Modularized test and verification method for automatic joint debugging system of power distribution terminal

Abstract

The invention relates to the technical field of power system automation, and discloses a power distribution terminal automatic joint debugging system modularization test and verification method comprising the following steps: building a modularization test model library: defining a series of basic test models according to functional characteristics of power distribution terminals, and instantiating the models according to specific power distribution terminal models to obtain a modular test model library; forming a test model instance set; and dynamic test case generation based on a behavior test strategy: designing a behavior description language to describe behavior characteristics of the power distribution terminal, and automatically generating a test case by using an algorithm according to the test model instance set and the behavior description language. The modular test and verification method for the automatic joint debugging system of the power distribution terminal aims to realize comprehensive and efficient verification of the functions of the power distribution terminal by constructing a test model library and dynamically generating a test case based on a strategy of combining a model-driven test and a behavior-based test.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, specifically to a modular testing and verification method for an automatic commissioning system for distribution terminals. Background Technology

[0002] The automatic commissioning system for distribution terminals is an advanced automated testing platform designed to solve the problems of time-consuming, labor-intensive, and incomplete coverage of all operating conditions in traditional distribution terminal testing and verification methods. By integrating distribution safety communication technology, the system achieves automated commissioning and verification of distribution terminals. It can automatically set test scenarios, simulate various operating conditions, and monitor and analyze the terminal's response in real time, thereby quickly and accurately identifying potential defects and problems. In addition, the automatic commissioning system for distribution terminals features a modular design, high test code reusability, and adaptability to the needs of rapid product development, significantly improving testing efficiency and quality while reducing development costs.

[0003] Traditional testing and verification methods for distribution terminals have significant drawbacks. They primarily rely on manually setting up test scenarios and verifying each function point one by one. This method is not only time-consuming and labor-intensive, but also inefficient. Furthermore, it is difficult to comprehensively cover all possible operating conditions, thus failing to fully guarantee the reliability and stability of distribution terminals in practical applications. Specifically, manual testing methods are limited by the experience and knowledge of testers, making it difficult to exhaust all possible test scenarios. This can easily lead to test omissions and defects not being discovered in a timely manner. At the same time, the method of verifying each function point one by one lacks systematic and modular design, resulting in low test code reusability and difficulty in automating and standardizing the testing process. In addition, with the rapid development of distribution terminal technology and the rapid iteration of products, traditional testing methods are no longer suitable for the needs of modern product development. Due to the lack of modular design, test code is difficult to maintain and extend. Each product update requires rewriting a large amount of test code, increasing development costs and time costs. Summary of the Invention

[0004] The purpose of this invention is to propose a modular testing and verification method for an automatic commissioning system of power distribution terminals, which combines model-driven testing and behavior-based testing strategies, and achieves comprehensive and efficient verification of the functions of power distribution terminals by constructing a test model library and dynamically generating test cases.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] A modular testing and verification method for an automatic commissioning system for power distribution terminals includes the following steps:

[0007] S10. Construct a modular test model library: Based on the functional characteristics of the power distribution terminal, define a series of basic test models, and instantiate these models for specific power distribution terminal models to form a test model instance set;

[0008] S20. Dynamic test case generation based on behavior testing strategy: Design a behavior description language to describe the behavior characteristics of power distribution terminals, and automatically generate test cases using an algorithm based on the test model instance set and the behavior description language.

[0009] S30. Test Case Execution and Result Verification: Build an automated testing framework, call instances in the test model library to execute generated test cases, and record test results; introduce machine learning algorithms to intelligently analyze test results and automatically identify abnormal data;

[0010] S40. Feedback and Optimization: Present test results to testers in a visual manner, adjust test model parameters based on test result feedback, optimize behavioral description language, and form a closed-loop optimization mechanism.

[0011] Based on the above technical solution, the present invention can be further improved as follows.

[0012] Furthermore, the specific method for constructing the modular test model library in step S10 includes:

[0013] Define basic test models such as communication models, telemetry models, and remote control models. Each model includes specific input parameters, expected outputs, and boundary conditions.

[0014] For a specific power distribution terminal model, instantiate the above basic model, specify specific communication protocols, baud rates, parity bits and other parameters to form a test model instance set for that model.

[0015] Furthermore, the specific methods for designing the behavior description language in step S20 include:

[0016] Design a behavior description language to describe the behavior characteristics of a power distribution terminal in various states, such as "when a remote preset command is received, the DTU should return an acknowledgment frame within a specified time".

[0017] The algorithm automatically generates test cases based on the test model instance set and behavioral description language. The algorithm needs to consider test coverage, dependencies between test cases, and execution order optimization.

[0018] Furthermore, the automatic test case generation algorithm employs a genetic algorithm or a simulated annealing algorithm to generate a high-coverage test case set through iterative optimization.

[0019] Furthermore, the specific method for constructing the automated testing framework in step S30 includes:

[0020] Build an automated testing framework based on Python or Java, integrating a test execution engine, logging module, and result analysis module;

[0021] The test execution engine is responsible for calling instances in the test model library to simulate a real power grid environment and execute the generated test cases;

[0022] Introducing machine learning algorithms, such as support vector machines or deep neural networks, allows for intelligent analysis of test results and automatic identification of abnormal data.

[0023] Furthermore, the machine learning algorithm uses the following formula to identify abnormal data:

[0024] in, For the test result vector, This is the weight matrix. For bias terms, For activation function, The output is used to determine whether the test results are abnormal data.

[0025] Furthermore, the specific methods for feedback and optimization mechanisms described in the steps described are not limited to basic presentation and parameter adjustment, but also include the following detailed steps.

[0026] Visualization of test results: Test results are presented to testers in a variety of visualization methods, including but not limited to bar charts, line charts, scatter plots, pie charts, and radar charts. These charts can intuitively show the test pass rate, the distribution of failed test cases, the frequency statistics of various errors, and possible causes of errors. In addition, an interactive interface is provided, allowing testers to obtain more detailed test information and error descriptions by clicking or hovering over data points on the charts.

[0027] Parameter adjustment and optimization strategy: Based on the test results feedback, the test model parameters are adjusted automatically or semi-automatically, including but not limited to the parameter settings of the communication protocol, the response time threshold of telemetry and remote control, and data verification rules. At the same time, machine learning-based optimization algorithms, such as genetic algorithms or reinforcement learning, are introduced to iteratively optimize the test model in order to improve the accuracy and efficiency of the test.

[0028] Optimization of Behavioral Description Language: Based on the error types and patterns that frequently occur in the test results, the Behavioral Description Language (BDL) is optimized to more accurately describe the behavioral characteristics of the power distribution terminal. In addition, BDL editing and verification tools are provided to ensure the accuracy and consistency of BDL descriptions.

[0029] Introduction of new test models: If it is found during the testing process that the existing test models cannot cover certain key functions or scenarios, a new test model will be introduced, and it will be instantiated and verified. At the same time, a test model library management system will be established to enable version control and updates of the test models.

[0030] Furthermore, the visualization presentation not only adopts forms such as bar charts, line charts, or scatter plots, but also combines advanced visualization technologies such as dynamic dashboards, heat maps, and 3D graphics to display test results and optimization effects in a more intuitive and dynamic way. In addition, it provides a custom report function, allowing testers to generate and export reports in various formats as needed, such as Excel, PDF, etc.

[0031] Furthermore, the specific steps for pre-configuring the power distribution terminal before the test begins include:

[0032] Communication parameter settings: According to the test requirements, set the communication protocol, baud rate, data bits, stop bits and parity bits of the power distribution terminal to ensure the reliability and consistency of communication during the test.

[0033] Telemetry and remote control point configuration: Based on the functional characteristics of the power distribution terminal, configure the number, type, address, and range of telemetry and remote control points. At the same time, set the data acquisition frequency and accuracy for telemetry points and configure the permissions and operation procedures for remote control points.

[0034] Test environment preparation: Set up a simulated power grid environment, including simulated substations, lines and loads, to simulate various operating scenarios and fault conditions in a real power grid. In addition, auxiliary equipment and tools required for testing, such as signal generators, oscilloscopes, ammeters, etc., need to be prepared.

[0035] Furthermore, the specific steps for archiving and managing test data after the test are completed include:

[0036] Test result storage: The test results are stored in a database, including information such as test time, tester, test terminal model, test case name, test result and error description. At the same time, an index and query mechanism are established to enable quick retrieval and query of test results.

[0037] Test case management: Implement version control and classification management for test cases to ensure their accuracy and reusability. At the same time, establish a review and update mechanism for test cases to revise and optimize them according to changes in test requirements and terminal functions.

[0038] Optimize record tracking: Record the optimization of test model parameters, behavioral description language and test environment after each test, including optimization time, optimization personnel, optimization content, optimization effect and other information. At the same time, establish a tracking and analysis mechanism for optimization records to evaluate the effectiveness of optimization and the direction of continuous improvement.

[0039] Compared with the prior art, the technical solution of this application has the following beneficial technical effects:

[0040] This invention addresses the time-consuming, labor-intensive, and inefficient problems caused by manually setting test scenarios and verifying function points one by one in traditional methods. By constructing a modular test model library, this invention achieves test model reuse and rapid instantiation. This feature not only reduces the workload of testers but also improves testing efficiency, making the testing process more systematic and automated. Furthermore, it allows for flexible instantiation of corresponding test models for different types of distribution terminals, ensuring the comprehensiveness and accuracy of the tests. This invention introduces a dynamic test case generation method based on behavioral testing strategies. It designs a behavioral description language to describe the behavioral characteristics of distribution terminals and uses algorithms to automatically generate test cases. Moreover, it addresses the shortcomings of traditional methods in testing... Addressing the issues of low code reusability, difficulty in maintenance and expansion, this invention constructs an automated testing framework to automate test case execution and intelligently analyze results. It introduces machine learning algorithms for intelligent analysis of test results, automatically identifying abnormal data and improving test accuracy and reliability. Furthermore, the modular design makes test code easier to maintain and expand, reducing development and time costs. Finally, this invention presents test results to testers in a visual manner and adjusts test model parameters and optimizes the behavior description language based on feedback. This feature forms a closed-loop optimization mechanism, enabling continuous improvement and optimization of the testing process, thereby constantly enhancing testing efficiency and quality. Attached Figure Description

[0042] Figure 1 This is a flowchart of a modular testing and verification method for an automatic commissioning system for power distribution terminals according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Combination Figure 1As shown, the present invention provides a modular testing and verification method for an automatic commissioning system for power distribution terminals, comprising the following steps:

[0046] S10. Construct a modular test model library: Based on the functional characteristics of the power distribution terminal, define a series of basic test models, and instantiate these models for specific power distribution terminal models to form a test model instance set;

[0047] S20. Dynamic test case generation based on behavior testing strategy: Design a behavior description language to describe the behavior characteristics of power distribution terminals, and automatically generate test cases using an algorithm based on the test model instance set and the behavior description language.

[0048] S30. Test Case Execution and Result Verification: Build an automated testing framework, call instances in the test model library to execute generated test cases, and record test results; introduce machine learning algorithms to intelligently analyze test results and automatically identify abnormal data;

[0049] S40. Feedback and Optimization: Present test results to testers in a visual manner, adjust test model parameters based on test result feedback, optimize behavioral description language, and form a closed-loop optimization mechanism.

[0050] In a preferred embodiment, the present invention can be further configured such that the specific method for constructing the modular test model library in step S10 includes:

[0051] Define basic test models such as communication models, telemetry models, and remote control models. Each model includes specific input parameters, expected outputs, and boundary conditions.

[0052] For a specific power distribution terminal model, the aforementioned basic model is instantiated, specifying specific communication protocols, baud rates, parity bits, and other parameters to form a test model instance set for that model. When defining the basic test model, in addition to communication models, telemetry models, and remote control models, fault simulation models and safety authentication models can be added to more comprehensively cover the various functions and characteristics of the power distribution terminal. Each model should not only include specific input parameters, expected outputs, and boundary conditions, but also describe its application scenario and test purpose in detail to ensure the accuracy and effectiveness of the test. When instantiating the aforementioned basic model, in addition to specifying specific communication protocols, baud rates, parity bits, and other parameters, factors such as the hardware characteristics, software version, and operating environment of the power distribution terminal should also be considered to ensure that the test model instance set can truly reflect the actual operating status of the power distribution terminal. Furthermore, a unique identifier and version number can be set for each test model instance to facilitate management and tracking during the test process. To further improve test efficiency and quality, steps such as model verification and test environment configuration can be introduced. In the model verification stage, simulation tools or actual hardware are used to verify the test model to ensure its correctness and reliability. During the test environment configuration phase, a corresponding test environment is built according to the test requirements, including a simulated power grid environment and a communication network environment, to simulate various scenarios and conditions of the power distribution terminal in actual operation.

[0053] By adding fundamental test models such as fault simulation models and safety authentication models, and by detailing the application scenarios and testing objectives of each model, the test model library becomes more comprehensive and detailed, accurately reflecting the various functions and characteristics of the distribution terminal, thereby improving the accuracy and effectiveness of testing. Secondly, by considering factors such as the hardware characteristics, software version, and operating environment of the distribution terminal, and by assigning a unique identifier and version number to each test model instance, the test model instance set becomes closer to actual operating conditions, facilitating management and tracking during the testing process. This helps improve the relevance and repeatability of testing, reducing testing costs and time. Finally, by introducing steps such as model verification and test environment configuration, the correctness and reliability of the test models, as well as the authenticity and effectiveness of the test environment, can be further ensured. This helps improve the reliability and stability of testing, providing stronger support for the automated commissioning and verification of the distribution terminal.

[0054] In a preferred embodiment, the present invention can be further configured such that the specific method for designing the behavioral description language in step S20 includes:

[0055] Design a behavior description language to describe the behavior characteristics of a power distribution terminal in various states, such as "when a remote preset command is received, the DTU should return an acknowledgment frame within a specified time".

[0056] The algorithm automatically generates test cases based on the test model instance set and the behavioral description language. The algorithm needs to consider test coverage, dependencies between test cases, and execution order optimization. The behavioral description language should be designed to be more detailed and flexible to cover the behavioral characteristics of the power distribution terminal in various complex scenarios. In addition to basic command response behavior, it should also include exception handling behavior, state transition behavior, and time synchronization behavior. For example, "When the communication link is interrupted and then restored, the DTU should re-establish the connection and synchronize time information"; "When multiple remote control commands are received consecutively, the DTU should process them sequentially and return the corresponding execution results." Secondly, to enhance the expressive power of the behavioral description language, some advanced features can be introduced, such as conditional statements, loop statements, and function calls. These features enable the behavioral description language to describe more complex and dynamic behavioral characteristics, improving the efficiency and accuracy of test case generation. When automatically generating test cases using the algorithm, in addition to considering test coverage, dependencies between test cases, and execution order optimization, intelligent optimization methods such as heuristic search algorithms or genetic algorithms should also be introduced to generate a more efficient and comprehensive test case set. These algorithms can automatically adjust test case generation strategies based on testing requirements, improving the relevance and effectiveness of testing. Furthermore, the test case generation process can be integrated with the test execution process, forming a closed-loop feedback mechanism. During test execution, test data is collected and analyzed in real time, and the test case generation strategy and behavioral description language are adjusted based on the analysis results to continuously improve the accuracy and reliability of the tests.

[0057] By introducing a more detailed and flexible behavioral description language, along with advanced features and intelligent optimization methods, test case generation becomes more efficient and accurate. This helps improve test coverage and relevance, while reducing testing costs and time. Through a closed-loop feedback mechanism, combining the test case generation process with the test execution process, the test case generation strategy and behavioral description language can be adjusted in real time to continuously improve test accuracy and reliability. This helps ensure the stability and reliability of power distribution terminals under various complex scenarios, improving their performance and safety in practical applications. Finally, by automatically generating test cases using algorithms and considering factors such as test coverage, dependencies between test cases, and execution order optimization, a more efficient and comprehensive test case set can be generated. This helps improve testing efficiency and quality, providing stronger support for the automated commissioning and verification of power distribution terminals.

[0058] In a preferred embodiment, the present invention can be further configured as follows: the automatic test case generation algorithm employs either a genetic algorithm or a simulated annealing algorithm to generate a high-coverage test case set through iterative optimization. When using a genetic algorithm, a fitness function needs to be defined to evaluate the quality of the test cases. The fitness function can comprehensively consider multiple dimensions such as test coverage, dependencies between test cases, and execution order. Simultaneously, reasonable genetic operations, such as selection, crossover, and mutation, need to be designed to continuously optimize the test case set during iteration. When using a simulated annealing algorithm, parameters such as initial temperature and cooling rate need to be set, and an objective function needs to be defined to guide the algorithm's search direction. The objective function also needs to comprehensively consider factors such as test coverage and dependencies between test cases. During iteration, the algorithm decides whether to accept new test cases based on the current temperature and the objective function value, and gradually converges to the optimal solution as the temperature decreases. To further improve the efficiency and accuracy of test case generation, the genetic algorithm and the simulated annealing algorithm can be combined to form a hybrid algorithm. The hybrid algorithm can flexibly switch algorithm strategies according to specific circumstances to fully utilize the advantages of both algorithms. For example, a genetic algorithm can be used for global search in the initial stage to quickly generate a batch of high-quality test cases; then, simulated annealing can be used for local optimization in subsequent stages to further improve the coverage and accuracy of the test cases. During algorithm implementation, the speed of test case generation and the time complexity of the algorithm also need to be considered. To balance these two factors, heuristic strategies can be used to accelerate the algorithm's search process. For example, existing test data and experiential knowledge can be used to guide the direction of test case generation, or parallel computing techniques can be used to accelerate the algorithm's execution speed.

[0059] By introducing intelligent optimization methods such as genetic algorithms and simulated annealing algorithms, high-coverage test case sets can be automatically generated. These test cases can comprehensively cover various functions and characteristics of the distribution terminal, thereby improving the accuracy and reliability of testing. Secondly, the use of hybrid algorithms can fully utilize the advantages of different algorithms, further improving the efficiency and accuracy of test case generation. At the same time, the application of heuristic strategies can also accelerate the algorithm search process and reduce the time complexity of the algorithm. Finally, by comprehensively considering multiple dimensions such as test coverage, dependencies between test cases, and execution order to evaluate the quality of test cases, a more efficient and comprehensive test case set can be generated. This helps to improve the efficiency and quality of testing, providing stronger support for the automated commissioning and verification of distribution terminals.

[0060] In a preferred embodiment, the present invention can be further configured such that the specific method for constructing the automated testing framework in step S30 includes:

[0061] Build an automated testing framework based on Python or Java, integrating a test execution engine, logging module, and result analysis module;

[0062] The test execution engine is responsible for calling instances in the test model library to simulate a real power grid environment and execute the generated test cases;

[0063] Introducing machine learning algorithms, such as support vector machines or deep neural networks, allows for intelligent analysis of test results and automatic identification of abnormal data. When building an automated testing framework based on Python or Java, in addition to integrating a test execution engine, logging module, and result analysis module, configuration management and test resource management modules can be added. The configuration management module manages the test environment's configuration information, such as IP addresses, port numbers, and test data paths, ensuring the consistency and repeatability of the test environment. The test resource management module is responsible for allocating and managing the resources required during the testing process, such as memory, CPU, and network bandwidth, to improve testing efficiency and stability. Secondly, in the design of the test execution engine, besides calling instances from the test model library to simulate a real power grid environment and execute generated test exceptions, exception handling and recovery mechanisms can be added. When an exception or error occurs during testing, the test execution engine can automatically capture the exception information and recover or retry according to a preset strategy to ensure the continuity and integrity of the test. When introducing machine learning algorithms for intelligent analysis of test results, in addition to support vector machines or deep neural networks, other algorithms suitable for anomaly detection can be considered, such as isolated forests and K-means clustering. These algorithms can be flexibly selected based on the characteristics and requirements of the test data to improve the accuracy and efficiency of anomaly identification. Furthermore, to further enhance the accuracy and reliability of intelligent analysis, multiple algorithms can be combined to form a hybrid model. The hybrid model can fully leverage the advantages of different algorithms, improving the overall performance of anomaly identification. In addition, the generation and management of test data must be considered during the construction of the automated testing framework. To simulate various scenarios and conditions in a real power grid environment, a large amount of test data needs to be generated. This data can include normal data, abnormal data, boundary data, etc. To effectively manage this data, a test data warehouse can be established to classify, store, and retrieve the test data. Simultaneously, data generation tools or scripts can be used to automatically generate test data to improve the efficiency and accuracy of test data generation.

[0064] By adding configuration management and test resource management modules, the automated testing framework becomes more comprehensive and flexible. This helps ensure the consistency and repeatability of the test environment, improving testing efficiency and stability. Secondly, by adding anomaly handling and recovery mechanisms, and introducing various machine learning algorithms for intelligent analysis, the automated testing framework can automatically identify and process abnormal data during the testing process, improving testing accuracy and reliability. This helps to promptly identify and fix potential problems in power distribution terminals, ensuring their performance and safety in practical applications. Finally, by considering the generation and management of test data, and establishing a test data warehouse, the automated testing framework can generate and manage large amounts of test data to simulate various scenarios and conditions in the real power grid environment. This helps to improve test coverage and relevance, reducing testing costs and time.

[0065] In a preferred embodiment, the present invention can be further configured such that the machine learning algorithm uses the following formula to identify abnormal data:

[0066]

[0067] in, For the test result vector, This is the weight matrix. For bias terms, For activation function, The output result, used to determine whether the test results are anomalous data, requires explicit definition and explanation for each parameter and variable in the formula. The test result vector can be represented as a set of values ​​containing multiple test indicators, which can be electrical parameters such as current, voltage, and power, or other performance indicators related to the function of the power distribution terminal. The weight matrix is ​​a trainable parameter matrix used to learn the importance of different test indicators for anomalous data identification. The bias term is a constant term used to adjust the threshold of the output result. The activation function is a nonlinear function used to introduce nonlinear factors and enhance the model's expressive power. The output result is a binary classification label used to determine whether the test result is anomalous data. Secondly, regarding the selection of machine learning algorithms, in addition to the model described in the above formula, other algorithms suitable for anomaly detection can be considered, such as Isolation Forest, Local Outlier Factor (LOF), and density-based clustering methods (such as DBSCAN). These algorithms each have their advantages and disadvantages, and can be flexibly selected according to the characteristics and needs of the test data. Furthermore, to improve the accuracy and efficiency of anomaly identification, multiple algorithms can be combined to form a hybrid model. Hybrid models can fully leverage the advantages of different algorithms to improve the overall performance of anomaly detection. During algorithm implementation, model training and optimization also need to be considered. To achieve better training results, data augmentation techniques can be used to expand the training dataset, such as adding random noise and data transformation. Simultaneously, regularization techniques can be employed to prevent overfitting, such as L1 regularization and L2 regularization. For optimization algorithms, gradient descent, stochastic gradient descent, and the Adam optimizer can be used to accelerate the model training process and improve training efficiency. Furthermore, to monitor and identify anomalous data in real time, machine learning algorithms need to be integrated into the automated testing framework, and corresponding interfaces and processes need to be designed. After the test execution engine completes the execution of test cases, it can pass the test result data to the machine learning algorithm for anomaly detection. The algorithm will determine whether the test result is anomalous data based on preset thresholds or rules and feed back the identification result to the tester or the system.

[0068] By clearly defining and explaining the parameters and variables in the formulas, the machine learning algorithms become clearer and more interpretable. This helps testers understand the algorithm's working principles and identification logic, thus enabling them to more accurately determine whether test results are abnormal data. Secondly, by considering the selection and combination of multiple machine learning algorithms, as well as model training and optimization, the accuracy and efficiency of anomaly identification are significantly improved. This helps to promptly discover and identify potential problems in power distribution terminals, ensuring their performance and safety in practical applications. Finally, by integrating machine learning algorithms into the automated testing framework and designing corresponding interfaces and processes, real-time monitoring and identification of test results are achieved. This helps reduce testing costs and time costs, and improves testing efficiency and accuracy. Simultaneously, it provides more intelligent support for the automated commissioning and verification of power distribution terminals.

[0069] In a preferred embodiment, the present invention can be further configured such that the specific method of the feedback and optimization mechanism in step S40 is not limited to basic presentation and parameter adjustment, but also includes the following detailed steps:

[0070] Visualization of test results: Test results are presented to testers in a variety of visualization methods, including but not limited to bar charts, line charts, scatter plots, pie charts, and radar charts. These charts can intuitively show the test pass rate, the distribution of failed test cases, the frequency statistics of various errors, and possible causes of errors. In addition, an interactive interface is provided, allowing testers to obtain more detailed test information and error descriptions by clicking or hovering over data points on the charts.

[0071] Parameter adjustment and optimization strategy: Based on the test results feedback, the test model parameters are adjusted automatically or semi-automatically, including but not limited to the parameter settings of the communication protocol, the response time threshold of telemetry and remote control, and data verification rules. At the same time, machine learning-based optimization algorithms, such as genetic algorithms or reinforcement learning, are introduced to iteratively optimize the test model in order to improve the accuracy and efficiency of the test.

[0072] Optimization of Behavioral Description Language: Based on the error types and patterns that frequently occur in the test results, the Behavioral Description Language (BDL) is optimized to more accurately describe the behavioral characteristics of the power distribution terminal. In addition, BDL editing and verification tools are provided to ensure the accuracy and consistency of BDL descriptions.

[0073] The introduction of new test models: During testing, if existing test models are found to be unable to cover certain key functions or scenarios, new test models are introduced, instantiated, and validated. Simultaneously, a test model library management system is established to manage model version control and updates. Regarding the visualization of test results, in addition to the aforementioned visualization methods such as bar charts, line charts, scatter plots, pie charts, and radar charts, advanced visualization techniques such as heatmaps and Sankey diagrams can be introduced to more intuitively display the complex relationships and trends of test results. Furthermore, to meet the needs of different testers and scenarios, customized visualization templates and report generation functions can be provided, enabling testers to quickly generate professional test reports according to their needs. Regarding parameter adjustment and optimization strategies, in addition to automatically or semi-automatically adjusting test model parameters, a parameter adjustment feedback mechanism can be established, dynamically adjusting parameters based on test result feedback and updating the test model in real time. Moreover, to further improve the efficiency and accuracy of optimization, optimization algorithms can be combined with visualization technology to form a visualization optimization platform. On this platform, testers can intuitively see the impact of parameter adjustments on test results, thus enabling more precise parameter optimization. Regarding the optimization of Behavioral Description Language (BDL), in addition to optimizing the BDL based on test results, natural language processing technology can be introduced. This allows testers to describe the behavioral characteristics of power distribution terminals using natural language and automatically generate the BDL. This will significantly reduce the difficulty and cost of writing the BDL and improve testing efficiency. Simultaneously, to ensure the accuracy and consistency of the BDL, an automated verification and testing mechanism can be established to rigorously validate and test the BDL. Regarding the introduction of new test models, besides introducing new test models and instantiating and verifying them, a test model evaluation and selection mechanism can be established. This mechanism can evaluate and select test models based on multiple dimensions such as test requirements, test environment, and test costs to ensure that the introduced test models meet actual needs and have a high cost-performance ratio. Furthermore, to version control and updates of test models, a version management system for the test model library can be established, recording the modification history and reasons for each version for subsequent analysis and traceability.

[0074] By introducing various visualization technologies and customized report generation functions, test results become more intuitive and easier to understand, improving the efficiency and accuracy of decision-making for testers and decision-makers. Simultaneously, visualization technology helps testers quickly locate problems and shorten testing cycles. By establishing a feedback mechanism for parameter adjustment and a visualization optimization platform, real-time and accurate parameter adjustments are achieved, improving testing accuracy and efficiency. Furthermore, the combination of optimization algorithms and visualization technology provides testers with more intelligent and efficient optimization solutions. Regarding BDL optimization, the introduction of natural language processing technology and automated verification testing mechanisms reduces the difficulty and cost of writing BDLs, improving testing efficiency. At the same time, the accuracy and consistency of BDLs are effectively guaranteed. Finally, by establishing an evaluation and selection mechanism for new test models and a version management system for the test model library, it is ensured that the introduced test models meet actual needs and have high cost-effectiveness, while also achieving effective management and updates of test models. This will provide more reliable and efficient support for the automated testing and verification of power distribution terminals.

[0075] In a preferred embodiment, this invention can be further configured such that the visualization presentation not only employs bar charts, line charts, or scatter plots, but also incorporates advanced visualization technologies such as dynamic dashboards, heatmaps, and 3D graphics to display test results and optimization effects in a more intuitive and dynamic way. Furthermore, it provides a custom report function, allowing testers to generate and export reports in various formats, such as Excel and PDF, as needed. Regarding the application of dynamic dashboards, an interactive dynamic dashboard system can be designed. This system can update and display key indicators of test results in real time, such as pass rate, error rate, and response time. These indicators can be presented intuitively through various gauges on the dashboard (such as speedometers, thermometers, progress bars, etc.), enabling testers to quickly grasp the overall test situation and trends. Simultaneously, the dashboard supports custom configuration, allowing testers to adjust the style, layout, and display content of the gauges according to actual needs. Secondly, in the application of heatmaps, the distribution of test results can be displayed. Heatmaps use color intensity to represent data magnitude or frequency, allowing testers to visually identify problematic test areas or items and their severity. Furthermore, heatmaps can be integrated with interactive interfaces, enabling testers to access more detailed test information and error descriptions by clicking or hovering over data points. In 3D graphics applications, the internal structure and operating status of power distribution terminals can be simulated and displayed. These 3D graphics can be interacted with through rotation, scaling, and translation, allowing testers to gain a deeper understanding of the working principles and performance characteristics of the power distribution terminals. Simultaneously, 3D graphics can be combined with test results, using attributes such as color and transparency to represent the quality of test results, thus providing a more intuitive demonstration of optimization effects. In terms of custom report functionality, in addition to supporting the generation and export of reports in common formats such as Excel and PDF, a wider and more flexible selection of report templates and styles is offered. Testers can choose suitable templates and styles according to their needs and customize the content, layout, and format of reports. The report generation function also supports batch processing and automated generation, significantly improving the work efficiency of testers.

[0076] By introducing advanced visualization technologies such as dynamic dashboards, heatmaps, and 3D graphics, test results and optimization effects can be presented to testers in a more intuitive and dynamic way. These advanced visualization technologies not only improve the readability and understandability of test results but also enable testers to more quickly identify, locate, and resolve problems, thereby improving testing efficiency and accuracy. Secondly, by providing custom reporting functionality, the system meets testers' diverse needs for report formats and content. Testers can generate and export reports in various formats, such as Excel and PDF, according to their needs, facilitating subsequent analysis and archiving. Simultaneously, the custom reporting function also supports batch processing and automated generation, greatly improving testers' work efficiency. Finally, by combining an interactive interface and a rich selection of report templates, testers can more easily view, analyze, and report test results. These features not only improve the user experience and work efficiency of testers but also provide more comprehensive and reliable support for the automated testing and verification of power distribution terminals.

[0077] In a preferred embodiment of the present invention, the specific steps of pre-configuring the power distribution terminal before the start of the test include:

[0078] Communication parameter settings: According to the test requirements, set the communication protocol, baud rate, data bits, stop bits and parity bits of the power distribution terminal to ensure the reliability and consistency of communication during the test.

[0079] Telemetry and remote control point configuration: Based on the functional characteristics of the power distribution terminal, configure the number, type, address, and range of telemetry and remote control points. At the same time, set the data acquisition frequency and accuracy for telemetry points and configure the permissions and operation procedures for remote control points.

[0080] Test Environment Preparation: A simulated power grid environment needs to be built, including simulated substations, lines, and loads, to simulate various operating scenarios and fault conditions in a real power grid. In addition, auxiliary equipment and tools required for testing need to be prepared, such as signal generators, oscilloscopes, and ammeters. Regarding communication parameter settings, in addition to setting basic parameters such as the communication protocol, baud rate, data bits, stop bits, and parity bits of the distribution terminal, configuration of parameters such as communication timeout, retry count, and communication log can be considered. The communication timeout parameter sets the time after which a communication failure is considered a failure; the retry count parameter sets the number of automatic retries upon communication failure; the communication log parameter records detailed information during the communication process, including sent and received data packets, timestamps, and communication status, for subsequent analysis and troubleshooting. Secondly, regarding the configuration of telemetry and remote control points, in addition to configuring basic parameters such as quantity, type, address, and range, configuration of status monitoring and alarm functions for telemetry and remote control points can be considered. For telemetry points, thresholds can be set to trigger an alarm when the collected data exceeds the threshold. For remote control points, restrictions on operation permissions and procedures can be set, such as requiring passwords or secondary confirmation, to ensure the security and accuracy of remote control operations. Regarding test environment preparation, in addition to setting up a simulated power grid environment, monitoring and recording functions for the test environment can be added. By installing sensors and monitoring equipment, parameters such as temperature, humidity, and electromagnetic interference in the test environment can be monitored in real time, and changes in these parameters can be recorded. This information helps analyze the accuracy and reliability of test results and troubleshoot potential problems during the test. Furthermore, it is advisable to add checks on the software and hardware versions of the distribution terminals during the pre-configuration phase. This ensures that the distribution terminals being tested match the requirements of the test environment and test cases, avoiding test failures due to version incompatibility or configuration errors.

[0081] By refining communication parameter settings, the reliability and consistency of communication were improved. Configuring parameters such as communication timeout, retry count, and communication logs helps to promptly identify and address communication failures, thus preventing test failures due to communication issues. Secondly, by improving the configuration of telemetry and remote control points, the monitoring and control capabilities of the distribution terminal were enhanced. The configuration of status monitoring and alarm functions enables the distribution terminal to promptly detect and respond to abnormal situations, improving the safety and accuracy of the test. Simultaneously, restrictions on remote control point operation permissions and procedures help prevent misoperation or malicious operation. Regarding test environment preparation, the controllability and repeatability of the test environment were improved by adding monitoring and recording functions. Real-time monitoring and recording of various parameters in the test environment helps to analyze the accuracy and reliability of test results and troubleshoot potential problems during the test process. This helps ensure the objectivity and impartiality of test results. Finally, by adding checks on the software and hardware versions of the distribution terminal, it was ensured that the tested distribution terminal matched the requirements of the test environment and test cases. This helps to avoid test failures due to version incompatibility or configuration errors, improving the efficiency and accuracy of the test.

[0082] In a preferred embodiment, the present invention can be further configured such that the specific steps for archiving and managing test data after the test are completed include:

[0083] Test result storage: The test results are stored in a database, including information such as test time, tester, test terminal model, test case name, test result and error description. At the same time, an index and query mechanism are established to enable quick retrieval and query of test results.

[0084] Test case management: Implement version control and classification management for test cases to ensure their accuracy and reusability. At the same time, establish a review and update mechanism for test cases to revise and optimize them according to changes in test requirements and terminal functions.

[0085] Optimization Record Tracking: Record the optimization status of test model parameters, behavioral description language, and test environment after each test, including optimization time, personnel involved, optimization content, and optimization effects. Simultaneously, establish a tracking and analysis mechanism for optimization records to evaluate the effectiveness of optimizations and identify directions for continuous improvement. Regarding test result storage, in addition to storing test results in a database, consider adding data backup and recovery mechanisms. Regularly back up the test result database to ensure data security and integrity. Provide data recovery functionality for rapid recovery in case of data loss or corruption. Secondly, in terms of test case management, in addition to version control and categorization management, consider adding automated execution and result analysis functions for test cases. By integrating automated testing tools, automate test case execution, reduce manual intervention, and improve testing efficiency. Simultaneously, automatically analyze test results to generate test reports, including pass rates and error distribution, to help testers quickly locate problems. Regarding optimization record tracking, in addition to recording the optimization status after each test, consider adding a verification and evaluation mechanism for optimization effects. Retest the test terminal after each optimization to verify whether the optimization effect meets expectations. Simultaneously, an evaluation system for the optimization effect should be established to quantitatively assess the optimization results, providing a basis for subsequent continuous improvement. Furthermore, adding access control functionality to the archive management system can be considered. Different permissions can be set for users with different roles; for example, testers can only view and edit the test results and test cases they participated in, while administrators can view all test results and test cases and perform operations such as data backup, recovery, and test case review. This helps ensure data security and integrity, preventing unauthorized access and modification.

[0086] By adding data backup and recovery mechanisms, the security and integrity of test data are ensured. Even in the event of data loss or corruption, data can be quickly recovered, avoiding interruptions and repetitive work in testing. Secondly, by adding automated test case execution and result analysis functions, testing efficiency and quality are improved. Automated testing tools reduce manual intervention, quickly execute test cases, and generate detailed test reports, helping testers quickly locate problems and improve testing efficiency. Simultaneously, automated analysis of test results allows for a more accurate assessment of the performance and stability of the test terminal, providing a basis for subsequent improvements. Regarding optimization record tracking, by adding verification and evaluation mechanisms for optimization effects, the effectiveness of optimizations and the direction for continuous improvement are ensured. After each optimization, retesting is performed to verify whether the optimization effect meets expectations, and the optimization effect is quantitatively evaluated, providing guidance and a basis for subsequent optimization work. Finally, by adding access control functions, the security and controllability of the archive management system are ensured. Different permissions are set for users with different roles to prevent unauthorized access and modification, protecting the security and integrity of test data. This also helps to standardize and streamline testing work, improving the quality and efficiency of testing.

[0087] The modular testing and verification method for automatic commissioning system of distribution terminals is an efficient and automated testing process designed to comprehensively verify the various functional characteristics of distribution terminals. The entire testing process begins with building a modular test model library. Based on the specific functions of the distribution terminal, a series of basic test models are defined, and these models are instantiated for different distribution terminal models to form a test model instance set for specific models.

[0088] Next, based on the behavior testing strategy, a behavior description language is designed to describe the behavior characteristics of the power distribution terminal under different states. Using this behavior description language and test model instance set, test cases are automatically generated by the algorithm. These test cases can cover the key functions and scenarios of the power distribution terminal.

[0089] During the test case execution and result verification phase, an automated testing framework is built. This framework integrates a test execution engine, a logging module, and a result analysis module. The test execution engine is responsible for calling instances in the test model library, simulating a real power grid environment, executing the generated test cases, and recording the test results. To analyze the test results more intelligently, machine learning algorithms, such as support vector machines or deep neural networks, are introduced to identify abnormal data in the test results, thereby improving the accuracy and efficiency of the test.

[0090] After the test is completed, the test results will be presented to the testers in a variety of visualization methods, including bar charts, line charts, scatter plots, pie charts, radar charts, as well as advanced visualization technologies such as dynamic dashboards, heat maps, and 3D graphics. These visualization charts can intuitively show the test pass rate, the distribution of failed test cases, the frequency statistics of various errors, and possible causes of errors. In addition, an interactive interface is provided, allowing testers to obtain more detailed test information and error descriptions by clicking or hovering over data points on the charts.

[0091] Based on the feedback from the test results, the parameters of the test model are adjusted automatically or semi-automatically, such as the parameter settings of the communication protocol, the response time thresholds of telemetry and remote control, and data verification rules. At the same time, machine learning-based optimization algorithms are introduced to iteratively optimize the test model to improve the accuracy and efficiency of the test. In addition, based on the error types and patterns that frequently occur in the test results, the behavioral description language is optimized to more accurately describe the behavioral characteristics of the power distribution terminal.

[0092] Before the test begins, the power distribution terminal is pre-configured, including setting communication parameters, configuring telemetry and remote control points, and preparing the test environment, to ensure the reliability and consistency of communication during the test and to simulate the real power grid environment. After the test, the test data is archived and managed, including storing test results, managing test cases, and recording optimizations, for subsequent analysis and continuous improvement.

[0093] In summary, this method forms a closed-loop testing and verification process through steps such as constructing a modular test model library, automatically generating test cases, automating test execution and result verification, visualizing results presentation, and providing feedback optimization. This process can efficiently and accurately verify the various functional characteristics of power distribution terminals, providing strong support for the research and development and testing of power distribution terminals.

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0095] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A modular testing and verification method for an automatic commissioning system for power distribution terminals, characterized in that, Includes the following steps: S10. Construct a modular test model library: Based on the functional characteristics of the power distribution terminal, define a series of basic test models, and instantiate these models for specific power distribution terminal models to form a test model instance set; S20. Dynamic test case generation based on behavior testing strategy: Design a behavior description language to describe the behavior characteristics of power distribution terminals, and automatically generate test cases using an algorithm based on the test model instance set and the behavior description language. S30. Test Case Execution and Result Verification: Build an automated testing framework, call instances in the test model library to execute the generated test cases, and record the test results; Machine learning algorithms are introduced to intelligently analyze test results and automatically identify abnormal data. S40. Feedback and Optimization: Present test results to testers in a visual manner, adjust test model parameters based on test result feedback, optimize behavioral description language, and form a closed-loop optimization mechanism.

2. The modular testing and verification method for an automatic commissioning system of a power distribution terminal according to claim 1, characterized in that, The specific methods for constructing the modular test model library in step S10 include: Define basic test models such as communication models, telemetry models, and remote control models. Each model includes specific input parameters, expected outputs, and boundary conditions. For a specific power distribution terminal model, instantiate the above basic model, specify specific communication protocols, baud rates, parity bits and other parameters to form a test model instance set for that model.

3. The modular testing and verification method for an automatic commissioning system of a power distribution terminal according to claim 1, characterized in that, The specific methods for designing the behavior description language in step S20 include: Design a behavior description language to describe the behavior characteristics of a power distribution terminal in various states, such as "when a remote preset command is received, the DTU should return an acknowledgment frame within a specified time"; The algorithm automatically generates test cases based on the test model instance set and behavioral description language. The algorithm needs to consider test coverage, dependencies between test cases, and execution order optimization.

4. The modular testing and verification method for an automatic commissioning system for power distribution terminals according to claim 3, characterized in that, The automatic test case generation algorithm uses a genetic algorithm or a simulated annealing algorithm to generate a high-coverage test case set through iterative optimization.

5. The modular testing and verification method for an automatic commissioning system for power distribution terminals according to claim 1, characterized in that, The specific methods for constructing the automated testing framework in step S30 include: Build an automated testing framework based on Python or Java, integrating a test execution engine, logging module, and result analysis module; The test execution engine is responsible for calling instances in the test model library to simulate a real power grid environment and execute the generated test cases; Introducing machine learning algorithms, such as support vector machines or deep neural networks, allows for intelligent analysis of test results and automatic identification of abnormal data.

6. The modular testing and verification method for an automatic commissioning system for power distribution terminals according to claim 5, characterized in that, The machine learning algorithm uses the following formula to identify abnormal data: ； in, For the test result vector, This is the weight matrix. For bias terms, For activation function, The output is used to determine whether the test results are abnormal data.

7. The modular testing and verification method for an automatic commissioning system for power distribution terminals according to claim 1, characterized in that, The specific methods of the feedback and optimization mechanism in step S40 are not limited to basic presentation and parameter adjustment, but also include the following detailed steps: Visualization of test results: Test results are presented to testers in a variety of visualization methods, including but not limited to bar charts, line charts, scatter plots, pie charts, and radar charts. These charts can intuitively show the test pass rate, the distribution of failed test cases, the frequency statistics of various errors, and possible causes of errors. In addition, an interactive interface is provided, allowing testers to obtain more detailed test information and error descriptions by clicking or hovering over data points on the charts. Parameter adjustment and optimization strategy: Based on the test results feedback, the test model parameters are adjusted automatically or semi-automatically, including but not limited to the parameter settings of the communication protocol, the response time threshold of telemetry and remote control, and data verification rules. At the same time, machine learning-based optimization algorithms, such as genetic algorithms or reinforcement learning, are introduced to iteratively optimize the test model in order to improve the accuracy and efficiency of the test. Optimization of Behavioral Description Language: Based on the error types and patterns that frequently occur in the test results, the Behavioral Description Language (BDL) is optimized to more accurately describe the behavioral characteristics of the power distribution terminal. In addition, BDL editing and verification tools are provided to ensure the accuracy and consistency of BDL descriptions. Introduction of new test models: If it is found during the testing process that the existing test models cannot cover certain key functions or scenarios, a new test model will be introduced, and it will be instantiated and verified. At the same time, a test model library management system will be established to enable version control and updates of the test models.

8. The modular testing and verification method for an automatic commissioning system of a power distribution terminal according to claim 7, characterized in that, The visualization presentation not only uses bar charts, line charts, or scatter plots, but also combines advanced visualization technologies such as dynamic dashboards, heat maps, and 3D graphics to display test results and optimization effects in a more intuitive and dynamic way. In addition, it provides a custom report function, allowing testers to generate and export reports in various formats, such as Excel and PDF, as needed.

9. The modular testing and verification method for an automatic commissioning system for power distribution terminals according to claim 1, characterized in that, The specific steps for pre-configuring the power distribution terminal before the test begin include: Communication parameter settings: According to the test requirements, set the communication protocol, baud rate, data bits, stop bits and parity bits of the power distribution terminal to ensure the reliability and consistency of communication during the test. Telemetry and remote control point configuration: Based on the functional characteristics of the power distribution terminal, configure the number, type, address, and range of telemetry and remote control points. At the same time, set the data acquisition frequency and accuracy for telemetry points and configure the permissions and operation procedures for remote control points. Test environment preparation: Set up a simulated power grid environment, including simulated substations, lines and loads, to simulate various operating scenarios and fault conditions in a real power grid. In addition, auxiliary equipment and tools required for testing, such as signal generators, oscilloscopes, ammeters, etc., need to be prepared.

10. The modular testing and verification method for an automatic commissioning system of a power distribution terminal according to claim 1, characterized in that, The specific steps for archiving and managing test data after the test are completed include: Test result storage: The test results are stored in a database, including information such as test time, tester, test terminal model, test case name, test result and error description. At the same time, an index and query mechanism are established to enable quick retrieval and query of test results. Test case management: Implement version control and classification management for test cases to ensure their accuracy and reusability. At the same time, establish a review and update mechanism for test cases to revise and optimize them according to changes in test requirements and terminal functions. Optimize record tracking: Record the optimization of test model parameters, behavioral description language and test environment after each test, including optimization time, optimization personnel, optimization content, optimization effect and other information. At the same time, establish a tracking and analysis mechanism for optimization records to evaluate the effectiveness of optimization and the direction of continuous improvement.

Images