Electric vehicle charging station recommendation method and system

Abstract

The invention provides an electric vehicle charging station recommendation method and system, and is applied to the related field of charging station recommendation. The method comprises the steps that user charging characteristics and influence degree values of user charging evaluation characteristics on user charging preferences are acquired, and the user charging evaluation characteristics are constructed based on evaluation information of a user on a charging station; calculating comprehensive scores of the user for different charging stations based on the influence degree values, and constructing a user preference matrix by taking the comprehensive scores of the user for different charging stations as elements; and decomposing the user preference matrix by adopting a singular value decomposition method to obtain a potential matching relationship between the user and the charging station characteristics, and recommending the charging station to the user based on the potential matching relationship. According to the method and the device, the problem that the charging stations cannot be recommended individually for different users in the prior art is solved, the personalized recommendation of the charging stations can be realized, and the user experience is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of charging station recommendation, in particular to a method and system for recommending charging stations for electric vehicles. BACKGROUND

[0002] With the popularity of electric vehicles, charging stations are increasingly used. The charging station recommendation system used by users when searching for charging stations is a key factor affecting user experience and charging station utilization.

[0003] Existing recommendation systems rely on simple rules or basic machine learning algorithms. For example, Chinese patent CN115345451A discloses a method for guiding electric vehicle charging based on charging station recommendation strategy. The method is based on real-time related data generated by users when they have charging needs and regular data required by the system. It uses time series analysis and machine learning algorithms to predict future power grid load and charging station load. Based on the load prediction results, the method combines the charging needs of electric vehicles and the real-time status of charging stations, and uses optimization algorithms to provide charging station recommendations based on comprehensive time and economic costs. Chinese patent CN115577863A discloses a method, system, device, and medium for recommending electric vehicle charging stations. The method obtains electric vehicle charging parameters for charging stations, calculates the minimum load difference value of the charging stations based on the electric vehicle charging parameters, obtains a scheduling scheme with minimum cost, and uses a price sensitivity pricing method to guide electric vehicle charging based on the scheduling scheme. This method minimizes the operating cost of charging stations and compensates users for charging costs.

[0004] The above methods categorize users and develop unified recommendation strategies based on statistical analysis of multi-user behavior. However, they cannot provide personalized recommendations tailored to different user preferences, resulting in poor user experience. SUMMARY

[0005] To solve the problem of existing technology that cannot provide personalized recommendations for different users, the present application proposes a method for recommending charging stations for electric vehicles, which includes:

[0006] Obtain the influence degree value of user charging characteristics and user charging evaluation characteristics on user charging preferences, wherein the user charging evaluation characteristics are constructed based on user evaluation information of charging stations;

[0007] Calculate the comprehensive score of different charging stations for the user based on the influence degree value, and construct a user preference matrix using the comprehensive score of different charging stations for the user as elements.

[0008] The user preference matrix is decomposed by using a singular value decomposition method to obtain a latent matching relationship of user and charging station characteristics, and a charging station is recommended to the user based on the latent matching relationship.

[0009] Optionally, the obtaining of the influence degree value of the user charging characteristics and the user charging evaluation characteristics on the user charging preference comprises:

[0010] The user charging characteristics and the user charging evaluation characteristics are obtained.

[0011] The user charging characteristics and the user charging evaluation characteristics are input into a LightGBM model to obtain the influence degree value.

[0012] Optionally, the obtaining of the user charging characteristics and the user charging evaluation characteristics comprises:

[0013] User charging data is obtained.

[0014] The user charging data is filtered to obtain target data including charging times greater than a preset value and corresponding evaluation information.

[0015] The target data is feature-extracted to obtain the user charging characteristics and the user charging evaluation characteristics.

[0016] Optionally, the feature-extraction of the target data to obtain the user charging characteristics and the user charging evaluation characteristics comprises:

[0017] The evaluation information data in the target data is one-hot encoded to obtain user scores, and the corresponding user charging evaluation characteristics are extracted based on the user scores.

[0018] The non-evaluation information data in the target data is feature-extracted to obtain user charging characteristics.

[0019] Optionally, after the user charging data is obtained and before the user charging data is filtered, the method further comprises:

[0020] The user charging data is cleaned.

[0021] Optionally, the training steps of the LightGBM model are:

[0022] User historical charging behavior data and corresponding evaluation information are collected.

[0023] The evaluation information is one-hot encoded to obtain user scores.

[0024] Training user charging characteristics are extracted based on the user historical charging behavior data, and training user charging evaluation characteristics are extracted based on the user scores.

[0025] The LightGBM model is trained by using the training user charging feature and the training user charging evaluation feature, and during the training process, mean square error or mean absolute error is used to evaluate the training result of each step until the mean square error or mean absolute error is less than a preset threshold.

[0026] Optionally, the comprehensive score of the user to different charging stations based on the influence degree value comprises:

[0027] For each charging station, the user charging feature and the user charging evaluation feature corresponding to the charging station are weighted and summed based on the influence degree value to obtain a comprehensive score of the charging station.

[0028] Optionally, the recommendation of the charging station to the user based on the potential matching relationship comprises:

[0029] The singular value decomposition method is used to obtain a potential score of the user to a to-be-selected charging station based on the potential matching relationship, and the to-be-selected charging station comprises at least one or more of the following: a charging station with an existing potential score and a charging station without a score.

[0030] The charging station is recommended to the user based on the potential score.

[0031] Optionally, the recommendation of the charging station to the user based on the potential score comprises:

[0032] According to the high and low of the potential score, the corresponding charging stations are sorted from high to low, and the first preset number of charging stations in the front are recommended to the user.

[0033] In another aspect, the application also provides an electric vehicle charging station recommendation system, comprising:

[0034] An acquisition module is configured to acquire an influence degree value of a user charging feature and a user charging evaluation feature on user charging preferences, wherein the user charging evaluation feature is constructed based on user evaluation information of a charging station.

[0035] A construction module is configured to calculate a comprehensive score of the user to different charging stations based on the influence degree value, and construct a user preference matrix with the comprehensive score of the user to different charging stations as an element.

[0036] A recommendation module is configured to decompose the user preference matrix by using a singular value decomposition method, obtain a potential matching relationship between the user and the charging station feature, and recommend the charging station to the user based on the potential matching relationship.

[0037] Optionally, the acquisition module is specifically configured to:

[0038] obtain the user charging feature and the user charging evaluation feature;

[0039] input the user charging feature and the user charging evaluation feature into a LightGBM model to obtain an influence degree value.

[0040] Optionally, the obtaining module obtains the user charging feature and the user charging evaluation feature, including:

[0041] obtain user charging data;

[0042] filter the user charging data to obtain target data including charging times greater than a preset value and corresponding evaluation information;

[0043] extract features from the target data to obtain the user charging feature and the user charging evaluation feature.

[0044] Optionally, the obtaining module extracts features from the target data to obtain the user charging feature and the user charging evaluation feature, including:

[0045] perform one-hot encoding on the evaluation information data in the target data to obtain a user score, and extract corresponding user charging evaluation features based on the user score;

[0046] extract features from the non-evaluation information data in the target data to obtain user charging features.

[0047] Optionally, after the obtaining module obtains the user charging data and before the user charging data is filtered, the method further includes:

[0048] clean the user charging data.

[0049] Optionally, the training step of the LightGBM model in the obtaining module is:

[0050] collect user historical charging behavior data and corresponding evaluation information;

[0051] perform one-hot encoding on the evaluation information to obtain a user score;

[0052] extract training user charging features based on the user historical charging behavior data, and extract training user charging evaluation features based on the user score;

[0053] train the LightGBM model using the training user charging features and the training user charging evaluation features, and use mean square error or mean absolute error to evaluate the training result of each step during the training process until the mean square error or mean absolute error is less than a preset threshold.

[0054] Optionally, the constructing module calculates a comprehensive score of the user to different charging stations based on the influence degree value, comprising:

[0055] For each charging station, the user charging feature and the user charging evaluation feature corresponding to the charging station are weighted and summed based on the influence degree value to obtain a comprehensive score of the charging station.

[0056] Optionally, the recommending module recommends the charging station to the user based on the potential matching relationship, comprising:

[0057] The singular value decomposition method is used to obtain a potential score of the user to the to-be-selected charging station based on the potential matching relationship, the to-be-selected charging station comprising at least one or more of the following: a charging station with an existing potential score and a charging station without a score;

[0058] The charging station is recommended to the user based on the potential score.

[0059] Optionally, the recommending module recommends the charging station to the user based on the potential score, comprising:

[0060] According to the high and low of the potential score, the corresponding charging station is sorted from high to low, and the first preset number of charging stations in the front is recommended to the user.

[0061] Compared with the prior art, the present application has the following advantages:

[0062] The present application provides a method and system for recommending a charging station for an electric vehicle, comprising obtaining a user charging feature and an influence degree value of a user charging evaluation feature of the user to the charging station on the user preference; calculating a comprehensive score of the user to different charging stations based on the influence degree value to construct a user preference matrix; decomposing the user preference matrix by using a singular value decomposition method to obtain a potential matching relationship between the user and the charging station feature, and recommending the charging station to the user based on the potential matching relationship; the influence degree value representing the user preference is constructed based on the user charging evaluation feature of the user to the charging station, the different charging stations are scored based on the influence degree value to construct the user preference matrix, and the potential matching relationship between the user preference and the charging station feature is obtained by decomposing the user preference matrix by using the singular value decomposition, the charging station is recommended to the user based on the matching relationship, and personalized recommendation considering the user preference is realized. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 The main flowchart of the method for recommending a charging station for an electric vehicle is shown in the figure.

[0064] Figure 2 The detailed flowchart of the method for recommending a charging station for an electric vehicle is shown in the figure.

[0065] Figure 3 The detailed flowchart of step S1 in the present application is shown in the following figure: Figure 2

[0066] Figure 4 The detailed flowchart of step S11 in the present application is shown in the following figure: Figure 3

[0067] Figure 5 The flowchart of feature extraction of target data in the present application is shown in the following figure:

[0068] Figure 6 The main flowchart of the training method of the LightGBM model in the present application is shown in the following figure:

[0069] Figure 7 The step diagram of the training method of the LightGBM model in the present application is shown in the following figure:

[0070] Figure 8 The application process flowchart of singular value decomposition in the present application is shown in the following figure:

[0071] Figure 9 The structure diagram of the electric vehicle charging station recommendation system in the present application is shown in the following figure. DETAILED DESCRIPTION

[0072] The existing electric vehicle charging station recommendation technology considers factors including distance, fast charging, discount, high quality, and idle, objective factors, and subjective factors of user charging order evaluation are not considered. The existing recommendation method cannot meet the complex personal preferences of users, cannot make targeted personalized customized recommendations for the preferences of different users, and the user experience is not good. Based on this, the present application provides an electric vehicle charging station recommendation method and system, as shown in the following figure, which is the overall flowchart of the present application. The method mainly includes: Figure 1

[0073] (1) obtaining related data of charging stations and user evaluation information based on user historical orders;

[0074] (2) cleaning and unifying the format of the data obtained in step (1), and performing statistics;

[0075] (3) performing feature extraction on the statistical results obtained in step (2) to obtain user charging features related to the objectivity of user charging behavior, and to obtain user charging evaluation features related to the subjectivity of user charging behavior;

[0076] (4) inputting the user charging features and the user charging evaluation features into a pre-trained LightGBM model to obtain the influence degree value of the user charging features and the user charging evaluation features on the user charging preferences; ​​​

[0077] (5) performing weighted summation on the user charging characteristics and the user charging evaluation characteristics based on the influence degree values to obtain a comprehensive score of the charging station, and constructing a user preference matrix based on the comprehensive score;

[0078] (6) decomposing the user preference matrix by using a singular value decomposition (SVD) method to obtain a latent matching relationship;

[0079] (7) recommending a charging station to be selected based on the latent matching relationship.

[0080] Embodiment 1:

[0081] A method for recommending an electric vehicle charging station, as shown in Figure 2 , includes the following steps S1-S3.

[0082] S1: obtaining an influence degree value of user charging characteristics and user charging evaluation characteristics on user charging preferences, wherein the user charging evaluation characteristics are constructed based on user evaluation information of the charging station.

[0083] In a further preferred scheme, as shown in Figure 3 , the step S1 includes the following steps S11 and S12.

[0084] S11: obtaining the user charging characteristics and the user charging evaluation characteristics.

[0085] The user charging characteristics are used to represent objective characteristics in the user charging process, and the user charging evaluation characteristics are used to represent subjective characteristics in the user charging process, as shown in Figure 4 , which is a process for obtaining user charging characteristics and user charging evaluation characteristics, including the following steps S111-S113.

[0086] S111: various user charging data can be obtained from users and charging stations, for example, user charging records, evaluation information, basic information of charging stations, etc.

[0087] S112: cleaning and screening the user charging data to obtain target data including charging times greater than a preset value and corresponding evaluation information.

[0088] The collected user charging data is cleaned and standardized to remove noise, outliers, etc. in the data, ensuring data quality and consistency. The basic information of the charging user, such as transaction amount, charging time, charging frequency, etc., is subjected to descriptive statistics to understand the distribution characteristics of the data, laying a foundation for subsequent analysis.

[0089] Obtain target data useful for user preferences from the cleaned data, for example, filter out users and their corresponding orders that have charged more than a preset number of times (e.g., 10 times) in a preset period (e.g., half a year) and contain user evaluation information. User evaluation is an important data source reflecting their preferences and satisfaction. Existing systems can collect this user evaluation, but have not yet conducted in-depth analysis and utilized these data for personalized recommendations.

[0090] S113: Feature extraction is performed on the target data to obtain the user charging features and user charging evaluation features. For example, 17 features are filtered out in this embodiment, which can be divided into user charging features with objective characteristics and user charging evaluation features with subjective characteristics. For example, in this embodiment, the user charging features include transaction electricity, discount amount, transaction amount, whether parking is free, charging time, one-time charging success rate, station position serious comment rate, abnormal order rate, station direct current pile proportion, whether plug and play, whether charging at peak or peak period, charging method, order source, and transaction end reason; and the user charging evaluation features include fault score, order score, and excessive scanning score.

[0091] In a further preferred scheme, feature extraction is performed on the target data to obtain the user charging features and user charging evaluation features, as shown in Figure 5 The steps include steps 1 and 2 as follows:

[0092] Step 1: One-hot encoding is performed on the evaluation information data in the target data to obtain user scores, and corresponding user charging evaluation features are extracted based on the user scores.

[0093] Step 2: Feature extraction is performed on the non-evaluation information data in the target data to obtain user charging features.

[0094] S12: The user charging features and user charging evaluation features are input into a LightGBM model to obtain the influence degree value.

[0095] LightGBM is a machine learning algorithm based on a gradient boosting framework, which has efficient data processing capability and accurate prediction performance, and is particularly suitable for processing large-scale data sets. Through feature selection and parallel computing optimization, it can significantly reduce the calculation time and resource consumption while maintaining high accuracy. Therefore, the pre-trained LightGBM model is used to obtain the influence degree value of the user charging features and user charging evaluation features on user preferences, which can not only achieve accurate prediction of the influence degree value, but also efficiently process a large amount of data for each user.

[0096] As Figure 6As shown, the training process of the LightGBM model mainly includes the following steps:

[0097] (1) cleaning the data of user historical orders;

[0098] (2) screening the cleaned data to screen out orders with more than a preset number of charging times and evaluation information;

[0099] (3) obtaining user ratings by one-hot encoding the evaluation information;

[0100] (4) extracting user charging evaluation features based on user ratings and extracting user charging features based on user historical charging behavior data;

[0101] (5) inputting the user charging evaluation features and user charging features into the LightGBM model to obtain a predicted influence degree value;

[0102] (6) using mean square error or mean absolute error to evaluate the LightGBM model and optimize the hyperparameters of the LightGBM model to train the LightGBM model.

[0103] In a further preferred scheme, as shown in Figure 7 The training process of the above LightGBM model includes the following steps S121-S124 in detail:

[0104] S121: collecting user historical charging behavior data and corresponding evaluation information;

[0105] As described above, various user charging data can be obtained from users and charging stations, such as user charging records, evaluation information, basic information of charging stations, etc. The user charging data is cleaned and screened to obtain user historical charging behavior data including charging times greater than a preset value and corresponding evaluation information, i.e. target data. A preset proportion of the target data can be set as a training set, and an exceptional proportion can be set as a test set, for example, 80% of the target data as the training set and 20% as the test set. Subsequently, the training set is used to train the LightGBM model, and the test set is used to test the trained LightGBM model.

[0106] S122: obtaining user ratings by one-hot encoding the evaluation information;

[0107] S123: extracting training user charging features based on the user historical charging behavior data and extracting training user charging evaluation features based on the user ratings.

[0108] S124: The LightGBM model is trained using the user charging features and user charging evaluation features used for training. During training, the mean squared error (MSE) or mean absolute error (MAE) is used to evaluate the training results at each step until the MSE or MAE is less than a preset threshold, thereby achieving iterative optimization of the LightGBM model. The trained model is then used to evaluate the influence of each feature in the user charging features and user charging evaluation features on user preferences, that is, to obtain the importance of each feature to the user.

[0109] S2: Calculate the user's comprehensive rating for different charging stations based on the influence level value, and construct a user preference matrix using the user's comprehensive rating for different charging stations as elements.

[0110] Based on the above influence values, user A's overall rating for different charging stations (charging station 1, charging station 2, charging station 3, ...) can be calculated.

[0111] Therefore, for each charging station, a weighted sum is performed on each of the user charging characteristics and user charging evaluation characteristics corresponding to the charging station based on the magnitude of the influence value. The influence value represents the degree of influence of each feature on the user. Therefore, the greater the influence on the user, the higher the corresponding influence value, and the higher the weight in the weighted summation process, thus obtaining a comprehensive score that meets customer preferences.

[0112] A user preference matrix is ​​constructed using users, charging stations, and corresponding comprehensive ratings as elements.

[0113] S3: The user preference matrix is ​​decomposed using the singular value decomposition method to obtain the potential matching relationship between user and charging station features, and charging stations are recommended to users based on the potential matching relationship.

[0114] This application employs Singular Value Decomposition (SVD) to decompose the user preference matrix. SVD is commonly used in signal processing and statistics to extract key features from the matrix. SVD is used in collaborative filtering processes to extract latent features of users and items by decomposing the user-item interaction matrix, thereby predicting ratings or interests for untouched items.

[0115] like Figure 8As shown, based on the above steps, the influence values ​​of user charging characteristics and user charging evaluation characteristics on user charging preferences can be obtained. Based on the influence values, the features are weighted and summed to obtain the user's comprehensive score for each charging station. The comprehensive score is used as the element to construct a user preference matrix. The SVD method in this application is used to solve the user preference matrix to obtain potential matching relationships. Based on the potential matching relationships, the selected charging stations are rated, and the Top-N charging stations are selected as recommendation objects.

[0116] In this embodiment, SVD is used to obtain the potential matching relationship between user and charging station features. Based on the potential matching relationship, the SVD method can calculate the potential rating of the user for the selected charging stations, wherein at least one or more of the following are included: charging stations with existing potential ratings and unrated charging stations. For example, when a user arrives in a new area, all the selected charging stations are unrated. When a user arrives in an area he has visited before, some new charging stations may have been added to that area, and the selected charging stations include charging stations with existing potential ratings or unrated charging stations.

[0117] Based on the potential ratings, the corresponding charging stations are sorted from highest to lowest. A preset number of the top-ranked charging stations are then recommended to the user, thus realizing a charging station recommendation method based on user preferences. This achieves personalized recommendations, making the recommendation results more in line with customer expectations and greatly enhancing the user experience.

[0118] The results show that the accuracy, recall, and F1 score of the LightGBM model combined with the SVD method in this application are 0.5604, 0.6040, and 0.5527, respectively. In contrast, the existing technology only considers the number of charging times as a factor influencing user preferences and then uses the SVD method for decomposition, resulting in accuracy, recall, and F1 scores of 0.4003, 0.5477, and 0.4231, respectively. It can be seen that the charging stations recommended in this application are more in line with human nature and user needs.

[0119] In summary, this application uses historical data and charging station information to obtain the degree of influence of evaluation features on user preferences, determines the importance of each feature, constructs a user preference matrix, and then uses the SVD method to decompose the preference matrix to predict the potential ratings of users for unrated charging stations, thereby optimizing the recommendation ranking algorithm. By considering users' charging preferences, this application can provide users with charging station recommendations that better meet their needs, thereby improving the user's charging experience. This personalized recommendation method helps reduce the time and effort users spend searching for suitable charging stations.

[0120] Example 2:

[0121] Based on the same inventive concept, this invention also provides an electric vehicle charging station recommendation system, the structural diagram of which is shown below. Figure 9 As shown, it includes:

[0122] Acquisition module: used to acquire user charging characteristics and the degree of influence of user charging evaluation characteristics on user preferences, wherein the user charging evaluation characteristics are constructed based on user evaluation information of charging stations;

[0123] Construction module: used to calculate the user's comprehensive rating of different charging stations based on the influence level value, and to construct a user preference matrix using the user's comprehensive rating of different charging stations as elements;

[0124] Recommendation module: Used to decompose the user preference matrix using the singular value decomposition method, obtain the potential matching relationship between user and charging station features, and recommend charging stations to users based on the potential matching relationship.

[0125] In one possible implementation, the acquisition module is specifically used for:

[0126] Obtain the user charging characteristics and user charging evaluation characteristics;

[0127] The user charging characteristics and user charging evaluation characteristics are input into the LightGBM model to obtain the degree of influence value.

[0128] In one possible implementation, the acquisition module acquires the user charging characteristics and user charging evaluation characteristics, including:

[0129] Obtain user charging data;

[0130] The user charging data is filtered to obtain target data that includes charging times greater than a preset value and corresponding evaluation information;

[0131] Feature extraction is performed on the target data to obtain the user charging features and user charging evaluation features.

[0132] In one possible implementation, the acquisition module performs feature extraction on the target data to obtain the user charging features and user charging evaluation features, including:

[0133] One-hot encoding is performed on the evaluation information data in the target data to obtain user scores, and corresponding user charging evaluation features are extracted based on the user scores.

[0134] Feature extraction is performed on the non-evaluation information data in the target data to obtain user charging features.

[0135] In one possible implementation, after the acquisition module acquires user charging data and before filtering the user charging data, it further includes:

[0136] The user charging data is cleaned.

[0137] In one possible implementation, the training steps of the LightGBM model in the acquisition module are as follows:

[0138] Collect the user's historical charging behavior data and corresponding evaluation information;

[0139] The user rating is obtained by performing one-hot encoding on the evaluation information;

[0140] Training user charging features are extracted based on the user's historical charging behavior data, and training user charging evaluation features are extracted based on the user ratings.

[0141] The LightGBM model is trained using the user charging features and user charging evaluation features used for training. During the training process, the mean squared error or mean absolute error is used to evaluate the training results at each step until the mean squared error or mean absolute error is less than a preset threshold.

[0142] In one possible implementation, the calculation of the user's overall rating of different charging stations based on the influence level value in the construction module includes:

[0143] For each charging station, a comprehensive score is obtained by weighted summation of the user charging characteristics and user charging evaluation characteristics corresponding to the charging station based on the magnitude of the influence value.

[0144] In one possible implementation, the recommendation module recommends charging stations to the user based on the potential matching relationship, including:

[0145] The singular value decomposition method is used to obtain the potential rating of the user for the selected charging station based on the potential matching relationship. The selected charging station includes at least one or more of the following: charging stations with existing potential ratings and charging stations without ratings.

[0146] Charging stations are recommended to users based on the potential ratings.

[0147] In one possible implementation, the recommendation module recommends charging stations to the user based on the potential rating, including:

[0148] Based on the potential ratings, the corresponding charging stations are sorted from highest to lowest, and the top-ranked charging stations are recommended to the user.

[0149] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. An electric vehicle charging station recommendation method characterized by, The method comprises the following steps: obtaining the influence degree value of the user charging feature and the user charging evaluation feature on the user charging preference, wherein the user charging evaluation feature is constructed based on the evaluation information of the user on the charging station; calculating the comprehensive score of the user on different charging stations based on the influence degree value, and constructing a user preference matrix with the comprehensive score of the user on different charging stations as elements; decomposing the user preference matrix by using the singular value decomposition method to obtain the latent matching relationship between the user and the charging station feature, and recommending the charging station to the user based on the latent matching relationship.

2. The electric vehicle charging station recommendation method of claim 1, wherein, The method comprises the following steps: obtaining the influence degree value of the user charging feature and the user charging evaluation feature on the user charging preference, wherein the user charging evaluation feature is constructed based on the evaluation information of the user on the charging station; calculating the comprehensive score of the user on different charging stations based on the influence degree value, and constructing a user preference matrix with the comprehensive score of the user on different charging stations as elements; 3. The electric vehicle charging station recommendation method of claim 2, wherein, decomposing the user preference matrix by using the singular value decomposition method to obtain the latent matching relationship between the user and the charging station feature, and recommending the charging station to the user based on the latent matching relationship. The method comprises the following steps: obtaining the influence degree value of the user charging feature and the user charging evaluation feature on the user charging preference, wherein the user charging evaluation feature is constructed based on the evaluation information of the user on the charging station; calculating the comprehensive score of the user on different charging stations based on the influence degree value, and constructing a user preference matrix with the comprehensive score of the user on different charging stations as elements; 4. The electric vehicle charging station recommendation method of claim 3, wherein, decomposing the user preference matrix by using the singular value decomposition method to obtain the latent matching relationship between the user and the charging station feature, and recommending the charging station to the user based on the latent matching relationship. The method comprises the following steps: obtaining the influence degree value of the user charging feature and the user charging evaluation feature on the user charging preference, wherein the user charging evaluation feature is constructed based on the evaluation information of the user on the charging station; 5. The electric vehicle charging station recommendation method according to claim 3 or 4, characterized by, calculating the comprehensive score of the user on different charging stations based on the influence degree value, and constructing a user preference matrix with the comprehensive score of the user on different charging stations as elements; decomposing the user preference matrix by using the singular value decomposition method to obtain the latent matching relationship between the user and the charging station feature, and recommending the charging station to the user based on the latent matching relationship.

6. The electric vehicle charging station recommendation method of claim 2, wherein, The method comprises the following steps: obtaining the influence degree value of the user charging feature and the user charging evaluation feature on the user charging preference, wherein the user charging evaluation feature is constructed based on the evaluation information of the user on the charging station; calculating the comprehensive score of the user on different charging stations based on the influence degree value, and constructing a user preference matrix with the comprehensive score of the user on different charging stations as elements; decomposing the user preference matrix by using the singular value decomposition method to obtain the latent matching relationship between the user and the charging station feature, and recommending the charging station to the user based on the latent matching relationship. The method comprises the following steps:

7. The electric vehicle charging station recommendation method of claim 1, wherein, obtaining the influence degree value of the user charging feature and the user charging evaluation feature on the user charging preference, wherein the user charging evaluation feature is constructed based on the evaluation information of the user on the charging station; calculating the comprehensive score of the user on different charging stations based on the influence degree value, and constructing a user preference matrix with the comprehensive score of the user on different charging stations as elements; 8. The electric vehicle charging station recommendation method of claim 1, wherein, decomposing the user preference matrix by using the singular value decomposition method to obtain the latent matching relationship between the user and the charging station feature, and recommending the charging station to the user based on the latent matching relationship. The method comprises the following steps: obtaining the influence degree value of the user charging feature and the user charging evaluation feature on the user charging preference, wherein the user charging evaluation feature is constructed based on the evaluation information of the user on the charging station; 9. The electric vehicle charging station recommendation method of claim 8, wherein, calculating the comprehensive score of the user on different charging stations based on the influence degree value, and constructing a user preference matrix with the comprehensive score of the user on different charging stations as elements; decomposing the user preference matrix by using the singular value decomposition method to obtain the latent matching relationship between the user and the charging station feature, and recommending the charging station to the user based on the latent matching relationship. According to the high and low of the potential score, the corresponding charging stations are sorted from high to low, and the top pre-set number of charging stations are recommended to the user.

10. An electric vehicle charging station recommendation system characterized by, Comprise: An acquisition module is configured to acquire a user charging feature and an influence degree value of a user charging evaluation feature on a user charging preference, wherein the user charging evaluation feature is constructed based on evaluation information of the user on the charging station; A construction module is configured to calculate a comprehensive score of the user on different charging stations based on the influence degree value, and construct a user preference matrix with the comprehensive score of the user on different charging stations as elements; A recommendation module is configured to decompose the user preference matrix by using a singular value decomposition method, acquire a potential matching relationship between the user and the charging station feature, and recommend the charging station to the user based on the potential matching relationship.

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