Power distribution automation practical training method, device, equipment and storage medium

Abstract

The invention relates to a power distribution automation practical training method and device, equipment and a storage medium, and the method comprises the steps: obtaining an operation demand type of a user, and determining a practical training scene according to the operation demand type of the user; acquiring corresponding practical training operation data, and transmitting the practical training operation data and the practical training scene to a pre-trained practical training model for comparative analysis to obtain an operation evaluation report; and performing analysis in combination with the practical training scene and the operation evaluation report to obtain practical training suggestion information, and transmitting the practical training suggestion information to the user side. The method has the effect of improving the teaching effect and the training efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution technology, and in particular to a power distribution automation training method, device, equipment and storage medium. Background Art

[0002] At present, the training and education of distribution automation technology mainly rely on theoretical teaching and on-site practice. However, due to the limitations of actual equipment and environment, on-site practice is difficult to carry out frequently and widely. Existing distribution automation training devices usually have single functions and cannot fully simulate the operating environment of the actual distribution network. The operation is complicated, which is not conducive to trainees to quickly master the operation of the distribution automation system.

[0003] The above-mentioned existing technical solutions have the following defects: the operation and fault simulation of the existing power distribution training are limited by the actual operating conditions and cannot fully simulate the operating environment of the actual distribution network, so there is room for improvement. Summary of the invention

[0004] In order to improve teaching effect and training efficiency, the present application provides a distribution automation training method, device, equipment and storage medium.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A distribution automation training method, the distribution automation training method comprising: Obtaining the user's operation requirement type, and determining a training scenario according to the user's operation requirement type; Acquire corresponding training operation data, transfer the training operation data and the training scenario to a pre-trained training model for comparison and analysis, and obtain an operation evaluation report; The training scenario and the operation evaluation report are analyzed to obtain training suggestion information, and the training suggestion information is transmitted to the user end.

[0006] By adopting the above technical solution, by obtaining the user's operation requirement type and determining the training scenario according to the user's operation requirement type, it is possible to set up personalized training scenarios according to the user's different operation requirements, thereby improving the pertinence and effectiveness of the training; by transmitting the acquired training operation data and training scenarios to a pre-trained training model for comparison and analysis, it is possible to accurately evaluate the user's operation and generate a detailed operation evaluation report; by combining the training scenarios and operation evaluation reports for comprehensive analysis, it is possible to provide users with personalized training suggestions, help users identify deficiencies in operations, and provide targeted improvement suggestions, thereby improving users' learning effects and actual operation capabilities.

[0007] In a preferred example, the present application may be further configured as follows: the obtaining of the user's operation requirement type and the determining of the training scenario according to the user's operation requirement type include: Acquiring user operation requirement information through a user input interface, wherein the operation requirement information includes operation device type, operation complexity, fault type, and operation environment; According to the operation requirement information, the requirement type of the user operation is determined, and according to the requirement type of the user operation, a corresponding operation scenario is selected from a predefined training scenario library to obtain the training scenario.

[0008] By adopting the above technical solution, the user's operation requirement information is obtained through the user input interface, which can ensure that the user's needs can be accurately conveyed to the system, and the type of operation requirement can be accurately determined based on the information input by the user; according to the user's operation requirement type, the corresponding operation scenario is selected from the predefined training scenario library, which can ensure that the training process is highly matched with actual needs, thereby improving the training effect and user satisfaction.

[0009] In a preferred example, the present application may be further configured as follows: before acquiring the corresponding training operation data, transmitting the training operation data and the training scenario to a pre-trained training model for comparison and analysis, and obtaining an operation evaluation report, the distribution automation training method further includes: Acquire historical training data, and preprocess the historical training data to obtain a training set; Based on the training set, a support vector machine is used to train the training model, and cross-validation and hyperparameter tuning methods are used to improve the prediction accuracy of the training model, and finally the pre-trained training model is obtained.

[0010] By adopting the above technical solution, by obtaining historical training data and preprocessing it, the noise and outliers in the data can be effectively removed to ensure the quality of the training set; by using support vector machines to train the training model, and using cross-validation and hyperparameter tuning methods, the prediction accuracy of the training model can be further improved, ensuring that the generated operation evaluation report is more reliable, thereby providing users with more accurate training feedback.

[0011] In a preferred example, the present application can be further configured as follows: the corresponding training operation data is obtained, and the training operation data and the training scenario are transmitted to a pre-trained training model for comparison and analysis, and the operation evaluation report obtained includes: When the user performs a training operation, the user's training operation data is recorded in real time, and the training operation data includes operation steps, operation time, and device response; The instant operation data and the training scenario are transmitted to the pre-trained training model for comparison and analysis to obtain the operation evaluation report, which includes an operation score and an error analysis.

[0012] By adopting the above technical solution, the real-time and accuracy of the data can be ensured by recording the training operation data in real time when the user performs the training operation; the operation data and the training scenario are transmitted to the pre-trained training model for comparison and analysis, which can accurately evaluate the user's operation performance, and generate operation scores and error analysis, thereby helping users to fully understand their own operation level and shortcomings, and guide subsequent improvements and learning.

[0013] In a preferred example, the present application may be further configured as follows: the analyzing the training scenario and the operation evaluation report to obtain training suggestion information, and transmitting the training suggestion information to the user terminal includes: Analyze the data in the operation evaluation report, identify deficiencies and common errors made by trainees during operation, and obtain analysis results; Generating the training suggestion information according to the analysis result, wherein the training suggestion information includes operation improvement suggestions, recommended learning resources and subsequent training plans; The training suggestion information is transmitted to the user terminal through the user interface to ensure that the trainees can receive feedback in a timely manner and make targeted improvements.

[0014] By adopting the above technical solution and analyzing the data in the operation evaluation report, it is possible to accurately identify the deficiencies and common errors made by trainees during the operation, thereby generating targeted practical training suggestion information; the practical training suggestion information is transmitted to the user end through the user interface, which can ensure that trainees receive feedback in a timely manner and make improvements based on the suggestions, thereby improving the effect of the practical training and the trainees' operation level.

[0015] In a preferred example, the present application can be further configured as follows: the distribution automation training method further includes: Establish a cloud data storage and analysis platform, where all training data and evaluation reports are stored in the cloud to facilitate centralized data management and analysis; By comprehensively analyzing the training data of all users at a preset time point, common problems and training effects can be identified to obtain comprehensive analysis results; Based on the comprehensive analysis results, optimizing the pre-trained training model and adjusting the training strategy to adapt to the latest operating specifications and technical developments; Support multi-user collaborative training mode, simulate team collaboration scenarios in real power distribution systems, and evaluate team collaboration effects and communication capabilities; Generate detailed operation logs and performance reports to record the specific operation details and evaluation results of each training, which is convenient for users and training managers to review and analyze.

[0016] By adopting the above technical solutions and establishing a cloud data storage and analysis platform, it is possible to achieve centralized management and efficient analysis of training data, thereby improving the efficiency of data management and the depth of analysis; by conducting a comprehensive analysis of the training data of all users, it is possible to identify common problems and training effects, and provide data support for subsequent training optimization; based on the comprehensive analysis results, the pre-trained training models are optimized and the training strategies are adjusted to ensure that the training content always keeps up with the latest operating specifications and technological developments, thereby improving the practical application value of the training; it supports multi-user collaborative training mode, which can simulate team collaboration scenarios in real distribution systems, thereby evaluating trainees' team collaboration capabilities and communication effects, and enhancing trainees' collaboration capabilities in actual work; it generates detailed operation logs and performance reports, which can help users and training managers comprehensively review the training process, analyze detailed issues in operations, and provide references and improvement directions for future operations.

[0017] The second object of the invention is achieved by the following technical solutions: A distribution automation training device, the distribution automation training device comprising: A training type acquisition module is used to acquire the user's operation requirement type and determine the training scenario according to the user's operation requirement type; A comparison module is used to obtain corresponding training operation data, transmit the training operation data and the training scenario to a pre-trained training model for comparison and analysis, and obtain an operation evaluation report; The analysis module is used to analyze the training scenario and the operation evaluation report to obtain training suggestion information, and transmit the training suggestion information to the user end.

[0018] By adopting the above technical solution, by obtaining the user's operation requirement type and determining the training scenario according to the user's operation requirement type, it is possible to set up personalized training scenarios according to the user's different operation requirements, thereby improving the pertinence and effectiveness of the training; by transmitting the acquired training operation data and training scenarios to a pre-trained training model for comparison and analysis, it is possible to accurately evaluate the user's operation and generate a detailed operation evaluation report; by combining the training scenarios and operation evaluation reports for comprehensive analysis, it is possible to provide users with personalized training suggestions, help users identify deficiencies in operations, and provide targeted improvement suggestions, thereby improving users' learning effects and actual operation capabilities.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned distribution automation training method when executing the computer program.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned distribution automation training method are implemented.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. By obtaining the user's operation requirement type and determining the training scenario according to the user's operation requirement type, it is possible to set up personalized training scenarios according to the user's different operation requirements, thereby improving the pertinence and effectiveness of the training; by transmitting the obtained training operation data and training scenarios to the pre-trained training model for comparison and analysis, it is possible to accurately evaluate the user's operation and generate a detailed operation evaluation report; 2. By combining practical training scenarios and operation evaluation reports for comprehensive analysis, we can provide users with personalized practical training suggestions, help users identify deficiencies in operations, and provide targeted improvement suggestions, thereby improving users' learning effects and practical operation capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a distribution automation training method in an embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 in a distribution automation training method in one embodiment of the present application; Figure 3 This is a flowchart for implementing step S20 in a distribution automation training method in an embodiment of the present application; Figure 4 It is a flowchart for implementing step S20 in a distribution automation training method in one embodiment of the present application; Figure 5 It is a flowchart for implementing step S30 in a distribution automation training method in one embodiment of the present application; Figure 6 This is a flow chart for implementing a distribution automation training method in an embodiment of the present application; Figure 7 This is a principle block diagram of a power distribution automation training device in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a distribution automation training method, which specifically includes the following steps: S10: Obtain the user's operation requirement type, and determine a training scenario according to the user's operation requirement type.

[0025] In this embodiment, the user's operation requirement type refers to the specific requirement type selected by the user on the training system for the training he / she wishes to perform.

[0026] Specifically, the system first identifies and collects the specific types of training needs that users want to conduct, including skill training, safety drills, troubleshooting, etc., to ensure that the system can provide corresponding training scenarios and content based on the specific needs of users. Based on the type of user's operation needs, the system determines or selects a suitable training scenario, which is a simulation environment, providing a specific and relevant training environment for the user's operation training.

[0027] S20: Obtain corresponding training operation data, transfer the training operation data and training scenarios to a pre-trained training model for comparison and analysis, and obtain an operation evaluation report.

[0028] Specifically, the system collects the operational data generated by the user during the training process, which may include action sequence, time response, operation accuracy, etc., and inputs the collected training operation data and training scenario information into a pre-trained training model. The training model evaluates the user's operating performance through comparative analysis and compares it with standard operating procedures or best practices. The training model generates a detailed operation evaluation report, which contains information such as the correctness, efficiency, potential errors, etc. of the user's operation, provides users with quantitative feedback on their operating performance, and helps users understand their performance in training.

[0029] S30: Analyze the training scenario and the operation evaluation report to obtain training suggestion information, and transmit the training suggestion information to the user end.

[0030] Specifically, the system further analyzes the evaluation report and training scenarios. Based on the analysis results, the system generates training suggestion information to identify specific problems or improvement points in user operations, and sends the training suggestion information to users through the user interface or notification system to ensure that users can receive feedback and suggestions so that they can learn and improve based on this information.

[0031] By adopting the above technical solution, by obtaining the user's operation requirement type and determining the training scenario according to the user's operation requirement type, it is possible to set up personalized training scenarios according to the user's different operation requirements, thereby improving the pertinence and effectiveness of the training; by transmitting the acquired training operation data and training scenarios to a pre-trained training model for comparison and analysis, it is possible to accurately evaluate the user's operation and generate a detailed operation evaluation report; by combining the training scenarios and operation evaluation reports for comprehensive analysis, it is possible to provide users with personalized training suggestions, help users identify deficiencies in operations, and provide targeted improvement suggestions, thereby improving users' learning effects and actual operation capabilities.

[0032] In one embodiment, if Figure 2 As shown, in step S10, the user's operation requirement type is obtained, and the training scenario is determined according to the user's operation requirement type, which specifically includes: S11: obtaining the user's operation requirement information through the user input interface, where the operation requirement information includes the operation device type, operation complexity, fault type, and operation environment.

[0033] Specifically, the system provides a user interface that allows users to input their specific operational requirements, including the type of equipment the user wishes to operate, such as machine tools, cranes, computer systems, etc.; the technical difficulty or complexity of the operation, which may be divided into elementary, intermediate, and advanced levels; the type of faults or problems the user wishes to learn to deal with, such as software failures, hardware failures, operational errors, etc.; and the environment in which the user operates, which may be a laboratory, an industrial site, a virtual environment, etc.

[0034] S12: Determine the user operation requirement type according to the operation requirement information, and select a corresponding operation scenario from a predefined training scenario library according to the user operation requirement type to obtain a training scenario.

[0035] Specifically, the system analyzes and determines the user's specific operation requirement type based on the operation requirement information input by the user, so as to understand the user's specific needs and prepare for selecting an appropriate training scenario in the next step. The system has a predefined training scenario library, which contains a variety of different operation scenarios. According to the user's operation requirement type, the system searches and selects the most matching operation scenario in the library. The system finally determines and provides the user with a specific training scenario that meets the user's operation requirements, namely the training scenario, to help users conduct targeted practice and learning.

[0036] In one embodiment, if Figure 3 As shown, before step S20, that is, obtaining corresponding training operation data, transmitting the training operation data and the training scenario to a pre-trained training model for comparison and analysis, and obtaining an operation evaluation report, a distribution automation training method further includes: S201: Acquire historical training data, pre-process the historical training data, and obtain a training set.

[0037] Specifically, data generated during past training is collected. This data may include user operation records, operation results, error types, user feedback, etc., to provide original materials for model training and help the model learn behavioral patterns and results in training. The collected data is cleaned, formatted, standardized and other preprocessing steps are performed to facilitate model processing, ensure data quality, eliminate noise and inconsistency, and improve the effect of model training. The preprocessed data is organized into a format suitable for model training to form a training set.

[0038] S202: Based on the training set, a support vector machine is used to train a training model, and cross-validation and hyperparameter tuning methods are used to improve the prediction accuracy of the training model, and finally a pre-trained training model is obtained.

[0039] Specifically, the support vector machine algorithm is used to train the training set and establish a training model. SVM is a supervised learning algorithm suitable for classification and regression problems. It can find the optimal boundary between data and is used to predict or evaluate training results. The cross-validation method is used to divide the training set into multiple small parts, and one of the parts is used as a test set in turn, and the rest are used as training sets to evaluate the generalization ability of the model, avoid overfitting, and improve the prediction accuracy of the model on unknown data. The hyperparameters of the SVM model, such as the penalty parameter C, kernel function parameters, etc., are adjusted to obtain the best model performance. After training, cross-validation and hyperparameter tuning, the pre-trained training model is finally obtained.

[0040] In one embodiment, if Figure 4 As shown, in step S20, the corresponding training operation data is obtained, and the training operation data and the training scene are transmitted to the pre-trained training model for comparison and analysis to obtain an operation evaluation report, which specifically includes: S21: When the user performs a training operation, the user's training operation data is recorded in real time, and the training operation data includes operation steps, operation time, and device response.

[0041] Specifically, when the user is conducting practical training, the system captures and records all the user's operation details in real time. The training operation data includes: the specific steps and sequence performed by the user; the time spent on each step or the entire operation process; the feedback or reaction of the device after the user's operation, such as indicator light changes, screen prompts, mechanical movements, etc.

[0042] S22: The current operation data and the training scenario are transmitted to a pre-trained training model for comparison and analysis to obtain an operation evaluation report, which includes an operation score and an error analysis.

[0043] Specifically, the user's operation data and corresponding training scenario information are input into a pre-trained training model. The training model can perform in-depth comparison and analysis based on the operation data and scenario information. The training model compares the user's operation with the standard operating process or expected operation. Through the comparison, the model can identify deviations, errors or improprieties in the operation, and finally generate a detailed operation evaluation report to summarize the user's operation performance. The operation evaluation report includes: quantitative scores given based on factors such as the correctness, efficiency, and standardization of user operations, as well as detailed analysis of errors or deficiencies in user operations, including the type of error, the cause of the error, etc.

[0044] In one embodiment, if Figure 5 As shown, in step S30, the training scenario and the operation evaluation report are analyzed to obtain training suggestion information, and the training suggestion information is transmitted to the user end, which specifically includes: S31: Analyze the data in the operation evaluation report, identify the deficiencies and common errors made by trainees during the operation, and obtain the analysis results.

[0045] Specifically, the system conducts an in-depth analysis of the operation evaluation report to identify the trainees' specific performance and problem points in the practical training operations. Through data analysis, the system extracts key information from the evaluation report, identifies the main problems encountered by trainees during the operation, and clarifies the specific areas where trainees need to improve, providing a basis for providing targeted feedback and suggestions. The system summarizes the problems and errors found during the analysis process to form clear analysis results, and provides trainees with a report containing specific problems and error types to help them understand their own operating status.

[0046] S32: Generate training suggestion information based on the analysis results, where the training suggestion information includes operation improvement suggestions, recommended learning resources, and subsequent training plans.

[0047] Specifically, based on the analysis results, the system generates practical training suggestion information to help trainees improve their operational skills. The practical training suggestion information includes: providing improvement measures or techniques for trainees' specific mistakes; recommending relevant learning materials, tutorials or courses based on trainees' deficiencies; and formulating personalized training plans for trainees to help them gradually improve their skills.

[0048] S33: Transmit practical training suggestion information to the user end through the user interface to ensure that trainees can receive feedback in a timely manner and make targeted improvements.

[0049] Specifically, through the user interface, practical training suggestion information is sent to trainees in real time to ensure that trainees can receive feedback in a timely manner, so that they can conduct targeted learning and training based on the suggestions. The system feedback information is delivered in a timely and easy-to-understand manner. Trainees can quickly adjust their learning strategies and training methods based on the feedback to achieve effective learning and skill improvement.

[0050] In one embodiment, if Figure 6 As shown, a distribution automation training method also includes: S40: Establish a cloud data storage and analysis platform. All training data and evaluation reports are stored in the cloud to facilitate centralized management and analysis of data.

[0051] Specifically, a cloud computing-based storage and analysis system is built to centrally store practical training data and evaluation reports, to achieve data security, accessibility and manageability, to facilitate large-scale data management and analysis, and to upload all data collected during the practical training and the generated evaluation reports to the cloud platform to ensure that the data will not be lost, to facilitate cross-device and cross-platform access, and to facilitate unified data analysis.

[0052] S50: By comprehensively analyzing the practical training data of all users at a preset time point, common problems and training effects are identified to obtain a comprehensive analysis result.

[0053] Preferably, before conducting a comprehensive analysis, the functions of the distribution automation training platform need to be tested to ensure that the data obtained by the training platform is accurate. The functional test of the training platform is relatively simple and the structure is relatively compact. When performing a functional test, wiring only requires the use of a power test line plugged into the banana plug on the training platform to perform relevant three-remote function tests and insulation function tests. On the training platform, the CT has been wrapped five times with a power line, and the terminal for applying a current value is led to the training platform. At this time, the CT value addition only requires the use of a relay protection tester to directly apply the value to the CT banana head socket on the training platform. This implementation method provides convenience for operators and greatly improves safety.

[0054] Specifically, at specific time points, such as weekly, monthly or quarterly, the system automatically conducts a comprehensive analysis of the collected training data to identify common problems and training effects of trainees in training, provide a basis for improving training content and methods, and through data analysis, find out the common problems of trainees and the effectiveness of training, help trainers understand which training content or methods need to be improved and which are successful, so as to make targeted teaching adjustments, and organize the analysis results into a report to provide clear data insights and conclusions.

[0055] S60: Based on the comprehensive analysis results, optimize the pre-trained training model and adjust the training strategy to adapt to the latest operating specifications and technological developments.

[0056] Specifically, based on the comprehensive analysis results, the algorithms and parameters of the training model are updated, and the training content and methods are adjusted to ensure that the training model and strategies are consistent with the latest operating specifications and technological developments, thereby improving the quality and effectiveness of training.

[0057] S70: Supports multi-user collaborative training mode, simulates team collaboration scenarios in real power distribution systems, and evaluates team collaboration effects and communication skills.

[0058] Specifically, the platform provides the function of supporting multiple users to conduct online training simultaneously, simulating team collaboration in a real working environment, and creating a simulation environment to allow multiple users to work together in the same training scenario, just like in a real power distribution system, providing a training experience close to actual combat, strengthening coordination and communication among team members, and improving the ability to cope with real work scenarios.

[0059] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0060] In one embodiment, a distribution automation training device is provided, and the distribution automation training device corresponds one-to-one to a distribution automation training method in the above embodiment. Figure 7 As shown, the distribution automation training device includes a training type acquisition module, a comparison module and an analysis module. The detailed description of each functional module is as follows: A training type acquisition module is used to acquire the user's operation requirement type and determine the training scenario according to the user's operation requirement type; The comparison module is used to obtain the corresponding training operation data, transfer the training operation data and training scenarios to the pre-trained training model for comparison and analysis, and obtain an operation evaluation report; The analysis module is used to analyze the practical training scenario and the operation evaluation report to obtain practical training suggestion information and transmit the practical training suggestion information to the user end.

[0061] Optionally, the distribution automation training device further includes: Storage module, used to establish a cloud data storage and analysis platform. All training data and evaluation reports are stored in the cloud to facilitate centralized data management and analysis; Comprehensive analysis module, which is used to conduct comprehensive analysis on the training data of all users at a preset time point, identify common problems and training effects, and obtain comprehensive analysis results; The adjustment and optimization module is used to optimize the pre-trained training model based on the comprehensive analysis results and adjust the training strategy to adapt to the latest operating specifications and technical developments; The collaboration module is used to support multi-user collaborative training mode, simulate team collaboration scenarios in real power distribution systems, and evaluate team collaboration effects and communication capabilities.

[0062] A historical data acquisition module is used to acquire historical training data, pre-process the historical training data, and obtain a training set; The model training module is used to train the training model based on the training set using a support vector machine, and to improve the prediction accuracy of the training model using cross-validation and hyperparameter tuning methods, ultimately obtaining a pre-trained training model.

[0063] Optionally, the practical training type module also includes: Determine the demand submodule, used to obtain the user's operation demand information through the user input interface, the operation demand information includes the operation device type, operation complexity, fault type and operation environment; The training scenario submodule is used to determine the user operation requirement type according to the operation requirement information, and select the corresponding operation scenario from the predefined training scenario library according to the user operation requirement type to obtain the training scenario.

[0064] Optionally, the comparison module also includes: The data recording submodule is used to record the user's training operation data in real time when the user performs training operations. The training operation data includes operation steps, operation time, and device response; The error analysis module is used to transfer the current operation data and the training scenario to the pre-trained training model for comparison and analysis to obtain an operation evaluation report, which includes an operation score and error analysis.

[0065] Optionally, the analysis module also includes: The analysis result submodule is used to analyze the data in the operation evaluation report, identify the deficiencies and common errors in the operation process of the trainees, and obtain the analysis results; Generate suggestion submodule, which is used to generate training suggestion information according to the analysis results. The training suggestion information includes operation improvement suggestions, recommended learning resources and subsequent training plans; The user-side display submodule is used to transmit the practical training suggestion information to the user side through the user interface, ensuring that trainees can receive feedback in a timely manner and make targeted improvements.

[0066] For the specific definition of a distribution automation training device, please refer to the definition of a distribution automation training method above, which will not be repeated here. Each module in the above-mentioned distribution automation training device can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0067] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-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 computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distribution automation training method is implemented.

[0068] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain the user's operation requirement type and determine the training scenario based on the user's operation requirement type; Obtain the corresponding training operation data, transfer the training operation data and training scenarios to the pre-trained training model for comparison and analysis, and obtain an operation evaluation report; The training scenario and the operation evaluation report are analyzed to obtain training suggestion information, and the training suggestion information is transmitted to the user end.

[0069] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtain the user's operation requirement type and determine the training scenario based on the user's operation requirement type; Obtain the corresponding training operation data, transfer the training operation data and training scenarios to the pre-trained training model for comparison and analysis, and obtain an operation evaluation report; The training scenario and the operation evaluation report are analyzed to obtain training suggestion information, and the training suggestion information is transmitted to the user end.

[0070] 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 may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may 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).

[0071] 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.

[0072] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distribution automation training method, characterized in that: The distribution automation training method comprises: Obtaining the user's operation requirement type, and determining a training scenario according to the user's operation requirement type; Acquire corresponding training operation data, transfer the training operation data and the training scenario to a pre-trained training model for comparison and analysis, and obtain an operation evaluation report; The training scenario and the operation evaluation report are analyzed to obtain training suggestion information, and the training suggestion information is transmitted to the user end.

2. A distribution automation training method according to claim 1, characterized in that: The obtaining of the user's operation requirement type and determining the training scenario according to the user's operation requirement type includes: Acquiring user operation requirement information through a user input interface, wherein the operation requirement information includes operation device type, operation complexity, fault type, and operation environment; According to the operation requirement information, the requirement type of the user operation is determined, and according to the requirement type of the user operation, a corresponding operation scenario is selected from a predefined training scenario library to obtain the training scenario.

3. A distribution automation training method according to claim 1, characterized in that: Before obtaining the corresponding training operation data, transmitting the training operation data and the training scenario to a pre-trained training model for comparison and analysis, and obtaining an operation evaluation report, the distribution automation training method further includes: Acquire historical training data, and preprocess the historical training data to obtain a training set; Based on the training set, a support vector machine is used to train the training model, and cross-validation and hyperparameter tuning methods are used to improve the prediction accuracy of the training model, and finally the pre-trained training model is obtained.

4. A distribution automation training method according to claim 1, characterized in that: The obtaining of corresponding training operation data, transmitting the training operation data and the training scenario to a pre-trained training model for comparison and analysis, and obtaining an operation evaluation report includes: When the user performs a training operation, the user's training operation data is recorded in real time, and the training operation data includes operation steps, operation time, and device response; The instant operation data and the training scenario are transmitted to the pre-trained training model for comparison and analysis to obtain the operation evaluation report, which includes an operation score and an error analysis.

5. A distribution automation training method according to claim 1, characterized in that: The analyzing the training scenario and the operation evaluation report to obtain training suggestion information, and transmitting the training suggestion information to the user terminal includes: Analyze the data in the operation evaluation report, identify deficiencies and common errors made by trainees during operation, and obtain analysis results; Generating the training suggestion information according to the analysis result, wherein the training suggestion information includes operation improvement suggestions, recommended learning resources and subsequent training plans; The training suggestion information is transmitted to the user terminal through the user interface to ensure that the trainees can receive feedback in a timely manner and make targeted improvements.

6. A distribution automation training method according to claim 1, characterized in that: The distribution automation training method further comprises: Establish a cloud data storage and analysis platform, where all training data and evaluation reports are stored in the cloud to facilitate centralized data management and analysis; By comprehensively analyzing the training data of all users at a preset time point, common problems and training effects can be identified to obtain comprehensive analysis results; Based on the comprehensive analysis results, optimizing the pre-trained training model and adjusting the training strategy to adapt to the latest operating specifications and technical developments; It supports multi-user collaborative training mode, simulates team collaboration scenarios in real power distribution systems, and evaluates team collaboration effects and communication skills.

7. A distribution automation training device, characterized in that: The distribution automation training device comprises: Obtaining the user's operation requirement type, and determining a training scenario according to the user's operation requirement type; Acquire corresponding training operation data, transfer the training operation data and the training scenario to a pre-trained training model for comparison and analysis, and obtain an operation evaluation report; The training scenario and the operation evaluation report are analyzed to obtain training suggestion information, and the training suggestion information is transmitted to the user end.

8. A distribution automation training device according to claim 7, characterized in that: A distribution automation training device also includes: Establish a cloud data storage and analysis platform, where all training data and evaluation reports are stored in the cloud to facilitate centralized data management and analysis; By comprehensively analyzing the training data of all users at a preset time point, common problems and training effects can be identified to obtain comprehensive analysis results; Based on the comprehensive analysis results, optimizing the pre-trained training model and adjusting the training strategy to adapt to the latest operating specifications and technical developments; It supports multi-user collaborative training mode, simulates team collaboration scenarios in real power distribution systems, and evaluates team collaboration effects and communication skills.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a distribution automation training method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a distribution automation training method as described in any one of claims 1 to 6 are implemented.

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