Data-driven electric vehicle charging amount and charging duration prediction method

Abstract

The invention discloses a data-driven electric vehicle charging amount and charging duration prediction method. The method comprises the following steps: step 1, carrying out data preprocessing on historical charging data of an electric vehicle; 2, carrying out feature extraction on the preprocessed charging segment and giving a label; step 3, realizing label prediction based on an LSTM classification model; and step 4, realizing charging amount and charging duration prediction based on the RF regression model. According to the method, the charging amount and the charging duration of the electric vehicle can be efficiently and accurately predicted, the operation efficiency of the charging station can be optimized, the charging experience of a user can be improved, and a greater contribution can be made to an intelligent power grid and a sustainable traffic system. Through accurate prediction, the charging station can better arrange the use of charging facilities, the queuing waiting time is reduced, and the utilization rate of the charging pile is improved; and the user can reasonably plan the charging time according to the prediction information, so that long-time waiting is avoided, and the flexibility and convenience of a travel plan are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of electric vehicle charging and discloses a data-driven method for predicting the charging amount and charging time of an electric vehicle. Background Art

[0002] As the proportion of renewable energy generation increases year by year, large-scale flexible resources are needed to maintain the smooth operation of the power system. In this context, vehicle-grid interaction, as a new type of energy regulation method, can not only provide flexible resource support and ensure the stable operation of the power system, but also promote the large-scale application of renewable energy and the development of intelligent energy management. Under the framework of the entire vehicle-grid interaction, it is particularly important to efficiently and accurately predict the charging amount and charging time of electric vehicles. Accurately predicting the charging amount and charging time not only helps to optimize the operating efficiency of charging stations, reduce the waiting time for charging, and improve user experience, but is also crucial for grid dispatching. These prediction information enables power resources to be allocated more efficiently, reasonably adjust the load during peak power consumption, and further promote the efficient use of renewable energy.

[0003] Therefore, there is an urgent need for a reasonable and feasible solution to accurately predict the charging amount and charging time of electric vehicle users, so as to ensure the stability of the power system and optimize the driver's charging experience. Summary of the invention

[0004] The present invention extracts representative data features of charging behavior by analyzing the historical operation data of electric vehicles, and establishes a GMM-LSTM-RF prediction model for electric vehicles. Using the established prediction model, the charging capacity and charging time of electric vehicles in the future time period can be accurately predicted.

[0005] The present invention discloses a data-driven electric vehicle charging capacity and charging time prediction method, the data-driven electric vehicle charging capacity and charging time prediction method comprising the following steps:

[0006] Perform data preprocessing on the historical charging data of electric vehicles;

[0007] Extract features from the pre-processed historical charging data and assign labels to generate charging behavior pattern features and add them to the electric vehicle charging segment data;

[0008] Predict charging behavior patterns based on electric vehicle charging segment data and LSTM classification model;

[0009] The charging amount and charging time are predicted based on the predicted charging behavior pattern, electric vehicle charging segment data and RF regression model.

[0010] Furthermore, the data preprocessing of the historical charging data of the electric vehicle includes the following steps:

[0011] Clean the historical charging data of electric vehicles and handle data missing, abnormal, and duplicate situations;

[0012] Standardize the data;

[0013] The charging cycle is divided. When the battery current is negative and the vehicle speed is zero, it is considered that the electric vehicle is in a charging state.

[0014] Furthermore, the feature extraction and labeling of the pre-processed historical charging data includes the following steps:

[0015] Each charging segment is extracted from the pre-processed historical charging data as:

[0016] S i ={F 1 ,F 2 ,F 3 ,F 4 ,F 5 ,F 6 ,Num n}

[0017] Among them, F 1 —F 6 The Num corresponding to the characteristics of charging start time, charging start date, charging start SOC, charging method, charging duration and charging power. n Indicates the corresponding electric vehicle number;

[0018] According to the electric vehicle number, the set of all charging segments for each vehicle is:

[0019] V n ={S 1 ,…,S i ,…} T

[0020] Among them, S i Indicates the charging segment characteristics;

[0021] Based on the set of all charging segments of all electric vehicles, the electric vehicle charging segment feature database is constructed as follows:

[0022] Data = {V 1 ,…,V n ,…} T

[0023] V n represents a charging segment feature set;

[0024] The optimal number of clusters is determined according to the BIC criterion, which is expressed as:

[0025]

[0026] Among them, BIC represents the score calculated by the BIC criterion, p(x n |Θ) is the observed data sample x under the condition of given model parameter Θ n The probability density function of , N is the number of samples, n represents the sample number, and K is the number of clusters;

[0027] GMM clustering is performed through the extracted features and the optimal number of clusters; the Num n Column deletion gives the feature matrix:

[0028]

[0029] Where N is the number of samples and f is the number of features;

[0030] The Gaussian distribution model is determined by solving the optimal Gaussian distribution model parameters for each sample using the expectation maximization method. The expectation maximization method is:

[0031] D={μ i ,∑ i ,α i}

[0032] Among them, μ i ,Σ i , α i Respectively represent the mean vector, covariance matrix, and mixing coefficient of the Gaussian distribution; each sample determines its most likely clustering affiliation according to the responsibility weight of each Gaussian distribution to which it belongs;

[0033] The clustering results are added to the electric vehicle charging segment data as charging behavior pattern features;

[0034] All charging segments of all vehicles are labeled with four types of labels representing four charging behavior modes: short-term low-power charging mode, balanced charging mode, long-term high-power charging mode, and high-efficiency fast charging mode;

[0035] According to Num n Divide all segments of all vehicles into a set of charging segments with each electric vehicle labeled with its charging behavior:

[0036] V n ′={S 1 ′,…,S i ′,…} T

[0037] V n ′ represents the charging segment set without the vehicle number, S i' indicates the charging segment without the vehicle number.

[0038] Furthermore, the method of predicting charging behavior patterns based on electric vehicle charging segment data and LSTM classification model includes the following steps:

[0039] Extract each charging segment S i F in ′ 1 ,F 2 ,F 3 ,F 4 ,F 7 Get new charging fragment x t ={F 1 ,F 2 ,F 3 ,F 4 ,F 7}, where F 1 —F 6 Corresponding to the proposed features charging start time, charging start date, charging start SOC, charging method, charging duration and charging power, F 7 As the charging behavior mode, all new charging segments x t Combining X = {F 1 ,…,x t ,…} T ;

[0040] For each new charging segment, it is used as the input of the LSTM unit for calculation to obtain the charging behavior pattern; the calculation process of the LSTM unit includes the forget gate, input gate, memory unit state update and output gate. The calculation of each unit can be described as follows:

[0041]

[0042] Among them, W f , W i , W C , W o They are the weight matrices of the forget gate, input gate, candidate memory unit state, and output gate, respectively. f , b i , b C , b o They are the forget gate, input gate, candidate memory cell state, and output gate bias, respectively. t-1 represents the hidden state of the previous time step, σ represents the sigmoid activation function, C t-1 Represents the memory cell state at the previous time step;

[0043] Before training begins, all weight matrices and bias items of the LSTM unit are randomly initialized. During the training process, the weight matrices and bias items are updated using the back propagation through time algorithm; finally, the hidden state h of the LSTM unit is obtained. t and the memory cell state C t Transmitted to the next time step, the hidden state h t and the memory cell state C t They are all vectors of length l, where l is the dimension of the LSTM hidden layer;

[0044] After processing all time steps in the time series, the hidden state h of the last time step is used T As the final feature vector for classification:

[0045] y=softmax(W y ·h T +b y )

[0046] Among them, y is the probability distribution vector of the classification, W y is the weight matrix of the output layer, b y is the bias term, h T Represents the hidden state of the last time step; the weight matrix and bias term are randomly initialized before training begins, and then the weight matrix and bias term are updated using the back propagation through time algorithm;

[0047] Finally, the charging behavior pattern F corresponding to the y with the highest probability is selected. 7 ’ as the predicted result of the label.

[0048] Furthermore, the prediction of charging amount and charging time is realized based on the predicted charging behavior pattern, the electric vehicle charging segment data and the RF regression model, including the following steps:

[0049] Extract each charging segment S i The charging start time F in ′ 1 , Charging start date F 2 , charging starts SOCF 3 ,Charging method F 4 , Charging time F 5 ,Charging capacity F 6 , and F predicted by the LSTM classification model 7 'Get the new charging fragment x t ′={F 1 ,F 2 ,F 3 ,F 4 ,F 7 ′,F 5 ,F 6}, and then all new charging fragments x t ′ combination to obtain X′={x 1 ′,…,x t ′,…} T ;

[0050] Randomly extract multiple subsamples from X′, and each subsample is used to train a decision tree;

[0051] Each decision tree predicts the input sample. For a sample x t ′, the prediction process of random forest can be expressed as:

[0052]

[0053] in, is the final prediction value, M is the number of decision trees, is the mth decision tree for sample x t ′’s prediction results;

[0054] Finally, the charging capacity and charging time are obtained through the final predicted values.

[0055] Furthermore, for the extracted historical charging data, if there is a gap of more than half an hour between two consecutive charging data frames, they are considered to be two independent charging cycles.

[0056] The present invention also provides a data-driven electric vehicle charging capacity and charging time prediction device, the device comprising a data preprocessing module, a feature extraction module, an LSTM classification module and an RF regression module;

[0057] The data preprocessing module is used to preprocess the historical charging data of the electric vehicle;

[0058] The feature extraction module is used to extract features from the pre-processed historical charging data and assign labels to generate data with charging behavior pattern features added to the charging segment of the electric vehicle;

[0059] The LSTM classification module is used to predict the charging behavior pattern based on the data of the electric vehicle charging segment and the LSTM classification model;

[0060] The RF regression module is used to predict the charging amount and charging time of the predicted charging behavior pattern and the electric vehicle charging segment data through the RF regression model.

[0061] An electronic device is also provided, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a data-driven electric vehicle charging amount and charging time prediction method.

[0062] A computer storage medium is also provided, in which at least one instruction, at least one program, code set or instruction set is stored, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement a data-driven electric vehicle charging amount and charging time prediction method.

[0063] The beneficial effects achieved by the present invention are:

[0064] The present invention can efficiently and accurately predict the charging amount and charging time of electric vehicles, which can not only optimize the operating efficiency of charging stations and improve the charging experience of users, but also make greater contributions to smart grids and sustainable transportation systems. Through accurate prediction, charging stations can better arrange the use of charging facilities, reduce waiting time in queues, and improve the utilization rate of charging piles; users can reasonably plan charging time based on the prediction information, avoid long waiting times, and improve the flexibility and convenience of travel plans.

[0065] The present invention uses the GMM clustering algorithm combined with the BIC criterion to determine the optimal number of clusters, and predicts future charging behavior through the LSTM model. This process makes the prediction results closer to the actual charging habits of drivers, reducing the waste of time and charging station resources. While improving the prediction accuracy, it also improves the user's charging experience, allowing users to arrange charging plans more reasonably, avoiding the inconvenience caused by charging uncertainty, and improving the utilization rate of charging facilities and user satisfaction.

[0066] The present invention uses the RF regression model to predict the charging amount and duration, providing data support for the construction and maintenance of charging infrastructure, so that charging stations can dynamically adjust service strategies according to the prediction results, such as optimizing the layout of charging piles, scheduling charging power, etc., thereby improving the operating efficiency and service level of the charging infrastructure. In addition, accurate charging amount prediction can also help power companies to rationally allocate power resources, reduce power waste, and achieve effective management and sustainable development of green energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of the flow chart of the data-driven electric vehicle charging capacity and charging time prediction method provided by the present invention;

[0068] Figure 2A flow chart of electric vehicle data preprocessing in the data-driven electric vehicle charging capacity and charging time prediction method provided by the present invention;

[0069] Figure 3 A flow chart of the GMM clustering marking charging behavior pattern in the data-driven electric vehicle charging capacity and charging time prediction method provided by the present invention;

[0070] Figure 4 A flow chart of the LSTM classification prediction charging behavior pattern in the data-driven electric vehicle charging capacity and charging time prediction method provided by the present invention;

[0071] Figure 5 This is a flow chart of RF regression prediction of charging capacity and charging time in the data-driven electric vehicle charging capacity and charging time prediction method provided by the present invention. DETAILED DESCRIPTION

[0072] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention may be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the scope of protection of the present invention.

[0073] The core idea of ​​this invention is to collect and analyze the historical charging data of electric vehicles, understand the charging preferences of drivers, and thus predict their charging behavior patterns, and further predict their accurate charging amount and charging time. Based on these data, a GMM-LSTM-RF prediction model is established to predict the driver's charging amount and charging time in a specific time period in the future.

[0074] Embodiment 1

[0075] like Figure 1 As shown, the present invention proposes a data-driven electric vehicle charging capacity and charging time prediction method, comprising the following steps:

[0076] Step 1, data preprocessing;

[0077] Due to noise pollution and signal transmission problems, the original sample data may be slightly missing, abnormal, or repeated. Therefore, it is necessary to clean and segment the data, which includes the following steps:

[0078] As attached Figure 2As shown, first, the historical data of electric vehicles is cleaned to deal with data missing, abnormal, and repeated situations. Then, the data is standardized. Finally, the charging cycle is divided. When the battery current is negative and the vehicle speed is zero, it is considered that the electric vehicle is in a charging state. For the extracted charging data, if there is a gap of more than half an hour between two consecutive charging data frames, it is considered to be two independent charging cycles.

[0079] Step 2, extract charging segment features and assign labels;

[0080] As attached Figure 3 As shown, the following steps are also included:

[0081] Step 21, extract features: each charging segment is obtained as:

[0082] S i ={F 1 ,F 2 ,F 3 ,F 4 ,F 5 ,F 6 ,Num n}

[0083] Among them, F 1 —F 6 The Num corresponding to the characteristics of charging start time, charging start date, charging start SOC, charging method, charging duration and charging power. n Indicates the corresponding electric vehicle number. According to the electric vehicle number, the set of all charging segments for each car is:

[0084] V n ={S 1 ,…,S i ,…} T

[0085] Among them, S i Represents the charging segment characteristics.

[0086] The electric vehicle charging segment feature database is constructed based on the set of all charging segments of all electric vehicles:

[0087] Data = {V 1 ,…,V n ,…} T

[0088] V n Represents a charging segment feature set.

[0089] Step 22, determine the optimal number of clusters according to the BIC criterion: In this study, the number of Gaussian mixture model clusters is determined by using the BIC criterion, which is expressed as:

[0090]

[0091] Among them, p(x n |Θ) is the observed data sample x under the condition of given model parameter Θ n The probability density function of N is the number of samples, n represents the sample number, and K is the number of clusters. Analysis of the data shows that as the number of clusters increases, the model's fitting accuracy to the data shows an upward trend, which is manifested in a gradual decrease in the BIC score, and the clustering effect is optimized accordingly. However, when the number of clusters exceeds 4, the change in the BIC score tends to be flat, and the improvement in model accuracy slows down significantly. At the same time, the computational complexity and time cost rise sharply. In view of this, weighing the accuracy gain and computational cost, the present invention sets the optimal number of clusters to 4, that is, K=4, as the basis for subsequent clustering analysis.

[0092] Step 23, perform GMM clustering using the extracted features and the optimal number of clusters: The core concept of GMM clustering is to use a set of linearly superimposed single Gaussian components to flexibly simulate data distribution, thereby achieving refined coverage of the sample space. n Column deletion gives the feature matrix:

[0093]

[0094] Where N is the number of samples, f is the number of features, and according to the above, f = 6. Assuming that all samples are independently distributed, the expectation maximization method is used to solve the optimal Gaussian distribution model parameter D = {μ i ,∑ i ,α i} to determine the Gaussian distribution model. Among them, μ i ,∑ i , α i They represent the mean vector, covariance matrix, and mixing coefficient of the Gaussian distribution respectively. Each statistic determines its most likely clustering affiliation according to the responsibility weight of each Gaussian distribution to which it belongs.

[0095] Step 24: Add the clustering results as new features to the electric vehicle charging segment data: label all charging segments of all vehicles, and the four labels represent four charging behavior modes, namely short-term low-power charging mode, balanced charging mode, long-term high-power charging mode, and high-efficiency fast charging mode. n Divide all segments of all vehicles into a set of charging segments with each electric vehicle labeled with its charging behavior:

[0096] V n ′={S 1 ′,…,S i ′,…} T

[0097] Step 3: LSTM classification model realizes label prediction;

[0098] As attached Figure 4 As shown, the following steps are also included:

[0099] Step 31, feature extraction: extract each charging segment S i F in ′ 1 ,F 2 ,F 3 ,F 4 ,F 7 Get x t ={F 1 ,F 2 ,F 3 ,F 4 ,F 7}, where F 7 As the charging behavior mode, all new charging segments x t Combining X = {x 1 ,…,x t ,…} T .

[0100] Step 32, the charging segment is calculated by the timing unit: For each charging segment, the calculation process of the LSTM unit includes the forget gate, input gate, memory unit state update and output gate. Each unit calculation can be described as follows:

[0101]

[0102] Among them, W f , W i , W C , W o They are the weight matrices of the forget gate, input gate, candidate memory unit state, and output gate, respectively. f 、b i 、b C 、b o They are the forget gate, input gate, candidate memory unit state, and output gate bias. Before training begins, all weight matrices and bias items are randomly initialized, and then back propagation is used to update the weight matrix and bias items through the time algorithm. Finally, the hidden state h of this unit is obtained. t and the memory cell state C t Transmitted to the next time step, the hidden state h t and the memory cell state C tis a vector of length l, where l is the dimension of the LSTM hidden layer.

[0103] Step 33, the output layer realizes the prediction of charging behavior pattern and adds the corresponding electric vehicle charging segment: After processing all time steps of the time series, the hidden state h of the last time step is used T As the final feature vector for classification:

[0104] y=softmax(W y ·h T +b y )

[0105] Among them, y is the probability distribution vector of the classification, W y is the weight matrix of the output layer, b y is the bias term. Before training begins, the weight matrix and the bias term are randomly initialized, and then the back propagation algorithm is used to update the weight matrix and the bias term. Finally, the charging behavior pattern F corresponding to the y with the highest probability is taken. 7 ’ is added as a feature to the corresponding electric vehicle charging segment.

[0106] Step 4: RF regression model realizes the prediction of charging capacity and charging time;

[0107] As attached Figure 5 As shown, the following steps are also included:

[0108] Step 41, feature extraction: extract each charging segment S i F in ′ 1 ,F 2 ,F 3 ,F 4 ,F 5 ,F 6 Compared with the F predicted by the LSTM classification model 7 ′Get x t ′={F 1 ,F 2 ,F 3 ,F 4 ,F 7 ′,F 5 ,F 6}, and then all new charging fragments x t ′ combination to obtain X′={x 1 ′,…,x t ′,…} T .

[0109] Step 42, extract subsets and build decision trees: randomly extract multiple subsamples from X′, and each subsample is used to train a decision tree. In the process of building a decision tree, each time a node splits, some features are randomly selected from all features for split selection. Each leaf node in the decision tree represents a predicted value, usually the mean of the samples in the leaf node.

[0110] Step 43, regression prediction of charging capacity and charging time: Each decision tree will make a prediction for the input sample, and the final prediction result is the average of the prediction results of all trees. Specifically, for a sample x t ′, the prediction process of random forest can be expressed as:

[0111]

[0112] in, is the final prediction value, M is the number of decision trees, is the mth decision tree for sample x t ′’s prediction results.

[0113] Embodiment 2

[0114] The present invention also provides a data-driven electric vehicle charging capacity and charging time prediction device, which includes a data preprocessing module, a feature extraction module, an LSTM classification module and an RF regression module;

[0115] The data preprocessing module is used to preprocess the historical charging data of the electric vehicle;

[0116] The feature extraction module extracts features from the preprocessed historical charging data and assigns labels to generate data with charging behavior pattern features added to the charging segment of the electric vehicle;

[0117] The LSTM classification module adds the charging behavior pattern characteristics to the data of the electric vehicle charging segment and uses the LSTM classification model to predict the charging behavior pattern;

[0118] The RF regression module predicts the charging amount and charging time based on the predicted charging behavior pattern and electric vehicle charging segment data through the RF regression model.

[0119] This embodiment also provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to predict the charging amount and charging time of an electric vehicle driven by the data in Example 1.

[0120] This embodiment also provides a computer storage medium, which stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the data-driven electric vehicle charging amount and charging time prediction method as in Example 1.

[0121] The above are only specific steps of the present invention and do not constitute any limitation to the protection scope of the present invention; any technical solutions formed by equivalent transformation or equivalent replacement shall fall within the protection scope of the present invention; some parts not elaborated in detail in the present invention belong to the known technologies of those skilled in the art.

Claims

1. A data-driven method for predicting the charging capacity and charging time of an electric vehicle, characterized in that: The data-driven electric vehicle charging capacity and charging time prediction method comprises the following steps: Perform data preprocessing on the historical charging data of electric vehicles; Extract features from the pre-processed historical charging data and assign labels to generate charging behavior pattern features and add them to the electric vehicle charging segment data; Predict charging behavior patterns based on electric vehicle charging segment data and LSTM classification model; The charging amount and charging time are predicted based on the predicted charging behavior pattern, electric vehicle charging segment data and RF regression model.

2. The data-driven electric vehicle charging capacity and charging time prediction method according to claim 1 is characterized in that: The data preprocessing of the historical charging data of the electric vehicle comprises the following steps: Clean the historical charging data of electric vehicles and handle data missing, abnormal, and duplicate situations; Standardize the data; The charging cycle is divided. When the battery current is negative and the vehicle speed is zero, it is considered that the electric vehicle is in a charging state.

3. The data-driven electric vehicle charging capacity and charging time prediction method according to claim 1 is characterized in that: The method of extracting features from the pre-processed historical charging data and assigning labels comprises the following steps: Each charging segment is extracted from the pre-processed historical charging data as: S i ={F1,F2,F3,F4,F5,F6,Num n } Among them, F1-F6 correspond to the proposed features charging start time, charging start date, charging start SOC, charging method, charging duration and charging power, respectively. n Indicates the corresponding electric vehicle number; According to the electric vehicle number, the set of all charging segments for each vehicle is: V n ={S1,…,S i ,…} T Among them, S i Indicates the charging segment characteristics; Based on the set of all charging segments of all electric vehicles, the electric vehicle charging segment feature database is constructed as follows: Data={V1,…,V n ,…} T V n represents a charging segment feature set; The optimal number of clusters is determined according to the BIC criterion, which is expressed as: Among them, BIC represents the score calculated by the BIC criterion, p(x n |Θ) is the observed data sample x under the condition of given model parameter Θ n The probability density function of , V is the number of samples, n is the sample number, and K is the number of clusters; GMM clustering is performed through the extracted features and the optimal number of clusters; the Num n Column deletion gives the feature matrix: Where N is the number of samples and f is the number of features; The Gaussian distribution model is determined by solving the optimal Gaussian distribution model parameters for each sample using the expectation maximization method. The expectation maximization method is: D={μ i ,∑ i ,a i } Among them, μ i ,∑ i , α i Respectively represent the mean vector, covariance matrix, and mixing coefficient of the Gaussian distribution; each sample determines its most likely clustering affiliation according to the responsibility weight of each Gaussian distribution to which it belongs; The clustering results are added to the electric vehicle charging segment data as charging behavior pattern features; All charging segments of all vehicles are labeled with four types of labels representing four charging behavior modes: short-term low-power charging mode, balanced charging mode, long-term high-power charging mode, and high-efficiency fast charging mode; Then according to Num n Divide all segments of all vehicles into a set of charging segments with each electric vehicle labeled with its charging behavior: V n ′={S1′,…,S i ′,…} T V n ′ represents the charging segment set without the vehicle number, S i ' indicates the charging segment without the vehicle number.

4. The data-driven electric vehicle charging capacity and charging time prediction method according to claim 1 is characterized in that: The method of predicting charging behavior patterns based on electric vehicle charging segment data and LSTM classification model includes the following steps: Extract each charging segment S i ′, F1, F2, F3, F4, F7 get the new charging fragment x t ={F1, F2, F3, F4, F7}, where F1-F6 correspond to the proposed features charging start time, charging start date, charging start SOC, charging method, charging duration and charging power, respectively, and F7 is the charging behavior mode. Then all new charging segments x t Combining X = {x1,…,x t ,…} T ; For each new charging segment, it is used as the input of the LSTM unit for calculation to obtain the charging behavior pattern; the calculation process of the LSTM unit includes the forget gate, input gate, memory unit state update and output gate. The calculation of each unit can be described as follows: Among them, W f , W i , W C , W o They are the weight matrices of the forget gate, input gate, candidate memory unit state, and output gate, respectively. f 、b i 、b C 、b o They are the forget gate, input gate, candidate memory cell state, and output gate bias, respectively. t-1 represents the hidden state of the previous time step, σ represents the sigmoid activation function, C t-1 Represents the memory cell state at the previous time step; Before training begins, all weight matrices and bias items of the LSTM unit are randomly initialized. During the training process, the weight matrices and bias items are updated using the back propagation through time algorithm; finally, the hidden state h of the LSTM unit is obtained. t and the memory cell state C t Transmitted to the next time step, the hidden state h t and the memory cell state C t They are all vectors of length l, where l is the dimension of the LSTM hidden layer; After processing all time steps in the time series, the hidden state h of the last time step is used T As the final feature vector for classification: y =softmax(W y ·h T +b y ) Among them, y is the probability distribution vector of the classification, W y is the weight matrix of the output layer, b y is the bias term, h T Represents the hidden state of the last time step; the weight matrix and bias term are randomly initialized before training begins, and then the weight matrix and bias term are updated using the back propagation through time algorithm; Finally, the charging behavior pattern F7′ corresponding to the y with the highest probability is taken as the prediction result of the label.

5. The data-driven electric vehicle charging capacity and charging time prediction method according to claim 1 is characterized in that: The charging amount and charging time prediction is realized based on the predicted charging behavior pattern, electric vehicle charging segment data and RF regression model, including the following steps: Extract each charging segment S i The charging start time F1, charging start date F2, charging start SOCF3, charging method F4, charging duration F5, charging power F6 in ′ are compared with F7′ predicted by the LSTM classification model to obtain a new charging segment x t ′={F1,F2,F3,F4,F7′,F5,F6}, and then all new charging segments x t ′ combination to obtain X′={x1′,…,x t ′,…} T ; Randomly extract multiple subsamples from X′, and each subsample is used to train a decision tree; Each decision tree predicts the input sample. For a sample x t ′, the prediction process of random forest can be expressed as: in, is the final prediction value, M is the number of decision trees, is the mth decision tree for sample x t ′’s prediction results; Finally, the charging capacity and charging time are obtained through the final predicted values.

6. The data-driven electric vehicle charging capacity and charging time prediction method according to claim 2 is characterized in that: For the extracted historical charging data, if there is a gap of more than half an hour between two consecutive charging data frames, they are considered to be two independent charging cycles.

7. A data-driven electric vehicle charging capacity and charging time prediction device, characterized in that: The device includes a data preprocessing module, a feature extraction module, an LSTM classification module and an RF regression module; The data preprocessing module is used to preprocess the historical charging data of the electric vehicle; The feature extraction module is used to extract features from the pre-processed historical charging data and assign labels to generate data with charging behavior pattern features added to the charging segment of the electric vehicle; The LSTM classification module is used to predict the charging behavior pattern based on the data of the electric vehicle charging segment and the LSTM classification model; The RF regression module is used to predict the charging amount and charging time of the predicted charging behavior pattern and the electric vehicle charging segment data through the RF regression model.

8. An electronic device, characterized in that: The device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the data-driven electric vehicle charging amount and charging time prediction method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that The computer storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the data-driven electric vehicle charging amount and charging time prediction method as described in any one of claims 1-6.

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