Power distribution network verification method, system and device based on multi-path intelligent switching and storage medium

Abstract

The invention discloses a power distribution network verification method, system and device based on multi-path intelligent switching and a storage medium, and relates to the technical field of power distribution network verification, and the method comprises the steps: building and initializing a verification platform, and configuring related rules. And continuously monitoring the operation of the power distribution network, judging switching conditions and evaluating indexes. And selecting an optimal path for switching after the conditions are met. And a test case is injected after switching, so that protocol compatibility is ensured. And monitoring a test response, and recording data. And after the test is completed, switching back equipment and performing closed-loop control. Analyzing data, diagnosing faults, monitoring own indexes, evaluating coverage rate, and adaptively optimizing internal parameters according to a result; according to the method, the switching efficiency of the power distribution network can be greatly improved, the equipment compatibility is enhanced, the verification precision is improved, the fault recovery time is shortened, the operation complexity is reduced, and reliable operation of the power distribution network is guaranteed.

Description

A method, system, device, and storage medium for power distribution network verification based on intelligent multi-path switching. Technical Field

[0001] This invention relates to the field of distribution network verification technology, and in particular to a distribution network verification method, system, device and storage medium based on multi-path intelligent switching. Background Technology

[0002] With the deepening of smart grid construction and the widespread application of active distribution network technology, the complexity and intelligence of distribution network systems are constantly increasing, and the requirements for system verification and testing are becoming increasingly stringent.

[0003] Traditional power distribution network verification platforms primarily employ static testing methods, lacking the ability to dynamically simulate actual operating environments and exhibiting significant shortcomings in system switching and control verification. Existing verification platforms cannot achieve automatic switching between the operating and testing systems, requiring manual rewiring and configuration, which is complex, error-prone, and severely impacts testing efficiency. Traditional platforms lack intelligent communication switching capabilities, are incompatible with various communication protocols and interface standards, and limit compatibility testing with equipment from different manufacturers. Existing methods lack unified management and intelligent scheduling capabilities for multi-path signals, making accurate control verification difficult in complex power distribution network environments. Furthermore, traditional verification platforms lack real-time status feedback mechanisms, failing to promptly detect and handle anomalies during switching processes, thus affecting the reliability and accuracy of verification results. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a method, system, device and storage medium for power distribution network verification based on multi-path intelligent switching.

[0005] Therefore, the technical problem solved by this invention is: the problems affecting the efficiency and reliability of distribution network system verification under the development of smart grids and active distribution networks, such as complex system switching, inconsistent communication protocols, difficulties in multipath management, and untimely status feedback.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: Firstly, this invention provides a distribution network verification method based on multi-path intelligent switching, comprising: building a verification platform and initializing its own state; initializing task scheduling and resource allocation algorithms; configuring protocol conversion rules; based on the built verification platform, continuously monitoring the distribution network operation status, calculating the current error and settling time to determine switching conditions, and evaluating switching delay and reliability indicators; when the verification platform determines that the switching conditions are met, executing a path selection algorithm based on real-time network conditions, comprehensively considering load, delay, and cost factors to select the optimal transmission path, and initiating the switching operation; after the switching is completed, the verification platform injects test cases into the test system, and simultaneously ensures [the following is incomplete and requires further context: "ensuring the switching is completed"] through protocol mapping and data frame reconstruction. The communication protocols between the operating system and the test system are compatible; the verification platform monitors the test system's response to injected test cases, performs interface impedance matching, level conversion, and bandwidth matching, and records all test data completely; after the test is completed, the verification platform controls the test system to exit safely, implements closed-loop control through the feedback controller, switches the distribution network equipment back to normal operation, and executes FTU equipment status monitoring and protection criteria; after the equipment is switched back, the verification platform analyzes the data throughout the process, performs fault diagnosis and impact assessment, predicts the distribution network system recovery time, and implements safety risk quantification and integrity verification; the verification platform monitors its own throughput, power consumption, and efficiency indicators, generates test cases and evaluates verification coverage, and adaptively optimizes internal parameters based on the comprehensive evaluation results.

[0007] As a preferred embodiment of a distribution network verification method based on multi-path intelligent switching, the following steps are included: Building the verification platform and initializing its own state, initializing task scheduling and resource allocation algorithms, and configuring protocol conversion rules. These steps involve: building the verification platform; establishing a system state vector containing switching state, communication state, and equipment state; pre-setting the switching mode, communication link, and equipment operating state during the initialization phase; constructing a switching state matrix to clarify the initial connection relationships of each input and output port; establishing a system conversion function to determine the output signal vector; initializing the task scheduling and resource allocation algorithms; establishing a communication protocol conversion mapping table and configuring protocol conversion rules; converting the input protocol format to the output protocol format using the protocol conversion matrix and offset vector; and unifying the data frame structure.

[0008] As a preferred embodiment of a distribution network verification method based on multi-path intelligent switching, the following steps are taken: Based on the established verification platform, the continuous monitoring of the distribution network operation status, calculation of current error and settling time to determine switching conditions, and evaluation of switching delay and reliability indicators include: continuously monitoring the distribution network operation status using the established verification platform; comparing the current error and settling time with preset switching thresholds and minimum settling time to determine whether switching conditions are met; calculating system state changes to capture system state changes, and setting anomaly detection threshold based on state mean, standard deviation, and confidence coefficient; when switching conditions are met, calculating the total switching delay composed of detection delay, processing delay, and execution delay; and evaluating switching reliability based on failure rate and operating time.

[0009] As a preferred embodiment of a distribution network verification method based on multi-path intelligent switching, the step of executing a path selection algorithm based on real-time network conditions after the verification platform determines that the switching conditions are met, comprehensively considering load, delay, and cost factors to select the optimal transmission path, and initiating the switching operation includes: executing a path selection algorithm after the verification platform determines that the switching conditions are met, comprehensively considering the load, delay, and cost factors of each path to select the optimal transmission path; implementing a load balancing strategy to reasonably distribute the load of each path according to service traffic and path capacity; evaluating signal quality by monitoring signal-to-noise ratio and bit error rate indicators, calculating channel capacity, and optimizing transmission parameter configuration based on the calculation results.

[0010] As a preferred embodiment of a distribution network verification method based on multi-path intelligent switching, the verification platform monitors the response of the injected test cases, performs interface impedance matching, level conversion, and bandwidth matching, and fully records all test data, including: the verification platform monitors the response of the injected test cases, performs interface impedance matching calculation, determines the matching impedance based on the source impedance and load impedance; performs signal level conversion, converting the input voltage into an output voltage adapted to different equipment level standards based on the voltage divider resistor and amplification factor; and achieves interface bandwidth matching, determining the effective bandwidth based on the bandwidth of the two interfaces and the matching efficiency.

[0011] As a preferred embodiment of a distribution network verification method based on multi-path intelligent switching, the following steps are taken: After the equipment is switched back, the verification platform analyzes the data throughout the process, performs fault diagnosis and impact assessment, predicts the distribution network system recovery time, and implements security risk quantification and integrity verification, including: predicting the system recovery time based on detection time, isolation time, repair time, and testing time, and formulating corresponding fault handling strategies; assessing system security by quantifying security risks by comprehensively considering threat probability, severity of consequences, and vulnerability index; performing access control verification by combining identity authentication, authorization, and audit trail to determine access permissions; implementing data integrity verification by comparing the received hash value with the calculated hash value to determine the data integrity flag; and initiating an automatic recovery program or issuing an alarm when a fault is detected.

[0012] The beneficial effects of this preferred technical solution are as follows: By analyzing data from the entire process, the operating status of the distribution network can be accurately grasped, and potential faults and safety hazards can be detected in a timely manner. Predicting system recovery time and formulating fault handling strategies helps to quickly restore the normal operation of the distribution network and reduce the impact of faults on users. Quantifying security risks, performing access control verification, and data integrity checks can improve the security and reliability of the distribution network and ensure the security and integrity of data.

[0013] As a preferred scheme for a distribution network verification method based on multi-path intelligent switching, the verification platform monitors its own throughput, power consumption, and efficiency indicators, generates test cases and evaluates verification coverage, and adaptively optimizes internal parameters based on the comprehensive evaluation results, including: calculating throughput by transmitting data volume and system efficiency, obtaining total power consumption by combining static and dynamic power consumption, and evaluating system efficiency based on useful output power, total input power, and conversion efficiency; executing intelligent verification strategies to generate test cases containing input vectors, expected outputs, and constraint sets, evaluating code coverage by the ratio of executed branches to total branches, and calculating verification completion by combining the number of passed tests, total number of tests, number of verified requirements, and total number of requirements; conducting a comprehensive system evaluation, comprehensively considering performance, reliability, security, and efficiency indicators to fully assess the system; and adaptively optimizing internal parameters based on the comprehensive evaluation results.

[0014] The beneficial effects of this preferred technical solution are as follows: Monitoring the throughput, power consumption, and efficiency indicators of the verification platform itself enables timely detection of system performance bottlenecks and resource waste. Generating test cases and evaluating verification coverage ensures the comprehensiveness and effectiveness of the verification work. Through comprehensive system evaluation and adaptive optimization of internal parameters, the performance and reliability of the verification platform can be continuously improved, providing better services for the verification work of the distribution network.

[0015] Secondly, this invention provides a distribution network verification system based on multi-path intelligent switching, comprising: a platform initialization module, used to build a verification platform and initialize the platform's own state, initialize task scheduling and resource allocation algorithms, and configure protocol conversion rules; a state monitoring and judgment module, used to continuously monitor the distribution network operation status based on the built verification platform, calculate the current error and settling time to determine switching conditions, and evaluate switching delay and reliability indicators; a path selection and switching module, used to execute a path selection algorithm based on real-time network conditions after the verification platform determines that the switching conditions are met, comprehensively consider load, delay, and cost factors to select the optimal transmission path, and initiate the switching operation; and a protocol adaptation and injection module, used to inject test cases into the test system after the switching is completed, and simultaneously ensure the compatibility between the operating system and the test system through protocol mapping and data frame reconstruction. The system features: communication protocol compatibility; a test response recording module to verify the platform's monitoring test system's response to injected test cases, performing interface impedance matching, level conversion, and bandwidth matching, and fully recording all test data; a device switchback control module to verify the platform's control of the test system's safe exit after testing, achieving closed-loop control through a feedback controller to switch the distribution network equipment back to normal operation, and executing FTU device status monitoring and protection criteria; a data diagnosis and evaluation module to verify the platform's analysis of the entire process data after device switchback, performing fault diagnosis and impact assessment, predicting the distribution network system's recovery time, and implementing safety risk quantification and integrity verification; and an indicator optimization and adjustment module to verify the platform's monitoring of its own throughput, power consumption, and efficiency indicators, generating test cases and evaluating verification coverage, and adaptively optimizing internal parameters based on the comprehensive evaluation results.

[0016] Thirdly, the present invention provides a computer device, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of a power distribution network verification method based on multi-path intelligent switching are implemented.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of a power distribution network verification method based on multi-path intelligent switching.

[0018] The beneficial effects of this invention are as follows: This invention achieves seamless automated switching between the operating system and the testing system through intelligent switching technology, reducing switching efficiency from 30 minutes manually to 2 minutes automatically, an improvement of 93.3%. A multi-protocol conversion mechanism is established, supporting compatibility testing of 35 types of power distribution equipment, increasing equipment compatibility from 60% to 95%, an improvement of 58.3%. A multi-path intelligent scheduling algorithm is developed, improving signal transmission reliability and efficiency, increasing verification accuracy from 85.6% to 98.7%. A real-time status monitoring and fault diagnosis system is constructed, reducing fault recovery time from 45 minutes to 5 minutes, a reduction of 88.9%. Operational complexity is significantly reduced, providing strong support for the reliable operation of the power distribution network system and solving problems such as complex system switching, inconsistent communication protocols, difficulties in multi-path management, and untimely status feedback during power distribution network verification. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 is an overall flowchart of a power distribution network verification method based on multi-path intelligent switching provided by the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1, is the first embodiment of the present invention. This embodiment provides a distribution network verification method based on multi-path intelligent switching, including: S1: Building a verification platform and initializing its own state, initializing task scheduling and resource allocation algorithms, and configuring protocol conversion rules; S2: Based on the built verification platform, continuously monitoring the distribution network operation status, calculating the current error and stabilization time to determine the switching conditions, and evaluating the switching delay and reliability indicators; S3: When the verification platform determines that the switching conditions are met, it executes a path selection algorithm based on the real-time network conditions, comprehensively considering load, delay, and cost factors to select the optimal transmission path, and initiates the switching operation; S4: After the switching is completed, the verification platform injects test cases into the test system, and simultaneously ensures operation through protocol mapping and data frame reconstruction. The communication protocols between the system and the test system are compatible; S5: The verification platform monitors the test system's response to the injected test cases, performs interface impedance matching, level conversion, and bandwidth matching, and records all test data completely; S6: After the test is completed, the verification platform controls the test system to exit safely, realizes closed-loop control through the feedback controller, switches the distribution network equipment back to normal operation, and executes FTU equipment status monitoring and protection criteria; S7: After the equipment is switched back, the verification platform analyzes the data of the entire process, performs fault diagnosis and impact assessment, predicts the recovery time of the distribution network system, and implements safety risk quantification and integrity verification; S8: The verification platform monitors its own throughput, power consumption, and efficiency indicators, generates test cases and evaluates the verification coverage, and adaptively optimizes internal parameters based on the comprehensive evaluation results.

[0023] It should be noted that through steps S1-S8, a closed-loop verification system was constructed, encompassing initialization preparation, intelligent switching, test execution, and analysis and optimization. Real-time status monitoring and multi-condition criteria ensured the accuracy of switching decisions; path optimization and protocol compatibility technologies guaranteed the efficiency and isolation of the testing process; and full data recording and multi-dimensional analysis enabled accurate assessment of the distribution network system's performance and reliability. Finally, through a parameter adaptive optimization mechanism, the entire verification platform possessed self-evolution capabilities, significantly improving the automation level, test coverage depth, and result reliability of distribution network verification work, providing effective technical support for the safe and stable operation of complex distribution systems.

[0024] Example 2, referring to Figure 1, is an embodiment of the present invention. Based on the previous embodiment, it provides a distribution network verification method based on multi-path intelligent switching, including: In this embodiment, the above step S1 of building a verification platform and initializing the state of the verification platform itself, initializing task scheduling and resource allocation algorithms, and configuring protocol conversion rules includes: building a verification platform, establishing a verification platform system state vector, including key information such as switching state, communication state, and equipment state, represented as: In the formula, Let i be the system state vector at time i. To switch mode status, This refers to the communication link status. For equipment operating status, For system timestamps, This is the status feedback signal.

[0025] During the initialization phase, the verification platform presets the switching mode, communication link, and device operating status.

[0026] Specifically, the initial connection relationships of each input / output port are defined by constructing a switching state matrix, which is defined as follows: In the formula, To switch the state matrix, This represents the switching state from the i-th input to the j-th output.

[0027] The system transformation function is established to lay the mathematical foundation for subsequent signal processing, and is expressed as follows: In the formula, For the output signal vector, The input signal vector, This is the noise vector.

[0028] In another possible implementation, the system transfer function can be established based on a mathematical model: according to the physical characteristics and working principle of the verification platform, corresponding mathematical equations are established to describe the relationship between the input and output signals. For example, for a power system verification platform, a mathematical model based on circuit theory can be established, and the system transfer function can be obtained by solving the circuit equations.

[0029] In another possible implementation, a data-driven approach can be used to establish the system transition function: a large amount of input-output data is collected, and data mining and machine learning algorithms, such as support vector regression, are used to learn the mapping relationship between inputs and outputs from the data, thereby establishing the system transition function. For example, the support vector regression model in Python's Scikit-learn library can be used to train the input-output data to obtain the system transition function.

[0030] Regarding task scheduling and resource allocation, the platform initializes the relevant algorithms. Task scheduling priority is determined by the following formula: In the formula, Let i be the priority of the i-th task. Due to the level of urgency, According to the degree of importance, As the deadline factor, , , This is the weighting factor.

[0031] The resource allocation algorithm uses the following formula: In the formula, The amount of resources allocated. For the amount of resources required, The amount of available resources, To improve allocation efficiency.

[0032] In addition, the platform has established a communication protocol conversion mapping table and configured corresponding protocol conversion rules. The protocol mapping function is specifically represented as follows: In the formula, To output the protocol format, For protocol conversion matrix, To input the protocol format, This is the offset vector.

[0033] The data frame structure is unified as follows: In the formula, Frame is the data frame structure, Header is the frame header, Length is the data length, Command is the command word, Data is the data field, and CRC is the checksum.

[0034] In another possible implementation, a cloud platform technology can be used to build the verification platform, leveraging the infrastructure and computing resources provided by cloud service providers for rapid deployment. Containerization technology can be used to package the various components of the verification platform into independent containers, enabling rapid deployment and elastic scaling. For example, Docker container technology can be used to encapsulate the verification platform's database, application server, and other components in different containers, with Kubernetes used for container orchestration and management.

[0035] In another possible implementation, a distributed architecture can be used to build the verification platform, distributing its functionality across multiple physical nodes. A distributed file system, such as Ceph, can be used to store data, enabling efficient data storage and sharing. The nodes communicate and collaborate via a network, improving the platform's processing power and reliability. For example, multiple nodes can be deployed in data centers in different regions to form the verification platform, connected by a high-speed network to achieve real-time data transmission and processing.

[0036] In this embodiment, step S2 above, based on the established verification platform, continuously monitors the operating status of the distribution network, calculates the current error and stabilization time to determine the switching conditions, and evaluates the switching delay and reliability indicators, including: the system continuously monitors the operating status, makes a switching judgment, and evaluates whether the current error and stabilization time meet the switching conditions.

[0037] Specifically, the switching judgment condition is as follows: In the formula, To switch the license signal, This is the current error value. To switch thresholds, To stabilize the time, This is the minimum settling time.

[0038] To capture changes in system state, the change in state is calculated using the following formula: In the formula, For state changes, This is the current state value. This represents the state value at the previous moment.

[0039] And set the anomaly detection threshold: In the formula, This is the anomaly detection threshold. The mean of the state. denoted as σ, and k is the confidence coefficient.

[0040] When the handover conditions are met, calculate the handover delay: In the formula, Total handover latency, To detect latency, To handle latency, Due to execution delay.

[0041] Assess switchover reliability: In the formula, To ensure reliability during switching, For failure rate, This refers to the runtime.

[0042] Initiate the handover preparation procedure to ensure a safe and reliable handover process.

[0043] In this embodiment, step S3 above, when the verification platform determines that the switching conditions are met, executes a path selection algorithm based on the real-time network conditions, comprehensively considers load, latency and cost factors to select the optimal transmission path, and initiates the switching operation, including: executing a path selection algorithm, comprehensively considering load, latency and cost factors to select the optimal transmission path.

[0044] Specifically, a signal routing algorithm is used: In the formula, The optimal path, Let i be the load of the i-th path. For delay, For cost, , , These are the weighting coefficients.

[0045] Implement a load balancing strategy to ensure reasonable load distribution across all paths; the load balancing strategy is expressed as: In the formula, Let i be the load rate of the i-th path. For business traffic, Where N is the path capacity, N is the number of services, and M is the number of paths.

[0046] In another possible implementation, load balancing strategies can be implemented based on traffic forecasting. Methods such as time series analysis can be used to predict service traffic, and the load allocation across paths can be adjusted in advance based on the forecast results. For example, using an ARIMA model to predict service traffic, bandwidth allocation across paths can be adjusted in advance based on predicted traffic peaks and troughs to avoid overloading some paths.

[0047] In another possible implementation, a dynamic feedback mechanism can be used to implement the load balancing strategy. The load status of each path is monitored in real time, and the load distribution is dynamically adjusted according to changes in load. When the load on a certain path is too high, some service traffic is diverted to other paths with lower load. For example, load information for each path can be obtained in real time via the SNMP protocol, and traffic distribution on the paths can be dynamically adjusted based on the load information.

[0048] The following formulas are used to evaluate signal quality and monitor signal-to-noise ratio and bit error rate: In the formula, SNR is a signal quality indicator, and SNR is the signal-to-noise ratio. For bit error rate, This represents the maximum permissible bit error rate.

[0049] The channel capacity is calculated using the following formula, and the transmission parameter configuration is optimized: In the formula, C is the channel capacity, B is the bandwidth, S is the signal power, and N is the noise power.

[0050] In another possible implementation, machine learning algorithms can be introduced to select the optimal transmission path. Historical network data, including information such as load, latency, and cost of each path, is collected to train a machine learning model, such as a neural network model. When selecting a transmission path in real time, the current network conditions are used as input, and the trained model predicts the performance of each path, selecting the path with the best performance. For example, a multilayer perceptron model can be trained using the TensorFlow framework to predict the performance of network paths.

[0051] In another possible implementation, a multi-objective optimization algorithm can be used to select the optimal transmission path. Factors such as load, delay, and cost are considered as multiple objectives, and a multi-objective optimization algorithm, such as the NSGA-II algorithm, is used to find a set of Pareto optimal solutions. Based on actual needs, the most suitable transmission path is selected from the Pareto optimal solutions. For example, in some cases where delay is a more critical factor, the path with the minimum delay is selected from the Pareto optimal solutions.

[0052] In this embodiment, after the switching in step S4 is completed, the verification platform injects test cases into the test system. Simultaneously, ensuring communication protocol compatibility between the running system and the test system through protocol mapping and data frame reconstruction includes: performing protocol mapping to convert the input protocol format into the target protocol format; specifically, the protocol mapping function is: In the formula, To output the protocol format, For protocol conversion matrix, To input the protocol format, This is the offset vector.

[0053] A data frame structure is constructed to ensure the integrity and accuracy of data transmission; specifically, the data frame structure is as follows: In the formula, Frame is the data frame structure, Header is the frame header, Length is the data length, Command is the command word, Data is the data field, and CRC is the checksum.

[0054] Communication efficiency can be evaluated and transmission success rate and protocol overhead monitored using the following formula: In the formula, For communication efficiency, To the number of successful transmissions, Total number of transmissions For the time spent on the agreement, This represents the total transmission time.

[0055] Analyze transmission delay and communication reliability; specifically, the transmission delay model is expressed as: In the formula, For total delay, To delay the spread, For transmission delay, To handle delays, Due to queuing delays.

[0056] Communication reliability is expressed as: In the formula, For communication reliability, Let be the failure probability of the i-th link, and n be the total number of communication links.

[0057] In this embodiment, step S5 above verifies the platform's monitoring and testing system's response to the injected test cases, performs interface impedance matching, level conversion, and bandwidth matching, and fully records all test data, including: performing interface impedance matching calculations to ensure signal transmission compatibility. In the formula, To match impedance, Source impedance, This is the load impedance.

[0058] Perform signal level conversion to adapt to the level standards of different devices: In the formula, For output voltage, Input voltage, and For voltage divider resistors, This is the magnification factor.

[0059] Achieve interface bandwidth matching and optimize data transmission efficiency: In the formula, For effective bandwidth, and For the bandwidth of the two interfaces, For matching efficiency.

[0060] Check the interface connection status to ensure the reliability of the physical connection.

[0061] In this embodiment, after the test in step S6 is completed, the verification platform control test system safely exits, and the feedback controller realizes closed-loop control to switch the power distribution network equipment back to normal operation. The FTU equipment status monitoring and protection criteria include: starting the feedback controller to realize closed-loop control of the system.

[0062] Specifically, the feedback controller is represented as: In the formula, To control the output, For error signals, , , These are proportional, integral, and differential gains, respectively.

[0063] Monitor system response time to ensure real-time requirements; system response time is expressed as: In the formula, For system response time, For queuing time, For service hours, This is for switching time.

[0064] Precise control of power distribution equipment such as FTUs (Feeder Terminal Units) is achieved through the following formula: FTU status monitoring: In the formula, For FTU status information, For switch position, This is the current value. This is the voltage value. This is a fault indicator.

[0065] The criteria for executing a protection action are expressed as follows: In the formula, Action represents the protection action, and I represents the current value. Here is the setpoint, and t is the duration. This is the delay time.

[0066] Apply the following formula to adapt to different communication protocols and ensure device compatibility: In the formula, This is the set of communication protocols supported by FTU.

[0067] In this embodiment, after the equipment is switched back in step S7 above, the verification platform analyzes the data of the entire process, performs fault diagnosis and impact assessment, predicts the recovery time of the power distribution network system, and implements safety risk quantification and integrity verification, including: performing fault diagnosis and calculating the fault probability and impact degree.

[0068] Specifically, the failure probability is calculated as follows: In the formula, This represents the probability of failure. This represents the time-varying failure rate.

[0069] Fault impact assessment: In the formula, Impact represents the degree of impact of the fault, Severity represents the severity, and Probability represents the probability of occurrence. This is to increase the difficulty of detection.

[0070] Predict recovery time and develop fault handling strategies: In the formula, For recovery time, For the detection time, For the quarantine period, For repair time, This refers to the test time.

[0071] Assess system security and implement security measures. When a fault is detected, initiate an automatic recovery process or issue an alarm.

[0072] Specifically, quantify security risks: In the formula, Risk is the total risk value. Let be the probability of the i-th threat. As for the severity of the consequences, This is a vulnerability index.

[0073] Access control verification: In the formula, Access means access permission, Authentication means identity authentication, Authorization means permission authorization, and Accounting means audit trail.

[0074] Data integrity verification: In the formula, Integrity is the integrity flag. For the received hash value, The calculated hash value.

[0075] In this embodiment, step S8 above verifies that the platform monitors its own throughput, power consumption, and efficiency indicators, generates test cases and evaluates the verification coverage, and adaptively optimizes internal parameters based on the comprehensive evaluation results, including monitoring system performance, including throughput, power consumption, and efficiency indicators.

[0076] Specifically, the system throughput is: In the formula, Throughput represents the system throughput. To transmit data volume, For system efficiency.

[0077] The power consumption optimization model is expressed as: In the formula, Total power consumption, This refers to static power consumption. For dynamic power consumption, is the switching activity factor, f is the operating frequency, and V is the operating voltage.

[0078] The efficiency evaluation indicators are: In the formula, Efficiency represents the system efficiency. For useful output power, Total input power, For conversion efficiency.

[0079] Implement intelligent verification strategies, generate test cases, and evaluate the verification completion rate.

[0080] Specifically, test case generation includes: In the formula, TestCase is a test case. For the input vector, For the desired output, This is the set of constraints.

[0081] Coverage assessment: In the formula, Coverage represents code coverage. The number of branches executed. This represents the total number of branches.

[0082] Verification completion rate: In the formula, Completeness represents the degree of verification completion. The number of tests passed. This represents the total number of tests. For the number of verified requirements, This represents the total demand.

[0083] Conduct a comprehensive system evaluation to assess the system's performance, reliability, security, and efficiency. Optimize system parameters based on the evaluation results to improve overall performance. In the formula, Evaluation represents the overall system evaluation, Performance is the performance indicator, Reliability is the reliability indicator, Safety is the safety indicator, and Efficiency is the efficiency indicator. , , , These are the weighting coefficients.

[0084] Example 3: The above is an illustrative scheme of a distribution network verification method based on multi-path intelligent switching according to this embodiment. It should be noted that the technical solution of a distribution network verification system based on multi-path intelligent switching and the technical solution of the above-described distribution network verification method based on multi-path intelligent switching belong to the same concept. Details not described in detail in the technical solution of the distribution network verification system based on multi-path intelligent switching in this embodiment can be found in the description of the technical solution of the above-described distribution network verification method based on multi-path intelligent switching.

[0085] This embodiment also provides a distribution network verification system based on multi-path intelligent switching, including: a platform initialization module, used to build a verification platform and initialize the platform's own state, initialize task scheduling and resource allocation algorithms, and configure protocol conversion rules; a state monitoring and judgment module, used to continuously monitor the distribution network operation status based on the built verification platform, calculate the current error and settling time to determine the switching conditions, and evaluate the switching delay and reliability indicators; a path selection and switching module, used to execute a path selection algorithm based on real-time network conditions after the verification platform determines that the switching conditions are met, comprehensively consider load, delay, and cost factors to select the optimal transmission path, and initiate the switching operation; and a protocol adaptation and injection module, used to inject test cases into the test system after the switching is completed, and simultaneously ensure communication between the operating system and the test system through protocol mapping and data frame reconstruction. Protocol compatibility; Test response recording module, used to verify the platform's monitoring test system's response to injected test cases, perform interface impedance matching, level conversion, and bandwidth matching, and fully record all test data; Device switchback control module, used to verify the platform's control test system to safely exit after testing, achieving closed-loop control through a feedback controller, switching the distribution network equipment back to normal operation, and executing FTU device status monitoring and protection criteria; Data diagnosis and evaluation module, used to verify the platform's analysis of the entire process data after device switchback, performing fault diagnosis and impact assessment, predicting the distribution network system recovery time, and implementing safety risk quantification and integrity verification; Indicator optimization and adjustment module, used to verify the platform's monitoring of its own throughput, power consumption, and efficiency indicators, generating test cases and evaluating verification coverage, and adaptively optimizing internal parameters based on comprehensive evaluation results.

[0086] This embodiment also provides an electronic device applicable to a power distribution network verification method based on multi-path intelligent switching, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the power distribution network verification method based on multi-path intelligent switching as proposed in the above embodiment.

[0087] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a power distribution network verification method based on multi-path intelligent switching as proposed in the above embodiments.

[0088] The storage medium proposed in this embodiment belongs to the same inventive concept as the power distribution network verification method based on multi-path intelligent switching proposed in the above embodiment. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0089] Example 4, referring to Tables 1-3, is an embodiment of the present invention, providing a power distribution network verification method based on multi-path intelligent switching. To verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0090] This embodiment constructs a comprehensive test platform encompassing various power distribution equipment to verify the effectiveness and practicality of the intelligent switching verification platform using the verification method of this invention. The test environment includes a 10kV ring main unit, distribution transformers, distributed power sources, and various protection devices, covering equipment from different manufacturers and multiple communication protocols. The experiment spanned six months, completing over 1000 switching operations and verification tests.

[0091] The experimental results show the system's performance under different operating conditions, as shown in Tables 1-3: Table 1: Intelligent switching performance test results

[0092] Table 2: Evaluation of Communication Protocol Conversion Effect

[0093] Table 3: System Comprehensive Performance Verification Results

[0094] Experimental results show that this platform (the platform using the verification method of this invention) significantly improves switching efficiency, verification accuracy, and equipment compatibility. The intelligent switching success rate reaches 99.8%, the switching latency is controlled within 50ms, and the verification accuracy is improved to 98.7%. The system supports 35 different types of power distribution equipment, and the communication protocol conversion accuracy exceeds 99.9%. Fault recovery time is reduced from 45 minutes to 5 minutes, and operational complexity is significantly reduced, providing strong technical support for the reliable operation of the power distribution network system.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for verifying distribution networks based on intelligent multi-path switching, characterized in that, include: The verification platform is built and its own state is initialized. The task scheduling and resource allocation algorithms are initialized, and the protocol conversion rules are configured. Based on the built verification platform, the operation status of the distribution network is continuously monitored, the current error and settling time are calculated to determine the switching conditions, and the switching latency and reliability indicators are evaluated. When the verification platform determines that the switching conditions are met, the path selection algorithm is executed according to the real-time network conditions. The optimal transmission path is selected by comprehensively considering load, latency and cost factors, and the switching operation is initiated. After the switching is completed, the verification platform injects test cases into the test system, and at the same time, the communication protocol compatibility between the running system and the test system is ensured through protocol mapping and data frame reconstruction. The verification platform monitors the test system's response to injected test cases, performs interface impedance matching, level conversion, and bandwidth matching, and records all test data completely. After the test is completed, the verification platform controls the test system to safely exit, implements closed-loop control through the feedback controller, switches the distribution network equipment back to normal operation, and executes FTU equipment status monitoring and protection criteria. After the equipment is switched back, the verification platform analyzes the data throughout the process, performs fault diagnosis and impact assessment, predicts the distribution network system recovery time, and implements security risk quantification and integrity verification. The verification platform monitors its own throughput, power consumption, and efficiency indicators, generates test cases, evaluates verification coverage, and adaptively optimizes internal parameters based on the comprehensive evaluation results.

2. The distribution network verification method based on multi-path intelligent switching as described in claim 1, characterized in that, The process of building and initializing the verification platform, initializing the task scheduling and resource allocation algorithms, and configuring protocol conversion rules includes: building the verification platform; establishing a system state vector containing switching state, communication state, and device state; pre-setting the switching mode, communication link, and device operating state during the initialization phase; constructing a switching state matrix to clarify the initial connection relationship of each input and output port; establishing a system conversion function to determine the output signal vector; initializing the task scheduling and resource allocation algorithms; establishing a communication protocol conversion mapping table and configuring protocol conversion rules; converting the input protocol format to the output protocol format through the protocol conversion matrix and offset vector; and unifying the data frame structure.

3. The distribution network verification method based on multi-path intelligent switching as described in claim 2, characterized in that, The aforementioned verification platform continuously monitors the distribution network's operating status, calculates the current error and settling time to determine switching conditions, and evaluates switching delay and reliability indicators. This includes: continuously monitoring the distribution network's operating status using the established verification platform; comparing the current error and settling time with preset switching thresholds and minimum settling time to determine if switching conditions are met; calculating system state changes to capture system state variations, and setting anomaly detection thresholds based on state mean, standard deviation, and confidence coefficients; calculating the total switching delay, composed of detection delay, processing delay, and execution delay, when switching conditions are met; and evaluating switching reliability based on failure rate and operating time.

4. The distribution network verification method based on multi-path intelligent switching as described in claim 3, characterized in that, When the verification platform determines that the switching conditions are met, the path selection algorithm is executed based on the real-time network conditions. The optimal transmission path is selected by comprehensively considering load, latency, and cost factors, and the switching operation is initiated. This includes: when the verification platform determines that the switching conditions are met, executing the path selection algorithm, comprehensively considering the load, latency, and cost factors of each path, and selecting the optimal transmission path; implementing a load balancing strategy to reasonably distribute the load of each path based on service traffic and path capacity; evaluating signal quality by monitoring signal-to-noise ratio and bit error rate indicators, calculating channel capacity, and optimizing transmission parameter configuration based on the calculation results.

5. The distribution network verification method based on multi-path intelligent switching as described in claim 4, characterized in that, The verification platform monitors the test system's response to the injected test cases, performs interface impedance matching, level conversion, and bandwidth matching, and fully records all test data, including: the verification platform monitors the test system's response to the injected test cases, performs interface impedance matching calculations, determines the matching impedance based on the source impedance and load impedance; performs signal level conversion, converting the input voltage into an output voltage adapted to different device level standards based on the voltage divider resistor and amplification factor; and achieves interface bandwidth matching, determining the effective bandwidth based on the bandwidth of the two interfaces and the matching efficiency.

6. The distribution network verification method based on multi-path intelligent switching as described in claim 5, characterized in that, After the equipment is switched back, the verification platform analyzes the data throughout the process, performs fault diagnosis and impact assessment, predicts the power distribution network system recovery time, and implements security risk quantification and integrity verification, including: predicting system recovery time based on detection time, isolation time, repair time, and testing time, and formulating corresponding fault handling strategies; assessing system security by quantifying security risks by comprehensively considering threat probability, severity of consequences, and vulnerability index; performing access control verification by combining identity authentication, authorization, and audit trail to determine access permissions; implementing data integrity verification by comparing the received hash value with the calculated hash value to determine data integrity flags; and initiating automatic recovery procedures or issuing alarm prompts when a fault is detected.

7. The distribution network verification method based on multi-path intelligent switching as described in claim 6, characterized in that, The verification platform monitors its own throughput, power consumption, and efficiency metrics, generates test cases, and evaluates verification coverage. Based on the comprehensive evaluation results, it adaptively optimizes internal parameters, including: calculating throughput by considering data transmission volume and system efficiency; obtaining total power consumption by combining static and dynamic power consumption; evaluating system efficiency based on useful output power, total input power, and conversion efficiency; executing intelligent verification strategies to generate test cases containing input vectors, expected outputs, and constraint sets; evaluating code coverage by the ratio of executed branches to total branches; calculating verification completion by combining the number of passed tests, total number of tests, number of verified requirements, and total number of requirements; conducting a comprehensive system evaluation, comprehensively considering performance, reliability, security, and efficiency metrics; and adaptively optimizing internal parameters based on the comprehensive evaluation results.

8. A distribution network verification system based on multi-path intelligent switching, using the method described in any one of claims 1 to 7, characterized in that, include: The platform initialization module is used to build the verification platform and initialize its own state, initialize task scheduling and resource allocation algorithms, and configure protocol conversion rules. The status monitoring and judgment module is used to continuously monitor the operation status of the distribution network based on the built verification platform, calculate the current error and stabilization time to determine the switching conditions, and evaluate the switching latency and reliability indicators. The path selection and switching module is used to execute the path selection algorithm based on the real-time network conditions when the verification platform determines that the switching conditions are met, comprehensively consider load, latency and cost factors to select the optimal transmission path, and initiate the switching operation. The protocol adaptation and injection module is used to inject test cases into the test system after the switching is completed, and at the same time ensure the communication protocol compatibility between the running system and the test system through protocol mapping and data frame reconstruction. The test response recording module is used to verify the platform monitoring test system's response to injected test cases, perform interface impedance matching, level conversion, and bandwidth matching, and record all test data completely; the device switchback control module is used to verify the platform control test system to safely exit after the test is completed, realize closed-loop control through the feedback controller, switch the distribution network equipment back to normal operation, and execute FTU device status monitoring and protection criteria. The data diagnosis and assessment module is used to verify that the platform analyzes the data throughout the entire process after the equipment is switched back, performs fault diagnosis and impact assessment, predicts the recovery time of the power distribution network system, and implements safety risk quantification and integrity verification. The metrics optimization and adjustment module is used to verify the platform's own throughput, power consumption, and efficiency metrics, generate test cases and evaluate verification coverage, and adaptively optimize internal parameters based on the comprehensive evaluation results.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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