also satisfied. In various embodiments, the second identity attribute may be collected automatically or by the person in response to a request from the identity management service 2702 to be provided. For example, if the first identity attribute is a card ID received from a card reader at a particular location within a building, the second identity attribute may be an image of a person taken by a camera in the same location as the card reader, or one Be a mobile device ID sent from a mobile device in the same location as the card reader. If the second identity attribute is also fulfilled (ie the second identity attribute matches an identity attribute in the same person entity 2732 the identity management service can 2702 allow access or report successful identity verification or authorization.

In some embodiments, the identity management service uses 2702 those in the person entity 2732 stored identity attributes to automatically give a person access to a system, device or area within a building without the person having to actively scan an ID card or enter a username or password. For example, the identity management service 2702 Track the locations of people within a building using location data generated by mobile devices carried by people 2720 , through IoT sensors 2724 provided IoT sensor data and / or camera data from the security system 2728 be reported. Some examples of systems and procedures for determining the locations of people within a building are detailed in the U.S. Patent Application No. 14 / 263,639 , filed April 28, 2014, the entire disclosure of which is hereby incorporated by reference.

The identity management service 2702 can that in the person entity 2732 Use stored identity attributes to determine each person's authorization. For example, the person entity 2732 one or more building areas (e.g. floors, rooms, zones, etc.), systems (e.g. HVAC systems, security systems, lighting systems, etc.) or devices (e.g. HVAC devices, lighting devices, card readers, etc.) ) for which the person's access is authorized. In some embodiments, the person entity identifies 2732 the role of the person (e.g. service technician, office administrator, nurse, etc.) and the identity management service 2702 automatically determines the person's authorization based on the identified role.

The identity management service 2702 can use the location information for a person in the building to determine whether the person is approaching an access point (e.g., a door, an entrance, an exit, etc.). If the identity attributes in the corresponding person entity 2732 For this person to indicate that the person is authorized to pass through the access point, the identity management service can 2702 Automatically open or unlock the access point to allow the person access without having to scan an ID card or enter a code key at the access point. In some embodiments, the identity management service opens or unlocks 2702 the access point automatically before the person reaches the access point (e.g. while the user is approaching the access point) to prevent a delay in reaching the access point.

Similarly, the identity management service 2702 Use the location information for a person in the building to determine whether the person is at a computer workstation, IT system, special building equipment, or other IT access point. If the identity attributes in the corresponding person entity 2732 For this person to indicate that the person is authorized to access a system or device through the IT access point, the identity management service can 2702 automatically log the person in or grant the person access to the building equipment to allow access without the person having to enter a username, password or other credentials. In some embodiments, the identity management service reports 2702 automatically approaches the person at the IT access point before the person reaches the IT access point (e.g., as the user approaches the IT access point) to prevent a delay in reaching the IT access point.

In some embodiments, the identity management service uses 2702 multi-factor authentication to verify the person's identity before access is granted. For example, the identity management service 2702 two or more identity attributes from the mobile device 2720 , the IT system 2722 , the IoT sensors 2724 , the building equipment 2726 and / or the security system 2728 collect at the location of a person. If all of the identity attributes have the same person entity 2732 can match, the identity management service 2702 confirm that the person's identity has been verified using multiple identity attributes. The identity management service 2702 can then automatically open or unlock a physical access point or provide access to an IT access point after successful multi-factor authentication or verification. In some embodiments, the identity management service 2702 Use attributes from two separate systems to increase security (e.g. a data item from the mobile device 2720 and another element from building equipment 2726 or the security system 2728 ). This can help prevent unauthorized access if the security of one of the devices is compromised.

Security service

Now referring to 28 Figure 3 is a block diagram of a security service 2800 shown according to an embodiment. In some embodiments, the security service is 2800 a service of the cloud building management platform 620a within the cloud building management platform 620 . In some embodiments, the security service includes 2800 some or all of the features and / or functions of the data platform described in the U.S. Patent Application No. 62 / 564,247 , filed on September 27, 2017, the entire disclosure of which is hereby incorporated by reference. The security service 2800 can be configured to perform device health monitoring, on-demand, offline, and online asset management through IoT technologies. It is shown that the backup service 2800 an identity and security service 2802 , a device management service 2804 , a transport and notification service 2806 , a device shadow / manifest service 2808 , a parcel service 2810 , an asset and security service 2812 , a manual upload service 2814 , Backup widgets 2816 and a seizure agent 2818 includes.

The identity and security service 2802 can be configured to ensure that every device of the building equipment 2726 and every user has the possibility to access configuration backups. For example, the identity and security service 2802 Monitor identity and authorization attributes to every user and every device of building equipment 2726 assigned, and can determine whether the set of identity and authorization attributes is sufficient to access configuration backups. The identity and security service 2802 can also ensure that every user has the ability to use the building equipment 2726 To give orders. In some embodiments, the identity and security service includes 2802 some or all of the features or functions of the identity management service 2702 as referring to 27th described.

The device management service 2804 can perform secure device registration. For example, the device management service 2804 with the building equipment installed at a customer site 2726 communicate to any device of building equipment 2726 at the cloud building management platform 620 to register. In some embodiments, this is through the device management service 2804 Device registration performed identical or similar to the device registration performed in the U.S. Patent Application No. 15 / 639,880 , filed on June 30, 2017, the entire disclosure of which is hereby incorporated by reference. For example, the device management service 2804 configured to provide a virtual representation of each building equipment device 2726 within the cloud building management platform 620 to create. In some embodiments, the representations of virtual devices are intelligent entities that contain attributes that correspond to the corresponding physical devices of the building equipment 2726 characterize. The device management service 2804 can use any device of building equipment 2726 assign to a special customer, a special department and / or a special user. The device management service 2804 can send and request firmware updates when needed when building equipment 2726 connected.

The transport and notification service 2806 can be configured to provide bidirectional communications between the security service and the building equipment installed at a customer site 2726 to facilitate. In some embodiments, the transportation and notification service provides 2806 an alarm and event notification in real time. For example, the transport and notification service 2806 real-time alarms or events from building equipment 2726 to the cloud building management platform 620 deliver. The transport and notification service 2806 can also provide real-time command and control functions for building equipment 2726 provide. For example, the transport and notification service 2806 Commands and control signals from the applications 630 or the cloud building management platform 620 to the building equipment in real time 2726 deliver. In some embodiments, the transportation and notification service 2806 configured to the building equipment 2726 to discover and firmware upgrades for building equipment 2726 to request.

The device shadow / manifest service 2808 can be configured to configure settings, parameters and other device-specific information between the building equipment 2726 and the cloud building management platform 620 to synchronize. In some embodiments, synchronization is asynchronous. The device shadow / manifest service 2808 can be configured to dynamically manage device properties. The device properties, configuration settings, parameters and other device-specific information can be shared between the building equipment 2726 and be synchronized with the intelligent entities provided by the cloud building management platform 620 generated and stored in this. In some embodiments, the device shadow / manifest service is 2808 configured to the health status of building equipment 2726 monitor and perform on-demand, online or offline asset management through IoT technologies.

In some embodiments, the device shadow / manifest service is 2808 configured to have a manifest for each device of the building equipment 2726 manage. The manifesto can represent a number of relationships between building equipment 2726 and various entities and / or the various entities and other entities. Furthermore, the manifesto may have a set of powers for the device of building equipment 2726 and / or specify powers of the various entities and / or other entities. The set of powers can be a device of building equipment 2726 and / or allow a user of the device to perform certain actions with the building equipment 2726 perform, such as adjusting a temperature setpoint, turning a connected system on and / or off, running certain pieces of software, requesting software updates, etc. In some embodiments, the entity is at least one of a group (e.g., a technician group, a resident group, a guest group) , a building manager group, etc.), a user (e.g., technician Bill, father, user A, user B, etc.) and a device (mobile device 1 , Smartphone A , Computer 4th , Actuator 9 etc.).

The parcel service 2810 can be configured to identify software releases by a developer or building equipment provider 2726 were published. For example, the parcel service 2810 a remote system or server for new software versions of building equipment 2726 monitor. If a new software version is available, the parcel service can 2810 generate a warning or notification. In some embodiments, the parcel service compares 2810 the installed version of the software in the building equipment 2726 with the version of software available on the remote system or server to determine whether the software version is new relative to the installed version is. In some embodiments, the parcel service 2810 the configurations of the building equipment 2726 Share as a compressed data object that is reliable and secure in the cloud building management platform 620 is saved.

In some embodiments, the parcel service is 2810 configured to fixture configurations, fixture parameters, fixture software, or other customizable building equipment parameters 2726 to install, back up and restore. For example, the parcel service 2810 Perform on-demand, offline, and online backups of device configurations and shared software packages. The parcel service 2810 can be a remote deployment of building equipment 2726 perform and version control for backup configurations and software. The parcel service 2810 can own and exchange any device of building equipment 2726 handle with new devices.

The asset and security service 2812 can be configured to connect to the applications 630 establish communication between the security service 2800 and the applications 630 to facilitate. For example, the asset and security service 2812 with the monitoring and reporting application 634 coupled to create and present a user interface, all of the assets installed at a customer site or facility (ie, building equipment devices 2726 ) lists. In some embodiments, the user interface identifies each asset and indicates whether the asset is configured on the cloud building management platform 620 was secured.

The manual upload service 2814 can be configured to manually back up device configuration parameters, software, firmware, and other customizable settings for building equipment 2726 perform. The manual backup can be the same or similar to the automatic backups made by other components of the backup service 2800 be performed. However, the manual backups can be triggered by a service technician or another user if necessary. In some embodiments, the manual upload service is 2814 configured to a device of building equipment 2726 manually on the cloud building management platform 620 and can upload various types of data associated with the registered device. For example, the manual upload service 2814 upload a heartbeat time series indicating whether the device is online and communicating, logs collected by the device, and / or configuration settings for the device.

Backup widgets 2816 may be configured to create various user interface elements (ie widgets) that contain information related to building equipment devices 2726 and / or the security service 2800 assigned. The widgets can act as extensions or components of user interfaces created by the applications 630 be generated. For example, the widgets can include a list view of connected assets, including asset details, and whether the asset is backed up to the cloud building management platform 620 is available. Backup widgets 2816 can use the building equipment 2726 remote control. For example, backup widgets 2816 a remote update request to the building equipment 2726 and can send registered commands securely to building equipment 2726 send. Backup widgets 2816 can also get real-time alarm and event data from building equipment 2726 Show.

The freezing agent 2818 can be configured with the services 2802-2814 , the backup widgets 2816 and building equipment 2726 to be coupled. In some embodiments, the backup agent is 2818 configured to the building equipment 2726 , the backup software and configuration settings to register and status information to the services 2802-2814 and the backup widgets 2816 to communicate. The freezing agent 2818 can be a requirement on building equipment 2726 send to perform a device discovery process to all of the building equipment devices 2726 identify who are at the customer location. An example of a device discovery process by the seizure agent 2818 can be triggered is detailed in the U.S. Patent Application No. 15 / 179,894 , filed June 10, 2016, the entire disclosure of which is hereby incorporated by reference. The freezing agent 2818 can also use the building equipment software 2726 update and the configuration settings of the building equipment 2726 restore. The freezing agent 2818 can send a device heartbeat time series, send logs, send configuration settings, send warnings and commands to and from building equipment 2726 hand off.

In some embodiments, the backup agent uses 2818 those in the entity database 2734 stored data to perform its various functions. As described above, the entity database 2732 store multiple interconnected intelligent entities. The intelligent entities may include object entities that represent multiple building equipment devices and data entities that represent data associated with the multiple building equipment devices. The intelligent entities can be linked by relational objects that indicate relationships between the object entities and the data entities. Each object entity can include a stored attribute that indicates a version of software installed on a building equipment device represented by the object entity.

The freezing agent 2818 may be configured to automatically detect a version of the software installed on each of the building equipment devices by storing the attributes of the object entities in the entity database 2734 to be read. The freezing agent 2818 The software installed on one or more of the building equipment devices may automatically update in response to a determination that the version of the software installed on the one or more building equipment devices is not a latest version of the software.

Configuration of the exemplary embodiments

The structure and arrangement of the systems and methods as shown in the various exemplary embodiments are only illustrative. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g. variations in the sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc .). For example, the position of elements can be reversed or otherwise varied and the type or number of discrete elements or positions can be changed or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The sequence or sequence of any process or method steps can be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.

The present disclosure provides methods, systems, and program products on any machine-readable medium for accomplishing various operations. The embodiments of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for a suitable system that is incorporated for this or other purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products that include machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. For example, such machine readable media may include RAM, ROM, EPROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium suitable for carrying or storing desired program code in the form of machine executable Instructions or data structures can be used and can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also within the scope of machine-readable media. For example, machine-executable instructions include instructions and data that cause a general-purpose computer, special-purpose computer, or special-purpose processing machine to perform a certain function or group of functions.

Although the figures show a specific order of the method steps, the order of the steps may differ from the one shown. Two or more steps can also be carried out simultaneously or partially simultaneously. Such a variation will depend on the software and hardware systems chosen and the choice of designer. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished using standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps, and decision steps.

The term “client or server” encompasses all types of devices, devices and machines for processing data, including, for example, a programmable processor, a computer, a system-on-chip or several or combinations of the above. The device may include special logic circuitry, e.g. B. a locally programmable gate array (FPGA) or an application specific integrated circuit (ASIC). In addition to the hardware, the device can also contain code that generates an execution environment for the computer program in question (e.g. code, the processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine or a combination of the two one or more of them). The device and the execution environment can implement various different computer model infrastructures, such as web services, distributed computing and grid computing infrastructures.

The systems and methods of the present disclosure can be completed by any computer program. A computer program (also called a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be used in any form , including as a stand-alone program or as a module, component, subroutine, object, or other entity suitable for use in a computing environment. A computer program can, among other things, correspond to a file in a file system. A program can be in a part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file that is dedicated to that program, or in several coordinated files (e.g. files that store one or more modules, subroutines or parts of code). A computer program can be used to run on one or more computers located at one location or distributed across multiple locations and connected by a communication network.

The processes and logic flows described in this patent may be performed by one or more programmable processors that execute one or more computer programs to perform actions by manipulating input data and generating output. The processes and logic flows can also be performed by special logic circuitry (e.g., an FPGA or an ASIC) and the device can also be implemented as such.

Processors suitable for executing a computer program include, for example, both general-purpose and specialty microprocessors and any one or more processors of any type of digital computer. In general, a processor will receive instructions and data from read-only memory or random access memory or both. The essential elements of a computer are a processor for performing actions according to instructions and one or more storage devices for storing instructions and data. In general, a computer will also include, or be operatively coupled to, one or more mass storage devices for storing data (e.g., magnetic, magneto-optical disks, or optical disks) to receive or transmit data from or to one or more mass storage devices, or both . However, a computer does not have to have such devices. In addition, a computer may be embedded in another device (e.g., a cell phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a GPS (Global Positioning System) receiver, or one portable storage device (e.g., USB: Universal Serial Bus), etc. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example Semiconductor memory devices (e.g. EPROM, EEPROM and flash memory devices; magnetic disks, e.g. internal hard drives or removable disks, magneto-optical disks; and CD-ROM and DVD-ROM disks). The processor and memory can be implemented by means of special logic circuitry be supplemented or integrated into it.

In various implementations, the steps and operations described here can be performed on one processor or in a combination of two or more processors. For example, in some implementations, the various operations could be performed on a central server or a set of central servers configured to receive data from one or more devices (e.g., edge computing devices / controllers) and perform the operations. In some implementations, the operations may be performed by one or more local controls or computing devices (e.g., edge devices), such as controls, that are dedicated to and / or located in a specific building or part of a building. In some implementations, the operations can be performed by a combination of one or more central or off-site computing devices / servers and one or more local controls / computing devices. All such implementations are intended to be within the scope of the present disclosure. Unless otherwise stated, unless the present disclosure references one or more computer readable storage media and / or one or more controls, such computer readable storage media and / or one or more controls as one or more central servers, one or more local controls or computing devices (e.g., edge devices), any combination thereof, or any other combination of storage media and / or controls regardless of the location of such devices.

To provide interaction with a user, implementations of the subject matter described in this patent may be implemented on a computer that includes a display device (e.g., a CRT (cathode ray tube) -, an LCD (liquid crystal display) -, an OLED (organic light emitting diode) -, TFT (thin film transistor) - or another flexible configuration or any other monitor for displaying information to the user and a keyboard, a pointing device, e.g. a mouse, a trackball etc., or a touchscreen, touchpad etc.) has, through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with a user; For example, feedback provided to the user can be any form of sensor feedback (e.g., visual feedback, acoustic feedback, or tactile feedback), and inputs from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending and receiving documents to and from a device used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.

Implementations of the subject matter described in this disclosure can be implemented in a computing system that includes a back-end component (e.g., such as a data server) or that contains a middleware component (e.g., an application server), or that includes a front -End component (e.g., a client computer) with a graphical user interface or web browser that allows a user to interact with an implementation of the subject matter described in this disclosure, or any combination of one or more of such Contains back-end, middleware, or front-end components. The components of the system can be interconnected by some form or medium of digital data communication (e.g. a communication network). Examples of communication networks include a LAN and a WAN, an inter-network (e.g. the Internet) and peer-to-peer networks (e.g. ad-hoc peer-to-peer networks).

The present disclosure can be embodied in various forms and should not be construed to be limited to only the embodiments illustrated herein. Rather, these embodiments are provided as examples so that this disclosure will be thorough and complete, and will fully convey the aspects and features of the present disclosure to those skilled in the art. Accordingly, processes, elements, and techniques that are not required by those of ordinary skill in the art to fully understand the aspects and features of the present disclosure may not be described. Unless otherwise indicated, like reference numbers refer to like elements throughout the accompanying drawings and the written description, and therefore descriptions thereof may not be repeated. Furthermore, features or aspects within each embodiment should typically be considered available for other similar features or aspects in other embodiments.

It is understood that although the terms "first", "second", "third", etc. may be used here to describe various elements, components, areas, layers and / or sections, these elements, components, areas, Layers and / or sections should not be limited by these terms. These terms are used to distinguish an element, component, area, layer, or section from another element, component, area, layer, or section. Thus, a first element, a first component, a first region, a first layer or a first section, which is described below, could be referred to as a second element, a second component, a second region, a second layer or a second section, without departing from the idea and scope of the present disclosure.

The terminology used here is for the purpose of describing specific embodiments and is not intended to limit the present disclosure. As used here, the singular forms “a” and “an” should also include the plural forms, unless the context clearly indicates otherwise. It is further understood that the terms “comprises”, “comprising”, “includes” and “including”, “has,” have “and“ has “, when used in this patent specification, the presence of the named Specify features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items. Expressions such as “at least one of” when in front of a list of items modify the entire list of items and do not modify the individual items in the list.

As used herein, the terms "substantially", "about" and similar terms are used as approximate terms and not as degrees and are intended to take into account the inherent variations in the measured or calculated values that one of ordinary skill in the art would recognize. Furthermore, the use of "may" in describing embodiments of the present disclosure refers to "one or more embodiments of the present disclosure". As used here, the terms "use", "using" and "used" can be considered synonymous with the terms "use", "using" and "used". In addition, the term “exemplary” is intended to refer to an example or an illustration.

A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright holder has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure as listed in the patent file or the records of the Patent and Trademark Office, but otherwise reserves all copyrights.

QUOTES INCLUDE IN THE DESCRIPTION

This list of documents listed by the applicant has been generated automatically and is only included for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

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Claims (100)

Non-volatile computer readable storage media or non-volatile computer readable storage media containing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations that include: Creating a database of interconnected intelligent entities, the intelligent entities object entities each representing a plurality of objects associated with one or more buildings, and wherein the plurality of objects each representing an area, a person, a building subsystem and / or a device, and comprise data entities representing data generated by the objects, the intelligent entities being interconnected by relational objects that indicate relationships between the object entities and the data entities; Receiving data from a first object of the plurality of objects; Determining a second object from a relational object for the first object based on the received data; and Modify a data entity associated with an object entity of the second object within the intelligent entity database based on the received data for the first object. Non-volatile computer readable media Claim 1 , wherein the first object is a sensor and the second object is an area within the building in which the sensor is located. Non-volatile computer readable media Claim 2 wherein the sensor is a temperature sensor, and the data entity associated with the object entity representing the area is configured to store an ambient temperature value of the area based on the data received from the temperature sensor. Non-volatile computer readable media Claim 1 wherein one or more of the object entities include a static attribute for identifying the object entity, a dynamic attribute for storing a data point associated with the object entity that changes over time, and a behavioral attribute that provides an expected response from the object entity in response to defines an input. Non-volatile computer readable media Claim 4 , wherein the data entity associated with the object entity of the second object is configured to store the dynamic attribute of the object entity. Non-volatile computer readable media Claim 5 , wherein a relational object semantically defines the connection between the data entity and the object entity of the second object. Non-volatile computer readable media Claim 1 wherein modifying the data entity associated with the object entity of the second object comprises: identifying a dynamic attribute in the data associated with the object entity of the second object; Determining a relational object that connects the data entity to the object entity of the second object; and storing a value of the data corresponding to the dynamic attribute in the data entity. Non-volatile computer readable media Claim 1 , wherein the first object is an access control device and the second object is a person associated with the building in which the access control device is located. Non-volatile computer readable media Claim 8 , wherein the data entity associated with the object entity representing the person is configured to store a location attribute of the person based on the data received from the access control device. Non-volatile computer readable media Claim 9 the instructions further causing the one or more processors to generate a shadow entity to store historical values of the data received from the access control device and calculate an average time of arrival of the person from the historical values stored in the shadow entity. Method for managing data relating to a plurality of objects which are assigned to one or more buildings, the plurality of objects each comprising an area, a person, a building subsystem and / or represent a device connected to one or more electronic communication networks, comprising: creating, by one or more processors, a database of interconnected intelligent entities, the intelligent entities object entities each representing the plurality of objects, and comprise data entities representing data generated by the objects, the intelligent entities being connected by relational objects that indicate relationships between the object entities and the data entities; Receiving, by the one or more processors, data from a first object of the plurality of objects; Determining, by the one or more processors, a second object from a relational object for the first object based on the received data; and modifying, by the one or more processors, a data entity associated with an object entity of the second object within the intelligent entity database based on the received data for the first object. Procedure according to Claim 11 , wherein the first object is a sensor and the second object is a space within the building in which the sensor is located. Procedure according to Claim 12 wherein the sensor is a temperature sensor, and the data entity associated with the object entity representing the area is configured to store an ambient temperature value of the area based on the data received from the temperature sensor. Procedure according to Claim 11 , wherein each of the object entities is a static attribute for identifying the object entity, a dynamic attribute for storing a data point associated with the object entity that changes over time, and a behavior attribute that is an expected response of the object entity in response to an input defined, includes. Procedure according to Claim 14 , wherein the data entity associated with the object entity of the second object is configured to store the dynamic attribute of the object entity. Procedure according to Claim 14 , wherein a relational object semantically defines the connection between the data entity and the object entity of the second object. Procedure according to Claim 11 wherein modifying the data entity associated with the object entity of the second object comprises: identifying, by the one or more processors, a dynamic attribute in the data associated with the object entity of the second object; Determining, by the one or more processors, a relational object that connects the data entity to the object entity of the second object; and storing, by the one or more processors, a value of the data corresponding to the dynamic attribute in the data entity. Procedure according to Claim 11 , wherein the first object is an access control device and the second object is a person associated with the building in which the access control device is located and the data entity associated with the object entity representing the person for storing a location attribute of the person is configured on the data received from the access control device. Building management cloud computing system for managing data related to multiple objects associated with one or more buildings, the multiple objects each representing an area, person, building subsystem, and / or device associated with one or more electronic communication networks , which comprises: one or more processors, and one or more computer-readable storage media which are communicatively coupled to the one or more processors and which have instructions stored thereon which, when executed by the one or more processors, the one or cause the multiple processors to: create a database of interconnected intelligent entities, the intelligent entities comprising object entities each representing the multiple objects and data entities representing data generated by the objects, the data in telligent entities are linked by relational objects that indicate relationships between the object entities and the data entities; Receiving data from a first object of the plurality of objects; Determining a second object from a relational object for the first object based on the received data; and modifying a data entity associated with an object entity of the second object within the intelligent entity database based on the received data for the first object. System according to Claim 19 wherein modifying the data entity associated with the object entity of the second object comprises: identifying a dynamic attribute in the data associated with the object entity of the second object; Determining a relational object that connects the data entity to the object entity of the second object; and storing a value of the data corresponding to the dynamic attribute in the data entity. Non-volatile computer readable storage media or non-volatile computer readable storage media containing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations that include: Receiving first raw data from a first object of a plurality of objects which are assigned to one or more buildings, the plurality of objects each representing an area, a person, a building subsystem and / or a device, and the first raw data generating one or more generated by the first object include first data points; Generating a first input time series according to the one or more data points; Access a database of interconnected intelligent entities, the intelligent entities comprising object entities each representing the plurality of objects and data entities representing data stored, the intelligent entities being connected by relational objects, the relationships between the object entities and specify the data entities; Identifying a first object entity representing the first object from a first identifier in the first input time series; Identifying a first data entity from a first relational object that indicates a relationship between the first object entity and the first data entity; and Store the first input time series in the first data entity. Non-volatile computer readable media Claim 21 , where the relational objects semantically define the relationships between the object entities and the data entities. Non-volatile computer readable media Claim 21 , wherein the input time series includes the first identifier, a time stamp that indicates a generation time of the one or more first data points, and a value of the one or more first data points. Non-volatile computer readable media Claim 21 wherein the instructions further cause the one or more processors to: identify a second data entity. representing a second object from a second relational object indicating a relationship between the first object entity and the second object entity; and identifying a second data entity from a third relational object indicating a relationship between the second object entity and the second data entity, the second data entity storing a second input time series corresponding to one or more second data points associated with the second object. Non-volatile computer readable media Claim 24 wherein the instructions further cause the one or more processors to: identify one or more processing workflows defining one or more processing operations to generate derived time series using the first and second input time series; Executing the one or more processing workflows to generate the derived time series; Identifying a third data entity from a fourth relational object that indicates a relationship between the second object entity and the third data entity; and storing the derived input time series in the third data entity. Non-volatile computer readable media Claim 25 , wherein the first object is an access control device and the second object is an access key card associated with a person, and wherein derived time series includes one or more virtual data points calculated according to the first and second input time series. Non-volatile computer readable media Claim 26 , wherein the one or more virtual data points include one or more location attributes of the person. Non-volatile computer readable media Claim 25 , wherein the first object is a temperature sensor and the second object is a variable air volume unit (VAV), and the derived time series includes an abnormal temperature attribute that corresponds to an area in which the temperature sensor is located and for which the VAV is configured to supply it. Non-volatile computer readable media Claim 25 wherein the first and / or second object is a temperature sensor and the instructions cause the one or more processors to: periodically receive temperature measurements from the temperature sensor; and updating at least the derived time series in the third data entity each time a new temperature measurement is received from the temperature sensor. Non-volatile computer readable media Claim 29 , wherein the derived time series includes an average ambient temperature of an area in which the temperature sensor is located. Method for managing time series data relating to a plurality of objects which are associated with one or more buildings, the plurality of objects each representing an area, a person, a building system and / or a device which are connected to one or more electronic communication networks, which The following includes: Receiving, by one or more processors, first raw data from a first object of the plurality of objects, the first raw data including one or more first data points generated by the first object; Generating, by the one or more processors, a first input time series according to the one or more data points; Access, by the one or more processors, a database of interconnected intelligent entities, the intelligent entities including object entities each representing the multiple objects and data entities representing stored data, the intelligent entities through relational objects together connected that indicate relationships between the object entities and the data entities; Identify, by the one or more processors, a first object entity representing the first object from a first identifier in the first input time series; Identify, by the one or more processors, a first data entity from a first relational object indicating a relationship between the first object entity and the first data entity; and Storing, by the one or more processors, the first input time series in the first data entity, the relational objects semantically defining the relationships between the object entities and the data entities, and wherein one or more of the object entities is a static attribute for identifying the object entity, a dynamic attribute for storing data associated with the object entity that changes over time, and a behavior attribute that is an expected response of the object entity in response to one Input defined, include. Procedure according to Claim 31 , wherein the first input time series corresponds to the dynamic attribute of the first object entity, and at least one of the first data points in the first input time series is stored in the dynamic attribute of the first object entity. Procedure according to Claim 31 , wherein the input time series includes the first identifier, a time stamp that indicates a generation time of the one or more first data points, and a value of the one or more first data points. Procedure according to Claim 31 , which also includes: Identify, by the one or more processors, a second data entity. representing a second object from a second relational object indicating a relationship between the first object entity and the second object entity; and identifying, by the one or more processors, a second data entity from a third relational object indicating a relationship between the second object entity and the second data entity, the second data entity storing a second input time series corresponding to one or more second data points, assigned to the second object. Procedure according to Claim 34 , further comprising: identifying, by the one or more processors, one or more processing workflows defining one or more processing operations to generate derived time series using the first and second input time series; Executing, by the one or more processors, the one or more processing workflows to generate the derived time series; Identify, by the one or more processors, a third data entity from a fourth relational object that indicates a relationship between the second object entity and the third data entity; and storing, by the one or more processors, the derived time series in the third data entity. Procedure according to Claim 35 , wherein the first object is an access control device and the second object is an access key card associated with a person, and wherein the derived time series includes one or more virtual data points calculated according to the first and second input time series. Procedure according to Claim 36 , wherein the one or more virtual data points include one or more location attributes of the person. Procedure according to Claim 35 , wherein the first object is a temperature sensor and the second object is a variable air volume unit (VAV), and the derived time series includes an abnormal temperature attribute that corresponds to an area in which the temperature sensor is located and for which the VAV is configured to supply it. Procedure according to Claim 35 wherein the first and / or second object is a temperature sensor and the method further comprises: periodically receiving, by the one or more processors, temperature measurements from the temperature sensor; and updating, by the one or more processors, at least the derived time series in the third data entity each time a new temperature measurement is received from the temperature sensor. Building management cloud computing system for managing time series data related to multiple objects associated with one or more buildings, the multiple objects each representing an area, person, building subsystem, and / or device associated with one or more electronic communication networks , which comprises: one or more processors communicatively connected to a database of interconnected intelligent entities, the intelligent entities comprising object entities each representing the plurality of objects and data entities representing stored data, the intelligent ones Entities are linked by relational objects that indicate relationships between the object entities and the data entities; and one or more computer readable storage media communicatively coupled to the one or more processors and having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: receive first raw data from one first object of the plurality of objects, the first raw data including one or more first data points generated by the first object; Generating a first input time series according to the one or more data points; Identifying a first object entity representing the first object from a first identifier in the first input time series; Identifying a first data entity from a first relational object that indicates a relationship between the first object entity and the first data entity; and Storing the first input time series in the first data entity, the relational objects semantically defining the relationships between the object entities and the data entities, one or more of the object entities having a static attribute for identifying the object entity, a dynamic attribute for storing data associated with the object entity that change over time and include a behavioral attribute that defines an expected response of the object entity in response to an input, the first input time series corresponding to the dynamic attribute of the first object entity, at least one of the first data points in the first The input time series is stored in the dynamic attribute of the first object entity, and the input time series includes the first identifier, a time stamp that indicates a generation time of the one or more first data points, and a value of the one or more first data points included. Building management system that includes: building equipment configured to provide samples of one or more data points in the building management system; a data collector configured to collect the samples from the building equipment and generate one or more input time series comprising multiple samples; a time series service configured to: Identify a first time series processing workflow that uses the input time series as an input and defines one or more processing operations to be applied to the samples of the input time series; Performing the one or more processing operations defined by the first time series processing workflow to generate a first derived time series comprising a first set of derived time series samples; Identify a second time series processing workflow that uses the first derived time series as an input and defines one or more processing operations to be applied to the samples of the first derived time series; and Performing the one or more processing operations defined by the second time series processing workflow to produce a second derived time series comprising a second set of derived time series samples; and a time series storage interface configured to store the input time series and the first and second derived time series in a time series database. Building management system according to Claim 41 wherein the building equipment includes sensors, HVAC equipment, lighting equipment, access control equipment and / or security equipment. Building management system according to Claim 41 wherein generating the first derived time series comprises: transforming one or more samples of the input time series into one or more samples of the first set of derived time series samples by applying the one or more samples of the input time series as an input to the first time series processing workflow; and assembling the first set of derived time series samples to form the first derived time series. Building management system according to Claim 41 wherein generating the second derived time series sample comprises: transforming one or more samples of the first set of derived time series samples into one or more samples of the second set of derived time series samples by applying the one or more samples of the first set of derived time series samples as an input to the second time series processing workflow; and assembling the second set of derived time series samples to form the second derived time series. Building management system according to Claim 41 wherein the time series service is configured to: identify one or more time series to be used as inputs to the first time series processing workflow; and Generate an enriched time series processing workflow that includes the first time series processing workflow, the input time series, and the one or more other time series. Building management system according to Claim 41 further comprising a directed acyclic graph (DAG) database that stores a plurality of DAGs, each of the DAGs defining a time series processing workflow, the time series service including a DAG identifier configured to determine whether any of the ones in the DAG Database stored DAGs used the input time series or the first derived time series as an input. Building management system according to Claim 41 wherein the time series service is configured to generate a derived time series to: (a) determine whether the derived time series is used as an input to any of a number of stored time series processing workflows; (b) in response to a determination that the derived time series are used as one input for at least one of the stored time series processing workflows, performing one or more processing operations defined by the time series processing workflows that use the derived time series as one input to another generate derived time series; and (c) iteratively repeating steps (a) and (b) until the derived time series generated in step (b) is not used as an input to any of the plurality of stored time series processing workflows. A building management system for managing time series data provided by building equipment, the building management system comprising: one or more computer-readable storage media with instructions stored thereon which, when executed by one or more processors, cause the one or more processors to: Collecting samples of the time series data from the building equipment and generating one or more input time series comprising multiple of the samples; Identify a first time series processing workflow that uses the input time series as an input and defines one or more processing operations to be applied to the samples of the input time series; Performing the one or more processing operations defined by the first time series processing workflow to generate a first derived time series comprising a first set of derived time series samples; Identify a second time series processing workflow that uses the first derived time series as an input and defines one or more processing operations to be applied to the samples of the first derived time series; Performing the one or more processing operations defined by the second time series processing workflow to produce a second derived time series comprising a second set of derived time series samples; and Store the input time series and the first and second derived time series in a time series database. Building management system according to Claim 48 wherein the building equipment includes sensors, HVAC equipment, lighting equipment, access control equipment and / or security equipment. Building management system according to Claim 48 wherein generating the first derived time series comprises: transforming one or more samples of the input time series into one or more samples of the first set of derived time series samples by applying the one or more samples of the input time series as an input to the first time series processing workflow; and assembling the first set of derived time series samples to form the first derived time series. Building management system according to Claim 48 wherein generating the second derived time series comprises: transforming one or more samples of the first set of derived time series samples into one or more samples of the second set of derived time series samples by applying the one or more samples of the first set of derived time series samples as an input to the second time series processing workflow; and Assembling the second set of derived time series samples to form the second derived time series. Building management system according to Claim 48 wherein the instructions cause the one or more processors to: identify one or more time series to be used as inputs to the first time series processing workflow; and generating an enriched time series processing workflow that includes the first time series processing workflow, the input time series, and the one or more other time series. Building management system according to Claim 48 further comprising a directed acyclic graph (DAG) database that stores a plurality of DAGs, each of the DAGs defining a time series processing workflow, the instructions causing the one or more processors to determine whether any of the ones in the DAG database stored DAGs uses the input time series or the first derived time series as an input. Building management system according to Claim 48 wherein the instructions cause the one or more processors to generate a derived time series: (a) determine whether the derived time series is used as an input to any of a number of stored time series processing workflows; (b) in response to a determination that the derived time series are used as one input for at least one of the stored time series processing workflows, performing one or more processing operations defined by the time series processing workflows that use the derived time series as one input to another generate derived time series; and (c) iteratively repeating steps (a) and (b) until the derived time series generated in step (b) is not used as an input to any of the plurality of stored time series processing workflows. A method of managing time series data provided by building equipment, the method comprising: Collecting samples of the time series data from the building equipment and generating one or more input time series comprising multiple of the samples; Identify a first time series processing workflow that uses the input time series as an input and defines one or more processing operations to be applied to the samples of the input time series; Performing the one or more processing operations defined by the first time series processing workflow to generate a first derived time series comprising a first set of derived time series samples; Identify a second time series processing workflow that uses the first derived time series as an input and defines one or more processing operations to be applied to the samples of the first derived time series; Performing the one or more processing operations defined by the second time series processing workflow to produce a second derived time series comprising a second set of derived time series samples; and Store the input time series and the first and second derived time series in a time series database. Procedure according to Claim 55 wherein the building equipment includes sensors, HVAC equipment, lighting equipment, access control equipment and / or security equipment. Procedure according to Claim 55 wherein generating the first derived time series comprises: transforming one or more samples of the input time series into one or more samples of the first set of derived time series samples by applying the one or more samples of the input time series as an input to the first time series processing workflow; and assembling the first set of derived time series samples to form the first derived time series. Procedure according to Claim 55 wherein generating the second derived time series comprises: transforming one or more samples of the first set of derived time series samples into one or more samples of the second set of derived time series samples by applying the one or more samples of the first set of derived time series samples as an input to the second time series processing workflow; and assembling the second set of derived time series samples to form the second derived time series. Procedure according to Claim 55 further comprising: identifying one or more time series to use as inputs to the first time series processing workflow; and generating an enriched time series processing workflow that includes the first time series processing workflow, the input time series, and the one or more other time series. Procedure according to Claim 55 further comprising, when generating a derived time series: (a) determining whether the derived time series is used as an input to any of a number of stored time series processing workflows; (b) in response to a determination that the derived time series are used as one input for at least one of the stored time series processing workflows, performing one or more processing operations defined by the time series processing workflows that use the derived time series as one input to another generate derived time series; and (c) iteratively repeating steps (a) and (b) until the derived time series generated in step (b) is not used as an input to any of the plurality of stored time series processing workflows. A building management platform for monitoring and controlling equipment of a building management system, the building management platform comprising: a data collector configured to collect feedback samples provided by one or more sensors of the building management system and generate one or more feedback time series comprising multiple of the feedback samples; a time series service configured to: Identify a first feedback control workflow that uses the feedback time series as an input and defines one or more processing operations to be applied to the feedback samples of the feedback time series; Performing the one or more processing operations defined by the feedback control workflow to generate a control signal time series comprising a set of control signal samples; and Providing a control signal, including the control signal samples and / or the control signal time series, as an output to controllable building equipment of the building management system that operates using the control signal as an input. Building management platform after Claim 61 wherein generating the control signal time series comprises: transforming one or more samples of the feedback time series into one or more samples of the control signal samples by applying the one or more samples of the feedback time series as an input to the first feedback control workflow; and compiling the control samples to form the control signal time series. Building management platform after Claim 61 wherein the time series service is configured to: identify one or more time series required as inputs to the first feedback control workflow, the one or more other time series comprising multiple setpoint time series comprising multiple setpoint samples, each of the setpoint samples defining a setpoint that defines corresponds to one of the feedback samples; and generating an enriched feedback control workflow that includes the feedback control workflow, the feedback time series, and the one or more other time series. Building management platform after Claim 61 further comprising a directed acyclic graph (DAG) database that stores a plurality of feedback DAGs, each of the DAGs defining a feedback control workflow, the time series service including a DAG identifier configured to determine whether any of the data in FIG feedback control DAGs stored in the DAG database uses the feedback time series as an input. Building management platform after Claim 61 where the feedback control workflow is a state based control workflow, an ESC (Extremum Seeking Control) workflow, a proportional integral (PI) control workflow, a proportional integral derivative (PID) - Control workflow and / or an MPC (Model Predictive Control) workflow that cause the time series service to transform the feedback time series into the control signal time series using a feedback control technique. Building management platform after Claim 61 , wherein the feedback control workflow includes a proportional integral derivative (PID) control workflow that causes time series service to: generate an error time series that includes multiple error samples, each of the error samples indicating a difference between one or more of the feedback samples and a corresponding setpoint; and generating the control signal time series by applying a set of PID control operations to the error time series. Building management platform after Claim 66 wherein applying the set of PID control operations to the error time series comprises: generating an integrated error time series based on a plurality of the error samples; Generating a derivative error time series based on a change in a value between successive samples of the error time series; Computing a proportional gain component by multiplying the error time series by a proportional gain parameter; Computing an integral gain component by multiplying the integrated error time series by an integral gain parameter; Computing a derivative gain component by multiplying the derivative error time series by a derivative gain parameter; and combining the proportional gain component, the integral gain component and the derivative gain component to produce the control signal time series. A building management platform for monitoring and controlling equipment of a building management system, the building management platform comprising: one or more computer-readable storage media with instructions stored thereon which, when executed by one or more processors, cause the one or more processors to: Collecting feedback samples provided by one or more sensors of the building management system and generating one or more feedback time series comprising several of the feedback samples; Identify a first feedback control workflow that uses the feedback time series as an input and defines one or more processing operations to be applied to the feedback samples of the feedback time series; Performing the one or more processing operations defined by the feedback control workflow to generate a control signal time series comprising a set of control signal samples; and Providing a control signal, including the control signal samples and / or the control signal time series, as an output to controllable building equipment of the building management system that operates using the control signal as an input. Building management platform after Claim 68 wherein generating the control signal time series comprises: transforming one or more samples of the feedback time series into one or more samples of the control signal samples by applying the one or more samples of the feedback time series as an input to the first feedback control workflow; and compiling the control samples to form the control signal time series. Building management platform after Claim 68 wherein the instructions cause the one or more processors to: identify one or more time series required as inputs to the first feedback control workflow, the one or more other time series comprising multiple setpoint time series that comprise a plurality of setpoint samples, each of the setpoint samples defining a setpoint that corresponds to one of the feedback samples; and generating an enriched feedback control workflow that includes the feedback control workflow, the feedback time series, and the one or more other time series. Building management platform after Claim 68 further comprising a directed acyclic graph (DAG) database that stores a plurality of feedback DAGs, each of the DAGs defining a feedback control workflow; the instructions causing the one or more processors to determine whether any of the feedback control DAGs stored in the DAG database use the feedback time series as an input. Building management platform after Claim 68 wherein the feedback control workflow is a state based control workflow, an ESC workflow (ESC), a proportional integral (PI) control workflow, a proportional integral derivative (PID) control workflow, and / or includes an MPC (Model Prediction Control) workflow that causes the one or more processors to transform the feedback time series into the control signal time series using a feedback control technique. Building management platform after Claim 68 wherein the feedback control workflow includes a proportional-integral-derivative (PID) control workflow that causes the one or more processors to: generate an error time series that includes multiple error samples, each of the error samples different from one or more of the feedback samples indicates the corresponding setpoint; and generating the control signal time series by applying a set of PID control operations to the error time series. Building management platform after Claim 73 wherein applying the set of PID control operations to the error time series comprises: generating an integrated error time series based on a plurality of the error samples; and generating a derivative error time series based on a change in a value between successive samples of the error time series; Computing a proportional gain component by multiplying the error time series by a proportional gain parameter; Computing an integral gain component by multiplying the integrated error time series by an integral gain parameter; Computing a derivative gain component by multiplying the derivative error time series by a derivative gain parameter; and combining the proportional gain component, the integral gain component and the derivative gain component to produce the control signal time series. A method of monitoring and controlling equipment of a building management system, the method comprising: Collecting feedback samples provided by one or more sensors of the building management system and generating one or more feedback time series comprising several of the feedback samples; Identify a first feedback control workflow that uses the feedback time series as an input and defines one or more processing operations to be applied to the feedback samples of the feedback time series; Performing the one or more processing operations defined by the feedback control workflow to generate a control signal time series comprising a set of control signal samples; and Providing a control signal, including the control signal samples and / or the control signal time series, as an output to controllable building equipment of the building management system that operates using the control signal as an input. Procedure according to Claim 75 wherein generating the control signal time series comprises: transforming one or more samples of the feedback time series into one or more samples of the control signal samples by applying the one or more samples of the feedback time series as an input to the first feedback control workflow; and Gathering the control samples to form the control signal time series. Procedure according to Claim 75 further comprising: identifying one or more time series required as inputs to the first feedback control workflow, the one or more other time series comprising multiple setpoint time series comprising multiple setpoint samples, each of the setpoint samples defining a setpoint that corresponds to one of the feedback samples corresponds; and generating an enriched feedback control workflow that includes the feedback control workflow, the feedback time series, and the one or more other time series. Procedure according to Claim 75 wherein the feedback control workflow is a state based control workflow, an ESC workflow (ESC), a proportional integral (PI) control workflow, a proportional integral derivative (PID) control workflow, and / or includes an MPC workflow (MCP) that cause the feedback time series to be transformed into the control signal time series using a feedback control technique. Procedure according to Claim 75 , wherein the feedback control workflow includes a proportional integral derivative (PID) control workflow, and performing the one or more processing operations defined by the feedback control workflow includes: generating an error time series that includes multiple error samples, each of the error samples one Indicates difference between one or the feedback samples and a corresponding setpoint; and generating the control signal time series by applying a set of PID control operations to the error time series. Procedure according to Claim 79 wherein applying the set of PID control operations to the error time series comprises: generating an integrated error time series based on a plurality of the error samples; and generating a derivative error time series based on a change in a value between successive samples of the error time series; Computing a proportional gain component by multiplying the error time series by a proportional gain parameter; Computing an integral gain component by multiplying the integrated error time series by an integral gain parameter; Computing a derivative gain component by multiplying the derivative error time series by a derivative gain parameter; and combining the proportional gain component, the integral gain component and the derivative gain component to produce the control signal time series. Building management system that includes: an entity database that stores multiple interconnected smart entities, the smart entities including object entities representing multiple people or physical devices and data entities representing data associated with the people or physical devices, the smart entities through relational objects with each other connected that indicate relationships between the object entities and the data entities, each of the object entities comprising a plurality of stored identity attributes; and an identity management service configured to: Receiving a first identity attribute from a first device within a building; Receiving a second identity attribute from a second device within the building; Comparing the first and second identity attributes with the stored identity attributes of an object entity from the plurality of interconnected intelligent entities; and Granting access to a building area, building equipment device and / or a computer system in response to the first and second identity attributes matching the stored identity attributes of the object entity. Building management system according to Claim 81 , wherein: the first device is an access card reader and the first identity attribute is a card ID attribute recorded by the access card reader; and the second device is a security camera and the second identity attribute is an image of a person that is captured by the security camera. Building management system according to Claim 81 , wherein: the first device is an access card reader and the first identity attribute is a card ID attribute recorded by the access card reader; and wherein the second device is a mobile device worn by a person and the second identity attribute is a mobile device ID attribute associated with the mobile device. Building management system according to Claim 81 , wherein: wherein the first device is a user interface device and the first identity attribute is a user ID received from a user via the user interface device; and the second device is a security camera and the second identity attribute is an image of a person that is captured by the security camera. Building management system according to Claim 81 , wherein: wherein the first device is a user interface device and the first identity attribute is a user ID received from a user via the user interface device; and wherein the second device is a mobile device worn by a person and the second identity attribute is a mobile device ID attribute associated with the mobile device. Building management system according to Claim 81 wherein: the first device is a mobile device, an information technology (IT) device, an Internet of Things (IoT) sensor, a building equipment device, and / or a security device; and the second device is another of the mobile device, the IT device, the IoT device, the building equipment device, or the security device. Building management system according to Claim 81 , wherein the identity management service is configured to: determine a location associated with the first device in response to the first device providing the first identity attribute; Identifying a building area in which the first device is located; and selecting the second device from a set of devices located in the same building area as the first device. Building management system according to Claim 81 wherein the identity management service is configured to deny access to the building area, building equipment device, and / or the computer system in response to the first and / or second identity attribute not matching the stored identity attributes of the object entity. A method of controlling access to a building area, a building equipment device, or a computer system in a building management system, the method comprising: Storing a plurality of interconnected intelligent entities in an entity database, the intelligent entities comprising object entities representing multiple people or physical devices and data entities representing data associated with the people or physical devices, the intelligent entities connected by relational objects are that indicate relationships between the object entities and the data entities, each of the object entities comprising a plurality of stored identity attributes; Receiving a first identity attribute from a first device within a building; Receiving a second identity attribute from a second device within the building; Comparing the first and second identity attributes with the stored identity attributes of an object entity from the plurality of interconnected intelligent entities; and Granting access to a building area, building equipment device and / or a computer system in response to the first and second identity attributes matching the stored identity attributes of the object entity. Procedure according to Claim 89 , wherein: the first device is an access card reader and the first identity attribute is a card ID attribute recorded by the access card reader; the second device is a security camera and / or a mobile device carried by a person; and the second identity attribute is an image of a person captured by the security camera and / or a mobile device ID attribute associated with the mobile device. Procedure according to Claim 89 , wherein: wherein the first device is a user interface device and the first identity attribute is a user ID received from a user via the user interface device; the second device is a security camera and / or a mobile device carried by a person; and the second identity attribute is an image of a person captured by the security camera and / or a mobile device ID attribute associated with the mobile device. Procedure according to Claim 89 further comprising: determining a location associated with the first device in response to the first device providing the first identity attribute; Identifying a building area in which the first device is located; and selecting the second device from a set of devices located in the same building area as the first device. Building management system that includes: several devices of a building equipment; an entity database that stores multiple interconnected smart entities, the smart entities include object entities representing multiple building equipment devices and data entities representing data associated with the multiple building equipment devices, the smart entities being interconnected by relational objects indicating relationships between the object entities and the data entities, each object entity includes a stored attribute that indicates a version of software installed on a building equipment device represented by the object entity; and a security service configured to: automatically detect a version of software installed on each of the building equipment devices by reading the stored attributes of the object entities, and automatically update the software installed on one or more of the building equipment devices , in response to a determination that the version of the software installed on the one or more building equipment devices is not a newest version of the software. Building management system according to Claim 93 wherein the security service includes an identity and security service configured to ensure that each building equipment device is capable of accessing configuration backups. Building management system according to Claim 93 , wherein the security service includes a device management service configured to create an intelligent entity for each device of the building equipment and register each device of the building equipment with the corresponding intelligent entity. Building management system according to Claim 93 wherein the security service includes a transport and notification service configured to enable bidirectional communication between the security service and the building equipment. Building management system according to Claim 93 wherein the security service includes a device shadow / manifest service configured to synchronize configuration settings, parameters, and / or device specific information between the building equipment and the security service. Building management system according to Claim 93 , wherein the backup service includes a parcel service configured to generate a compressed data object that includes a configuration of the building equipment and store the compressed data object as a backup of the configuration. Building management system according to Claim 93 wherein the security service includes an asset and backup service configured to create and present a user interface that lists each device of the building equipment and indicates whether a backup configuration of each device has been stored in the security service. Building management system according to Claim 93 wherein the backup service includes a manual upload service configured to upload a backup configuration in response to a user request for the backup configuration.

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