Exception recognition method and system based on edge gateway cloud edge collaboration

Abstract

The invention relates to an anomaly identification method and system based on edge gateway cloud edge collaboration, and the method specifically comprises the steps: uploading an algorithm model to a background model management module of an Internet of Things platform, configuring a calculation rule, issuing the algorithm model and the calculation rule to an edge gateway through a cloud server, the edge gateway pulls cloud agent sub-equipment information and calculation configuration data, loads a corresponding algorithm model and a calculation rule, monitors an event of the cloud agent sub-equipment, performs logic processing according to the calculation rule, processes a processing result into an equipment exception event, and uploads the equipment exception event to the gateway server; and the gateway server processes the abnormal event according to the service demand. Therefore, through the combination of cloud collaboration and edge computing, the rapid identification and processing of the equipment exception are realized, the efficiency and accuracy of exception identification are improved, the dependence on specific hardware equipment is reduced, the equipment exception identification adaptation cost and the later operation and maintenance cost are reduced, and the network transmission pressure and the cloud processing burden are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things and device docking, and in particular to an anomaly identification method and system based on edge gateway cloud-edge collaboration, which automatically sends algorithm models and calculation rules to the edge gateway through cloud configuration, realizes intelligent processing of device anomaly identification, and reduces device adaptation cost and operation and maintenance management cost. Background Art

[0002] The Internet of Things (IOT) is an extended application and network extension of the communication network and the Internet. Through information sensing devices such as radio frequency identification (RFID), sensors, cameras, locators, laser scanners, etc., any object can be connected to the Internet to achieve perception, recognition, control, network interconnection and intelligent processing of objects, and realize the interaction and seamless connection between people, objects and information, so as to achieve the purpose of real-time perception, precise management and scientific decision-making of the physical world.

[0003] With the continuous development of IoT technology, its application in various fields is becoming more and more extensive, especially in the financial industry. IoT technology provides new solutions for solving the pain points of credit business, realizing accurate control of movable property, credit assessment and post-loan supervision. However, in the technical architecture of IoT financial service platform, the existing technology usually adopts the following methods for abnormal identification:

[0004] One option is to purchase third-party hardware devices and use smart cameras with integrated anomaly recognition algorithms. However, in this solution, the camera algorithm needs to be customized according to specific anomaly recognition scenarios, and the purchased hardware devices cannot use the anomaly recognition algorithm for other scenarios, resulting in high costs and lack of flexibility.

[0005] The other is to connect the device directly to the cloud IoT platform, capture frames through the device and send the on-site information to the cloud, which will then identify abnormalities in the image information. However, this solution will result in a large amount of invalid image and video data being sent to the cloud, occupying a large amount of network bandwidth, resulting in high network bandwidth costs. At the same time, the cloud's processing of large amounts of data also increases the demand and cost of computing resources.

[0006] In addition, with the development of business, the application demand of IoT technology in the field of value-added services continues to increase, such as access control channels, card payment, security monitoring, crowd monitoring, face recognition and other scenarios. These scenarios have put forward higher requirements on the abnormal recognition ability of IoT devices. Therefore, a new technical solution is needed to solve the above problems, reduce the adaptation cost and operation and maintenance cost of equipment abnormal recognition, and at the same time improve the accuracy and efficiency of abnormal recognition. Summary of the invention

[0007] In order to solve the deficiencies of the prior art, the present invention proposes an anomaly identification method and system based on edge gateway cloud-edge collaboration.

[0008] To achieve the above objectives, the technical solutions adopted by the present invention include:

[0009] An anomaly identification method based on edge gateway cloud-edge collaboration, characterized in that it includes the following steps:

[0010] S1. Pack the algorithm model and upload it to the background model management module of the Internet of Things platform, and configure the calculation rules in the background model management module;

[0011] S2. Send the algorithm model and the calculation rules to the edge gateway through the cloud service end of the Internet of Things platform;

[0012] S3. The edge gateway communicates with the gateway server of the IoT platform, pulls the cloud proxy sub-device information and computing configuration data, and loads the corresponding algorithm model and computing rules;

[0013] S4. The edge gateway monitors the events of the cloud proxy sub-device, performs logical processing according to the calculation rules, and processes the logical processing results into device abnormal events and uploads them to the gateway server;

[0014] S5. The gateway server processes the abnormal event according to business requirements.

[0015] Furthermore, the step S1 also includes: training the algorithm model, packaging it into a file of a specified format and uploading it to a background model management module of the Internet of Things platform.

[0016] Furthermore, the step S2 also includes: the cloud service end dynamically selects and sends the algorithm model and calculation rules to the designated edge gateway according to business needs.

[0017] Further, the step S4 specifically includes:

[0018] S41. The edge gateway monitors the image data obtained by the cloud proxy sub-device;

[0019] S42. The image data is scored using the algorithm model, and the scoring results are processed into device abnormal events and uploaded to the gateway server.

[0020] Further, after the edge gateway in step S3 communicates with the gateway server of the Internet of Things platform, the following steps are further included:

[0021] The edge gateway authenticates with the gateway server of the Internet of Things platform through a preset digital certificate and key, and pulls data after the authentication is passed.

[0022] Further, the step S5 specifically includes:

[0023] S51. The gateway server analyzes abnormal events according to business needs;

[0024] S52. According to the analysis result of the abnormal event, the cloud gateway server triggers the corresponding business process, and the business process includes: sending an alarm notification or logging.

[0025] Furthermore, training the algorithm model specifically includes: collecting sample data and preprocessing it, and using the preprocessed data for model training.

[0026] In addition, the present invention also relates to an anomaly identification system based on edge gateway cloud-edge collaboration, which is characterized by comprising:

[0027] The backend model management module is used to store and manage packaged algorithm models and configure calculation rules;

[0028] An initialization module is used to package the algorithm model and upload it to the background model management module of the Internet of Things platform, and configure the calculation rules in the background model management module;

[0029] A gateway service module is used to send the algorithm model and the calculation rules to the edge gateway through the cloud service end of the Internet of Things platform;

[0030] A communication module, used for the edge gateway to communicate with the gateway server of the Internet of Things platform, to pull the cloud proxy sub-device information and calculation configuration data, and to load the corresponding algorithm model and calculation rules;

[0031] An abnormal monitoring module is used to monitor the events of the cloud proxy sub-device based on the edge gateway, perform logical processing according to the calculation rules, and process the logical processing results into device abnormal events and upload them to the gateway server;

[0032] The exception handling module is used to enable the gateway server to handle the exception event according to business requirements.

[0033] In addition, the present invention also relates to an electronic device, characterized in that it comprises a processor and a memory;

[0034] The memory is used to store operation instructions;

[0035] The processor is used to execute the above method by calling the operation instruction.

[0036] In addition, the present invention also relates to a computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and the computer program implements the above method when executed by a processor.

[0037] The present invention discloses a method and system for abnormal identification based on edge gateway cloud-edge collaboration, which is suitable for the scene of IoT device docking and abnormal identification, and aims to realize efficient identification and management of device abnormalities through IoT platform. The method includes uploading the algorithm model to the background model management module of the IoT platform and configuring the calculation rules, sending the algorithm model and calculation rules to the edge gateway through the cloud service end, the edge gateway pulls the cloud proxy sub-device information and calculation configuration data, and loads the corresponding algorithm model and calculation rules, the edge gateway monitors the events of the cloud proxy sub-device, performs logical processing according to the calculation rules, and processes the processing results into device abnormal events and uploads them to the gateway service end, and the gateway service end processes the abnormal events according to business needs. The present invention realizes rapid identification and processing of device abnormalities through the combination of cloud collaboration and edge computing, improves the efficiency and accuracy of abnormal identification, reduces the dependence on specific hardware devices, reduces the adaptation cost of device abnormal identification and the cost of later operation and maintenance, and performs on-site data capture and preliminary processing through the edge gateway, reduces the situation of sending invalid image and video data to the cloud, reduces the network transmission pressure and cloud processing burden, and improves the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flowchart of an anomaly identification method based on edge gateway cloud-edge collaboration provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of the structure of an anomaly identification system based on edge gateway cloud-edge collaboration provided in an embodiment of the present application;

[0040] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present invention.

[0042] Those skilled in the art can understand that, unless otherwise stated, the singular forms "one", "an", "said" and "the" used herein may also include plural forms, and the "first", "second", etc., are only used to distinguish and define objects for the purpose of introducing the scheme clearly, and do not limit the objects themselves. Of course, the objects defined by "first" and "second" may be the same terminal, device, user, etc., or the same type of terminal, device and user. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0043] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0044] like Figure 1 As shown, an anomaly identification method based on edge gateway cloud-edge collaboration is characterized by comprising:

[0045] S1. Package the algorithm model into an SO or DLL file and upload it to the background model management module of the Internet of Things platform, and configure the calculation rules in the background model management module;

[0046] Specifically, before the algorithm model is packaged and uploaded, it also includes: training the algorithm model, and packaging it into a file of a specified format and uploading it to the background model management module of the Internet of Things platform; training the algorithm model specifically includes: collecting sample data and preprocessing it, and using the preprocessed data for model training.

[0047] S2. Send the algorithm model and the calculation rules to the edge gateway through the cloud service end of the Internet of Things platform;

[0048] Specifically, the cloud server dynamically selects and sends the algorithm model and calculation rules to the designated edge gateway according to business needs, and applies different algorithm models and calculation rules in different business scenarios to improve the flexibility and scalability of the system.

[0049] S3. The edge gateway communicates with the gateway server of the IoT platform. The edge gateway verifies with the gateway server through a preset digital certificate and key. After verification, the cloud proxy sub-device information and computing configuration data are pulled, and the corresponding algorithm model and computing rules are loaded;

[0050] S4. The edge gateway monitors the events of the cloud proxy sub-device, performs logical processing according to the calculation rules, and processes the logical processing results into device abnormal events and uploads them to the gateway server;

[0051] Specifically, step S4 includes:

[0052] S41. The edge gateway monitors the image data obtained by the cloud proxy sub-device;

[0053] S42. The image data is scored using the algorithm model, and the scoring results are processed into device abnormal events and uploaded to the gateway server.

[0054] S5. The gateway server processes the abnormal event according to business requirements.

[0055] Specifically, step S5 includes:

[0056] S51. The gateway server analyzes abnormal events according to business needs;

[0057] S52. According to the analysis result of the abnormal event, the cloud gateway server triggers the corresponding business process, and the business process includes: sending an alarm notification or logging.

[0058] In order to more clearly introduce the technical solution of the present application, the following will be described in detail using the vault anomaly recognition scenario as an example to help understand the anomaly recognition method based on edge gateway cloud-edge collaboration disclosed in the present application:

[0059] 1. System deployment and configuration

[0060] Cloud configuration: The algorithm model for vault anomaly identification is packaged into an SO file through the pipeline and uploaded to the background model management module of the IoT platform; the calculation rules are configured in the background model management module. The rule sets that when a dynamic event (such as people entering or leaving) is detected in the vault, the capture command is executed, and the captured image is identified through the uploaded algorithm model. If the identification result shows that there is only one person in the vault, it is determined to be an abnormal event.

[0061] 2. Edge gateway configuration: Send the configured calculation rules and algorithm models to the edge gateway IOT_Agent in the vault area.

[0062] 3. Start and connect the edge gateway IOT_Agent: After the edge gateway IOT_Agent is started, it communicates with the gateway service of the IoT platform based on the packaged digital certificate and key, and passes the verification.

[0063] After verification, the edge gateway IOT_Agent automatically pulls the sub-device information (such as the camera in the vault), algorithm model and recognition calculation configuration data of the cloud agent.

[0064] According to the pulled configuration, the edge gateway IOT_Agent connects to the camera device in the vault and loads the algorithm model and anomaly recognition calculation rules.

[0065] Send the sub-device (camera) online message to the cloud gateway service. At this time, the edge gateway IOT_Agent has established communication with the camera and can subscribe to process the camera's events.

[0066] 4. Abnormal identification process: The edge gateway IOT_Agent monitors the dynamic monitoring events of the camera. When it detects that someone is entering or leaving the vault, it executes the capture command according to the calculation rules to obtain the on-site perception image data.

[0067] The acquired image data is scored using the loaded algorithm model, and the scoring results are processed into device abnormal events.

[0068] The abnormal event data is sent to the cloud gateway service through the MQTTS protocol for subsequent processing or alarm.

[0069] By adopting the solution provided by the present application, the algorithm model is uploaded to the background model management module of the Internet of Things platform and the calculation rules are configured. The algorithm model and calculation rules are sent to the edge gateway through the cloud server. The edge gateway pulls the cloud proxy sub-device information and calculation configuration data, and loads the corresponding algorithm model and calculation rules. The edge gateway monitors the events of the cloud proxy sub-device, performs logical processing according to the calculation rules, and processes the processing results into device abnormal events and uploads them to the gateway server. The gateway server processes the abnormal events according to business needs. Therefore, through the technical solution of the present invention, abnormal identification does not need to purchase specific hardware equipment, but can be achieved by using existing camera equipment. The cloud automatically sends algorithm models and calculation rules, supports automatic cloud update and reuse of algorithm models, improves the efficiency and accuracy of abnormal identification, reduces the situation of sending invalid image and video data to the cloud, reduces network transmission pressure and cloud processing burden, and reduces operation and maintenance costs.

[0070] In addition, another aspect is to provide an anomaly identification system based on edge gateway cloud-edge collaboration, the structure of which is as follows: Figure 2 As shown, including:

[0071] The backend model management module 201 is used to store and manage the packaged algorithm models and configure the calculation rules;

[0072] Initialization module 202, used to package the algorithm model and upload it to the background model management module of the Internet of Things platform, and configure calculation rules in the background model management module;

[0073] The gateway service module 203 is used to send the algorithm model and the calculation rules to the edge gateway through the cloud service end of the Internet of Things platform;

[0074] The communication module 204 is used for the edge gateway to communicate with the gateway server of the Internet of Things platform, to pull the cloud proxy sub-device information and calculation configuration data, and to load the corresponding algorithm model and calculation rules;

[0075] The abnormal monitoring module 205 is used to monitor the events of the cloud proxy sub-device based on the edge gateway, perform logical processing according to the calculation rules, and process the logical processing results into device abnormal events and upload them to the gateway server;

[0076] The exception handling module 206 is used to enable the gateway server to handle the exception event according to business requirements.

[0077] Furthermore, the initialization module 202 is also used to train the algorithm model, and package it into a file of a specified format and upload it to the background model management module of the Internet of Things platform.

[0078] Furthermore, the gateway service module 203 is also used by the cloud service end to dynamically select and send the algorithm model and calculation rules to a designated edge gateway according to business needs.

[0079] Furthermore, the communication module 204 is also used for the edge gateway to authenticate with the gateway server of the Internet of Things platform through a preset digital certificate and key, and to pull data after the authentication is passed.

[0080] Furthermore, the abnormal monitoring module 205 is specifically used for: the edge gateway monitors the image data obtained by the cloud proxy sub-device; scores the image data through the algorithm model, and processes the scoring results into device abnormal events and uploads them to the gateway server.

[0081] Furthermore, the exception handling module 206 is specifically used for: the gateway server analyzes abnormal events according to business needs; according to the analysis results of the abnormal events, the cloud gateway server triggers corresponding business processes, and the business processes include: sending alarm notifications or logging.

[0082] By using this system, the above method can be executed and the corresponding technical effects can be achieved.

[0083] An embodiment of the present invention further provides an electronic device for executing the above method, as an implementation device of the method, comprising a processor and a memory;

[0084] A memory, used for storing operation instructions;

[0085] The processor is used to execute an exception identification method based on edge gateway cloud-edge collaboration provided in any embodiment of the present application by calling an operation instruction.

[0086] As an example, Figure 3 A schematic diagram of the structure of an electronic device applicable to an embodiment of the present application is shown, and the electronic device 300 includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as being connected through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one. It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the specific structure of the electronic device 300. In other embodiments of the present application, the electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. Optionally, the electronic device may also include a display screen 305 for displaying images, or receiving user operation instructions when necessary.

[0087] The processor 301 is applied in the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 304 may include a receiver and a transmitter. The transceiver 304 is applied in the embodiment of the present application to implement the function of the electronic device of the embodiment of the present application communicating with other devices when executed.

[0088] Processor 301 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0089] The processor 301 may also include one or more processing units, for example: the processor 301 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a memory, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and / or a neural network processor (Neural-network Processing Unit, NPU), etc. Different processing units can be independent devices or integrated into one or more processors. Among them, the controller can be the nerve center and command center of the electronic device 300. The controller can generate an operation control signal according to the instruction opcode and the timing signal to complete the control of fetching and executing instructions. A memory can also be set in the processor 301 for storing instructions and data. In some embodiments, the memory in the processor 301 is a cache memory. The memory can save instructions or data that the processor 301 has just used or circulated.

[0090] The processor 301 can run the anomaly identification method based on edge gateway cloud-edge collaboration provided in the embodiment of the present application. The processor 301 may include different devices. For example, when the CPU and GPU are integrated, the CPU and the GPU can cooperate to execute the anomaly identification method based on edge gateway cloud-edge collaboration provided in the embodiment of the present application. Part of the algorithm is executed by the CPU, and the other part of the algorithm is executed by the GPU to obtain faster processing efficiency.

[0091] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0092] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or may include a high-speed random access memory, or may include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0093] Optionally, the memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the abnormality identification method based on edge gateway cloud-edge collaboration provided in any embodiment of the present application.

[0094] The memory 303 can be used to store computer executable program codes, which include instructions. The processor 301 executes various functional applications and data processing of the electronic device 300 by running the instructions stored in the memory 303. The memory 303 may include a program storage area and a data storage area. Among them, the program storage area may store the code of the operating system, application program, etc. The data storage area may store data created during the use of the electronic device 300 (such as images, videos, etc. collected by the camera application), etc.

[0095] The memory 303 may also store one or more computer programs corresponding to the abnormal identification method based on edge gateway cloud-edge collaboration provided in the embodiment of the present application. The one or more computer programs are stored in the above-mentioned memory 303 and are configured to be executed by the one or more processors 301, and the one or more computer programs include instructions, which can be used to execute each step in the above-mentioned corresponding embodiment.

[0096] Of course, the code of the anomaly identification method based on edge gateway cloud-edge collaboration provided in the embodiment of the present application can also be stored in an external memory.

[0097] The display screen 305 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 300 may include 1 or N display screens 305, where N is a positive integer greater than 1. The display screen 305 can be used to display information input by a user or information provided to a user and various graphical user interfaces (GUI). For example, the display screen 305 can display photos, videos, web pages, or files.

[0098] The electronic device provided in the embodiment of the present application is applicable to any embodiment of the above method. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

[0099] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps in the method in the above embodiment, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps in the method in the above embodiment are implemented.

[0100] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0102] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0104] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An anomaly identification method based on edge gateway cloud-edge collaboration, characterized in that: include: S1. Pack the algorithm model and upload it to the background model management module of the Internet of Things platform, and configure the calculation rules in the background model management module; S2. Send the algorithm model and the calculation rules to the edge gateway through the cloud service end of the Internet of Things platform; S3. The edge gateway communicates with the gateway server of the IoT platform, pulls the cloud proxy sub-device information and computing configuration data, and loads the corresponding algorithm model and computing rules; S4. The edge gateway monitors the events of the cloud proxy sub-device, performs logical processing according to the calculation rules, and processes the logical processing results into device abnormal events and uploads them to the gateway server; S5. The gateway server processes the abnormal event according to business requirements.

2. The method according to claim 1, characterized in that: The step S1 also includes: training the algorithm model, packaging it into a file of a specified format and uploading it to the background model management module of the Internet of Things platform.

3. The method according to claim 1, characterized in that The step S2 also includes: the cloud service end dynamically selects and sends the algorithm model and calculation rules to the designated edge gateway according to business needs.

4. The method according to claim 1, characterized in that: The step S4 specifically includes: S41. The edge gateway monitors the image data obtained by the cloud proxy sub-device; S42. The image data is scored using the algorithm model, and the scoring results are processed into device abnormal events and uploaded to the gateway server.

5. The method according to claim 1, characterized in that After the edge gateway in step S3 communicates with the gateway server of the Internet of Things platform, the method further includes: The edge gateway authenticates with the gateway server of the Internet of Things platform through a preset digital certificate and key, and pulls data after the authentication is passed.

6. The method according to claim 1, characterized in that The step S5 specifically includes: S51. The gateway server analyzes abnormal events according to business needs; S52. According to the analysis result of the abnormal event, the cloud gateway server triggers the corresponding business process, and the business process includes: sending an alarm notification or logging.

7. The method according to claim 2, characterized in that Training the algorithm model specifically includes: collecting sample data and preprocessing it, and using the preprocessed data for model training.

8. An anomaly identification system based on edge gateway cloud-edge collaboration, characterized in that: include: The backend model management module is used to store and manage packaged algorithm models and configure calculation rules; An initialization module is used to package the algorithm model and upload it to the background model management module of the Internet of Things platform, and configure the calculation rules in the background model management module; A gateway service module is used to send the algorithm model and the calculation rules to the edge gateway through the cloud service end of the Internet of Things platform; A communication module, used for the edge gateway to communicate with the gateway server of the Internet of Things platform, to pull the cloud proxy sub-device information and calculation configuration data, and to load the corresponding algorithm model and calculation rules; An abnormal monitoring module is used to monitor the events of the cloud proxy sub-device based on the edge gateway, perform logical processing according to the calculation rules, and process the logical processing results into device abnormal events and upload them to the gateway server; The exception handling module is used to enable the gateway server to handle the exception event according to business requirements.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store operation instructions; The processor is used to execute the method according to any one of claims 1 to 7 by calling the operation instruction.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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