A vehicle-pile consistency detection method, charging pile selection method and system

Abstract

The present invention provides a vehicle-pile consistency detection method, a charging pile selection method and a system, obtains historical charging records, establishes a charging pile data matrix, a vehicle data matrix and a charging start time data matrix; clusters the vehicle data matrix according to vehicle classification to obtain each type of set matrix, and splits the charging start time data matrix into multiple charging time matrices; performs calculations on each vehicle classification element of each charging time matrix separately, calculates the average start time of each type of vehicle for each charging pile; judges the vehicles and charging piles with abnormal charging time of the corresponding type of vehicles for the corresponding charging pile according to the average start time; calculates the abnormality of the vehicle and the charging pile, and records the corresponding type of vehicles and charging piles whose abnormality exceeds the set threshold. The present invention can meet the detection diversity requirements of vehicle models and charging piles, and greatly shortens the time for detection and problem discovery.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy vehicles, and specifically relates to a vehicle-charging pile consistency detection method, a charging pile selection method and a charging pile system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Building a dense fast charging network is a key factor in the popularization of electric vehicles. With the popularization of new energy electric vehicles, electric vehicle charging equipment has also entered a stage of rapid development. However, the consistency detection between electric vehicles and charging piles has not received enough attention. Comprehensive testing in this regard is essential for new energy vehicle manufacturers, charging pile manufacturers, and charging operators. At present, there are more and more types of electric vehicles and charging piles. There are consistency issues between old cars and new piles, new cars and old piles, and new cars and new piles. Simply relying on a single detection device to test is far from being able to guarantee and discover consistency issues that occur during the release of new products and product operation.

[0004] At present, researchers have noticed the above problems. For example, Chinese invention patent CN111884776A provides a "Charging Pile Protocol Consistency Test Method", which simulates the communication between the BMS system in the electric vehicle and the charging pile through a BMS simulation device, and determines which protocol it belongs to through the period, byte length and content of the message between different protocols, thereby judging the consistency of the protocol. However, this patent still does not solve the problem of how to conduct timely, effective and comprehensive testing to discover the consistency problem of the vehicle and charging pile when there are various types of electric vehicles and charging piles (old car and new pile, new car and old pile, new car and new pile). Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a vehicle-pile consistency detection method, a charging pile selection method and a system. The present invention can meet the detection diversity requirements of vehicle models and charging piles, and greatly shortens the time for detection and problem discovery.

[0006] According to some embodiments, the present invention adopts the following technical solutions:

[0007] A method for detecting consistency of a vehicle and a pile, comprising the following steps:

[0008] Obtain historical charging records, and establish charging pile data matrix, vehicle data matrix, and charging start time data matrix;

[0009] According to the vehicle classification, the vehicle data matrix is ​​clustered to obtain the set matrix of each type, and the charging start time data matrix is ​​correspondingly split into multiple charging time matrices;

[0010] Each vehicle classification element of each charging time matrix is ​​calculated separately to calculate the average starting time of each type of vehicle for each charging pile;

[0011] According to the average startup time, determine the vehicles and charging piles with abnormal startup time for the corresponding type of vehicles and the corresponding charging piles;

[0012] Calculate the abnormality of vehicles and charging piles, and record the corresponding type of vehicles and charging piles whose abnormality exceeds the set threshold.

[0013] As an optional implementation method, the specific process of establishing the charging pile data matrix includes:

[0014] From the charging records, count the number of used charging piles, establish a data set of all charging piles, and obtain a charging pile matrix.

[0015] As an optional implementation, the specific process of establishing the vehicle data matrix includes: charging records, counting the number of electric vehicles therein, establishing a data set of all electric vehicles, and obtaining the vehicle data matrix.

[0016] As an optional implementation, the specific process of establishing a charging start time data matrix includes: calculating the corresponding charging start time according to the charging request time and the charging voltage output start time in the charging record, and establishing a two-dimensional data matrix of the vehicle charging start time.

[0017] As an optional implementation, according to the vehicle classification, the specific process of clustering the vehicle data matrix includes: performing cluster calculation on the vehicle data matrix according to the brand, model and configuration similarity of the electric vehicle to form a set matrix of different vehicle classifications.

[0018] As an optional implementation, the specific process of splitting the charging start time data matrix into multiple charging time matrices includes: according to the vehicle type classification, splitting the charging start time matrix into multiple charging time matrices classified according to the vehicle type to which the electric vehicle belongs.

[0019] As an optional implementation, the specific process of calculating the average start-up time of each type of vehicle for each charging pile includes: calculating the average start-up time of all vehicles of the type for a certain charging pile, wherein the average start-up time is the median of the sequence obtained after sorting the charging start-up time of all electric vehicles of the type;

[0020] The average starting time of all electric vehicles of this model for other charging piles is calculated in turn to obtain a multi-dimensional row vector.

[0021] As an optional implementation, the specific process of determining whether a corresponding type of vehicle has abnormal charging start time at a corresponding charging pile includes: if the charging start time of a vehicle of this type at the corresponding charging pile is greater than the sum of the average start time of vehicles of this type at the charging pile and a reasonable deviation value, then the vehicle is considered to have an abnormality.

[0022] As an optional implementation, the specific process of determining the charging piles with abnormal charging start time for the corresponding type of vehicles includes: determining the charging piles with charging start time variations exceeding a set value for different types of vehicles;

[0023] Obtain all charging start time records of the charging pile in the vehicle pile start time data matrix;

[0024] Using the median and absolute deviation of the data records, estimate the overall mean and standard deviation of the charging start time;

[0025] The judgment threshold is constructed based on the median, absolute deviation, mean and standard deviation. When the charging start time of the charging pile for a certain type of vehicle exceeds the judgment threshold, it is considered that the charging pile is abnormal for this type of vehicle.

[0026] As an optional implementation, the specific process of calculating the abnormality of vehicles and charging piles includes: if the proportion of abnormal charging piles for a certain type of vehicle to all piles exceeds a set value, the abnormality is considered unqualified and the vehicle of this type is marked.

[0027] A charging pile selection method comprises the following steps:

[0028] Obtain configuration parameters of the vehicle to be charged and determine the type of vehicle to which it belongs;

[0029] Based on the above vehicle-pile consistency detection method, determine the charging piles that are abnormal for this type of vehicle and eliminate them;

[0030] Predict the charging start time of the remaining charging piles;

[0031] The charging piles whose predicted values ​​are greater than the abnormal charging pile startup time threshold are excluded, and a charging pile is selected from the remaining charging piles to charge the vehicle.

[0032] As an optional implementation, when determining the vehicle type, it is directly determined based on the vehicle configuration parameters, or the similarity of the average startup time values ​​of vehicles of this type or similar types is calculated in sequence, and the average startup time values ​​of several most similar electric vehicle models are found to determine the vehicle type.

[0033] As a further limitation, when predicting the charging start time of the remaining charging piles, the predicted value is calculated based on the average start time value of the vehicle of this type or the average start time value of the vehicle of a similar type.

[0034] A vehicle-pile consistency detection system, comprising:

[0035] A parameter acquisition module is configured to acquire historical charging records, establish a charging pile data matrix, a vehicle data matrix, and a charging start time data matrix;

[0036] A clustering module is configured to cluster the vehicle data matrix according to the vehicle classification to obtain each type set matrix, and to split the charging start time data matrix into a plurality of charging time matrices accordingly;

[0037] A calculation module is configured to perform calculations on each vehicle classification element of each charging time matrix separately to calculate the average starting time of each type of vehicle for each charging pile;

[0038] A judgment module is configured to judge, based on the average starting time, vehicles and charging piles with abnormal starting time of corresponding types of vehicles for corresponding charging piles;

[0039] The abnormality recording module is configured to calculate the abnormality of the vehicle and the charging pile, and record the corresponding type of vehicles and charging piles whose abnormality exceeds a set threshold.

[0040] A charging pile selection system, comprising:

[0041] Obtain configuration parameters of the vehicle to be charged and determine the type of vehicle to which it belongs;

[0042] The vehicle-pile consistency detection system is used to identify and eliminate abnormal charging piles for this type of vehicle;

[0043] A prediction module is used to predict the charging start time of the remaining charging piles;

[0044] The screening module is used to exclude charging piles whose predicted values ​​are greater than the abnormal charging pile startup time threshold, and select a charging pile from the remaining charging piles to charge the vehicle.

[0045] An electronic device comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the above method are completed.

[0046] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps in the above method are completed.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention proposes a vehicle-pile consistency detection method, which evaluates the service quality of the charging pile by comparing the differences in charging start time between various types of vehicle models, and optimizes the differences; it can find the vehicle models that cannot be well adapted to the charging piles, solve the problems according to the characteristics of the vehicle models, and optimize them.

[0049] The present invention also provides a charging pile screening method, which predicts the charging start-up time of a new charging pile for a certain vehicle, excludes abnormal electric vehicles in combination with vehicle-pile consistency detection, and recommends the best or better charging pile for the vehicle in the application.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0052] Figure 1 This is a flow chart of vehicle-pile consistency detection according to an embodiment of the present invention;

[0053] Figure 2 The figure is a schematic diagram of the charging pile selection process in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0056] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0057] As described in the background technology, the existing charging pile consistency test has the following shortcomings: due to the single test sample of the single detection device, there are problems such as incomplete detection and repeated detection during the detection work, resulting in high detection costs, which is unaffordable for some small automobile manufacturers and suppliers; electric vehicle manufacturers, charging pile manufacturers, and charging operators have different detection needs, and there is currently no mature, stable and comprehensive detection database solution.

[0058] A vehicle-pile consistency detection method, comprising:

[0059] (1) Establish a charging record library. Obtain relevant charging process data from the charging record to calculate the estimated value of the charging start time. Read the maximum allowable charging voltage of the charging pile in the charging record: V pmax , Current battery voltage: V bat , insulation voltage: V insul , this startup time: T start , the standard startup time of the module: T mod . , the charging start time T is obtained to evaluate the charging process.

[0060] Among them: Charging start time

[0061] The closer T is to 0, the smoother the communication interaction.

[0062] (2) Establish the charging pile matrix Piles. From the charging records, count the number of used charging piles, establish a data set of all charging piles, and obtain the charging pile matrix Piles.

[0063] Where: Piles = {Pile 1 ,Pile 2 ,…,Pile m}

[0064] (3) Establish the electric vehicle data matrix Car. Obtain all charging records, count the number of electric vehicles in them, establish a data set of all electric vehicles, and obtain the electric vehicle data matrix Cars.

[0065] Where: Cars = {Car 1 ,Car 2 ,…,Car n}

[0066] (4) Establishing the data matrix Times of the vehicle charging start time. Based on all charging records, establish the two-dimensional data matrix Times of the vehicle charging start time, and count the charging start time of each charging record. Among them:

[0067] Each element in the matrix Time ijIndicates electric car Car i Use the charging pile j The startup time.

[0068] Electric Car i The total charging start time is the row vector Time i , Indicates the arithmetic mean of the startup duration of all charging services of the pile.

[0069] (5) Clustering vehicle models. Clustering calculations are performed on the electric vehicle data matrix Cars according to the similarity of the electric vehicle brand, model, and configuration, forming a collection matrix {Cars 1 ,Cars 2 ,…,Cars t}.

[0070] (6) According to the vehicle type classification, the charging start time matrix Times is split into t charging time matrices Times'={Times 1’ ,Times 2’ ,…,Times t’}.

[0071] (7) Perform calculations on each vehicle type classification element in Times' separately.

[0072] (8) Calculate the average startup time Tm of all electric vehicles of this model on charging pile j center,j .Tm center,j After sorting the charging start time of all electric vehicles of this model, take the median of the sequence.

[0073] (9) The same method is used to calculate the average startup time of all electric vehicles of this model for each charging pile, and an m-dimensional row vector Tm is obtained. center .

[0074] (10) Find out the electric vehicles of this model that have abnormal charging start time on charging pile j. The judgment criteria are:

[0075]

[0076] in: β is the start-up time adjustment correlation coefficient of the vehicle model, which is 5 in this case.

[0077] By comparing different vehicle models, we can identify charging stations with abnormal reactions and conduct focused testing and analysis based on the characteristics of each vehicle.

[0078] (11) Identify abnormal charging piles for different vehicle models. The judgment criteria are that the charging start time of different vehicle models varies significantly:

[0079] Take out the charging pile information in Piles one by one, such as charging pile P.

[0080] Read all charging start time records of pile p in the car pile start time data matrix Times to establish a data matrix TP;

[0081] Use the sample median med and absolute deviation MAD to estimate the overall mean of the charging start time and standard deviation

[0082] Calculate the median charging start time med i

[0083] med i =median(TP)

[0084] Calculate the median absolute deviation (MAD) of the charging start time

[0085] MAD=med i (|TP i -med j (TP)|), where i=1,…k, j=1,…k.

[0086] The duration of each charging record in TP is taken to determine whether there is an abnormal amount of charging start duration for the electric vehicle in the charging record. k is the total number of charging records.

[0087] The median charging start time is used as the overall mean charging start time

[0088]

[0089] The median absolute deviation (MAD) of the charging start time is used as the standard deviation.

[0090]

[0091] If and only if It indicates that charging pile p is abnormal for electric vehicle model M.

[0092] Among them, region(j) represents the model of electric vehicle j.

[0093] After finding the charging station that reacts abnormally to a certain model, you can conduct special testing and analysis for that model.

[0094] (12) Calculate the abnormality of the vehicle and pile model

[0095] Calculate the ratio of abnormal charging piles for this electric vehicle model to all charging piles.

[0096] If the abnormality is greater than the threshold τ (20% in this embodiment), it indicates that the electric vehicle model has a common charging startup abnormality problem, indicating that its consistency with most charging piles is relatively poor, and the charging piles need to be adapted and rectified for the electric vehicle model.

[0097] The present invention also proposes predicting the charging start-up time of an unused charging pile, comprising the following steps:

[0098] For the electric car vcar and its unused pile, the following steps are used to predict the startup time:

[0099] 1. Find the model κ according to the vehicle configuration. If not found, create a new model library κ containing the vehicle.

[0100] 2. Check whether the pile is an abnormal pile for the model of electric vehicle. If it is an abnormal pile, due to the particularity of the electric vehicle model, the predicted startup time is Tm center,κ

[0101] 3. Calculate the similarity of the average starting time of the electric car model m and other electric car models that have tested the pile, and find the most similar average starting time of the first k electric car models {V 1 ,V 2 ,…,V K}

[0102] Calculate the similarity between model m and model n:

[0103]

[0104] Where S(m) and S(n) represent the charging piles used by vehicle type m and vehicle type n respectively.

[0105] Where sim(m,n) indicates that the correlation between m and n is calculated using the PCC algorithm.

[0106] Based on the similarity ranking, the models with higher scores are the most similar.

[0107] 4. If the charging pile is not an abnormal pile for the model, and the model has the charging start time Tm of the charging pile center,κ , then the predicted value of the charging start time of the electric vehicle vcar is:

[0108]

[0109] 5. If the charging pile is not an abnormal pile for the model, and the model does not have the charging start time Tm of the charging pile center,κ, but similar models have the charging start time value of the pile, then the predicted value is:

[0110]

[0111] 6. Otherwise, the charging pile is an abnormal pile for this model and no prediction is made.

[0112] 7. Determine whether the predicted value is greater than the above abnormal charging pile startup time judgment formula If it is greater, it means that the startup time may be abnormal, indicating that the car and the pile do not match, or more historical data is needed for prediction.

[0113] like Figure 2 As shown, based on the above prediction of the charging start time of the unused charging pile and the above vehicle-pile consistency detection process, the abnormal charging piles of this type of vehicle are screened and excluded, which can be used to select a more suitable or most suitable charging pile for the electric vehicle. The charging pile can be an old pile or a new pile, and the applicable car can be a new electric car or an old electric car that has been charged before.

[0114] 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.

[0115] 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.

[0116] 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 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] 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 A step that specifies a function in one or more boxes.

[0118] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for detecting consistency between a vehicle and a pile, characterized in that: The following steps are involved: Obtain historical charging records, and establish charging pile data matrix, vehicle data matrix, and charging start time data matrix; The specific process of establishing the charging pile data matrix includes: From the charging records, count the number of used charging piles, establish a data set of all charging piles, and obtain a charging pile data matrix; The specific process of establishing the vehicle data matrix includes: charging records, counting the number of electric vehicles, establishing a data set of all electric vehicles, and obtaining a vehicle data matrix; The specific process of establishing the charging start time data matrix includes: calculating the corresponding charging start time according to the charging request time and the charging voltage output start time in the charging record, and establishing a two-dimensional data matrix of the vehicle pile charging start time; According to the vehicle classification, the vehicle data matrix is ​​clustered to obtain the set matrix of each type, and the charging start time data matrix is ​​correspondingly split into multiple charging time matrices; The specific process of splitting the charging start time data matrix into a plurality of charging time matrices includes: according to the classification of vehicle models, splitting the charging start time matrix into a plurality of charging time matrices classified according to the vehicle models to which the electric vehicles belong; Each vehicle classification element of each charging time matrix is ​​calculated separately to calculate the average starting time of each type of vehicle for each charging pile; The specific process of calculating the average starting time of each type of vehicle for each charging pile includes: calculating the average starting time of all vehicles of the type for a certain charging pile, wherein the average starting time is the median of the sequence after sorting the charging starting time of all electric vehicles of the type; Calculate the average starting time of all electric vehicles of this model for other charging piles in turn, and obtain a multi-dimensional row vector; According to the average startup time, determine the vehicles and charging piles with abnormal startup time for the corresponding type of vehicles and the corresponding charging piles; The specific process of judging whether the corresponding type of vehicle has abnormal charging time start at the corresponding charging pile includes: if the charging start time of a certain type of vehicle at the corresponding charging pile is greater than the sum of the average start time of the type of vehicle at the charging pile and a reasonable deviation value, then the vehicle is considered to be abnormal; The specific process of determining the charging piles with abnormal charging start time for the corresponding type of vehicles for the corresponding charging piles includes: determining the charging piles with charging start time variations exceeding a set value for different types of vehicles; Obtain all charging start time records of the charging pile in the vehicle pile start time data matrix; Using the median and absolute deviation of the data records, estimate the overall mean and standard deviation of the charging start time; The judgment threshold is constructed based on the median, absolute deviation, mean and standard deviation. When the charging start time of the charging pile for a certain type of vehicle exceeds the judgment threshold, it is considered that the charging pile is abnormal for this type of vehicle. Calculate the abnormality of vehicles and charging piles, and record the corresponding type of vehicles and charging piles whose abnormality exceeds the set threshold.

2. A vehicle-pile consistency detection method as claimed in claim 1, characterized in that: According to the vehicle classification, the specific process of clustering the vehicle data matrix includes: performing clustering calculations on the vehicle data matrix according to the brand, model and configuration similarity of the electric vehicles to form a collection matrix of different vehicle model classifications.

3. A vehicle-pile consistency detection method as claimed in claim 1, characterized in that: The specific process of calculating the abnormality of vehicles and charging piles includes: if the proportion of abnormal charging piles for a certain type of vehicle to all piles exceeds a set value, the abnormality is considered unqualified and the vehicle of this type is marked.

4. A charging pile selection method, characterized in that: The following steps are involved: Obtain configuration parameters of the vehicle to be charged and determine the type of vehicle to which it belongs; Based on the vehicle-pile consistency detection method described in any one of claims 1 to 3, determine the charging piles that are abnormal for the type of vehicle and exclude them; Predict the charging start time of the remaining charging piles; The charging piles whose predicted values ​​are greater than the abnormal charging pile startup time threshold are excluded, and a charging pile is selected from the remaining charging piles to charge the vehicle.

5. A charging pile selection method as claimed in claim 4, characterized in that: When determining the type of vehicle to which it belongs, it is directly determined based on the vehicle configuration parameters, or the similarity of the average starting time values ​​of vehicles of this type or similar types is calculated in sequence, and the average starting time values ​​of several most similar electric vehicle models are found to determine the vehicle type.

6. A charging pile selection method as claimed in claim 5, characterized in that: When predicting the charging start time of the remaining charging piles, the predicted value is calculated based on the average start time value of the type of vehicle or the average start time value of similar types of vehicles.

7. A vehicle-pile consistency detection system, characterized in that: include: A parameter acquisition module is configured to acquire historical charging records, establish a charging pile data matrix, a vehicle data matrix, and a charging start time data matrix; The specific process of establishing the charging pile data matrix includes: From the charging records, count the number of used charging piles, establish a data set of all charging piles, and obtain a charging pile data matrix; The specific process of establishing the vehicle data matrix includes: charging records, counting the number of electric vehicles, establishing a data set of all electric vehicles, and obtaining a vehicle data matrix; The specific process of establishing the charging start time data matrix includes: calculating the corresponding charging start time according to the charging request time and the charging voltage output start time in the charging record, and establishing a two-dimensional data matrix of the vehicle pile charging start time; A clustering module is configured to cluster the vehicle data matrix according to the vehicle classification to obtain each type set matrix, and to split the charging start time data matrix into a plurality of charging time matrices accordingly; The specific process of splitting the charging start time data matrix into a plurality of charging time matrices includes: according to the classification of vehicle models, splitting the charging start time matrix into a plurality of charging time matrices classified according to the vehicle models to which the electric vehicles belong; A calculation module is configured to perform operations on each vehicle classification element of each charging time matrix separately to calculate the average starting time of each type of vehicle for each charging pile; The specific process of calculating the average starting time of each type of vehicle for each charging pile includes: calculating the average starting time of all vehicles of the type for a certain charging pile, wherein the average starting time is the median of the sequence after sorting the charging starting time of all electric vehicles of the type; Calculate the average starting time of all electric vehicles of this model for other charging piles in turn, and obtain a multi-dimensional row vector; A judgment module is configured to judge, based on the average starting time, vehicles and charging piles with abnormal starting time of corresponding types of vehicles for corresponding charging piles; The specific process of judging whether the corresponding type of vehicle has abnormal charging time start at the corresponding charging pile includes: if the charging start time of a certain type of vehicle at the corresponding charging pile is greater than the sum of the average start time of the type of vehicle at the charging pile and a reasonable deviation value, then the vehicle is considered to be abnormal; The specific process of determining the charging piles with abnormal charging start time for the corresponding type of vehicles for the corresponding charging piles includes: determining the charging piles with charging start time variations exceeding a set value for different types of vehicles; Obtain all charging start time records of the charging pile in the vehicle pile start time data matrix; Using the median and absolute deviation of the data records, estimate the overall mean and standard deviation of the charging start time; The judgment threshold is constructed based on the median, absolute deviation, mean and standard deviation. When the charging start time of the charging pile for a certain type of vehicle exceeds the judgment threshold, it is considered that the charging pile is abnormal for this type of vehicle. The abnormality recording module is configured to calculate the abnormality of the vehicle and the charging pile, and record the corresponding type of vehicles and charging piles whose abnormality exceeds a set threshold.

8. A charging pile selection system, characterized in that: include: Obtain configuration parameters of the vehicle to be charged and determine the type of vehicle to which it belongs; The vehicle-pile consistency detection system according to claim 7 is used to determine and eliminate abnormal charging piles for the type of vehicle; A prediction module is used to predict the charging start time of the remaining charging piles; The screening module is used to exclude charging piles whose predicted values ​​are greater than the abnormal charging pile startup time threshold, and select a charging pile from the remaining charging piles to charge the vehicle.

9. An electronic device, characterized in that: It includes a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps of the vehicle-pile consistency detection method described in any one of claims 1 to 3 are completed, or the steps of the charging pile selection method described in any one of claims 4 to 6 are completed.

10. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps of the vehicle-pile consistency detection method described in any one of claims 1-3, or complete the steps of the charging pile selection method described in any one of claims 4-6.

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