Substation monitoring system detection method and device, medium and electronic equipment

Abstract

The invention discloses a transformer substation monitoring system detection method and device, a medium and electronic equipment. The method comprises the following steps: determining a monitored object of a to-be-detected monitoring system and a test data set corresponding to the monitored object based on a detection demand; in response to a test operation control instruction of a user, controlling the monitored object to perform a test operation according to the test data set; acquiring image data acquired by a preset image acquisition device and generated after the monitored object executes the test operation; and identifying the image data by adopting a preset image identification model to obtain a detection result of the to-be-detected monitoring system. According to the method, the detection efficiency and accuracy of the substation monitoring system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of substations, and in particular to a detection method, device, medium and electronic equipment for a substation monitoring system. Background Art

[0002] As one of the key links in the construction of smart grid, substation is the information perception source of grid operation and the command execution terminal of grid control. It is the core node of power flow and information flow, and the basic support for the safe and stable operation of the grid. With the development of technology, substation monitoring system plays an increasingly important role in improving the operation efficiency of substation, preventing accidents, and responding to faults in a timely manner. The basic functions of substation monitoring system cover real-time monitoring and data acquisition, fault detection and early warning, remote control and automatic operation, data management and report generation, etc. These are key technical measures to ensure the efficient and stable operation of substation. Among them, real-time data can help managers understand the operating status of equipment, discover potential problems in time, and take corresponding measures; when equipment is abnormal or fails, the system can issue an alarm in time to remind managers to deal with it, thereby reducing the risk of equipment damage and power outage. Therefore, the accuracy of telemetry and telesignaling signals is the basis for the safe and stable operation of substations. Traditional automatic detection of substation monitoring system adopts the process layer equipment to collect data or the interval layer equipment to forward data to realize automatic detection of substation monitoring system. Although abnormalities can be detected, the detection results are not accurate. Summary of the invention

[0003] In view of this, the present invention provides a substation monitoring system detection method, device, medium and electronic equipment, the main purpose of which is to solve the current problem of inaccurate detection results of automatic detection of substation monitoring systems.

[0004] To solve the above problems, the present application provides a substation monitoring system detection method, comprising:

[0005] Determine a monitored object of the monitoring system to be tested and a test data set corresponding to the monitored object based on the detection requirements;

[0006] In response to a test operation control instruction of a user, controlling the monitored object to perform a test operation according to the test data set;

[0007] Acquire image data generated by the monitored object after the monitored object performs the test operation and acquired by a preset image collector;

[0008] The image data is identified using a preset image recognition model to obtain a detection result of the monitoring system to be detected.

[0009] Optionally, in response to a test operation control instruction of a user, controlling the monitored object to perform a test operation according to the test data set specifically includes:

[0010] Sending the telemetry test instruction in the control instruction to a preset tester to control the preset tester to perform a telemetry control operation on the monitored object according to the telemetry data in the test data set;

[0011] The telesignaling test instruction in the control instruction is sent to a preset simulator to control the preset simulator to perform a telesignaling control operation on the monitored object according to the telesignaling data in the test data set.

[0012] Optionally, before adopting a preset image recognition model to recognize the image data, the method further includes constructing the preset image recognition model;

[0013] The constructing of the preset image recognition model specifically includes:

[0014] Obtain some historical primary wiring image data;

[0015] Preprocessing each of the historical primary wiring image data to obtain an initial training data set, wherein the preprocessing process includes data cleaning, data normalization processing, and data enhancement processing;

[0016] Annotating the initial training data set to obtain a labeled data set;

[0017] The initial neural network recognition model is trained based on the label data set as a training sample to generate the preset image recognition model.

[0018] Optionally, the performing model training on the initial neural network recognition model based on the label data set as a training sample to generate the preset image recognition model specifically includes:

[0019] Preprocessing each label data in the label data set using the input layer of the initial neural network recognition model to obtain first image data corresponding to each label data;

[0020] Using the convolution layer of the initial neural network recognition model to extract local features of each of the first image data, to obtain first feature data corresponding to each of the first image data;

[0021] Down-sampling each of the feature data using the pooling layer of the initial neural network recognition model to obtain second feature data corresponding to each of the first feature data;

[0022] Using the fully connected layer of the initial neural network recognition model to recognize each of the second feature data to obtain an initial prediction data set;

[0023] Based on the initial prediction data set and the label data set, a preset cross entropy loss function is used to update the model parameters of the initial neural network recognition model to generate a current neural network recognition model;

[0024] Based on the label data set as a training sample, the current neural network recognition model is trained in an iterative manner until the loss value obtained by calculating and processing using the preset cross entropy loss function meets the preset conditions, and the current neural network recognition model is determined as the preset image recognition model.

[0025] Optionally, the using a preset image recognition model to recognize the image data to obtain the detection result of the monitoring system to be detected specifically includes:

[0026] Preprocessing the image data to obtain an image to be recognized;

[0027] Using the preset image recognition model to recognize the image to be recognized, and obtaining a prediction data set corresponding to the monitored object in the image to be recognized;

[0028] The predicted data set is tested based on the test data set to obtain a test result of the monitoring system to be tested.

[0029] Optionally, using the preset image recognition model to recognize the image to be recognized to obtain a prediction data set corresponding to the monitored object in the image to be recognized specifically includes:

[0030] Preprocessing the image to be recognized by using the input layer of the preset image recognition model to obtain second image data corresponding to the image to be recognized;

[0031] Using the convolution layer of the preset image recognition model to extract local features of the second image data, to obtain first target feature data corresponding to the second image data;

[0032] Down-sampling the first target feature data using the pooling layer of the preset image recognition model to obtain second target feature data corresponding to the first target feature data;

[0033] Using the fully connected layer of the preset image recognition model to identify the second target feature data, and obtain a prediction data set corresponding to the monitored object in the image to be identified;

[0034] The prediction data set includes prediction equipment information, prediction category information, prediction telemetry data and prediction telesignaling data.

[0035] Optionally, after the image data is recognized by using a preset image recognition model to obtain the detection result of the monitoring system to be detected, the method further includes:

[0036] A test report of the monitoring system to be tested is generated based on the test results and a preset template.

[0037] In order to solve the above problems, the present application provides a substation monitoring system detection device, including:

[0038] A determination module, used to determine a monitored object of the monitoring system to be detected and a test data set corresponding to the monitored object based on the detection requirements;

[0039] An operation module, configured to control the monitored object to perform a test operation according to the test data set in response to a test operation control instruction of a user;

[0040] An acquisition module, used for acquiring image data generated by a preset image collector after the monitored object performs the test operation;

[0041] The recognition module is used to recognize the image data using a preset image recognition model to obtain the detection result of the monitoring system to be detected.

[0042] In order to solve the above problem, the present application provides a storage medium, characterized in that the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned substation monitoring system detection method are implemented.

[0043] To solve the above problem, the present application provides an electronic device, characterized in that it includes at least a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above-mentioned substation monitoring system detection method when executing the computer program on the memory.

[0044] The beneficial effects of the present application are as follows: the present application collects a wiring diagram as image data to detect the monitoring system to be detected, and uses a preset image recognition model to identify the image data, so as to discover potential electrical connection errors or equipment defects, and make corrections in time to avoid electrical accidents caused by wiring errors. At the same time, the wiring diagram can display the connection status of the equipment in real time, helping operation and maintenance personnel to quickly locate the fault point and improve the efficiency and accuracy of fault handling.

[0045] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0047] Figure 1 A schematic diagram of a process flow of a substation monitoring system detection method provided by an embodiment of the present application is shown;

[0048] Figure 2 A schematic diagram of a process flow of a substation monitoring system detection method provided by another embodiment of the present application is shown;

[0049] Figure 3 A schematic diagram showing the principle of detection by the monitoring system to be detected in a substation according to an embodiment of the present application is shown;

[0050] Figure 4 A structural block diagram of a substation monitoring system detection device provided in another embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0052] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.

[0053] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0054] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0055] It should also be understood that although the present application has been described with reference to some specific examples, those skilled in the art will be able to readily implement many other equivalent forms of the present application.

[0056] The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

[0057] Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments applied for are merely examples of the present application, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that obscure the present application. Therefore, the specific structural and functional details applied for herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any suitable detailed structure.

[0058] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present application.

[0059] The present application embodiment provides a substation monitoring system detection method, such as Figure 1 As shown, including:

[0060] Step S101: determining a monitored object of a monitoring system to be detected and a test data set corresponding to the monitored object based on a detection requirement;

[0061] In the specific implementation process of this step, the detection demand can be the demand for detecting one or more monitored objects of the monitoring system to be detected; the monitored object can be each intelligent electronic device (Intelligent Electronic Device, referred to as IED) monitored by the monitoring system to be detected; the test data set can be obtained from the substation configuration description (SCD) file. The SCD file describes the instance configuration and communication parameters of all IEDs, the communication configuration between IEDs, and the substation primary system structure. The monitoring system to be detected realizes the unified access, storage and management of substation information such as power grid and equipment operation information, status monitoring information, and metering information through system integration optimization and information sharing, and realizes substation operation monitoring, operation and control, comprehensive information analysis and intelligent alarm. Operation management and auxiliary application functions, and provide unified substation operation and access services for master station systems such as scheduling and production. Based on the user's detection needs, the monitored object of the monitoring system to be detected is determined; the monitored object is at least one device to be monitored. Based on the monitored object, the substation system configuration file is queried to obtain the test data set corresponding to the monitored object.

[0062] Step S102: determining a monitored object of the monitoring system to be tested and a test data set corresponding to the monitored object based on the detection requirement;

[0063] In the specific implementation process of this step, in response to the test operation clicked by the user, a control instruction for the detection requirement is generated; the control instruction is a control instruction for controlling the monitored object to perform a test operation according to the test data set. Specifically, the control instruction includes a telemetry test instruction for performing a test operation on the monitored object and a telesignaling test instruction for performing a test operation on the monitored object.

[0064] Step S103: acquiring image data generated by the monitored object after the monitored object performs the test operation and collected by a preset image collector;

[0065] During the specific implementation of this step, the substation monitoring system receives the operating data sent by the monitored object through the station control layer network; the output end of the substation monitoring system is configured with an image subassembly connector; the image subassembly connector copies the image signal sent by the substation monitoring system to obtain two identical first image signals; one of the first image signals is subjected to signal conversion to generate a first fully digital audio and video transmission interface signal, i.e., an HDMI signal, and the HDMI signal is sent to a preset display for display; the other first image signal is subjected to signal conversion to generate a second fully digital audio and video transmission interface signal HDMI signal and then sent to a preset image collector; the image data generated by the monitored object after executing the control instruction collected by the preset image collector is received, and the image data is a primary wiring diagram; the primary wiring diagram is a substation configuration description language, which describes the substation primary system structure and associated logical nodes, and describes the instance configuration and communication parameters of all IEDs, and the communication configuration between IEDs; it lays the foundation for the subsequent use of a preset image recognition model to identify the image data.

[0066] Step S104: using a preset image recognition model to recognize the image data to obtain a detection result of the monitoring system to be detected.

[0067] In the specific implementation process of this step, the image data is preprocessed to obtain the image to be identified; the image to be identified is identified using the preset image recognition model to obtain a prediction data set corresponding to the monitored object in the image to be identified; the prediction data set includes data such as prediction device information, prediction category information, prediction telemetry data, and prediction telesignaling data. The prediction data set is tested based on the test data set to obtain the detection result of the monitoring system to be detected. Specifically, the detection is performed based on the prediction device information, prediction category information, prediction telemetry data, prediction telesignaling data, and the initial device information, initial category information, initial telemetry data, and initial telesignaling data corresponding to the monitored object in the pre-acquired test data set to obtain the detection result of the monitoring system to be detected.

[0068] The present application collects a wiring diagram as image data to detect the monitoring system to be detected, and uses a preset image recognition model to identify the image data. It can discover potential electrical connection errors or equipment defects, and make corrections in time to avoid electrical accidents caused by wiring errors. At the same time, the wiring diagram can display the connection status of the equipment in real time, helping operation and maintenance personnel to quickly locate the fault point and improve the efficiency and accuracy of fault handling.

[0069] Another embodiment of the present application provides another substation monitoring system detection method, such as Figure 2 As shown, including:

[0070] Step S201: determining a monitored object of a monitoring system to be detected and a test data set corresponding to the monitored object based on a detection requirement;

[0071] In the specific implementation process of this step, the detection demand can be the demand for detecting one or more monitored objects of the monitoring system to be detected; the monitored object can be each intelligent electronic device (Intelligent Electronic Device, referred to as IED) monitored by the monitoring system to be detected; the test data set can be obtained from the substation configuration description (SCD) file. The SCD file describes the instance configuration and communication parameters of all IEDs, the communication configuration between IEDs, and the substation primary system structure. The monitoring system to be detected realizes the unified access, storage and management of substation information such as power grid and equipment operation information, status monitoring information, and metering information through system integration optimization and information sharing, and realizes substation operation monitoring, operation and control, comprehensive information analysis and intelligent alarm. Operation management and auxiliary application functions, and provide unified substation operation and access services for master station systems such as scheduling and production. Based on the user's detection needs, the monitored object of the monitoring system to be detected is determined; the monitored object is at least one device to be monitored. Based on the monitored object, the substation system configuration file is queried to obtain the test data set corresponding to the monitored object.

[0072] Step S202: In response to a test operation control instruction from a user, controlling the monitored object to perform a test operation according to the test data set;

[0073] In the specific implementation process of this step, in response to the test operation clicked by the user, a control instruction for the detection requirement is generated; the control instruction is a control instruction for controlling the monitored object to perform a test operation according to the test data set. Specifically, the control instruction includes a telemetry test instruction for performing a test operation on the monitored object and a telesignaling test instruction for performing a test operation on the monitored object. The telemetry test instruction in the control instruction is sent to a preset tester to control the preset tester to perform a telemetry control operation on the monitored object according to the telemetry data in the test data set; Figure 3As shown, it is a schematic diagram of the principle of testing the monitoring system to be tested in the substation of the present application; the substation monitoring system detection method is applied to an automatic test host, and the automatic test host sends the telemetry test instruction in the control instruction to a preset tester to control the preset tester to perform telemetry control on the monitored object according to the telemetry data in the test data set. The telesignaling test instruction in the control instruction is sent to a preset simulator to control the preset simulator to perform telesignaling control on the monitored object according to the telesignaling data in the test data set; the automatic test host sends the telesignaling test instruction in the control instruction to the preset simulator to control the preset tester to perform telesignaling control on the monitored object according to the telesignaling data in the test data set.

[0074] Step S203: constructing a preset image recognition model;

[0075] In the specific implementation process of this step, a number of historical primary wiring image data are obtained; each of the historical primary wiring image data is preprocessed to obtain an initial training data set, and the preprocessing process includes data cleaning, data normalization processing and data enhancement processing; the initial training data set is labeled to obtain a label data set; based on the label data set as a training sample, the initial neural network recognition model is trained to generate the preset image recognition model. Specifically, the input layer of the initial neural network recognition model is used to preprocess each label data in the label data set to obtain first image data corresponding to each label data; the convolution layer of the initial neural network recognition model is used to extract local features of each first image data to obtain first feature data corresponding to each first image data; the pooling layer of the initial neural network recognition model is used to downsample each feature data to obtain second feature data corresponding to each first feature data; the fully connected layer of the initial neural network recognition model is used to recognize each second feature data to obtain an initial prediction data set; based on the initial prediction data set and the label data set, the model parameters of the initial neural network recognition model are updated using a preset cross entropy loss function to generate a current neural network recognition model; based on the label data set as a training sample, the current neural network recognition model is iteratively trained until the loss value calculated using the preset cross entropy loss function meets the preset conditions, and the current neural network recognition model is determined as the preset image recognition model.

[0076] Step S204: pre-processing the image data to obtain an image to be recognized;

[0077] In the specific implementation process of this step, the image data is cleaned, noise is removed, and the size of the image is adjusted to generate a first image to be identified; the first image to be identified is subjected to data normalization processing to obtain a second image to be identified; and the second image to be identified is subjected to data enhancement processing to obtain the image to be identified.

[0078] Step S205: using the preset image recognition model to recognize the image to be recognized, and obtaining a prediction data set corresponding to the monitored object in the image to be recognized;

[0079] In the specific implementation process of this step, the input layer of the preset image recognition model is used to preprocess the image to be recognized, so as to obtain second image data corresponding to the image to be recognized; the convolution layer of the preset image recognition model is used to extract local features of the second image data, so as to obtain first target feature data corresponding to the second image data; the pooling layer of the preset image recognition model is used to downsample the first target feature data, so as to obtain second target feature data corresponding to the first target feature data; the fully connected layer of the preset image recognition model is used to recognize the second target feature data, so as to obtain a predicted data set corresponding to the monitored object in the image to be recognized; wherein the predicted data set includes predicted device information, predicted category information, predicted telemetry data and predicted telesignaling data. The predicted device information includes information such as device name, device ID, etc. The predicted category information is the device type, and the predicted category information is device type information such as switches and knife switches; the predicted telemetry data includes information such as predicted voltage data and predicted current data; the predicted telesignaling data includes data information such as the state of the graphic element being open and closed; the predicted data set also includes predicted alarm character information, and the predicted alarm character information is information such as interruption.

[0080] Step S206: testing the predicted data set based on the test data set to obtain a test result of the monitoring system to be tested;

[0081] During the specific implementation of this step, detection is performed based on the predicted device information, predicted category information, predicted telemetry data, predicted telesignaling data, and the initial device information, initial category information, initial telemetry data, and initial telesignaling data corresponding to the monitored object in the pre-acquired test data set to obtain the detection result of the monitoring system to be detected. Specifically, the predicted device information is compared with the initial device information corresponding to the monitored object to obtain the device information detection result; the predicted category information is compared with the initial category information corresponding to the monitored object to obtain the device category detection result; the predicted telemetry data is compared with the initial telemetry data corresponding to the monitored object to obtain the telemetry data detection result; the predicted telesignaling data is compared with the initial telesignaling data corresponding to the monitored object to obtain the telesignaling data detection result; when any one of the device information detection result, the device category detection result, the telemetry data detection result and the telesignaling data detection result does not meet the preset conditions, an alarm is given for the fault, and the fault is located based on the detection result that does not meet the preset conditions, so as to identify potential electrical connection errors or equipment defects, make corrections in time, and avoid electrical accidents caused by wiring errors. At the same time, a wiring diagram can display the connection status of the equipment in real time, help operation and maintenance personnel quickly locate the fault point, and improve the efficiency and accuracy of fault handling.

[0082] Step S207: Generate a test report of the monitoring system to be tested based on the test result and a preset template.

[0083] During the specific implementation of this step, the device information detection results, device category detection results, telemetry data detection results and telesignaling data detection results are used to construct a test report using a preset template to generate a test report of the monitoring system that reflects the device information detection results, device category detection results, telemetry data detection results and telesignaling data detection results, and prompts the fault information in the detection results.

[0084] The present application collects a wiring diagram as image data to detect the monitoring system to be detected, and uses a preset image recognition model to identify the image data. It can discover potential electrical connection errors or equipment defects, and make corrections in time to avoid electrical accidents caused by wiring errors. At the same time, the wiring diagram can display the connection status of the equipment in real time, helping operation and maintenance personnel to quickly locate the fault point and improve the efficiency and accuracy of fault handling.

[0085] Another embodiment of the present application provides a substation monitoring system detection device, such as Figure 4 As shown, including:

[0086] Determination module 1, used to determine the monitored object of the monitoring system to be detected and the test data set corresponding to the monitored object based on the detection requirements;

[0087] An operation module 2, configured to control the monitored object to perform a test operation according to the test data set in response to a test operation control instruction of a user;

[0088] An acquisition module 3 is used to acquire image data generated by a preset image collector after the monitored object performs the test operation;

[0089] The recognition module 4 is used to recognize the image data using a preset image recognition model to obtain the detection result of the monitoring system to be detected.

[0090] During the specific implementation process, the operation module 2 is specifically used to: send the telemetry test instruction in the control instruction to a preset tester to control the preset tester to perform telemetry control operations on the monitored object according to the telemetry data in the test data set; send the telesignaling test instruction in the control instruction to a preset simulator to control the preset simulator to perform telesignaling control operations on the monitored object according to the telesignaling data in the test data set.

[0091] In the specific implementation process, the substation monitoring system detection device also includes a preset image recognition model construction module, and the preset image recognition model is specifically used to: obtain a number of historical primary wiring image data; preprocess each of the historical primary wiring image data to obtain an initial training data set, and the preprocessing process includes data cleaning, data normalization processing and data enhancement processing; annotate the initial training data set to obtain a label data set; based on the label data set as a training sample, perform model training on the initial neural network recognition model to generate the preset image recognition model.

[0092] In the specific implementation process, the preset image recognition model construction module is also used to: use the input layer of the initial neural network recognition model to preprocess each label data in the label data set to obtain the first image data corresponding to each label data; use the convolution layer of the initial neural network recognition model to extract local features of each first image data to obtain the first feature data corresponding to each first image data; use the pooling layer of the initial neural network recognition model to downsample each feature data to obtain the second feature data corresponding to each first feature data; use the fully connected layer of the initial neural network recognition model to recognize each second feature data to obtain an initial prediction data set; based on the initial prediction data set and the label data set, use the preset cross entropy loss function to update the model parameters of the initial neural network recognition model to generate a current neural network recognition model; based on the label data set as a training sample, iteratively train the current neural network recognition model until the loss value calculated by the preset cross entropy loss function meets the preset conditions, and the current neural network recognition model is determined as the preset image recognition model.

[0093] In the specific implementation process, the recognition module 4 is specifically used to: preprocess the image data to obtain the image to be recognized; use the preset image recognition model to recognize the image to be recognized to obtain a predicted data set corresponding to the monitored object in the image to be recognized; detect the predicted data set based on the test data set to obtain the detection result of the monitoring system to be detected.

[0094] In the specific implementation process, the recognition module 4 is also used to: use the input layer of the preset image recognition model to preprocess the image to be recognized, and obtain second image data corresponding to the image to be recognized; use the convolution layer of the preset image recognition model to extract local features of the second image data, and obtain first target feature data corresponding to the second image data; use the pooling layer of the preset image recognition model to downsample the first target feature data, and obtain second target feature data corresponding to the first target feature data; use the fully connected layer of the preset image recognition model to recognize the second target feature data, and obtain a predicted data set corresponding to the monitored object in the image to be recognized; wherein the predicted data set includes predicted device information, predicted category information, predicted telemetry data, and predicted telesignaling data.

[0095] In a specific implementation process, the substation monitoring system detection device further includes a test report generation module, and the test report generation module is specifically used to generate a test report of the monitoring system based on the detection result and a preset template.

[0096] The present application collects a wiring diagram as image data to detect the monitoring system to be detected, and uses a preset image recognition model to identify the image data. It can discover potential electrical connection errors or equipment defects, and make corrections in time to avoid electrical accidents caused by wiring errors. At the same time, the wiring diagram can display the connection status of the equipment in real time, helping operation and maintenance personnel to quickly locate the fault point and improve the efficiency and accuracy of fault handling.

[0097] Another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the following method steps are implemented:

[0098] Step 1: determining a monitored object of the monitoring system to be tested and a test data set corresponding to the monitored object based on the detection requirements;

[0099] Step 2: In response to a test operation control instruction of a user, controlling the monitored object to perform a test operation according to the test data set;

[0100] Step 3: acquiring image data generated by the monitored object after executing the test operation and collected by a preset image collector;

[0101] Step 4: Use a preset image recognition model to identify the image data to obtain the detection result of the monitoring system to be detected.

[0102] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0103] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0104] The specific implementation process of the above method steps can refer to the embodiments of any of the above substation monitoring system detection methods, and this embodiment will not be repeated here.

[0105] The present application collects a wiring diagram as image data to detect the monitoring system to be detected, and uses a preset image recognition model to identify the image data. It can discover potential electrical connection errors or equipment defects, and make corrections in time to avoid electrical accidents caused by wiring errors. At the same time, the wiring diagram can display the connection status of the equipment in real time, helping operation and maintenance personnel to quickly locate the fault point and improve the efficiency and accuracy of fault handling.

[0106] Another embodiment of the present application provides an electronic device, which may be a server, and the electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external client via a network connection. When the electronic device program is executed by the processor, it implements the functions or steps of a substation monitoring system detection method service side.

[0107] In one embodiment, an electronic device is provided, which may be a client. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external server via a network connection. When the electronic device program is executed by the processor, the functions or steps on the client side of a substation monitoring system detection method are implemented.

[0108] Another embodiment of the present application provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the following method steps when executing the computer program in the memory:

[0109] Step 1: determining a monitored object of the monitoring system to be tested and a test data set corresponding to the monitored object based on the detection requirements;

[0110] Step 2: In response to a test operation control instruction of a user, controlling the monitored object to perform a test operation according to the test data set;

[0111] Step 3: acquiring image data generated by the monitored object after executing the test operation and collected by a preset image collector;

[0112] Step 4: Use a preset image recognition model to identify the image data to obtain the detection result of the monitoring system to be detected.

[0113] The specific implementation process of the above method steps can refer to the embodiments of any of the above substation monitoring system detection methods, and this embodiment will not be repeated here.

[0114] The present application collects a wiring diagram as image data to detect the monitoring system to be detected, and uses a preset image recognition model to identify the image data. It can discover potential electrical connection errors or equipment defects, and make corrections in time to avoid electrical accidents caused by wiring errors. At the same time, the wiring diagram can display the connection status of the equipment in real time, helping operation and maintenance personnel to quickly locate the fault point and improve the efficiency and accuracy of fault handling.

[0115] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A substation monitoring system detection method, characterized in that: include: Determine a monitored object of the monitoring system to be tested and a test data set corresponding to the monitored object based on the detection requirements; In response to a test operation control instruction of a user, controlling the monitored object to perform a test operation according to the test data set; Acquire image data generated by the monitored object after the monitored object performs the test operation and acquired by a preset image collector; The image data is identified using a preset image recognition model to obtain a detection result of the monitoring system to be detected.

2. The method according to claim 1, characterized in that The step of responding to the test operation control instruction of the user and controlling the monitored object to perform the test operation according to the test data set specifically includes: Sending the telemetry test instruction in the control instruction to a preset tester to control the preset tester to perform a telemetry control operation on the monitored object according to the telemetry data in the test data set; The telesignaling test instruction in the control instruction is sent to a preset simulator to control the preset simulator to perform a telesignaling control operation on the monitored object according to the telesignaling data in the test data set.

3. The method according to claim 1, characterized in that Before using the preset image recognition model to recognize the image data, the method further includes constructing the preset image recognition model; The constructing of the preset image recognition model specifically includes: Obtain some historical primary wiring image data; Preprocessing each of the historical primary wiring image data to obtain an initial training data set, wherein the preprocessing process includes data cleaning, data normalization processing, and data enhancement processing; Annotating the initial training data set to obtain a labeled data set; The initial neural network recognition model is trained based on the label data set as a training sample to generate the preset image recognition model.

4. The method according to claim 4, characterized in that The performing model training on the initial neural network recognition model based on the label data set as a training sample to generate the preset image recognition model specifically includes: Preprocessing each label data in the label data set using the input layer of the initial neural network recognition model to obtain first image data corresponding to each label data; Using the convolution layer of the initial neural network recognition model to extract local features of each of the first image data, to obtain first feature data corresponding to each of the first image data; Down-sampling each of the feature data using the pooling layer of the initial neural network recognition model to obtain second feature data corresponding to each of the first feature data; Using the fully connected layer of the initial neural network recognition model to recognize each of the second feature data to obtain an initial prediction data set; Based on the initial prediction data set and the label data set, a preset cross entropy loss function is used to update the model parameters of the initial neural network recognition model to generate a current neural network recognition model; Based on the label data set as a training sample, the current neural network recognition model is trained in an iterative manner until the loss value obtained by calculating and processing using the preset cross entropy loss function meets the preset conditions, and the current neural network recognition model is determined as the preset image recognition model.

5. The method according to claim 1, characterized in that The adopting a preset image recognition model to recognize the image data to obtain the detection result of the monitoring system to be detected specifically includes: Preprocessing the image data to obtain an image to be recognized; Using the preset image recognition model to recognize the image to be recognized, and obtaining a prediction data set corresponding to the monitored object in the image to be recognized; The predicted data set is tested based on the test data set to obtain a test result of the monitoring system to be tested.

6. The method according to claim 5, characterized in that The preset image recognition model is used to recognize the image to be recognized, and a prediction data set corresponding to the monitored object in the image to be recognized is obtained, which specifically includes: Preprocessing the image to be recognized by using the input layer of the preset image recognition model to obtain second image data corresponding to the image to be recognized; Using the convolution layer of the preset image recognition model to extract local features of the second image data, to obtain first target feature data corresponding to the second image data; Down-sampling the first target feature data using the pooling layer of the preset image recognition model to obtain second target feature data corresponding to the first target feature data; Using the fully connected layer of the preset image recognition model to identify the second target feature data, and obtain a prediction data set corresponding to the monitored object in the image to be identified; The prediction data set includes prediction equipment information, prediction category information, prediction telemetry data and prediction telesignaling data.

7. The method according to claim 1, characterized in that After the image data is identified by using a preset image recognition model to obtain the detection result of the monitoring system to be detected, the method further includes: A test report of the monitoring system to be tested is generated based on the test results and a preset template.

8. A substation monitoring system detection device, characterized in that: include: A determination module, used to determine a monitored object of the monitoring system to be detected and a test data set corresponding to the monitored object based on the detection requirements; An operation module, configured to control the monitored object to perform a test operation according to the test data set in response to a test operation control instruction of a user; An acquisition module, used for acquiring image data generated by a preset image collector after the monitored object performs the test operation; The recognition module is used to recognize the image data using a preset image recognition model to obtain the detection result of the monitoring system to be detected.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the substation monitoring system detection method described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The device at least comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the substation monitoring system detection method according to any one of claims 1 to 7 when executing the computer program on the memory.

Images