Charging gun abnormity identification and determination method based on data driving

Abstract

The invention discloses a charging gun abnormity identification and judgment method based on data driving, and relates to the technical field of charging piles. The charging gun abnormity identification and determination method based on data driving comprises the following steps: obtaining a query order; performing user statistics; screening data features; and making a data perspective table. The charging order is obtained by querying the date, user statistics is performed on the charging order, then the obtained user data table is analyzed to obtain the abnormal number and the abnormal rate, data feature screening is performed in combination with the user data table to generate the fault information, and the fault file table of the charging gun to be detected is obtained in combination with the charging gun file information. And finally, a data perspective table is made according to the fault file table, and fault gun information is summarized, so that the effect of improving the abnormality detection accuracy of the charging gun is achieved, and the problem of inaccurate abnormality detection of the charging gun in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and in particular to a method for identifying and determining abnormalities of charging guns based on data-driven. Background Art

[0002] With the popularization and promotion of new energy vehicles, the construction and operation of charging infrastructure has become particularly important. As a key component of charging infrastructure, the stability and reliability of charging guns directly affect the user's charging experience and the promotion effect of new energy vehicles. During the long-term use of charging guns, various abnormalities may occur, such as poor contact, high temperature, excessive current, etc. These abnormalities will not only affect the charging efficiency, but also may bring safety hazards. Therefore, efficient and accurate abnormal detection and maintenance of charging guns has become the key to improving the service level of charging facilities.

[0003] Traditional methods of detecting abnormalities in charging guns mainly rely on the built-in sensors and abnormality diagnosis systems of the equipment. These sensors include temperature sensors, current sensors, and voltage sensors. By real-time monitoring of changes in various parameters during the charging process, once abnormal data is detected, the abnormality diagnosis system will determine whether there is an abnormality based on the preset threshold and algorithm.

[0004] For example, the charging gun self-test device and charging pile announced in the invention patent with announcement number: CN116298594B include: a self-test control circuit for connecting the gun holder wiring harness of the charging gun holder, the self-test control circuit is used to output a detection signal to the charging gun through the gun holder wiring harness when the charging gun currently meets the detection conditions; the detection conditions include the charging gun being inserted into the charging gun holder; the detection signal includes a timing signal for testing the working status of the charging gun line; a detection circuit for connecting the charging gun wiring harness of the charging gun, the detection circuit is used to obtain the electrical signal generated by the charging gun line when the detection signal flows through the charging gun through the charging gun wiring harness; and if it is confirmed that the charging gun is abnormal according to the electrical signal, the abnormal status information is output; the abnormal status information is used to instruct the charging pile to output prompt information to the station control platform.

[0005] For example, the invention patent with announcement number: CN117890825B announces a leakage current test method, device, equipment and storage medium for a charging gun, including: performing a current test on a target charging gun to obtain charging condition data; inputting the charging condition data into a principal component feature extraction model to extract condition features to obtain a condition-sensitive feature set; discretizing the condition-sensitive feature set to obtain a discrete working condition feature; feature encoding the discretized working condition feature to obtain a coded feature vector; inputting the coded feature vector of each test voltage into a current anomaly detection model to perform current anomaly detection to obtain current anomaly detection data for each test voltage; inputting the current anomaly detection data into a preset Bayesian network to perform charging gun current leakage interface analysis to obtain a leakage current test result.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, the charging gun abnormality detection method mainly relies on the built-in sensors and abnormality diagnosis system of the equipment. However, the sensors cannot identify problems such as damage to the mechanical structure of the charging gun lock and damage to the gun wire. They may also fail due to aging, resulting in the inability to accurately upload abnormal information. The abnormality diagnosis system may also falsely report or miss abnormalities due to software bugs or algorithm defects, resulting in inaccurate abnormality detection of the charging gun. Summary of the invention

[0008] The embodiment of the present application solves the problem of inaccurate detection of abnormalities in charging guns in the prior art by providing a method for identifying and determining abnormalities in charging guns based on data-driven, thereby improving the accuracy of detection of abnormalities in charging guns.

[0009] The embodiment of the present application provides a method for identifying and determining charging gun abnormalities based on data-driven, comprising the following steps: S1, filtering charging orders from a database of a station to be tested according to a query date to obtain a query order, wherein the query date indicates a specified query day for extracting charging orders from the database; S2, performing user statistics on the query order to obtain a user data table, and analyzing the user data table to obtain the number of abnormalities and the abnormality rate, wherein the user data table is used to store various types of user information obtained from the query order statistics; S3, performing data feature screening according to the number of abnormalities, the abnormality rate and the user data table to generate fault information, and obtaining a fault file table of the charging gun to be tested according to the charging gun file information and the fault information, wherein the fault file table indicates a file recording charging gun fault conditions generated in combination with the charging gun file data; S4, creating a pivot table based on the fault file table and summarizing the faulty gun information, and pushing the faulty gun information to relevant personnel, wherein the pivot table is used to analyze the data in the fault file table to obtain the faulty gun information.

[0010] Furthermore, the specific process of filtering charging orders from the database of the station to be tested according to the query date to obtain the query order is as follows: all charging orders within the query days are extracted from the database of the station to be tested by means of a written database query statement, and the extracted charging orders are sorted according to the order date to obtain the query order, and the database query statement is generated according to the start date and the query days; the query date includes the start date and the query days; the charging order includes the order date, charging power, user ID, charging gun ID and charging pile ID.

[0011] Furthermore, the user statistics of the queried orders are performed to obtain a user data table, which also includes marking the queried orders to obtain order status. The specific process is: determine whether the order power is greater than a preset power threshold. If the order power is greater than the preset power threshold, the corresponding order status is marked as a successful order, otherwise the corresponding order status is marked as a failed order. The successful order indicates a charging order that is successfully charged, and the failed order indicates a charging order that fails to charge.

[0012] Furthermore, the specific steps of performing user statistics on the query orders to obtain the user data table are as follows: A1, grouping the query orders according to the charging pile ID, charging gun ID and order date to obtain a test gun order group, wherein the test gun order group represents a collection of test gun orders; A2, obtaining the number of charging users of the test gun order by counting the query orders corresponding to the test gun orders, wherein the number of charging users refers to the number of user IDs that placed orders on the same test charging gun on the same day; A3, obtaining the number of normal users, failed users and gun-jumping users of the test gun order through the order status of the test gun order and the corresponding query order. The number of normal users refers to the number of user IDs who charged on the charging gun to be tested on the same day and whose order status is all successful orders, the number of failed users refers to the number of user IDs who charged on the charging gun to be tested on the same day and whose order status is all failed orders, and the number of users who jumped the gun refers to the number of user IDs who charged on the charging gun to be tested on the same day and whose order status has both successful orders and failed orders; A4, obtains the number of consecutive days without orders for the order of the gun to be tested through the order date of the order of the gun to be tested and the corresponding query order; A5, integrates the number of charging users, the number of normal users, the number of failed users, the number of users who jumped the gun and the number of consecutive days without orders into a user data table.

[0013] Furthermore, the specific process of analyzing the user data table to obtain the number of anomalies and the anomaly rate is as follows: the number of anomalies is obtained based on the sum of the number of failed users and the number of users who jumped the gun on the same order date of the charging gun to be tested, and the anomaly rate is obtained in combination with the number of charging users. The specific calculation formula of the anomaly rate is as follows:

[0014]

[0015] In the formula, m is the number of the charging gun to be tested, m = 1, 2, ..., M, M is the total number of charging guns to be tested in the test site, b is the number of the order date, b = 1, 2, ..., B, B is the query day, R m.b is the abnormal rate of the mth charging gun to be tested on day b, NL m.b is the number of failed users of the mth charging gun to be tested on day b, NJ m.b is the number of users who jumped the charging gun of the mth charging gun under test on the bth day, NC m.b is the number of users who use the mth charging gun to be tested on the bth day.

[0016] Furthermore, the specific steps of generating fault information by screening data features according to the number of anomalies, the anomaly rate and the user data table are as follows: B1, obtaining the maximum number of users through the charging gun ID and the user data table, the maximum number of users including the maximum number of users of the gun in a single day, the maximum number of users of the station in a single day, the maximum number of users of the pile on the same day and the maximum number of users of the station on the same day, the maximum number of users of the gun in a single day refers to the maximum value of the number of charging users in a single day within the date range of the charging gun to be tested, the maximum number of users of the pile on the same day refers to the maximum value of the number of charging users of all the charging guns to be tested in the same charging pile on the same day, the maximum number of users of the station on the same day refers to the maximum value of the number of charging users of all the charging guns to be tested in the station to be tested on the same day, and the maximum number of users of the station on the same day refers to the maximum value of the number of charging users of all the charging guns to be tested in the station to be tested on the same day. The maximum number of users per day at the station refers to the maximum value of the maximum number of users per day at the station within the date range of the charging gun to be tested; B2, the maximum number of users and the number of charging users are recorded as fault row parameters, and the fault row conditions are obtained through the fault row parameters. The fault row parameters represent the maximum number of users per day at the gun, the maximum number of users per day at the station, the maximum number of users per day at the station, the maximum number of users per day at the pile, and the number of charging users within the query date range; B3, the order group of the gun to be tested is filtered according to the fault row conditions to obtain the fault row, and all the fault rows are integrated to obtain the fault information. The filtering means comparing whether the order group of the gun to be tested meets the fault row conditions, and the fault row refers to the order group of the gun to be tested that meets any fault row condition.

[0017] Furthermore, the specific process of obtaining the fault row condition through the fault row parameters is as follows: obtaining a preset set from a database, the preset set including a preset user number threshold, a preset user number range, a preset abnormal number threshold, a preset charging pile threshold, a preset station threshold, a preset maximum user number threshold, a preset day threshold and a preset abnormal rate threshold, the preset user number threshold including a preset first user number threshold, a preset second user number threshold and a preset third user number threshold, the preset user number range including a preset first user number range, a preset second user number range and a preset third user number range, the preset abnormal number threshold including a preset first abnormal number threshold, a preset second abnormal number threshold and a preset third abnormal number threshold; obtaining the fault row condition by comparing the preset set and the fault row parameters, the fault row condition including a first condition, a second condition, a third condition, a fourth condition, a fifth condition, a sixth condition, a seventh condition and an eighth condition.

[0018] Furthermore, the specific process of obtaining the fault file table of the charging gun to be tested based on the charging gun file information and the fault information is as follows: obtain the charging gun file information, the charging gun file information including the charging gun ID, the last charging time and the device status; summarize the fault information to obtain the total number of faults and the number of consecutive fault days of the charging gun to be tested, the total number of faults refers to the number of fault rows with the same charging gun ID in the fault information, and the consecutive number of fault days refers to the number of days in which the same charging gun ID in the fault information has consecutive fault rows from the start date; merge the charging gun file information and the total number of faults according to the charging gun ID to obtain the fault file table of the charging gun to be tested.

[0019] Furthermore, the method of making a pivot table based on the fault file table and summarizing the faulty gun information also includes obtaining a fault coefficient based on the fault file table, and the fault coefficient indicates the degree of fault of the charging gun to be tested. The specific process is as follows: obtaining a fault assessment weight from a preset database, and the fault assessment weight includes a first weight, a second weight, and a third weight. The first weight is used to describe the influence of the total number of faults and the abnormality rate on the fault coefficient, the second weight is used to describe the influence of the last charging time on the fault coefficient, and the third weight is used to describe the influence of the device status on the fault coefficient; the total number of uses is obtained based on the user data of the charging gun to be tested, and the total number of uses indicates the sum of all charging users of the charging gun to be tested; the fault coefficient is obtained based on the total number of uses, the total number of faults, the abnormality rate of the charging gun to be tested, the last charging time, and the device status. The specific calculation formula of the fault coefficient is as follows:

[0020]

[0021] In the formula, F m is the failure coefficient of the mth charging gun to be tested, TA m is the total number of failures of the mth charging gun to be tested, Cm is the total number of times the mth charging gun to be tested is used, D m is the last charging time of the mth charging gun to be tested, D 0 is the current time, Z m is the device status of the mth charging gun to be tested, a 1 is the first weight, a 2 is the second weight, a 3 The third weight.

[0022] Furthermore, the specific process of making a pivot table based on the fault file table and summarizing the fault gun information is as follows: import the fault file table into a data processing tool, pre-process the fault file table and then fill it into the pivot table; sort the pivot table in order according to the fault coefficient and derive the charging gun ID, and record a preset number of charging gun IDs in the fault gun information; obtain the average fault coefficient and the average number of consecutive fault days by counting the fault coefficient and the number of consecutive faults, the average fault coefficient refers to the average of the fault coefficients of all the charging guns to be tested in the fault file, and the average number of consecutive fault days refers to the average of the consecutive fault days of all the charging guns to be tested in the fault file; obtain the stability evaluation index through the fault coefficient, average fault coefficient, consecutive fault days and average consecutive fault days of the charging gun to be tested, and judge whether the stability evaluation index is not greater than the preset stability threshold. If the stability evaluation index is not greater than the preset stability threshold, the corresponding charging gun ID is recorded in the fault gun information, otherwise no processing is performed. The stability evaluation index is used to describe the stability of the charging gun to be tested.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. Get the charging order by querying the date, and make user statistics on the charging order. Then, analyze the obtained user data table to get the number of anomalies and the anomaly rate. Combine the user data table to perform data feature screening to generate fault information. Then, combine the charging gun file information to get the fault file table of the charging gun to be tested. Finally, make a pivot table based on the fault file table and summarize the faulty gun information, so as to timely identify the faulty charging gun, thereby improving the accuracy of charging gun anomaly detection and effectively solving the problem of inaccurate charging gun anomaly detection in the prior art.

[0025] 2. The maximum number of users in a single day is obtained through the charging gun ID and the user data table, and the total number of users on the day is obtained through the charging pile ID and the user data table. Then, the fault row condition is obtained by combining the number of charging users and the preset set obtained from the database. According to the fault row condition, the data features of the order group of the gun to be tested are screened to obtain the fault row, and all the fault rows are integrated to obtain the fault information, so as to effectively screen out the faulty orders of the charging gun to be tested, thereby realizing the abnormal recognition efficiency of the charging gun abnormal recognition and judgment method.

[0026] 3. The fault number of the charging gun to be tested is obtained by summarizing the fault information, and then the actual number of changes is obtained by the first fault number and the second fault number. The stability evaluation index of the charging gun to be tested is obtained in combination with the fault number. By comparing the stability evaluation index of the charging gun to be tested with the preset stability threshold, it is determined whether to record the corresponding charging gun ID into the faulty gun information, thereby accurately locating the unstable charging gun to be tested, thereby achieving improved stability of the station to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of a method for identifying and determining abnormalities of a charging gun based on data drive provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of the change of the fault coefficient provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiment of the present application solves the problem of inaccurate abnormality detection of charging guns in the prior art by providing a data-driven charging gun abnormality identification and determination method. Charging orders are screened from the database of the station to be tested by querying the date, and user statistics are performed on the obtained query orders to obtain a user data table. The user data table is then analyzed to obtain the number of abnormalities and the abnormality rate. Data feature screening is performed in combination with the user data table to generate fault information. Subsequently, a fault file table of the charging gun to be tested is obtained based on the charging gun file information and the fault information. Finally, a pivot table is created based on the fault file table to summarize the fault gun information, and the fault gun information is pushed to relevant personnel, thereby improving the accuracy of abnormality detection of charging guns.

[0030] The technical solution in the embodiment of the present application is to solve the problem of inaccurate abnormal detection of the charging gun mentioned above, and the overall idea is as follows:

[0031] By conducting user statistics on charging orders, we obtain a user data table, and analyze it to obtain the number of anomalies and the anomaly rate. Then, we use the user data table to perform data feature screening to generate fault information. This is combined with the charging gun file information to obtain a fault file table. Finally, we create a pivot table and summarize the faulty gun information, thereby improving the accuracy of charging gun anomaly detection.

[0032] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0033] like Figure 1 As shown, it is a flow chart of a method for identifying and determining abnormalities of charging guns based on data-driven according to an embodiment of the present application, the method comprising the following steps: S1, filtering charging orders from the database of the station to be tested according to the query date to obtain query orders, the query date indicates the specified query days for extracting charging orders from the database, the station to be tested indicates the charging station that needs to be analyzed, the database is used to store all past charging orders of the station to be tested, and the query order indicates the charging order in the station to be tested within the specified query date; S2, performing user statistics on the query order to obtain a user data table, and analyzing the user data table to obtain the number of abnormalities and the abnormality rate, the user data table is used to store various types of user information obtained from the query order statistics, the number of abnormalities indicates the number of abnormal orders of the charging gun to be tested per day within the query date, and the abnormality rate indicates the proportion of abnormal orders of the charging gun to be tested per day within the query date. , the charging gun to be tested represents the specific charging gun that needs to be analyzed in the station to be tested; S3, generates fault information by filtering data features according to the number of abnormalities, abnormality rate and user data table, obtains the fault archive table of the charging gun to be tested according to the charging gun archive information and fault information, the fault information represents the charging gun fault condition obtained by data feature screening, the charging gun archive data is used to record the basic information of the charging gun to be tested, and the fault archive table represents the archive of recording the charging gun fault condition generated in combination with the charging gun archive data; S4, makes a pivot table based on the fault archive table and summarizes the fault gun information, pushes the fault gun information to relevant personnel, the pivot table is used to analyze the data in the fault archive table to obtain the fault gun information, the fault gun information represents the charging gun extracted from the fault archive table that needs to be handled in time by relevant personnel, and the relevant personnel represent the maintenance personnel of the equipment manufacturer of the charging gun to be tested.

[0034] In this embodiment, the query date is determined by the preset personnel according to the specific time period for the charging gun abnormality identification and judgment as needed. The specific time period can be a few days, weeks or months, depending on the frequency of use and maintenance requirements of the charging gun. In this embodiment, MySQL is used as the database management system. MySQL is a relational database management system used to store and manage detailed information on all charging orders related to the station to be tested. MySQL can efficiently and reliably store and manage charging order data, providing a solid data foundation and analysis tools for the charging gun abnormality identification and judgment system; the accuracy of charging gun abnormality detection is improved, and the problem of inaccurate charging gun abnormality detection in the prior art is effectively solved.

[0035] Furthermore, the specific process of filtering charging orders from the database of the station to be tested according to the query date to obtain the query order is as follows: all charging orders within the query days are extracted from the database of the station to be tested through the written database query statement, and the extracted charging orders are sorted according to the order date to obtain the query order. The database query statement is determined by the specific type of the management system of the database of the station to be tested, and the database query statement is generated according to the start date and the query days; the query date includes the start date and the query days, the start date indicates the start date of the query, and the query days indicate the dates continuously intercepted from the start date to the past; the charging order includes the order date, charging power, user ID, charging gun ID and charging pile ID.

[0036] In this embodiment, the start date can be any specified date, and the query days represent the number of days from the start date. According to the determined query date, the corresponding database query statement is written. In this embodiment, the SELECT statement is used to extract charging orders, and the charging orders that meet the query date are filtered out in combination with the WHERE condition, and then the ORDER BY keyword is used to sort the extracted charging orders; the sorting operation makes the query orders have good time series, which improves the efficiency of user statistics in the charging gun abnormality identification and judgment system.

[0037] Furthermore, user statistics are performed on the queried orders to obtain a user data table, which also includes marking the queried orders to obtain the order status. The specific process is: determine whether the order power is not greater than the preset power threshold. If the order power is not greater than the preset power threshold, the corresponding order status is marked as a successful order, otherwise the corresponding order status is marked as a failed order. A successful order indicates a charging order that is successfully charged, and a failed order indicates a charging order that fails to charge.

[0038] In this embodiment, the charging power is recorded in real time by the metering device of the charging gun, and the pre-charge power is set by the user before starting charging; in this embodiment, the preset power threshold should be determined according to the one-minute charging power of the specific model of the charging gun to be tested. For example, if a charging gun can stably charge 5 kWh of electricity for an electric vehicle in one minute, then the preset power threshold can be set to one tenth of the one-minute charging power, that is, 0.5 kWh. If the order power of the charging order is not higher than 0.5 kWh, it means that the charging amount of the order is extremely small and it is regarded as a failed order. Otherwise, it is recorded as a successful order, ensuring that the charging status of the charging order can be accurately reflected in various usage scenarios, thereby achieving the accuracy of the order status marking in the charging gun abnormality identification and judgment system.

[0039] Furthermore, the specific steps for performing user statistics on the query orders to obtain the user data table are as follows: A1, grouping the query orders according to the charging pile ID (Identity document), charging gun ID and order date to obtain the order group for the gun to be tested, where the order group for the gun to be tested represents the collection of orders for the gun to be tested, and the order for the gun to be tested represents the query orders for each day for the charging gun to be tested within the query days; A2, obtaining the number of charging users of the order for the gun to be tested by counting the query orders corresponding to the order for the gun to be tested, where the number of charging users refers to the number of user IDs that placed orders on the same charging gun to be tested on the same day; A3, obtaining the number of normal users, the number of failed users and the number of gun-jumping users of the order for the gun to be tested through the order status of the order for the gun to be tested and the corresponding query order, where the number of normal users refers to the number of user IDs that charged on the charging gun to be tested on the same day and whose order status are all successful orders, and the number of failed users The number indicates the number of user IDs who charged on the charging gun to be tested on the same day and whose order status is all failed orders. The number of users who jumped the gun indicates the number of user IDs who charged on the charging gun to be tested on the same day and whose order status has both successful orders and failed orders. A4, obtains the number of consecutive days without orders for the order of the gun to be tested through the start date of the order of the gun to be tested and the corresponding query order. The number of consecutive days without orders indicates the number of consecutive days without charging orders for the charging gun to be tested from the start date. A5, integrates the number of charging users, the number of normal users, the number of failed users, the number of users who jumped the gun and the number of consecutive days without orders into a user data table. The user data table includes the charging pile ID, charging gun ID, order date, number of charging users, number of normal users, number of users who jumped the gun and number of failed users.

[0040] In this embodiment, the GROUPBY keyword is used to group the query orders with the same charging gun ID and the same order date in the query order into a group to obtain a test gun order group; the COUNT function is used to count the number of user IDs in each group to obtain the number of charging users; the CASE WHEN function is used to determine the order status of the test gun order, and the SUM function is used to sum to obtain the number of normal users, the number of failed users, and the number of users who jumped the gun; the DATED IFF function is used to calculate the difference between the most recent test gun order and the start date in each group as the number of consecutive days without orders; finally, the CREATE statement is used to integrate the data of the number of charging users, the number of normal users, the number of failed users, the number of users who jumped the gun, and the number of consecutive days without orders to obtain a user data table; a structured user data table is constructed through the above steps, which is convenient for subsequent abnormal rate analysis and data feature screening, thereby improving the abnormal recognition efficiency of the charging gun abnormal recognition and judgment system.

[0041] Furthermore, the specific process of analyzing the user data table to obtain the number of anomalies and the anomaly rate is as follows: the number of anomalies is obtained by summing the number of failed users and the number of users who jumped the gun on the same order date of the charging gun to be tested, and the anomaly rate is obtained by combining the number of charging users. The specific calculation formula of the anomaly rate is as follows:

[0042]

[0043] In the formula, m is the number of the charging gun to be tested, m = 1, 2, ..., M, M is the total number of charging guns to be tested in the test site, b is the number of the order date, b = 1, 2, ..., B, B is the query day, R m.b is the abnormal rate of the mth charging gun to be tested on day b, NL m.b is the number of failed users of the mth charging gun to be tested on day b, NJ m.b is the number of users who jumped the charging gun of the mth charging gun under test on the bth day, NC m.b is the number of users who use the mth charging gun to be tested on the bth day.

[0044] In this embodiment, the number of failed users is used to describe the number of user IDs that charge on the charging gun to be tested and whose order status is all failed orders on the same day. The number of users who jump the gun is used to describe the number of user IDs that charge on the charging gun to be tested and whose order status has both successful orders and failed orders on the same day. The sum of the number of users who jump the gun and the number of failed users represents the number of user IDs that have failed orders on the charging gun to be tested on that day. The ratio obtained by combining the number of charging users represents the proportion of user IDs that have failed orders among the user IDs that have generated orders on the charging gun to be tested on that day. The larger the proportion, the worse the stability of the charging gun, and therefore the higher the abnormality rate. The statistical table of changes in the abnormality rate is shown in Table 1:

[0045] Table 1 Statistics of abnormal rate changes

[0046]

[0047]

[0048] The algorithm of this embodiment combines "the number of failed users of the mth charging gun to be tested on the bth day", "the number of users whose charging gun jumped on the bth day" and "the number of charging users of the mth charging gun to be tested on the bth day", and comprehensively analyzes to obtain "the abnormal rate of the mth charging gun to be tested on the bth day", and each parameter is related to each other and does not exist independently. It is a whole, and the "abnormal rate of the mth charging gun to be tested on the bth day" cannot be obtained by simple combination and addition. For example, in this formula, "the number of failed users of the mth charging gun to be tested on the bth day" is conducive to increasing the "abnormal rate of the mth charging gun to be tested on the bth day", which does not necessarily mean that the "abnormal rate of the mth charging gun to be tested on the bth day" is large. At this time, it is also necessary to consider that "the number of users whose charging gun jumped on the bth day" can also determine the failure condition of the charging gun. The correlation and mutual influence between various factors are considered in a quantitative way, and the algorithm itself is obtained by comprehensive analysis, which can accurately and quantitatively analyze the failure condition of the charging gun, indicating the effect of comprehensive evaluation of the abnormal rate. Therefore, accurate analysis is performed by establishing a mathematical form.

[0049] Furthermore, the specific steps of generating fault information by performing data feature screening based on the number of anomalies, the anomaly rate and the user data table are as follows: B1, obtaining the maximum number of users through the charging gun ID and the user data table, the maximum number of users including the maximum number of users of the gun in a single day, the maximum number of users of the station in a single day, the maximum number of users of the pile on the same day and the maximum number of users of the station on the same day, the maximum number of users of the gun in a single day refers to the maximum value of the number of charging users in a single day within the date range of the charging gun to be tested, the maximum number of users of the pile on the same day refers to the maximum value of the number of charging users of all charging guns to be tested in the same charging pile on the same day, the maximum number of users of the station on the same day refers to the maximum value of the number of charging users of all charging guns to be tested in the station to be tested on the same day, and the maximum number of users of the station on the same day refers to the maximum value of the number of charging users of the charging gun to be tested on the date The maximum value of the maximum number of users on the day at the station within the range; B2, record the maximum number of users and the number of charging users as fault row parameters, and obtain the fault row conditions through the fault row parameters. The fault row parameters represent the maximum number of users of the gun in a single day, the maximum number of users of the station in a single day, the maximum number of users of the station on the day of each day, the maximum number of users of the pile on the day, and the number of charging users within the query date range; B3, filter the gun order group to be tested according to the fault row conditions to obtain the fault row, and integrate all the fault rows to obtain the fault information. The screening means comparing whether the gun order group to be tested meets the fault row conditions. The fault row refers to the gun order group to be tested that meets any fault row condition. The integration means sorting the fault rows from small to large according to the charging gun ID to obtain the fault information.

[0050] In this embodiment, the SUM function is used to count the maximum number of charging users with the same charging gun ID in the user data table on the same day to obtain the number of users of the gun on that day, and the maximum number of users of the gun on that day within the query date is recorded as the maximum number of users of the gun on a single day, and the maximum number of users of the charging gun to be tested with the same charging pile ID on that day is counted to obtain the maximum number of users of the pile on that day, and the maximum number of users of the gun on that day of all the charging guns to be tested in the station to be tested on that day is counted to obtain the maximum number of users of the station on that day, and the maximum number of the maximum number of users of the station on that day within the query date is recorded as the maximum number of users of the station on a single day; the CASEWHEN function is used to perform data feature screening on the order group of the gun to be tested to obtain the fault row; finally, the ORDER BY statement is used to sort the fault rows to obtain the fault information; by integrating all fault rows into fault information, the fault information is systematized and structured, and the accuracy of the charging gun abnormality identification and judgment system is effectively improved.

[0051] Furthermore, the specific process of obtaining the fault row condition through the fault row parameter is as follows: obtain a preset set from the database, the preset set includes a preset user number threshold, a preset user number range, a preset abnormal number threshold, a preset charging pile threshold, a preset station threshold, a preset maximum user number threshold, a preset day threshold and a preset abnormal rate threshold, the preset user number threshold includes a preset first user number threshold, a preset second user number threshold and a preset third user number threshold, the preset user number range includes a preset first user number range, a preset second user number range and a preset third user number range, and the preset abnormal number threshold includes a preset first abnormal number threshold, a preset second abnormal number threshold and a preset third abnormal number threshold; obtain the fault row condition by comparing the preset set and the fault row parameter, the fault row condition includes the first condition, the second condition, the third condition, the fourth condition, the fifth condition, the sixth condition, the seventh condition and the eighth condition; the first condition indicates that the maximum number of users of the charging gun to be tested in a single day is not less than the preset first user number threshold, and the number of users of the charging gun to be tested on that day is within the preset first user number range, and the abnormal number is not less than the preset first abnormal number threshold; the second condition indicates that the charging gun to be tested The maximum number of users of the charging gun in a single day is not less than the preset second user number threshold, and the number of users of the charging gun to be tested on that day is within the preset second user number range, and the number of abnormalities is not less than the preset second abnormal number threshold; the third condition indicates that the maximum number of users of the charging gun to be tested in a single day is not less than the preset third user number threshold, and the number of users of the charging gun to be tested on that day is within the preset third user number range, and the number of abnormalities is not less than the preset third abnormal number threshold; the fourth condition indicates that the maximum number of users of the charging gun to be tested in a single day is not less than the preset third user number threshold, and the abnormal rate is not less than the preset abnormal rate threshold , and; the fifth condition indicates that the number of charging users of the charging gun to be tested on that day is equal to 0, and the number of charging users on the previous day is not less than the preset third user number threshold; the sixth condition indicates that the maximum number of users of the station in a single day is not less than the preset maximum number of users threshold, and the number of consecutive days without orders for the charging gun to be tested is not less than the preset number of days threshold; the seventh condition indicates that the maximum number of users of the pile on that day is not less than the preset charging pile threshold, and the number of charging users of the charging gun to be tested on that day is equal to 0; the eighth condition indicates that the maximum number of users of the station on that day is not less than the predicted station threshold, and the number of charging users of the charging gun to be tested on that day is equal to 0.

[0052] In this embodiment, the preset user number threshold, the preset user number range, and the preset abnormal number threshold should be determined according to the charging user number and threshold relationship table. The construction process of the charging user number and threshold relationship table is as follows: assuming that the charging user number is x, the threshold is set to y=0.3x+k, let x be an integer, and the decimal of y is rounded down. When x is in the range of 1-3, y is in the range of 2-2.9, and is rounded down to 2, so the threshold is 2. When x is in the range of 4-6, y is in the range of 3.2-3.8, and is rounded down to 3, so the threshold is 4. When x is in the range of 7-9, y is in the range of 4.1-4.7, and is rounded down to 4, so the threshold is 4. The obtained charging user number and threshold relationship table is shown in Table 2:

[0053] Table 2 Relationship between the number of charging users and thresholds

[0054] Number of charging users Threshold 1-3 1 4-,6 3 7-9 4

[0055] Therefore, the preset first user number range is set to 1 to 3, the preset second user number range is set to 4 to 6, the preset third user number range is set to 7 to 9, the preset first abnormal number threshold is set to 1, the preset second abnormal number threshold is set to 3, and the preset third abnormal number threshold is set to 4. Then, the preset user number threshold is obtained according to the maximum number of charging users of the charging guns to be tested numbered 1-6 in the historical time period. For example, if the maximum number of charging users of the charging guns to be tested numbered 1-6 in the past week are [3, 7, 11, 9, 7, 13] respectively, then the preset first user number threshold is set to (3+7) / 2=5, the preset second user number threshold is set to (11+9) / 2=10, and the preset third user number threshold is set to (7+13) / 2=10.

[0056] Specifically, the preset charging pile threshold and the predicted station threshold are obtained according to the maximum number of users of the pile and the maximum number of users of the station on the same day in the historical time period, respectively. The maximum number of users of the pile and the maximum number of users of the station on the same day of the charging gun to be tested in the past period of time are obtained from the database, and the mode of the corresponding data is obtained by statistics. For example, Figure 2 As shown, this is a graph of the changes in the number of charging users in a week provided in an embodiment of the present application. It can be seen from the graph that the mode of the maximum number of users on the pile on that day is 10, and the mode of the maximum number of users on the station on that day is 20. Therefore, the preset charging pile threshold is set to 10, and the preset charging station threshold is set to 20.

[0057] Specifically, the preset maximum user number threshold, the preset day number threshold and the preset abnormal rate threshold are determined by the distribution of the maximum number of users in a single day at the station, the number of consecutive days without orders and the abnormal rate in the historical time period, and the mean and standard deviation of the maximum number of users in a single day at the station, the number of consecutive days without orders and the abnormal rate of the charging gun to be tested in the past period of time are obtained from the database, and the maximum number of users in a single day at the station, the number of consecutive days without orders and the abnormal rate are obtained respectively. The preset maximum user number threshold is obtained by subtracting twice the standard deviation from the mean of the maximum number of users in a single day at the station, and the preset day number threshold and the preset abnormal rate threshold are obtained by adding twice the standard deviation to the mean of the number of consecutive days without orders and the preset abnormal rate. Comprehensive fault line conditions are provided through diversified thresholds and ranges, which effectively improves the reliability of fault line detection in the charging gun abnormal identification and judgment system.

[0058] Furthermore, the specific process of obtaining the fault file table of the charging gun to be tested based on the charging gun file information and the fault information is as follows: obtain the charging gun file information, the charging gun file information includes the charging gun ID, the last charging time and the device status, the last charging time indicates the order date of the last charging order of the charging gun to be tested, the device status indicates the device status information sent by the charging gun to be tested, which is the charging gun status obtained by the built-in sensor of the charging gun; summarize the fault information to obtain the total number of faults and the number of consecutive fault days of the charging gun to be tested, the total number of faults refers to the number of fault rows with the same charging gun ID in the fault information, and the number of consecutive fault days refers to the number of consecutive days with the same charging gun ID in the fault information from the start date; merge the charging gun file information and the total number of faults according to the charging gun ID to obtain the fault file table of the charging gun to be tested.

[0059] In this embodiment, the device charging time is in the format of a timestamp, and the device status includes a normal status and a fault status, which are represented by 0 and 1 respectively. Since the built-in sensor of the charging gun is affected by many factors, the device status is not necessarily accurate and can only be used as one of the references for subsequent calculation of the fault coefficient; the GROUPBY keyword is used to group the fault rows with the same charging gun ID in the fault information, and the COUNT function is used to count the number of fault rows in each group to obtain the total number of faults; the charging gun file information and the total number of faults are merged by performing a JOI N operation according to the charging gun ID, and a CREATE statement is used to generate a fault file table containing the charging gun ID, the last charging time, the device status, and the total number of faults; through the above steps, the charging gun file information and the fault information are fully integrated, which facilitates the subsequent calculation of the fault coefficient and the stability evaluation index, thereby improving the operating efficiency of the charging gun fault detection system.

[0060] Furthermore, a pivot table is made based on the fault file table and the fault gun information is summarized. Previously, the fault coefficient is obtained based on the fault file table. The fault coefficient indicates the degree of fault of the charging gun to be tested. The specific process is as follows: the fault assessment weight is obtained from the preset database. The fault assessment weight includes the first weight, the second weight and the third weight. The first weight is used to describe the influence of the total number of faults and the abnormality rate on the fault coefficient. The second weight is used to describe the influence of the last charging time on the fault coefficient. The third weight is used to describe the influence of the equipment status on the fault coefficient; the total number of uses is obtained based on the user data of the charging gun to be tested. The total number of uses indicates the sum of all charging users of the charging gun to be tested; the fault coefficient is obtained based on the total number of uses, the total number of faults, the abnormality rate of the charging gun to be tested, the last charging time and the equipment status. The specific calculation formula of the fault coefficient is as follows:

[0061]

[0062] In the formula, F m is the failure coefficient of the mth charging gun to be tested, TA m is the total number of failures of the mth charging gun to be tested, C m is the total number of times the mth charging gun to be tested is used, D m is the last charging time of the mth charging gun to be tested, D 0 is the current time, Z m is the device status of the mth charging gun to be tested, a 1 is the first weight, a 2 is the second weight, a 3 The third weight.

[0063] In this embodiment, the current time is the time when the fault coefficient is obtained, which is in the format of a timestamp; the total number of faults is used to describe the number of faults of the charging gun to be tested. The higher the total number of faults, the more serious the problem of the charging gun to be tested, so the higher the fault coefficient; the abnormality rate is used to describe the fault ratio of the charging gun in a specific time period. A high abnormality rate means that the fault frequency of the charging gun to be tested is high in the time period, so the fault coefficient is higher; the difference between the last charging time and the current time is used to describe the idle time of the charging gun. The higher the difference between the last charging time and the current time, the longer the charging gun to be tested has not been used, so the fault coefficient is higher; the device status is used to describe the device status information sent by the charging gun to be tested. The device status of 1 indicates that the charging gun to be tested is in a fault state, so the fault coefficient is higher; the first weight, the second weight and the third weight are used to represent the influence of the total number of faults and the abnormality rate, the last charging time and the device status on the fault coefficient respectively. The greater the fault assessment weight, the greater the influence; the fault coefficient is a comprehensive consideration of the total number of faults, the total number of uses and the abnormality rate, and is used to describe the overall fault situation of the charging gun to be tested. The higher the fault coefficient, the more serious the fault degree of the charging gun.

[0064] In order to simplify the custom description of the failure coefficient, define the abnormal rate index RP m Indicates the abnormal rate index of the mth charging gun to be tested, and defines the charging time index DP m It represents the charging time index of the mth charging gun to be tested. The simplified formula is F m =tanh(a 1 *RP m +a 2 *DP m +a 3 *Z m ), with Z m Take 0, a 1 Take 0.3, a 2 Take 0.5 as an example, Figure 2 As shown, it is a schematic diagram of the change of the fault coefficient provided in the embodiment of the present application. It can be seen from the figure that the abnormal rate index and the charging time index are positively correlated with the fault coefficient, which realizes the quantitative evaluation of the overall fault condition of the charging gun to be tested and improves the accuracy of the charging gun fault detection system.

[0065] Specifically, the first weight is obtained from a preset database. In a specific embodiment, a mapping set of the abnormality rate index and the first weight is constructed according to the relationship between the abnormality rate index and the failure coefficient in the historical time period, and the real-time abnormality rate index is input into the mapping set to obtain the corresponding first weight.

[0066] Specifically, the second weight is obtained from a preset database. In a specific embodiment, a mapping set of the charging time index and the second weight is constructed according to the relationship between the charging time index and the fault coefficient in the historical time period, and the real-time charging time index is input into the mapping set to obtain the corresponding second weight.

[0067] Specifically, the third weight is obtained from a preset database. In a specific embodiment, a mapping set of device status and first weight is constructed according to the relationship between device status and fault coefficient in a historical time period, and the real-time device status is input into the mapping set to obtain the corresponding third weight.

[0068] Furthermore, the specific process of making a pivot table based on the fault file and summarizing the faulty gun information is as follows: import the fault file into the data processing tool, pre-process the fault file and fill it into the pivot table. The data processing tool is used to create data processing software for the pivot table. The preprocessing includes setting the charging gun ID as the row index, and setting the total number of faults, the last charging time, the equipment status and the fault coefficient as the column index; after sorting the pivot table in order according to the fault coefficient, the charging gun ID is exported, and a preset number of charging gun IDs are recorded in the faulty gun information. The ordered sorting means sorting from high to low according to the fault coefficient. The preset number is determined by the preset personnel according to the actual operation and maintenance needs of the station to be tested; the average is obtained by counting the fault coefficient and the number of consecutive faults. Failure coefficient and average continuous failure days. The average failure coefficient refers to the average of the failure coefficients of all charging guns to be tested in the fault file, and the average continuous failure days refers to the average of the continuous failure days of all charging guns to be tested in the fault file. The stability evaluation index is obtained by the failure coefficient, average failure coefficient, continuous failure days and average continuous failure days of the charging gun to be tested, and it is judged whether the stability evaluation index is not greater than the preset stability threshold. If the stability evaluation index is not greater than the preset stability threshold, the corresponding charging gun ID is recorded in the fault gun information, otherwise no processing is performed. The stability evaluation index is used to describe the stability of the charging gun to be tested. The preset stability threshold is determined by the preset personnel according to the actual operation and maintenance standards of the station to be tested. The specific calculation formula of the stability evaluation index is as follows:

[0069]

[0070] In the formula, Q m is the stability evaluation index of the mth charging gun to be tested, F 0 is the average failure coefficient, E m is the number of consecutive failure days of the mth charging gun to be tested, E 0 is the mean number of consecutive failure days.

[0071] In this embodiment, the preset number is the number of charging guns to be tested that need to be paid special attention to, which is determined according to the scale and maintenance capacity of the station to be tested. For example, the preset number can be set to 10, indicating that the top ten charging guns to be tested in the fault coefficient ranking are recorded in the fault gun information; the standard deviation of the stability evaluation index is obtained by analyzing the distribution of the stability evaluation index, and twice the standard deviation is set as the preset stability threshold. The preset stability threshold is used to evaluate the stability of the charging gun to be tested. For example, assuming that the preset stability threshold is set to 5, if the stability evaluation index of a charging gun to be tested is greater than 5, the charging gun to be tested is considered to be highly unstable and should be recorded in the fault gun information for key inspection and maintenance, otherwise it will not be processed; through the pivot table and the stability evaluation index, it is ensured that the charging guns to be tested that are most likely to have problems are given priority within a reasonable range, thereby improving the operation and maintenance efficiency and stability of the charging gun fault detection system.

[0072] In summary, the embodiment of the present application obtains charging orders by querying the date, and performs user statistics on the charging orders, then analyzes the obtained user data table to obtain the number of anomalies and the anomaly rate, and combines the user data table to perform data feature screening to generate fault information, and then combines the charging gun file information to obtain the fault file table of the charging gun to be tested, and finally, a pivot table is made according to the fault file table and the faulty gun information is summarized, so as to timely identify the faulty charging gun, thereby achieving improved accuracy in charging gun anomaly detection, and effectively solving the problem of inaccurate charging gun anomaly detection in the prior art.

[0073] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A data-driven charging gun abnormality identification and judgment method, characterized in that: The following steps are involved: S1, filtering charging orders from a database of the station to be tested according to a query date to obtain a query order, wherein the query date represents a specified query day for extracting charging orders from the database; S2, performing user statistics on the query order to obtain a user data table, and analyzing the user data table to obtain the number of exceptions and the exception rate, wherein the user data table is used to store various types of user information obtained from the query order statistics; S3, generating fault information by filtering data features according to the number of abnormalities, the abnormality rate and the user data table, and obtaining a fault file table of the charging gun to be tested according to the charging gun file information and the fault information, wherein the fault file table represents a file recording the fault conditions of the charging gun generated in combination with the charging gun file data; S4, create a pivot table based on the fault file table and summarize the faulty gun information, and push the faulty gun information to relevant personnel. The pivot table refers to a table used to summarize and analyze the data in the fault file table.

2. The data-driven charging gun abnormality identification and determination method according to claim 1, characterized in that: The specific process of filtering charging orders from the database of the station to be tested according to the query date to obtain the query order is as follows: Extract all charging orders within the query days from the database of the station to be tested by writing a database query statement, sort the extracted charging orders according to the order date to obtain the query order, and the database query statement is generated according to the start date and the query days; The query date includes the start date and query days; The charging order includes the order date, charging power, user ID, charging gun ID and charging pile ID.

3. The data-driven charging gun abnormality identification and determination method according to claim 2, characterized in that: The user statistics of the query order are obtained to obtain the user data table, and before that, the query order is also marked to obtain the order status, and the specific process is as follows: Determine whether the order power is greater than the preset power threshold. If the order power is greater than the preset power threshold, the corresponding order status is marked as a successful order, otherwise the corresponding order status is marked as a failed order. The successful order indicates a charging order in which charging is successful, and the failed order indicates a charging order in which charging fails.

4. The data-driven charging gun abnormality identification and determination method according to claim 1, characterized in that: The specific steps of performing user statistics on the query order to obtain the user data table are as follows: A1, grouping the query orders according to the charging pile ID, charging gun ID and order date to obtain a test gun order group, where the test gun order group represents a collection of test gun orders; A2, by counting the query orders corresponding to the orders of the tested guns, the number of charging users of the orders of the tested guns is obtained, where the number of charging users refers to the number of user IDs that placed orders on the same tested charging gun on the same day; A3, obtain the number of normal users, failed users and jumped users of the order of the test gun through the order status of the order of the test gun and the corresponding query order, the normal user number refers to the number of user IDs who charged on the test gun on the same day and whose order status is all successful orders, the failed user number refers to the number of user IDs who charged on the test gun on the same day and whose order status is all failed orders, and the jumped user number refers to the number of user IDs who charged on the test gun on the same day and whose order status has both successful orders and failed orders; A4, obtain the number of consecutive days without orders for the order of the gun to be tested according to the start date of the order of the gun to be tested and the corresponding query order; A5, integrate the number of charging users, normal users, failed users, failed users, and consecutive days without orders into a user data table.

5. The data-driven charging gun abnormality identification and determination method according to claim 4, characterized in that: The specific process of analyzing the user data table to obtain the number of anomalies and the anomaly rate is as follows: The number of anomalies is obtained by summing the number of failed users and the number of users who jumped the gun on the same order date of the charging gun to be tested. The anomaly rate is obtained by combining the number of charging users. The specific calculation formula of the anomaly rate is as follows: In the formula, m is the number of the charging gun to be tested, m = 1, 2, ..., M, M is the total number of charging guns to be tested in the test site, b is the number of the order date, b = 1, 2, ..., B, B is the query day, R m.b is the abnormal rate of the mth charging gun to be tested on day b, NL m.b is the number of failed users of the mth charging gun to be tested on day b, NJ m.b is the number of users who jumped the charging gun of the mth charging gun under test on the bth day, NC m.b is the number of users who use the mth charging gun to be tested on the bth day.

6. The data-driven charging gun abnormality identification and determination method according to claim 5, characterized in that: The specific steps of generating fault information by filtering data features according to the number of exceptions, the exception rate and the user data table are as follows: B1, obtain the maximum number of users through the charging gun ID and the user data table, the maximum number of users includes the maximum number of users of the gun in a single day, the maximum number of users of the station in a single day, the maximum number of users of the pile on the same day and the maximum number of users of the station on the same day. The maximum number of users of the gun in a single day refers to the maximum value of the number of charging users in a single day within the date range of the charging gun to be tested, the maximum number of users of the pile on the same day refers to the maximum value of the number of charging users of all charging guns to be tested in the same charging pile on the same day, the maximum number of users of the station on the same day refers to the maximum value of the number of charging users of all charging guns to be tested in the station to be tested on the same day, and the maximum number of users of the station in a single day refers to the maximum value of the maximum number of users of the station on the same day within the date range of the charging gun to be tested; B2, record the maximum number of users and the number of charging users as fault row parameters, and obtain the fault row condition through the fault row parameters. The fault row parameters represent the maximum number of users of the gun in a single day, the maximum number of users of the station in a single day, and the maximum number of users of the station, the maximum number of users of the charging pile, and the number of charging users on the same day within the query date range; B3, according to the fault row condition, the order group of the gun to be tested is screened to obtain the fault row, and all the fault rows are integrated to obtain the fault information. The screening means comparing whether the order group of the gun to be tested meets the fault row condition. The fault row refers to the order group of the gun to be tested that meets any one of the fault row conditions.

7. The data-driven charging gun abnormality identification and determination method according to claim 6, characterized in that: The specific process of obtaining the fault row condition through the fault row parameter is as follows: Obtain a preset set from a database, the preset set including a preset user number threshold, a preset user number range, a preset abnormal number threshold, a preset charging pile threshold, a preset station threshold, a preset maximum user number threshold, a preset day threshold, and a preset abnormal rate threshold, the preset user number threshold including a preset first user number threshold, a preset second user number threshold, and a preset third user number threshold, the preset user number range including a preset first user number range, a preset second user number range, and a preset third user number range, the preset abnormal number threshold including a preset first abnormal number threshold, a preset second abnormal number threshold, and a preset third abnormal number threshold; The faulty row condition is obtained by comparing the preset set with the faulty row parameter, and the faulty row condition includes a first condition, a second condition, a third condition, a fourth condition, a fifth condition, a sixth condition, a seventh condition and an eighth condition.

8. The data-driven charging gun abnormality identification and determination method according to claim 6, characterized in that: The specific process of obtaining the fault file table of the charging gun to be tested according to the charging gun file information and fault information is as follows: Obtain charging gun file information, where the charging gun file information includes the charging gun ID, the last charging time, and the device status; Summarize the fault information to obtain the total number of faults and the number of consecutive fault days of the charging gun to be tested. The total number of faults refers to the number of fault rows with the same charging gun ID in the fault information, and the consecutive fault days refers to the number of consecutive days with the same charging gun ID in the fault information from the start date. Combine the charging gun file information and the total number of faults according to the charging gun ID to obtain the fault file table of the charging gun to be tested.

9. The data-driven charging gun abnormality identification and determination method according to claim 8, characterized in that: The process of making a pivot table based on the fault file table and summarizing the faulty charging gun information also includes obtaining a fault coefficient based on the fault file table. The fault coefficient indicates the degree of fault of the charging gun to be tested. The specific process is as follows: Obtain a fault assessment weight from a preset database, the fault assessment weight including a first weight, a second weight and a third weight, the first weight is used to describe the influence of the total number of faults and the abnormality rate on the fault coefficient, the second weight is used to describe the influence of the last charging time on the fault coefficient, and the third weight is used to describe the influence of the device status on the fault coefficient; obtain the total number of uses according to the user data of the charging gun to be tested, the total number of uses represents the sum of all charging users of the charging gun to be tested; obtain the fault coefficient according to the total number of uses, the total number of faults, the abnormality rate of the charging gun to be tested, the last charging time and the device status, and the specific calculation formula of the fault coefficient is as follows: In the formula, F m is the failure coefficient of the mth charging gun to be tested, TA m is the total number of failures of the mth charging gun to be tested, C m is the total number of times the mth charging gun to be tested is used, D m is the last charging time of the mth charging gun to be tested, D0 is the current time, Z m is the device status of the mth charging gun to be tested, a1 is the first weight, a2 is the second weight, and a3 is the third weight.

10. The data-driven charging gun abnormality identification and determination method according to claim 9, characterized in that: The specific process of making a pivot table based on the fault file table and summarizing the fault gun information is as follows: Import the fault file table into the data processing tool, pre-process the fault file table and fill it into the pivot table; After sorting the pivot table in order according to the failure coefficient, the charging gun ID is exported, and a preset number of charging gun IDs are recorded in the fault gun information; The average failure coefficient and the average continuous failure days are obtained by statistically analyzing the failure coefficient and the number of consecutive failures. The average failure coefficient refers to the average of the failure coefficients of all charging guns to be tested in the failure file, and the average continuous failure days refers to the average of the continuous failure days of all charging guns to be tested in the failure file. The stability evaluation index is obtained by the failure coefficient, average failure coefficient, continuous failure days and average continuous failure days of the charging gun to be tested, and it is determined whether the stability evaluation index is not greater than the preset stability threshold. If the stability evaluation index is not greater than the preset stability threshold, the corresponding charging gun ID is recorded in the faulty gun information, otherwise no processing is performed. The stability evaluation index is used to describe the stability of the charging gun to be tested.

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