An operating status detection and analysis method suitable for high-power devices of charging piles

Abstract

The present invention proposes an operation status detection and analysis method applicable to a high-power device of a charging pile, which belongs to the technical field of charging piles, and comprises the following steps: step S1: acquiring detection data of a charging pile in different scenarios based on a charging pile detection device, and establishing a data model library; step S2: classifying the detection data according to the power and type of the charging gun, preprocessing the classified detection data, and processing missing values ​​and noise data; step S3: embedding the preprocessed detection data into a data model, processing the preprocessed detection data using the least squares fitting method in a high-order polynomial fitting algorithm, and finally calculating an optimal fitting curve; step S4: deploying the optimal fitting curve, evaluating the fitting result, acquiring the current detection data of the charging pile, comparing and analyzing the fitting result of the optimal fitting curve with the current detection data, and judging the existing faults of the charging pile and the faults that may occur.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging piles, and in particular relates to an operating status detection and analysis method applicable to high-power devices of a charging pile. Background Art

[0002] With the rapid development of new energy technology, the number of new energy vehicles is growing, and the utilization rate and failure rate of charging piles are also increasing. The damage rate of high-power devices in charging piles, such as charging guns, is on the rise with the increase of service life. On the one hand, high-power devices such as charging guns are prone to abnormal heating, insulation degradation, failure of safety protection measures and other safety hazards after long-term use. After the charging gun has safety hazards, it is more likely to have safety problems in the future use. At the same time, the existence of the above safety hazards also creates great service pressure on the operation and maintenance personnel of charging facilities. Therefore, it is of great significance to monitor and predict the abnormal status of high-power key accessories such as charging guns in order to discover hidden dangers in advance and deal with them as soon as possible.

[0003] At present, there are some testing instruments and other types of testing equipment on the market that can detect high-power devices such as charging guns, including the collection and display of basic data information such as the charging gun's electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation resistance, and conduction polarity. When abnormalities are detected in the data information, maintenance personnel can be reminded to handle it.

[0004] However, the detection data obtained by the above-mentioned detection instruments or detection equipment are relatively discrete and not systematic. In addition, for high-power devices such as charging guns that have safety hazards, before the detection data reaches the standard value of abnormal data, traditional testing instruments or detection equipment are difficult to detect the safety hazards in the devices. Their fault detection efficiency is relatively low and their accuracy is poor. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, a method for detecting and analyzing the operating status of high-power devices in a charging pile is provided. By performing curve fitting and comparison on the acquired detection data, the safety performance of high-power devices such as charging guns after running for a period of time is automatically analyzed, and the operating risks of high-power devices in the charging pile are predicted in advance.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] This technical solution proposes an operating status detection and analysis method suitable for high-power devices in charging piles.

[0008] The following steps are involved:

[0009] Step S1: acquiring detection data of charging piles in different scenarios based on charging pile detection equipment, and establishing a data model library;

[0010] Step S2: classify the detection data according to the power and type of the charging gun, pre-process the classified detection data, and process missing values ​​and noise data;

[0011] Step S3: embed the preprocessed detection data into the data model, process the preprocessed detection data using the least squares fitting method in the high-order polynomial fitting algorithm, and finally calculate the optimal fitting curve;

[0012] Step S4: deploy the best fitting curve, obtain the current detection data of the charging pile, compare and analyze the fitting result of the best fitting curve with the current detection data, and judge the fault of the charging pile and the possible fault.

[0013] Furthermore, in S1, the detection data includes S2 switch, electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation detection, and withstand voltage detection data.

[0014] Further, in S2, the preprocessing includes the following steps:

[0015] The clustering algorithm is used to determine the endpoints of the missing intervals in the detection data. The formula is as follows:

[0016] (1);

[0017] In the formula, Indicates The center of the cluster, Indicates cluster center and center The farthest point, i.e. the potential missing interval endpoint, represents the distance weight of a point relative to the center of cluster t, Indicates the distance cluster of this point The distance weight of the center is defined as the smaller the weight is, the farther away from the center of the cluster, Represents the difference between a point in the tth cluster and the abscissa of the center of the cluster;

[0018] Use the linear interpolation algorithm to estimate the missing values ​​of the missing intervals in the detection data. The formula is as follows:

[0019] (2);

[0020] In the formula, Indicates The vertical coordinates of missing values, Indicates The horizontal axis of missing values, and Respectively represent The coordinate values ​​of the two endpoints of the missing interval;

[0021] Low-pass filtering technology is used to filter out noise data in the detection data.

[0022] Furthermore, in S3, the least squares fitting includes the following steps:

[0023] Draw a scatter plot of the test data, and calculate and determine the highest order of the optimal fit equation through the scatter plot And the coefficients of the corresponding polynomials of various orders ;

[0024] According to the highest order And the coefficients of the corresponding polynomials of various orders Construct the optimal fitting equation, the formula is as follows:

[0025] (3);

[0026] In the formula, represents the result calculated according to the best fitting equation, Indicates the observed value in the test data, that is, the value of one of the S2 switch, electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation detection, and withstand voltage detection data. Represents the coefficients of each order polynomial, that is Different powers of The degree of influence, Representing the polynomial The highest order of

[0027] According to the best fitting equation, the best fitting curve is calculated.

[0028] Further, determining the order of the fitting polynomial and the coefficients of the polynomial includes the following steps:

[0029] Step A1: Determine the total number of data points by examining the scatter plot of the data , determine the highest order Provisional value of ;

[0030] Step A2: Based on the provisional value of the highest order Determine the tentative fitting equation, the formula is as follows:

[0031] (4);

[0032] In the formula, represents the coefficients of the corresponding polynomial in the tentative fitting equation;

[0033] Step A3: The coordinates of the data points Substitute them into the tentative fitting equation one by one and establish The matrix of the corresponding polynomial is calculated by the matrix The value of is as follows:

[0034] (5);

[0035] Assumptions Then formula (5) becomes:

[0036] (6);

[0037] but (7);

[0038] In the formula, for The transposed matrix of For the matrix The reversible matrix of

[0039] Step A4: Solve Substitute into the tentative fitting equation and draw the corresponding fitting curve;

[0040] Step A5: Evaluate the fitting effect and determine whether the fitting effect meets the optimal fitting condition. If it does, exit the loop and the current provisional fitting equation is the optimal fitting equation. Return the highest order in the optimal fitting equation. And the coefficients of the corresponding polynomial If it is not satisfied, the highest order is corrected to , return to step A2 until the optimal fitting condition is met.

[0041] Furthermore, in S4, the evaluation of the fitting result includes the following steps:

[0042] According to the fitting equation, the correlation coefficient R is calculated as follows:

[0043] (8);

[0044] In the formula, Indicates The observed value of the test data of the data point, that is, the actual measured or observed data value, Indicates The fitted value is calculated by fitting equation for each data point. Represents the mean of all observations in the test data, that is, all The average of represents the average of all fitted values, which is the The average of represents the total number of samples, that is and The number of data points in is the number of relevant data points involved in the calculation;

[0045] Calculate the statistic F using the following formula:

[0046] (9);

[0047] (10);

[0048] (11);

[0049] In the formula, Indicates The observed value of the independent variable for each data point, Indicates The fitted value is calculated by fitting equation for each data point. represents the mean of the fitted values, represents the total number of observations, Indicates the number of model parameters;

[0050] Calculate the residual standard deviation , the formula is as follows:

[0051] (12);

[0052] In the formula, Indicates The observed value of the independent variable for each data point, Indicates The fitted value is calculated by fitting equation for each data point. Represents the total number of data points involved in the analysis, Indicates the number of model parameters.

[0053] Furthermore, the criteria for determining the optimal fitting condition are: The absolute value of ≥ 0.99, ≤0.001.

[0054] Furthermore, in S4, determining the fault of the charging pile and the possible faults includes the following steps:

[0055] According to the best fitting curve, the best fitting result is calculated to obtain the reference standard of the charging pile detection data;

[0056] Compare the actual value of the current test data obtained by the charging pile detection equipment with the reference standard, determine the component category with large data difference, draw analysis conclusions, and locate the reliability and fault location of the charging gun components;

[0057] Extract specific optimization strategies from the database based on the analysis conclusions.

[0058] Compared with the prior art, the present invention has the following advantages:

[0059] 1. This application analyzes the acquired status data of the electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation resistance, and conduction polarity of the charging gun, determines the fitting curve by high-order polynomial least squares method, and performs fitting comparison with the acquired status data, so as to automatically analyze the safety performance of the charging gun after running for a period of time, make advance predictions on the operating risks of high-power components of the charging pile, and prevent impending failures of the charging gun in advance, and propose targeted optimization strategies, which are beneficial for the operation and maintenance personnel of the charging facilities to carry out targeted management of charging guns that may fail or frequently fail, and ensure charging safety.

[0060] 2. This application analyzes and processes the status data and determines the fitting curve of the operating status to establish a scientific, objective and accurate charging gun safety life and reliability evaluation system. This system can comprehensively evaluate the performance and safety of the charging gun after long-term operation, forming a complete set of charging gun fault prediction and rapid diagnosis technology, providing an objective and accurate evaluation basis for the electric vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0062] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0063] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0064] like Figure 1 As shown, this embodiment proposes an operating status detection and analysis method suitable for high-power devices of a charging pile, wherein the status data corresponding to the operating status includes S2 switch, electromagnetic lock, temperature resistor, control signal resistor, power conductor resistor, insulation detection, and withstand voltage detection data.

[0065] The present invention provides a charging gun comprehensive function detection device, which is used to collect the above status data. The charging gun comprehensive function detection device is mainly based on PLC data communication and detection logic control. In the whole device, according to the requirements of the detection project, high-precision withstand voltage, insulation detection, LCR bridge tester, electronic lock drive control, stepper motor control and drive device are integrated internally. It can automatically complete the charging gun withstand voltage, insulation, temperature sensing, electronic lock, S2 switch, R2, R3 resistance detection functions, and can upload data to the background.

[0066] The background analyzes these status data, uses high-order polynomial least squares method to determine the fitting curve, and fits and compares it with the acquired status data. It can automatically analyze the safety performance of the charging gun after running for a period of time, and make advance predictions on the operating risks of high-power components of the charging pile. It can prevent impending failure problems of the charging gun in advance and propose targeted optimization strategies. The specific optimization strategies are stored in the background database, so that specific optimization strategies can be quickly given according to the existing failure problems, which is beneficial for the operation and maintenance personnel of the charging facilities to carry out targeted management of charging guns that may fail or frequently fail, to ensure charging safety.

[0067] The operating status detection and analysis method applicable to the high-power device of the charging pile provided by the present invention comprises the following steps:

[0068] Step S1: Acquire detection data of charging piles in different scenarios based on charging pile detection equipment and establish a data model library;

[0069] Step S2: classify the detection data according to the power and type of the charging gun, pre-process the classified detection data, and process missing values ​​and noise data;

[0070] Step S3: embed the preprocessed detection data into the data model, process the preprocessed detection data using the least squares fitting method in the high-order polynomial fitting algorithm, and finally calculate the optimal fitting curve;

[0071] Step S4: deploy the best fitting curve, obtain the current detection data of the charging pile, compare and analyze the fitting result of the best fitting curve with the current detection data, and judge the fault of the charging pile and the possible fault.

[0072] In S1, the detection data includes S2 switch, electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation detection, and withstand voltage detection data.

[0073] In S2, the preprocessing includes the following steps:

[0074] The clustering algorithm is used to determine the endpoints of the missing intervals in the detection data. The formula is as follows:

[0075] (1);

[0076] In the formula, Indicates The center of the cluster, Indicates cluster center and center The farthest point, i.e. the potential missing interval endpoint, represents the distance weight of a point relative to the center of cluster t, Indicates the distance cluster of this point The distance weight of the center is defined as the smaller the weight is, the farther away from the center of the cluster, Represents the difference between a point in the tth cluster and the abscissa of the center of the cluster;

[0077] In addition, in the present embodiment, argmax() is a function, which belongs to the prior art and is a function for finding a parameter (set) of a function, that is, a function for finding the largest independent variable. In practical applications, the argmax function can be applied to various optimization problems. For example, in machine learning, we often need to find a parameter combination that optimizes model performance. In addition, the argmax function is also commonly used in data analysis to help us identify patterns and trends in data.

[0078] Use the linear interpolation algorithm to estimate the missing values ​​of the missing intervals in the detection data. The formula is as follows:

[0079] (2);

[0080] In the formula, Indicates The vertical coordinates of missing values, Indicates The horizontal axis with missing values, and Respectively represent The coordinate values ​​of the two endpoints of the missing interval;

[0081] Low-pass filtering technology is used to filter out noise data in the detection data.

[0082] Low-pass filter is a filtering method, which belongs to the existing technology. The low-pass filter rule is that low-frequency signals can pass normally, while high-frequency signals exceeding the set threshold are blocked and weakened. However, the degree of blocking and weakening will change according to different frequencies and different filtering procedures (purposes).

[0083] In S3, the least squares fitting includes the following steps:

[0084] Draw a scatter plot of the test data, and calculate and determine the highest order of the optimal fit equation through the scatter plot And the coefficients of the corresponding polynomials of various orders ;

[0085] According to the highest order And the coefficients of the corresponding polynomials of various orders Construct the optimal fitting equation, the formula is as follows:

[0086] (3);

[0087] In the formula, represents the result calculated according to the best fitting equation, Indicates the observed value in the test data, that is, the value of one of the S2 switch, electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation detection, and withstand voltage detection data. Represents the coefficients of each order polynomial, that is Different powers of The degree of influence, Representing the polynomial The highest order of

[0088] According to the best fitting equation, the best fitting curve is calculated.

[0089] In this embodiment, a scatter plot of the detection data is drawn to determine the trend, distribution range and local fluctuation of the detection data in the scatter plot.

[0090] In addition, determining the order of the fitting polynomial and the coefficients of the polynomial includes the following steps:

[0091] Step A1: Determine the total number of data points by examining the scatter plot of the data , determine the highest order Provisional value of It should be noted that the more data points there are in the scatter plot, the higher the selectable order. In this embodiment, The initial value of is set to 1;

[0092] Step A2: Based on the provisional value of the highest order Determine the tentative fitting equation, the formula is as follows:

[0093] (4);

[0094] In the formula, represents the coefficients of the corresponding polynomial in the tentative fitting equation;

[0095] Step A3: The coordinates of the data points Substitute them into the tentative fitting equation one by one and establish The matrix of the corresponding polynomial is calculated by the matrix The value of is as follows:

[0096] (5);

[0097] Assumptions Then formula (5) becomes:

[0098] (6);

[0099] but (7);

[0100] In the formula, for The transposed matrix of For the matrix The reversible matrix of

[0101] Step A4: Solve the Substitute into the tentative fitting equation and draw the corresponding fitting curve;

[0102] Step A5: Evaluate the fitting effect and determine whether the fitting effect meets the optimal fitting condition. If it does, exit the loop and the current provisional fitting equation is the optimal fitting equation. Return the highest order in the optimal fitting equation. And the coefficients of the corresponding polynomial If it is not satisfied, the highest order is corrected to , return to step A2 until the optimal fitting condition is met.

[0103] The fitting effect evaluation includes the following steps:

[0104] According to the fitting equation, the correlation coefficient R is calculated as follows:

[0105] (8);

[0106] In the formula, Indicates The observed value of the test data of the data point, that is, the actual measured or observed data value, Indicates The fitted value is calculated by fitting equation for each data point. Represents the mean of all observations in the test data, that is, all The average of represents the average of all fitted values, which is the The average of represents the total number of samples, that is and The number of data points in is the number of relevant data points involved in the calculation;

[0107] Calculate the statistic F using the following formula:

[0108] (9);

[0109] (10);

[0110] (11);

[0111] In the formula, Indicates The observed value of the independent variable for each data point, Indicates The fitted value is calculated by fitting equation for each data point. represents the mean of the fitted values, represents the total number of observations, Indicates the number of model parameters;

[0112] Calculate the residual standard deviation , the formula is as follows:

[0113] (12);

[0114] In the formula, Indicates The observed value of the independent variable for each data point, Indicates The fitted value is calculated by fitting equation for each data point. Represents the total number of data points involved in the analysis, Indicates the number of model parameters;

[0115] It should be noted that the above The absolute value of the coefficient is infinitely close to 1, which means , The stronger the linear relationship, the The larger the value, The closer it is to 0, the better the fitting result. In this embodiment, the criterion for judging the optimal fitting condition is: The absolute value of ≥ 0.99, ≤0.001.

[0116] In S4, judging the fault of the charging pile and the possible faults includes the following steps:

[0117] According to the best fitting curve, the best fitting result is calculated to obtain the reference standard of the charging pile detection data;

[0118] Compare the actual value of the current test data obtained by the charging pile detection equipment with the reference standard, determine the component category with large data difference, draw analysis conclusions, and locate the reliability and fault location of the charging gun components;

[0119] Extract specific optimization strategies from the database based on the analysis conclusions.

[0120] The present invention also provides an operating status detection and analysis system applicable to high-power devices of a charging pile, wherein the system implements an operating status detection and analysis method applicable to high-power devices of a charging pile, and the system includes the aforementioned comprehensive function detection device of a charging gun and a background system.

[0121] Through the comprehensive function detection equipment of the charging gun and combined with the background system, the status data of the electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation resistance, and conduction polarity of the charging gun are collected and analyzed. The fitting curve is determined by the high-order polynomial least squares method, and the fitting curve is compared with the acquired status data. This can automatically analyze the safety performance of the charging gun after running for a period of time, make advance predictions on the operating risks of the high-power components of the charging pile, and prevent the impending failure of the charging gun in advance. Targeted optimization strategies are proposed, which are beneficial for the operation and maintenance personnel of the charging facilities to carry out targeted management of charging guns that may fail or frequently fail, to ensure charging safety.

[0122] In addition, by analyzing and processing the status data and determining the fitting curve of the operating status, a scientific, objective and accurate charging gun safety life and reliability evaluation system is established. This system can comprehensively evaluate the performance and safety of the charging gun after long-term operation, forming a complete set of charging gun fault prediction and rapid diagnosis technology, providing an objective and accurate evaluation basis for the electric vehicle industry.

[0123] By using big data technology and combining the operation data of charging gun comprehensive function detection equipment, a scientific, objective and accurate charging gun safety life and reliability evaluation system has been established for an operation status detection and analysis system suitable for high-power devices in charging piles. This system will be able to comprehensively evaluate the performance and safety of charging guns after long-term operation, forming a complete set of charging gun fault prediction and rapid diagnosis technology.

[0124] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for detecting and analyzing the operating status of a charging pile high-power device, characterized in that: The following steps are involved: Step S1: acquiring detection data of charging piles in different scenarios based on charging pile detection equipment, and establishing a data model library; Step S2: classify the detection data according to the power and type of the charging gun, pre-process the classified detection data, and process missing values ​​and noise data; Step S3: embed the preprocessed detection data into the data model, process the preprocessed detection data using the least squares fitting method in the high-order polynomial fitting algorithm, and finally calculate the optimal fitting curve; Step S4: deploy the best fitting curve, obtain the current detection data of the charging pile, compare and analyze the fitting result of the best fitting curve with the current detection data, and judge the fault of the charging pile and the possible fault; In the S1, the detection data includes the S2 switch, electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation detection, and withstand voltage detection data; In S2, the preprocessing comprises the following steps: The clustering algorithm is used to determine the endpoints of the missing intervals in the detection data. The formula is as follows: （1）； In the formula, Indicates The center of the cluster, Indicates cluster center and center The farthest point, i.e. the potential missing interval endpoint, represents the distance weight of a point relative to the center of cluster t, Indicates the distance cluster of this point The distance weight of the center is defined as the smaller the weight is, the farther away from the center of the cluster, Represents the difference between a point in the tth cluster and the abscissa of the center of the cluster; Use the linear interpolation algorithm to estimate the missing values ​​of the missing intervals in the detection data. The formula is as follows: （2）； In the formula, Indicates The vertical coordinates of missing values, Indicates The horizontal axis with missing values, and Respectively represent The coordinate values ​​of the two endpoints of the missing interval; Low-pass filtering technology is used to filter out noise data in the detection data.

2. The operating status detection and analysis method applicable to a charging pile high-power device according to claim 1 is characterized in that: In S3, the least squares fitting includes the following steps: Draw a scatter plot of the test data, and calculate and determine the highest order of the optimal fit equation through the scatter plot And the coefficients of the corresponding polynomials of various orders ; According to the highest order And the coefficients of the corresponding polynomials of various orders Construct the optimal fitting equation, the formula is as follows: （3）； In the formula, represents the result calculated according to the best fitting equation, Indicates the observed value in the test data, that is, the value of one of the S2 switch, electromagnetic lock, temperature resistance, control signal resistance, power conductor resistance, insulation detection, and withstand voltage detection data. Represents the coefficients of each order polynomial, that is Different powers of The degree of influence, Representing the polynomial The highest order of According to the best fitting equation, the best fitting curve is calculated.

3. The operating status detection and analysis method applicable to a charging pile high-power device according to claim 2 is characterized in that: Determining the highest order of the best fitting equation and the corresponding polynomial coefficients of each order includes the following steps: Step A1: Determine the total number of data points by examining the scatter plot of the data m , determine the highest order Provisional value of ; Step A2: Based on the provisional value of the highest order Determine the tentative fitting equation, the formula is as follows: （4）； In the formula, represents the coefficients of the corresponding polynomial in the tentative fitting equation; Step A3: m The coordinates of the data points Substitute them into the tentative fitting equation one by one and establish The matrix of the corresponding polynomial is calculated by the matrix The value of is as follows: （5）； Assumptions Then formula (5) becomes: （6）； but (7); In the formula, for The transposed matrix of For the matrix The reversible matrix of Step A4: Solve the Substitute into the tentative fitting equation and draw the corresponding fitting curve; Step A5: Evaluate the fitting effect and determine whether the fitting effect meets the optimal fitting condition. If it does, exit the loop and the current provisional fitting equation is the optimal fitting equation. Return the highest order in the optimal fitting equation. And the coefficients of the corresponding polynomial If it is not satisfied, the highest order is corrected to , return to step A2 until the optimal fitting condition is met.

4. The operating status detection and analysis method applicable to a charging pile high-power device according to claim 3 is characterized in that: The fitting effect evaluation includes the following steps: According to the fitting equation, the correlation coefficient R is calculated as follows: （8）； In the formula, X q Indicates q The observed value of the test data of the data point, that is, the actual measured or observed data value, Y q Indicates q The fitted value is calculated by fitting equation for each data point. Represents the mean of all observations in the test data, that is, all X q The average of represents the average of all fitted values, which is the Y q The average of represents the total number of samples, that is and The number of data points in is the number of relevant data points involved in the calculation; Calculate the statistic F using the following formula: （9）； （10）； （11）； In the formula, X q Indicates q The observed value of the independent variable for each data point, Y q Indicates q The fitted value is calculated by fitting equation for each data point. represents the mean of the fitted values, represents the total number of observations, Indicates the number of model parameters; Calculate the residual standard deviation , the formula is as follows: （12）； In the formula, X q Indicates q The observed value of the independent variable for each data point, Y q Indicates q The fitted value is calculated by fitting equation for each data point. Represents the total number of data points involved in the analysis, Indicates the number of model parameters.

5. The operating status detection and analysis method applicable to a high-power device of a charging pile according to claim 4 is characterized in that: The criteria for determining the optimal fitting condition are: The absolute value of ≥ 0.99, ≤0.

001.

6. The operating status detection and analysis method applicable to a charging pile high-power device according to claim 4 is characterized in that: In S4, judging the fault of the charging pile and the possible faults includes the following steps: According to the best fitting curve, the best fitting result is calculated to obtain the reference standard of the charging pile detection data; Compare the actual value of the current test data obtained by the charging pile detection equipment with the reference standard, determine the component category with large data difference, draw analysis conclusions, and locate the reliability and fault location of the charging gun components; Extract specific optimization strategies from the database based on the analysis conclusions.

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