TW202212945A - Environmental adjustment using artificial intelligence - Google Patents

Abstract

Changing environmental characteristics of an enclosure are controlled to promote health, wellness, and/or performance for occupant(s) of the enclosure using sensor data, three-dimensional modeling, physical properties of the enclosure, and machine learning (e.g., Artificial Intelligence).

Description

Environmental adjustments using artificial intelligence

related applications

This application claims U.S. Provisional Patent Application No. 63/029,301, filed on May 22, 2020, and entitled "ENVIRONMENTAL ADJUSTMENT USING ARTIFICIAL INTELLIGENCE" and filed on June 2, 2020 Priority to US Provisional Patent Application No. 63/033,474 for "ENVIRONMENTAL ADJUSTMENT USING ARTIFICIAL INTELLIGENCE". This application is also a continuation-in-part of the International Patent Application No. PCT/US21/30798 filed on May 5, 2021, entitled "DEVICE ENSEMBLES AND COEXISTENCE MANAGEMENT OF DEVICES", The international patent application claims U.S. Provisional Patent Application No. 63/079,851, filed on September 17, 2020, entitled "DEVICE ENSEMBLES AND COEXISTENCE MANAGEMENT OF DEVICES," June 4, 2020 U.S. Provisional Patent Application Serial No. 63/034,792, entitled "DEVICE ENSEMBLES AND COEXISTENCE MANAGEMENT OF DEVICES", filed on May 6, 2020, and entitled "DEVICE ENSEMBLES AND COEXISTENCE MANAGEMENT OF DEVICES" Priority of U.S. Provisional Patent Application No. 63/020,819 in DEVICE ENSEMBLES AND COEXISTENCE MANAGEMENT OF DEVICES. This application is also US Patent Application Serial No. 16/447,169, filed on June 20, 2019, entitled "SENSING AND COMMUNICATIONS UNIT FOR OPTICALLY SWITCHABLE WINDOW SYSTEMS" A continuation-in-part application of No. , which claims priority to the following applications: (I) filed on June 22, 2018, entitled "SENSING AND COMMUNICATION UNIT FOR OPTICAL SWITCHABLE WINDOW SYSTEM" COMMUNICATIONS UNIT FOR OPTICALLY SWITCHABLE WINDOW SYSTEMS" U.S. Provisional Patent Application No. 62/688,957; (II) filed June 6, 2019, entitled "SENSING AND COMMUNICATION UNIT FOR OPTICALLY SWITCHABLE WINDOW SYSTEMS" AND COMMUNICATIONS UNIT FOR OPTICALLY SWITCHABLE WINDOW SYS SENSING AND COMMUNICATIONS UNIT FOR OPTICALLY SWITCHABLE WINDOW SYSTEMS)" U.S. Provisional Patent Application No. 62/803,324; (IV) filed on November 16, 2018, entitled "Sensing and Communication Unit for Optical Switchable Window Systems" (SENSING AND COMMUNICATIONS UNIT FOR OPTICALLY SWITCHABLE WINDOW SYSTEMS)" U.S. Provisional Patent Application No. 62/768,775. This application is also a continuation-in-part application of International Patent Application No. PCT/US21/15378 filed on January 28, 2021, entitled "Sensor Calibration and Operation". The application claims priority to US Provisional Patent Application Serial No. 62/967,204, filed January 29, 2020, entitled "SENSOR CALIBRATION AND OPERATION." This application is also a continuation-in-part of International Patent Application No. PCT/US21/17603, filed on February 11, 2021, entitled "PREDICTIVE MODELING FOR TINTABLE WINDOWS" , the international patent application claims 63/145,333, filed on February 3, 2021, entitled "PREDICTIVE MODELING FOR TINTABLE WINDOWS," filed on September 8, 2020, entitled "PREDICTIVE MODELING FOR TINTABLE WINDOWS" 63/075,569 of "PREDICTIVE MODELING FOR TINTABLE WINDOWS" and filed February 12, 2020, entitled "Virtual Sky Sensor and Sensor Radiation for Weather Modeling 62/975,677 of VIRTUAL SKY SENSORS AND SUPERVISED CLASSIFICATION OF SENSOR RADIATION FOR WEATHER MODELING. This application is also the No. 1 US patent application entitled "CONTROL METHODS AND SYSTEMS USING EXTERNAL 3D MODELING AND NEURAL NETWORKS" filed on February 5, 2021 17/250,586, a continuation-in-part of US patent application filed on August 14, 2019, entitled "CONTROL METHODS AND SYSTEMS USING EXTERNAL 3D MODELING AND NEURAL NETWORKS)” in the national entry phase of International Patent Application No. PCT/US19/46524, which claims priority to the following applications: (I) filed on August 15, 2018, entitled “Using US Provisional Patent Application No. 62/764,821 of "CONTROL METHODS AND SYSTEMS USING EXTERNAL 3D MODELING AND NEURAL NETWORKS"; (II) filed on October 15, 2018 U.S. Provisional Patent Application No. 62/745,920, entitled "CONTROL METHODS AND SYSTEMS USING EXTERNAL 3D MODELING AND NEURAL NETWORKS"; and (III) 2019 2 U.S. Provisional Patent Application No. 62/805,841, filed on March 14, entitled "CONTROL METHODS AND SYSTEMS USING EXTERNAL 3D MODELING AND NEURAL NETWORKS"; International Patent Application No. PCT/US19/46524, also filed on March 20, 2019, is entitled "CONTROL METHODS AND SYSTEMS USING EXTERNAL 3D MODELING AND SCHEDULE- BASED COMPUTING)", a continuation-in-part of International Patent Application No. PCT/US19/23268, which claims filed on March 21, 2018, entitled "Use of Cloud Detection to Control Tintable Windows" METHODS AND SY STEMS FOR CONTROLLING TINTABLE WINDOWS WITH CLOUD DETECTION)" U.S. Provisional Patent Application No. 62/646,260 and filed on May 3, 2018, entitled "Control Method and System Using External 3D Modeling and Schedule-Based Computation ( CONTROL METHODS AND SYSTEMS USING EXTERNAL 3D MODELING AND SCHEDULE-BASED COMPUTING)" of U.S. Provisional Patent Application No. 62/666,572. This application is also filed on September 18, 2020 in the United States entitled "CONTROL METHODS AND SYSTEMS USING EXTERNAL 3D MODELING AND SCHEDULE-BASED COMPUTING" A continuation-in-part of Patent Application No. 16/982,535, which is the national entry phase of PCT/US19/23268 filed on March 20, 2019. Each of the aforementioned patent applications is incorporated herein by reference in its entirety.

Occupants of enclosures (eg, facilities, buildings, or offices) may benefit from certain environmental characteristics. For example, when factors such as light (eg, visual comfort), heat (eg, thermal comfort), air quality, noise (eg, noise privacy), carbon dioxide levels, VOCs, humidity, potential pathogen load, The environmental properties of ventilation and the like can improve the health, wellness and/or performance of an individual in an enclosed environment when the environment is adjusted. Environmental characteristics may be adjusted to match requested comfort, health, and/or safety standards. Enclosures may include workplaces, hospitals, transportation hubs, buildings, vehicles or facilities. Aware sensor feedback of environmental inputs such as HVAC systems may not be sufficient to achieve this goal. For example, this sensor feedback does not take into account changing environmental conditions, such as the number of people and/or activity in an enclosure. For example, conventional sensor networks may not consider use cases and/or occupant behavior that can lead to sub-optimal, unhealthy, and/or hazardous conditions, including, for example, occupant proximity, pathogen load, increased virus exposure, visual Glare, thermal discomfort and/or reduced privacy. In some cases, it may be difficult and/or expensive to provide sensor placement at a density high enough to accurately characterize the sensed environmental conditions for all sites of interest within the enclosure.

The various aspects disclosed herein alleviate at least some of the disadvantages associated with monitoring and adjusting the environmental characteristics of enclosures.

Various aspects disclosed herein may relate to environmental characteristics of the enclosure and its control (eg, monitoring and/or adjustment). Environmental characteristics of the enclosure can be monitored and adjusted to promote enhanced health, wellness, disease reduction, and/or contamination risk and/or performance of the enclosure's occupants. The control can utilize machine learning. The machine learning may include at least one artificial intelligence (AI) engine. Environmental characteristics can be monitored by one or more sensors placed in the enclosure. Models can be constructed using baseline readings from sensors, a three-dimensional (abbreviated "3D") schematic view of the enclosure, and/or physical properties (eg, material properties and/or configuration) of the enclosure's fixtures. The control system can use the AI engine to improve the model using sensor readings of the enclosure's environment, monitor and adjust the enclosure's environment. The AI engine may refine the model based at least in part on trends and/or expected physical parameters, eg, using predictive extrapolation. The environment can be regulated, for example, by direct system management to various devices (eg, lighting; heating, ventilation, and air conditioning systems, abbreviated herein as "HVAC") and/or by using a building management system (abbreviated herein). for "BMS"). AI modeling of closed bodies can include using parts on a mesh. The grid may be adjustable. The grid may have a higher spatial resolution than the spacing between the sensors. The mesh can have constant resolution or varying resolution over some of its parts. The mesh can be homogeneous or heterogeneous.

In another aspect, a method of environment adjustment, the method comprising: (a) using one or more of (i) a virtual representation of a physical enclosure, (ii) a virtual mesh of vertices, and (iii) a physical enclosure material properties to generate a virtual enclosure model of the physical enclosure; (b) use the virtual enclosure model to generate a map of one or more environmental properties of the physical enclosure; and (c) use the map to control one of the physical enclosures or multiple environmental properties.

In some embodiments, the method further includes receiving a selection of the first vertex from the virtual mesh as the first point of interest. In some embodiments, the method further includes analyzing one or more environmental properties at the first vertex and the second vertex of the virtual mesh. In some embodiments, greater precision is used for the first vertex relative to the second vertex. In some embodiments, the method further includes receiving a selection of a second point of interest that is not a vertex of the virtual mesh. In some embodiments, the method further includes performing (a) altering the virtual mesh in response to receiving the selection of the second point of interest and/or (b) migrating the second point of interest to the nearest vertex of the virtual mesh. In some embodiments, the first vertex from the virtual mesh is identified as the first point of interest. In some embodiments, one or more environmental properties are obtained at the first vertex and the second vertex of the virtual mesh. In some embodiments, greater precision is applied to the first vertex relative to the second vertex. In some embodiments, a second point of interest that is not a vertex of the virtual mesh is identified. In some embodiments, the first point of interest has a similar first location in the physical enclosure, the first location including the sensor. In some embodiments, the first point of interest is a distance from the closest sensor. In some embodiments, the second point of interest has a similar first location in the physical enclosure, the first location being at a distance from the closest sensor. In some embodiments, the method further includes inputting data into the virtual closed volume model from one or more sensors disposed at physical locations similar to virtual mesh vertices adjacent to the first point of interest for use in The sensed attribute at the first point of interest is extrapolated. In some embodiments, the virtual mesh of vertices is an inhomogeneous mesh. In some embodiments, the heterogeneity of the virtual grid is related to the region of interest. In some embodiments, the heterogeneity of the virtual mesh is related to mesh density. In some embodiments, the heterogeneity of the virtual grid is related to the grid resolution. In some embodiments, the virtual enclosure model includes consideration of one or more structural features of the physical enclosure. In some embodiments, the virtual enclosure model includes consideration of one or more fixtures of the physical enclosure. In some embodiments, the physical enclosure includes one or more sensors. In some embodiments, the method further includes receiving baseline readings from one or more sensors. In some embodiments, the method further includes constructing a virtual closed volume model using the baseline readings. In some embodiments, the method further includes constructing a virtual closed volume model using the three-dimensional schematic representation of the physical closed volume. In some embodiments, the method further includes constructing a virtual closed volume model using the building information model. In some embodiments, the method further includes constructing a virtual enclosure model using one or more physical properties of one or more fixtures of the physical enclosure. In some embodiments, the method further includes constructing a virtual enclosure model using one or more material properties of one or more fixtures of the physical enclosure. In some embodiments, the method further includes using an artificial intelligence engine to refine the virtual closed volume model. In some embodiments, the physical enclosure includes one or more sensors. In some embodiments, the artificial intelligence engine receives readings from one or more sensors. In some embodiments, the method further includes using an artificial intelligence engine to model (i) the location of the one or more sensors, (ii) the operation of the one or more sensors, (iii) the operation of the one or more sensors Spatial distribution of at least one attribute sensed by a plurality of sensors and/or (iv) evolution over time of at least one attribute sensed by one or more sensors. In some embodiments, the method further comprises using predictive extrapolation to improve the modeling. In some embodiments, the predictive extrapolation is based at least in part on trends in sensor data. In some embodiments, the predictive extrapolation is based at least in part on expected physical parameters. In some embodiments, the one or more sensors are not at locations similar to the vertices of the virtual mesh. In some embodiments, the method further includes controlling one or more environmental properties of the physical enclosure using the hierarchical control system. In some embodiments, the method further includes the control system controlling one or more environmental characteristics of the physical enclosure. In some embodiments, one or more environmental properties of the physical enclosure are controlled by adjusting (i) heating, ventilation, and air conditioning (HVAC) systems, (ii) security systems, (iii) lighting systems, and/or (iv) It can be done by tinting the hue of the window. In some embodiments, one or more environmental properties of the physical enclosure are controlled by adjusting the velocity of air flowing through the vents to and/or from the enclosure. In some embodiments, controlling one or more environmental properties of the physical enclosure is performed by controlling a building management system. In some embodiments, the hierarchical control system includes a master controller configured to control one or more floor controllers. In some embodiments, one of the one or more floor controllers is configured to control one or more home controllers. In some embodiments, one of the one or more home controllers is configured to control one or more tintable windows. In some embodiments, one of the one or more local controllers is configured to control one or more sensors. In some embodiments, one of the one or more home controllers is configured to control one or more output devices. In some embodiments, the master controller is configured to be operatively coupled to the building management system. In some embodiments, the master controller is configured to be operatively coupled to the database. In some embodiments, the master controller is configured to be operatively coupled to the network. In some embodiments, the master controller and/or the floor controller are in the cloud. In some embodiments, the main controller is disposed in a physical enclosure. In some embodiments, the floor controller is disposed in a physical enclosure. In some embodiments, the main controller is positioned at a location other than that of the physical enclosure. In some embodiments, the floor controller is positioned at a location other than the location of the physical enclosure. In some embodiments, the building management system is configured to control one or more environmental characteristics of the physical enclosure. In some embodiments, controlling one or more environmental characteristics of the physical enclosure includes providing energy consumption savings for operating the physical enclosure. In some embodiments, the enclosure is a facility. In some embodiments, the enclosure is a building. In some embodiments, the virtual grid is a three-dimensional grid spanning at least a portion of the volume of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a two-dimensional grid spanning at least a portion of the surface of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a one-dimensional grid spanning at least a portion of the line of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a time-varying four-dimensional grid spanning at least a portion of the volume of the virtual representation of the solid enclosure. In some embodiments, the method further includes varying the virtual grid over time.

In another aspect, an apparatus for environmental conditioning, the apparatus comprising one or more controllers comprising at least one circuitry and configured to: (a) use (i) A virtual representation of the solid enclosure, a virtual mesh of vertices, and (iii) one or more material properties of the physical enclosure to generate or direct the generation of a virtual enclosure model of the physical enclosure; (b) use or direct the utilization of the virtual enclosure modeling to generate a map of one or more environmental properties of the physical enclosure; and (c) using or directing utilization of the map to control one or more environmental properties of the physical enclosure.

In some embodiments, the one or more controllers are configured to receive a selection from a first vertex of the virtual mesh as the first point of interest. In some embodiments, the one or more controllers are configured for analyzing one or more environmental characteristics at the first vertex and the second vertex of the virtual mesh. In some embodiments, greater precision is used for the first vertex relative to the second vertex. In some embodiments, the one or more controllers are configured for receiving a selection of a second point of interest that is not any of the vertices of the virtual mesh. In some embodiments, one or more controllers are configured to perform or direct the execution of (a) changing the virtual grid in response to receiving a selection of the second point of interest and/or (b) changing the second point of interest The focus is shifted to the nearest vertex of the virtual mesh. In some embodiments, the first vertex from the virtual mesh is identified as the first point of interest. In some embodiments, one or more environmental properties are obtained at the first vertex and the second vertex of the virtual mesh. In some embodiments, greater precision is applied to the first vertex relative to the second vertex. In some embodiments, the second point of interest is not on a vertex of the virtual mesh. In some embodiments, the first points of interest correspond to respective locations in the physical enclosure where the sensors are placed. In some embodiments, the first point of interest corresponds to a respective location in the physical enclosure that is at a distance from the nearest sensor. In some embodiments, the second point of interest corresponds to a respective location in the physical enclosure that is at a distance from the closest sensor. In some embodiments, the one or more controllers are configured for inputting data into the virtual closed volume model from one or more sensors positioned at mesh vertices adjacent to the first point of interest. In some embodiments, the input of the data is used to extrapolate the sensed attribute at the first point of interest. In some embodiments, the virtual mesh of vertices is an inhomogeneous mesh. In some embodiments, the heterogeneity of the virtual grid is related to the region of interest. In some embodiments, the heterogeneity of the virtual mesh is related to mesh density. In some embodiments, the heterogeneity of the virtual grid is related to the grid resolution. In some embodiments, the virtual enclosure model includes consideration of one or more structural features of the physical enclosure. In some embodiments, the virtual enclosure model includes consideration of one or more fixtures of the physical enclosure. In some embodiments, the physical enclosure includes one or more sensors. In some embodiments, one or more controllers are configured to receive baseline readings from one or more sensors. In some embodiments, the apparatus further includes circuitry configured to construct a virtual closed volume model using the baseline readings. In some embodiments, the one or more controllers are configured for constructing a virtual enclosed volume model using a three-dimensional schematic representation of the physical enclosed volume. In some embodiments, one or more controllers are configured for constructing a virtual enclosed volume model using the building information model. In some embodiments, the one or more controllers are configured for constructing a virtual enclosure model using one or more physical properties of one or more fixtures of the physical enclosure. In some embodiments, the one or more controllers are configured for constructing a virtual enclosure model using one or more material properties of one or more fixtures of the physical enclosure. In some embodiments, the one or more controllers are configured for using an artificial intelligence engine to improve or direct the improvement of the solid closed volume model. In some embodiments, the physical enclosure includes one or more sensors. In some embodiments, the artificial intelligence engine is configured to receive readings from one or more sensors. In some embodiments, the artificial intelligence engine is configured to model (i) the location of one or more sensors, (ii) the operation of one or more sensors, (iii) the operation of one or more sensors Spatial distribution of at least one attribute sensed by a plurality of sensors and/or (iv) evolution over time of at least one attribute sensed by one or more sensors. In some embodiments, the operation includes a state including standard operation or failure of at least one of the one or more sensors. In some embodiments, the artificial intelligence engine is configured for improving modeling using predictive extrapolation. In some embodiments, the predictive extrapolation is based at least in part on trends. In some embodiments, the predictive extrapolation is based at least in part on expected physical parameters. In some embodiments, the one or more sensors are not at the vertices of the virtual mesh. In some embodiments, one or more controllers are configured for controlling one or more environmental properties of the physical enclosure using a hierarchical control system. In some embodiments, one or more controllers are configured for controlling one or more environmental characteristics of the physical enclosure. In some embodiments, one or more controllers are configured to adjust (a) heating, ventilation and air conditioning (HVAC) systems, (b) security systems, (c) lighting systems, and/or (d) You can shade windows to control one or more environmental properties. In some embodiments, the one or more controllers are configured to control the flow of the physical enclosure by adjusting or directing adjustment of the speed of air flowing to and/or from the physical enclosure through the vents One or more environmental properties. In some embodiments, one or more controllers are configured for controlling one or more environmental characteristics of the physical enclosure by controlling the building management system. In some embodiments, the one or more controllers include a master controller that controls one or more floor controllers. In some embodiments, one of the one or more floor controllers is configured to control one or more home controllers. In some embodiments, one of the one or more home controllers is configured to control one or more devices that include tintable windows. In some embodiments, one of the one or more local controllers is configured to control a device including one or more sensors. In some embodiments, one of the one or more local controllers is configured to control a device that includes one or more output devices. In some embodiments, the master controller is configured to be operatively coupled to the building management system. In some embodiments, the master controller is configured to be operatively coupled to the database. In some embodiments, the master controller is configured to be operatively coupled to the network. In some embodiments, the master controller is located in the cloud. In some embodiments, the floor controller is located in the cloud. In some embodiments, the main controller is disposed in a physical enclosure. In some embodiments, the floor controller is disposed in a physical enclosure. In some embodiments, the main controller is positioned at a location other than the physical enclosure. In some embodiments, the floor controller is positioned at a location other than the physical enclosure. In some embodiments, the building management system is configured to control one or more environmental characteristics of the physical enclosure. In some embodiments, the building management system is configured to control one or more environmental characteristics to provide energy consumption savings for the physical enclosure. In some embodiments, the virtual grid is a three-dimensional grid spanning at least a portion of the volume of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a two-dimensional grid spanning at least a portion of the surface of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a one-dimensional grid spanning at least a portion of the line of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a time-varying four-dimensional grid spanning at least a portion of the volume of the virtual representation of the solid enclosure. In some embodiments, the one or more controllers are configured to vary or direct the virtual grid to vary over time.

In another aspect, a non-transitory computer-readable medium comprising instructions for environmental adjustment, when the instructions are executed by one or more processors, cause the one or more processors to execute the following: The operation of: (a) use (i) a virtual representation of the physical enclosure, a mesh of vertices, and (iii) one or more material properties of the physical enclosure to generate a virtual enclosure model of the physical enclosure; (b) using the virtual enclosure model to generate a map of one or more environmental properties of the physical enclosure; and (c) using the map to control one or more environmental properties of the physical enclosure.

In some embodiments, the non-transitory computer-readable medium further includes instructions for receiving a selection of a first vertex from the virtual mesh as the first point of interest. In some embodiments, the non-transitory computer-readable medium further comprises instructions for analyzing or directing analysis of one or more environmental properties at the first vertex and the second vertex of the virtual mesh. In some embodiments, greater precision is used for the first vertex relative to the second vertex. In some embodiments, the non-transitory computer-readable medium further includes instructions for receiving a selection of a second point of interest that is not any of the vertices of the virtual mesh. In some embodiments, the non-transitory computer-readable medium further comprises means for performing or directing the execution of (a) changing the virtual grid in response to receiving the selection of the second point of interest and/or (b) placing the second point of interest Command for point migration to the nearest vertex of the virtual mesh. In some embodiments, the first vertex from the virtual mesh is identified as the first point of interest. In some embodiments, one or more environmental properties are obtained at the first vertex and the second vertex of the virtual mesh. In some embodiments, greater precision is applied to the first vertex relative to the second vertex. In some embodiments, a second point of interest that does not coincide with a vertex of the virtual mesh is identified. In some embodiments, the first point of interest includes a corresponding location in the physical enclosure where the sensor is placed. In some embodiments, the first point of interest is a certain distance from the corresponding portion of the physical enclosure where the closest sensor is located. In some embodiments, the second point of interest is at the corresponding location in the physical enclosure where the closest sensor is located. In some embodiments, the non-transitory computer-readable medium further includes inputting data or directing data from one or more sensors disposed in the physical enclosure at locations corresponding to mesh vertices adjacent to the first point of interest Data is entered into a virtual closed body model. In some embodiments, the non-transitory computer-readable medium further includes utilization or instruction utilization data for extrapolating the sensed attribute at the first point of interest. In some embodiments, the virtual mesh of vertices is an inhomogeneous mesh. In some embodiments, the heterogeneity of the virtual grid is related to regions and/or points of interest. In some embodiments, the heterogeneity of the virtual grid is related to the density of the virtual grid. In some embodiments, the heterogeneity of the virtual grid is related to the resolution of the virtual grid. In some embodiments, the construction and/or use of the virtual enclosure model includes consideration of one or more structural features of the physical enclosure. In some embodiments, the construction and/or use of the virtual enclosure model includes consideration of one or more fixtures of the physical enclosure. In some embodiments, the physical enclosure includes one or more sensors. In some embodiments, the operations include receiving or directing receipt of baseline readings from one or more sensors. In some embodiments, the operations include using the baseline readings to construct or guide the construction of a solid closed body model. In some embodiments, the operations include using a three-dimensional representation of the physical enclosure to construct or guide the construction of a virtual enclosure model. In some embodiments, the operations include using the building information model to construct or guide the construction of a virtual enclosed volume model. In some embodiments, the operations include using one or more physical properties of one or more fixtures of the physical enclosure to construct or direct construction of a virtual enclosure model. In some embodiments, the operations include constructing or directing construction of a virtual enclosure model using one or more material properties of one or more fixtures of the physical enclosure. In some embodiments, the operations include using an artificial intelligence engine to refine or direct refinement of the virtual closed volume model. In some embodiments, the physical enclosure includes one or more sensors. In some embodiments, the artificial intelligence engine is configured to receive readings from one or more sensors. In some embodiments, the non-transitory computer-readable medium further comprises instructions for the artificial intelligence engine to model (i) the location of one or more sensors, (ii) one or more sensing operation of the sensor, (iii) spatial distribution of at least one attribute sensed by one or more sensors and/or (iv) evolution of at least one attribute sensed by one or more sensors over time . In some embodiments, the non-transitory computer-readable medium further comprises instructions for the artificial intelligence engine to improve the artificial intelligence engine model by using predictive extrapolation. In some embodiments, the predictive extrapolation is based at least in part on trends. In some embodiments, the predictive extrapolation is based at least in part on expected physical parameters. In some embodiments, the one or more sensors are positioned at one or more locations in the physical enclosure that do not correspond to vertices of the virtual mesh. In some embodiments, operating includes directing the hierarchical control system to control one or more environmental characteristics of the physical enclosure. In some embodiments, the operation includes directing the hierarchical control system to adjust the hue of (I) heating, ventilation, and air conditioning systems (HVAC), (II) security systems, (III) lighting systems, and/or (IV) tintable windows . In some embodiments, the operations include directing the building management system to control one or more environmental characteristics of the physical enclosure. In some embodiments, the operation includes directing the hierarchical control system to adjust or directing the adjustment of the speed of air flow to and/or from the physical enclosure (eg, via a vent). In some embodiments, the hierarchical control system includes a master controller that controls one or more floor controllers. In some embodiments, one of the one or more floor controllers is configured to control one or more home controllers. In some embodiments, one of the one or more home controllers is configured to control one or more tintable windows. In some embodiments, one of the one or more local controllers is configured to control a device including one or more sensors. In some embodiments, one of the one or more home controllers is configured to control a device that includes one or more output devices. In some embodiments, the master controller is configured to be operatively coupled to the building management system. In some embodiments, the master controller is configured to be operatively coupled to the database. In some embodiments, the master controller is configured to be operatively coupled to the network. In some embodiments, the master controller is located in the cloud. In some embodiments, the floor controller is located in the cloud. In some embodiments, the main controller is disposed in a physical enclosure. In some embodiments, the floor controller is disposed in a physical enclosure. In some embodiments, the main controller is positioned at a location other than the physical enclosure. In some embodiments, the floor controller is positioned at a location other than the physical enclosure. In some embodiments, the operations include directing the building management system to control one or more environmental characteristics of the physical enclosure. In some embodiments, controlling one or more environmental characteristics of the physical enclosure includes providing energy consumption savings in the operation of the physical enclosure (eg, devices associated with the environment and/or devices controlling the environment). In some embodiments, the virtual grid is a three-dimensional grid spanning at least a portion of the volume of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a two-dimensional grid spanning at least a portion of the surface of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a one-dimensional grid spanning at least a portion of the line of the virtual representation of the solid enclosure. In some embodiments, the virtual grid is a time-varying four-dimensional grid spanning at least a portion of the volume of the virtual representation of the solid enclosure. In some embodiments, the operations include causing or directing the virtual grid to vary over time.

In some embodiments, the network is a local area network. In some embodiments, the network includes cables configured to transmit power and communications in a single cable. The communication can be one or more types of communication. The communication may include cellular communication complying with at least second generation (2G), third generation (3G), fourth generation (4G) or fifth generation (5G) cellular communication protocols. In some embodiments, the communication includes media communication that facilitates still images, music or animation streaming (eg, movies or video). In some embodiments, the communication includes data communication (eg, sensor data). In some embodiments, the communication includes control communication, eg, to control one or more nodes operatively coupled to a network. In some embodiments, the network includes a first (eg, cabling) network installed in the facility. In some embodiments, the network includes a (eg, cabling) network installed in the envelope of the facility (eg, in the envelope of a building included in the facility).

In another aspect, the present disclosure provides a network configured for facilitating the transfer of any communication (eg, signals) and/or (eg, electrical) power of any of the operations disclosed herein. Communications may include control communications, cellular communications, media communications, and/or data communications. Data communications may include sensor data communications and/or processed data communications. The network can be configured to adhere to one or more protocols that facilitate this communication. For example, the communication protocol used by the network (eg, by the BMS) may be the Building Automation and Control Network Protocol (BACnet). For example, the communication protocol may facilitate cellular communications that adhere to at least 2nd, 3rd, 4th, or 5th generation cellular communication protocols.

In another aspect, the present disclosure provides a system, apparatus (eg, controller) and/or one or more non-transitory computer-readable media (eg, software) implementing any of the methods disclosed herein.

In another aspect, the present disclosure provides methods of using any of the systems, computer-readable media, and/or devices disclosed herein, eg, for their intended purposes.

In another aspect, an apparatus includes at least one controller programmed to direct a mechanism for implementing (eg, performing) any of the methods disclosed herein, the at least one controller being programmed configured to be operatively coupled to the mechanism. In some embodiments, at least two operations (eg, of the method) are directed/performed by the same controller. In some embodiments, at least two operations are directed/performed by different controllers.

In another aspect, an apparatus includes at least one controller configured (eg, programmed) to implement (eg, perform) any of the methods disclosed herein. At least one controller can implement any of the methods disclosed herein. In some embodiments, at least two operations (eg, of the method) are directed/performed by the same controller. In some embodiments, at least two operations are directed/performed by different controllers.

In some embodiments, one of the at least one controllers is configured to perform two or more operations. In some embodiments, two different ones of the at least one controller are configured to each perform different operations.

In another aspect, a system includes at least one controller programmed to direct operation of at least one other device (or component thereof) and the device (or component thereof), wherein the at least one The controller is operatively coupled to the device (or components thereof). The apparatus (or components thereof) may include any apparatus (or components thereof) disclosed herein. At least one controller can be configured to direct any of the devices (or components thereof) disclosed herein. At least one controller can be configured to be operatively coupled to any of the devices (or components thereof) disclosed herein. In some embodiments, at least two operations (eg, of a device) are directed by the same controller. In some embodiments, at least two operations are directed by different controllers.

In another aspect, a computer software product storing program instructions (eg, recorded on one or more non-transitory media) that, when read by at least one processor (eg, a computer), cause the At least one processor directs a mechanism disclosed herein to implement (eg, perform) any of the methods disclosed herein, wherein the at least one processor is configured to be operatively coupled to the mechanism. The mechanism may include any device disclosed herein (any component thereof). In some embodiments, at least two operations (eg, of a device) are directed/performed by the same processor. In some embodiments, at least two operations are directed/performed by different processors.

In another aspect, the present disclosure provides non-transitory computer-readable program instructions comprising machine-executable code (included in a program product comprising one or more non-transitory media), the machine-executable program The code, when executed by one or more processors, implements any of the methods disclosed herein. In some embodiments, at least two operations (eg, of a method) are directed/performed by the same processor. In some embodiments, at least two operations are directed/performed by different processors.

In another aspect, the present disclosure provides one or more non-transitory computer-readable media comprising machine-executable code that, when executed by one or more processors, executes control of a controller (eg, as disclosed herein). In some embodiments, at least two operations (eg, of a controller) are directed/performed by the same processor. In some embodiments, at least two operations are directed/performed by different processors.

In another aspect, the present disclosure provides a computer system comprising one or more computer processors and one or more non-transitory computer-readable media coupled thereto, the non-transitory computer-readable media comprising a machine Executable code that, when executed by one or more processors, implements any of the methods disclosed herein and/or executes instructions for one or more controllers disclosed herein.

In another aspect, the present disclosure provides non-transitory computer-readable program instructions that, when read by one or more processors, cause the one or more processors to performing any operation of the methods disclosed herein, performed (or configured to perform) by the apparatus disclosed herein, and/or directed (or configured to direct) by the apparatus disclosed herein any operation.

In some embodiments, the program instructions are recorded on one or more non-transitory computer-readable media. In some embodiments, at least two of the operations are performed by one of the one or more processors. In some embodiments, at least two of the operations are each performed by a different one of the one or more processors.

The contents of this Summary section are provided as a simplified introduction to the present disclosure and are not intended to limit the scope of any invention disclosed herein or the scope of the appended claims.

Additional aspects and advantages of the present disclosure will become apparent to those skilled in the art from the following description, in which only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and descriptions are to be regarded as illustrative and not restrictive in nature.

These and other features and embodiments will be described in more detail with reference to the drawings. incorporated by reference All publications, patents and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be by reference way incorporated into the general.

While various embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Numerous changes, changes, and substitutions can occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be used.

Terms such as "a/an" and "the" are not intended to refer to only the singular entity, but rather include the general class of particular instances that can be used for description. The terminology herein is used to describe specific embodiments of the invention, but their use does not delimit the invention.

Unless otherwise specified, when referring to a range, the range is intended to be inclusive. For example, a range between value 1 and value 2 is intended to be inclusive and includes both value 1 and value 2. An inclusive range will span any value from about 1 to about 2. The terms "adjacent" or "adjacent to" as used herein include "proximate," "adjacent," "contacting," and "proximity."

As used herein, including within the scope of the claims, the conjunction "and/or" in a phrase such as "including X, Y, and/or Z" is meant to include any combination or combination of X, Y, and Z. plural. For example, this phrase is intended to include X. For example, this phrase is intended to include Y. For example, this phrase is intended to include Z. For example, this phrase is intended to include X and Y. For example, this phrase is intended to include X and Z. For example, this phrase is intended to include Y and Z. For example, this phrase is intended to include multiple X's. For example, this phrase is intended to include plural Ys. For example, this phrase is intended to include multiple Z's. For example, this phrase is intended to include plural X and plural Y. For example, this phrase is intended to include plural X and plural Z. For example, this phrase is intended to include Y's and Z's. For example, this phrase is intended to include a plurality of X and Y. For example, this phrase is intended to include a plurality of X and Z. For example, this phrase is intended to include a plurality of Y and Z. For example, this phrase is intended to include X and Y's. For example, this phrase is intended to include X and Z. For example, this phrase is intended to include Y and a plurality of Z. The conjunction "and/or" is intended to have the same effect as the phrase "X, Y, Z, or any combination or plural thereof." The conjunction "and/or" is intended to have the same effect as the phrase "one or more of X, Y, Z, or any combination thereof."

The terms "operatively coupled" or "operatively connected" refer to the coupling (eg, connection) of a first element (eg, mechanism) to a second element to allow intended operation of the second and/or first element . Coupling may include physical or non-physical coupling (eg, communicative coupling). Non-physical coupling may include signal-induced coupling (eg, wireless coupling). Coupling may include physical coupling (eg, a physical connection) or non-physical coupling (eg, via wireless communication). Operationally coupled may include communicative coupling.

An element (eg, mechanism) that is "configured to" perform a function includes structural features that cause the element to perform that function. Structural features may include electrical features, such as circuitry or circuit elements. Structural features may include actuators. Structural features may include circuitry (eg, including electrical or optical circuitry). A circuit system may contain one or more wires. Optical circuitry may include at least one optical element (eg, a beam splitter, mirror, lens, and/or optical fiber). Structural features may include mechanical features. Mechanical features may include latches, springs, closures, hinges, chassis, supports, fasteners or cantilevers, and the like. Performing functions may include utilizing logical features. Logical features may include programmed instructions. The programmed instructions are executable by at least one processor. Programming instructions can be stored or encoded on a medium that can be accessed by one or more processors. Additionally, in the following description, the phrases "operable with," "adapted with," "configured with," "designed with," "programmed with," or "capable of" may be used interchangeably as appropriate use.

In some embodiments, the enclosure includes an area bounded by at least one structure. At least one structure may comprise at least one wall. An enclosure may contain and/or enclose one or more sub-closures. At least one wall may comprise metal (eg, steel), clay, stone, plastic, glass, plaster (eg, gypsum), polymer (eg, polyurethane, styrene, or vinyl), asbestos, fibers Glass, concrete (eg reinforced concrete), wood, paper or ceramic. At least one wall may contain wires, bricks, blocks (eg, cinder blocks), tiles, drywall, or framing (eg, steel frames).

In some embodiments, the closure includes one or more openings. One or more of the openings may be reversibly closed. One or more openings may be permanently open. The basic length dimension of the one or more openings may be relatively small relative to the basic length dimension of the walls defining the closure. The basic length dimension may include the diameter, length, width or height of the bounding circle. The surface of the one or more openings may be relatively small relative to the surface of the walls defining the enclosure. The open surface may be a percentage of the total surface of the wall. For example, the open surface may measure up to about 30%, 20%, 10%, 5%, or 1% of the wall. Walls can include floors, ceilings, or side walls. The closable opening may be closed by at least one window or door. The enclosure may be at least a portion of the facility. Facilities may contain buildings. The enclosure may contain at least a portion of the building. The buildings can be private buildings and/or commercial buildings. A building can contain one or more floors. A building (eg, its floors) may include at least one of: rooms, halls, foyers, attics, basements, balconies (eg, interior or exterior balconies), stairwells, hallways, elevator shafts, facades , mezzanine, attic, garage, porch (eg, enclosed porch), patio (eg, enclosed patio), cafeteria, and/or plumbing. In some embodiments, the enclosure may be stationary and/or movable (eg, a train, plane, ship, vehicle, or rocket).

In some embodiments, the enclosure encloses the atmosphere. The atmosphere may contain one or more gases. Gases may include inert gases (eg, including argon or nitrogen) and/or non-inert gases (eg, including oxygen or carbon dioxide). The enclosure atmosphere can be similar to the atmosphere outside the enclosure (eg, the ambient atmosphere) in at least one external atmospheric characteristic including temperature, relative gas content, gas type (eg, humidity and/or oxygen content) ), propagating agents (eg, pollutants, volatile organic compounds, dust and/or pollen), and/or gas velocity. The enclosure atmosphere may differ from the atmosphere outside the enclosure in at least one external atmospheric characteristic including temperature, relative gas content, gas type (eg, humidity and/or oxygen content), propagating agent (eg, dust and/or pollen) and/or gas velocity. For example, the enclosure atmosphere may be less humid (eg, drier) than the outer (eg, ambient) atmosphere. For example, the enclosure atmosphere and the atmosphere outside the enclosure may contain the same (eg, or substantially similar) ratio of oxygen to nitrogen. The velocity of the gas in the enclosure may be (eg, substantially) similar throughout the enclosure. The velocity of the gas in the enclosure can be different in different parts of the enclosure (eg, by flowing the gas through a vent coupled to the enclosure).

Certain disclosed embodiments provide network infrastructure in an enclosure (eg, a facility such as a building). The network infrastructure may be used for various purposes, such as for providing communication and/or power services. Communication services may include high bandwidth (eg, wireless and/or wireline) communication services. Communication services may be directed to occupants of the facility and/or users external to the facility (eg, a building). The network infrastructure may work in conjunction with or replace part of the infrastructure of one or more cellular operators. The network infrastructure may be provided in a facility that includes electrically switchable windows. Examples of components of a network infrastructure include high-speed backhaul. The network infrastructure may include at least one cable, switch, physical antenna, transceiver, sensor, transmitter, receiver, radio, processor and/or controller (which may include a processor). The network infrastructure is operably coupled to and/or includes a wireless network. Network infrastructure can include cabling. One or more sensors may be deployed (eg, installed) in the environment as part of and/or after installing the network. The network may be a local area network. A network may include cables configured to transmit power and communications in a single cable. The communication can be one or more types of communication. The communication may include cellular communication complying with at least second generation (2G), third generation (3G), fourth generation (4G) or fifth generation (5G) cellular communication protocols. Communications may include media communications that facilitate streaming of still images, music, or animation (eg, movies or video). Communication may include data communication (eg, sensor data). Communication may include control communication, eg, to control one or more nodes operatively coupled to a network. The network may include the first (eg, cabling) network installed in the facility. The network may include (eg, cabling) installed in the envelope of the facility (eg, in the envelope of the enclosure of the facility. For example, included in the envelope of the building in the facility) network.

In various embodiments, the network infrastructure supports a control system for one or more windows, such as tintable (eg, electrochromic) windows. The control system may include one or more controllers operatively coupled (eg, directly or indirectly) to the one or more windows. Although the disclosed embodiments describe tintable windows (also referred to herein as "optically switchable windows" or "smart windows"), such as electrochromic windows, the concepts disclosed herein may be applied to other types of Switchable optical devices, including liquid crystal devices, electrochromic devices, suspended particle devices (SPD), NanoChromics displays (NCD), organic electroluminescent displays (OELD), suspended particle devices (SPD), NanoChromics displays (NCD) or have Electroluminescent Displays (OELDs). The display element may be attached to a portion of the transparent body, such as a window. Tintable windows can be placed in (non-transitory) installations such as buildings, and/or in temporary installations such as automobiles, RVs, buses, trains, airplanes, helicopters, ships, or boats (eg, vehicles) middle. Tintable windows may be placed in (non-transitory) installations such as buildings, and/or in temporary vehicles such as automobiles, RVs, buses, trains, airplanes, helicopters, ships, or boats.

In some embodiments, a tintable window exhibits a (eg, controllable and/or reversible) change in at least one optical property of the window, eg, upon application of a stimulus. The changes may be continuous changes. The changes may be for discrete hue levels (eg, at least about 2, 4, 8, 16, or 32 hue levels). Optical properties can include hue or transmittance. Hue can contain color. Transmittance can have one or more wavelengths. The wavelengths may include ultraviolet, visible or infrared wavelengths. Stimulation may include optical, electrical and/or magnetic stimulation. For example, stimulation can include applying voltage and/or current. One or more tintable windows may be used to control lighting and/or glare conditions, eg, by adjusting the transmission of solar energy propagating therethrough. One or more tintable windows can be used to control the temperature within a building, for example, by regulating the transmission of solar energy propagating through the windows. Control of solar energy can control the thermal load imposed on the interior of a facility (eg, a building). Control can be manual and/or automatic. Controls may be used to maintain one or more requested (eg, environmental) conditions, such as occupant comfort. Controls may include reducing energy consumption of heating, ventilation, air conditioning and/or lighting systems. At least two of heating, ventilation and air conditioning can be induced by individual systems. At least two of heating, ventilation and air conditioning can be induced by a system. Heating, ventilation and air conditioning can be induced by a single system (abbreviated herein as "HVAC"). In some cases, the tintable window may be responsive to (eg, and communicatively coupled to) one or more environmental sensors and/or user controls. Tintable windows can include (eg, can be) electrochromic windows. Windows may be located in a range from the interior to the exterior of a structure (eg, a facility such as a building). However, this need not be the case. Tintable windows can be operated using liquid crystal devices, suspended particle devices, microelectromechanical systems (MEMS) devices (such as micro-shutters), or any technology now known or later developed that is configured to control the transmission of light through a window. Windows (eg, with MEMS devices for coloring) are described in "Multi-Window Format Windows Including Electrochromic Devices and Electromechanical Systems Devices (MULTI -PANE WINDOWS INCLUDING ELECTROCHROMIC DEVICES AND ELECTROMECHANICAL SYSTEMS DEVICES)" in US Patent No. 10,359,681, which is incorporated herein by reference in its entirety. In some cases, one or more tintable windows may be located inside the building, such as between a conference room and a hallway. In some cases, one or more tinted windows may be used in automobiles, trains, airplanes, and other vehicles, eg, in place of passive and/or non-tinted windows.

In some embodiments, the tintable window comprises an electrochromic device (referred to herein as an "EC device" (abbreviated herein as ECD) or "EC"). The EC device may comprise at least one coating comprising at least one layer. At least one layer may contain an electrochromic material. In some embodiments, the electrochromic material exhibits a change from one optical state to another, such as when a potential is applied across the EC device. The transition of an electrochromic layer from one optical state to another can be caused, for example, by reversible, semi-reversible or irreversible ion insertion into the electrochromic material (eg, by means of intercalation) and corresponding injection of charge balancing electrons. For example, the transition of an electrochromic layer from one optical state to another can be caused, for example, by reversible ion insertion into the electrochromic material (eg, by means of intercalation) and corresponding injection of charge balancing electrons. Reversible can target the life expectancy of the ECD. A semi-reversible refers to a measurable (eg, significant) degradation in the reversibility of the hue of the window over one or more tinting cycles. In some cases, a portion of the ions responsible for the optical transition is irreversibly incorporated into the electrochromic material (eg, and thus the induced (altered) hue state of the window is irreversible to its original colored state). In various EC devices, at least some (eg, all) of the irreversibly bound ions can be used to compensate for "blind charges" in the material (eg, ECD).

In some embodiments, suitable ions include cations. Cations can include lithium ions (Li+) and/or hydrogen ions (H+) (ie, protons). In some embodiments, other ions may be suitable. Cations can be intercalated into (eg, metal) oxides. Changes in the intercalation state of ions (eg, cations) into the oxide can induce visible changes in the hue (eg, color) of the oxide. For example, oxides can transition from a colorless state to a colored state. For example, intercalation of lithium ions into tungsten oxide (WO3-y (0 < y <about 0.3)) can cause tungsten oxide to change from a transparent state to a colored (eg, blue) state. The EC device coating as described herein is located within the viewable portion of the tintable window so that the tint of the EC device coating can be used to control the optical state of the tintable window.

1 shows a schematic example of a computer system 100 programmed or otherwise configured to perform one or more operations of any of the methods provided herein. The computer system may control (eg, direct, monitor, and/or adjust) various features of the methods, apparatus, and systems of the present disclosure, such as controlling heating, cooling, lighting, and/or ventilation of the enclosure, or any combination thereof. The computer system may be part of or in communication with any set of sensors or devices disclosed herein. A computer may be coupled to one or more of the mechanisms disclosed herein and/or any portion thereof. For example, a computer may be coupled to one or more sensors, valves, switches, lights, windows (eg, IGUs), motors, pumps, optical components, or any combination thereof.

A computer system may include a processing unit (eg, 106) ("processor", "computer" and "computer processor" are also used herein). A computer system may include memory or memory locations (eg, 102) (eg, random access memory, read-only memory, flash memory), electronic storage units (eg, 104) (eg, hard disks), Communication interfaces (eg, 103) (eg, network adapters) for communicating with one or more other systems, and peripheral devices (eg, 105), such as cache, other memory, data storage and/or electronic display adapter. In the example shown in FIG. 1, memory 102, storage unit 104, interface 103, and peripheral devices 105 communicate with processing unit 106 via a communication bus (solid line) such as a motherboard. The storage unit may be a data storage unit (or data repository) for storing data. The computer system may be operatively coupled to a computer network ("network") (eg, 101) by means of a communication interface. The network can be the Internet, the internet and/or an inter-enterprise network, or an intra-corporate and/or inter-enterprise network that communicates with the Internet. In some cases, the network is a telecommunications and/or data network. A network may include one or more computer servers that may enable distributed computing, such as cloud computing. In some cases, a network may implement a peer-to-peer network with the aid of a computer system, which may enable devices coupled to the computer system to act as clients or servers.

The processing unit can execute a sequence of machine-readable instructions that can be embodied in a program or software. Instructions may be stored in a memory location such as memory 102 . The instructions may be directed to a processing unit, which may then be programmed or otherwise configured to implement the methods of the present disclosure. Examples of operations performed by a processing unit may include fetching, decoding, executing, and writing back. The processing unit may interpret and/or execute the instructions. Processors may include microprocessors, data processors, central processing units (CPUs), graphics processing units (GPUs), system-on-chips (SOCs), co-processors, network processors, application-specific integrated circuits (ASICs) , Application Specific Instruction Set Processor (ASIP), controller, Programmable Logic Device (PLD), Chipset, Field Programmable Gate Array (FPGA) or any combination thereof. The processing unit may be part of a circuit such as an integrated circuit. One or more other components of system 100 may be included in the circuit.

The storage unit can store files, such as drivers, libraries, and saved programs. The storage unit may store user data (eg, user preferences and user programs). In some cases, the computer system may include one or more additional data storage units external to the computer system, such as on remote servers that communicate with the computer system via an intranet or the Internet superior.

The computer system may communicate with one or more remote computer systems via a network. For example, a computer system may communicate with a remote computer system of a user (eg, an operator). Examples of remote computer systems include personal computers (eg, portable PCs), tablet or tablet PCs (eg, Apple® iPad, Samsung® Galaxy Tab), telephones, smart phones (eg, Apple® iPhone, Android-enabled device, Blackberry®) or personal digital assistant. A user (eg, a client) can access the computer system via a network.

The methods as described herein may be implemented by means of machine (eg, computer processor) executable code stored on an electronic storage location of a computer system, such as in memory 102 or an electronic storage unit 104 on. Machine-executable or machine-readable code may be provided in the form of software. During use, processor 106 can execute code. In some cases, the code may be retrieved from the storage unit and stored on memory ready for access by the processor. In some cases, the electronic storage unit may be eliminated, and the machine-executable instructions stored on memory.

The code may be precompiled and configured for use by a machine with a processor adapted to execute the code, or may be compiled during the execution phase. The code may be supplied in a programming language that may be selected to enable the code to be executed in a precompiled or as-compiled manner.

In some embodiments, the processor includes program code. The code may be program instructions. Program instructions may cause at least one processor (eg, a computer) to direct the feedforward and/or feedback control loop. In some embodiments, the program instructions cause at least one processor to direct a closed loop and/or open loop control scheme. Control may be based, at least in part, on one or more sensor readings (eg, sensor data). One controller can direct multiple operations. At least two operations may be directed by different controllers. In some embodiments, a different controller may direct at least two of operations (a), (b), and (c). In some embodiments, a plurality of different controllers may direct at least two of operations (a), (b), and (c). In some embodiments, the non-transitory computer-readable medium causes each distinct computer to direct at least two of operations (a), (b), and (c). In some embodiments, different non-transitory computer-readable media cause each different computer to direct at least two of operations (a), (b), and (c). A controller and/or computer-readable medium may direct any of the apparatuses disclosed herein or components thereof. A controller and/or computer-readable medium may direct any operation of the methods disclosed herein.

In some embodiments, at least one sensor is operatively coupled to a control system (eg, a computerized control system). Sensors may include light sensors, acoustic sensors, vibration sensors, chemical sensors, electrical sensors, magnetic sensors, mobility sensors, motion sensors, speed sensors, position sensors sensor, pressure sensor, force sensor, density sensor, distance sensor or proximity sensor. Sensors may include temperature sensors, weight sensors, material (eg, powder) content sensors, metrology sensors, gas sensors, or humidity sensors. Metrology sensors may include measurement sensors (eg, height, length, width, angle, and/or volume). Metrology sensors may include magnetic, acceleration, orientation, or optical sensors. Sensors can transmit and/or receive acoustic (eg, echo), magnetic, electronic, or electromagnetic signals. The signal may include a radio signal, which includes an ultra-wideband radio signal. The signal may comprise visible light, infrared light or ultraviolet light. Infrared sensors can detect living objects (eg, people). The signal may include an audio signal (eg, a human audio signal). Electromagnetic signals may include visible light, infrared, ultraviolet, ultrasonic, radio waves, or microwave signals. The gas sensor can sense any of the gases described herein. The distance sensor may be one type of metrology sensor. Distance sensors may include optical sensors or capacitive sensors. Temperature sensors can include bolometers, bimetals, calorimeters, exhaust thermometers, flame detection, Gardon meters, Golay cells, heat flux sensors, infrared thermometers, micro- bolometers, microwave radiometers, net radiometers, quartz thermometers, resistance temperature detectors, resistance thermometers, silicon bandgap temperature sensors, special sensors microwave/imagers, thermometers, thermistors, thermocouples, thermometers (eg resistance thermometer) or pyrometer. The temperature sensor may include an optical sensor. The temperature sensor may include image processing. The temperature sensor may include a camera (eg, IR camera, visible light camera, CCD camera). The camera may be a high-resolution camera (eg, a resolution may be at least 2 kilopixel (K), 3K, 4K, or 5K camera). The sensors may include accelerometers. The sensor can sense the location and/or presence of a person. The sensor can sense and/or locate the enclosure occupant. Pressure sensors may include barometers, barometers, booster gauges, Bourdon gauges, hot filament ionization gauges, ionization gauges, McLeod gauges, U-shaped oscillating tubes , permanent downhole manometer, manometer, Pirani gauge, pressure sensor, manometer, tactile sensor or time pressure gauge. Position sensors may include growth meters, capacitive displacement sensors, capacitive sensing, free fall sensors, gravimeters, gyroscope sensors, crash sensors, inclinometers, integrated circuit piezoelectric sensing sensors, laser rangefinders, laser surface velocimeters, lidars, linear encoders, linear variable differential transformers (LVDTs), liquid capacitance inclinometers, odometers, photoelectric sensors, piezoelectric accelerometers, Rate Sensors, Rotary Encoders, Rotary Variable Differential Transformers, Synchronizers, Shock Detectors, Shock Data Loggers, Tilt Sensors, Tachometers, Ultrasonic Thickness Gauges, Variable Reluctance Sensors or speed receiver. Optical sensors can include charge-coupled devices, colorimeters, contact image sensors, electro-optical sensors, infrared sensors, dynamic inductance detectors, light emitting diodes (eg, light sensors), Optically addressable potentiometric sensors, Nichols radiometers, fiber optic sensors, optical position sensors, photodetectors, photodiodes, photomultipliers, phototransistors, photoinductors detectors, photoionization detectors, photomultipliers, photoresistors, photoswitches, photocells, scintillation counters, Shack-Hartmann wavefront sensors (Shack-Hartmann), single-photon burst diodes, super Conductive nanowire single-photon detectors, transition edge sensors, visible light photon counters or wavefront sensors. One or more sensors can be connected to the control system (eg, to a processor, to a computer).

In some embodiments, one or more of the devices includes a sensor (eg, as part of a transceiver). In some embodiments, the transceiver may be configured to transmit and receive one or more signals using a personal area network (PAN) standard such as IEEE 802.15.4. In some embodiments, the signals may include Bluetooth, Wi-Fi, or EnOcean signals (eg, broadband). The one or more signals may include ultra-wideband (UWB) signals (eg, having frequencies in the range of about 2.4 to about 10.6 gigahertz (GHz) or about 7.5 GHz to about 10.6 GHz). An ultra-wideband signal can be a signal having a partial bandwidth greater than about 20%. Ultra-wideband (UWB) radio frequency signals may have a bandwidth of at least about 500 megahertz (MHz). One or more signals may use very low energy levels for short range. Signals (eg, having radio frequencies) may use a spectrum capable of penetrating solid structures (eg, walls, doors, and/or windows). The low power may be up to about 25 milliwatts (mW), 50 mW, 75 mW, or 100 mW. The low power can be any value between the foregoing values (eg, from 25 mW to 100 mW, from 25 mW to 50 mW, or from 75 mW to 100 mW). Sensors and/or transceivers can be configured to support wireless technology standards for exchanging data between stationary and mobile devices, such as over short distances. The signal may comprise ultra high frequency (UHF) radio waves, such as about 2.402 gigahertz (GHz) to about 2.480 GHz. This signal can be configured for use in building a personal area network (PAN).

In some embodiments, the device is configured to enable geolocation technology (eg, Global Positioning System (GPS), Bluetooth (BLE), Ultra Wide Band (UWB), and/or dead reckoning). Geolocation techniques can facilitate determining the location of a signal source (eg, the location of a tag) with an accuracy of at least 100 centimeters (cm), 75 cm, 50 cm, 25 cm, 20 cm, 10 cm, or 5 cm. In some embodiments, the electromagnetic radiation of the signal includes ultra-wideband (UWB) radio waves, ultra-high frequency (UHF) radio waves, or radio waves used in global positioning systems (GPS). In some embodiments, the electromagnetic radiation includes electromagnetic waves having a frequency of at least about 300 MHz, 500 MHz, or 1200 MHz. In some embodiments, the signal includes location and/or time data. In some embodiments, the geolocation technology includes Bluetooth, UWB, UHF and/or Global Positioning System (GPS) technology. In some embodiments, the signal has a spatial capacity of at least about 1013 bits per second per square meter (bit/s/m²).

In some embodiments, pulse-based ultra-wideband (UWB) technology (eg, ECMA-368 or ECMA-369) is used for ' or 150') at low power (eg, less than about 1 millivolt (mW), 0.75 mW, 0.5 mW, or 0.25 mW) to transmit large amounts of data wireless technology. UWB signals may occupy at least about 750 MHz, 500 MHz, or 250 MHz of the bandwidth spectrum, and/or at least about 30%, 20%, or 10% of its center frequency. UWB signals can be transmitted by one or more pulses. Components that broadcast digital signal pulses can simultaneously time (eg, precisely) a carrier signal across several channels. Information may be conveyed, for example, by modulating the timing and/or positioning of signals (eg, pulses). Signal information may be transmitted by encoding the polarity of the signal (eg, pulses), its amplitude, and/or by using quadrature signals (eg, pulses). The UWB signal may be a low power information transfer protocol. UWB technology can be used for (eg, indoor) positioning applications. The broad range of the UWB spectrum includes low frequencies with long wavelengths that allow UWB signals to penetrate a variety of materials, including various building fixtures (eg, walls). A wide frequency range (eg, including low penetration frequencies) may reduce the chance of multipath propagation errors (without wishing to be bound by theory, since some wavelengths may have line-of-sight trajectories). UWB communication signals (eg, pulses) may be short (eg, up to about 70 cm, 60 cm, or 50 cm for pulses of about 600 MHz, 500 MHz, or 400 MHz wide; or up to about 1 GHz, 1.2 GHz, 1.3 GHz or 1.5 GHz bandwidth, up to approximately 20 cm, 23 cm, 25 cm or 30 cm). A short communication signal (eg, pulse) may reduce the chance that the reflected signal (eg, pulse) will overlap the original signal (eg, pulse).

In some embodiments, the plurality of devices are operably (eg, communicatively) coupled to the control system. A plurality of devices may be placed in a facility (eg, including buildings and/or rooms). A control system may include a hierarchy of controllers. A device may include a transmitter, a sensor, or a window (eg, an IGU). A device can damage radio transmitters and/or receivers (eg, broadband or ultra-wideband radio transmitters and/or receivers). The device may include a positioning device. The device may include a global positioning system (GPS) device. The device may include a Bluetooth device. The device can be any device as disclosed herein. At least two of the plurality of devices may be of the same type. For example, two or more IGUs may be coupled to the control system. At least two of the plurality of devices may be of different types. For example, sensors and transmitters can be coupled to a control system. The plurality of devices may sometimes comprise at least 20, 50, 100, 500, 1000, 2500, 5000, 7500, 10000, 50000, 100000 or 500000 devices. The plurality of devices can have any number of devices between the foregoing numbers (eg, 20 devices to 500,000 devices, 20 devices to 50 devices, 50 devices to 500 devices, 500 devices to 2500 devices, 1000 devices to 5,000 devices, 5,000 devices to 10,000 devices, 10,000 devices to 100,000 devices, or 100,000 devices to 500,000 devices). For example, the number of windows in a floor may be at least 5, 10, 15, 20, 25, 30, 40 or 50. The number of windows in a floor can be any number between the foregoing numbers (eg, 5 to 50, 5 to 25, or 25 to 50). In some cases, devices may be in multi-storey buildings. At least a portion of the floors of the multi-story building may have devices controlled by the control system (eg, at least a portion of the floors of the multi-story building may be controlled by the control system). For example, a multi-story building may have at least 2, 8, 10, 25, 50, 80, 100, 120, 140 or 160 floors controlled by the control system. The number of floors (eg, devices therein) controlled by the control system can be any number between the foregoing numbers (eg, 2 to 50, 25 to 100, or 80 to 160). A floor may have an area of at least about 150 m ² , 250 m ² , 500 m ² , 1000 m ² , 1500 m ² or 2000 square meters (m ² ). Floors can have an area between any of the foregoing floor area values (eg, about 150 ^m2 to about 2000 ^m2 , about 150 ^m2 to about 500 ^m2 , about ²⁵⁰ m2 to about 1000 ^m2 , or about 1000 ^m2 to about 2000 m ² ). A building may comprise an area of at least about 1,000 square feet (sqft), 2,000 square feet, 5,000 square feet, 10,000 square feet, 100,000 square feet, 150,000 square feet, 200,000 square feet, or 500,000 square feet. A building may comprise an area between any of the foregoing areas (eg, from about 1,000 square feet to about 5,000 square feet, from about 5,000 square feet to about 500,000 square feet, or from about 1,000 square feet to about 500,000 square feet). A building may comprise an area of at least about 100 m ² , 200 m ² , 500 m ² , 1000 m ² , 5000 m ² , 10000 m ² , 25000 m ² or 50000 m ² . A building may comprise an area between any of the foregoing areas (eg, about 100 m ² to about 1000 m ² , about 500 m ² to about 25,000 m ² , about 100 m ² to about 50,000 m ² ). Facilities may contain commercial or residential buildings. Commercial buildings may include tenants and/or owners. Residential facilities may contain multi-family or single-family buildings. Residential facilities may include residential complexes. Residential facilities may include single-family dwellings. Residential facilities may include multi-family dwellings (eg, apartments). Residential facilities may include townhouses. Facilities may contain both residential and commercial components. A facility can include at least about 1, 2, 5, 10, 50, 100, 150, 200, 250, 300, 350, 400, 420, 450, 500, or 550 windows (eg, tintable windows). The windows may be divided into partitions (eg, based at least in part on the location, facade, floor, ownership, utilization, any other assignment measure, random assignment of the enclosure (eg, room) in which the windows are placed) Or any combination thereof. The assignment of windows to partitions may be static or dynamic (eg, based on heuristics). There may be at least about 2, 5, 10, 12, 15, 30, 40, or 46 windows per partition.

In some embodiments, the sensor is operatively coupled to at least one controller and/or processor. Sensor readings may be obtained by one or more processors and/or controllers. A controller may include a processing unit (eg, a CPU or GPU). The controller can receive input (eg, from at least one sensor). The controller may include circuitry, electrical wiring, optical wiring, communication terminals and/or sockets. The controller can deliver output. A controller may contain multiple (eg, sub) controllers. The controller may be part of a control system. The control system may include a main controller, a floor (eg, including a network controller) controller, and a local controller. The local controller may be a window controller (eg, controlling an optically switchable window), an enclosure controller, or a component controller. For example, the controller may be a hierarchical control system (eg, including a master controller that directs one or more controllers, such as floor controllers, local controllers (eg, window controllers), closed body controller and/or component controller). The physical part of the controller type in a hierarchical control system can be changed. For example: at the first time: the first processor can assume the role of the main controller, the second processor can assume the role of the floor controller, and the third processor can assume the role of the local controller. At the second time: the second processor can assume the role of the main controller, the first processor can assume the role of the floor controller, and the third processor can maintain the role of the local controller. At the third time: the third processor can assume the role of the main controller, the second processor can assume the role of the floor controller, and the first processor can assume the role of the local controller. The controller may control one or more devices (eg, directly coupled to the devices). A controller may be positioned proximate one or more devices it controls. For example, the controller may control optically switchable devices (eg, IGUs), antennas, sensors, and/or output devices (eg, light sources, sound sources, odor sources, gas sources, HVAC outlets, or heaters).

In one embodiment, the floor controls may indicate one or more window controls, one or more enclosure controls, one or more component controls, or any combination thereof. Floor controllers may include floor controllers. For example, a floor (eg, including a network) controller may control a plurality of local (eg, including a window) controller. A plurality of local controllers may be located in a portion of a facility (eg, in a portion of a building). A portion of the facility may be the floor of the facility. For example, floor controllers can be assigned to floors. In some embodiments, a floor may include a plurality of floor controllers, eg, depending on the floor size and/or the number of local controllers coupled to the floor controllers. For example, a floor controller can be assigned to a portion of a floor. For example, a floor controller can be assigned to a portion of the local controllers located in the facility. For example, a floor controller can be assigned to a portion of a floor of a facility.

The master controller may be coupled to one or more floor controllers. Floor controllers can be placed in the facility. The main controller may be located in the facility or located outside the facility. The main controller can be located in the cloud. The controller may be part of or operatively coupled to the building management system. The controller can receive one or more inputs. A controller can generate one or more outputs. The controller may be a single-input single-output controller (SISO) or a multiple-input multiple-output controller (MIMO). The controller can interpret the received input signal. The controller may obtain data from one or more components (eg, sensors). Acquiring can include receiving or extracting. Data may include measurements, estimates, determinations, generation, or any combination thereof. The controller may contain feedback control. The controller may include feedforward control. Control may include on-off control, proportional control, proportional-integral (PI) control, or proportional-integral-derivative (PID) control. Control can include open loop control or closed loop control. The controller may contain closed loop control. The controller may contain open loop control. The controller may include a user interface. The user interface may include (or be operatively coupled to) a keyboard, keypad, mouse, touch screen, microphone, speech recognition package, camera, imaging system, or any combination thereof. The output may include a display (eg, a screen), speakers, or a printer.

FIG. 2 shows a schematic example of a control system architecture 200 that includes a master controller 208 that controls floor controllers 206 , which in turn control local controllers 204 . In some embodiments, the local controller controls one or more IGUs, one or more sensors, one or more output devices (eg, one or more emitters), or any combination thereof. In the illustrative configuration of FIG. 2 , the master controller is operatively coupled (eg, wirelessly and/or communicatively coupled) to a building management system (BMS) 224 and database 220 . Arrows in FIG. 2 indicate communication paths. The controller is operably coupled (eg, directly/indirectly and/or wired and/wirelessly) to the external source 210 . External sources can include networks. The external source may include one or more sensors or output devices. External sources may include cloud-based applications and/or databases. Communication may be wired and/or wireless. External sources may be located outside the facility. For example, the external source may include one or more sensors and/or antennas disposed on, for example, a wall or ceiling of a facility. Communication can be one-way or two-way. In the example shown in Figure 2, all communication arrows may be bidirectional.

A controller may monitor and/or direct (eg, physical) changes to the operating conditions of the apparatus, software, and/or methods described herein. Controlling may include regulating, manipulating, limiting, directing, monitoring, adjusting, modulating, altering, altering, restraining, checking, directing or managing. Controlling (eg, by a controller) may include attenuating, modulating, changing, managing, suppressing, discipline, regulating, constraining, supervising, manipulating, and/or directing. Controlling may include controlling control variables (eg, temperature, power, voltage, and/or profile). Control can include instant or offline control. Calculations utilized by the controller may be performed in real-time and/or off-line. The controller can be manual or non-manual. The controller may be an automatic controller. The controller can operate on request. The controller may be a programmable controller. The controller can be programmed. A controller may include a processing unit (eg, a CPU or GPU). The controller can receive input (eg, from at least one sensor). The controller can deliver output. A controller may contain multiple (eg, sub) controllers. The controller may be part of a control system. The control system may include master controllers, floor controllers, local controllers (eg, enclosure controllers or window controllers). The controller can receive one or more inputs. A controller can generate one or more outputs. The controller may be a single-input single-output controller (SISO) or a multiple-input multiple-output controller (MIMO). The controller can interpret the received input signal. The controller may obtain data from one or more sensors. Acquiring can include receiving or extracting. Data may include measurements, estimates, determinations, generation, or any combination thereof. The controller may contain feedback control. The controller may include feedforward control. Control may include on-off control, proportional control, proportional-integral (PI) control, or proportional-integral-derivative (PID) control. Control can include open loop control or closed loop control. The controller may contain closed loop control. The controller may contain open loop control. The controller may include a user interface. The user interface may include (or be operatively coupled to) a keyboard, keypad, mouse, touch screen, microphone, speech recognition package, camera, imaging system, or any combination thereof. The output may include a display (eg, a screen), speakers, or a printer.

The methods, systems and/or apparatus described herein may include a control system. The control system can communicate with any of the devices (eg, sensors) described herein. The sensors may be of the same type or of different types, eg, as described herein. For example, the control system may communicate with the first sensor and/or the second sensor. The control system may control one or more sensors. The control system may control one or more components of the building management system (eg, including lighting, security, occupancy, occupant behavior, HVAC, sensors, transmitters, alarms, and/or air conditioning systems). The controller can adjust at least one (eg, environmental) characteristic of the enclosure. The control system may use any component of the building management system to regulate the enclosure environment. For example, the control system may regulate the energy supplied by the heating element and/or the cooling element. For example, the control system may regulate the speed of air flowing to and/or from the enclosure through the vent. The control system may include a processor. The processor may be a processing unit. The controller may include a processing unit. The processing unit may be a central processing unit. The processing unit may include a central processing unit (abbreviated herein as "CPU"). The processing unit may be a graphics processing unit (abbreviated herein as "GPU"). A controller or control mechanism (eg, including a computer system) can be programmed to implement one or more methods of the present disclosure. A processor can be programmed to implement the methods of the present disclosure. A controller may control at least one component forming the systems and/or apparatus disclosed herein.

In some embodiments, the building network infrastructure has vertical data planes (between building floors) and horizontal data planes (all within a single floor or multiple (eg, consecutive) floors). In some cases, the horizontal and vertical data planes have at least one (eg, all) data carrying capabilities and/or components that carry (eg, substantially) the same or similar data. In other cases, the two data planes have at least one (eg, both) different data carrying capabilities and/or components. For example, a vertical data plane may contain one or more components for fast data transfer rates and/or bandwidth. In one example, the vertical data plane contains support for at least about 10 gigabits per second (Gbit/s) or faster (eg, Ethernet) data transfer (eg, using a first type of wiring (eg, UTP) electrical and/or fiber optic cables)), while the horizontal data plane contains support for at least about 8 Gbit/s, 5 Gbit/s, or 1 Gbit/s (eg, Ethernet) data transfer (eg, via a second type of wiring (eg, coaxial cable). In some cases, the horizontal data plane supports data transfer via d.hn or MoCA standards (eg, MoCA 2.5 or MoCA 3.0). In some embodiments, the connections between floors on the vertical data plane use control panels with high-speed (eg, Ethernet) switches that connect the horizontal and vertical data planes to and from the /or communication pairing between different types of wiring. These control panels can communicate with those on a given floor (eg , IP) addressable node (eg, device) communication. Horizontal and vertical data planes in a single building structure are depicted in Figure 3.

Data transmission offerings and, in some embodiments, voice services may be provided in the building via wireless and/or wired communications to and/or from occupants of the building. In third, fourth or fifth generation (3G, 4G or 5G) cellular communications, data transmission and/or voice services may be attenuated in part by building structures such as walls, floors, ceilings and windows become difficult. Attenuation becomes more severe using higher frequency protocols such as 5G relative to 3G and 4G communications. To address this challenge, buildings can be equipped with components that act as gateways or ports for cellular signals. Such gateways are coupled to the infrastructure inside the building that provides wireless services (eg, via internal antennas and other infrastructure that implements Wi-Fi, small cell services (eg, via picocell or femtocell devices), CBRS, etc. architecture). Gateways or entry points for such services may include high-speed cables from the operator's central office (eg, underground) and/or antennas at strategic locations on the exterior of the building (eg, on the roof of the building). The wireless signal received at the donor antenna and/or sky sensor). High-speed cables to buildings may be referred to as "returns."

3 shows an example of a building with a collection of devices (eg, assemblies). As connection points, the building may include a plurality of rooftop donor antennas 305, 305b and a sky sensor 307 for transmitting electromagnetic radiation (eg, infrared, ultraviolet and/or visible light). Wireless signals from a network (eg, provided via an antenna) may allow the building services network to wirelessly (at least partially) interface with one or more communication service provider systems. The building depicted in the example shown in FIG. 3 has, for example, a control panel 313 for connecting to a provider central office 311 via a physical line 309 (eg, optical fiber such as single-mode fiber or coaxial fiber). Control panel 313 may include hardware and/or software configured to provide functions such as signal source carrier headends, fiber distribution headends, and/or (eg, bidirectional) amplifiers or repeaters. Rooftop donor antennas 305a and 305b may allow building occupants and/or devices to access (eg, a 3rd party) provider's wireless system communication services. The antenna and/or controller may provide access to the same service provider system, different service provider systems, or a variant, such as two interface elements providing access to the first service provider's system, and different interfaces The element provides access to the second service provider's system.

As shown in the example of FIG. 3, the vertical data plane may include (eg, high-capacity or high-speed) data-carrying wires 319, such as (eg, single-mode) optical fibers, coaxial cables, and/or UTP copper wires (sufficient gauge) ). In some embodiments, at least one control panel may be provided on at least a portion of the floors of the building (eg, on each floor). The control panel is associated with the controller. The controller may be part of a control system (eg, as disclosed herein). In some embodiments, one (eg, high-capacity) communication line may connect a control panel in another floor (eg, in the top floor) with the (eg, main) control panel 313 in the bottom floor (or in the basement floor) direct connection. It should be noted that wire 319 is directly connected to rooftop antennas 305a, 305b and/or sky sensor 307, while control panel 313 is also directly connected (eg, 3rd party) to service provider central office 311.

FIG. 3 shows an example of a horizontal data plane that may include one or more of a control panel and data and/or power-carrying wiring (eg, wires) that includes mains 321. In certain embodiments, the trunk may be made of coaxial cable, optical cable, twisted pair, or any combination thereof. Trunks can include any of the wiring disclosed herein. The control panel can be configured to provide data on trunk 321 via data communication protocols such as MoCA and/or G.hn. Data communication protocols may include (i) Next Generation Home Network Connectivity Protocol (abbreviated herein as the "G.hn" protocol), (ii) communications that transmit digital information over power lines traditionally used to deliver (eg, only) electrical power technology, or (iii) hardware devices designed to communicate and transmit data (eg, Ethernet, USB, and Wi-Fi) through the electrical wiring of a building. Data transfer protocols can facilitate data transfer rates of at least about 1 gigabit per second (Gbit/s), 2 Gbit/s, 3 Gbit/s, 4 Gbit/s, or 5 Gbit/s. Data transfer protocols can operate on telephone wiring, coaxial cables, power lines, and/or optical fibers (eg, plastic or glass). Data transfer protocols may be facilitated using chips (eg, including semiconductor devices).

Each horizontal data plane may provide high-speed network access to one or more sets of devices 323 (eg, sets of one or more devices in a housing comprising a device assembly) and/or antennas 325 in which Some or all of them are optionally integrated with device set 323 . Antenna 325 (and associated radio, not shown) may be configured to provide wireless access by any of a variety of protocols, including, for example, cellular (eg, at or near 28 GHz, one or more of frequency bands), Wi-Fi (eg, in one or more of the 2.4, 5, and 60 GHz frequency bands), CBRS, and the like. A drop-in line may connect the device set 323 to the trunk line 321 . In some embodiments, horizontal data planes are deployed on floors of buildings. Devices in a device set may include sensors, transmitters, transceivers, processors, controllers, memory, network connectivity, or antennas. A set of devices may include circuitry (eg, disposed on one or more circuit boards). The devices in the device set are operably coupled to the circuitry. Plane 350 shows a vertical plane in the building.

One or more donor antennas 305a, 305b may be connected to the control panel 313 via high speed wires (eg, single mode fiber or copper). In the depicted example of Figure 3, the control panel 313 is located in the lower level of the building. Connections to the donor antennas 305a, 305b may be via one or more vRAN radios and wiring (eg, coaxial cables). The communications service provider's central office 311 is connected to the underlying control panel 313 via a high-speed line 309 (eg, optical fiber serving as part of the backhaul). This point of entry for the service provider to the building is sometimes referred to as the main point of entry (MPOE), and it can be configured to allow the building to distribute both voice and data traffic.

In some cases, small cell systems are available to buildings, at least in part, via one or more antennas. Examples of antennas, sky sensors, and control systems can be found in US Patent Application Serial No. 15/287,646, filed October 6, 2016, which is incorporated herein by reference in its entirety. The use of rooftop antennas may provide other advantages, such as facilitating cellular coverage of increased areas (geographically). In some cases, small cell systems are available to buildings, at least in part, via one or more donor antennas. Figure 4 depicts a block diagram of an embodiment of a building network 400 for buildings. Building network 400 may use any number of different communication protocols, including BACnet. As shown, building network 400 includes main network controller 405 , lighting control panel 410 , building management system 415 , security control system 420 , and user console 425 . These various controllers and systems in the building may be used to receive input from and/or control the following in the building: HVAC system 430, lights 435, security sensors 440, door locks 445, cameras 450 and Tintable window 455.

The master network controller 405 may function in a similar manner as the master controller 208 described with respect to FIG. 2 . Lighting control panel 410 (FIG. 4) may include circuitry to control any of the devices disclosed herein (eg, interior lighting operatively coupled to the controller). The device may include interior lighting, exterior lighting, emergency warning lights, emergency exit signs, and emergency floor exit lighting associated with the building and operatively coupled to the controller. Lighting control panel 410 may include other devices (eg, occupancy sensors). Building management system (BMS) 415 may include computer servers that receive data from and/or issue commands to other systems and controllers operatively coupled to network 400 . For example, BMS 415 may receive data from each of network controller 405, lighting control panel 410, and security control system 420 and send data to each of the network controller, lighting control panel, and security control system Issue an order. Security control system 420 may include magnetic card access, turnstiles, solenoid actuated door locks, surveillance cameras, burglar alarms, metal detectors, and the like. User console 425 may be a computer terminal that can be used by building managers to schedule the operation of the various systems of the building, control, monitor, optimize, and troubleshoot such systems. Software from Tridium Corporation can generate visual representations of data from various systems for the user console 425 .

Each of the different controls can control an individual device/apparatus. The main network controller 405 can control the window 455 . Lighting control panel 410 may control lights 435 . The BMS 415 can control the HVAC 430 . The security control system 420 can control the security sensor 440 , the door lock 445 and the camera 450 . Data may be exchanged and/or shared between different devices and controllers that are part of building network 400 (eg, all).

In some cases, at least a portion of the system of BMS 415 and/or building network 400 may operate on a daily, monthly, quarterly, or annual schedule. For example, lighting control systems, window control systems, HVAC, and security systems may operate on a 24-hour schedule that takes into account when people are in the building during the workday. At least two device classes (eg, 430, 435, 440, 445, 450, and 455) may operate according to different schedules from each other. At least two device classes (eg, 430, 435, 440, 445, 450, and 455) may operate according to (eg, substantially) the same schedule. For example, at night, a building may enter an energy saving mode, while during the day, the system may operate in a manner that minimizes the building's energy consumption while providing occupant comfort, safety, and health. As another example, the system may shut down or enter an energy saving mode during a vacation.

Scheduling information can be combined with geographic information. The geographic information may include the latitude and/or longitude of the building. The geographic information may include information about the direction in which at least one side of the building faces. Using this information, different rooms on different sides of the building can be controlled in different ways. For example, in winter, for an east-facing room of a building, the window controller may instruct the window to have no tint in the morning, causing the room to warm due to sunlight shining in the room, and because of the lighting from the sunlight, the lighting control panel The indicator light can be dimmed. The west facing windows can be controlled by the room occupants in the morning because the hue of the windows on the west side may not affect energy savings. The operating modes of the east-facing and west-facing windows can be switched at night (eg, when the sun sets, the west-facing windows may not be tinted to allow sunlight in for heat and illumination).

In some embodiments, a plurality of assemblies (eg, sets of devices) are deployed throughout a particular enclosure (eg, a building), in portions thereof (eg, rooms or floors), or across a plurality of such enclosures Addressable nodes (eg, devices) that handle interconnections (eg, IP) within a system. 5 shows a schematic example of a network system within an enclosure (eg, a building) having a plurality of sub-enclosures (eg, floors). In the example of FIG. 5, the enclosure 500 is a building with floor 1, floor 2, and floor 3. The enclosure 500 includes a network 520 (eg, a wired network) provided to communicatively couple any addressable circuitry (eg, an addressable node), such as a device or set of devices collectively represented by 510 (in the Also referred to herein as a "component cluster" (eg, a device cluster). In the example shown in FIG. 5 , three floors are sub-enclosures within enclosure 500 . At least two devices may be of different types from each other. At least two devices may be of the same type. At least two device sets may be of different types from each other. At least two device sets may be of the same type.

In some embodiments, the enclosure includes one or more sensors. Sensors can facilitate controlling the environment of an enclosure, eg, so that the occupants of the enclosure can be more comfortable, desirable, beautiful, healthy, productive (eg, in terms of resident performance), easier to live (eg, work), or any combination of environments. The sensors can be configured as low or high resolution sensors. A sensor can provide an on/off indication of the occurrence and/or presence of an environmental event (eg, a pixel sensor). In some embodiments, the accuracy and/or resolution of the sensor may be improved through analysis of the sensor's measurements by artificial intelligence (abbreviated herein as "AI"). Examples of artificial intelligence techniques that may be used include: reactivity, limited memory, theory of mind, and/or self-perception techniques known to those skilled in the art. A sensor (including its circuitry) can be configured to process, measure, analyze, detect and/or respond to: data, temperature, humidity, sound, force, pressure, concentration, Electromagnetic waves, location, distance, movement, flow, acceleration, velocity, vibration, dust, light, glare, color, gas type, and/or any other aspect (eg, property) of the environment (eg, enclosure). Gases may include volatile organic compounds (VOCs). Gases may include carbon monoxide, carbon dioxide, water vapor (eg, humidity), oxygen, radon, and/or hydrogen sulfide. One or more sensors may be calibrated at the factory and/or in the facility. A sensor can be optimized to perform accurate measurements of one or more environmental characteristics that exist in a factory setting and/or in the facility in which the sensor is deployed. Artificial intelligence technology, machine learning, and examples of its use to control environments and/or tintable windows, sensors, control systems and networks can be found in International Patent Application No. PCT/US21/17603 filed on February 11, 2021 and International Patent Application No. PCT/US19/46524 filed on August 14, 2019, each of which is incorporated herein by reference in its entirety.

Sensors coupled to the network can be configured to sense properties including temperature, relative humidity (RH), illuminance (eg, in lux), temperature (in degrees Celsius), Correlated Color Temperature (CCT, e.g. in Kjeldahl), Carbon Dioxide (e.g., parts per million (ppm)), Volatile Organic Compounds (VOC, e.g. as an indicator value), Pressure (e.g., as a sound pressure in decibels), powdered materials, infrared, ultraviolet or visible light. The sensor can have accuracy. Sensors can have random variability. Stochastic variability (eg, a statistical measure of long-term stochastic variability). The random variability of the temperature sensor may be at most about 0.5 degrees Celsius (°C), 0.3°C, 0.2°C, or 0.1°C. The random variability of the RH sensor may be at most about 3%, 2%, 1.5%, or 1%. The random variability of the illuminance sensor can be up to about 20 lux (LUX), 15 LUX, 10 LUX, or 5 LUX. The random variability of the CCT sensor can be up to about 250 Kelvin (K), 220K, 210K, 200K, 190K or 150K. The random variability of the carbon dioxide sensor can be up to about 25 ppm, 23 ppm, 20 ppm, 19 ppm, or 15 ppm. The random variability of the VOC sensor can be up to about 15 index values (IV), 12IV, 11IV, 10IV, or 5IV. The random variability of the sound pressure sensor can be up to about 10 decibels (dB), 8 dB, 5 dB, 4 dB, or 2 dB. Occasionally, the sensor set may include measuring the temperature of the set of devices (eg, the temperature of the internal set of devices) and/or the temperature outside the set of devices (eg, the temperature of the set of external devices, such as the temperature in the room in which the set of devices is housed) . In some embodiments, data from the sensors is processed and/or analyzed. Data processing may include removing gaps, removing anomalies (eg, outside of range data), performing spatial extrapolation or calibration. Data processing may vary for data obtained by different types of sensors. For example, data from temperature sensors may be subject to different processing and/or analysis than data from VOC sensors. Data processing may include data manipulation. Data processing may include data filtering. Data filtering may vary for data obtained by different types of sensors. Data filtering can include median, mean, standard deviation, or select the minimum value as the filtering mechanism. The absolute value of the standard deviation can be at most about 1 sigma (σ), 2σ, 3σ, or 4σ. Data filtering may include obtaining absolute deviations (eg, mean absolute deviations and/or median absolute deviations). In some cases, median-based methods can be better than mean-based methods. Media may contain median absolute deviations. Sometimes, data processing and/or analysis may involve obtaining the standard deviation of the minimum value, eg, to derive long-term variation (eg, in a particular location of the sensor). The median absolute deviation may contain the absolute distance from the median median. The mean absolute deviation can include the mean absolute distance from the mean. Filtering may include removing ambient noise (eg, fluctuations). Spatial extrapolation may have properties measured by the sensor for the space in which the sensor is disposed, eg, to provide a sensor property map of the space. For example, the sensor data may be temperature data, and the spatial map may be a temperature map of the room in which the temperature sensor is placed. The calibration engine can account for device-based long-term drift. An example of sensor calibration can be found in International Patent Application No. PCT/US21/15378, entitled "SENSOR CALIBRATION AND OPERATION", filed on January 28, 2021, which starts with Incorporated herein by reference in its entirety. Data processing and/or analysis may be refreshed, for example, periodically. For example, sensor sampling may be performed at most every 10 seconds (s), 20 s, 30 s, 45 s, 60 s, 2 minutes (min), 5 min, or 10 min. Sensor sampling may be performed between any of the foregoing values (eg, every 10 s to every 10 min). For example, spatial mapping of sensed attributes may be performed at most every 1 minute (min), 2.5 min, 5 min, or 10 min. Spatial mapping can be performed between any of the foregoing values (eg, every 1 min to every 10 min). Sensor sampling and/or spatial mapping may be performed during periods of high and/or low occupancy of the facility. Sensor sampling and/or spatial mapping may be performed during periods of high and/or low activity of a facility (eg, people and/or machines). Sensor sampling and/or spatial mapping may be performed randomly and/or arbitrarily.

In some embodiments, a device (eg, a sensor) may be designated as a gold device, which may be used as a reference (eg, as a gold standard) for calibrating other sensors (eg, here or another sensors of the same type in the facility). A golden device may be the device that is most calibrated in a facility or part thereof (eg, in a building, in a floor, and/or in a room). The calibrated and/or positioned device can be used as a standard for calibrating and/or positioning other devices (eg, of the same type). Such devices may be referred to as "golden devices". Gold fixtures are used as reference fixtures. A golden device may be the device that is most calibrated and/or most accurately positioned in a facility (eg, among devices of the same type).

In some embodiments, multiple sensors of the same type may be distributed in multiple locations or housings. For example, at least one of the plurality of sensors of the same type may be part of a set. For example, at least two of the plurality of sensors of the same type may be part of at least two different sets. The set of devices may be distributed in the enclosure. Enclosures may contain meeting rooms or cafeterias. For example, a plurality of sensors of the same type can measure environmental characteristics (eg, parameters) in a conference room. In response to measurements of environmental parameters of the enclosure, a parametric topology of the enclosure can be generated. The parametric topology can be generated using output signals from, for example, any type of sensor or set of devices as disclosed herein. Any facility that may be directed to facilities such as meeting rooms, hallways, bathrooms, cafeterias, garages, auditoriums, utility rooms, storage rooms, machine rooms, partitions (eg, electrical and/or elevator partitions) and/or elevators A closed volume produces a parametric topology. Examples of artificial intelligence techniques that may be used include: reactivity, limited memory, theory of mind, and/or self-perception techniques known to those skilled in the art. Sensors can be configured to process, measure, analyze, detect and/or respond to one or more of the following: data, temperature, humidity, Sound, force, pressure, electromagnetic waves, position, distance, movement, flow, acceleration, speed, vibration, dust, light, glare, color, gas, pathogen exposure (or possible pathogen exposure) and/or (eg, enclosures) other aspects (eg, properties) of the environment. Gases may include volatile organic compounds (VOCs). Gases may include carbon monoxide, carbon dioxide, formaldehyde, naphthalene, taurine, water vapor (eg, humidity), oxygen, radon, and/or hydrogen sulfide. One or more sensors can be calibrated in the factory settings. The sensors can be optimized to be able to perform accurate measurements of one or more environmental characteristics that exist in a factory setting. In some cases, a factory calibrated sensor may not be suitable for operation in the target environment. For example, the factory settings may contain a different environment than the target environment. The target environment may be the environment in which the sensor is deployed. The target environment may be the environment in which the sensor is expected and/or specified to operate. The target environment can be different from the factory environment. The factory environment corresponds to where the sensor is assembled and/or built. The target environment may include factories where sensors are not assembled and/or built. In some cases, the factory settings may differ from the target environment insofar as sensor readings captured in the target environment are erroneous (eg, within a measurable range). In this context, "error" may refer to a sensor reading that deviates from a specified accuracy (eg, as specified by the sensor's manufacturer). In some cases, factory calibrated sensors may provide readings that do not meet accuracy specifications (eg, provided by the manufacturer) when operating in the target environment.

In some embodiments, processing sensor data includes performing sensor data analysis. Sensor data analysis may include at least one rational decision-making process and/or learning. Sensor data analysis can be used to adjust the environment, for example, by adjusting one or more components that affect the environment of the enclosure. Data analysis can be performed by machine-based systems (eg, circuitry). The circuitry may belong to a processor. Sensor data analysis can leverage artificial intelligence. Sensor data analysis may rely on one or more models (eg, mathematical models). In some embodiments, sensor data analysis includes linear regression, least squares fitting, Gaussian procedure regression, kernel regression, nonparametric multiplicative regression (NPMR), regression trees, local regression, semiparametric regression, isotonic regression, multiple Variable Adaptive Regression Splines (MARS), Logistic Regression, Robust Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, Principal Component Analysis (PCA), Singular Value Decomposition, Fuzzy Measure Theory, Polet Borel measure, Han measure, Risk neutral measure, Lebesgue measure, Grouped data disposition method (GMDH), Naive Bayes classifier, k nearest neighbor algorithm (k-NN) ), support vector machines (SVM), neural networks, support vector machines, classification and regression trees (CART), random forests, gradient boosting, generalized linear model (GLM) techniques or deep learning techniques. Neural networks may include dense neural networks or along short-term memory (LSTM) networks. Neural networks may include LSTM networks or deep neural networks (DNNs). Example DNN architectures that can be used in some implementations include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), and the like.

In one embodiment, the input features (eg, a set of two hundred (200) or more input features) are fed into the neural network. One example of a neural network architecture is a deep dense neural network, such as a network with at least seven (7) layers and at least fifty-five (55) total nodes. In some DNN architectures, at least one (eg, each) input feature is connected to at least one (eg, each) first layer node, and at least one (eg, each) node is connected to at least one (eg, each) node ) placeholder for other node connections (variable X). Nodes in the first layer model the relationship between all input features. A node in a subsequent layer learns one of the relationships modeled in at least one of the previous layers. When performing a DNN, the error can be iteratively minimized, eg, by updating the coefficient weights of at least one (eg, each) node placeholder.

6 shows an example of a diagram 600 of a configuration of sensors distributed among an enclosure. In the example shown in FIG. 6, the controller 605 is associated with sensors located in enclosure A (sensors 610A, 610B, 610C, ... 610Z), sensors in enclosure B (sensors 610Z) 615A, 615B, 615C, 615Z), sensors in enclosure C (sensors 620A, 620B, 620C, ... 620Z) and sensors in enclosure Z (sensors 685A, 685B, 685C, ... 685Z) communication link (608). Communication links include wired and/or wireless communications. In some embodiments, the set of devices includes at least two sensors of different types. In some embodiments, the set of devices includes at least two transmitters of different types. In some embodiments, the set of devices includes at least two sensors of the same type (eg, sensor arrays). In some embodiments, the set of devices includes at least two emitters of the same type (eg, an array of emitters, such as an array of light emitting diodes).

In some embodiments, the set of devices includes at least two sensors of the same type. In the example shown in FIG. 6, sensors 610A, 610B, 610C, . . . 610Z of enclosure A represent sets. A set of sensors may refer to a collection of various sensors. In some embodiments, at least two of the concentrated sensors cooperate to determine, for example, environmental parameters of the enclosure in which the sensors are located. For example, a set of devices may include carbon dioxide sensors, carbon monoxide sensors, VOC sensors, environmental noise sensors, light (visible, UV and IR) sensors, temperature sensors and/or humidity sensor. The set of devices may include other types of sensors, and the claimed subject matter is not so limited. The enclosure may contain one or more sensors that are not part of the sensor set. A closed volume can contain multiple sets. At least two of the plurality of sets may differ in at least one of their sensors. At least two of the plurality of sets can have at least one of their similar (eg, the same type) sensors. For example, a set may have two motion sensors and one temperature sensor. For example, a set may have a carbon dioxide sensor and an IR sensor. A set may include one or more devices that are not sensors. One or more other devices that are not sensors may include sound emitters (eg, buzzers) and/or electromagnetic radiation emitters (eg, light emitting diodes). In some embodiments, a single sensor (eg, not in a cluster) may be positioned adjacent (eg, in close proximity, such as in contact with) another device that is not a sensor.

The sensors in the device set can cooperate with each other. One type of sensor can be related to at least one other type of sensor. Data from multiple sensor types can be synthesized to provide results. The results may be related to properties measured by at least one of the plurality of sensor types. The results may be related to properties not measured by any of the plurality of sensor types. Various sensors in a facility (eg, of the same type and/or different types) can work together, eg, to produce a requested result (eg, to adjust the environment of the facility). The sensors may be included in an array of sensors disposed in the facility. The conditions in the enclosure can affect one or more of the different sensors. The sensor readings of one or more different sensors may be related to and/or affected by the situation. The correlation may be predetermined. Correlations can be determined over a period of time (eg, using a learning program). This period of time may be predetermined. The period can have a cutoff value. The cutoff value may consider, for example, an error threshold (eg, a percentage value) between the predicted sensor data and the measured sensor data in a similar situation. The time can be continuous. Correlations can be derived from a learning set (also referred to herein as a "training set"). A learning set may contain and/or may be derived from real-time observations in a closed volume. Observations may include data collection (eg, from sensors). The learning set may contain sensor data from similar closed bodies. A learning set may include third-party data sets (eg, of sensor data). A study set may be derived from, for example, a simulation that affects one or more environmental conditions within the enclosure. A learning set may constitute detected (eg, historical) signal data with the addition of one or more types of noise. Correlation can utilize historical data, third-party data, and/or real-time (eg, sensor) data. The correlation between two sensor types may be assigned a value. The value may be a relative value (eg, a strong correlation, a moderate correlation, or a weak correlation). Learning sets that are not derived from real-time measurements may serve as benchmarks (eg, baselines) to initiate operation of sensors and/or various components that affect the environment (eg, HVAC systems and/or tinting windows). The real-time sensor data may supplement the learning set, eg, on an ongoing basis or over a defined period of time. (eg, supplemental) The size of the learning set may increase during deployment of the sensor in the environment. For example, with the inclusion of additional (i) real-time measurements, (ii) sensor data from other (eg, similar) enclosures, (iii) third-party data, (iv) other and/or updated simulations , the size of the initial learning set can be increased.

In some embodiments, data from sensors can be correlated. Once a correlation between two or more sensor types is established, deviating correlations (eg, correlation values) may indicate irregularities and/or malfunctions of the sensors of the related sensors . Failures can include calibration offsets. A failure may indicate that the sensor needs to be recalibrated. A failure can include a complete failure of the sensor. In one example, the motion sensor may cooperate with the carbon dioxide sensor. In one example, in response to the movement sensor detecting movement of one or more individuals in the enclosure, the carbon dioxide sensor may be activated to begin taking carbon dioxide measurements. An increase in movement in the enclosure can be correlated with an increase in the level of carbon dioxide. In another example, the detection of individuals in the enclosure by the motion sensor may be correlated with an increase in noise detected by the noise sensor in the enclosure.

In some embodiments, a detection by a sensor of the first type not accompanied by a detection by a sensor of the second type may cause the sensor to issue an error message. For example, if the motion sensor detects a large number of individuals in the enclosure without detecting an increase in carbon dioxide and/or noise, the carbon dioxide sensor and/or noise sensor may be identified as malfunctions or has incorrect output. Error messages can be posted. The first plurality of different associated sensors in the first set may include one sensor of a first type, and a second plurality of sensors of a different type. If the second plurality of sensors indicate a correlation, and one sensor indicates a reading that differs from the correlation, the likelihood of one sensor failing increases. If the first correlation is detected by the first plurality of sensors in the first set, and the second correlation different from the first correlation is detected by the third plurality of correlation sensors in the second set There is an increased likelihood that one sensor set is exposed to a different situation than the third sensor set is exposed to. The sensors in the device set can cooperate with each other. Collaborating may include considering sensor data from another sensor in a concentration (eg, of a different type). Collaboration may include trends predicted by another sensor (eg, type) of focus. Collaboration can include trends predicted by data related to another sensor (eg, type) in the collection. Other sensor data may be derived from another sensor in the set, a sensor of the same type in another set, or the type of data collected by another sensor in the set that is not derived from another sensor. For example, the first set may include pressure sensors and temperature sensors. The cooperation between the pressure sensor and the temperature sensor may include considering the pressure sensor data when analyzing and/or predicting the temperature data of the temperature sensors in the first set. The pressure data may be (i) pressure data from a first set of pressure sensors, (ii) pressure data from one or more other sets of pressure sensors, (iii) pressure data from other sensors, and/or (iv) third party pressure information. FIG. 7 shows an example of a diagram 700 of a configuration of a set of devices distributed within an enclosure. In the example shown in FIG. 7 , a group 710 of individuals are seated in a conference room 702 . The meeting room includes an "X" dimension to indicate length, a "Y" dimension to indicate height, and a "Z" dimension to indicate depth. XYZ are directions in a Cartesian coordinate system. Device sets 705A, 705B, and 705C include sensors that may operate similar to those described with reference to device set 323 of FIG. 3 . At least two sets of devices (eg, 705A, 705B, and 705C) can be integrated into a single sensor module. Device sets 705A, 705B, and 705C may include carbon dioxide ( _CO2 ) sensors, ambient noise sensors, or any other sensor disclosed herein. In the example shown in FIG. 7, the first set of devices 705A is positioned (eg, installed) near a point 715A, which may correspond to a ceiling, a location in a wall, or a table at a table where the group of individuals 710 is seated other parts of the side. In the example shown in FIG. 7, the second set of devices 705B is positioned (eg, mounted) near a point 715B, which may correspond to a ceiling, a location in a wall, or above a table where the group of individuals 710 is seated ( For example, directly above) other parts. In the example shown in FIG. 7, a third set of devices 705C is positioned (eg, installed) near a point 715C, which may correspond to a ceiling, a location in a wall, or where a relatively small group of individuals 710 are seated Other parts on the side of the table. Any number of additional sensors and/or sensor modules may be positioned elsewhere in conference room 702 . The set of devices can be placed anywhere in the enclosure. The locations of the sensor sets in the enclosure may have coordinates (eg, in a Cartesian coordinate system). At least one coordinate (eg, of x, y, and z) may differ between two or more sets of devices, eg, disposed in an enclosure. At least two coordinates (eg, of x, y, and z) may differ between two or more sets of devices, eg, disposed in an enclosure. All coordinates (eg, of x, y, and z) may differ between two or more sets of devices, eg, disposed in an enclosure. For example, two sets of devices may have the same x-coordinate and different y and z-coordinates. For example, two device sets may have the same x and y coordinates and different z coordinates. For example, the two sets of devices may have different x, y, and z coordinates. In some embodiments, one or more sensors of the set of devices provide readings. In some embodiments, the sensor is configured to sense the parameter. Parameters can include temperature, particulate matter, volatile organic compounds, electromagnetic energy, pressure, acceleration, time, radar, lidar, glass vibration, glass breakage, movement, or gas. The gas may comprise Nobel gas. The gas may be harmful to ordinary people. The gas may be a gas present in the surrounding atmosphere (eg, oxygen, carbon dioxide, ozone, chlorinated carbon compounds, or nitrogen compounds). The gas may include radon, carbon monoxide, hydrogen sulfide, hydrogen, oxygen, water (eg, humidity), nitric oxide (NO), or nitrogen _dioxide (NO2). Electromagnetic sensors may include infrared, visible light, ultraviolet sensors. The infrared radiation may be passive infrared radiation (eg, blackbody radiation). Electromagnetic sensors can sense radio waves. Radio waves may include broadband or ultra-broadband radio signals. The radio waves may include pulsed radio waves. Radio waves may include radio waves used for communication. The radio waves can be at an intermediate frequency of at least about 300 kilohertz (KHz), 500 KHz, 800 KHz, 1000 KHz, 1500 KHz, 2000 KHz, or 2500 KHz. Radio waves can be at intermediate frequencies of up to about 500 KHz, 800 KHz, 1000 KHz, 1500 KHz, 2000 KHz, 2500 KHz, or 3000 KHz. The radio waves can be at any frequency between the aforementioned frequency ranges (eg, about 300 KHz to about 3000 KHz). Radio waves can be at high frequencies of at least about 3 megahertz (MHz), 5 MHz, 8 MHz, 10 MHz, 15 MHz, 20 MHz, or 25 MHz. Radio waves can be at high frequencies up to about 5 MHz, 8 MHz, 10 MHz, 15 MHz, 20 MHz, 25 MHz, or 30 MHz. The radio waves can be at any frequency between the aforementioned frequency ranges (eg, about 3 MHz to about 30 MHz). Radio waves can be at very high frequencies of at least about 30 megahertz (MHz), 50 MHz, 80 MHz, 100 MHz, 150 MHz, 200 MHz, or 250 MHz. Radio waves can be at very high frequencies of up to about 50 MHz, 80 MHz, 100 MHz, 150 MHz, 200 MHz, 250 MHz, or 300 MHz. The radio waves can be at any frequency between the aforementioned frequency ranges (eg, about 30 MHz to about 300 MHz). Radio waves can be at ultra-high frequencies of at least about 300 kilohertz (MHz), 500 MHz, 800 MHz, 1000 MHz, 1500 MHz, 2000 MHz, or 2500 MHz. Radio waves can be at ultra-high frequencies of up to about 500 MHz, 800 MHz, 1000 MHz, 1500 MHz, 2000 MHz, 2500 MHz, or 3000 MHz. The radio waves can be at any frequency between the aforementioned frequency ranges (eg, about 300 MHz to about 3000 MHz). Radio waves can be at ultra-high frequencies of at least about 3 gigahertz (GHz), 5 GHz, 8 GHz, 10 GHz, 15 GHz, 20 GHz, or 25 GHz. Radio waves may be at ultra-high frequencies of up to about 5 GHz, 8 GHz, 10 GHz, 15 GHz, 20 GHz, 25 GHz, or 30 GHz. Radio waves can be at any frequency between the aforementioned frequency ranges (eg, about 3 GHz to about 30 GHz).

Gas sensors can sense gas type, flow (eg, velocity and/or acceleration), pressure, and/or concentration. Readings can have amplitude ranges. Readings can have parameter ranges. For example, the parameter can be the electromagnetic wavelength and the range can be the range of detected wavelengths.

In some embodiments, the sensor data is responsive to the environment in the enclosure and/or any inducers of changes in this environment (eg, any environmental disturbers). Sensor data may be in response to transmitters (eg, occupants, appliances (eg, heaters, coolers, ventilators, and/or vacuums) operatively coupled to the enclosure (eg, in the enclosure) ), opening). For example, sensor data may be in response to air conditioning ducts, or to open windows. Sensor data may be responsive to activity occurring in the room. Activities may include human activities and/or non-human activities. Activities may include electronic activities, gaseous activities, and/or chemical activities. Activities can include sensory activities (eg, sight, touch, smell, hearing, and/or taste). Activity may include electronic and/or magnetic activity. Personnel may sense activity. Personnel may not feel activity. The sensor data may be responsive to occupants in the enclosure, substance (eg, gas) flow, substance (eg, gas) pressure and/or temperature. In one example, sets of devices 705A, 705B, and 705C may include carbon dioxide ( _CO2 ) sensors and ambient noise sensors. The carbon dioxide sensor of device set 705A may provide readings as depicted in sensor output reading distribution 725A. The noise sensor of device set 705A may provide the readings depicted in sensor output reading distribution 725A. The carbon dioxide sensor of device set 705B may provide readings as depicted in sensor output reading distribution 725B. The noise sensor of device set 705B may provide readings also as depicted in sensor output reading distribution 725B. Relative to sensor output reading distribution 725A, sensor output reading distribution 725B may indicate higher carbon dioxide levels and noise levels. Relative to sensor output reading distribution 725B, sensor output reading distribution 725C may indicate lower carbon dioxide levels and noise levels. Sensor output reading profile 725C may indicate carbon dioxide levels and noise levels similar to sensor output reading profile 725A. Sensor output reading distributions 725A, 725B, and 725C may include indications representing other sensor readings, such as temperature, humidity, particulate matter, volatile organic compounds, ambient light, pressure, acceleration, time, radar, lidar , UWB radio signals, passive infrared and/or glass breakage, motion detectors. In some embodiments, data is collected and/or processed (eg, analyzed) from sensors in sensors in enclosures (eg, and in device sets). Data processing can be done by the processor of the sensor, by the processor of the device set, by another sensor, by another set in the cloud, by the processor of the controller, by the processor in the enclosure, by outside the enclosure The processor is executed by a remote processor (eg, in a different facility), by a manufacturer (eg, of sensors, windows, and/or building networks). The sensor data may have a time indicator (eg, may be time stamped). Sensor data may have sensor site identification (eg, with site stamps). The sensors may be identifiably coupled to the one or more controllers. In certain embodiments, sensor output reading distributions 725A, 725B, and 725C may be processed. For example, as part of processing (eg, analysis), a distribution of sensor output readings can be plotted to depict sensor readings versus size (eg, "X" dimension) of an enclosure (eg, meeting room 702 ) and change on the graph. In one example, the carbon dioxide level indicated in the sensor output reading distribution 725A may be indicated as point 735A of the CO ₂ graph 730 of FIG. 7 . In one example, the carbon dioxide content of sensor output reading distribution 725B may be indicated as point 735B of CO ₂ graph 730 . In one example, the carbon dioxide level indicated in the sensor output reading distribution 725C may be indicated as point 735C of the CO ₂ graph 730 . In one example, the ambient noise level indicated in sensor output reading distribution 725A may be indicated as point 745A of noise graph 740 . In one example, the ambient noise level indicated in sensor output reading distribution 725B may be indicated as point 745B of noise graph 740 . In one example, the ambient noise level indicated in sensor output reading distribution 725C may be indicated as point 745C of noise graph 740 . In some embodiments, processing data derived from the sensor includes applying one or more models. Models may contain mathematical models. Processing may include fitting of models (eg, curve fitting). Models can be multi-dimensional (eg, two-dimensional or three-dimensional). The model can be represented as a graph (eg, a 2- or 3-dimensional graph). For example, the model may be represented as a contour map (eg, as depicted in Figure 7). Modeling can contain one or more matrices. Models can contain topological models. The model can be related to the topology of the sensed parameter in the enclosure. The model can be related to the temporal variation of the topology of the sensed parameter in the enclosure. Models can be environment and/or enclosure specific. The model may take into account one or more properties of the enclosure (eg, size, openings, and/or environmental distractors (eg, transmitters)). The processing of sensor data may utilize historical sensor data and/or current (eg, real-time) sensor data. Data processing (eg, using models) can be used to predict environmental changes in enclosures, and/or suggest actions to mitigate, adjust, or otherwise respond to changes. In certain embodiments, the set of devices 705A, 705B, and/or 705C may have access to a model to allow curve fitting of sensor readings as a function of one or more dimensions of the enclosure. In one example, the model can be accessed to generate sensor profiles 750A, 750B, 750C, 750D, and 750E using points 735A, 735B, and 735C of the _CO2 plot 730 . In one example, the model can be accessed to generate sensor profiles 751A, 751B, 751C, 751B, and 751E using points 745A, 745B, and 745C of noise graph 740 . Additional models may utilize additional readings from device sets (eg, 705A, 705B, and/or 705C) to provide curves other than sensor distribution curves 750 and 751 of FIG. 7 . A sensor profile generated in response to the use of the model can sense the output reading distribution, indicating specificity as a function of enclosure dimensions (eg, "X" dimension, "Y" dimension, and/or "Z" dimension) The value of the environment parameter. In certain embodiments, one or more of the models used to form curves 750A-750E and 751A-751E may provide a parametric topology of the closed volume. In one example, a parametric topology (as represented by curves 750A-750E and 751A-751E) can be synthesized or generated from the sensor output reading distribution. The parametric topology may be the topology of any sensed parameter disclosed herein. In one example, a parameter topology for a meeting room (eg, meeting room 702 ) may include locations with relatively low values at locations far from the meeting room table, and locations above (eg, directly above) the meeting room table distribution of carbon dioxide with relatively high values. In one example, a parametric topology for a conference room may include a multidimensional noise distribution with relatively low values at locations far from the conference table and slightly higher values above (eg, directly above) the conference room table . In one example, for a carbon dioxide sensor, the relevant parameter may correspond to carbon dioxide concentration. In one example, the time window during which the carbon dioxide sensor may determine that fluctuations in carbon dioxide concentration may be minimal corresponds to a two hour period, eg, between 5:00 AM and 7:00 AM. Self-calibration may begin at 5:00 AM and continue while searching for the duration of measurement stability (eg, minimal fluctuation) during these two hours. In some embodiments, the duration is long enough to allow separation between signal and noise. In one example, data from a carbon dioxide sensor may facilitate determining a 5-minute duration within a time window between 5:00 AM and 7:00 AM (eg, between 5:25 AM and 5:30 AM) Forms the optimal time period to collect the lower baseline. The determination may be performed at least partially (eg, fully) at the sensor level. Determining may be performed by one or more processors operatively coupled to the sensor. During the selected duration, the sensor may collect readings to establish a baseline that may correspond to a lower threshold value. In one example, for a gas sensor placed in a room (eg, in an office environment), the relevant parameter may correspond to gas (eg, CO ₂ ) levels, where the desired levels are typically in the range of about 1000 ppm or less Inside. In one example, a CO ₂ sensor may determine that self-calibration should occur during a time window in which CO ₂ levels are at a minimum, such as when there are no occupants in the vicinity of the sensor. A time window during which fluctuations in _CO2 levels are minimal may correspond to, for example, a one-hour period during lunch from about 12:00 PM to about 1:00 and during off hours. 8 shows an example of a contour map of a horizontal (eg, top) view of an office environment depicting various _CO2 concentration levels. The office environment may include a first occupant 801, a second occupant 802, a third occupant 803, a fourth occupant 804, a fifth occupant 805, a sixth occupant 806, a seventh occupant 807, an eighth occupant 808 and ninth occupant 809. Gas (CO ₂ ) concentrations can be measured by sensors placed at various locations in an enclosure (eg, an office). In one example, for an ambient noise sensor placed in a crowded area such as a cafeteria, the relevant parameter may correspond to a level of sound pressure (eg, noise) measured in decibels above background atmospheric pressure allow. In one example, the ambient noise sensor may determine that the self-calibration should occur during a time window during which fluctuations in the sound pressure level are minimal. The time window in which the fluctuation in sound pressure is minimal may correspond to a one-hour period from about 12:00 AM to about 1:00 AM. Self-calibration may continue for the duration that a baseline (eg, an upper threshold value) can be established during the period within the sensor decision window. In one example, the ambient noise sensor may determine that a 10 minute duration within a time window of about 12:00 AM to about 1:00 AM (eg, about 12:30 AM to about 12:40 AM) formed The best time that can correspond to a baseline above the upper threshold is collected.

At least two of the plurality of sensors may be of different types (eg, configured to measure different properties). Various sensor types can be assembled together (eg, bundled together) and form a device set. A plurality of sensors can be coupled to an electronic board. The electrical connection of at least two of the plurality of sensors in the sensor set may be controlled (eg, manually and/or automatically). For example, a set of devices is operably coupled to or includes a controller (eg, a microcontroller). The controller can control and switch on/off the connectivity of the sensor to power. The controller can thus control the time (eg, period) when the sensor will be operational.

In some embodiments, the baseline of one or more sensors of a set of devices may drift. Recalibration may include one or more (eg, but not all) sensors of a set of devices. For example, in a given set of devices, collective baseline drift can occur in at least two sensor types. A baseline drift in one sensor of a device set may indicate sensor failure. Baseline drift measured in a plurality of sensors in a device set may be indicative of a change in the environment sensed by the sensors in the device set (eg, rather than a failure of these baseline drift sensors). These sensor data baseline drifts can be used to detect environmental changes. For example, (i) building/destroying a building next to a set, (ii) changing (eg, damaging) ventilation passages next to a set, (iii) building/removing a refrigerator next to a set, (iv) opposing (eg, and proximate) the work site of the person changing the device set, (v) the device set has undergone an electronic change (eg, malfunction), (vi) the structure (eg, inner wall) has been changed, or (vii) any combination thereof. In this way, the data can be used, for example, to update a three-dimensional (3D) model of the enclosed volume. In some embodiments, one or more sensors are added or removed from, for example, sensor clusters disposed in enclosures and/or device sets. A newly added sensor can inform other members of the sensor cluster (eg, beacon) of its presence and relative location within the cluster topology. Examples of sensor clusters can be found, for example, in US Provisional Patent Application Serial No. 62/958,653, entitled "SENSOR AUTOLOCATION," filed on January 8, 2020, which is incorporated by reference in its entirety. manner is incorporated herein. The sensors of a device set can be organized into sensor modules. A set of devices may include at least one circuit board, such as a printed circuit board, to which several devices (eg, sensors and/or transmitters) are adhered or attached. Devices can be removed from the device set. For example, the sensor can be inserted and/or unplugged from the circuit board. The sensors can be individually activated and/or deactivated (eg, using switches). The circuit board may contain polymers. The circuit board may be transparent or opaque. Circuit boards may include metals (eg, elemental metals and/or metal alloys). The circuit board may contain conductors. The circuit board may contain insulators. The circuit board may contain any geometric shape (eg, rectangular or oval). The circuit board can be configured (eg, can have a shape) to allow the set to be placed in a mullion (eg, within a window). The circuit board can be configured (eg, can have a shape) to allow the set to be placed in a frame (eg, door and/or window frames). The mullion, traverse, and/or frame may include one or more apertures to allow the sensor to obtain (eg, accurate) readings. The sensor set may contain a housing. The housing may contain one or more holes to facilitate sensor readings. The circuit board may include electrical connectivity ports (eg, sockets). The circuit board can be connected to a power source (eg, electricity). The power source can include renewable or non-renewable power sources. 9 shows an example of a system 900 that includes a sensor set organized into sensor modules. Sensors 910A, 910B, 910C, and 910D are shown included in device set 905. A set of devices organized into sensor modules, including device set 905, may include at least 1, 2, 4, 5, 8, 10, 20, 50, or 500 sensors. A sensor module may include a number of sensors in a range between any of the foregoing values (eg, about 1 to about 1000, about 1 to about 500, or about 500 to about 1000). Sensors in a sensor module may include sensors configured or designed to sense parameters including temperature, humidity, carbon dioxide, particulate matter (eg, between 2.5 µm and 10 µm). time), total volatile organic compounds (eg, changes in voltage potential caused by surface adsorption of volatile organic compounds), ambient light, audio noise levels, pressure (eg, gases and/or liquids), acceleration , time, radar, lidar, radio signals (eg, UWB radio signals), passive infrared, glass breakage, or motion detectors. A set of devices (eg, 905) may include non-sensor devices such as buzzers and light emitting diodes. Examples of device sets and their use can be found in US patent application entitled "SENSING AND COMMUNICATIONS UNIT FOR OPTICALLY SWITCHABLE WINDOW SYSTEMS" filed on June 20, 2019 In Ser. No. 16/447169, this application is incorporated herein by reference in its entirety. In some embodiments, an increase in the number and/or type of sensors may be used to increase the probability that one or more measured attributes are accurate and/or that a particular event measured by one or more sensors has occurred. In some embodiments, sensors in a device set and/or different device sets may cooperate with each other. In one example, the radar sensor of the device set may determine that several individuals are present in the enclosure. A processor (eg, processor 915) may determine that the detection of the presence of several individuals in the enclosure is positively correlated with an increase in carbon dioxide concentration. In one example, the processor-accessible memory can determine that the detected increase in infrared energy is positively correlated with the temperature increase as detected by the temperature sensor. In some embodiments, the network interface (eg, 950) may communicate with other sets of devices similar to the set of devices. The network interface may additionally communicate with the controller. Individual sensors of a device set (eg, sensor 910A, sensor 910D, etc.) may include and/or utilize at least one dedicated processor. A set of devices may utilize a remote processor (eg, 954) that utilizes wireless and/or wired communication links. The set of devices may utilize at least one processor (eg, processor 952), which may include a cloud-based processor coupled to the set of devices via the cloud (eg, 951). The processors (eg, 952 and/or 954) may be located in the same building, in a different building, in a building owned by the same or different entities, in a facility owned by the manufacturer of the window/controller/device set in the middle or any other location. In various embodiments, as indicated by the dashed lines of FIG. 9, device set 905 is not required to include individual processors and network interfaces. These entities may be individual entities and are operably coupled to set 905 . The dashed lines in Figure 9 indicate optional features. In some embodiments, on-board processing and/or memory of one or more sensor sets may be used to support other functions (eg, by allocating set memory and/or processing power to a building's network infrastructure) . In some embodiments, a plurality of sensors of the same type may be distributed in the enclosure. At least one of the plurality of sensors of the same type may be part of a set. For example, at least two of the plurality of sensors of the same type may be part of at least two sets. The set of devices may be distributed in the enclosure. The enclosure may contain meeting rooms. For example, a plurality of sensors of the same type can measure environmental parameters in a conference room. In response to measurements of environmental parameters of the enclosure, a parametric topology of the enclosure can be generated. The parametric topology can be generated using output signals from any type of sensor, such as a set of devices as disclosed herein. A parametric topology can be generated for any enclosure of a facility such as conference rooms, hallways, bathrooms, cafeterias, garages, auditoriums, utility rooms, storage rooms, machine rooms, and/or elevators. FIG. 10 shows an example of a device set (eg, an assembly) 1000 with a protective housing 1001 . The housing may include mounting features that enable it to be captured in a wall mount adapter, a window frame portion section (eg, a mullion), or a ceiling mount adapter. The enclosure may expose its front face (eg, only) to a viewer in the enclosure because its main body may be positioned within a wall mount portion, ceiling mount portion, or frame portion, eg, as shown in the example of 1002. The front side can be a (eg, reversibly openable and closed) cover of the housing. The housing 1001 may include one or more features desired for optimum performance of the module in the corresponding location, such as one or more openings for admitting external environmental characteristics into the housing to facilitate sensing thereof by the sensor. For example, the housing may include one or more openings (eg, holes 1003) that facilitate air contact with temperature, humidity, pressure, and dust sensors. The opening can have any shape. The openings may contain straight lines or curvatures. The plurality of openings can form a (eg, natural or abstract) shape such as a petal. The outer shell cover may contain smooth and/or rough portions. The roughness can visually obscure the hole. The outer cover may have at least a portion that includes a rough texture, such as a screen, cloth, embossing, scoring, or dimples. Other examples of housing features include speaker or microphone grills, and apertures for camera lenses, motion sensors, or ambient light sensors. One or more openings may be exposed in the front of the housing facing the occupant of the enclosure. The housing may be covered by a textured area surrounding and/or surrounding the opening. The textured regions may be patterned or irregular. Patterns can include any geometric shape, such as space-filling polygons (eg, squares, rectangles, hexagons, or triangles). Patterns may not contain space-filling polygons. Space-filling polygons can be of a single type or of multiple types (eg, at least 2 or 3 types). Texture patterns can contain curved or straight lines. Texture patterns may not contain curved or straight lines. The textured pattern may be a mesh. The textured pattern can be formed from the same or different material as the rest of the housing. For example, the housing may be formed of plastic, and the textured region may be a mesh and/or cloth that at least partially covers the open portion of the housing. The textured pattern may include shapes similar to openings. The openings can resemble petals or leaves. The textured pattern may cover at least one eighth, one fifth, one quarter, one third or one half of the front portion of the housing, the front portion facing the occupant. The enclosure may be attached directly to fixtures, such as walls and/or ceilings, using a frame and/or by means of posts or rails. In some embodiments, the housing surrounds at least one circuit board. A circuit board can be configured to house (or house) one or more devices. These devices can be integrated into the circuit board in a reversible manner. For example, at least one device can be inserted or extracted from the circuit board (eg, for maintenance, repair, replacement, or removal). The board may have a side on which circuitry and/or devices are disposed. This side may face the front of the housing. The front of the enclosure may face an occupant in the enclosure in which the enclosure is placed. The side may face the back of the housing. The back of the shell may face away from the occupant in the enclosure in which the shell is positioned. The board may have two sides on which circuitry and/or devices are disposed. The plate may have one or more holes. The holes may facilitate passage of at least one environmental property. The holes facilitate the passage of gas, sound or electromagnetic radiation. For example, a sensor may be positioned at the back of the circuit board and sense environmental qualities from the enclosure through one or more holes to the sensor. The board can have a first device disposed on a first side and circuitry disposed on a second side. The board may have a first device disposed on a first side and a second device disposed on a second side. The board may include one or more heat sinks. The heat sinks can be placed at locations where heat is likely to be generated and/or accumulated. The plate is operably coupled to and/or includes a baffle. Spacers can be used to reduce undesired consequences (eg, interference) of coexistence of devices in the enclosure and/or on the board. The housing may contain one or more circuit boards. The circuit boards are communicatively coupled (eg, directly or indirectly) to each other. The circuit boards may be operatively (eg, communicatively) coupled to each other by wiring and/or wirelessly. The circuit board is operatively (eg, communicatively) coupled to network wiring and/or wireless. The housing may comprise elemental metals, metal alloys, polymers, resins, glass or sapphire. The shell can be made transparent or opaque. Can be transparent to any range of electromagnetic radiation disclosed herein (eg, UV, IR, and/or visible radiation).

In some embodiments, environmental properties of the enclosure can be monitored and adjusted to promote enhanced health, wellness, and/or performance of the enclosure occupant. Control may utilize at least one artificial intelligence (AI) engine. Environmental characteristics can be monitored by one or more sensors placed in the enclosure. The model may be constructed using baseline readings from sensors, a three-dimensional (abbreviated "3D") schematic view of the enclosure, and/or physical properties (eg, material properties) of the enclosure's fixtures. The control system can use the AI engine to improve the model using sensor readings of the enclosure's environment, monitor and adjust the enclosure's environment. The AI engine may refine the model based at least in part on trends and/or expected physical parameters, eg, using predictive extrapolation. The environment can be regulated, for example, by direct system management of various devices (eg, heating, ventilation, and air conditioning systems, abbreviated herein as "HVAC") and/or by using a building management system (abbreviated herein as "HVAC"). BMS") to adjust. AI modeling of closed bodies can include using parts on a mesh. The grid may be adjustable. The grid may have a higher spatial resolution than the spacing between the sensors. The grid may have varying resolutions on some of its parts. The mesh may be heterogeneous.

In some embodiments, an artificial intelligence (AI) engine may be used to control and/or predict environmental characteristics in the environment. The AI engine may provide recommendations for changes to one or more environmental characteristics based at least in part on the results (eg, predictions) of the AI engine. The AI engine can use data from one or more buildings. A facility can contain one or more buildings. The AI engine may use facility structural data (eg, buildings and/or layouts, such as workplace layouts), (eg, real-time) sensor data, simulated data, third-party data, and/or experiments (eg, sensors )material. Structural data for a facility may be configured and/or updated in real-time for historical interiors (eg, workplaces) of the current plan. Data collected from buildings may be received by a physics engine. The physics engine may use the subject property properties (eg, its preparation in time and space), the material property properties of one or more buildings, and the physics-based interaction of the topic properties with at least one material property of the one or more buildings. simulation. Physical simulations can use energy distribution simulations. The physical model may utilize occupancy characteristics (eg, predicted number of occupants, their physical properties, and/or predicted occupancy time). The physics engine may utilize the digital twin of the building that may incorporate the structure of the building and various (eg, network connections) devices in the building. The physics engine may be implemented, for example, using a processing system programmed with ray tracing software. The physics engine may generate simulation data based at least in part on material properties of the building data. The model (eg, material properties and/or configuration) can be constructed using baseline readings from sensors, a three-dimensional (abbreviated "3D") schematic diagram of the enclosure, and/or the physical properties (eg, material properties and/or configuration) of the enclosure's fixtures. Used by the physics engine and/or the AI engine). In some embodiments, the physics engine does not use baseline readings from sensors. For example, the simulation data can be used to construct a first model for use by an AI engine. Experimental (eg, sensor) data can be used to construct a second model for use by the AI engine. Experimental data can be obtained by placing a plurality of sensors that sense subject attributes in the enclosure (eg, when the enclosure is unoccupied and/or not in typical operational service (eg, undeployed)) or with substantial Collected in another enclosure similar to the subject enclosure (eg, building). Experimental data can be collected from a set of test sensing devices (eg, sensors). Models may be constructed using experimental data and/or experimental data collected from a set of devices. Raw data can be collected by sensors and/or device sets. For example, structures can be excited by signals and/or conditions, whereby a plurality of sensors measure the response signals, eg, to develop an accurate AI engine. The raw data may be cleaned (eg, using at least one filter to remove noise) to produce cleaned data (also referred to herein as "silver data") for use by the AI engine. Vertex meshes can be superimposed on closed volumes (eg, inside buildings or rooms). One or more points of interest (POIs) may be defined with reference to a vertex mesh. The AI engine may analyze the silver data using one or more models to produce a result (eg, a set of one or more values at one or more POIs). A set of one or more values at one or more POIs may be stored in a database. The control system may use the database, eg, for predictions, for recommendations, to improve models using sensor readings of the enclosure's environment, and/or to control (eg, monitor and adjust) the enclosure's environment.

FIG. 11 schematically depicts an artificial intelligence (AI) engine 1105 . Data collected from the first building 1111 , the second building 1112 , and the Nth building 1115 is received by the physics engine 1121 . N can be 1 or any integer greater than 1. Physics engine 1121 may be implemented, for example, using a processing system programmed with ray tracing software. The physics engine 1121 prepares the simulation data 1117 based at least in part on the building data. Baseline readings from sensors, three-dimensional (herein abbreviated "3D") schematics of the enclosure, and/or physical properties (eg, material properties) of the enclosure's fixtures may be used to construct a model (by a physics engine and/or or AI engine use). In some embodiments, the physics engine does not use baseline readings from sensors. For example, the simulation data 1117 may be used to construct a first model 1119 (eg, a physical simulation model) for use by the AI engine 1105 . Experimental (eg, sensor) data 1125 may be used to construct a second model 1127 (eg, using real sensor data from experiments and/or deployed sensors) for use by the AI engine. Experimental data 1125 is collected from a set of test sensing devices 1123 (eg, sensors used for testing). A second model 1127 is constructed using the experimental data 1125 and the data collected by the device set 1101 . In the example shown in FIG. 11 , the raw data is collected by device set 1101 . Raw data is cleaned and/or filtered by cleaning and/or filtering module 1103 to generate silver data for use by AI engine 1105. The vertex mesh can be superimposed within a virtual representation of an enclosure 1107 (eg, a building, floor, or room). A virtual closed volume model including mesh vertices may include regions of interest and/or points of interest (POIs). One or more points of interest (POIs) may be defined with reference to a vertex mesh. The AI engine 1105 analyzes the silver data using a first model 1119 and a second model 1127 (eg, a real sensor data-driven model) to generate a set of one or more values at one or more POIs. A set of one or more values at one or more POIs may be stored in the insights database 1109 . The AI engine can compute the results of sensor values (eg, at points of interest) and store them in the insights database 1109 . The control system can use the insight database 1109 to improve the model using sensor readings of the enclosure environment, monitor and adjust the environment of the enclosure 1107.

In some embodiments, one or more environmental characteristics are measured within the enclosure using one or more sensors. Virtual (eg, electronic) maps are used to model enclosures and control environmental properties. The virtual map may be a terrain type map. The map may include one or more levels of at least one sensed environmental characteristic. In some embodiments, the enclosure may be divided into sections that form a mesh. A mesh may divide an enclosed volume into mesh parts (eg, mesh segments). In some embodiments, the mesh includes several vertices. The user can define a point of interest in the enclosure (abbreviated herein as "POI"). The POI may include a sensor and/or may be at a distance from the sensor. When the POI is in a sensor-free location, data from one or more sensors (eg, placed at mesh vertices adjacent to the point of interest) can be input into the model for extrapolation of the point of interest Location sensing properties.

12 shows an example of a flow diagram illustrating one example of a set of environmental properties for a simulated facility. In block 1201, the set of environmental properties of the facility is simulated by meshing the facility using the vertices. In block 1202, a first selection designating a vertex from the mesh as the first POI is received. In block 1203, the set of environmental properties at the first point of interest is simulated with greater accuracy relative to non-selected vertices of the mesh. In block 1205, a second selection is received specifying a second POI that is not on the grid. At block 1209, in some embodiments, the grid is altered in response to the second selection. At block 1207, in some embodiments, the second POI is migrated to the closest vertex on the mesh.

In some embodiments, the enclosure may be divided into a plurality of mesh portions that form a mesh. A mesh divides a closed volume into mesh parts. In some embodiments, the mesh includes several vertices. A grid can be defined according to a coordinate system. The coordinate system may include a Cartesian coordinate system, a polar coordinate system, a cylindrical coordinate system, a canonical coordinate system, or a trilinear coordinate system. For example, a grid may be defined according to an x, y, z Cartesian coordinate system. The mesh can contain space-filling polygons. Grids can contain tessellation. A mesh may contain (eg, within a closed volume) a boundary representing a topology model. A mesh can be defined such that there is a minimum distance and a maximum distance between any two adjacent vertices of the mesh. Two adjacent vertices of the mesh can be placed in a closed volume. For example, the minimum distance may be about 1 foot, and the maximum distance may be about 3 feet. A mesh can be defined such that there is a constant distance between any two adjacent vertices of the mesh. For example, the constant distance can be about 2 feet. A mesh divides a closed volume into sections. The portions may have the same basic length dimension. The basic length dimension may include the length, width, height or radius of the bounding circle. The basic length scale is abbreviated herein as "FLS". The FLS of the mesh portion may be smaller than the FLS of the closed volume. There may be multiple mesh sections in a closed volume.

In some embodiments, one or more sensors are placed throughout the enclosure (eg, facility). Sensors of one or more portions may be positioned at grid coordinates (eg, at the vertices of the grid). The sensors may be located (i) at the vertices, or (ii) between the vertices. At least one sensor may be located at the vertices, with one or more sensors located between the vertices. The user can define the points of interest in the enclosure. The POI includes the sensor or is at a distance from the sensor. When the point of interest is in a location without sensors, data from one or more sensors (eg, placed at mesh vertices adjacent to the point of interest) can be input into the model for extrapolation of the point of interest Sensing property at the point. When mesh points (eg, vertices) do not contain sensors, data from one or more sensors (eg, placed at other mesh vertices adjacent to the vertex of interest) may be input into the model for use in The sensed attribute at the vertex of interest without the sensor is extrapolated.

In some embodiments, segmenting the facility using a grid is performed. Segmentation can be performed manually and/or automatically. For example, a user can manually alter the mesh, eg, by adding one or more vertices to the mesh. The mesh may be automatically segmented, eg, in response to receiving user input specifying POIs that are not mesh vertices. A mesh can include intersections that can be considered vertices. The mesh can be automatically divided, eg, by specifying and/or changing the mesh size of the mesh. The mesh can be automatically divided, eg, by adding one or more additional points to the mesh. The parts of the grid can have the same FLS. The parts of the grid can have different FLSs. The mesh can be formed from space-filling polygons. Space-filling polygons can be of at least one type, two types, three types, or more than three types. POIs can be visualized as precise points on a grid (mesh). The grid may be placed manually (eg, for some properties such as sound) and/or automatically (eg, for other properties such as temperature). The determination of whether to place the grid manually or automatically can be performed based on attributes (eg, humidity, temperature, _CO2 , VOC, atmospheric movement, and/or ventilation velocity) and/or covered (eg, significant) variability of the data. Manual or automatic grid placement can be performed depending on the room (eg, size, location, opening) and/or depending on grid density (eg, an enclosure may have multiple sensors or a single sensor). determination. A mesh can have multiple vertices or a single vertex in a closed volume. This minimizes the required number of grid points.

In some embodiments, if the POI is not on the grid, the POI will migrate to the closest grid point. Migration can be facilitated using "snap to grid" procedures (eg, algorithms). The POI may coincide with the location of the sensor. The POI can be coincident with the location without the sensor. The POI can be a certain distance from the sensor.

In some embodiments, sensor data from relevant sensors is input into the model for extrapolation of sensed properties, such as to compensate for the absence of sensors at grid points (eg, vertices). The behavior of attributes in space and/or time can be calculated and/or estimated. The calculation and/or estimation may utilize the physical behavior of the sensed property and/or the accumulated data about the sensed property. Cumulative data can be in enclosures or in similar enclosures. Similar enclosures may be in the facility or outside the facility (eg, in the distal site). Similar enclosures may have similar settings and/or experience similar environmental conditions. The behavior of a sensed attribute (eg, its data) sensed by a first sensor at a first location can be extrapolated to a second location at a distance from the first sensor that does not have the first sensor. Two sensors. For example, a first set of measured attribute data from the first sensor can be used to simulate a virtual second set of attribute data, the attributes being environmental characteristics sensed by the first sensor. The first parts may be mesh vertices. The second location may be a mesh vertex, or may be a location outside the mesh vertex. The third portion may have a third sensor of sensing properties. Data from the third sensor can be used to simulate a virtual second set of attribute data at the second location. The third location may be at another mesh vertex. A second data set derived from the first sensor data can be compared to a second data set derived from the third sensor data. The comparison can measure differences that can be used to optimize model and/or extrapolation of virtual sensor data. The model can be optimized iteratively. Iterative optimization can use (i) data from different sensors, (ii) data from the same sensor sensing different times, or (iii) any combination thereof. Predictive models of physical parameters can be compared between themselves (eg, using different sets of sensed data to estimate environmental properties at the site) and/or with those from sensors (eg, placed on mesh vertices and/or the actual (eg, real-world) readings outside the mesh vertices. This comparison can be used to further improve the model.

In some embodiments, an initial physics simulation is performed to simulate the propagation of environmental properties within the enclosure. Individual simulations may be performed for environmental characteristics (eg, for each environmental characteristic). The results of the physical simulation can be compared to sample (eg, naturally occurring and/or manually orchestrated) real-time sensor readings of the environment. This comparison can be performed during the experimental phase. A delta (eg, a difference) may be formed between the physical simulation and the reality sensed in the environment. In response to the delta, the neural network model can be modified. Based on the comparison results, the neural network model may take into account a physics engine (eg, a model). A physics engine can contain several models. One or more sensor samples can be used to simulate additional samples. Parametric analysis can be performed to feed into the model. Analysis can be focused on representative samples. The analysis may utilize information from the Building Performance Database (BPD) in the jurisdiction, such as the database maintained by the US Department of Energy. BPD may combine, cleanse, and/or anonymize information collected from buildings by jurisdictional authorities (eg, federal, state, and/or local governments), utilities, energy efficiency programs, building owners, and/or private companies. BPD makes this information available to the public. Various physical and operational characteristics for multiple building types can be stored in the BPD, for example to document trends in energy performance.

In some embodiments, one or more sensors are placed in mesh vertices for experiments that correlate (eg, verify) measurements. Learning models (eg, using artificial intelligence (AI), such as neural networks, linear regression, or polynomials) can be used to modify tunable coefficients in the model based on sensor samples and based on deltas. In some embodiments, physical simulations may not be used to learn the update procedure in the model. A weighted average can be used to populate sensor readings for grid points that do not contain sensors.

13 shows an example of a flow diagram depicting an illustrative modification of the learning model. At block 1301, a learning model of the facility is generated. The learning model associates building fixtures with corresponding materials. Next, at block 1302, any devices of the facility (eg, sensors) are incorporated into the model. At block 1305, any non-fixed materials are incorporated into the model. At block 1306, any facility openings (eg, windows, doors, and/or ventilation openings) are incorporated into the model. At block 1307, the date, time, weather conditions, sun location, and/or solar radiation (eg, penetration into the facility) are incorporated into the model. At block 1308, environmental characteristics are simulated in the environment of at least a portion of the facility. A model may utilize a mesh of nodes (eg, vertices).

14 shows an example of a flowchart depicting instance modeling of a mesh using vertices. At block 1401, nodes (eg, vertices) of the mesh from the learning model are designated as first points of interest (POIs). At block 1403, specified nodes (eg, vertices) are simulated with greater accuracy relative to non-specified nodes (eg, vertices). Next, at block 1405, a second point of interest (POI) that is not on the grid is specified. According to one embodiment, at block 1409, the grid is changed in response to user input. According to one embodiment, at block 1407, the second point of interest is migrated to the closest point on the grid.

15 shows an example of a flowchart depicting an example collection of sensor data. At block 1501, a first data set is collected from the sensor. At block 1503, a first environment dataset is simulated at mesh vertices using the first dataset. Next, at block 1505, a second data set is collected from the sensor. At block 1507, a second set of environmental data is simulated at mesh vertices using the second data set. Next, at block 1509, any differences between the first set of environmental data and the second set of environmental data are determined. At block 1511 , the predicted model of the environmental parameter is iteratively improved using the determined differences, and the sequence of operations loops back to block 1501 . Optionally, the operational sequence flow forms blocks 1511 through 1513, where sensor readings, historical data, and/or third-party data are applied to the model to improve the model. The sequence of operations then loops back to block 1501.

In some embodiments, a deep convolutional neural network (eg, deep learning) may be used to populate sensor readings of grid points that do not contain sensors. One or more sensors may be placed in missing grid points for training, model tuning, and/or model validation purposes. This placement can occur in samples of enclosed volumes. Regression analysis can be performed to populate sensor readings for grid points that do not contain sensors. Analysis can be performed using the input and output of the function. Linear regression (eg, weighted average) can be used to populate sensor readings for grid points that do not contain sensors. A non-linear function can be used to populate sensor readings for grid points that do not contain sensors. Non-linear functions can be used, for example, in situations of constant variation (eg, light sensors measure a minimum amount of natural light at night, no fluctuations in CO ₂ at night). A linear function can be used to populate sensor readings for grid points that do not contain sensors, such as where there are unequal variations (eg, non-linear flux and temperature during the day).

16 shows an example of a flow diagram depicting an illustrative execution of environmental adjustments. At block 1601, any discrepancies between analog sensor readings and real-time (and/or on-site ) sensor readings in at least a portion of the facility are determined. At block 1603, this difference is used to adjust the set of coefficients in the learned model. Next, at block 1605, simulated virtual sensor readings are derived in mesh vertices that do not contain physical sensors. Optionally, during the training phase, at block 1607, physical sensors are placed at mesh vertices that do not contain physical sensors. The sequence of operations proceeds from block 1605 or block 1607 to block 1609, where the model is used to adjust the environment in at least a portion of the facility. At block 1611, the model is updated with adjustments to account for the environment.

In some embodiments, the learned model continues to be used to update the model after system deployment. Using the learned model, the control system can be directed to adjust or can be directed to adjust the environment of the enclosure (eg, preemptively).

In some embodiments, various environmental properties of the enclosure are controlled (eg, monitored and/or adjusted). These characteristics can be controlled to provide an optimal occupant environment (eg, in terms of hygiene, health, and/or comfort). One or more environmental characteristics can be monitored by sensors. The sensor may be placed in the enclosure. One or more models may be constructed using baseline readings and/or a 3D representation of the space. At least one controller (eg, a control system) and/or processor may use an AI algorithm. AI algorithms may include predictive extrapolation. Predictive extrapolation can be based, at least in part, on trends and/or expected physical parameters. AI algorithms can be used to further refine the model using sensor readings from enclosed volume spaces. AI algorithms can be used to control the environment of a closed body. Controlling the environment may include directly or indirectly controlling any device. The device may be operatively coupled to a building (eg, HVAC). Indirect control may include the use of a building management system (BMS). The BMS may or may not be communicatively coupled to the controller. The BMS may or may not be communicatively coupled to the processor. AI modeling of closed volume spaces can include locations on meshes. AI modeling of closed volume space can utilize the parts on the grid. Portions of the grid may have a different spatial resolution (eg, higher or lower) than the spacing between the sensors.

In some embodiments, the enclosure includes one or more sensors. Sensors can facilitate controlling the environment of an enclosure, eg, so that the occupants of the enclosure can be more comfortable, desirable, beautiful, healthy, productive (eg, in terms of resident performance), easier to live (eg, work), or any combination of environments. The sensors can be configured as low or high resolution sensors. The sensor may provide an on/off indication of the occurrence and/or presence of an environmental event (eg, a pixel sensor). In some embodiments, the accuracy and/or resolution of the sensor may be improved through analysis of the sensor's measurements by artificial intelligence (abbreviated herein as "AI"). Examples of artificial intelligence techniques that may be used include: reactivity, limited memory, theory of mind, and/or self-awareness techniques.

In some embodiments, sensor data analysis includes linear regression, least squares fitting, Gaussian procedure regression, kernel regression, nonparametric multiplicative regression (NPMR), regression trees, local regression, semiparametric regression, isotonic regression, multiple Variable Adaptive Regression Splines (MARS), Logistic Regression, Robust Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, Principal Component Analysis (PCA), Singular Value Decomposition, Fuzzy Measure Theory, Polet Borel measure, Han measure, Risk neutral measure, Lebesgue measure, Grouped data disposition method (GMDH), Naive Bayes classifier, k nearest neighbor algorithm (k-NN) ), support vector machine (SVM), neural network, support vector machine, classification and regression tree (CART), random forest, gradient boosting or generalized linear model (GLM) techniques. Sensors can be configured to process, measure, analyze, detect and/or respond to: data, temperature, humidity, sound, force, pressure, concentration, electromagnetic waves, location, distance, Movement, flow, acceleration, speed, vibration, dust, light, glare, color, gas, type, and/or other aspects (eg, properties) of the environment (eg, enclosure). Gases may include volatile organic compounds (VOCs). Gases may include carbon monoxide, carbon dioxide, water vapor (eg, humidity), oxygen, radon, and/or hydrogen sulfide. One or more sensors may be calibrated at the factory and/or in the facility. A sensor can be optimized to perform accurate measurements of one or more environmental characteristics that exist in a factory setting and/or in the facility in which the sensor is deployed.

In some embodiments, the processor interfaces with actuators and/or sensors. This interface may be provided for control purposes. The processor may include a hierarchy of controllers. The processor can control enclosures such as smart buildings. A smart building can be any structure that uses one or more automation programs to automatically control the operation of the building. Such automated procedures may include heating, ventilation, air conditioning, lighting, security, shade control and/or other systems. Smart buildings can use sensors, actuators, and/or microchips to collect data. Smart buildings can use this data to manage the building's environment. This infrastructure helps owners, operators and facility managers to enhance the comfort of building occupants. Energy usage can be reduced. Improves the way space is used. It can reduce the environmental impact of the building.

In some embodiments, the enclosure may have an interactive system. An enclosure can be a collection of parts of a facility, room, and/or multiple buildings. The closure can be any of the closures disclosed herein. A processor may operate in a network environment, eg, the processor may be operatively (eg, communicatively and/or physically) coupled to a network. The network environment can be configured for remote (eg, cloud) interaction. Remote interactions may include users and/or service providers. The network environment may include wired and/or wireless communications. The processor may execute the control scheme. Control schemes may include feed forward, fast forward, open loop and/or closed loop. The processor may control the BMS and/or any controllable devices, such as sensors, transmitters, antennas, or tintable windows (eg, IGUs). The controllable device may comprise an optically controllable electrochromic device. The processor may be communicatively coupled to the sensor and/or the transmitter. Multiple sensors, transmitters, actuators, transmitters and/or receivers may be integrated into a single assembly. A single assembly is available as a digital building element. The general-purpose processor may be communicatively coupled to other output devices. Other output devices may include the HVAC system and/or one or more antennas.

In some embodiments, processing data derived from the sensor includes applying one or more models. Models may contain mathematical models. Processing may include model fitting (eg, curve fitting). Models can be multi-dimensional (eg, two-dimensional or three-dimensional). Models can contain linear or nonlinear equations. Models can contain exponential or logarithmic equations. A model can contain one or more Boolean operations. The model can consider closed volumes. It is contemplated that the enclosure may include the structure and/or composition of the enclosure. The composition of the enclosure can include the material composition of any immobilized and/or non-immobilized form in the enclosure. The model may take into account building information modeling (BIM) (eg, a Revit file) of the enclosure before, during, and/or after its construction. Models may consider two-dimensional (eg, floor plans) and/or three-dimensional modeling (eg, 3D model renderings) of enclosed volumes. The model may or may not contain finite element analysis. Models can contain simulations or be used in simulations. The simulation may be a simulation of at least one environmental property of at least a portion of the enclosure (eg, depicting states in various locations within the enclosure, such as POIs). The model can be represented as a graph (eg, a 2- or 3-dimensional graph). For example, a model can be represented as a contour map. Modeling can contain one or more matrices. Models can contain topological models. The model can be related to the topology of the sensed parameter in the enclosure. The model can be related to the temporal variation of the topology of the sensed parameter in the enclosure. Models can be environment and/or enclosure specific. The model may take into account one or more properties of the enclosure (eg, size, openings, and/or environmental distractors (eg, transmitters)). Processing of sensor data may utilize historical sensor data and/or current (eg, real-time) sensor data. Data processing (eg, using models) may be used to predict environmental changes in enclosures and/or suggest actions to mitigate, adjust, or otherwise respond to changes.

In some embodiments, the model of the enclosure includes the architecture of the building (eg, including one or more fixtures). The model may be a 3D model. The model identifies one or more materials contained in these fixtures. The model may include a building information modeling (BIM) software (eg, Autodesk Revit) product (eg, file). BIM products allow users to design buildings using parametric modeling and drawing components. In some embodiments, BIM is a computer-aided design (CAD) paradigm that allows intelligent, 3D, and/or parametric object-based design. A BIM model can contain information about the entire life cycle of a building from concept to construction to decommissioning. This functionality may be provided by the underlying relational database architecture of the BIM model which may be referred to as a parameter change engine. BIM products can use .RVT files to store BIM models. Parametric objects (whether 3D building objects (such as windows or doors) or 2D drawing objects) can be called families and can be saved in .RFA files and imported into the RVT database. Many sources of pre-drawn RFA libraries exist.

BIM (eg, Revit) allows users to create parametric components in a graphical "family editor". The model captures the relationships between components, views, and annotations so that any component changes are automatically propagated to keep the model consistent. For example, moving walls updates adjacent walls, floors, and roofs, corrects placement and values of dimensions and annotations, adjusts floor areas reported in schedules, redraws sections, and more. BIM may facilitate continuous connection, update and/or coordination between models of a facility and (eg, all) documents, eg, to simplify instant updates and/or instant revisions of models. The concept of bidirectional associativity between components, views and annotations can be a feature of BIM.

BIM models can use a single file database that can be shared among multiple users. Plans, sections, elevations, legends and schedules can be interconnected. BIM can provide (eg, full) two-way associativity. Therefore, if the user makes changes in one view, the other views can be automatically updated. Likewise, the BIM file can be automatically updated in response to input received from the sensor. The BIM schema and/or schedule can be fully coordinated as far as the building objects shown in the schema are concerned. 3D objects may be used to draw infrastructure (eg, buildings) to create fixtures (eg, walls, floors, roofs, structures, windows and/or doors) and other objects as desired. A BIM model (eg, a BIM virtual model or a BIM virtual file) may incorporate information about the structure and/or materials associated with the facility. In general, if components of the design are to be visible in more than one view, then the components can be created using 3D objects. Users can create their own 3D and 2D objects for modeling and drawing purposes. Small scale views of building components can be created using a combination of 3D and 2D drawing objects or by importing drawing work done in another computer aided design (CAD) platform, eg via DWG, DXF, DGN, SAT or SKP.

In some embodiments, when using BIM to share a project database, a central file may be created that stores the master of the project database on a file server. Users can work with copies of central files (called local files) stored on their workstations. Users can save to the central file to update the central file with their changes, and receive changes from other users. Whenever a user starts working on an object in the database, the BIM model can be checked against the central file to see if another user is editing the object. This procedure prevents two people from making the same changes at the same time and creating a conflict. Multiple Disciplines working on the same project together can generate their own project database and link the database from other consultants for validation. BIM can perform interference checks, which detect whether different components of a building are occupying the same physical space.

In some embodiments, when a structural change occurs in a facility, the BIM model may require manual updating of at least one file associated with the facility to document the change and keep it updated. The control system (eg, using the facility's sensors) may feed structural updates (eg, automatically) to the BIM model, AI engine, and/or physics engine. Structural updates fed by the control system can be done on-the-fly (eg, when changes occur) or when the facility is not occupied (eg, at night, during weekends, or during holidays). Updates can be scheduled (eg, pre-scheduled). The update may occur within the timeframe closest to the structural change made (eg, the first time the facility is idle after the structural change has been made). Updates and/or sensor scans may be performed at predetermined (eg, pre-scheduled) intervals.

In some embodiments, one or more models (as disclosed herein) are used by an AI engine. Models can incorporate non-fixed materials such as water occupied by pipes, heat capacity of materials, optical absorptivity/reflectivity, thermal characteristics, acoustic properties, and/or exhaust/VoC contrast temperatures of materials. Models may incorporate openings, time of day, sun angle and/or penetration depth. The model can be applied to situations where room assignments and/or walls are unknown. The model can be applied to situations known to drywall, corridors, open areas, reception areas, stairs and/or enclosed areas. Models may include building elements such as fixtures and non-fixes. Building elements may include partitions, walls, floors, roofs, structures, windows, doors, ceilings, cabinets, furniture, tables, compartments, benches, chairs, ventilation pipes, electrical conduits, lighting fixtures, water supply lines, roof ventilation Ports and/or pipes for utilities. The model may associate the fixture with one or more physical properties, such as the fixture's material, the fixture's heat capacity, the fixture's acoustic properties, and/or any of several other physical properties.

Models may include information on energy-related properties of commercial and/or residential buildings. For example, as previously mentioned, the model may include information from the Building Performance Database (BPD) maintained by the US Department of Energy. In some embodiments, BPD may be combined, cleaned, and/or anonymized from buildings by jurisdictional authorities (eg, federal, state, and/or local governments), utilities, energy efficiency programs, building owners, and/or private companies data collected. Various physical and operational characteristics for multiple building types can be stored in the BPD, for example to document trends in energy performance. BPD may allow users to create and/or save custom data sets based on certain variables including, for example, building type, location, size, age, equipment and/or operating characteristics. BPD may allow users to compare buildings using statistical or actuarial methods. The BPD may include a graphical web interface and/or a web application programming interface (API), which may allow applications and/or services to query the BPD dynamically.

In some embodiments, an initial physics simulation is performed to simulate the propagation of environmental properties within the enclosure. Individual simulations may be performed for environmental characteristics (eg, for each environmental characteristic). The AI model can be configured using the output of the physical simulation. The AI model may be an AI engine including a neural network, or any other sensor analysis method and/or mathematical model disclosed herein. Physical simulations can be lengthy, eg, on the order of hours or days. Physics simulations can simulate the interior as well as the exterior of closed volumes. A physics simulation can simulate the (eg, entire) internal environment of an enclosed volume. The internal environment may encompass areas beyond the perimeter skin of the enclosure. The interior of the enclosed volume can be modeled based at least in part on a mesh of nodes (eg, vertices). The mesh of vertices may be the intersections of 3D mesh lines. There can be any number of vertices in the mesh. The grid can have constant density or varying density. For example, at least a portion of the grid can have a higher density (eg, adjacent to and including POIs). One or more sensors may be placed throughout the enclosure. At least one of the sensors can be included in a sensor set (eg, a sensor kit). A sensor set may include any of the devices disclosed herein (eg, sensors, transmitters, controllers, and/or antennas). Sensors can be placed at the coordinates of the grid. The mesh may have vertices occupied by at least one sensor. A mesh can have vertices that do not contain any sensors.

In some embodiments, the model uses a variable value model. The variable value model may be a univariate value model or a multivariate value model. A univariate model can be applied to one type of environmental characteristic (and uses a corresponding one type of sensor data). Multivariate value models can be applied to multiple types of environmental characteristics (and use corresponding multiple types of sensor data). Multivariate value models can be applied to one type of environmental characteristic (and use multiple types of sensor data). Variable-value models determine missing value imputation. Missing value imputation can be used to increase confidence in sensor readings (eg, to verify that sensor readings are correct). A multivariate value model may use sensor readings for different properties (eg, different environmental characteristics). A multivariate model can use sensor readings at different parts of the enclosure (eg, different rooms in a floor, different floors in a building, or different buildings in a facility). A univariate model may use (eg, only) one sensor property. Variation models may use anomaly detection for sudden peaks and/or outliers.

17 shows an example of a schematic cross-section of an electrochromic device 1700 in accordance with some embodiments. EC device coating is attached to substrate 1702, transparent conductive layer (TCL) 1704, electrochromic layer (EC) 1706 (sometimes referred to as cathodically colored layer or cathodically colored layer), ion conducting layer or zone (IC) 1708 , a counter electrode layer (CE) 1710 (sometimes referred to as an anodically colored layer or anodically colored layer), and a second TCL 1714. Elements 1704 , 1706 , 1708 , 1710 , and 1714 are collectively referred to as electrochromic stack 1720 . A voltage source 1716 operable to apply a potential across the electrochromic stack 1720 effects the transition of the electrochromic coating from, for example, a clear state to a colored state. In other embodiments, the order of the layers is reversed with respect to the substrate. That is, the layers are in the following order: substrate, TCL, opposing electrode layer, ion conducting layer, electrochromic material layer, TCL.

In various embodiments, the ion conductor region (eg, 1708) may be formed from a portion of the EC layer (eg, 1706) and/or from a portion of the CE layer (eg, 1710). In such embodiments, an electrochromic stack (eg, 1720) may be deposited to include a cathodically dyed electrochromic material (EC layer) in direct physical contact with an anodically dyed counter electrode material (CE layer). Ionic conductor regions (sometimes referred to as interfacial regions or ionically conductive substantially electronically insulating layers or regions) can be formed where the EC and CE layers meet, eg, via heating and/or other processing steps. Examples of electrochromic devices (eg, including those fabricated without deposition of phase dissimilar ionic conductor materials) can be found in a May 2, 2012 filing entitled "ELECTROCHROMIC DEVICES)" in US Patent Application Serial No. 13/462,725, which is incorporated herein by reference in its entirety. In some embodiments, the EC device coating may include one or more additional layers, such as one or more passive layers. Passive layers can be used to improve certain optical properties, to provide moisture and/or to provide scratch resistance. These and/or other passive layers can be used to hermetically seal the EC stack 1720. Various layers including transparent conductive layers (such as 1704 and 1714) may be treated with anti-reflective and/or protective layers (eg, oxide and/or nitride layers).

In certain embodiments, the electrochromic device is configured to cycle (eg, substantially) reversibly between a transparent state and a colored state. Reversible can be within the expected lifetime of the ECD. The life expectancy can be at least about 5, 10, 15, 25, 50, 75 or 100 years. The life expectancy can be any value between the foregoing values (eg, about 5 years to about 100 years, about 5 years to about 50 years, or about 50 years to about 100 years). A potential can be applied to the electrochromic stack (eg, 1720 ) such that available ions in the stack that can cause the electrochromic material (eg, 1706 ) to be in a colored state have the window in a first hue state (eg, clear) It resides primarily in the opposite electrode (eg, 1710). When the potential applied to the electrochromic stack is reversed, ions can be transported across the ion conducting layer (eg, 1708) to the electrochromic material and bring the material into a second hue state (eg, a colored state).

It should be understood that the reference to the transition between the clear state and the colored state is non-limiting and suggests only one example of many examples of electrochromic transitions that may be implemented. Unless otherwise specified, herein, whenever a clear to colored transition is referenced, the corresponding device or procedure encompasses other optical state transitions, such as non-reflective to reflective and/or transparent to opaque transitions. In some embodiments, the terms "clear" and "bleached" refer to an optically neutral state, such as unpigmented, transparent, and/or translucent. In some embodiments, the "color" or "hue" of an electrochromic transition is not limited to any wavelength or range of wavelengths. Selection of appropriate electrochromic materials and opposing electrode materials can control the relative optical transition (eg, from a colored state to an uncolored state).

In certain embodiments, at least a portion (eg, all) of the materials that make up the electrochromic stack are inorganic, solid (ie, in a solid state), or both inorganic and solid. Because various organic materials tend to degrade over time, especially when tinted building windows are exposed to heat and UV light, inorganic materials give the advantage of reliable electrochromic stacks that can function for extended periods of time. In some embodiments, materials in a solid state may provide the advantage of minimal contamination and minimize leakage problems, as materials in a liquid state do sometimes. One or more of the layers in the stack may contain an amount of organic material (eg, measurable). The ECD or any portion thereof (eg, one or more of the layers) may contain little or no measurable organic matter. The ECD or any portion thereof (eg, one or more of the layers) may contain one or more liquids which may be present in very small amounts. Few can be at most about 100 ppm, 10 ppm or 1 ppm of ECD. Solid state materials may be deposited (or otherwise formed) using one or more procedures employing liquid components, such as certain procedures employing sol-gel, physical vapor deposition, and/or chemical vapor deposition.

18 shows an example of a cross-sectional view of a tintable window embodied in an insulating glass unit ("IGU") 1800, according to some implementations. The terms "IGU", "tintable window" and "optically switchable window" are used interchangeably herein. When an IGU is provided for installation in a building, it may be desirable for the IGU to serve as the basic construct for holding the electrochromic pane (also referred to herein as a "window"). IGU windows can be of single-substrate or multi-substrate construction. The window may comprise, for example, a laminate of two substrates. An IGU (eg, having a two-pane or three-pane configuration) can provide several advantages over a single-pane configuration. For example, a multi-pane configuration may provide enhanced thermal isolation, noise isolation, environmental protection, and/or durability when compared to a single-pane configuration. A multi-pane configuration provides enhanced protection for ECDs. For example, an electrochromic film (eg, and associated layers and conductive interconnects) can be formed on the inner surface of a multi-pane IGU and protected by an inert gas filling the interior volume (eg, 1808) of the IGU. The inert gas filling can provide at least some (thermal) insulating function to the IGU. Electrochromic IGUs can have thermal barrier capabilities, for example, by means of colorable coatings that absorb (and/or reflect) heat and light.

In some embodiments, an "IGU" includes two (or more) substantially transparent substrates. For example, an IGU may include two panes of glass. At least one substrate of the IGU can include electrochromic devices disposed thereon. One or more panes of the IGU may have separators disposed therebetween. The IGU may be of hermetically sealed construction, eg, having an interior region isolated from the surrounding environment. A "window assembly" may include an IGU. A "window assembly" may include (eg, stand alone) laminates. A "window assembly" may include one or more electrical leads, eg, for connecting to the IGU and/or laminate. The electrical leads can operatively couple (eg, connect) one or more electrochromic devices to voltage sources, switches, and the like, and can include a frame that supports the IGU or laminate. The window assembly may include a window control, and/or components of the window control (eg, a docking piece).

18 shows an exemplary implementation of an IGU 1800 that includes a first pane 1804 having a first surface S1 and a second surface S2. In some implementations, the first surface S1 of the first pane 1804 faces an external environment, such as an outdoor or external environment. The IGU 1800 includes a second pane 1806 having a first surface S3 and a second surface S4. In some implementations, the second surface (eg, S4 ) of the second pane (eg, 1806 ) faces the interior environment, such as a house, building, vehicle, or compartment thereof (eg, an enclosure therein, such as a room) the internal environment.

In some implementations, the first and second panes (eg, 1804 and 1806) are transparent or translucent, eg, transparent or translucent at least for light in the visible spectrum. For example, each of the panes (eg, 1804 and 1806) may be formed from a glass material. Glass materials may include architectural glass and/or shatterproof glass. The glass may contain silicon oxide (SO _x ). The glass may comprise soda lime glass or float glass. The glass may contain at least about 75% silicon dioxide (SiO ₂ ). _The glass may contain oxides such as Na2O or CaO. The glass may contain alkali metal or alkaline earth oxides. The glass may contain one or more additives. The first and/or second panes may comprise any material having suitable optical, electrical, thermal and/or mechanical properties. Other materials (eg, substrates) that may be included in the first and/or second panes are plastic, semi-plastic and/or thermoplastic materials such as poly(methyl methacrylate), polystyrene, polycarbonate, Allyl diethylene glycol carbonate, styrene acrylonitrile copolymer (SAN), poly(4-methyl-1-pentene), polyester and/or polyamide. The first and/or second panes may include a specular material (eg, silver). In some implementations, the first and/or second panes can be enhanced. Strengthening may include tempering, heating and/or chemical strengthening.

Sometimes the relationship between a measured attribute (eg, measured by one or more sensors) and time exhibits repetitive behavior. Such properties can lead to various predictive behaviors. Alerts may be provided when the predicted behavior does not occur as predicted (eg, within a threshold). Alerts may signal unacknowledged behavior (eg, may indicate unacknowledged behavior). Unacknowledged signals may be due to environmental changes, sensor changes, or both. FIG. 19 shows examples of various sensors measuring temperature over several days, with the sensor data superimposed on the graph shown in FIG. 19 . Temperature values are higher during the day than at night, hence the daily pattern. For each cycle such as 1900, an elevation such as 1920 appears to increase with a steeper slope (eg, a higher absolute value of the slope) than the slope of temperature drop such as 1910. Time series and values such as the daily maximum of 1930 may also be visible.

While preferred embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. It is not intended that the present invention be limited to the specific examples provided within this specification. While the invention has been described with reference to the foregoing specification, the description and illustration of the embodiments herein are not intended to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it is to be understood that all aspects of the invention are not limited to the specific depictions, configurations, or relative proportions set forth herein, which depend upon various conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. Accordingly, it is intended that the present invention also covers any such alternatives, modifications, variations or equivalents. It is intended that the following patent claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

100: Computer system 101: Computer network 102: Memory/memory part 103: Communication interface 104: Electronic storage unit 105: Peripheral device 106: Processing unit/processor 200: Control system architecture 204: Local controller 206: Floor Controller 208: Main Controller 210: External Source 220: Database 224: Building Management System (BMS) 305: Roof Donor Antenna 305a: Roof Donor Antenna 305b: Roof Donor Antenna 307: Sky Sensor 309: Solid Line /High Speed Line 311: CSP 313: Control Panel 319: Data Carrying Line 321: Trunk Line 323: Device Collection 325: Antenna 350: Plane 400: Building Network 405: Main Network Controller 410: Lighting Control Panel 415: Building Management System 420: Security Control System 425: User Console 430: HVAC System/HVAC 435: Lights 440: Security Sensors 445: Door Locks 450: Cameras 455: Tintable Windows 500: Closures 510 : device or device set 520: network 600: graph 605: controller 608: communication link 610A: sensor 610B: sensor 610C: sensor 610Z: sensor 615A: sensor 615B: sensor 615C: Sensor 615Z: Sensor 620A: Sensor 620B: Sensor 620C: Sensor 620Z: Sensor 685A: Sensor 685B: Sensor 685C: Sensor 685Z: Sensor 700: Diagram 702: Meeting Room 705A: First Device Set 705B: Second Device Set 705C: Third Device Set 710: Group 715A: Point 715B: Point 715C: Point 725A: Sensor Output Reading Distribution 725B: Sensing Sensor Output Reading Distribution 725C: Sensor Output Reading Distribution 730: CO ₂ Graph 735A: Dot 735B: Dot 735C: Dot 740: Noise Graph 745A: Dot 745B: Dot 745C: Dot 750: Sensor Distribution Curve 750A :Curve 750B:Curve 750C:Curve 750D:Curve 750E:Curve 751:Sensor Distribution Curve 751A:Curve 751B:Curve 751C:Curve 751D:Curve 751E:Curve 801:First Occupant 802:Second Occupant 803: Third occupant 804: Fourth occupant 805: Fifth occupant 806: Sixth occupant 807: Seventh occupant 808: Eighth occupant 809: Ninth occupant 900: System 905: Device set 910A: Sense Sensor 910B: Sensor 910C: Sensor 910D: Sensor 915: Processor 950: Network Interface 951: Cloud 952: Processor 954: Remote Processor 1000: Device Set (eg, Assembly) 1001 : Protective Enclosure 1002: Instance 1003: Hole 1101: Device Set 1103: Cleaning and/or Cleaning Filter Module 1105: Artificial Intelligence (AI) Engine 1107: Enclosure 1109: Insight Database 1111: First Building 1113: Second Building 1115: Nth Building 1117: Simulation Data 1119: First Model 1121: Physics Engine 1123: Test Sensing Device 1125: Experimental Data 1127: Second Model 1201: Block 1202: Block 1203: Block 1205: Block 1207: Block 1209: Block 1301: Block 1302: Block 1305: Block 1306: Block 1307: Block 1308: Block 1401: Block 1403: Block 1405: Block 1407: Block 1409: Block 1501: Block 1503: Block 1505: Block 1507: Block 1509: Block 1511: Block 1513: Block 1601: Block 1603: Block 1605: Block 1607: Block 1609: Block 1611: Block 1700: Electrochromic device 1702: Substrate 1704: Transparent conductive layer (TCL) 1706: Electrochromic Layer (EC) 1708: Ion Conductive Layer or Region (IC) 1710: Counter Electrode Layer (CE) 1714: Second TCL 1716: Voltage Source 1720: Electrochromic Stack 1800: Insulating Glass Unit 1804: First Pane 1806: Second Pane 1808: Internal Volume 1900: Loop S1: First Surface S2: Second Surface S3: First Surface S4: Second Surface

The novel features of the present invention are set forth in detail in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following description and accompanying drawings (also referred to herein as "FIG./FIGS.") illustrating illustrative embodiments utilizing the principles of the invention. ,in:

[FIG. 1] schematically shows a processing system;

[Fig. 2] Schematically shows the control system architecture and buildings;

[Figure 3] A schematic representation of buildings and networks;

[FIG. 4] A block diagram showing the network of the device;

[FIG. 5] Schematically depicts communication networks housed in various enclosures;

[FIG. 6] shows a schematic example of a sensor configuration;

[FIG. 7] shows a schematic example of sensor configuration and sensor data;

[Fig. 8] A topographic map showing the measured attribute values;

[Figure 9] Shows the device, its components and connectivity options;

[FIG. 10] schematically shows various views and configurations of the assembly housing;

[FIG. 11] schematically depicts an artificial intelligence (AI) engine and associated components;

[Fig. 12] is a flow chart depicting the construction of the learning model;

[FIG. 13] is a flowchart depicting the improvement of the learning model;

[FIG. 14] is a flowchart depicting modeling of meshes using vertices;

[FIG. 15] is a flowchart depicting the collection of sensor data;

[FIG. 16] is a flowchart depicting the execution of environmental adjustments;

[FIG. 17] An electrochromic device is schematically shown;

[FIG. 18] schematically shows a cross-section of an integrated glass unit (IGU); and

[FIG. 19] Various graphs depicting temperature changes with time.

The figures and components therein may not be drawn to scale. Various components of the figures described herein may not be drawn to scale.

1101: Device Set

1103: Cleaning and/or Filtering Modules

1105: Artificial Intelligence (AI) Engine

1107: Closure

1109: Insight Repository

1111: First Building

1113: Second Building

1115: Building N

1117: Simulation data

1119: The first model

1121: Physics Engine

1123: Test Sensing Device

1125: Experimental data

1127: Second Model

Claims (165)

A method of environmental adjustment, the method includes: (a) use (i) a virtual representation of a physical enclosure, (ii) a virtual mesh of vertices, and (iii) one or more material properties of the physical enclosure to generate a virtual enclosure of the physical enclosure body model; (b) use the virtual enclosure model to generate a map of one or more environmental properties of the physical enclosure; and (c) using the map to control the one or more environmental properties of the physical enclosure. The method of claim 1, further comprising receiving a selection from a first vertex of the virtual mesh as a first point of interest. The method of claim 2, further comprising analyzing the one or more environmental properties at the first vertex and a second vertex of the virtual mesh, wherein a greater accuracy is used with respect to the second vertex at the first vertex. The method of claim 2, further comprising receiving a selection of a second point of interest that is not a vertex of the virtual mesh. The method of claim 4, further comprising performing (a) changing the virtual mesh in response to receiving the selection of the second point of interest and/or (b) migrating the second point of interest to the virtual mesh One of the nearest vertices of the lattice. The method of claim 1, wherein a first vertex from the virtual mesh is identified as a first point of interest. The method of claim 6, wherein the one or more environmental properties are obtained at the first vertex and a second vertex of the virtual mesh, and wherein a greater accuracy is applied to the second vertex relative to the second vertex the first vertex. The method of claim 6, wherein a second point of interest that is not a vertex of the virtual mesh is identified. 6. The method of claim 6, wherein the first point of interest has a similar first location in the physical enclosure, the first location including a sensor. The method of claim 6, wherein the first point of interest is a distance from the closest sensor. The method of claim 8, wherein the second point of interest has a similar first location in the physical enclosure, the first location being a distance from the closest sensor. The method of claim 6, further comprising inputting data into the virtual closed volume model from one or more sensors disposed at a physical location similar to a virtual mesh vertex adjacent to the first point of interest, for extrapolating one of the sensed attributes at the first point of interest. The method of claim 1, wherein the virtual mesh of vertices is an inhomogeneous mesh. The method of claim 13, wherein the heterogeneity of the virtual grid is related to a region of interest. The method of claim 13, wherein the heterogeneity of the virtual grid is related to a grid density. The method of claim 13, wherein the heterogeneity of the virtual grid is related to a grid resolution. The method of claim 1, wherein the virtual enclosure model includes a consideration of one or more structural features of the physical enclosure. The method of claim 1, wherein the virtual enclosure model includes a consideration of one or more fixtures of the physical enclosure. The method of claim 1, wherein the physical enclosure includes one or more sensors. The method of claim 19, further comprising receiving baseline readings from the one or more sensors. The method of claim 19, further comprising using the baseline readings to construct the virtual closed volume model. The method of claim 1, further comprising using a three-dimensional representation of the physical enclosure to construct the virtual enclosure model. The method of claim 1, further comprising using a building information model to construct the virtual closed volume model. The method of claim 18, further comprising constructing the virtual enclosure model using one or more physical properties of the one or more fixtures of the physical enclosure. The method of claim 18, further comprising constructing the virtual enclosure model using one or more material properties of the one or more fixtures of the physical enclosure. The method of claim 1, further comprising using an artificial intelligence engine to refine the virtual closed volume model. The method of claim 26, wherein the physical enclosure includes one or more sensors. The method of claim 27, wherein the artificial intelligence engine receives readings from the one or more sensors. The method of claim 28, further comprising using the artificial intelligence engine to model (i) the location of the one or more sensors, (ii) the operation of the one or more sensors, (iii) by Spatial distribution of at least one attribute sensed by the one or more sensors and/or (iv) evolution over time of at least one attribute sensed by the one or more sensors. The method of claim 29, further comprising the artificial intelligence engine using predictive extrapolation to improve the modeling. The method of claim 30, wherein the predictive extrapolation is based at least in part on a trend in sensor data. The method of claim 30, wherein the predictive extrapolation is based at least in part on an expected physical parameter. The method of claim 29, wherein the one or more sensors are not at a location similar to a vertex of the virtual mesh. The method of claim 1, further comprising using a hierarchical control system to control the one or more environmental properties of the physical enclosure. The method of claim 34, further comprising the control system controlling the one or more environmental properties of the physical enclosure. The method of claim 35, wherein the one or more environmental properties of the physical enclosure are controlled by adjusting (i) a heating, ventilation and air conditioning (HVAC) system; (ii) a security system; (iii) ) a lighting system and/or (iv) a tint of a tintable window. The method of claim 35, wherein controlling the one or more environmental properties of the physical enclosure is by adjusting a velocity of an air flowing to and/or from the enclosure through a vent. The method of claim 35, wherein controlling the one or more environmental properties of the physical enclosure is performed by controlling a building management system. The method of claim 34, wherein the hierarchical control system includes a master controller configured to control one or more floor controllers. The method of claim 39, wherein one of the one or more floor controllers is configured to control one or more local controllers. The method of claim 40, wherein a local controller of the one or more local controllers is configured to control one or more tintable windows. The method of claim 40, wherein a local controller of the one or more local controllers is configured to control one or more sensors. The method of claim 40, wherein a local controller of the one or more local controllers is configured to control one or more output devices. The method of claim 39, wherein the master controller is configured to be operatively coupled to a building management system. The method of claim 39, wherein the host controller is configured to be operatively coupled to a database. The method of claim 39, wherein the host controller is configured to be operatively coupled to a network. The method of claim 39, wherein the main controller and/or the floor controller are in the cloud. The method of claim 39, wherein the main controller is disposed in the physical enclosure. The method of claim 40, wherein the floor controller is disposed in the physical enclosure. The method of claim 39, wherein the main controller is positioned at a location different from the location of the physical enclosure. The method of claim 40, wherein the floor controller is positioned at a location different from the location of the physical enclosure. The method of claim 38, wherein the building management system is configured to control the one or more environmental characteristics of the physical enclosure. The method of claim 53, wherein controlling the one or more environmental characteristics of the physical enclosure includes providing an energy consumption savings for operating the physical enclosure. The method of claim 1, wherein the enclosure is a facility. The method of claim 1, wherein the enclosure is a building. An apparatus for environmental conditioning, the apparatus comprising one or more controllers comprising at least one circuitry and configured to: (a) use (i) a virtual representation of a physical enclosure, (ii) a virtual mesh of vertices, and (iii) one or more material properties of the physical enclosure to generate or guide the creation of the physical enclosure a virtual closed body model; (b) use or direct the use of the virtual enclosure model to generate a map of one or more environmental properties of the physical enclosure; and (c) use or direct the use of the map to control the one or more environmental properties of the physical enclosure. The apparatus of claim 56, wherein the one or more controllers are configured for receiving a selection from a first vertex of the virtual mesh as a first point of interest. The apparatus of claim 56, wherein the one or more controllers are configured for analyzing the one or more environmental properties at a first vertex and a second vertex of the virtual mesh, wherein relative to the For the second vertex, a greater precision is used for the first vertex. The apparatus of claim 57, wherein the one or more controllers are configured for receiving a selection of a second point of interest that is not any of the vertices of the virtual mesh. The apparatus of claim 59, wherein the one or more controllers are configured to execute or direct execution of (a) changing the virtual grid and/or responsive to receiving the selection of the second point of interest (b) Migrating the second point of interest to one of the closest vertices of the virtual mesh. The apparatus of claim 56, wherein a first vertex from the virtual mesh is identified as a first point of interest. The apparatus of claim 61, wherein the one or more environmental properties are obtained at the first vertex and a second vertex of the virtual mesh, and wherein a greater precision is applied to the second vertex relative to the second vertex the first vertex. The apparatus of claim 61, wherein a second point of interest is not on a vertex of the virtual mesh. The apparatus of claim 61, wherein the first point of interest corresponds to a respective portion of the physical enclosure where a sensor is disposed. The apparatus of claim 61, wherein the first point of interest corresponds to a respective location in the physical enclosure that is at a distance from the nearest sensor. The apparatus of claim 63, wherein the second point of interest corresponds to a respective location in the physical enclosure at a distance from the nearest sensor. The apparatus of claim 61, wherein the one or more controllers are configured for inputting data to the virtual enclosure from one or more sensors positioned at mesh vertices adjacent to the first point of interest in the model. The apparatus of claim 67, wherein the input of the data is used to extrapolate a sensed attribute at the first point of interest. The apparatus of claim 56, wherein the virtual mesh of vertices is an inhomogeneous mesh. The apparatus of claim 69, wherein the heterogeneity of the virtual grid is related to a region of interest. The apparatus of claim 69, wherein the heterogeneity of the virtual grid is related to a grid density. The apparatus of claim 69, wherein the heterogeneity of the virtual grid is related to a grid resolution. The apparatus of claim 56, wherein the virtual enclosure model includes an consideration of one or more structural features of the physical enclosure. The apparatus of claim 56, wherein the virtual enclosure model includes a consideration of one or more fixtures of the physical enclosure. The apparatus of claim 56, wherein the physical enclosure includes one or more sensors. The apparatus of claim 75, wherein the one or more controllers are configured for receiving baseline readings from the one or more sensors. The apparatus of claim 76, further comprising circuitry configured for constructing the virtual closed volume model using the baseline readings. The apparatus of claim 56, wherein the one or more controllers are configured for constructing the virtual enclosure model using a three-dimensional representation of the physical enclosure. The apparatus of claim 56, wherein the one or more controllers are configured for constructing the virtual enclosed volume model using a building information model. The apparatus of claim 74, wherein the one or more controllers are configured for constructing the virtual enclosure model using one or more physical properties of the one or more fixtures of the physical enclosure. The apparatus of claim 74, wherein the one or more controllers are configured for constructing the virtual enclosure model using one or more material properties of the one or more fixtures of the physical enclosure. The apparatus of claim 56, wherein the one or more controllers are configured for using an artificial intelligence engine to improve or direct the improvement of the physical closed body model. The apparatus of claim 82, wherein the physical enclosure includes one or more sensors. The apparatus of claim 83, wherein the artificial intelligence engine is configured for receiving readings from the one or more sensors. The apparatus of claim 84, wherein the artificial intelligence engine is configured for modeling (i) the location of the one or more sensors, (ii) the operation of the one or more sensors, (iii) ) the spatial distribution of the at least one attribute sensed by the one or more sensors and/or (iv) the evolution of the at least one attribute sensed by the one or more sensors over time. The apparatus of claim 85, wherein operation includes a state comprising standard operation or failure of at least one of the one or more sensors. The apparatus of claim 85, wherein the artificial intelligence engine is configured for improving the modeling using predictive extrapolation. The apparatus of claim 87, wherein the predictive extrapolation is based at least in part on a trend. The apparatus of claim 87, wherein the predictive extrapolation is based at least in part on an expected physical parameter. The apparatus of claim 85, wherein the one or more sensors are not at a vertex of the virtual mesh. The apparatus of claim 56, wherein the one or more controllers are configured for controlling the one or more environmental properties of the physical enclosure using a hierarchical control system. The apparatus of claim 91, wherein the one or more controllers are configured for controlling the one or more environmental properties of the physical enclosure. The apparatus of claim 92, wherein the one or more controllers are configured to adjust by adjusting (a) a heating, ventilation and air conditioning (HVAC) system, (b) a security system, (c) a lighting system and/or (d) a tintable window to control the one or more environmental properties. The apparatus of claim 91, wherein the one or more controllers are configured for regulating or directing regulation of an air flow to and/or from the physical enclosure through a vent. A velocity controls the one or more environmental properties of the physical enclosure. The apparatus of claim 91, wherein the one or more controllers are configured for controlling the one or more environmental properties of the physical enclosure by controlling a building management system. The apparatus of claim 91, wherein the one or more controllers comprise a master controller that controls the one or more floor controllers. The apparatus of claim 96, wherein one of the one or more floor controllers is configured to control one or more local controllers. The apparatus of claim 97, wherein a local controller of the one or more local controllers is configured to control one or more devices including a tintable window. The apparatus of claim 97, wherein one of the one or more local controllers is configured to control a device including one or more sensors. The apparatus of claim 97, wherein a local controller of the one or more local controllers is configured to control a device including one or more output devices. The apparatus of claim 96, wherein the master controller is configured to be operatively coupled to a building management system. The apparatus of claim 96, wherein the host controller is configured to be operatively coupled to a database. The apparatus of claim 96, wherein the host controller is configured to be operatively coupled to a network. The apparatus of claim 96, wherein the host controller is located in the cloud. The apparatus of claim 97, wherein the floor controller is located in the cloud. The apparatus of claim 96, wherein the main controller is disposed in the physical enclosure. The apparatus of claim 97, wherein the floor controller is disposed in the physical enclosure. The apparatus of claim 96, wherein the main controller is positioned at a location other than the physical enclosure. The apparatus of claim 97, wherein the floor controller is positioned at a location other than the physical enclosure. The apparatus of claim 95, wherein the building management system is configured to control the one or more environmental characteristics of the physical enclosure. The apparatus of claim 110, wherein the building management system is configured to control the one or more environmental characteristics to provide an energy consumption savings for the physical enclosure. A non-transitory computer-readable medium comprising instructions for environmental adjustment that, when executed by one or more processors, cause the one or more processors to perform operations comprising: (a) use (i) a virtual representation of a physical enclosure, (ii) a mesh of vertices, and (iii) one or more material properties of the physical enclosure to generate a virtual enclosure of the physical enclosure Model; (b) use the physical enclosure model to generate a map of one or more environmental properties of the physical enclosure; and (c) using the map to control the one or more environmental properties of the physical enclosure. The non-transitory computer-readable medium of claim 112, further comprising instructions for receiving a selection from a first vertex of the virtual mesh as a first point of interest. The non-transitory computer-readable medium of claim 113, further comprising instructions for analyzing or directing analysis of the one or more environmental properties at the first vertex and a second vertex of the virtual mesh, wherein the relative For the second vertex, a greater precision is used for the first vertex. The non-transitory computer-readable medium of claim 113, further comprising instructions for receiving a selection of one of a second point of interest that is not any of the vertices of the virtual mesh. The non-transitory computer-readable medium of claim 115, further comprising means for executing or directing execution of (a) changing the virtual grid in response to receiving the selection of the second point of interest and/or (b) An instruction to migrate the second point of interest to one of the closest vertices of the virtual mesh. The non-transitory computer-readable medium of claim 112, wherein a first vertex from the virtual mesh is identified as a first point of interest. The non-transitory computer-readable medium of claim 117, wherein the one or more environmental properties are obtained at the first vertex and a second vertex of the virtual mesh, and wherein relative to the second vertex, a Greater precision is applied to this first vertex. The non-transitory computer-readable medium of claim 117, wherein a second point of interest that does not coincide with the vertices of the virtual mesh is identified. The non-transitory computer-readable medium of claim 117, wherein the first point of interest includes a corresponding portion of the physical enclosure where a sensor is disposed. The non-transitory computer-readable medium of claim 117, wherein the first point of interest is a distance from a corresponding portion of the physical enclosure where a closest sensor is located. The non-transitory computer-readable medium of claim 119, wherein the second point of interest is located in the physical enclosure at a corresponding location of a closest sensor. The non-transitory computer-readable medium of claim 117, further comprising inputting data from one or more sensors disposed in the physical enclosure at locations corresponding to mesh vertices adjacent to the first point of interest Or guide the input of data into the virtual closed body model. The non-transitory computer-readable medium of claim 123, further comprising utilizing or directing utilizing the data for extrapolating a sensed attribute at the first point of interest. The non-transitory computer-readable medium of claim 112, wherein the virtual mesh of vertices is an inhomogeneous mesh. The non-transitory computer-readable medium of claim 125, wherein the heterogeneity of the virtual grid is associated with a region of interest and/or a point of interest. The non-transitory computer-readable medium of claim 125, wherein the heterogeneity of the virtual grid is related to a density of the virtual grid. The non-transitory computer-readable medium of claim 125, wherein the heterogeneity of the virtual grid is related to a resolution of the virtual grid. The non-transitory computer-readable medium of claim 112, wherein the construction and/or use of the virtual enclosure model includes a consideration of one or more structural features of the physical enclosure. The non-transitory computer-readable medium of claim 112, wherein the construction and/or use of the virtual enclosure model includes a consideration of one or more fixtures of the physical enclosure. The non-transitory computer-readable medium of claim 112, wherein the physical enclosure includes one or more sensors. The non-transitory computer-readable medium of claim 131, wherein the operations comprise receiving or directing receipt of baseline readings from one or more sensors. The non-transitory computer-readable medium of claim 132, wherein the operations include using the baseline readings to construct or guide construction of a physical closed volume model. The non-transitory computer-readable medium of claim 112, wherein the operations comprise using a three-dimensional representation of the physical enclosure to construct or direct construction of the virtual enclosure model. The non-transitory computer-readable medium of claim 112, wherein the operations include using a building information model to construct or direct construction of the virtual enclosure model. The non-transitory computer-readable medium of claim 130, wherein the operations comprise using one or more physical properties of the one or more fixtures of the physical enclosure to construct or direct construction of the virtual enclosure model. The non-transitory computer-readable medium of claim 130, wherein the operations comprise using one or more material properties of the one or more fixtures of the physical enclosure to construct or direct construction of the virtual enclosure model. The non-transitory computer-readable medium of claim 112, wherein the operations include using an artificial intelligence engine to refine or direct refinement of the virtual closed volume model. The non-transitory computer-readable medium of claim 138, wherein the physical enclosure includes one or more sensors. The non-transitory computer-readable medium of claim 139, wherein the artificial intelligence engine is configured to receive readings from the one or more sensors. The non-transitory computer-readable medium of claim 140, further comprising a portion for the artificial intelligence engine to model (i) the one or more sensors, (ii) the one or more sensors Operation, (iii) spatial distribution of at least one attribute sensed by the one or more sensors and/or (iv) evolution over time of at least one attribute sensed by the one or more sensors instruction. The non-transitory computer-readable medium of claim 141, further comprising instructions for the artificial intelligence engine to improve the artificial intelligence engine model by using predictive extrapolation. The non-transitory computer-readable medium of claim 142, wherein the predictive extrapolation is based at least in part on a trend. The non-transitory computer-readable medium of claim 142, wherein the predictive extrapolation is based at least in part on an expected physical parameter. The non-transitory computer-readable medium of claim 141, wherein the one or more sensors are disposed at one or more locations in the physical enclosure that do not correspond to the vertices of the virtual mesh. The non-transitory computer-readable medium of claim 112, wherein the operations include directing a hierarchical control system to control the one or more environmental characteristics of the physical enclosure. The non-transitory computer readable medium of claim 146, wherein the operations comprise directing a hierarchical control system to adjust (I) a heating, ventilation and air conditioning system (HVAC), (II) a security system, (III) A lighting system and/or (IV) the tint of a tintable window. The non-transitory computer-readable medium of claim 146, wherein the operations comprise directing a building management system to control the one or more environmental characteristics of the physical enclosure. The non-transitory computer-readable medium of claim 146, wherein the operations comprise directing a hierarchical control system to adjust or directing to adjust a speed of an air flow to and/or from the physical enclosure. The non-transitory computer readable medium of claim 149, wherein the hierarchical control system includes a master controller that controls one or more floor controllers. The non-transitory computer readable medium of claim 150, wherein one of the one or more floor controllers is configured to control one or more local controllers. The non-transitory computer-readable medium of claim 151, wherein one of the one or more local controllers is configured to control one or more tintable windows. The non-transitory computer-readable medium of claim 151, wherein one of the one or more local controllers is configured to control a device including one or more sensors. The non-transitory computer-readable medium of claim 151, wherein one of the one or more local controllers is configured to control a device including one or more output devices. The non-transitory computer-readable medium of claim 150, wherein the host controller is configured to be operatively coupled to a building management system. The non-transitory computer-readable medium of claim 150, wherein the host controller is configured to be operatively coupled to a database. The non-transitory computer-readable medium of claim 150, wherein the host controller is configured to be operatively coupled to a network. The non-transitory computer-readable medium of claim 150, wherein the host controller is located in the cloud. The non-transitory computer-readable medium of claim 151, wherein the floor controller resides in the cloud. The non-transitory computer-readable medium of claim 150, wherein the host controller is disposed in the physical enclosure. The non-transitory computer-readable medium of claim 151, wherein the floor controller is disposed in the physical enclosure. The non-transitory computer-readable medium of claim 150, wherein the host controller is disposed at a location other than the physical enclosure. The non-transitory computer-readable medium of claim 151, wherein the floor controller is disposed at a location other than the physical enclosure. The non-transitory computer-readable medium of claim 148, wherein the operations comprise directing a building management system to control the one or more environmental characteristics of the physical enclosure. The non-transitory computer-readable medium of claim 164, wherein controlling the one or more environmental characteristics of the physical enclosure includes providing an energy consumption savings for operation of the physical enclosure.

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