A high-reliability charging pile redundancy control system

Abstract

The application discloses a high-reliability charging pile redundancy control system and relates to the technical field of charging pile redundancy control.The high-reliability charging pile redundancy control system realizes real-time collection of operation state data of a charging gun, a contactor, a communication module and the like and preprocessing through a multi-source data acquisition subsystem, early identification of a fault trend through comparison and analysis of the fault trend preliminary diagnosis subsystem and benchmark data, accurate identification of a fault mode with the support of a support vector machine model of a fault diagnosis subsystem, and finally, optimal strategy matching according to a historical state vector and a reward value by a redundancy control strategy generation subsystem, early warning, accurate diagnosis and dynamic redundancy control of faults are realized, the reliability, fault response capability and operation efficiency of the charging pile system are improved, the charging interruption risk and operation and maintenance cost caused by faults are reduced, and the problems that a traditional charging pile cannot be charged due to single control board faults, fault diagnosis lags and redundancy strategies are rigid are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging pile redundancy control, in particular to a high-reliability charging pile redundancy control system. BACKGROUND

[0002] The current market double-gun direct current charging all-in-one machine adopts a single-gun single-control power sharing control strategy. Once a control board malfunctions, the charging gun on that path will fail, and charging cannot be performed. Although the charging pile can report the fault status in time, the maintenance of the charging pile still takes several days, which not only affects the user's charging experience, but also affects the income of the charging station.

[0003] A charging gun redundancy control system for a charging pile is disclosed in Chinese Patent No. CN117774760B, which belongs to the technical field of charging piles. The technical problem to be solved is that the control board fails to charge under the condition of single-gun single-control. The system includes a double-path control board, which is a state acquisition board and a charging control board. The state acquisition board is configured with a gun line state acquisition interface, a CAN interface, and a processor MCU, wherein the processor MCU serves as a state acquisition MCU. The charging control board is configured with a processor MCU, a BMS communication interface, an inter-board communication interface, a charging module communication interface, and a charging module control line interface. The charging control MCU controls the charging process based on the gun line state and vehicle BMS state stored in the on-board FLASH.

[0004] However, the traditional charging pile has the problems of failure to charge due to single-control board failure, lagging fault diagnosis, and rigid redundancy strategy. SUMMARY

[0005] To overcome the shortcomings of the prior art, the present application provides a high-reliability charging pile redundancy control system, which solves the problems of failure to charge due to single-control board failure, lagging fault diagnosis, and rigid redundancy strategy of the traditional charging pile.

[0006] To achieve the above object, the application is implemented by the following technical solutions: a high-reliability charging pile redundancy control system, comprising a charging pile multi-source data acquisition subsystem, a fault trend preliminary diagnosis subsystem, a fault diagnosis subsystem and a redundancy control strategy generation subsystem, wherein: the charging pile multi-source data acquisition subsystem is used for acquiring charging pile multi-source operating state data and preprocessing to obtain processed charging pile multi-source operating state data; the fault trend preliminary diagnosis subsystem is used for real-time diagnosis on the processed charging pile multi-source operating state data acquired by the charging pile multi-source data acquisition subsystem to determine whether the charging pile has a trend of failure, and obtain a preliminary diagnosis result; the fault diagnosis subsystem is used for fault mode recognition based on a fault diagnosis model when the preliminary diagnosis result is that the charging pile has a trend of failure; and the redundancy control strategy generation subsystem is used for determining a redundancy control strategy according to the processed charging pile multi-source operating state data after fault mode recognition.

[0007] Further, the processed charging pile multi-source operating state data comprises charging gun operating characteristics A1, contactor operating characteristics B1 and communication module operating characteristics C1, wherein: the charging gun operating characteristics A1 comprises gun head temperature, cable temperature, interface temperature and current fluctuation amplitude; the contactor operating characteristics B1 comprises contact resistance and vibration signal amplitude; and the communication module operating characteristics C1 comprises signal strength, packet loss rate and response delay.

[0008] Further, the charging pile multi-source data acquisition subsystem comprises a charging gun monitoring module, a contactor monitoring module, a communication module monitoring module and a data preprocessing module, wherein: the charging gun monitoring module is used for real-time monitoring of the operating state of the charging gun to obtain charging gun initial operating state data; the contactor monitoring module is used for real-time monitoring of the operating state of the contactor to obtain contactor initial operating state data; the communication module monitoring module is used for real-time monitoring of the operating state of the communication module to obtain communication module initial operating state data; and the data preprocessing module is used for receiving the charging gun initial operating state data, the contactor initial operating state data and the communication module initial operating state data and performing standardization processing to obtain the processed charging pile multi-source operating state data.

[0009] Further, the process of obtaining the preliminary diagnosis result whether the charging pile has a tendency to fail is as follows: determining the charging pile multi-source operation state reference data, including charging gun operation reference characteristics A2, contactor operation reference characteristics B2 and communication module operation reference characteristics C2; performing similarity analysis on the processed charging pile multi-source operation state data and the charging pile multi-source operation state reference data to determine the charging pile operation abnormality evaluation index Gzx; if the charging pile operation abnormality evaluation index Gzx is greater than the charging pile operation abnormality evaluation threshold obtained from the database, the preliminary diagnosis result is that the charging pile does not have a tendency to fail; if the charging pile operation abnormality evaluation index Gzx is not greater than the charging pile operation abnormality evaluation threshold obtained from the database, the preliminary diagnosis result is that the charging pile has a tendency to fail.

[0010] Further, the calculation formula of the charging pile operation abnormality evaluation index Gzx is as follows:

[0011] Gzx = a1 * s (A1, A2) + a2 * s (B1, B2) + a3 * s (C1, C2);

[0012] Wherein, a1, a2 and a3 are weight factors, and s(·) is a cosine similarity function.

[0013] Further, the process of determining the charging pile multi-source operation state reference data is as follows: obtaining the corresponding charging pile multi-source operation state reference data from the database based on the type of the charging pile; obtaining the charging pile historical working environment data, including the total use time St, the environmental temperature fluctuation amplitude Sw, the environmental humidity fluctuation amplitude Sh and the monthly average working overload rate Sf; obtaining the matching data-correction coefficient mapping data set stored in the database, the matching data including the total use time matching value Ct, the environmental temperature fluctuation amplitude matching value Cw, the environmental humidity fluctuation amplitude matching value Ch and the monthly average working overload rate matching value Cf, and the correction coefficient including the charging gun operation reference characteristic correction factor, the contactor operation reference characteristic correction factor and the communication module operation reference characteristic correction factor; determining the correction coefficient based on the charging pile historical working environment data and the matching data-correction coefficient mapping data set, and correcting the charging pile multi-source operation state reference data based on the correction coefficient to obtain the charging pile multi-source operation state reference data.

[0014] Further, the process of determining the correction coefficient based on the charging pile historical working environment data and the matching data-correction coefficient mapping data set is as follows: comparing each matching data in the charging pile historical working environment data with the matching data-correction coefficient mapping data set one by one to obtain the comparison value Bd:

[0015]

[0016] Determine the minimum matching value corresponding to the matching data, and obtain the corresponding correction coefficient.

[0017] Further, the process of fault mode recognition based on the fault diagnosis model is as follows: obtaining charging pile fault features, including charging gun fault features, contactor fault features and communication module fault features, wherein: the charging gun fault features include temperature gradient, current fluctuation variance and plug force change rate; the contactor fault features include contact resistance increment, action delay time and arc energy accumulation; the communication module fault features include signal strength attenuation rate, packet loss rate fluctuation coefficient and communication success rate with mainstream vehicle models; performing standardization processing on the charging pile fault features to obtain standardized charging pile fault features; inputting the standardized charging pile fault features into the trained support vector machine model to obtain the fault mode.

[0018] Further, the process of determining the redundancy control strategy according to the processed charging pile multi-source running state data is as follows: splicing the processed charging pile multi-source running state data and the standardized charging pile fault features to obtain a charging pile state vector; obtaining a historical charging pile state vector dataset obtained from the database; determining the most similar historical charging pile state vector to the charging pile state vector from the historical charging pile state vector dataset based on a cosine similarity function; obtaining a redundancy control strategy set corresponding to the most similar historical charging pile state vector from the database, each redundancy control strategy in the redundancy control strategy set corresponding to a reward value; selecting the redundancy control strategy with the maximum reward value to perform redundancy control, and updating the charging pile state vector; performing fault trend analysis based on the updated charging pile state vector and updating the reward value at the same time, if there is a fault trend, reselecting the redundancy control strategy until there is no fault trend.

[0019] Further, the updating process of the reward value is as follows: obtaining the updated processed charging pile multi-source running state data in the updated charging pile state vector; processing the updated processed charging pile multi-source running state data based on the fault trend preliminary diagnosis subsystem to obtain an updated charging pile running abnormality evaluation index; calculating the difference between the updated charging pile running abnormality evaluation index and the charging pile running abnormality evaluation index before updating to obtain an evaluation value deviation; obtaining the reward additional value corresponding to the evaluation value deviation from the database by querying the table; adding the reward value before updating and the reward additional value to obtain the updated reward value.

[0020] The present application has the following beneficial effects:

[0021] The high-reliability charging pile redundancy control system realizes early warning, accurate diagnosis and dynamic redundancy control of faults by real-time collection of operation state data of charging guns, contactors, communication modules and the like through a multi-source data acquisition subsystem, preliminary diagnosis of fault trends, comparison and analysis with reference data, early identification of fault trends, accurate identification of fault modes with the support of a support vector machine model of a fault diagnosis subsystem, and generation of an optimal strategy by a redundancy control strategy generation subsystem according to a historical state vector and a reward value.

[0022] Of course, it is not necessary for any product embodying the present application to achieve all of the above advantages simultaneously. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The high-reliability charging pile redundancy control system flowchart of the present application. DETAILED DESCRIPTION

[0024] Referring to Figure 1 The present application provides a technical solution: a high-reliability charging pile redundancy control system, comprising a charging pile multi-source data acquisition subsystem, a fault trend preliminary diagnosis subsystem, a fault diagnosis subsystem and a redundancy control strategy generation subsystem, wherein: the charging pile multi-source data acquisition subsystem is used to acquire charging pile multi-source operation state data and perform preprocessing to obtain processed charging pile multi-source operation state data.

[0025] The processed charging pile multi-source operation state data includes charging gun operation characteristics A1, contactor operation characteristics B1 and communication module operation characteristics C1, wherein: the charging gun operation characteristics A1 include gun head temperature, cable temperature, interface temperature and current fluctuation amplitude; the contactor operation characteristics B1 include contact resistance and vibration signal amplitude; and the communication module operation characteristics C1 include signal strength, packet loss rate and response delay.

[0026] The charging pile multi-source data acquisition subsystem includes a charging gun monitoring module, a contactor monitoring module, a communication module monitoring module and a data preprocessing module. The charging gun monitoring module is used for real-time monitoring of the running state of the charging gun to obtain initial running state data of the charging gun. The contactor monitoring module is used for real-time monitoring of the running state of the contactor to obtain initial running state data of the contactor. By using the charging gun monitoring module, the contactor monitoring module and the communication module monitoring module, the temperature and current fluctuation of the charging gun, the contact resistance and vibration signal of the contactor, the signal strength and packet loss rate of the communication module and other key operating characteristic data can be obtained in real time, and the overall monitoring of the state of the core components of the charging pile can be realized to avoid fault omission caused by incomplete data acquisition.

[0027] The communication module monitoring module is used for real-time monitoring of the running state of the communication module to obtain initial running state data of the communication module. The data preprocessing module is used for receiving the initial running state data of the charging gun, the initial running state data of the contactor and the initial running state data of the communication module, and performing standardization processing to obtain processed charging pile multi-source running state data. The data preprocessing module can eliminate the dimensional differences of different sensor data by standardizing the initial data, ensure the accuracy of subsequent fault trend diagnosis and fault mode recognition, lay a data foundation for the system to discover component abnormal trends and accurately locate fault types, and thus improve the reliability of the charging pile running state evaluation, reduce the risk of misjudgment caused by data errors, and provide a scientific basis for dynamic generation of redundancy control strategy.

[0028] The fault trend preliminary diagnosis subsystem is used for real-time diagnosis of the processed charging pile multi-source running state data obtained by the charging pile multi-source data acquisition subsystem to determine whether the charging pile has a tendency to fail, and obtain a preliminary diagnosis result.

[0029] The process of determining whether the charging pile has a tendency to fail and obtaining a preliminary diagnosis result is as follows: determining charging pile multi-source running state reference data, including charging gun running reference feature A2, contactor running reference feature B2 and communication module running reference feature C2; performing similarity analysis on the processed charging pile multi-source running state data and the charging pile multi-source running state reference data to determine a charging pile running abnormality evaluation index Gzx; if the charging pile running abnormality evaluation index Gzx is greater than the charging pile running abnormality evaluation threshold obtained from the database, the preliminary diagnosis result is that the charging pile does not have a tendency to fail; if the charging pile running abnormality evaluation index Gzx is not greater than the charging pile running abnormality evaluation threshold obtained from the database, the preliminary diagnosis result is that the charging pile has a tendency to fail.

[0030] By comparing the evaluation index with a threshold, it is possible to quickly determine whether a charging pile has a fault trend. When the index is not greater than the threshold, an early warning is issued in a timely manner, changing the passive mode of traditional post-event maintenance, realizing early detection of faults, and buying time for subsequent accurate fault diagnosis and the generation of redundant control strategies, effectively reducing the fault rate and downtime losses.

[0031] The formula for calculating the charging pile operation anomaly assessment index Gzx is as follows:

[0032] Gzx=α1*σ(A1,A2)+α2*σ(B1,B2)+α3*σ(C1,C2);

[0033] Where α1, α2 and α3 are all weighting factors, and σ(·) is the cosine similarity function.

[0034] By calculating the similarity between processed data and baseline data using the cosine similarity function, an anomaly assessment index for charging piles is generated, transforming abstract fault trends into quantifiable numerical indicators. This index differentiates the importance of charging guns, contactors, and communication modules through weighting factors, achieving a comprehensive assessment of multi-source data and ensuring the comprehensiveness of anomaly detection.

[0035] The process of determining the benchmark data for the multi-source operation status of charging piles is as follows: Based on the type of charging pile, obtain the corresponding multi-source operation status parameter data from the database; obtain historical working environment data of the charging pile, including total usage time St, ambient temperature fluctuation amplitude Sw, ambient humidity fluctuation amplitude Sh, and average monthly overload rate Sf; obtain the matching data-correction coefficient mapping dataset stored in the database. The matching data includes the matching value Ct for total usage time, the matching value Cw for ambient temperature fluctuation amplitude, the matching value Ch for ambient humidity fluctuation amplitude, and the matching value Cf for average monthly overload rate. The correction coefficients include the correction factors for the charging gun operation benchmark characteristics, the contactor operation benchmark characteristics, and the communication module operation benchmark characteristics; determine the correction coefficients based on the historical working environment data of the charging pile and the matching data-correction coefficient mapping dataset; and correct the multi-source operation status parameter data of the charging pile based on the correction coefficients to obtain the benchmark number of the multi-source operation status of the charging pile.

[0036] By determining the benchmark data of the multi-source operation status of charging piles, and combining the charging pile type and historical working environment (usage time, temperature and humidity fluctuations, overload rate, etc.) to correct the benchmark data, the benchmark data is made to better fit the actual operating conditions of the charging piles, avoiding the evaluation deviation caused by static benchmarks, and solving the problem of benchmark unification under different usage scenarios.

[0037] The correction coefficient is determined based on the historical operating environment data of charging piles and the matching data-correction coefficient mapping dataset. The process is as follows: Each matching data point in the historical operating environment data of charging piles and the matching data-correction coefficient mapping dataset is compared one by one to obtain the comparison value Bd. ：

[0038]

[0039] The matching data corresponding to the smallest alignment value is determined, and the corresponding correction coefficient is obtained.

[0040] By matching the data with correction coefficients in the dataset, historical operating environment data is transformed into specific correction coefficients, thus quantifying the impact of environmental factors on baseline data. The mechanism of determining correction coefficients based on the minimum comparison value ensures a high degree of matching between the baseline data and the actual historical operating conditions of the charging piles. This allows the anomaly assessment index obtained through similarity analysis to better reflect the true fault trends, avoiding anomaly assessment biases caused by a "one-size-fits-all" approach to baseline data. This provides a more reliable basis for early fault warning, thereby improving the reliability and fault prevention capabilities of the charging pile system.

[0041] The fault diagnosis subsystem is used to identify fault modes based on the fault diagnosis model when the preliminary diagnosis results indicate that the charging pile has a tendency to malfunction.

[0042] The system acquires fault characteristics of charging piles, including charging gun fault characteristics, contactor fault characteristics, and communication module fault characteristics. Specifically, charging gun fault characteristics include temperature gradient, current fluctuation variance, and insertion / extraction force variation rate; contactor fault characteristics include contact resistance increment, action delay time, and arc energy accumulation; and communication module fault characteristics include signal strength attenuation rate, packet loss rate fluctuation coefficient, and communication success rate with mainstream vehicle models. Core fault characteristics such as temperature gradient, contact resistance increment, and signal strength attenuation rate are extracted for the charging gun, contactor, and communication module, covering key indicators of typical fault modes such as component aging, poor contact, and communication anomalies. This avoids missed fault detection due to single features and achieves multi-dimensional fault characterization.

[0043] The fault characteristics of charging piles are standardized to obtain standardized fault characteristics. Standardization of fault characteristics eliminates the influence of differences in the dimensions of different characteristics, making heterogeneous data such as temperature, current, and signal strength comparable. This ensures that the SVM model can accurately learn the inherent laws of fault modes and improves the model's adaptability to different charging piles and different operating scenarios.

[0044] The standardized charging pile fault features are input into a trained support vector machine (SVM) model to obtain fault modes. The trained SVM model is then used to classify the standardized features. Its nonlinear mapping capability can effectively handle the complex correlations between fault features (such as the coupling relationship between temperature gradient and current fluctuation), enabling accurate identification of specific fault modes such as poor charging gun contact, contactor adhesion, and communication protocol incompatibility. Compared with traditional rule matching, it is more robust and has greater generalization ability, providing accurate fault type basis for the subsequent generation of redundancy control strategies, thereby improving the targeting and efficiency of charging pile fault handling.

[0045] During the training phase, the support vector machine model uses the RBF kernel function, and the optimal parameters are determined through grid search. The range of C (penalty coefficient) is [0.1, 1, 10, 100], and the range of γ (kernel function width) is [0.01, 0.1, 1, 10].

[0046] The redundancy control strategy generation subsystem is used to determine the redundancy control strategy based on the processed multi-source operating status data of the charging pile after fault mode identification.

[0047] The processed multi-source operating status data of charging piles and the standardized fault features of charging piles are concatenated to obtain the charging pile state vector. A dataset of historical charging pile state vectors is obtained from the database. Based on the cosine similarity function, the historical charging pile state vector most similar to the current charging pile state vector is determined from the historical charging pile state vector dataset. A set of redundant control strategies corresponding to the most similar historical charging pile state vectors is obtained from the database; each redundant control strategy in the set corresponds to a reward value. The redundant control strategy with the largest reward value is selected for redundant control, and the charging pile state vector is updated. Fault trend analysis is performed based on the updated charging pile state vector, and the reward value is updated simultaneously. If a fault trend exists, a new redundant control strategy is selected until no fault trend exists.

[0048] The processed operational data and fault characteristics are concatenated into a state vector. By matching historical optimal strategies using cosine similarity, efficient reuse of past fault handling experience is achieved. Each redundant control strategy corresponds to a reward value, which is dynamically updated based on the evaluation value deviation, forming a closed-loop optimization mechanism of "strategy execution - effect evaluation - reward update". This enables the system to automatically select high-value strategies and gradually eliminate inefficient strategies.

[0049] The reward value update process is as follows: Obtain the updated multi-source operating status data of the charging pile from the updated charging pile status vector; process the updated multi-source operating status data of the charging pile based on the fault trend preliminary diagnosis subsystem to obtain the updated charging pile operating anomaly assessment index; calculate the difference between the updated charging pile operating anomaly assessment index and the original charging pile operating anomaly assessment index to obtain the assessment value deviation; retrieve the corresponding reward bonus value from the database through a lookup table; add the original reward value to the reward bonus value to obtain the updated reward value.

[0050] Updating the charging pile state vector and iteratively evaluating fault trends ensures the effectiveness of the strategy in complex scenarios. If a fault trend persists after the initial strategy execution, the system will re-match the strategy until the fault is eliminated, avoiding processing failures caused by static strategies. Reward value updates are based on the quantified deviation of the anomaly evaluation index, avoiding the limitations of subjective rule design. Linking the evaluation deviation with the reward bonus through database queries makes the strategy's effectiveness quantifiable and traceable.

[0051] An electronic device includes: a processor; and a memory storing computer program instructions, which, when executed by the processor, cause the processor to perform the highly reliable redundant control system for charging piles as described above.

[0052] A computer-readable storage medium for storing a program that, when executed by a processor, implements the highly reliable redundant control system for charging piles as described above.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0058] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A highly reliable redundant control system for charging piles, characterized in that, It includes a charging pile multi-source data acquisition subsystem, a preliminary fault trend diagnosis subsystem, a fault diagnosis subsystem, and a redundancy control strategy generation subsystem, among which: The charging pile multi-source data acquisition subsystem is used to acquire multi-source operating status data of charging piles and preprocess it to obtain processed multi-source operating status data of charging piles. The preliminary fault trend diagnosis subsystem is used to perform real-time diagnosis on the processed multi-source operating status data of the charging piles acquired by the multi-source data acquisition subsystem, to determine whether there is a trend of fault occurrence in the charging piles and to obtain preliminary diagnostic results. The fault diagnosis subsystem is used to identify fault modes based on the fault diagnosis model when the preliminary diagnosis results indicate that the charging pile has a tendency to malfunction. The redundancy control strategy generation subsystem is used to determine the redundancy control strategy based on the processed multi-source operating status data of the charging pile after fault mode identification. The process of obtaining preliminary diagnostic results regarding whether a charging station shows a tendency to malfunction is as follows: Determine the multi-source operational status benchmark data of charging piles, including the operational benchmark characteristics of charging guns. Contactor operating reference characteristics and communication module operating baseline characteristics ; A similarity analysis was performed between the processed multi-source operation status data of charging piles and the benchmark data of multi-source operation status of charging piles to determine the anomaly assessment index of charging pile operation. ; If the charging pile operation abnormality assessment index If the value is greater than the threshold for assessing abnormal operation of the charging pile obtained from the database, the preliminary diagnosis result is that the charging pile does not show a trend of failure. If the charging pile operation abnormality assessment index If the value is not greater than the threshold for assessing abnormal operation of charging piles obtained from the database, the preliminary diagnosis result is that the charging pile has a tendency to malfunction. The process of determining the baseline data for the multi-source operation status of charging piles is as follows: Based on the type of charging pile, retrieve the corresponding multi-source operation status parameter data of the charging pile from the database; Obtain historical operating environment data of charging piles, including total usage time. The range of temperature fluctuations in the surrounding environment The amplitude of humidity fluctuation in the surrounding environment and the average number of times per month overload rate ; Retrieve the matching data - correction coefficient mapping dataset stored in the database. The matching data includes matching values ​​using the total duration. Matching value of ambient temperature fluctuation range Matching value of ambient humidity fluctuation range Matching value with the average number of times the work overload rate is per month The correction factors include the charging gun operating reference characteristic correction factor, the contactor operating reference characteristic correction factor, and the communication module operating reference characteristic correction factor. The correction coefficients are determined based on the historical working environment data of the charging pile and the matching data-correction coefficient mapping dataset. The multi-source operation status parameter data of the charging pile is corrected based on the correction coefficients to obtain the multi-source operation status benchmark data of the charging pile. The correction coefficients are determined based on historical operating environment data of charging piles and a matching data-correction coefficient mapping dataset. The process is as follows: The historical operating environment data of the charging piles is compared one by one with each matching data in the matching data-correction coefficient mapping dataset to obtain the comparison value. : ； Determine the matching data corresponding to the smallest alignment value, and obtain the corresponding correction coefficient; The process of fault mode identification based on the fault diagnosis model is as follows: Obtain charging pile fault characteristics, including charging gun fault characteristics, contactor fault characteristics, and communication module fault characteristics, among which: Fault characteristics of charging guns include temperature gradient, current fluctuation variance, and insertion / extraction force variation rate; Contactor fault characteristics include increased contact resistance, operating delay time, and accumulated arc energy. Fault characteristics of the communication module include signal strength attenuation rate, packet loss rate fluctuation coefficient, and communication success rate with mainstream vehicle models; The fault characteristics of charging piles are standardized to obtain standardized charging pile fault characteristics. The standardized charging pile fault features are input into the trained support vector machine model to obtain the fault modes.

2. The highly reliable redundant control system for charging piles according to claim 1, characterized in that, The processed multi-source operating status data of the charging pile includes the operating characteristics of the charging gun. Contactor operating characteristics and communication module operating characteristics ,in: Charging gun operating characteristics This includes the nozzle temperature, cable temperature, interface temperature, and current fluctuation amplitude; Contactor operating characteristics This includes contact resistance and vibration signal amplitude; Communication module operating characteristics This includes signal strength, packet loss rate, and response latency.

3. The highly reliable redundant control system for charging piles according to claim 2, characterized in that, The charging pile multi-source data acquisition subsystem includes a charging gun monitoring module, a contactor monitoring module, a communication module, and a data preprocessing module, among which: The charging gun monitoring module is used to monitor the operating status of the charging gun in real time and obtain the initial operating status data of the charging gun. The contactor monitoring module is used to monitor the contactor's operating status in real time and obtain the initial operating status data of the contactor; The communication module monitoring module is used to monitor the operating status of the communication module in real time and obtain the initial operating status data of the communication module; The data preprocessing module receives the initial operating status data of the charging gun, the initial operating status data of the contactor, and the initial operating status data of the communication module, and performs standardization processing to obtain the processed multi-source operating status data of the charging pile.

4. The highly reliable redundant control system for charging piles according to claim 3, characterized in that, Charging pile operation anomaly assessment index The calculation formula is: ； in, , and All are weighting factors. This is the cosine similarity function.

5. A highly reliable redundant control system for charging piles according to claim 4, characterized in that, The process of determining the redundancy control strategy based on the processed multi-source operating status data of the charging piles is as follows: The processed multi-source operating status data of the charging pile and the standardized fault characteristics of the charging pile are spliced ​​together to obtain the charging pile status vector. Obtain the historical charging pile status vector dataset from the database; Based on the cosine similarity function, the historical charging pile state vector that is most similar to the charging pile state vector is determined from the historical charging pile state vector dataset. Retrieve the set of redundant control strategies corresponding to the most similar historical charging pile state vector from the database. Each redundant control strategy in the set of redundant control strategies corresponds to a reward value. Select the redundancy control strategy with the highest reward value for redundancy control and update the charging pile state vector; Fault trend analysis is performed based on the updated charging pile state vector, and the reward value is updated simultaneously. If a fault trend exists, a redundancy control strategy is reselected until no fault trend exists.

6. A highly reliable redundant control system for charging piles according to claim 5, characterized in that, The update process for reward values ​​is as follows: Obtain the updated and processed multi-source operating status data of the charging pile from the updated charging pile status vector; The fault trend preliminary diagnosis subsystem processes the updated multi-source operation status data of the charging piles to obtain the updated charging pile operation anomaly assessment index. The difference between the updated charging pile operation anomaly assessment index and the previous charging pile operation anomaly assessment index is calculated to obtain the assessment value deviation. The corresponding bonus value for the evaluation deviation is retrieved from the database by querying the table. The updated reward value is obtained by adding the original reward value to the bonus value.

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