Installation quality testing method and system for compact grounding circuit breaker

Abstract

The present application provides a method and system for testing the installation quality of a compact grounding circuit breaker, which relates to the field of circuit breaker testing technology, including: calling the circuit breaker operation record, combining the quality standards, and mining the installation test points; configuring the pre-test scenario; performing the installation circuit loop simulation, and determining the scenario simulation data; constructing the quality analysis module; transmitting the scenario simulation data to the quality analysis module, performing abnormal frequency component identification and feature vector dimension transformation, performing scenario quality assessment, and determining the scenario test results; generating a test single column, performing weighted calculation, and determining the quality test coefficient; and visualizing the terminal display interface. This application can solve the technical problem in the prior art that the test results are not accurate enough due to the incomplete test scenario, which further affects the test efficiency. By mining the installation test points that meet the fault probability threshold, performing the scenario operation simulation, and performing a comprehensive test on the circuit breaker, the efficiency and accuracy of the test are improved.

Description

Technical Field

[0001] The present application relates to the technical field of circuit breaker testing, and in particular to an installation quality testing method and system for a compact grounding circuit breaker. Background Art

[0002] With the continuous advancement of science and technology, the role of power system protection devices in power systems is becoming increasingly important. Among them, circuit breakers, as key protection devices in power systems, have the main function of quickly cutting off the circuit when an overload or short circuit is detected in the circuit, preventing damage to electrical equipment and safety accidents such as fire. However, there are some problems with the installation and testing of circuit breakers in the existing technology. Traditional testing methods often only cover basic fault scenarios, such as simple short circuits and overloads, while ignoring more complex fault modes, such as ground faults. Due to the incompleteness of test scenarios and the limitations of test methods, the existing testing process is often time-consuming and inefficient.

[0003] In summary, the existing technology has a technical problem that the test results are not accurate enough due to the incomplete test scenarios, which further affects the test efficiency. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for testing the installation quality of a compact grounding circuit breaker, so as to solve the technical problem in the prior art that the test results are not accurate enough due to incomplete test scenarios, which further affects the test efficiency.

[0005] In view of the above problems, the present application provides a method and system for testing the installation quality of a compact grounding circuit breaker.

[0006] In a first aspect, the present application provides an installation quality testing method for a compact grounding circuit breaker, which is implemented by an installation quality testing system for a compact grounding circuit breaker. The installation quality testing method comprises: calling circuit breaker operation records, combining quality standards, and mining installation test points, wherein the installation test points meet the fault probability threshold and are marked with abnormal frequency components; traversing the installation test points, configuring pre-test scenarios, wherein the pre-test scenarios include grounding fault and overload protection categories, and using continuous test scenarios as test point configuration requirements; and performing installation test on the target circuit breaker. The circuit is simulated to perform an operation simulation based on the pre-test scenario and determine the scenario simulation data; a quality analysis module is constructed, wherein the quality analysis module includes a front feature transformation unit and a static decision branch and a dynamic decision branch based on the twin network principle connected to the back; the scenario simulation data is transmitted to the quality analysis module, abnormal frequency component identification and feature vector dimension transformation are performed, and the scenario quality assessment is performed by combining static and dynamic to determine the scenario test results; the scenario test results are integrated and a test column is generated, and a weighted calculation is performed based on the scenario frequency to determine the quality test coefficient; the test column and the quality test coefficient are visualized on the terminal display interface.

[0007] In a second aspect, the present application further provides an installation quality testing system for a compact grounding circuit breaker, which is used to execute the installation quality testing method for a compact grounding circuit breaker as described in the first aspect, wherein the installation quality testing system for a compact grounding circuit breaker includes: an operation record calling module, the operation record calling module is used to call the circuit breaker operation record, and mine the installation test points in combination with the quality standards, wherein the installation test points meet the fault probability threshold and are marked with abnormal frequency components; a test point mining module, the test point mining module is used to traverse the installation test points and configure pre-test scenarios, wherein the pre-test scenarios include grounding fault class and overload protection class, and the continuous test scenario is used as the test point configuration requirement; a circuit loop simulation module, the circuit loop simulation module is used to perform installation circuit loop simulation on the target circuit breaker, and execute based on The operation simulation of the pre-test scenario determines the scenario simulation data; a data analysis module, the data analysis module is used to construct a quality analysis module, wherein the quality analysis module includes a front-end feature transformation unit, and a rear-end static decision branch and a dynamic decision branch constructed based on the twin network principle; a scenario evaluation module, the scenario evaluation module is used to transmit the scenario simulation data to the quality analysis module, perform abnormal frequency component identification and feature vector dimension transformation, and perform scenario quality evaluation by combining static and dynamic methods to determine the scenario test results; a weighted calculation module, the weighted calculation module is used to integrate the scenario test results and generate a test column, perform weighted calculation based on the scenario frequency, and determine the quality test coefficient; an interface visualization module, the interface visualization module is used to visualize the test column and the quality test coefficient on the terminal display interface.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By calling the circuit breaker operation record and combining it with the quality standard, the installation test points are mined, wherein the installation test points meet the fault probability threshold and are marked with abnormal frequency components; the installation test points are traversed and pre-test scenarios are configured, wherein the pre-test scenarios include ground fault type and overload protection type, and the continuous test scenario is used as the test point configuration requirement; the installation circuit loop of the target circuit breaker is simulated, and an operation simulation based on the pre-test scenario is performed to determine the scenario simulation data; a quality analysis module is constructed, wherein the quality analysis module includes a front-end feature transformation unit and a static decision branch and a dynamic decision branch based on the twin network principle connected to the back-end; the scenario simulation data is transmitted to the quality analysis module, abnormal frequency component identification and feature vector dimension transformation are performed, and scenario quality assessment is performed by combining static and dynamic methods to determine the scenario test results; the scenario test results are integrated and a test list is generated, and a weighted calculation is performed based on the scenario frequency to determine the quality test coefficient; the test list and the quality test coefficient are visualized on the terminal display interface. In other words, by mining the installation test points that meet the fault probability threshold, executing the scenario operation simulation, and conducting a comprehensive test on the circuit breaker, the efficiency and accuracy of the test are improved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 A flowchart of a method for testing the installation quality of a compact grounding circuit breaker according to the present application;

[0013] Figure 2 This is a schematic diagram of the structure of the installation quality testing system for the compact grounding circuit breaker of this application.

[0014] Explanation of the accompanying symbols: operation record calling module 11, test point mining module 12, circuit loop simulation module 13, data analysis module 14, scenario evaluation module 15, weighted calculation module 16, interface visualization module 17. DETAILED DESCRIPTION

[0015] This application provides a method and system for testing the installation quality of compact grounding circuit breakers, resolving the existing technical issues of inaccurate test results and lowering test efficiency due to incomplete testing scenarios. By identifying installation test points that meet a failure probability threshold and executing scenario-based operational simulations, comprehensive testing of the circuit breaker is conducted, improving both test efficiency and accuracy.

[0016] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0017] For example, see the attached Figure 1 The present application provides a method for testing the installation quality of a compact grounding circuit breaker. The method is applied to a system for testing the installation quality of a compact grounding circuit breaker. The method specifically includes the following steps:

[0018] Step 1: Call the circuit breaker operation record and, in combination with the quality standard, mine the installation test points, wherein the installation test points meet the failure probability threshold and are marked with abnormal frequency components.

[0019] Specifically, historical operating data, including switching operations, fault records, current and voltage readings, etc., is extracted from the circuit breaker's monitoring system or maintenance management system. This data reflects the circuit breaker's actual operating status and performance. The operating record contains operational data for the circuit breaker over a period of time, such as the number of switching cycles, number of faults, and current load. For example, if a circuit breaker frequently experiences current overloads in its operating records, this point is a fault point requiring focused testing. The circuit breaker's operating status is assessed by referring to relevant industry quality standards, such as IEC and GB standards. By analyzing the operating records, problematic circuit breaker components or functional points are identified. The failure probability threshold is a pre-set value used to determine the acceptable level of equipment failure risk. If this value is exceeded, the test point is considered to have a high failure risk. By analyzing historical operating data, areas where the failure probability exceeds the pre-defined threshold are identified. These test points are identified as containing abnormal frequency components, indicating potential failure modes or performance issues. For example, the components in electrical data or signals that may cause anomalies are included. By analyzing operating records and quality standards, test points with a high probability of failure can be identified, allowing resources and attention to be focused on these critical areas.

[0020] Step 2: traverse the installation test points and configure pre-test scenarios, which include ground fault and overload protection scenarios, with continuous test scenarios as the test point configuration requirements.

[0021] Specifically, pay attention to the installation test points and configure pre-test scenarios for each point. Pre-test scenarios refer to test conditions or scenarios defined before the actual test, which are used to simulate different fault conditions. Pre-test scenarios include two categories: ground fault and overload protection. The ground fault category simulates a fault in the circuit breaker's grounding system, such as a disconnected grounding line or excessively high grounding resistance. The overload protection category simulates the protective action of the circuit breaker when the circuit is overloaded, such as simulating a situation where the rated current is exceeded to verify whether the circuit breaker can properly disconnect the circuit when overloaded. By configuring multiple pre-test scenarios, it is ensured that the circuit breaker is fully tested under various possible fault conditions.

[0022] Step 3: Simulate the installation circuit loop of the target circuit breaker, perform an operation simulation based on the pre-test scenario, and determine scenario simulation data.

[0023] Specifically, a test environment is created that is as similar as possible to the actual operating environment, including simulating the circuit breaker's connection method in the actual power system, load conditions, and other factors that may affect its performance. Circuit loop simulation refers to the use of software tools to simulate the behavior of circuits to predict how the circuits will perform under actual conditions. A series of tests are performed in a simulated environment to simulate different operating conditions that the circuit breaker may encounter, including ground faults, overloads, short circuits, etc. By simulating these scenarios, the response of the circuit breaker under different fault conditions is observed and relevant data is collected. By simulating circuit loops, the performance of the circuit breaker in the actual working environment is simulated, and the performance of the circuit breaker can be accurately evaluated.

[0024] Step 4: Construct a quality analysis module, wherein the quality analysis module includes a front-end feature transformation unit and a rear-end static decision branch and a dynamic decision branch constructed based on the twin network principle.

[0025] Specifically, a module for analyzing and evaluating product quality is constructed, which is capable of processing input data and outputting quality assessment results. The quality analysis module contains a pre-processing feature transformation unit to preprocess and extract features from the raw test data. The feature transformation unit includes algorithms such as filtering, denoising, and feature selection, and its purpose is to convert complex data into features that are easier to analyze and understand. The twin network is a special neural network structure consisting of two nearly identical networks, which is used to compare the similarity of two input data. In the quality analysis module, the static decision branch and the dynamic decision branch are used to handle different decision problems respectively. The static decision branch is used to handle decision problems that do not require a real-time response, that is, static data, such as the fixed parameters of a circuit breaker. The dynamic decision branch is used to handle decision problems that require a real-time response, that is, dynamic data, such as the real-time operating data of a circuit breaker. By combining static and dynamic data analysis, the installation quality of the circuit breaker is comprehensively evaluated.

[0026] Step 5: The scenario simulation data is transmitted to the quality analysis module, abnormal frequency component identification and feature vector dimension transformation are performed, and the scenario quality assessment is performed by combining dynamic and static methods to determine the scenario test results.

[0027] Specifically, the scenario simulation data is transmitted to the quality analysis module, which processes the data and identifies abnormal frequency components. Once the abnormal frequency components are identified, the quality analysis module will perform feature vector dimension transformation, including filtering, standardization, dimensionality reduction and other operations on the data. The first valid feature vector is segmented to determine the node feature vector and the continuous feature vector. The vectors are processed using static and dynamic dimensions respectively to determine the dynamic detection results and static detection results. The static decision branch processes static data, such as the inherent characteristics of the circuit breaker; the dynamic decision branch processes dynamic data, such as the behavior of the circuit breaker in actual operation. The dynamic detection results and the static detection results are integrated to determine the scenario test results. By identifying abnormal frequency components and comparing and analyzing the static and dynamic characteristics of the circuit breaker, faults can be detected and diagnosed more accurately, thereby improving the fault detection and diagnosis capabilities.

[0028] Step 6: Integrate the scenario test results and generate a test column, perform weighted calculation based on the scenario frequency, and determine the quality test coefficient.

[0029] Specifically, multiple scenarios are tested, including overload protection, short-circuit response, and temperature stability. The test results obtained in different test scenarios are integrated, including performance data of the circuit breaker under different load, temperature, voltage, and other conditions. A test list is generated according to a specific format. A test list is a document that records the performance data of the circuit breaker, including various test scenarios and test results. In the test list, weighting calculations are performed based on the frequency of occurrence of different scenarios. The more times a scenario appears in the test, the greater its impact on the overall performance evaluation, and the higher the corresponding weight. For example, if overload protection is a common test scenario, a higher weight is assigned. Through weighting calculations, the contribution of each test scenario to the overall performance is determined, resulting in a quality test coefficient that reflects the performance level of the circuit breaker under different conditions. Through weighting calculations, the performance of the circuit breaker in different scenarios can be more accurately evaluated, which helps to optimize the performance evaluation and selection of circuit breakers.

[0030] Step 7: Visualize the test column and the quality test coefficient on a terminal display interface.

[0031] Specifically, the test columns and the quality test coefficients calculated based on the test results are integrated. A terminal display interface is designed to showcase the integrated data, presenting complex test data and performance indicators in charts and graphs. On the terminal display interface, visualization tools such as charts and graphs are used to display the test columns and quality test coefficients. For example, a bar chart or line graph can be used to display performance data under different test scenarios, and a pie chart or histogram can be used to show the distribution of quality test coefficients. This visualization allows for a quick understanding of the circuit breaker's performance under different test scenarios.

[0032] Furthermore, the present application further comprises the following steps:

[0033] Interacting with the redundant structure of the target circuit breaker, wherein the redundant structure includes installation circuit redundancy and circuit breaker structure redundancy; identifying the redundant structure and determining redundant protection logic; performing redundant fault tolerance analysis of scenario testing based on the redundant protection logic to determine redundant test features; and adding the redundant test features to the test list.

[0034] Specifically, the redundant structure of the target circuit breaker refers to the redundant parts or mechanisms designed into the circuit breaker to improve reliability and safety. The redundant structure includes installation circuit redundancy and circuit breaker structure redundancy. Installation circuit redundancy refers to the spare or backup components included in the installation circuit of the circuit breaker, which take over its functions when the main component fails. Circuit breaker structure redundancy involves spare components or mechanisms in the internal design of the circuit breaker to improve its reliability and safety. Identify the redundant structure in the circuit breaker, including spare circuit breakers, spare contacts, spare circuits, etc. After identifying the redundant structure, it is necessary to formulate the corresponding protection logic, including determining under what circumstances to start the backup component and how to ensure that the backup component can smoothly take over the function of the main component. For example, if the main circuit breaker fails, the protection logic includes automatically switching to the backup circuit breaker and ensuring that the backup circuit breaker can correctly cut off the circuit.

[0035] Based on the identified and determined redundant protection logic, test scenarios are designed to simulate various fault conditions that may occur in actual operation, such as main circuit breaker failure, circuit short circuit, etc. Evaluate how the redundant structure of the circuit breaker responds to and handles these faults, including observing whether the backup components can smoothly take over the functions of the main components, as well as the response time and recovery capabilities of the entire system. Through scenario testing, key characteristics related to redundancy testing are determined, such as the response time of the backup components, the recovery speed of the system, the accuracy of fault detection, etc. The key characteristics are added to the test list to demonstrate the performance of the redundant structure in the test report. The test list is a document that records the test results of the circuit breaker, which contains various test scenarios and test results. Adding redundant test features to the test list can more comprehensively record the performance and reliability of the circuit breaker. By recording redundant test features, possible problems with the circuit breaker can be diagnosed and predicted, thereby improving the ability to diagnose and predict faults.

[0036] Furthermore, step three of this application includes:

[0037] Determine a first pre-test scenario and set time zone scenario data, wherein the time zone scenario data includes multiple scenario nodes based on continuous scenario variables; perform operation simulation and scenario adjustment management based on the time zone scenario data to determine first scenario simulation data.

[0038] Specifically, a specific test scenario is selected to simulate a specific operating condition a circuit breaker might encounter. For example, a circuit breaker's response to a ground fault condition is simulated. A series of data points are defined for the selected test scenario, based on continuous scenario variables. Continuous scenario variables are parameters that continuously change during the test, such as time, current, and voltage. Each scenario node represents a data point at a specific moment or condition. Based on the defined time zone scenario data, simulation runs and scenario adjustments are managed. Tests are executed in the simulated environment, and test conditions are adjusted based on the scenario node data. In this way, the circuit breaker's response under actual operating conditions is simulated and relevant data is collected. For example, suppose a circuit breaker used to protect a power system is being tested, and a simulated load change is selected as the first pre-test scenario. A series of scenario nodes are defined, each representing a load value at a specific moment, to simulate a gradual load increase. By defining the time zone scenario data and continuous scenario variables, the circuit breaker's performance in actual operating conditions is precisely simulated.

[0039] Furthermore, step 4 of this application includes:

[0040] Determine a feature transformation standard, wherein the feature transformation standard is determined based on a multi-dimensional and multi-scale combination; perform unit domain division based on the feature transformation standard, determine the unit structure and perform data-driven training to determine multiple feature transformation areas; integrate the multiple feature transformation areas, perform independent calculation domain configuration, and determine the feature transformation unit, wherein the multiple feature transformation areas are selectively triggered.

[0041] Specifically, feature transformation standards are determined based on multiple data dimensions (such as time, space, and frequency) and scales (such as coarse-grained and fine-grained). Feature transformation standards refer to a set of rules or algorithms used to transform raw test data into a format more suitable for analysis and evaluation. The dataset is divided into multiple units or regions, each containing data points with similar characteristics. A structure is defined for each unit, and a model is trained using a data-driven approach to identify features. The model is trained using data to enable it to recognize and understand patterns and features within the data. Different regions are identified, and specific feature transformations are applied to each region. Multiple feature transformation regions are integrated into a unified system, combining feature transformation results from different time periods or under different conditions. Independent computing resources and algorithms are allocated to each feature transformation region, allowing each to be optimized based on its own characteristics and requirements, thereby improving the performance and efficiency of the entire system. Different feature transformation regions are selectively triggered based on specific conditions or requirements. For example, when a specific fault mode is detected, only the feature transformation regions associated with that fault mode are triggered, improving the accuracy and efficiency of fault diagnosis. The multi-dimensional and multi-scale combination ensures the comprehensiveness and accuracy of data conversion, and the selective triggering mechanism of the feature transformation unit improves the flexibility and responsiveness of the system, enabling it to dynamically adjust the processing flow according to different data characteristics.

[0042] Furthermore, step five of this application includes:

[0043] The first scenario simulation data is transmitted to the feature transformation unit to identify abnormal frequency components and trigger the dimensional transformation layer to perform dimensional transformation processing to determine the first valid feature vector; the first valid feature vector is identified, feature vector segmentation and branch decision processing are performed based on the dynamic and static dimensions, and the first scenario test result is output.

[0044] Specifically, the first scenario simulation data, including measured values of parameters such as current, voltage, and temperature, is transmitted to the feature transformation unit. The feature transformation unit processes the transmitted data and identifies abnormal frequency patterns associated with specific faults or performance issues. Once the abnormal frequency components are identified, the feature transformation unit triggers the dimensionality transformation layer to perform further dimensionality transformation on the data, including filtering, normalization, and dimensionality reduction. Through dimensionality transformation, the feature transformation unit determines the first valid feature vector, which contains key information about the circuit breaker's performance. Key features that reflect circuit breaker performance are identified and the feature vectors are classified into different categories based on the dynamic characteristics of the data. The feature vectors are divided into node feature vectors and continuous feature vectors. The node feature vectors are transferred to the static decision branch for processing. The static decision branch typically processes relatively static data features, such as the inherent characteristics of the circuit breaker or performance indicators under steady-state operating conditions. The continuous feature vectors are transferred to the dynamic decision branch, which typically processes data features that continuously change over time, such as the dynamic behavior of the circuit breaker in actual operation or time series data. The first static and dynamic test results are combined to determine the test results for the first scenario, such as whether the circuit breaker passed the load change and continuous operation tests. By combining the static and dynamic test results, the overall performance of the circuit breaker in the first scenario is comprehensively evaluated, reflecting the circuit breaker's comprehensive performance in the specific test scenario, including its static and dynamic performance.

[0045] Furthermore, the present application further comprises the following steps:

[0046] Match the first abnormal frequency component of the first pre-test scenario and set a first self-attention mechanism; identify the first scenario simulation data, use the first self-attention mechanism as a guide, identify the abnormal frequency component based on the feature transformation unit, and determine the first eigenvector; perform feature transformation area matching on the first eigenvector, perform feature vector compression and dimensionality reduction, and determine the first effective eigenvector, wherein the effective eigenvector corresponds one-to-one to the multiple scene nodes.

[0047] Specifically, the first abnormal frequency component in the first pre-test scenario is matched, and spectral analysis, such as Fourier transform, is performed on the test data to identify abnormal frequency components in the data. For example, if the pre-test scenario simulates the response of a circuit breaker under a specific load, it is necessary to identify abnormal frequency components associated with that load. A first self-attention mechanism is established. Self-attention is an advanced neural network technology that allows a model to focus on specific portions of the input data, thereby improving processing efficiency and accuracy. In circuit breaker testing, the self-attention mechanism is used to more accurately identify and process abnormal frequency components. The first self-attention mechanism is applied in the feature transformation unit to identify abnormal frequency components in the simulated data of the first scenario. After identifying abnormal frequency components, a set of feature vectors containing important feature information is determined. The first feature vector, after feature transformation, is matched against a pre-defined feature transformation region. The feature transformation region is divided according to the different requirements and conditions of the test scenario, and each region may contain specific feature transformation rules or algorithms. This matching ensures that the feature vector meets the specific transformation requirements. After matching the feature transformation region, the first feature vector is compressed and reduced to reduce its dimensionality, thereby improving data processing efficiency and reducing computational complexity. For example, feature vectors can be compressed using principal component analysis (PCA) or other dimensionality reduction techniques. By matching the feature transformation region and performing compression and dimensionality reduction, the first valid feature vector can be determined. These feature vectors contain key information about the circuit breaker's performance and correspond one-to-one with multiple pre-defined scenario nodes. This means that each scenario node has a corresponding valid feature vector, reflecting the circuit breaker's performance characteristics in that scenario. By using a self-attention mechanism and feature transformation units, key features are accurately extracted, and a specific feature vector is assigned to each scenario node. This allows for a comprehensive assessment of the circuit breaker's performance in different scenarios and helps identify potential issues.

[0048] Furthermore, the present application further comprises the following steps:

[0049] The first valid feature vector is segmented to determine the node feature vector and the continuous feature vector; the node feature vector is transferred to the static decision branch, and the feature state is used as the detection and judgment target to determine the first static detection result; the continuous feature vector is transferred to the dynamic decision branch, and the feature trend is used as the detection and judgment target to determine the first dynamic detection result, wherein the static decision branch and the dynamic decision branch establish a lateral interaction channel; the first static detection result and the first dynamic detection result are integrated to determine the first scenario test result.

[0050] Specifically, the first valid feature vector, which contains feature values at multiple time points, is divided into node feature vectors and continuous feature vectors. A node feature vector represents a feature vector at a specific time point, such as the characteristics of a circuit breaker at different load levels. A continuous feature vector represents a feature vector that continuously changes over time, such as the characteristics of a circuit breaker during continuous operation. After undergoing feature transformation and compression and dimensionality reduction, the node feature vector is transferred to the static decision branch. The static decision branch typically processes relatively static data features, such as the inherent characteristics of a circuit breaker or performance indicators under steady-state operating conditions. In the static decision branch, the transferred feature state is detected and determined. Machine learning algorithms or expert systems are used to analyze the feature vector and determine whether it conforms to the expected state. For example, parameters such as current and voltage in the feature vector are detected to determine whether they are within safe ranges or whether there are any abnormal patterns. Based on this detection and determination, the static decision branch outputs a first static detection result, which reflects the performance status of the circuit breaker in its static state, including the presence of potential problems or faults.

[0051] The continuous feature vectors, after feature transformation and compression and dimensionality reduction, are transferred to the dynamic decision branch. The dynamic decision branch typically processes data features that continuously change over time, such as the dynamic behavior of a circuit breaker during actual operation or time series data. Within the dynamic decision branch, the transferred continuous feature vectors are detected and judged, focusing on changes in feature trends. Machine learning algorithms or time series analysis methods are used to analyze the feature vectors and determine whether the trends conform to expected behavior. For example, the dynamic decision branch detects the temporal trends of parameters such as current and voltage in the feature vectors to identify possible abnormal patterns. After detection and judgment, the dynamic decision branch outputs the first dynamic detection result, which reflects the performance of the circuit breaker under dynamic conditions. During circuit breaker testing, a lateral interaction channel is established between the static and dynamic decision branches, allowing the two branches to share information and results for a more comprehensive assessment of circuit breaker performance. The lateral interaction channel is a communication mechanism established between the two decision branches, allowing them to share information and influence each other. For example, if the dynamic decision branch detects an abnormal trend, it can pass the relevant information to the static decision branch for further analysis.

[0052] The test results from the static and dynamic decision branches are integrated to form a complete test result for the first scenario. In a specific example, assume that the simulation data for the first scenario includes parameters such as current intensity, frequency, and temperature. First, the first valid feature vector is determined and then split into a node feature vector and a continuous feature vector. The node feature vector represents the characteristics at a specific time point, such as the characteristics of a circuit breaker at 10% load, 50% load, and 100% load. The continuous feature vector represents the characteristic changes over a period of time, such as the characteristic changes of a circuit breaker over a period of 10 consecutive minutes. The node feature vector is then transferred to the static decision branch, where the first static test result is determined based on the test objective. For example, this could be testing the performance stability of a circuit breaker under different load levels. Similarly, the continuous feature vector is transferred to the dynamic decision branch, where the first dynamic test result is determined based on the characteristic trend. For example, this could be testing the performance stability of a circuit breaker during continuous operation. By combining the static and dynamic decision branches, a comprehensive assessment of circuit breaker performance can be achieved. The establishment of a lateral interaction channel allows the two decision branches to influence each other, improving decision accuracy.

[0053] In summary, the installation quality testing method for a compact grounding circuit breaker provided in this application has the following technical effects:

[0054] By calling the circuit breaker operation record and combining it with the quality standard, the installation test points are mined, wherein the installation test points meet the fault probability threshold and are marked with abnormal frequency components; the installation test points are traversed and pre-test scenarios are configured, wherein the pre-test scenarios include ground fault type and overload protection type, and the continuous test scenario is used as the test point configuration requirement; the installation circuit loop of the target circuit breaker is simulated, and an operation simulation based on the pre-test scenario is performed to determine the scenario simulation data; a quality analysis module is constructed, wherein the quality analysis module includes a front-end feature transformation unit and a static decision branch and a dynamic decision branch based on the twin network principle connected to the back-end; the scenario simulation data is transmitted to the quality analysis module, abnormal frequency component identification and feature vector dimension transformation are performed, and scenario quality assessment is performed by combining static and dynamic methods to determine the scenario test results; the scenario test results are integrated and a test list is generated, and a weighted calculation is performed based on the scenario frequency to determine the quality test coefficient; the test list and the quality test coefficient are visualized on the terminal display interface. In other words, by mining the installation test points that meet the fault probability threshold, executing the scenario operation simulation, and conducting a comprehensive test on the circuit breaker, the efficiency and accuracy of the test are improved.

[0055] In the second embodiment, based on the same inventive concept as the installation quality testing method of the compact grounding circuit breaker in the above embodiment, the present application also provides an installation quality testing system for the compact grounding circuit breaker, see the attached Figure 2, the installation quality test system of the compact grounding circuit breaker includes:

[0056] The operation record calling module 11 is used to call the circuit breaker operation record, and mine the installation test points in combination with the quality standard, wherein the installation test points meet the failure probability threshold and are marked with abnormal frequency components.

[0057] The test point mining module 12 is used to traverse the installation test points and configure pre-test scenarios. The pre-test scenarios include ground fault and overload protection categories, and the continuous test scenario is used as the test point configuration requirement.

[0058] The circuit loop simulation module 13 is used to simulate the installation circuit loop of the target circuit breaker, perform operation simulation based on the pre-test scenario, and determine scenario simulation data.

[0059] The data analysis module 14 is used to construct a quality analysis module, wherein the quality analysis module includes a front feature transformation unit and a rear-connected static decision branch and a dynamic decision branch constructed based on the twin network principle.

[0060] The scenario evaluation module 15 is used to transmit the scenario simulation data to the quality analysis module, perform abnormal frequency component identification and feature vector dimension transformation, perform scenario quality evaluation by combining dynamic and static methods, and determine scenario test results.

[0061] The weighted calculation module 16 is used to integrate the scenario test results and generate a test list, perform weighted calculation based on the scenario frequency, and determine the quality test coefficient.

[0062] The interface visualization module 17 is used to visualize the test column and the quality test coefficient on a terminal display interface.

[0063] Furthermore, the installation quality testing system for compact grounding circuit breakers further includes a testing single-row module for:

[0064] Interacting with the redundant structure of the target circuit breaker, wherein the redundant structure includes installation circuit redundancy and circuit breaker structure redundancy; identifying the redundant structure and determining redundant protection logic; performing redundant fault tolerance analysis of scenario testing based on the redundant protection logic to determine redundant test features; and adding the redundant test features to the test list.

[0065] Furthermore, the circuit loop simulation module 13 in the installation quality testing system for the compact grounding circuit breaker is further configured to:

[0066] Determine a first pre-test scenario and set time zone scenario data, wherein the time zone scenario data includes multiple scenario nodes based on continuous scenario variables; perform operation simulation and scenario adjustment management based on the time zone scenario data to determine first scenario simulation data.

[0067] Furthermore, the data analysis module 14 in the installation quality testing system for the compact grounding circuit breaker is further configured to:

[0068] Determine a feature transformation standard, wherein the feature transformation standard is determined based on a multi-dimensional and multi-scale combination; perform unit domain division based on the feature transformation standard, determine the unit structure and perform data-driven training to determine multiple feature transformation areas; integrate the multiple feature transformation areas, perform independent calculation domain configuration, and determine the feature transformation unit, wherein the multiple feature transformation areas are selectively triggered.

[0069] Furthermore, the scenario assessment module 15 in the installation quality testing system for the compact grounding circuit breaker is further configured to:

[0070] The first scenario simulation data is transmitted to the feature transformation unit to identify abnormal frequency components and trigger the dimensional transformation layer to perform dimensional transformation processing to determine the first valid feature vector; the first valid feature vector is identified, feature vector segmentation and branch decision processing are performed based on the dynamic and static dimensions, and the first scenario test result is output.

[0071] Furthermore, the scenario assessment module 15 in the installation quality testing system for the compact grounding circuit breaker is further configured to:

[0072] Match the first abnormal frequency component of the first pre-test scenario and set a first self-attention mechanism; identify the first scenario simulation data, use the first self-attention mechanism as a guide, identify the abnormal frequency component based on the feature transformation unit, and determine the first eigenvector; perform feature transformation area matching on the first eigenvector, perform feature vector compression and dimensionality reduction, and determine the first effective eigenvector, wherein the effective eigenvector corresponds one-to-one to the multiple scene nodes.

[0073] Furthermore, the scenario assessment module 15 in the installation quality testing system for the compact grounding circuit breaker is further configured to:

[0074] The first valid feature vector is segmented to determine the node feature vector and the continuous feature vector; the node feature vector is transferred to the static decision branch, and the feature state is used as the detection and judgment target to determine the first static detection result; the continuous feature vector is transferred to the dynamic decision branch, and the feature trend is used as the detection and judgment target to determine the first dynamic detection result, wherein the static decision branch and the dynamic decision branch establish a lateral interaction channel; the first static detection result and the first dynamic detection result are integrated to determine the first scenario test result.

[0075] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The installation quality testing method and specific examples of the compact grounding circuit breaker in the first embodiment are also applicable to the installation quality testing system for the compact grounding circuit breaker in this embodiment. The detailed description of the installation quality testing method for the compact grounding circuit breaker will clearly indicate the installation quality testing system for the compact grounding circuit breaker in this embodiment. For the sake of brevity, a detailed description will not be given here. Since the system disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively brief; refer to the method description for relevant details.

[0076] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for testing the installation quality of a compact grounding circuit breaker, characterized in that: include: Retrieving circuit breaker operation records and, in combination with quality standards, identifying installation test points that meet a failure probability threshold and are identified with abnormal frequency components; Traverse the installation test points and configure pre-test scenarios, wherein the pre-test scenarios include ground fault and overload protection, and the continuous test scenario is used as the test point configuration requirement; Performing installation circuit loop simulation on the target circuit breaker, executing operation simulation based on the pre-test scenario, and determining scenario simulation data; Constructing a quality analysis module, wherein the quality analysis module includes a front-end feature transformation unit and a back-end static decision-making branch and a dynamic decision-making branch constructed based on the twin network principle; The scenario simulation data is transmitted to the quality analysis module, abnormal frequency component identification and feature vector dimension transformation are performed, and the scenario quality assessment is performed by combining dynamic and static methods to determine the scenario test results; Integrate the scenario test results and generate a test column, perform weighted calculation based on the scenario frequency, and determine the quality test coefficient; Visualizing the test column and the quality test coefficient on a terminal display interface; The step of performing an operation simulation based on the pre-test scenario and determining scenario simulation data includes: Determine a first pre-test scenario and set time zone scenario data, wherein the time zone scenario data includes a plurality of scenario nodes based on continuous scenario variables; Based on the time zone scenario data, perform operation simulation and scenario adjustment management to determine first scenario simulation data; The execution of abnormal frequency component identification and feature vector dimension transformation, combining dynamic and static scene quality assessment, and determining scene test results includes: The first scene simulation data is transmitted to the feature conversion unit to identify abnormal frequency components and trigger the dimensionality conversion layer to perform dimensionality conversion processing to determine a first valid feature vector; Identify the first valid feature vector, perform feature vector segmentation and branch decision processing based on the dynamic and static dimensions, and output the first scenario test result.

2. The installation quality testing method of a compact grounding circuit breaker according to claim 1, wherein: The method further comprises: Interchanging the redundant structure of the target circuit breaker, wherein the redundant structure includes installation circuit redundancy and circuit breaker structure redundancy; Identifying the redundant structure and determining redundant protection logic; Based on the redundant protection logic, perform redundant fault tolerance analysis of the scenario test to determine redundant test characteristics; The redundant test feature is added to the test list.

3. The installation quality testing method of a compact grounding circuit breaker according to claim 1, wherein: The quality analysis module includes a front-end feature conversion unit, including: Determining a feature transformation standard, wherein the feature transformation standard is determined based on a multi-dimensional and multi-scale combination; Based on the feature transformation standard, unit domain is divided, unit structure is determined, and data-driven training is performed to determine multiple feature transformation regions; The plurality of feature conversion areas are integrated and combined, and independent calculation domain configuration is performed to determine the feature conversion unit, wherein the plurality of feature conversion areas are selectively triggered.

4. The installation quality testing method of a compact grounding circuit breaker according to claim 1, wherein: The abnormal frequency component identification and triggering of the dimensionality transformation layer to perform dimensionality transformation processing include: Matching a first abnormal frequency component of the first pre-test scenario and setting a first self-attention mechanism; Identify the first scenario simulation data, and identify abnormal frequency components based on the feature transformation unit using the first self-attention mechanism as a guide to determine a first feature vector; Perform feature transformation area matching on the first feature vector, perform feature vector compression and dimensionality reduction, and determine the first valid feature vector, wherein the valid feature vector corresponds one-to-one to the multiple scene nodes.

5. The installation quality testing method of a compact grounding circuit breaker according to claim 1, wherein: The feature variable segmentation and branch decision processing based on the dynamic and static dimensions include: Segmenting the first valid feature vector to determine a node feature vector and a continuous feature vector; Transferring the node feature vector to the static decision branch, determining a first static detection result for the feature state as a detection determination target; Transferring the continuous feature vector to the motion decision branch, taking the feature trend as the detection and judgment target, and determining a first motion detection result, wherein a lateral interaction channel is established between the static decision branch and the motion decision branch; The first static detection result and the first dynamic detection result are integrated to determine the first scene test result.

6. Installation quality test system for compact grounding circuit breakers, characterized in that, The steps for implementing the installation quality testing method of a compact grounding circuit breaker according to any one of claims 1 to 5 are as follows: An operation record calling module is used to call the circuit breaker operation record and, in combination with the quality standard, to mine installation test points, wherein the installation test points meet the failure probability threshold and are marked with abnormal frequency components; A test point mining module, the test point mining module is used to traverse the installation test points and configure pre-test scenarios, the pre-test scenarios including ground fault and overload protection categories, with continuous test scenarios as test point configuration requirements; a circuit loop simulation module, the circuit loop simulation module being used to simulate the installation circuit loop of the target circuit breaker, perform an operation simulation based on the pre-test scenario, and determine scenario simulation data; A data analysis module, which is used to construct a quality analysis module, wherein the quality analysis module includes a front-end feature transformation unit and a back-end static decision-making branch and a dynamic decision-making branch based on the twin network principle; A scenario assessment module, which is used to transmit the scenario simulation data to the quality analysis module, perform abnormal frequency component identification and feature vector dimension transformation, conduct a dynamic and static combination of scenario quality assessment, and determine the scenario test results; A weighted calculation module is used to integrate the scenario test results and generate a test column, perform weighted calculation based on the scenario frequency, and determine the quality test coefficient; An interface visualization module is used to visualize the test column and the quality test coefficient on a terminal display interface.

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