A method for fault diagnosis of charging piles based on charging big data

Abstract

The application discloses a charging pile fault diagnosis method based on charging big data, discards the data model for fault diagnosis based on the hardware self-check of the existing charging pile equipment, adopts power grid charging big data which is completely orthogonal to the hardware parameters of the charging pile to construct a fault diagnosis database, and obtains a brand-new data model which is completely independent of the hardware self-check model of the charging pile, so that the independence of the data source and the data model makes the new and old data models have excellent complementarity and synergy. The application provides a brand-new technical model and a data model which are independent of and complementary to the hardware self-check of the charging pile for solving the charging pile fault diagnosis.

Description

Technical Field

[0001] This invention relates to the field of smart and information-based power grid technology, and in particular to a charging pile fault diagnosis data model, method and application based on charging big data. Background Technology

[0002] With the increasing popularity of electric vehicles, the construction of charging stations in my country has entered a fast track. Because public charging stations are numerous and widely distributed, most are unmanned self-service stations. If a charging station malfunctions and cannot be detected in a timely manner, it will not only inconvenience customers but also affect the revenue and corporate image of the charging facility operators.

[0003] Currently, the shortcomings and deficiencies of existing technologies include at least the following: To promptly detect charging pile malfunctions, most public charging piles are designed with self-diagnostic functions to automatically detect common faults such as offline charging pile faults and abnormal electricity meter faults, and send fault alarm information to the platform. However, in our actual work, we have found that some charging piles, while showing normal self-diagnostic functions, cannot complete charging normally, manifesting as the charging pile failing to start normally or stopping charging after a few seconds. Our analysis suggests that the reason for this is that the charging pile's self-diagnostic function, which displays normal equipment but cannot charge, is based on sudden changes in the charging pile's status parameters. However, for some charging interruptions during the charging process, the self-diagnostic function cannot determine whether the cause is the vehicle, the customer's operation, or the charging pile itself. Therefore, to prevent a large number of false alarms, the charging pile's self-diagnostic function must not report anomalies occurring during the charging process. This results in some charging pile faults not being detected by the self-diagnostic function. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the various shortcomings of the existing technology and provide a method for diagnosing and processing charging pile faults based on big data of charging pile network. It has a brand-new technical solution and provides another feasible technical model and data model for solving charging pile fault diagnosis.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows.

[0006] The method for constructing a charging pile fault diagnosis database based on charging big data abandons the existing data model for fault diagnosis based on the hardware self-inspection of charging pile equipment. Instead, it uses big data on power grid charging that is completely orthogonal to the hardware parameters of the charging pile to construct the fault diagnosis database, resulting in a completely new data model that is independent of the charging pile hardware self-inspection model. The independence of the data source and the data model makes the new and old data models have excellent complementarity and synergy.

[0007] As a preferred embodiment of the present invention, the method specifically includes the following construction steps:

[0008] A. The full charging order data of charging piles within a set time period from the power grid data platform is used as the bottom database layer. The endpoint of the set time period is: the data of the day when the bottom database layer is built or updated minus 2. The starting point of the set time period is set as: the duration of the time period is set to n days based on the specified endpoint, where n is a data range with 15 as the median, and the swing radius r of n is set to r = 5-50 days. The full charging order data is the acquisition of all order data within the time range. Each order contains at least the charging pile identification number, the name of the charging station where the charging pile is located, and the charging amount. The charging pile identification number is the identification information that distinguishes different charging piles. Each charging pile has its own unique identification number.

[0009] B. Construction of the Basic Progressive Data Layer: The full charging order data obtained in step A from the bottom database layer is cumulatively analyzed. The analysis rules are set as follows: B-1, the cumulative number of charging times and cumulative charging amount of each charging pile in the geographic charging area network covered by the bottom database layer within a set time period; B-2, the charging piles with cumulative charging amount less than or equal to the power fault threshold are selected as the basic fault data nodes of the progressive data layer, corresponding to the preliminary suspected fault piles; wherein the power fault threshold is set as any value between (0-8) kW·h, based on big data simulation and compared with real-world fault data, currently set as follows: the power fault threshold of DC fast charging piles is anchored at 2 kW·h or fluctuates slightly, and the power fault threshold of AC slow charging piles is anchored at 0.5 kW·h or fluctuates slightly; wherein the median data anchor and the data fluctuation range are allowed to change in real time with the update of the data model and / or the change in the scale of the data model; additionally, the cumulative number of charging times and cumulative charging amount or other information contained in the preliminary suspected fault pile information are externally bound to the basic fault data nodes.

[0010] C. Integration of Yesterday's Cumulative Data Layer: Obtain yesterday's full charging order data when constructing the bottom database layer. The order data includes orders that were charged normally and orders that ended abnormally. Each order contains at least the charging pile identification number, the name of the charging station where the charging pile is located, and the charging amount. Then, perform cumulative analysis on the obtained yesterday's order data and the preliminary suspected faulty charging piles on the basic fault data nodes in step B. Filter out the charging pile information whose cumulative charging amount is less than or equal to the power fault threshold and mark it as yesterday's fault data node, which includes suspected faulty charging pile information up to yesterday.

[0011] D: Constructing a fault data layer based on a probability model: The suspected faulty charging pile information screened in step C is analyzed according to the following rules: the more cumulative charging times but the less cumulative charging amount, the higher the probability that the charging pile is a faulty charging pile; based on a pre-defined threshold, charging piles with a cumulative charging times greater than the charging times fault threshold and a cumulative charging amount less than the charging amount fault threshold are selected to construct a fault data layer; based on the fault data layer, work orders are generated and transmitted to the fault handling personnel. The work order content includes the charging pile identification number, the charging station information to which the charging pile belongs, or other information.

[0012] As a preferred technical solution of the present invention, the method further includes the following subsequent data steps: E. The suspected fault pile information filtered in step C up to yesterday is accumulated and analyzed with the full order data of the next day to obtain the latest suspected fault pile information, and the data model is updated to date according to the data rules.

[0013] As a preferred technical solution of the present invention, in step A, since the source and presentation form of this data model are big data models, the final value of n determines the scale of the big data model and satisfies the data rule that the larger the model, the higher the accuracy and the higher the data load. Therefore, under normal circumstances, the value of n is based on the median and fluctuates slightly. In other words, when updating the bottom database layer, the priority data rule is: new data replaces the end data; the alternative data rule is: new data is merged and accumulated.

[0014] As a preferred technical solution of the present invention, in step D, the charging number fault threshold is set to an integer value between (3-10), with 5 as the optimal data anchor point; the power fault threshold is set to any value between (0-8) kW·h. Based on big data simulation and comparison with real-world fault data, it is currently set as follows: the power fault threshold for DC fast charging piles is set to 2 kW·h as the median data anchor point or fluctuates slightly, and the power fault threshold for AC slow charging piles is set to 0.5 kW·h as the median data anchor point or fluctuates slightly; wherein, the median data anchor point and the data fluctuation range are allowed to change in real time with the update of the data model and / or the change in the scale of the data model.

[0015] As a preferred technical solution of the present invention, in step D, when the faulty charging pile is transmitted to the fault handling personnel in the form of a work order, the urgency of the work order is classified according to the cumulative number of charging times. The work order with more cumulative charging times indicates that the demand is high and is given a priority handling weight coefficient.

[0016] As a preferred technical solution of the present invention, in step D, the fault handling information reported by the fault handling personnel is checked in batches and efficiently. The specific method is as follows: after the fault handling personnel repair the fault, they are required to charge the charging pile, and the charging amount is greater than the power fault threshold, and report the fault handling date; then, according to the method of the present invention, the suspected fault piles up to the previous day are counted daily. If the faulty charging pile is not in the suspected fault pile information up to the fault handling date reported by the fault handling personnel, it indicates that the fault has been repaired. If the pile is still in the suspected fault pile information list, it indicates that the fault handling personnel have not completed the equipment fault repair.

[0017] The present invention also includes a database model constructed based on the above method.

[0018] The present invention also includes, based on the above-mentioned method or data model, performing charging pile fault diagnosis independently of the charging pile hardware self-testing system.

[0019] The present invention also includes, based on the above-mentioned method or data model, performing dual-system complementary fault diagnosis of charging piles in conjunction with existing charging pile hardware self-testing systems.

[0020] The beneficial effects of adopting the above technical solution are as follows: This invention abandons the existing data model for fault diagnosis based on the hardware self-test of charging pile equipment. Instead, it constructs a fault diagnosis database using big data from the power grid that is completely orthogonal to the charging pile hardware parameters, resulting in a completely new data model independent of the charging pile hardware self-test model. The independence of the data source and the data model ensures excellent complementarity and synergy between the old and new data models. This invention overcomes the problems of inaccurate fault information judgment and inability to accurately report faults in the interaction process between the charging pile and the vehicle during charging, which are present in existing charging pile fault self-test technologies. In addition, the additional technical solution of this invention can implement closed-loop management of charging pile faults at different urgency levels. This invention is an important supplement to the existing charging pile fault diagnosis technology, and can significantly improve the accuracy of charging pile fault diagnosis and the normal health level of the equipment. Detailed Implementation

[0021] The following embodiments detail the present invention. In the description of the following embodiments, specific details such as particular system structures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail. It should be understood that, as used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases “if determined” or “if [the described condition or event] is detected” can be interpreted, depending on the context, as meaning “once determined” or “in response to determined” or “once the described condition or event] is detected” or “in response to the detection of [the described condition or event]”. Furthermore, in the description of this application and the appended claims, the terms “first,” “second,” “third,” etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. References to “one embodiment” or “some embodiments”, etc., described in this application mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases “in one embodiment,” “in some embodiments,” “in other embodiments,” “in still other embodiments,” etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean “one or more, but not all, embodiments,” unless otherwise specifically emphasized. The terms “comprising,” “including,” “having,” and variations thereof mean “including, but not limited to,” unless otherwise specifically emphasized.

[0022] Example 1, Data Model

[0023] The construction of a data model specifically includes the following steps:

[0024] A. The full charging order data of charging piles within a set time period from the power grid data platform is used as the bottom database layer; the end point of the set time period is: the data of the day when the bottom database layer is built or updated minus 2; the start point of the set time period is set as: the duration of the time period is set to n days based on the specified end point, where n is a data range with 15 as the median.

[0025] Since the source and presentation form of this data model are big data models, the final value of n determines the scale of the big data model and satisfies the data rule that the larger the model, the higher the accuracy and the higher the data load. Therefore, under normal circumstances, the value of n is based on the median and fluctuates slightly. In other words, when updating the bottom database layer, the priority data rule is: new data replaces the end data; the alternative data rule is: new data is merged and accumulated.

[0026] The full charging order data refers to all order data within the time range, including orders that are charging normally and orders that have ended abnormally. Each order contains at least the charging pile identification number, the name of the charging station where the charging pile is located, and the charging amount. The charging pile identification number is a unique identification number that distinguishes different charging piles.

[0027] B. Construction of the basic cumulative data layer: The full charging order data obtained in step A is cumulatively analyzed, and the analysis rules are set as follows:

[0028] B-1. The cumulative number of charging sessions and the cumulative amount of charging for each charging pile in the geographic charging area network covered by the bottom database layer within a set time period;

[0029] B-2. Select charging piles with cumulative charging amount less than or equal to the power fault threshold as basic fault data nodes of the progressive data layer, corresponding to the preliminary suspected fault piles; wherein the power fault threshold is set to any value between (0-8) kW·h, based on big data simulation and compared with real-world fault data, currently set as follows: the power fault threshold of DC fast charging piles is anchored at 2 kW·h or fluctuates slightly, and the power fault threshold of AC slow charging piles is anchored at 0.5 kW·h or fluctuates slightly; wherein the median data anchor and the data fluctuation range are allowed to change in real time with the update of the data model and / or the change of the data model size; additionally, the cumulative number of charging times and cumulative charging amount or other information contained in the preliminary suspected fault pile information are externally bound to the basic fault data node;

[0030] C. Integration of Yesterday's Cumulative Data Layer: Obtain yesterday's full charging order data when constructing the bottom database layer. The order data includes orders that were charged normally and orders that ended abnormally. Each order contains at least the charging pile identification number, the name of the charging station where the charging pile is located, and the charging amount. Then, perform cumulative analysis on the obtained yesterday's order data and the preliminary suspected faulty charging piles on the basic fault data nodes in step B. Filter out the charging pile information whose cumulative charging amount is less than or equal to the power fault threshold and mark it as yesterday's fault data node, which includes suspected faulty charging pile information up to yesterday.

[0031] D: Constructing a fault data layer based on a probability model: The suspected faulty charging pile information screened in step C is analyzed according to the following rules: the more cumulative charging times but the less cumulative charging amount, the higher the probability that the charging pile is faulty; based on a pre-defined threshold, charging piles with a cumulative charging times greater than the charging times fault threshold and a cumulative charging amount less than the charging amount fault threshold are selected to construct a fault data layer; based on the fault data layer, work orders are generated and delivered to the fault handling personnel. The work order content includes the charging pile's identification number, the charging station information to which the charging pile belongs, or other information; when delivering faulty charging piles to the fault handling personnel in the form of work orders, the urgency of the work orders is classified according to the cumulative charging times. Those with more cumulative charging times indicate higher usage demand and are given a priority processing weight coefficient;

[0032] In addition, the fault handling information reported by the fault handling personnel can be efficiently verified in batches. The specific method is as follows: after the fault handling personnel repair the fault, they are required to charge the charging pile, and the charging amount is greater than the power fault threshold. They are also required to report the fault handling date. Then, according to the method of this invention, the suspected fault piles up to the previous day are counted daily. If the faulty charging pile is not in the suspected fault pile information up to the fault handling date reported by the fault handling personnel, it means that the fault has been repaired. If the pile is still in the suspected fault pile information list, it means that the fault handling personnel have not completed the equipment fault repair.

[0033] E. Accumulate and analyze the suspected faulty pile information filtered in step C up to yesterday with the full order data of the next day to obtain the latest suspected faulty pile information, and update the data model for date in accordance with the data rules.

[0034] Example 2, Application Process

[0035] This embodiment provides a method for diagnosing and handling charging pile faults based on charging pile order data, corresponding to the above data model. It offers a novel technical solution and provides another feasible approach to diagnosing charging pile faults. Specifically, it includes the following steps:

[0036] Step 1: Obtain all charging order data from the period before the day before yesterday to the day before yesterday. The period before the day before yesterday includes the day before yesterday, and the period is a time range of more than 2 days. Because this invention uses big data technology, the larger the data time range, the higher the accuracy. However, excessively large data can also cause other problems. Based on our experience, it is generally more suitable to take about 15 days. For example, if today is August 30th and the day before yesterday is August 28th, the order data from the period before the day before yesterday to the day before yesterday is the order data from 00:00 on August 13th (or August 20th, July 1st, or other dates) to 24:00 on August 28th. The full charging order data is all order data within the time range, including orders that are charging normally and orders that have ended abnormally. The order must contain at least the charging pile identification number, the name of the charging station where the charging pile is located, and the charging amount. The charging pile identification number is the identification information that distinguishes different charging piles. Each charging pile has its own unique identification number.

[0037] Step 2: Perform cumulative analysis on all charging order data from the period before the day before yesterday to the day before yesterday obtained in Step 1. Analyze the cumulative number of charging sessions and the cumulative charging amount for each charging pile within the time range of the period before the day before yesterday to the day before yesterday. Select charging piles with a cumulative charging amount less than or equal to the power fault threshold as preliminary suspected fault piles. The power fault threshold can be any value between (0-8) kW·h. Based on our experience, a power fault threshold of 2 kW·h is more suitable for DC fast charging piles, and a power fault threshold of 0.5 kW·h is more suitable for AC slow charging piles. The information of the selected preliminary suspected fault piles includes the cumulative number of charging sessions and the cumulative charging amount within the obtained time range.

[0038] Step 3: Obtain all charging order data from yesterday. The order data includes orders that were charged normally and orders that ended abnormally. Each order must contain at least the charging pile identification number, the name of the charging station where the charging pile is located, and the charging amount.

[0039] Step 4: Perform cumulative analysis on yesterday's order data obtained in Step 3 and the preliminary suspected faulty charging piles selected in Step 2, and select charging piles whose cumulative charging amount is less than or equal to the power fault threshold to obtain the suspected faulty charging pile information up to yesterday.

[0040] Step 5: Analyze the suspected faulty charging pile information screened in Step 4. Charging piles with a high number of cumulative charging attempts but low cumulative charging volume are considered faulty, with a higher probability of being faulty. Charging piles with a cumulative charging attempt exceeding the charging attempt threshold and a cumulative charging volume below the charging volume threshold are identified as faulty charging piles and are sent to the fault handling personnel in the form of a work order. The work order includes the charging pile's identification number and the charging station information to which the charging pile belongs. The charging attempt threshold is an integer value between 3 and 10; based on our operational experience, 5 attempts are considered suitable. When sending faulty charging piles to the fault handling personnel in the form of a work order, the urgency of the work order can be classified according to the cumulative charging attempt; a higher cumulative charging attempt indicates higher demand and should be prioritized.

[0041] Step 6: By summing and analyzing the suspected faulty pile information filtered in Step 4 up to yesterday, and the full order data for the next day, we can obtain the latest suspected faulty pile information. For example, if today is August 30th, we can obtain the suspected faulty pile and faulty pile information up to yesterday (August 29th) through Steps 1-5 above. On August 31st, we can sum and analyze the suspected faulty pile information up to August 29th and the order data on August 30th to obtain the suspected faulty pile and faulty pile information up to August 30th.

[0042] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0043] In various embodiments, the hardware implementation of the technology can directly utilize existing smart devices, including but not limited to industrial control computers, PCs, smartphones, handheld devices, and floor-standing devices. The input device preferably uses an on-screen keyboard, the data storage and calculation modules utilize existing memory, calculators, and controllers, the internal communication module utilizes existing communication ports and protocols, and the remote communication utilizes existing GPRS networks, the World Wide Web, etc. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy distinction and are not intended to limit the scope of protection of this application. The specific working processes of the units and modules in the above system can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the embodiments provided by this invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate; components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of this embodiment.

[0044] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for constructing a charging pile fault diagnosis database based on charging big data, characterized in that: The method comprises the following steps: A. Set the total charging order data of the charging piles in the set time period from the grid data as the bottom database layer; the end point of the set time period is the data of the day when the bottom database layer is constructed or updated minus 2, and the start point of the set time period is set as n days based on the specified end point, wherein n is a data interval with 15 as the median, and the swing radius r of n is set as r=5-50 days; the total charging order data is all the order data in the acquisition time range, and the order at least contains the charging pile identity number, the charging station name where the charging pile is located, and the charging power, wherein the charging pile identity number is the number information for distinguishing different charging piles, and each charging pile has its own unique identity number; B. Construction of the basic progressive data layer: cumulative analysis is performed on the total charging order data in the bottom database layer obtained in step A, and the analysis rule is set as: B-1, the cumulative charging times and the cumulative charging power of each charging pile in the set time period in the geographical charging area network covered by the bottom database layer; B-2, the charging piles with cumulative charging power less than or equal to the power failure threshold are selected as the basic failure data nodes of the progressive data layer, corresponding to the preliminary suspected fault piles; Wherein the power failure threshold is set as any value between (0-8) kW•h, based on big data simulation and comparison with real world failure data, at present, the direct current fast charging pile power failure threshold is set as 2 kW•h as the median data anchor point or small amplitude swing, and the alternating current slow charging pile power failure threshold is set as 0.5 kW•h as the median data anchor point or small amplitude swing; wherein the median data anchor point and the data swing amplitude can be changed in real time with the update of the data model and / or the change of the data model size; additionally, the cumulative charging times and the cumulative charging power or other information contained in the preliminary suspected fault pile information are externally bound on the basic failure data nodes; C. Integration of yesterday's progressive data layer: obtain the total charging order data of yesterday when the bottom database layer is constructed, wherein the order data contains normal charging orders and abnormal ending orders, and the order at least contains the charging pile identity number, the charging station name where the charging pile is located, and the charging power; and cumulative analysis is performed on the preliminary suspected fault charging piles on the basic failure data nodes in step B with the obtained yesterday's order data, and the charging pile information with cumulative charging power less than or equal to the power failure threshold is selected and marked as the yesterday's failure data node, containing the suspected fault charging pile information up to yesterday. D: Based on the probability model to filter and build the fault data layer: the suspected fault pile information screened out in step C is analyzed according to the following rules: the more the cumulative charging times but the less the cumulative charging capacity, the higher the probability of the charging pile being a fault charging pile; based on the pre-set threshold, the charging pile with cumulative charging times greater than the charging times fault threshold and cumulative charging capacity less than the charging capacity fault threshold is assigned to build a fault data layer; based on the fault data layer, work orders are generated and delivered to fault handling personnel, and the work order content includes charging pile identity number, charging pile information or other information. 2.The charging pile fault diagnosis database construction method based on charging big data according to claim 1, characterized in that: The method further comprises the following subsequent data steps: E, cumulative analysis of the suspected fault pile information screened out in step C up to yesterday and the full order data of the next day to obtain the latest suspected fault pile information, and date updating of the data model is performed in sequence according to the data rules. 3.The charging pile fault diagnosis database construction method based on charging big data according to claim 1, characterized in that: In step A, since the data model construction source and presentation form is a big data model, the final value of n determines the size of the big data model, and satisfies the data rule that the larger the model, the higher the accuracy and the higher the data load, so generally the value of n is determined by the median value with a small swing; in other words, when updating the bottom database layer, the priority data rule is: new data replaces the end data; the alternative data rule is: new data fusion accumulation. 4.The charging pile fault diagnosis database construction method based on charging big data according to claim 1, characterized in that: In step D, the charging times fault threshold is set to an integer value between 3 and 10, and the value 5 is the optimal data anchor point; the charging capacity fault threshold is set to any value between 0 and 8 kW•h, based on big data simulation and comparison with real world fault data, currently set to: the DC fast charging pile charging capacity fault threshold is set to 2 kW•h as the median data anchor point or with a small swing, and the AC slow charging pile charging capacity fault threshold is set to 0.5 kW•h as the median data anchor point or with a small swing; wherein the median data anchor point and the data swing amplitude are allowed to be changed in real time with the update of the data model and / or the change of the data model size. 5.The charging pile fault diagnosis database construction method based on charging big data according to claim 1, characterized in that: In step D, when the fault charging pile is delivered to the fault handling personnel in the form of a work order, the emergency level of the work order is classified according to the cumulative charging times, and a high use demand indicates a high priority processing weight coefficient. 6.The charging pile fault diagnosis database construction method based on charging big data according to claim 1, characterized in that: In step D, the fault handling information fed back by the fault handling personnel is checked in batches and efficiently, and the specific method is: requiring the fault handling personnel to charge the charging pile after repairing the fault, the charging capacity is greater than the charging capacity fault threshold, and the fault handling date is fed back; Then according to the method of the present application, the suspected fault pile information up to the previous day is counted daily, if the suspected fault pile information up to the fault handling date fed back by the fault handling personnel does not include the fault charging pile, it means that the fault has been repaired, if the pile is still in the suspected fault pile information list, it means that the fault handling personnel has not completed the equipment fault repair.

7. A database model constructed based on the method of any one of claims 1-6.

8. The method of any one of claims 1-6 or the data model of claim 7, for charging pile fault diagnosis independent of the charging pile hardware self-checking system.

9. The method of any one of claims 1-6 or the data model of claim 7, in conjunction with an existing charging pile hardware self-checking system to perform complementary fault diagnosis of the charging pile in a dual system.