Charging behavior classification method and device, terminal and storage medium

Abstract

The application discloses a charging behavior classification method and device, a terminal and a storage medium. The method comprises the following steps: acquiring charging behavior data of a vehicle; weighting the charging behavior data to obtain weighted charging behavior data; and determining a charging behavior category of the vehicle based on the weighted charging behavior data. The application reduces the dimensionality of high-dimensional input charging behavior data by weighting the charging behavior data, thereby improving the accuracy of charging behavior category identification of electric vehicle users.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a method, apparatus, terminal, and storage medium for classifying charging behavior. Background Technology

[0002] The charging behaviors of Chinese electric vehicle users exhibit several characteristics: regardless of holidays or weekdays, and regardless of the vehicle's intended use, peak charging times are concentrated in the early morning and late at night; and users choose DC charging far more often than AC charging. Identifying these charging behavior categories can be beneficial for the development of electric vehicles.

[0003] Currently, determining the charging behavior categories of electric vehicle users primarily involves first collecting data to obtain vehicle charging behavior data. This data includes parameters such as average charging termination level, the range of most frequent charging termination levels, and the standard deviation of charging termination levels. Then, the charging behavior categories of electric vehicle users are identified using this vehicle charging behavior data.

[0004] However, the vehicle charging behavior data in the above methods cannot truly reflect the characteristics of charging behavior, resulting in low accuracy in judging the charging behavior category of electric vehicle users. Summary of the Invention

[0005] The main objective of this application is to provide a charging behavior classification method, device, terminal, and storage medium to solve the problem of low accuracy in judging the charging behavior category of electric vehicle users in related technologies.

[0006] To achieve the above objectives, firstly, this application provides a charging behavior classification method, including:

[0007] Obtain vehicle charging behavior data;

[0008] The charging behavior data is weighted to obtain the weighted charging behavior data.

[0009] Based on the weighted charging behavior data, the charging behavior category of the vehicle is determined.

[0010] In one possible implementation, charging behavior data is represented by a probability matrix, where the columns of the probability matrix represent multiple charge ranges, and the rows of the probability matrix represent the probability of the vehicle's charging behavior in any of the multiple charge ranges.

[0011] In one possible implementation, the charging behavior data is weighted to obtain weighted charging behavior data, including:

[0012] Obtain a symmetric matrix;

[0013] Multiplying the probability matrix by the symmetric matrix yields the weighted charging behavior data.

[0014] In one possible implementation, the vehicle's charging behavior category is determined based on weighted charging behavior data, including:

[0015] The weighted charging behavior data is input into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data.

[0016] Clustering algorithms are used to perform cluster analysis on the probability of charging behavior categories to obtain the target classification results;

[0017] The charging behavior category with the highest probability density is selected from the target classification results as the vehicle's charging behavior category.

[0018] In one possible implementation, before inputting the weighted charging behavior data into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data, the following steps are also included:

[0019] Obtain the initial self-organizing map network and training data;

[0020] The initial self-organizing map network is trained using training data, and the trained self-organizing map network is obtained when a preset threshold is reached.

[0021] In one possible implementation, the method also includes:

[0022] The charging behavior data and the weighted charging behavior data are respectively input into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the charging behavior data and the charging behavior category probability corresponding to the weighted charging behavior data.

[0023] Clustering algorithms were used to perform cluster analysis on the charging behavior category probabilities corresponding to the charging behavior data and the weighted charging behavior data to obtain the first classification result and the second classification result. The second classification result is the same as the target classification result.

[0024] Based on the first classification result and the second classification result, a first charging behavior category and a second charging behavior category are determined, wherein the second charging behavior category is the charging behavior category of the vehicle.

[0025] In one possible implementation, the method also includes:

[0026] Obtain the vehicle's original charging behavior category;

[0027] The first charging behavior category, the second charging behavior category, and the original charging behavior category are compared to obtain the comparison results. The comparison results are used to verify the consistency between the first charging behavior category, the second charging behavior category, and the original charging behavior category.

[0028] Secondly, embodiments of the present invention provide a charging behavior classification device, comprising:

[0029] The acquisition module is used to acquire vehicle charging behavior data;

[0030] The weighting module is used to weight the charging behavior data to obtain weighted charging behavior data;

[0031] The classification module is used to determine the charging behavior category of a vehicle based on the weighted charging behavior data.

[0032] Thirdly, embodiments of the present invention provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the charging behavior classification methods described above.

[0033] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the charging behavior classification methods described above.

[0034] This invention provides a charging behavior classification method, device, terminal, and storage medium, comprising: first acquiring vehicle charging behavior data; then weighting the charging behavior data to obtain weighted charging behavior data; and finally determining the vehicle's charging behavior category based on the weighted charging behavior data. This invention improves the accuracy of identifying the charging behavior category of electric vehicle users by weighting the charging behavior data, thereby reducing the dimensionality of the high-dimensional input charging behavior data. Attached Figure Description

[0035] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings:

[0036] Figure 1 This is a flowchart illustrating the implementation of a charging behavior classification method provided in an embodiment of the present invention.

[0037] Figure 2 This is a schematic diagram of the comparison results provided in an embodiment of the present invention;

[0038] Figure 3This is a schematic diagram of the structure of a charging behavior classification device provided in an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of the terminal provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0042] It should be understood that in the various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0043] It should be understood that in this invention, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0044] It should be understood that in this invention, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, "and / or B" can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "Contains A, B, and C", "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0045] It should be understood that in this invention, "B corresponding to A", "B corresponding to A", "A and B correspond", or "B and A correspond" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information. Matching A and B is defined as a similarity between A and B that is greater than or equal to a preset threshold.

[0046] Depending on the context, "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection."

[0047] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0049] In one embodiment, such as Figure 1 As shown, a charging behavior classification method is provided, including the following steps:

[0050] Step S101: Obtain vehicle charging behavior data.

[0051] The charging behavior data in this application is mainly represented by a probability matrix of the charging behavior of electric vehicle users.

[0052] Depending on the number of input data types, the probability matrix can be multidimensional, and the specific types and number of input data should not limit the scope of protection of this patent. In this embodiment, a one-dimensional probability matrix is ​​used, in which case the data can be regarded as a column vector. The index of this column vector is the power range (here, 10 ranges), such as 0-10% SOC, 10%-20% SOC, 20%-30% SOC, 30%-40% SOC, 40%-50% SOC, 50%-60% SOC, 60%-70% SOC, 70%-80% SOC, 80%-90% SOC, and 90%-100% SOC, representing the SOC at the end of charging; and the only column of this column vector is the frequency with which the SOC at the end of charging falls within the above power range during multiple charging processes of the vehicle.

[0053] Then, the charging behavior of electric vehicle users is converted into probabilities corresponding to the above-mentioned power range, thus forming the charging behavior data of electric vehicle users.

[0054] Suppose there are three electric vehicles: electric vehicle A, electric vehicle B, and electric vehicle C. Electric vehicle A is always fully charged; electric vehicle B is fully charged half the time and charged to 80%-90% the other half; electric vehicle C is fully charged 80% of the time and remains almost completely depleted 20% of the time.

[0055] In this case, the charging behavior data of electric vehicle A, electric vehicle B, and electric vehicle C are [0,0,0,0,0,0,0,0,0,1], [0,0,0,0,0,0,0,0,0.5,0.5], and [0.2,0,0,0,0,0,0,0,0,0.8], respectively.

[0056] To facilitate understanding the process of converting electric vehicle user charging behavior into charging behavior data, we will use the charging behavior data of electric vehicle A as an example. Since electric vehicle A charges to a full charge every time, the probability of its charging behavior falling within the 90%–100% SOC range is 1, while the probabilities of falling within the 0–10%, 10%–20%, 20%–30%, 30%–40%, 40%–50%, 50%–60%, 60%–70%, 70%–80%, and 80%–90% SOC ranges are all 0. Therefore, the charging behavior data for electric vehicle A is represented as [0,0,0,0,0,0,0,0,0,1]. The conversion process for the charging behavior data of electric vehicles B and C is similar to that of electric vehicle A, and will not be repeated here.

[0057] Step S102: Weight the charging behavior data to obtain weighted charging behavior data.

[0058] After obtaining the charging behavior data of electric vehicles A, B, and C through the above embodiments, since the charging behavior data is high-dimensional, in order to prevent the input of high-dimensional data from affecting the classification effect, this invention weights the high-dimensional data. Essentially, it multiplies the high-dimensional data by a symmetric matrix. The purpose is to "distribute" the values ​​of the first dimension exponentially to the second, third, and so on dimensions according to distance. This reduces the influence of high-dimensional data on determining the charging behavior category, thereby improving the accuracy of the charging behavior category determination.

[0059] Specifically, the charging behavior data is weighted to obtain weighted charging behavior data. This mainly involves obtaining a symmetric matrix and then multiplying the probability matrix with the symmetric matrix to obtain the weighted charging behavior data.

[0060] The symmetric matrix D used in this application is represented as follows:

[0061]

[0062] Let a = 0.5 and e be the natural logarithm in the symmetric matrix D. Multiply the charging behavior data of electric vehicles A, B, and C by the symmetric matrix D respectively to obtain the weighted charging behavior data of electric vehicle A: E[0,0.01,0.01,0.02,0.03,0.05,0.09,0.15,0.24,0.40], the weighted charging behavior data of electric vehicle B: F[0.01,0.01,0.01,0.02,0.04,0.06,0.10,0.17,0.28,0.30], and the weighted charging behavior data of electric vehicle C: G[0.08,0.05,0.04,0.03,0.04,0.05,0.07,0.12,0.19,0.32].

[0063] Step S103: Determine the vehicle's charging behavior category based on the weighted charging behavior data.

[0064] Among them, the charging behavior category is used to characterize the charging selection behavior adopted by the user during the charging process of the vehicle, and the same or similar charging selection behavior is regarded as the same charging behavior category.

[0065] To determine the charging behavior category of a vehicle based on weighted charging behavior data, it is necessary to first train a self-organizing map network using the weighted charging behavior data. The trained self-organizing map network is a dimensionality reduction result of the charging behavior data.

[0066] Among them, the Self-organizing Map (SOM) network is an unsupervised neural network. Unlike general neural networks that are trained based on backpropagation of the loss function, it uses a competitive learning strategy, relying on the competition between neurons to gradually optimize the network. Because it has fewer network layers, faster training, and a two-dimensional output layer structure, which is beneficial for visualization, it is also used for data dimensionality reduction tasks. In this embodiment, due to the limited number of input data intervals (10... 1 Its advantages are unclear. However, if the number of input data categories increases to 3, the probability matrix becomes a three-dimensional matrix, and the number of input data intervals will increase to 10. 3 When the data is too sparse, using SOM dimensionality reduction can ensure the effectiveness of the clustering algorithm.

[0067] Then, the initial self-organizing map network is trained using weighted charging behavior data until a preset threshold is reached, resulting in the trained self-organizing map network. During the training process, the output layer of the self-organizing map network uses a 4×5 grid, with hexagonal adjacencies between grid points and a Gaussian nearest neighbor function. Random initialization is used, and the training process takes 2000 steps.

[0068] After obtaining the trained self-organizing map network, a clustering algorithm is used to perform cluster analysis on the charging behavior data that has been mapped to two-dimensional network data to obtain the target classification results. Then, the charging behavior category with the highest probability density is selected from the target classification results as the vehicle's charging behavior category.

[0069] Clustering algorithms include, but are not limited to, the k-means algorithm. The k-means algorithm is an iterative clustering analysis algorithm. Its execution steps are as follows: First, the data is pre-divided into K groups. Then, K objects are randomly selected as initial cluster centers. Next, the distance between each object and each seed cluster center is calculated, and each object is assigned to the nearest cluster center. The cluster centers and the objects assigned to them represent a cluster. Each time a sample is assigned, the cluster centers are recalculated based on the existing objects in the cluster. This process is repeated until a termination condition is met. The termination condition may be that no (or a minimum number) objects are reassigned to different clusters, no (or a minimum number) cluster centers change, or the sum of squared errors reaches a local minimum.

[0070] To demonstrate that the weighted charging behavior data is more effective in classifying charging behavior, this application provides the following example:

[0071] The charging behavior data and weighted charging behavior data are input into the trained self-organizing map network (SAMR) to obtain the charging behavior category probabilities corresponding to the individual charging behavior data and the weighted charging behavior data, respectively. Then, a clustering algorithm is used to perform cluster analysis on both the individual charging behavior category probabilities and the weighted charging behavior category probabilities, yielding a first classification result and a second classification result. The second classification result is identical to the target classification result. The first classification result represents the first charging behavior category, and the second classification result represents the second charging behavior category.

[0072] Next, it is necessary to obtain the vehicle's original charging behavior category. The original charging behavior category of the vehicle is pre-labeled and can be obtained by recognizing the category label. The category label can be represented by text, numbers, or a combination thereof.

[0073] Once the vehicle's original charging behavior category is obtained, the first charging behavior category, the second charging behavior category, and the original charging behavior category can be compared to obtain the comparison results. The comparison results are used to verify the consistency between the first charging behavior category, the second charging behavior category, and the original charging behavior category.

[0074] Since the charging behaviors of electric vehicles A and B are more similar (both charge to near full capacity each time), while the charging behavior of electric vehicle C is relatively different (most of the time it charges to near full capacity, but sometimes it stops charging at a low SOC), we can classify the charging behaviors of electric vehicles A and B into one category and the charging behavior of electric vehicle C into another category. Therefore, we can set the original charging behavior category of electric vehicle A as 1, the original charging behavior category of electric vehicle B as 1, and the original charging behavior category of electric vehicle C as 0.

[0075] like Figure 2 The example shows the prediction of vehicle charging behavior categories using raw data, i.e., charging behavior data. The first charging behavior category is output: electric vehicles A and C both belong to category 1, while electric vehicle B belongs to category 0. The second example uses modified data, i.e., weighted charging behavior data, to predict vehicle charging behavior categories, outputting a second charging behavior category: electric vehicles A and B both belong to category 1, while electric vehicle C belongs to category 0. Here, category 1 and category 0 simply represent different categories; the specific categories are not specifically defined here.

[0076] The comparison shows that the second charging behavior category is the same as the original charging behavior category. Therefore, the specification indicates that the accuracy of predicting the charging behavior category using weighted charging behavior data is higher.

[0077] This invention provides a charging behavior classification method, comprising: first acquiring vehicle charging behavior data; then weighting the charging behavior data to obtain weighted charging behavior data; and finally determining the vehicle's charging behavior category based on the weighted charging behavior data. This invention improves the accuracy of identifying the charging behavior category of electric vehicle users by weighting the charging behavior data, thereby reducing the dimensionality of the high-dimensional input charging behavior data.

[0078] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0079] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0080] Figure 3 The diagram shows a schematic representation of a charging behavior classification device according to an embodiment of the present invention. For ease of explanation, only the parts relevant to the embodiment of the present invention are shown. The charging behavior classification device includes an acquisition module 301, a weighting module 302, and a classification module 303, as detailed below:

[0081] The acquisition module 301 is used to acquire vehicle charging behavior data;

[0082] Weighting module 302 is used to weight the charging behavior data to obtain weighted charging behavior data;

[0083] The classification module 303 is used to determine the charging behavior category of the vehicle based on the weighted charging behavior data.

[0084] In one possible implementation, charging behavior data is represented by a probability matrix, where the columns of the probability matrix represent multiple charge ranges, and the rows of the probability matrix represent the probability of the vehicle's charging behavior in any of the multiple charge ranges.

[0085] In one possible implementation, the weighting module 302 is also used to obtain a symmetric matrix;

[0086] Multiplying the probability matrix by the symmetric matrix yields the weighted charging behavior data.

[0087] In one possible implementation, the classification module 303 is also used to input the weighted charging behavior data into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data.

[0088] Clustering algorithms are used to perform cluster analysis on the probability of charging behavior categories to obtain the target classification results;

[0089] The charging behavior category with the highest probability density is selected from the target classification results as the vehicle's charging behavior category.

[0090] In one possible implementation, before the classification module 303, there is also a model training module, which is used to obtain the initial self-organizing map network and training data.

[0091] The initial self-organizing map network is trained using training data, and the trained self-organizing map network is obtained when a preset threshold is reached.

[0092] In one possible implementation, the device further includes a comparison module, which is used to input the charging behavior data and the weighted charging behavior data into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the charging behavior data and the charging behavior category probability corresponding to the weighted charging behavior data.

[0093] Clustering algorithms were used to perform cluster analysis on the charging behavior category probabilities corresponding to the charging behavior data and the weighted charging behavior data to obtain the first classification result and the second classification result. The second classification result is the same as the target classification result.

[0094] Based on the first classification result and the second classification result, a first charging behavior category and a second charging behavior category are determined, wherein the second charging behavior category is the charging behavior category of the vehicle.

[0095] In one possible implementation, the device further includes a result output module for obtaining the vehicle's original charging behavior category;

[0096] The first charging behavior category, the second charging behavior category, and the original charging behavior category are compared to obtain the comparison results. The comparison results are used to verify the consistency between the first charging behavior category, the second charging behavior category, and the original charging behavior category.

[0097] This invention provides a charging behavior classification device, specifically used to first acquire vehicle charging behavior data, then weight the charging behavior data to obtain weighted charging behavior data, and finally determine the vehicle's charging behavior category based on the weighted charging behavior data. This invention improves the accuracy of identifying the charging behavior category of electric vehicle users by weighting the charging behavior data, thus reducing the dimensionality of the high-dimensional input charging behavior data.

[0098] Figure 4 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 4 As shown, the terminal 4 in this embodiment includes a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, it implements the steps described in the various charging behavior classification method embodiments above, for example... Figure 1 Steps 101-103 are shown. Alternatively, when processor 401 executes computer program 403, it implements the functions of each module / unit in the above-described embodiments of the charging behavior classification device, for example... Figure 3 The functions of modules / units 301-303 shown.

[0099] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, is used to implement the charging behavior classification method provided in the various embodiments described above, including:

[0100] Obtain vehicle charging behavior data;

[0101] The charging behavior data is weighted to obtain the weighted charging behavior data.

[0102] Based on the weighted charging behavior data, the charging behavior category of the vehicle is determined.

[0103] In one possible implementation, charging behavior data is represented by a probability matrix, where the columns of the probability matrix represent multiple charge ranges, and the rows of the probability matrix represent the probability of the vehicle's charging behavior in any of the multiple charge ranges.

[0104] In one possible implementation, the charging behavior data is weighted to obtain weighted charging behavior data, including:

[0105] Obtain a symmetric matrix;

[0106] Multiplying the probability matrix by the symmetric matrix yields the weighted charging behavior data.

[0107] In one possible implementation, the vehicle's charging behavior category is determined based on weighted charging behavior data, including:

[0108] The weighted charging behavior data is input into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data.

[0109] Clustering algorithms are used to perform cluster analysis on the probability of charging behavior categories to obtain the target classification results;

[0110] The charging behavior category with the highest probability density is selected from the target classification results as the vehicle's charging behavior category.

[0111] In one possible implementation, before inputting the weighted charging behavior data into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data, the following steps are also included:

[0112] Obtain the initial self-organizing map network and training data;

[0113] The initial self-organizing map network is trained using training data, and the trained self-organizing map network is obtained when a preset threshold is reached.

[0114] In one possible implementation, the method also includes:

[0115] The charging behavior data and the weighted charging behavior data are respectively input into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the charging behavior data and the charging behavior category probability corresponding to the weighted charging behavior data.

[0116] Clustering algorithms were used to perform cluster analysis on the charging behavior category probabilities corresponding to the charging behavior data and the weighted charging behavior data to obtain the first classification result and the second classification result. The second classification result is the same as the target classification result.

[0117] Based on the first classification result and the second classification result, a first charging behavior category and a second charging behavior category are determined, wherein the second charging behavior category is the charging behavior category of the vehicle.

[0118] In one possible implementation, the method also includes:

[0119] Obtain the vehicle's original charging behavior category;

[0120] The first charging behavior category, the second charging behavior category, and the original charging behavior category are compared to obtain the comparison results. The comparison results are used to verify the consistency between the first charging behavior category, the second charging behavior category, and the original charging behavior category.

[0121] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0122] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the charging behavior classification method provided in the various embodiments described above, including:

[0123] Obtain vehicle charging behavior data;

[0124] The charging behavior data is weighted to obtain the weighted charging behavior data.

[0125] Based on the weighted charging behavior data, the charging behavior category of the vehicle is determined.

[0126] In one possible implementation, charging behavior data is represented by a probability matrix, where the columns of the probability matrix represent multiple charge ranges, and the rows of the probability matrix represent the probability of the vehicle's charging behavior in any of the multiple charge ranges.

[0127] In one possible implementation, the charging behavior data is weighted to obtain weighted charging behavior data, including:

[0128] Obtain a symmetric matrix;

[0129] Multiplying the probability matrix by the symmetric matrix yields the weighted charging behavior data.

[0130] In one possible implementation, the vehicle's charging behavior category is determined based on weighted charging behavior data, including:

[0131] The weighted charging behavior data is input into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data.

[0132] Clustering algorithms are used to perform cluster analysis on the probability of charging behavior categories to obtain the target classification results;

[0133] The charging behavior category with the highest probability density is selected from the target classification results as the vehicle's charging behavior category.

[0134] In one possible implementation, before inputting the weighted charging behavior data into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data, the following steps are also included:

[0135] Obtain the initial self-organizing map network and training data;

[0136] The initial self-organizing map network is trained using training data, and the trained self-organizing map network is obtained when a preset threshold is reached.

[0137] In one possible implementation, the method also includes:

[0138] The charging behavior data and the weighted charging behavior data are respectively input into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the charging behavior data and the charging behavior category probability corresponding to the weighted charging behavior data.

[0139] Clustering algorithms were used to perform cluster analysis on the charging behavior category probabilities corresponding to the charging behavior data and the weighted charging behavior data to obtain the first classification result and the second classification result. The second classification result is the same as the target classification result.

[0140] Based on the first classification result and the second classification result, a first charging behavior category and a second charging behavior category are determined, wherein the second charging behavior category is the charging behavior category of the vehicle.

[0141] In one possible implementation, the method also includes:

[0142] Obtain the vehicle's original charging behavior category;

[0143] The first charging behavior category, the second charging behavior category, and the original charging behavior category are compared to obtain the comparison results. The comparison results are used to verify the consistency between the first charging behavior category, the second charging behavior category, and the original charging behavior category.

[0144] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0145] The above 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 charging behavior classification method, characterized by, include: Obtain vehicle charging behavior data; The charging behavior data is weighted to obtain weighted charging behavior data; Based on the weighted charging behavior data, the charging behavior category of the vehicle is determined; The charging behavior data is represented by a probability matrix, where the columns of the probability matrix represent multiple battery level ranges, and the rows of the probability matrix represent the probability of the vehicle's charging behavior in any of the multiple battery level ranges. The step of weighting the charging behavior data to obtain weighted charging behavior data includes: Obtain a symmetric matrix; Multiplying the probability matrix by the symmetric matrix yields the weighted charging behavior data.

2. The charging behavior classification method as described in claim 1, characterized in that, The process of determining the vehicle's charging behavior category based on the weighted charging behavior data includes: The weighted charging behavior data is input into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data; Clustering algorithms are used to perform cluster analysis on the probability of the charging behavior categories to obtain the target classification results; The charging behavior category with the highest probability density is selected from the target classification results as the charging behavior category of the vehicle.

3. The method of claim 2, wherein the charging behavior classification is based on a comparison of the charging behavior to a predetermined charging behavior. Before inputting the weighted charging behavior data into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the weighted charging behavior data, the method further includes: Obtain the initial self-organizing map network and training data; The initial self-organizing map network is trained using training data, and once a preset threshold is reached, the trained self-organizing map network is obtained.

4. The method of claim 2, wherein the charging behavior classification is based on a comparison of the charging behavior with a predetermined charging behavior. The method further includes: The charging behavior data and the weighted charging behavior data are respectively input into the trained self-organizing map network to obtain the charging behavior category probability corresponding to the charging behavior data and the charging behavior category probability corresponding to the weighted charging behavior data. Clustering algorithms are used to perform clustering analysis on the charging behavior category probabilities corresponding to the charging behavior data and the charging behavior category probabilities corresponding to the weighted charging behavior data, respectively, to obtain a first classification result and a second classification result, wherein the second classification result is the same as the target classification result; Based on the first classification result and the second classification result, a first charging behavior category and a second charging behavior category are determined, wherein the second charging behavior category is the charging behavior category of the vehicle.

5. The method of claim 4, wherein the charging behavior classification is based on a comparison of the charging behavior with a predetermined charging behavior. The method further includes: Obtain the vehicle's original charging behavior category; The first charging behavior category, the second charging behavior category, and the original charging behavior category are compared to obtain the comparison result, wherein the comparison result is used to verify the consistency between the first charging behavior category, the second charging behavior category, and the original charging behavior category.

6. A charging behavior classification device, characterized in that, include: The acquisition module is used to acquire vehicle charging behavior data; The weighting module is used to weight the charging behavior data to obtain weighted charging behavior data; A classification module is used to determine the charging behavior category of the vehicle based on the weighted charging behavior data; The charging behavior data is represented by a probability matrix, where the columns of the probability matrix represent multiple battery level ranges, and the rows of the probability matrix represent the probability of the vehicle's charging behavior in any of the multiple battery level ranges. The step of weighting the charging behavior data to obtain weighted charging behavior data includes: Obtain a symmetric matrix; Multiplying the probability matrix by the symmetric matrix yields the weighted charging behavior data.

7. A terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the charging behavior classification method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the charging behavior classification method as described in any one of claims 1 to 5.

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