CN116149862A - Industrial edge computing platform and method - Google Patents
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Abstract
The invention relates to the technical field of edge computing, and particularly discloses an industrial edge computing platform and method, wherein the platform comprises a cloud platform, an edge computing frame and bottom equipment, and the edge computing frame comprises: the device service module is used for accessing the bottom layer device and acquiring data uploaded by the bottom layer device; the core service module is used for intensively storing the data uploaded by the bottom layer equipment in a built-in data center; the support service module is used for acquiring stored data from the data center and cleaning the data according to a preset rule engine; the application service module is used for accessing the cloud platform through a preset interface, acquiring cleaned data from the data center, and making a decision according to a preset application rule. The technical scheme of the invention has higher compatibility, can integrate the data produced by the edge terminal, effectively control the edge terminal equipment and coordinate the edge computing resources.
Description
Technical Field
The invention relates to the technical field of edge computing, in particular to an industrial edge computing platform and an industrial edge computing method.
Background
Edge computing (EdgeComputing) is a distributed computing mode that moves computing and data processing capabilities to edge nodes closer to the data source to reduce delay and bandwidth occupation of data transmission in the network, improving the response speed and performance of the system. Edge computation can be applied in a number of fields. For example, in the industry, there is a need for rapid processing and analysis of real-time data in order to take appropriate measures in time, and edge computing can process and analyze the data locally on the device, so as to respond to the device's requirements more quickly, and at the same time reduce the dependency on network bandwidth, and reduce the risk of delays and network congestion.
However, as more and more edge devices are installed, higher demands are placed on the management of these devices and the processing of the generated data.
Therefore, there is a need for an industrial edge computing platform and method that can integrate edge production data, effectively control edge devices, and coordinate edge computing resources.
Disclosure of Invention
One of the purposes of the present invention is to provide an industrial edge computing platform, which can integrate the data produced by the edge, effectively control the edge equipment and coordinate the edge computing resources.
In order to solve the technical problems, the application provides the following technical scheme:
an industrial edge computing platform comprising a cloud platform, an edge computing framework, and underlying devices, the edge computing framework comprising:
the device service module is used for accessing the bottom layer device and acquiring data uploaded by the bottom layer device;
the core service module is used for intensively storing the data uploaded by the bottom layer equipment in a built-in data center;
the support service module is used for acquiring stored data from the data center and cleaning the data according to a preset rule engine;
the application service module is used for accessing the cloud platform through a preset interface, acquiring cleaned data from the data center, and making a decision according to a preset application rule.
Further, when the application service module makes a decision according to a preset application rule, judging whether the current data trigger an alarm or not; if the alarm is triggered, reporting data to a cloud platform and issuing a control instruction;
if the alarm is not triggered, judging whether reporting is needed, and if the reporting is not needed, discarding the data; and if the data needs to be reported, reporting the data to the cloud platform.
Further, the application service module is further used for sending a control instruction to the core service module; the core service module is also used for issuing a control command to the equipment service module; the device service module is also used for controlling the bottom layer device according to the control instruction.
Further, the application service module is further configured to receive a control command issued by the cloud platform, and convert the control command into a control command.
Further, the edge computing framework also comprises a security service module for providing an API gateway and storing confidential information, wherein the confidential information comprises a token, a password and a certificate.
Further, the edge computing framework further comprises a management service module, which is used for controlling the service modules in the edge computing framework and also used for acquiring the running conditions of the service modules in the edge computing framework.
The second objective of the present invention is to provide an industrial edge computing method, which is applied to the industrial edge computing platform, and includes the following uplink contents:
s101, collecting data generated by bottom equipment through an equipment service module, and placing the data into a data center of a core service module;
s102, cleaning data of a data center through a rule engine supporting a service module, and eliminating useless data;
s103, acquiring cleaned data from a data center through an application service module, processing the data according to a preset application rule, and judging whether the current data triggers an alarm or not;
if the alarm is triggered, reporting data and issuing a control instruction;
if the alarm is not triggered, judging whether reporting is needed, and if the reporting is not needed, discarding the data; if the data needs to be reported, reporting the data;
s104, when the data is reported, the application service module uploads the data to the docked IOT platform;
s105, when a control instruction is issued, the control instruction is sent to the equipment service module; and the device service module controls the bottom layer device according to the control instruction.
Further, the method also comprises the following downlink contents:
s201, issuing a control command on a cloud platform;
s202, an application service module acquires a control command through an interface, converts the control command into a control command, and sends the control command to a core service module;
s203, the core service module issues a control command to the equipment service module;
s204, the equipment service module controls the bottom equipment according to the control instruction.
Further, the step S202 specifically includes: the application service module acquires a control command through an interface, performs splitting processing according to the purpose of the control command, converts the control command into a control command through a preset command library, and sends the control command to the core service module according to the action target of the control command;
the step S203 specifically includes: when the core service module receives the control instruction, the control instruction is stored, the configuration information of the target bottom layer device is detected, whether the current control instruction is available or not is judged, if the current control instruction is available, the control instruction is converted into an internal unified command format, and the control instruction is issued to the device service module.
Compared with the prior art, the invention has the beneficial effects that:
1. the deployment efficiency is high, and the deployment pressure is reduced by adopting a Docker containerized deployment scheme to achieve 'use while taking'.
2. The compatibility of the device with the control is high, and the device is decoupled with hardware, so that most of devices can be compatible. Can be deployed on different architecture devices, avoiding additional adaptation work on different devices, thereby realizing compatibility for most devices.
3. The modular design architecture and the light-weight design can be flexibly expanded, split and deployed. For example: the device service module is deployed at the edge end, and the application service module is deployed to the gateway, so that the same set of application service can be used for managing all edge devices under the gateway, and the management is convenient.
4. The compatibility with external systems is high, and the external systems can be conveniently and flexibly accessed. For example: various commercial IOT platforms can also be accessed to the self-built IOT platform. In the edge computing framework, according to the information access requirements of each IOT platform, only the interfaces in the application service module are subjected to code adjustment, and the access can be conveniently performed.
5. The compatibility to the bottom layer equipment is high, and various bottom layer equipment can be conveniently accessed: in the edge computing framework, the device service module is responsible for accessing the bottom layer device, digitizing various real world states, and transmitting the real world states to a data center of the core service module, so that other service modules can be used conveniently. Through the conventional protocol analysis, the device service module can use the data information of the bottom device in simple configuration. Meanwhile, a plurality of different bottom devices are supported to be simultaneously accessed into the system, and various data information is provided for the system.
6. The decision is made at the edge side, so that the data can be processed and analyzed rapidly in real time, and only the calculation result is uploaded to the cloud end, so that the transmission efficiency is improved, meanwhile, the consumption of bandwidth is greatly reduced, and resources are saved.
Drawings
FIG. 1 is a system architecture diagram of an industrial edge computing platform according to an embodiment;
fig. 2 is a flow chart of an industrial edge computing method according to an embodiment.
Detailed Description
The following is a further detailed description of the embodiments:
example 1
As shown in fig. 1, an industrial edge computing platform of the present embodiment includes a cloud platform, an edge computing framework, and an underlying device.
The cloud platform is responsible for displaying data information of the edge side and controlling bottom equipment of the edge side; in this embodiment, the cloud platform is deployed at the cloud, and is an IOT platform, which may be an existing ali cloud, hua cloud, or a self-built IOT platform, and in other embodiments, the cloud platform may also be an external management platform of a third party, such as an upper computer, a private management platform, or the like.
The edge computing framework is a brain which is deployed at the edge side and is responsible for controlling the whole edge side;
the bottom layer equipment comprises data service equipment, input equipment and the like which are arranged on the edge side, wherein the data service equipment comprises AI equipment, streaming media service equipment and the like, and the input equipment comprises a camera, a sensor and the like.
The edge computing framework comprises a device service module, a core service module, an application service module, a support service module, a security service module and a management service module.
The device service module is a connector which is interacted with the bottom layer device, and is used for accessing various bottom layer devices, providing a standard input interface for the bottom layer device and obtaining the data uploaded by the bottom layer device. For example, a plurality of sensor protocols are preset to facilitate direct access of the sensor, thereby collecting data uploaded by the sensor.
The equipment service module also has a control function of the bottom layer equipment, and is used for receiving the control instruction and controlling the bottom layer equipment according to the control instruction.
The core service module is used for providing data storage service and storing the data uploaded by the bottom layer equipment in a centralized manner in a built-in data center;
the system is also used for centralizing service configuration data, simplifying configuration flow and connecting service modules in all systems through a RestAPI;
the metadata is also used for providing metadata of the bottom layer device for other service modules to know the bottom layer device and how to communicate with the bottom layer device;
and the system is also used for providing control instructions, so that other service modules or external systems can execute commands and operations on the underlying equipment. The core service module is also used for receiving the control instruction and transmitting the control instruction to the equipment service module;
the support service module is used for acquiring stored data from a data center of the core service module, cleaning the data according to a preset rule engine and removing useless data; useless data such as: 1. recording data which does not reach the threshold value of alarm; 2. temporary data normally exchanged between services; 3. stored expiration data; 4. incomplete data such as missing data and truncated data.
And also to provide the system with preset software application functions such as log services, alarms and notifications, planning tasks, rules engines, etc.
The application service module is used for acquiring the cleaned data from the data center, making a decision according to a preset application rule, judging the operation required to be performed, wherein the operation comprises data reporting, control instruction issuing and the like.
Specifically, judging whether the current data triggers an alarm or not; if the alarm is triggered, reporting data and issuing a control instruction;
if the alarm is not triggered, judging whether reporting is needed, and if the reporting is not needed, discarding the data; and if the data needs to be reported, reporting the data. For example, whether the worker wears the safety helmet or not is judged according to the video data, if the worker does not wear the safety helmet to trigger an alarm, a screenshot is reported, and a control instruction of sound-light alarm is achieved.
The application service module is also used for rapidly accessing various IOT platforms through a preset interface and reporting data to the IOT platforms;
the control command is converted into a control command and sent to the core service module; in other words, the operator defined control commands are translated into machine control instructions.
The security service module is used for providing a secure API gateway, is a single point entry of REST traffic in all edge computing frameworks, and serves as a barrier between an external system and an internal system to prevent unauthorized REST pi access;
and is also used for storing confidential information, such as a token, a password, a certificate and the like, can be stored, created and retrieved, and is used for protecting the confidential information from being exposed.
The management service module is used for providing a centralized contact function for an external management system and controlling the starting, stopping and restarting of the edge computing framework;
the method is also used for acquiring the state and the running state of each service module in the edge computing framework and providing monitoring service;
and is also used for obtaining configuration for each service module.
As shown in fig. 2, based on the industrial edge computing platform, the embodiment further provides an industrial edge computing method, which includes the following steps:
and (3) uplink control:
s101, collecting data generated by bottom equipment through an equipment service module, and placing the data into a data center of a core service module to wait for other service modules to use; data generated by the bottom layer device such as position, size, temperature and the like acquired by a sensor, AI reasoning results generated by the AI device, audio-video image data generated by the streaming media service device and the like.
S102, cleaning data of the data center through a rule engine supporting the service module, and eliminating useless data. And useless data is removed through automatic cleaning of the data, so that the service module can use the data more accurately.
S103, the application service module acquires cleaned data from the data center, processes the data according to preset application rules, and judges whether the current data triggers an alarm or not;
if the alarm is triggered, reporting data and issuing a control instruction;
if the alarm is not triggered, judging whether reporting is needed, and if the reporting is not needed, discarding the data; if the data needs to be reported, reporting the data;
s104, when data is reported, the application service module uploads the data to the docked IOT platform through network protocols such as MQTT or HTTP;
s105, when a control instruction is issued, the control instruction is sent to the equipment service module through an API interface; the equipment service module controls the bottom layer equipment according to the control instruction;
and (3) downlink control:
s201, when a manager confirms that the bottom layer equipment needs to be controlled, a control command is issued on the IOT platform;
s202, an application service module acquires a control command through an interface, performs splitting processing according to the purpose of the control command, converts the control command into a control command through a preset command library, and sends the control command to a core service module according to the action target of the control command;
s203, when the core service module receives the control instruction, the control instruction is subjected to local temporary/persistent storage, meanwhile, the configuration information of the target bottom layer equipment is detected, whether the current control instruction is available or not is judged, if the current control instruction is available, the control instruction is converted into an internal unified command format, and finally, the control instruction is issued to the equipment service module;
s204, the device service module controls the corresponding bottom layer device according to the control instruction.
The scheme of the embodiment has the advantages that:
1. the deployment efficiency is high, a Docker containerized deployment scheme can be adopted, so that the deployment pressure is reduced.
2. The device with the control is high in compatibility, is decoupled from hardware, can be deployed on devices with different architectures (arm architecture and X86 architecture), and avoids additional adaptation work on different devices, so that the compatibility of most devices is realized.
3. The modular design architecture and the light-weight design can be flexibly expanded, split and deployed. For example: the device service module is deployed at the edge end, and the application service module is deployed to the gateway, so that the same set of application service can be used for managing all edge devices under the gateway, and the management is convenient.
4. The compatibility with external systems is high, and the external systems can be conveniently and flexibly accessed. For example: various IOT platforms, commonly used cloud IOT platforms such as cloud, ali cloud, tencent cloud and the like, can be accessed into the self-built IOT platform. In the edge computing framework, according to the information access requirements of each IOT platform, only the interfaces in the application service module are subjected to code adjustment, and the access can be conveniently performed.
5. The compatibility to the bottom layer equipment is high, and various data production ends such as the bottom layer equipment (onvif, modbus, gpio protocol and the like) are conveniently accessed:
in the edge computing framework, the device service module is responsible for accessing the bottom layer device, digitizing various real world states, and transmitting the real world states to a data center of the core service module, so that other service modules can be used conveniently. Through the conventional protocol analysis, the device service module can use the data information of the bottom device in simple configuration. Meanwhile, a plurality of different bottom devices are supported to be simultaneously accessed into the system, and various data information is provided for the system.
6. The decision is made at the edge side, so that the data can be processed and analyzed rapidly in real time, and only the calculation result is uploaded to the cloud end, so that the transmission efficiency is improved, meanwhile, the consumption of bandwidth is greatly reduced, and resources are saved.
In summary, the scheme has high compatibility, modularized light-weight design and high deployment efficiency, can effectively integrate data produced by the edge, control the edge equipment and coordinate edge computing resources.
Example two
The difference between the embodiment and the first embodiment is that, in the platform of the embodiment, the cloud platform is further configured to configure a detection area for each path of camera and set detection timeliness. For example, when configuring the detection area, a user-defined quadrilateral drawing is supported, and the quadrilateral can be arbitrarily dragged to four corners, in this embodiment, at most three quadrilaterals are supported by one picture, so as to realize coverage when detecting a scene special topography. After the detection aging is set, the detection function is only effective within a set time range. The AI equipment is used for acquiring the monitoring video from the camera, and in the detection time, carrying out safety detection on the detection area of the monitoring video, and uploading the detection result.
Reporting data including alarm events; for example, dangerous behavior alarm events such as call receiving, smoking and the like of AI equipment, scene alarm events such as fire, ponding, oil leakage and the like.
The cloud platform is further configured to receive an alarm event, label the alarm event with a handling type and remark information, where the handling type includes a normal event and a false alarm event, and in this embodiment, the handling type and the remark information are labeled for a manager.
The application service module is also used for issuing a control instruction to enable the AI equipment to carry out safety detection again on the whole area of the video frame corresponding to the false alarm event when the handling type of the alarm event is the false alarm event, and judging whether the alarm event exists or not; if an alarm event exists, generating detection area abnormal information and sending the detection area abnormal information to a cloud platform;
if no alarm event exists, the cloud platform is further used for adding the corresponding video frame into a preset training image set; recording the alarm type of the false alarm event, the camera to which the false alarm event belongs and the video frame area where the detected target is located; the detected object is, for example, an object, a human body, or the like. The video frame area where the target is located, for example, the configuration detection area is divided into 4 small areas in a cross mode, and one of the small areas where the target is located is the video frame area where the target is located.
The application service module is also used for judging whether the video frame area of the camera corresponding to the alarm event and the detected target is the same as the video frame area of the camera corresponding to the false alarm event or not when the alarm event of the same alarm type as the false alarm event is generated again after detection, and if not, sending the alarm event to the cloud platform; if the video frames are the same, the AI device selects a preset number of other video frames within the time range of the video frames corresponding to the alarm event, and in the embodiment, 5 frames are selected within 1 second; the equipment service module is also used for estimating the processing time of the newly selected other video frames according to the current load and judging whether the processing time is less than the preset time which is 1-5 seconds; if the time is less than the preset time, the AI equipment is controlled to carry out safety detection on other video frames, and when at least one detection result has an alarm event, the alarm event is sent to the cloud platform; if the processing time is greater than or equal to the preset time, judging whether the processing time is greater than the average response time, and if the processing time is greater than or equal to the average response time, sending an alarm event to the cloud platform; if the character number of remark information of the false alarm event exceeds a preset value, sending the alarm event to a cloud platform, carrying out safety detection on other video frames by using AI equipment, and carrying out remark on the sent alarm event according to the detection result of the other video frames; if the alarm event exceeds the preset value, sending the alarm event to the cloud platform, namely not detecting again.
After an error alarm event occurs, if a subsequent alarm event is similar to the error alarm time, namely the alarm type, the camera to which the alarm belongs and the video frame area where the detected target is located are the same, when the re-detection processing time is short (the influence on the alarm timeliness is small in the preset time), safety detection is performed on other video frames so as to increase the number of detection samples and improve the detection accuracy; when the re-detection processing time is long (exceeds the preset time), judging whether re-detection is needed or not according to the average response time (the interval between each time of receiving the alarm event and marking the alarm event is the response time, and the average response time is the average value of the response time). If the processing time exceeds the average response time, the probability that the manager checks the alarm event is high, and the alarm event is re-detected only when the reason description of the false alarm event is unclear (the character number of remark information does not exceed a preset value), so that the calculation force can be saved, meanwhile, the alarm event is fully analyzed, and the workload of the manager is reduced.
The foregoing is merely an embodiment of the present invention, the present invention is not limited to the field of this embodiment, and the specific structures and features well known in the schemes are not described in any way herein, so that those skilled in the art will know all the prior art in the field before the application date or priority date, and will have the capability of applying the conventional experimental means before the date, and those skilled in the art may, in light of the teaching of this application, complete and implement this scheme in combination with their own capabilities, and some typical known structures or known methods should not be an obstacle for those skilled in the art to practice this application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (9)
1. An industrial edge computing platform, comprising a cloud platform, an edge computing framework, and an underlying device, wherein the edge computing framework comprises:
the device service module is used for accessing the bottom layer device and acquiring data uploaded by the bottom layer device;
the core service module is used for intensively storing the data uploaded by the bottom layer equipment in a built-in data center;
the support service module is used for acquiring stored data from the data center and cleaning the data according to a preset rule engine;
the application service module is used for accessing the cloud platform through a preset interface, acquiring cleaned data from the data center, and making a decision according to a preset application rule.
2. The industrial edge computing platform of claim 1, wherein: when the application service module makes a decision according to a preset application rule, judging whether the current data trigger an alarm or not; if the alarm is triggered, reporting data to a cloud platform and issuing a control instruction;
if the alarm is not triggered, judging whether reporting is needed, and if the reporting is not needed, discarding the data; and if the data needs to be reported, reporting the data to the cloud platform.
3. The industrial edge computing platform of claim 2, wherein: the application service module is also used for sending a control instruction to the core service module; the core service module is also used for issuing a control command to the equipment service module; the device service module is also used for controlling the bottom layer device according to the control instruction.
4. An industrial edge computing platform as claimed in claim 3, wherein: the application service module is also used for receiving a control command issued by the cloud platform and converting the control command into a control command.
5. The industrial edge computing platform of claim 4, wherein: the edge computing framework also comprises a security service module for providing an API gateway and storing confidential information, wherein the confidential information comprises a token, a password and a certificate.
6. The industrial edge computing platform of claim 5, wherein: the edge computing framework further comprises a management service module used for controlling the service modules in the edge computing framework and also used for acquiring the running conditions of the service modules in the edge computing framework.
7. An industrial edge computing method applied to the industrial edge computing platform according to any one of claims 1 to 6, and comprising the following uplink contents:
s101, collecting data generated by bottom equipment through an equipment service module, and placing the data into a data center of a core service module;
s102, cleaning data of a data center through a rule engine supporting a service module, and eliminating useless data;
s103, acquiring cleaned data from a data center through an application service module, processing the data according to a preset application rule, and judging whether the current data triggers an alarm or not;
if the alarm is triggered, reporting data and issuing a control instruction;
if the alarm is not triggered, judging whether reporting is needed, and if the reporting is not needed, discarding the data; if the data needs to be reported, reporting the data;
s104, when the data is reported, the application service module uploads the data to the docked IOT platform;
s105, when a control instruction is issued, the control instruction is sent to the equipment service module; and the device service module controls the bottom layer device according to the control instruction.
8. The industrial edge computing method of claim 7, wherein: the method also comprises the following steps:
s201, issuing a control command on a cloud platform;
s202, an application service module acquires a control command through an interface, converts the control command into a control command, and sends the control command to a core service module;
s203, the core service module issues a control command to the equipment service module;
s204, the equipment service module controls the bottom equipment according to the control instruction.
9. The industrial edge computing method of claim 8, wherein: the step S202 specifically includes: the application service module acquires a control command through an interface, performs splitting processing according to the purpose of the control command, converts the control command into a control command through a preset command library, and sends the control command to the core service module according to the action target of the control command;
the step S203 specifically includes: when the core service module receives the control instruction, the control instruction is stored, the configuration information of the target bottom layer device is detected, whether the current control instruction is available or not is judged, if the current control instruction is available, the control instruction is converted into an internal unified command format, and the control instruction is issued to the device service module.
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