Alternating current and direct current charging pile full-function test method and platform

Abstract

The invention discloses an AC / DC charging pile full-function test method and platform, and relates to the technical field of charging pile testing, and the method comprises the steps: setting a full-function test table based on a plurality of AC / DC charging piles of a target power station; establishing a full-function test model; introducing a simulation power supply matrix of a target power station, and drawing up an interaction test scene of a plurality of AC / DC charging piles; according to the configured interaction test scene, acquiring simulation test data, and outputting a full-function test report; and fault positioning mining is carried out by combining the power supply function test branch, the communication protocol test branch, the safety protection test branch and the compatibility test branch, and fault reminding is carried out. The technical problems that an existing alternating current and direct current charging pile is not comprehensive in testing, and faults are difficult to accurately and rapidly position and repair are solved, and the technical effects that the comprehensive function testing of the alternating current and direct current charging pile is achieved, the faults can be found in time, a repair scheme and reminding can be provided, and stable and reliable operation of the charging pile is guaranteed are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile testing, and particularly to a full-function testing method and platform for AC / DC charging piles. Background Art

[0002] With the booming development of the electric vehicle industry, as a key supporting facility, the performance and quality reliability of AC / DC charging piles are of crucial importance. In the current technical environment, there are many deficiencies in the traditional testing means for AC / DC charging piles. On the one hand, the testing content is often limited to some functions, and it is impossible to comprehensively and integrally evaluate the power supply function, communication protocol, safety protection and compatibility of the charging piles, resulting in some potential problems being difficult to be discovered. On the other hand, in terms of fault detection and handling, there is a lack of effective methods to accurately locate the root cause of the fault, and there is also a lack of a systematic solution for fault repair. When a charging pile fails, the repair time is long and the efficiency is low, which seriously affects the charging experience of users and increases the operation cost. At the same time, due to the failure to discover and solve faults in a timely manner, the stability and reliability of the charging pile operation are greatly reduced, and there are potential safety hazards.

[0003] There are technical problems in the existing AC / DC charging piles that the testing is not comprehensive and the faults are difficult to be accurately, quickly located and repaired. Summary of the Invention

[0004] The present application provides a full-function testing method and platform for AC / DC charging piles, which are used to solve the technical problems that the existing AC / DC charging piles have incomplete testing and the faults are difficult to be accurately, quickly located and repaired.

[0005] In view of the above problems, the present application provides a full-function testing method and platform for AC / DC charging piles.

[0006] In the first aspect of the present application, a full-function testing method for AC / DC charging piles is provided, and the method includes:

[0007] Based on multiple AC-DC charging piles in the target power station, a full-function test table is set up. The full-function test table includes power supply function test items, communication protocol test items, safety protection test items, and compatibility test items. According to the full-function test table, a charging interface plug-in is used to simulate the electric vehicle charging interface to establish a full-function test model. The full-function test model includes a power supply function test branch, a communication protocol test branch, a safety protection test branch, and a compatibility test branch. The simulated power matrix of the target power station is introduced to draw up the interactive test scenarios of the multiple AC-DC charging piles, and the configuration is carried out in combination with the real-time operation status of the multiple AC-DC charging piles uploaded to the cloud monitoring center. According to the configured interactive test scenarios, the full-function test model is used to obtain simulated test data, and the data is compared with the expected standard values of the power supply function test items, communication protocol test items, safety protection test items, and compatibility test items to output a full-function test report. Based on the full-function test report, fault location and excavation are carried out in combination with the power supply function test branch, communication protocol test branch, safety protection test branch, and compatibility test branch to generate a single column of fault repair and give a fault reminder.

[0008] In the second aspect of the present application, a full-function test platform for AC-DC charging piles is provided. The platform includes:

[0009] A full-function test table setting module, which is used to set up a full-function test table based on multiple AC-DC charging piles in the target power station. The full-function test table includes power supply function test items, communication protocol test items, safety protection test items, and compatibility test items; a full-function test model establishment module, which is used to use a charging interface plug-in to simulate the electric vehicle charging interface according to the full-function test table to establish a full-function test model. The full-function test model includes a power supply function test branch, a communication protocol test branch, a safety protection test branch, and a compatibility test branch; an interactive test scenario drawing-up module, which is used to introduce the simulated power matrix of the target power station to draw up the interactive test scenarios of the multiple AC-DC charging piles and configure them in combination with the real-time operation status of the multiple AC-DC charging piles uploaded to the cloud monitoring center; a full-function test report output module, which is used to obtain simulated test data according to the configured interactive test scenarios by using the full-function test model, and compare the data with the expected standard values of the power supply function test items, communication protocol test items, safety protection test items, and compatibility test items to output a full-function test report; a fault reminder module, which is used to carry out fault location and excavation in combination with the power supply function test branch, communication protocol test branch, safety protection test branch, and compatibility test branch based on the full-function test report to generate a single column of fault repair and give a fault reminder.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] Based on multiple AC-DC charging piles of the target power station, a full-function test table is set up; according to the full-function test table, a charging interface plug-in is used to simulate the charging interface of an electric vehicle to establish a full-function test model; the simulated power matrix of the target power station is introduced, an interactive test scenario for the multiple AC-DC charging piles is drawn up, and it is configured in combination with the real-time operating status of the multiple AC-DC charging piles uploaded to the cloud monitoring center; according to the configured interactive test scenario, the full-function test model is used to obtain simulated test data, and it is compared with the expected standard values of the power supply function test item, communication protocol test item, safety protection test item, and compatibility test item, and a full-function test report is output; based on the full-function test report, a list of fault repairs is generated and a fault reminder is given. It achieves the technical effect of comprehensively testing the functions of AC-DC charging piles, being able to detect faults in a timely manner and provide repair solutions and reminders, and ensuring the stable and reliable operation of charging piles. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 Schematic flow chart of a method for full-function testing of AC-DC charging piles provided by an embodiment of the present application;

[0014] Figure 2 Schematic structural diagram of a full-function test platform for AC-DC charging piles provided by an embodiment of the present application.

[0015] Description of reference numerals: full-function test table setting module 10, full-function test model establishment module 20, interactive test scenario drawing-up module 30, full-function test report output module 40, fault reminder module 50. Detailed Embodiments

[0016] The present application provides a method and platform for full-function testing of AC-DC charging piles to solve the technical problems of incomplete testing of existing AC-DC charging piles and difficult accurate and rapid positioning and repair of faults.

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0018] Embodiment 1, asFigure 1 As shown in the figure, the present application provides a full-function test method for AC-DC charging piles, and the method includes:

[0019] Step S100: Based on multiple AC-DC charging piles of a target power station, set up a full-function test form, and the full-function test form includes a power supply function test item, a communication protocol test item, a safety protection test item, and a compatibility test item.

[0020] Specifically, to set up a full-function test form based on multiple AC-DC charging piles of a target power station, it is necessary to deeply understand the specification parameters, technical standards of various AC-DC charging piles in the target power station, and common problems in actual use. For the power supply function test item, details are planned for the test content of the output voltage and current stability of the charging pile, the power adjustment range and accuracy, etc., to ensure that its power supply performance meets relevant standards and satisfies the charging requirements of electric vehicles. For the communication protocol test item, it focuses on the accuracy, timeliness, and integrity of data transmission between the charging pile and electric vehicles, the background management system, etc., and sets test key points for the version compatibility of the communication protocol, the correctness of the message format, the standardization of the communication handshake process, etc. The safety protection test item focuses on whether the protection actions of the charging pile are rapid and effective under abnormal conditions such as overcurrent, overvoltage, leakage, and overheating, and formulates test schemes for indicators such as the accuracy of the overcurrent protection threshold, the response time of the leakage protection, and the starting temperature of the overheating protection. The compatibility test item aims to investigate the adaptation of the charging pile to the charging interfaces of electric vehicles of different brands and models, as well as the support for different charging standards and protocols, and determines the test details for aspects such as the physical size matching degree of the charging interface, the charging protocol compatibility, and the charging parameter adaptation ability, so as to construct a comprehensive, scientific, and rigorous full-function test form and lay a solid foundation for the subsequent test work.

[0021] Step S200: According to the full-function test form, use a charging interface plug-in to simulate the electric vehicle charging interface and establish a full-function test model, and the full-function test model includes a power supply function test branch, a communication protocol test branch, a safety protection test branch, and a compatibility test branch.

[0022] Specifically, after setting up the full-functional test form, an appropriate charging interface plug-in is selected according to the set full-functional test form to simulate the electric vehicle charging interface. This charging interface plug-in is highly similar to the real electric vehicle charging interface in terms of electrical characteristics, physical structure, and communication protocol, and can accurately simulate various situations when an electric vehicle is connected to a charging pile. With the help of this plug-in, a full-functional test model is started to be constructed. In this model, the power supply function test branch simulates the charging requirements of electric vehicles, and monitors in real time the power supply parameters such as voltage, current, and power output by the charging pile to determine whether its power supply performance meets the standards; the communication protocol test branch focuses on analyzing and verifying the accuracy and stability of the communication messages between the charging pile and the simulated electric vehicle, and checks the execution of the communication protocol; the safety protection test branch simulates various abnormal working conditions such as overcurrent, overvoltage, and leakage to test the effectiveness of the charging pile safety protection mechanism; the compatibility test branch connects different types of simulated electric vehicle charging interfaces to test the adaptability of the charging pile under different conditions. Through this series of operations, a full-functional test model including power supply function, communication protocol, safety protection, and compatibility test branches is successfully established, providing strong support for the subsequent comprehensive testing of the functions of AC and DC charging piles.

[0023] Step S300: Introduce the simulated power matrix of the target power station, formulate the interactive test scenarios for the multiple AC and DC charging piles, and configure them in combination with the real-time operating states of the multiple AC and DC charging piles uploaded to the cloud monitoring center.

[0024] Specifically, after introducing the simulated power matrix of the target power station, the interactive test scenarios for the multiple AC and DC charging piles are started to be formulated. According to the historical operating data of the charging piles, a spatio-temporal distribution map of charging requirements is drawn and a state space is established to present the changing rules and possible states of the charging pile operating states. Then, a reward mechanism is set in combination with the charging efficiency and fault avoidance rate to generate a candidate test scenario set, and a protocol conflict probability prediction channel that processes the communication protocol feature sequence using a bidirectional LSTM network is embedded at the decision node to predict the possibility of communication protocol conflicts and screen out more reasonable test scenarios. At the same time, it is configured in combination with the real-time operating states of the multiple AC and DC charging piles uploaded to the cloud monitoring center. The real-time grid dispatching data is determined according to the real-time operating state, and the scenario parameters are dynamically adjusted. For example, the output power of the charging pile is adjusted according to the current load situation of the grid to optimize the coverage density of the interactive test scenarios, ensure that the test can cover more actual operating situations, and set appropriate test frequency standards. The interactive test scenarios are pre-executed in the digital twin power station model, and by comparing the KL divergence value between the actual device response curve and the simulation result, the scenario parameter configuration is continuously iteratively optimized, and a test scenario execution sequence marked with a timestamp is output to ensure that the test scenarios are closely combined with the actual situation and executed in an orderly manner, making the test more scientific, accurate, and practical, so as to provide a reliable basis for the subsequent test work.

[0025] Step S400: According to the configured interactive test scenario, use the full-function test model to obtain simulated test data, compare it with the expected standard values of the power supply function test item, communication protocol test item, safety protection test item, and compatibility test item, and output a full-function test report.

[0026] Specifically, after completing the configuration of the interactive test scenario, based on the configured interactive test scenario, start the full-function test model for simulation testing. During the testing process, use each branch of the full-function test model, namely the power supply function test branch, communication protocol test branch, safety protection test branch, and compatibility test branch, to synchronously collect simulated test data. For the power supply function test branch, accurately monitor parameters such as output voltage, current, and power of the charging pile during simulated charging; the communication protocol test branch details record the transmission of communication messages, including message accuracy, transmission delay, etc.; the safety protection test branch simulates various abnormal working conditions and records relevant data when the charging pile triggers the safety protection mechanism; the compatibility test branch collects adaptation data when the charging pile interacts with different simulated electric vehicle charging interfaces. During the data acquisition process, construct a time-series sliding window for outlier detection to promptly discover abnormal data points. For communication message loss events, embed a protocol fuzzing test engine in the communication protocol test branch to generate a set of mutant test cases (mutation factors include bit flipping, length extension, and timing perturbation, and the triggering probability is positively correlated with the real-time load rate of AC and DC charging piles), dynamically evaluate the fault tolerance of the protocol stack state machine, and trigger a retransmission verification mechanism linked to the protocol stack state machine to ensure the accuracy of communication data. Use a parallel processing architecture to extract features from the power quality data stream and the communication protocol data stream respectively, establish a cross-dimensional correlation analysis matrix, identify hidden abnormal patterns, and generate an abnormal probability distribution heat map. Based on the fault knowledge base, construct an abnormal pattern classifier, calculate the cosine similarity between the expected standard values of the power supply function, communication protocol, safety protection, and compatibility test items and the conventional fault modes, output abnormal type labels with confidence ratings, and classify and label the abnormal probability distribution heat map accordingly. Finally, comprehensively and meticulously compare the obtained simulated test data with the expected standard values of the power supply function test item, communication protocol test item, safety protection test item, and compatibility test item respectively, comprehensively analyze all test data and comparison results, and output a detailed full-function test report, which clearly presents the passing situation of each test, existing problems, and analysis of abnormal situations, providing a strong basis for subsequent fault location and repair.

[0027] Step S500: Based on the full-function test report, combine the power supply function test branch, communication protocol test branch, safety protection test branch, and compatibility test branch to conduct fault location and mining, generate a single column of fault repairs, and issue a fault reminder.

[0028] Specifically, conduct an in-depth analysis of the full-functional test report. For the power supply function test branch, use data analysis software supporting Fluke power quality analyzers to import the collected data such as voltage, current, and power. This software has powerful waveform analysis, harmonic analysis, and power quality index calculation functions. By comparing with standard power supply parameters and using its built-in threshold judgment and trend analysis algorithms, it can accurately locate faults such as unstable output power caused by overheating of the power module. For example, when it is detected that the voltage fluctuation exceeds the standard range and the power curve shows an abnormal downward trend, it can be judged that the fault may be caused by poor heat dissipation of the power module.

[0029] For the communication protocol test branch, use network protocol analysis tools such as Wireshark to parse the communication message data. Wireshark supports the decoding and analysis of multiple communication protocols and carefully compares the parsed messages with the standard communication protocol specifications. If message format errors or transmission interruptions are found, combined with the protocol stack state machine logs and using its provided state transition analysis function, it can be determined whether it is a communication interface chip failure or a software protocol parsing program error. For example, when specific protocol field errors are detected and abnormal jumps occur in the protocol stack state machine, it can be preliminarily judged that there are loopholes in the software protocol parsing part.

[0030] For the safety protection test branch, input the test data under simulated abnormal working conditions into a dedicated electrical safety fault diagnosis system, such as Siemens electrical safety analysis software. This software has built-in rich electrical safety standards and fault diagnosis models. By matching with the preset safety protection thresholds and action logics, it can find the root causes of faults such as misoperation of the leakage protection device due to sensor failures or control circuit short circuits. For example, when the leakage current does not reach the action threshold but the leakage protection is triggered, the software determines the location of the fault by analyzing the accuracy of the data collected by the sensor and the logic relationship of the control circuit.

[0031] For the compatibility test branch, record the data of the charging pile interacting with different simulated electric vehicle charging interfaces and use compatibility test tools such as Tektronix charging compatibility test software to analyze the data. Based on the interface standards and charging protocol specifications and combined with hardware interface adaptability detection equipment, this software can judge whether it is a problem caused by mismatched hardware interface sizes or incompatible communication protocol versions. For example, when an error prompt of protocol mismatch appears during the charging handshake process, the software can quickly locate the compatibility problem caused by inconsistent communication protocol versions.

[0032] After determining the fault point, use the fault management system to generate a single column for fault repair. According to the fault type, location, and severity, extract the corresponding repair suggestions from the predefined repair solution library, and automatically generate a detailed list containing the fault description, repair steps, and required tools. At the same time, through the message push function of the operation and maintenance management platform, send fault reminders to the mobile APP and computer client of relevant operation and maintenance personnel to ensure that they can be aware of and handle the fault in a timely manner, and guarantee the stable operation of the charging pile.

[0033] In a possible implementation manner, step S300 further includes:

[0034] Step S310: Introduce the simulated power matrix of the target power station, set the spatio-temporal distribution map of charging demand according to the historical operation data of the charging pile, and establish a state space.

[0035] Step S320: Configure a reward mechanism according to the charging efficiency and fault avoidance rate, and generate a set of candidate test scenarios. Embed a protocol conflict probability prediction channel at the decision node, and the protocol conflict probability prediction channel uses a bidirectional LSTM network to process the communication protocol feature sequence.

[0036] Step S330: In the state space, combine the protocol conflict probability prediction channel to establish a digital twin verification loop, and the digital twin verification loop is used to verify the set of candidate test scenarios and determine the interactive test scenario.

[0037] Specifically, introduce the simulated power matrix of the target power station, which can accurately simulate the power supply characteristics of the target power station, including parameters such as voltage fluctuation range, frequency change, and power output ability. At the same time, extract the historical operation data of the charging pile from the power station's database system, which details the usage time, charging duration, charging power, geographical location of the charging pile, and relevant status information during each charging. Use data analysis libraries in Python, such as Pandas and Matplotlib, to process and visualize the historical data. By grouping and statistically analyzing the time series data, calculate the charging demand frequency and intensity of each charging pile in different time periods; based on the geographical location information of the charging pile, combined with geographic information system (GIS) technology, analyze the charging demand distribution in different regions. Based on these analysis results, draw a spatio-temporal distribution map of charging demand to visually present the changing rules of charging demand over time and space. In terms of establishing the state space, use state space modeling technology to quantitatively represent the operating states of the charging pile (such as charging, idle, fault, etc.) and related electrical parameters (voltage, current, power, etc.), and construct a mathematical space model that can describe all possible states of the charging pile.

[0038] Clarify the calculation methods of the charging efficiency and the fault avoidance rate. The charging efficiency is measured by the ratio of the actual charging amount to the theoretical charging amount, and the fault avoidance rate is determined according to the ratio of the number of times of avoiding faults to the total number of operating times in historical data. Based on these two indicators, use machine learning libraries in Python, such as Scikit-learn, to construct a reward mechanism. Set an initial reward value for each possible test scenario. Scenarios with high charging efficiency and high fault avoidance rate are given higher reward points, while those with low values are deducted points. By continuously iterating and optimizing the reward values, a set of candidate test scenarios is generated. Embed a protocol conflict probability prediction channel at the decision-making node, and use Python's deep learning frameworks TensorFlow or PyTorch to build a bidirectional LSTM network. Convert the key features in the communication protocol, such as message format, communication frequency, data transmission volume, etc., into a feature sequence and input it into the bidirectional LSTM network for training. After being trained with a large amount of historical communication data, the network can learn the patterns in the communication protocol feature sequence, so as to predict the probability of protocol conflicts in different test scenarios. According to the prediction results, filter out the test scenarios with a lower probability of protocol conflicts, and further optimize the set of candidate test scenarios.

[0039] Use digital twin technology to establish a virtual model corresponding to the actual charging pile system. With the help of 3D modeling software, such as Blender or Maya, create a physical model of the charging pile, and combine a physics engine to simulate its operation under different working conditions. Combine this virtual model with the previously established state space and protocol conflict probability prediction channel to construct a digital twin verification loop. Input the set of candidate test scenarios into the digital twin model for simulation operation in sequence. During the operation, the protocol conflict probability prediction channel monitors the execution of the communication protocol in real time. Once a potential protocol conflict risk is detected, the scenario parameters are adjusted in time or the feasibility of the scenario is re-evaluated. At the same time, according to the state space model, verify whether the test scenario covers all possible operating states of the charging pile. Through the analysis and evaluation of the simulation operation results, such as charging efficiency, fault occurrence, protocol execution, etc., select the most interaction test scenarios that can best reflect the actual operation situation and have the most test value from the set of candidate test scenarios to ensure that the subsequent full-functional test can more comprehensively and accurately detect the performance and functions of the charging pile.

[0040] In a possible implementation manner, step S300 further includes:

[0041] Step S340: Determine the real-time grid dispatching data according to the real-time operating states of multiple AC / DC charging piles, dynamically adjust the scenario parameters, optimize the coverage density of the interaction test scenario, and set the test frequency standard;

[0042] Step S350: In the digital twin power plant model, pre-execute the interactive test scenario. By comparing the KL divergence value between the actual device response curve and the simulation result, iteratively optimize the scenario parameter configuration, and output a test scenario execution sequence with timestamp markings; wherein, the test scenario execution sequence is always synchronized with the full-functional test report in terms of time sequence.

[0043] Specifically, with the help of the Internet of Things technology and the data acquisition system, collect the real-time operation status data of multiple AC and DC charging piles. By deploying sensors in the charging pile devices, obtain the operation parameters such as the output power, charging current, and voltage of the charging piles in real time, as well as the working status (such as charging, idle, fault, etc.) information of the charging piles, and upload this data to the data center. At the same time, conduct data docking with the power grid dispatching system to obtain real-time power grid dispatching data, including information such as the load condition of the power grid, electricity price fluctuations, and power supply stability. Use time series analysis, combined with the real-time operation status of the charging piles and the power grid dispatching data, to dynamically adjust the parameters of the interactive test scenario. For example, if the power grid load is high, in order to avoid causing too much impact on the power grid, appropriately reduce the simultaneous charging power of the charging piles in the test scenario; if it is detected that the usage frequency of charging piles in a certain area increases, then increase the occurrence frequency of the charging piles in that area in the test scenario. By adjusting these parameters, optimize the coverage density of the interactive test scenario to ensure that the test scenario can comprehensively cover the operation conditions of the charging piles under different working conditions. At the same time, according to factors such as the importance of the charging piles, the historical fault frequency, and the real-time operation stability, use the Analytic Hierarchy Process (AHP) to determine the test frequency standard and set a reasonable test frequency for different charging piles or test scenarios.

[0044] Build a digital twin power station model based on digital twin technology. Use 3D modeling software (such as Unity, 3ds Max) to build the virtual scene of the power station, including the models of equipment such as charging piles, power grid lines, and transformers, and combine with a physics engine (such as PhysX) to simulate the physical behavior and electrical characteristics of the equipment. Input the optimized interactive test scenario into the digital twin power station model for pre-execution. During the pre-execution process, deploy sensors at the key nodes of the actual charging pile equipment and the digital twin model to collect real-time data on the actual device response and the simulation result data of the digital twin model, such as voltage change curves, current fluctuation curves, charging times, etc. Use scientific computing libraries in Python (such as Numpy, Scipy) to calculate the KL divergence value between the actual device response curve and the simulation result. The KL divergence value is used to measure the degree of difference between two probability distributions. The smaller the value, the closer the actual device response is to the simulation result. If the KL divergence value exceeds the set threshold, it indicates that the current scenario parameter configuration is not accurate enough and needs to be optimized. Use the genetic algorithm to iteratively optimize the scenario parameters, adjust parameters such as the layout of the charging piles, charging time intervals, and charging power distribution, re-pre-execute the test scenario in the digital twin power station model, and calculate the KL divergence value again until the KL divergence value reaches a satisfactory range. After the optimization is completed, output the test scenario execution sequence with timestamp marks, and the timestamp marks accurately record the execution time of each test scenario. At the same time, when generating a full-functional test report, associate the timestamps of the test scenario execution sequence with the test data times in the report to ensure that the test scenario execution sequence and the full-functional test report are always synchronized in time sequence, facilitating accurate analysis and traceability of the test results in the future.

[0045] In a possible implementation manner, step S400 further includes:

[0046] Step S410: Construct a time series sliding window, perform outlier detection, and set up a retransmission verification mechanism linked to the protocol stack state machine for communication message loss events.

[0047] Step S420: Use a parallel processing architecture to extract features from the power supply quality data stream and the communication protocol data stream respectively, establish a cross-dimensional correlation analysis matrix, identify hidden abnormal patterns, and generate an abnormal probability distribution heat map.

[0048] Specifically, to effectively analyze the test data, a time series sliding window is constructed with a certain time interval as the window size, which slides over the continuous test data sequence, and the data is analyzed window by window. Within each window, statistical methods are used for outlier detection. For example, points that deviate from the mean by a certain multiple of the standard deviation are set as outliers. When an outlier is found, its cause is further analyzed to determine whether it is caused by an abnormality in the test system or an actual operation failure of the charging pile. For the communication message loss event, a retransmission verification mechanism linked to the protocol stack state machine is designed. When a message loss is detected, the protocol stack state machine triggers a retransmission instruction and records relevant information at the same time. The retransmission verification mechanism dynamically adjusts the retransmission interval based on the priority of the communication message and the real-time network condition to ensure that the message can be transmitted accurately and in a timely manner. After each retransmission, it is verified whether the receiving party has successfully received the message. If it is still not successful, the retransmission strategy is continuously adjusted until the message is successfully transmitted or the maximum number of retransmissions is reached.

[0049] To improve the data processing efficiency and mine potential anomalies, a parallel processing architecture is adopted. Using multi-threaded or distributed computing technologies, the power quality data stream and the communication protocol data stream are respectively assigned to different processing units for parallel processing. In the process of feature extraction of the power quality data stream, features such as the change trend, fluctuation range, and harmonic content of parameters such as voltage, current, and power are analyzed; for the communication protocol data stream, features such as communication frequency, message length, and message content are extracted. Then, a cross-dimensional correlation analysis matrix is established to correlate the power quality features with the communication protocol features. By calculating metrics such as the correlation coefficient between different features, the matrix elements are filled to show the degree of association between different types of data features. Based on this matrix, data mining algorithms such as clustering analysis algorithms are used to identify hidden anomaly patterns. For example, an abnormal combination pattern in which the power supply voltage fluctuates and a specific communication protocol error occur simultaneously is found. Finally, according to the identified hidden anomaly patterns, combined with historical data and probability statistics methods, an anomaly probability distribution heat map is generated. The heat map intuitively shows the probability of anomalies under different data feature combinations in shades of color, helping testers quickly locate and analyze potential anomaly situations, providing strong support for subsequent fault diagnosis and test report generation.

[0050] In a possible implementation manner, step S420 further includes:

[0051] Step S421: Construct an anomaly pattern classifier based on the fault knowledge base, and output an anomaly type label with a confidence rating through the cosine similarity between the expected standard values of the power supply function test item, communication protocol test item, safety protection test item, and compatibility test item and the conventional fault modes.

[0052] Step S422: Classify and label the anomaly probability distribution heat map based on the anomaly type label.

[0053] Specifically, by means of a database management system (such as MySQL), a fault knowledge base is built, and a large number of collected historical fault cases of AC and DC charging piles in terms of power supply function, communication protocol, safety protection, and compatibility are sorted out and entered. Each case details the fault phenomenon, abnormal data of relevant test items, and the corresponding fault cause. The scikit-learn library of Python is used to construct an abnormal pattern classifier, and the expected standard values of the power supply function, communication protocol, safety protection, and compatibility test items and the conventional fault modal data in the fault knowledge base are respectively converted into numerical vectors. By calling the cosine_similarity function in scikit-learn, the cosine similarity between the test data vector and each conventional fault modal vector is calculated. The higher the similarity value, the closer the test data is to the characteristics of the corresponding fault modal. Based on the similarity results, the fault modal with the highest similarity is selected as the predicted abnormal type. To obtain an abnormal type label with a confidence rating, the cross-validation method is used to evaluate the performance of the classifier, and the confidence is comprehensively determined according to the accuracy of multiple cross-validations and the similarity between the current test data and the selected fault modal. For example, the abnormal type corresponding to high similarity and high accuracy is marked as "high confidence" to form the final output of the abnormal type label.

[0054] Using the OpenCV and pandas libraries of Python, the abnormal probability distribution heat map data and the generated abnormal type label data are read respectively. According to the abnormal type label, the corresponding data area is found in the heat map, and classification annotation is carried out by drawing text or adding color marks on the heat map image. For example, if a certain area is marked as "Power supply function - Voltage anomaly (high confidence)", this area is framed with a prominent color (such as red), and the corresponding text information is marked beside it, so as to intuitively present the distribution of different types of anomalies in the heat map, facilitate testers to quickly identify and analyze the fault types of the charging pile and their positions in the data space, and provide an intuitive basis for subsequent fault diagnosis and processing.

[0055] In a possible implementation manner, step S410 further includes:

[0056] Step S411: Embed a protocol fuzz testing engine in the communication protocol test branch to generate a set of mutant test cases. The mutation factors corresponding to the set of mutant test cases include bit flipping, length extension, and timing perturbation; wherein, the triggering probabilities of the bit flipping, length extension, and timing perturbation are positively correlated with the real-time load rate of the AC and DC charging piles.

[0057] Step S412: According to the set of variant test cases, the fault tolerance capability of the protocol stack state machine is dynamically evaluated, and a retransmission verification mechanism linked to the protocol stack state machine is triggered, wherein the retransmission verification mechanism dynamically adjusts the retransmission interval according to the priority of the communication message and the real-time network status.

[0058] Specifically, an open source protocol fuzz testing framework, such as American Fuzzy Lop (AFL), is selected for secondary development to adapt to the communication protocol testing requirements of AC and DC charging piles. The protocol fuzz testing engine is reasonably embedded in the code logic of the communication protocol test branch. In order to generate a set of mutation test cases containing three mutation factors, namely bit flipping, length extension, and timing perturbation, the corresponding script is written in a programming language (such as Python). The normal communication message is processed by the script to realize the mutation operation. For bit flip mutation, the values ​​of certain bits in the message are randomly changed according to certain rules with the help of bit operation operations. For example, a random number generation function is set to randomly select several bits in the message and change their values ​​from 0 to 1 or from 1 to 0. For length extension mutation, random bytes are inserted at the head, tail or middle of the message according to the pre-set extension strategy to achieve the extension of the message length. For example, a piece of random data in a specific format is added to the tail of the message to simulate the possible abnormality of the message length. For timing perturbation mutation, it is achieved by adjusting the time interval for sending messages. Using the time control function, we randomly increase or decrease a certain time delay based on the normal message sending interval to simulate different network transmission delay conditions. In order to make the trigger probability of bit flip, length extension, and timing disturbance positively correlated with the real-time load rate of the AC and DC charging piles, a real-time load rate monitoring mechanism is established. By exchanging data with the control system of the charging pile, the load rate data of the charging pile is obtained in real time. Using the function P mutation =λ·(1-e -γ·L(t) ), (L(t) is the charging pile load rate at time t, λ is the baseline probability coefficient, and γ is the load sensitive adjustment factor), to calculate the trigger probability of each mutation factor. According to the calculation results, each time a test case is generated, the random number generator is compared with the trigger probability to decide whether to apply the corresponding mutation operation to the current test case.

[0059] Using the monitoring tools in the test framework and a customized evaluation algorithm, dynamically evaluate the fault tolerance of the protocol stack state machine according to the mutant test case set. During the test, when the protocol stack receives the mutant test case message, monitor the state changes of the protocol stack state machine, record whether the state machine can correctly process the abnormal message, and whether there are errors, abnormal jumps, etc. during the processing. By statistically analyzing indicators such as the number of times the state machine successfully processes abnormal messages, the processing time, and the types and frequencies of errors, comprehensively evaluate the fault tolerance of the protocol stack state machine. When it is found that a communication message is lost or an error occurs, trigger the retransmission verification mechanism linked to the protocol stack state machine. During the implementation of the retransmission verification mechanism, obtain the priority information of the communication message (for example, set the control instruction message to high priority, the data transmission message to medium priority, etc.), and at the same time use the network monitoring tool to obtain network status information such as network delay and packet loss rate in real time.

[0060] According to the priority F of the communication message and the real-time network delay D(t), through the formula dynamically adjust the retransmission interval. After each retransmission, compare the checksum of the sent message with the checksum feedback by the receiver to determine whether the message is correctly transmitted. If the checksum fails, further adjust the retransmission interval according to the retransmission times and the current network status until the message is successfully received or the maximum retransmission times is reached. In this way, ensure the reliability of communication, and at the same time can more comprehensively test the performance of the protocol stack under various abnormal conditions.

[0061] In a possible implementation manner, step S411 further includes:

[0062] Step S4111: Define the trigger probability calculation function of the mutant factor: P mutation =λ·(1 - e -γ·L(t) ), where L(t) is the charging pile load rate at time t, λ is the reference probability coefficient, and γ is the load-sensitive adjustment factor.

[0063] Step S4112: Based on the priority F of the communication message and the network delay D(t), dynamically calculate the retransmission interval: D base is the reference network delay, compare the residual between the actual communication recovery time T retry and the model prediction time T pred to evaluate the effectiveness of the retransmission verification mechanism, and feedback and adjust the reference probability coefficient and the load-sensitive adjustment factor according to the evaluation result.

[0064] Specifically, by defining the trigger probability calculation function P mutation =λ·(1 - e -γ·L(t)) The triggering probabilities of the three mutation factors, namely bit flipping, length extension, and timing perturbation, are closely associated with the charging pile load rate L(t) at time t, where λ is the reference probability coefficient and γ is the load-sensitive adjustment factor. This means that as the real-time load rate of the charging pile changes, the triggering probabilities of the mutation factors will also change accordingly. The higher the load rate, the greater the likelihood of the mutation factors being triggered, thereby simulating communication anomalies more in line with the actual complex working conditions.

[0065] When retransmission is required due to problems in the communication message transmission, based on the priority F of the communication message and the network delay D(t), the retransmission interval is dynamically calculated using the formula Here, D base is the reference network delay. During the actual testing process, after each retransmission, the residual between the actual communication recovery time T retry and the model prediction time T pred is carefully compared to evaluate the effectiveness of the retransmission verification mechanism. If the residual is large, it indicates that the effect of the retransmission verification mechanism is not ideal. At this time, the reference probability coefficient λ and the load-sensitive adjustment factor γ are adjusted according to the evaluation results. By continuously adjusting these two parameters, the triggering probability of the mutation factors can be made more reasonable, and the retransmission interval can be optimized, thereby continuously improving the accuracy of the test and ensuring that the reliability and stability of the AC / DC charging pile communication protocol can be comprehensively and accurately tested under different load rates and network conditions, providing strong support for the quality inspection and performance optimization of the charging pile.

[0066] Embodiment 2, based on the same inventive concept as the AC / DC charging pile full-function test method in the foregoing embodiment, as Figure 2 shown, the present application provides an AC / DC charging pile full-function test platform. The platform in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the platform includes:

[0067] A full-function test table setting module 10 for setting a full-function test table based on multiple AC / DC charging piles of the target power station. The full-function test table includes power supply function test items, communication protocol test items, safety protection test items, and compatibility test items.

[0068] A full-function test model establishment module 20 for establishing a full-function test model using a charging interface plug-in to simulate an electric vehicle charging interface according to the full-function test table. The full-function test model includes a power supply function test branch, a communication protocol test branch, a safety protection test branch, and a compatibility test branch.

[0069] An interactive test scenario formulation module 30 for introducing the simulated power matrix of the target power station, formulating the interactive test scenarios of the multiple AC / DC charging piles, and configuring them in combination with the real-time operating states of the multiple AC / DC charging piles uploaded to the cloud monitoring center.

[0070] The full - function test report output module 40 is used to obtain simulated test data using the full - function test model according to the configured interactive test scenario, compare it with the expected standard values of the power supply function test item, communication protocol test item, safety protection test item, and compatibility test item, and output a full - function test report.

[0071] The fault reminder module 50 is used to perform fault location and mining based on the full - function test report, combined with the power supply function test branch, communication protocol test branch, safety protection test branch, and compatibility test branch, generate a single column of fault repair, and perform fault reminder.

[0072] Furthermore, the interactive test scenario formulation module 30 further includes:

[0073] The state - space establishment unit is used to introduce the simulated power matrix of the target power station, set the spatio - temporal distribution map of charging demand according to the historical operation data of the charging pile, and establish a state - space; the protocol - conflict probability prediction channel embedding unit is used to configure a reward mechanism according to the charging efficiency and fault avoidance rate, generate a candidate test scenario set, and embed a protocol - conflict probability prediction channel at the decision node, and the protocol - conflict probability prediction channel uses a bidirectional LSTM network to process the communication protocol feature sequence; the digital - twin verification loop establishment unit is used to establish a digital - twin verification loop in the state - space, combined with the protocol - conflict probability prediction channel, and the digital - twin verification loop is used to verify the candidate test scenario set and determine the interactive test scenario.

[0074] Furthermore, the interactive test scenario formulation module 30 further includes:

[0075] The test frequency standard setting unit is used to determine the real - time power grid dispatching data according to the real - time operation status of multiple AC - DC charging piles, dynamically adjust the scenario parameters, optimize the coverage density of the interactive test scenario, and set the test frequency standard; the test scenario execution sequence output unit is used to pre - execute the interactive test scenario in the digital - twin power station model, iteratively optimize the scenario parameter configuration by comparing the KL divergence value between the actual device response curve and the simulation result, and output a test scenario execution sequence with timestamp marks; wherein, the test scenario execution sequence and the full - function test report are always synchronized in time series.

[0076] Furthermore, the full - function test report output module 40 further includes:

[0077] An outlier detection unit, which is used to construct a time - series sliding window, perform outlier detection, and set up a re - transmission verification mechanism linked with the protocol stack state machine for communication message loss events; An abnormal probability distribution heatmap generation unit, which is used to use a parallel processing architecture to extract features from the power supply quality data stream and the communication protocol data stream respectively, establish a cross - dimensional correlation analysis matrix, identify hidden abnormal patterns, and generate an abnormal probability distribution heatmap.

[0078] Furthermore, the abnormal probability distribution heatmap generation unit further includes:

[0079] An abnormal type label output unit, which is used to construct an abnormal pattern classifier based on a fault knowledge base, and output an abnormal type label with a confidence rating through the cosine similarity between the expected standard values of the power supply function test item, communication protocol test item, safety protection test item, and compatibility test item and the conventional fault modes; A classification annotation unit, which is used to classify and annotate the abnormal probability distribution heatmap based on the abnormal type label.

[0080] Furthermore, the outlier detection unit further includes:

[0081] A mutant test case set generation unit, which is used to embed a protocol fuzzing test engine in the communication protocol test branch to generate a mutant test case set, and the mutant factors corresponding to the mutant test case set include bit - flipping, length extension, and timing perturbation; Among them, the triggering probabilities of the bit - flipping, length extension, and timing perturbation are positively correlated with the real - time load rate of the AC - DC charging pile; A fault - tolerance ability evaluation unit, which is used to dynamically evaluate the fault - tolerance ability of the protocol stack state machine according to the mutant test case set, and trigger a re - transmission verification mechanism linked with the protocol stack state machine, and the re - transmission verification mechanism dynamically adjusts the re - transmission interval according to the priority of the communication message and the real - time network condition.

[0082] Furthermore, the mutant test case set generation unit further includes:

[0083] A triggering probability calculation function definition unit, which is used to define the triggering probability calculation function of the mutant factor: P mutation =λ·(1 - e -γ·L(t) ), where L(t) is the load rate of the charging pile at time t, λ is the reference probability coefficient, and γ is the load - sensitive adjustment factor; A re - transmission verification mechanism evaluation unit, which is used to dynamically calculate the re - transmission interval based on the priority F of the communication message and the network delay D(t): D base is the reference network delay, compare the actual communication recovery time T retry with the model - predicted time T pred of the residual, evaluate the effectiveness of the re - transmission verification mechanism, and feedback and adjust the reference probability coefficient and the load - sensitive adjustment factor according to the evaluation results.

[0084] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the specific embodiments of this specification have been described. Further, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0086] This specification and the drawings are merely exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the 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 equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A full-function test method for AC-DC charging piles, characterized in that, The method includes: Based on multiple AC-DC charging piles of the target power station, a full-function test table is set up. The full-function test table includes power supply function test items, communication protocol test items, safety protection test items, and compatibility test items; According to the full-function test table, a charging interface plug-in is used to simulate the electric vehicle charging interface, and a full-function test model is established. The full-function test model includes a power supply function test branch, a communication protocol test branch, a safety protection test branch, and a compatibility test branch; The simulated power matrix of the target power station is introduced, the interactive test scenarios of the multiple AC-DC charging piles are formulated, and the configuration is combined with the real-time operation status of the multiple AC-DC charging piles uploaded to the cloud monitoring center; According to the configured interactive test scenarios, the full-function test model is used to obtain simulated test data, and the simulated test data is compared with the expected standard values of the power supply function test items, communication protocol test items, safety protection test items, and compatibility test items, and a full-function test report is output; Based on the full-function test report, combined with the power supply function test branch, communication protocol test branch, safety protection test branch, and compatibility test branch, fault location and mining are carried out, a single column of fault repair is generated, and a fault reminder is given.

2. The all-function test method for an AC / DC charging pile according to claim 1, characterized in that The simulated power matrix of the target power station is introduced, and the interactive test scenarios of the multiple AC-DC charging piles are formulated. The method includes: The simulated power matrix of the target power station is introduced. According to the historical operation data of the charging piles, a spatio-temporal distribution map of charging demand is set up, and a state space is established; According to the charging efficiency and fault avoidance rate, a reward mechanism is configured, and a candidate test scenario set is generated. A protocol conflict probability prediction channel is embedded at the decision node, and the protocol conflict probability prediction channel uses a bidirectional LSTM network to process the communication protocol feature sequence; In the state space, combined with the protocol conflict probability prediction channel, a digital twin verification loop is established. The digital twin verification loop is used to verify the candidate test scenario set and determine the interactive test scenario.

3. The all-function test method for an AC / DC charging pile according to claim 2, wherein, The configuration is combined with the real-time operation status of the multiple AC-DC charging piles uploaded to the cloud monitoring center. The method further includes: According to the real-time operation status of the multiple AC-DC charging piles, the real-time power grid dispatching data is determined, the scenario parameters are dynamically adjusted, the coverage density of the interactive test scenarios is optimized, and the test frequency standard is set; In the digital twin power station model, the interactive test scenarios are pre-executed. By comparing the KL divergence value between the actual device response curve and the simulation result, the scenario parameter configuration is iteratively optimized, and a test scenario execution sequence with timestamp marks is output; Among them, the test scenario execution sequence is always synchronized with the full-function test report in time series.

4. The full - function test method for an AC - DC charging pile as described in claim 1, characterized in that, The comparison with the expected standard values of the power supply function test items, communication protocol test items, safety protection test items, and compatibility test items, and the output of the full-function test report. The method further includes: A time series sliding window is constructed for outlier detection, and for communication message loss events, a retransmission verification mechanism linked to the protocol stack state machine is set up. Using a parallel processing architecture, feature extraction is performed on the power supply quality data stream and the communication protocol data stream respectively, a cross-dimensional correlation analysis matrix is established, latent abnormal patterns are identified, and an abnormal probability distribution heat map is generated.

5. The full - function test method for an AC - DC charging pile as described in claim 4, wherein, Based on a fault knowledge base, an abnormal pattern classifier is constructed, and through the cosine similarity between the expected standard values of the power supply function test items, communication protocol test items, safety protection test items, and compatibility test items and the conventional fault modes, abnormal type labels with confidence ratings are output. Based on the abnormal type labels, the abnormal probability distribution heat map is classified and labeled.

6. The all-function test method for an AC / DC charging pile according to claim 4, characterized in that For communication message loss events, a retransmission verification mechanism linked to the protocol stack state machine is set up. The method includes: Embedding a protocol fuzz testing engine in the communication protocol test branch to generate a set of mutant test cases. The mutation factors corresponding to the set of mutant test cases include bit flipping, length extension, and timing perturbation. Among them, the triggering probabilities of the bit flipping, length extension, and timing perturbation are positively correlated with the real-time load rate of the AC / DC charging pile. According to the set of mutant test cases, the fault tolerance of the protocol stack state machine is dynamically evaluated, and a retransmission verification mechanism linked to the protocol stack state machine is triggered. The retransmission verification mechanism dynamically adjusts the retransmission interval based on the priority of the communication message and the real-time network condition.

7. The full - function test method for an AC - DC charging pile according to claim 6, characterized in that, The method includes: Define the trigger probability calculation function of the mutation factor: P mutation = λ·(1 - e -γ·L(t) ), where L(t) is the charging pile load rate at time t, λ is the reference probability coefficient, and γ is the load sensitivity adjustment factor; Dynamically calculate the retransmission interval based on the priority F of the communication message and the network delay D(t): D base is the reference network delay, compare the actual communication recovery time T retry with the model prediction time T pred residuals, evaluate the effectiveness of the retransmission verification mechanism, and feedback and adjust the reference probability coefficient and the load-sensitive adjustment factor according to the evaluation results.

8. A full-functional test platform for AC-DC charging piles, characterized in that, The platform is used to implement a full-function test method for an AC / DC charging pile according to any one of claims 1-7. The platform includes: A full-function test table setting module, which is used to set a full-function test table based on multiple AC / DC charging piles of a target power station. The full-function test table includes power supply function test items, communication protocol test items, safety protection test items, and compatibility test items. A full-function test model establishment module, which is used to use a charging interface plug-in to simulate an electric vehicle charging interface according to the full-function test table to establish a full-function test model. The full-function test model includes a power supply function test branch, a communication protocol test branch, a safety protection test branch, and a compatibility test branch. An interactive test scenario formulation module, which is used to introduce the simulated power matrix of the target power station, formulate the interactive test scenarios of the multiple AC / DC charging piles, and configure them in combination with the real-time operating states of the multiple AC / DC charging piles uploaded to the cloud monitoring center. A full-function test report output module, which is used to obtain simulated test data using the full-function test model according to the configured interactive test scenario, compare it with the expected standard values of the power supply function test items, communication protocol test items, safety protection test items, and compatibility test items, and output a full-function test report. A fault reminder module, which is used to perform fault location and mining based on the full-function test report in combination with the power supply function test branch, communication protocol test branch, safety protection test branch, and compatibility test branch, generate a single column for fault repair, and perform fault reminder.

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