Electric vehicle charging guidance method, device, electronic equipment and storage medium

Abstract

The embodiments of the present application provide a method, device, electronic device and computer-readable storage medium for guiding charging of electric vehicles. The method includes: obtaining charging behavior information of electric vehicle users, wherein the charging behavior information includes station load analysis information and user charging behavior portrait; determining the day-ahead invitation price and the intraday guidance price according to the preset station load prediction model and station load analysis information; performing day-ahead invitation processing on electric vehicle users according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait; and performing intraday invitation processing on electric vehicle users according to the station load analysis information and the intraday guidance price. According to the scheme of the embodiment of the present invention, in the day-ahead stage, the day-ahead invitation price is set for the user to invite the electric vehicle user to charge; in the intraday stage, the intraday guidance price is set for the user to guide the charging behavior of the electric vehicle user, thereby reducing the deviation cost of the charging station.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of power system automation, and in particular to an electric vehicle charging guidance method, device, electronic device, and computer-readable storage medium. Background Art

[0002] With the rapid development and popularity of electric vehicles, the demand for electric vehicle charging stations is also increasing. The rapid growth in the number of electric vehicles has brought challenges to charging station operators, ensuring that charging stations are available at all times while also being profitable. One of the main challenges facing charging station operators is managing the load at the charging station. If the demand for charging exceeds the capacity of the charging station, customers may not be able to charge their vehicles, resulting in frustration and loss of revenue for the operator. On the other hand, if the charging station is not fully utilized, the operator may not generate enough revenue to cover the costs. Therefore, it is critical for charging station operators to effectively manage the electric vehicle load at the charging station. Summary of the invention

[0003] The embodiments of the present application aim to solve at least one of the technical problems existing in the prior art.

[0004] To this end, an embodiment of the present application proposes an electric vehicle charging guidance method, which sets a day-ahead invitation price for users in the day-ahead stage to invite electric vehicle users to charge; in the intra-day stage, sets an intra-day guidance price for users to guide the charging behavior of electric vehicle users, thereby reducing the deviation cost of charging stations.

[0005] The embodiment of the present application also proposes a device that applies the above-mentioned electric vehicle charging guidance method.

[0006] The embodiment of the present application also provides an electronic device using the above-mentioned electric vehicle charging guidance method.

[0007] The embodiment of the present application also proposes a computer-readable storage medium that applies the above-mentioned electric vehicle charging guidance method.

[0008] According to the first aspect of the present application, the electric vehicle charging guidance method comprises:

[0009] Acquiring charging behavior information of electric vehicle users, wherein the charging behavior information includes station load analysis information and user charging behavior portrait;

[0010] Determine the day-ahead invitation price and the intraday guidance price according to the preset station load forecasting model and the station load analysis information;

[0011] The electric vehicle user is invited on the day before according to the station load analysis information, the day before invitation price and the user charging behavior portrait; and the electric vehicle user is invited on the day after according to the station load analysis information and the day before guidance price.

[0012] According to some embodiments of the embodiments of the present application, the station load prediction model is obtained by:

[0013] Obtain historical charging record information and auxiliary prediction data information of charging stations;

[0014] Training a preset station load prediction initial model according to the historical charging record information and the auxiliary prediction data information to obtain the station load prediction model;

[0015] The auxiliary prediction data information includes at least one of the following: temperature data, meteorological data, season data and date type data.

[0016] According to some embodiments of the embodiments of the present application, the user charging behavior portrait is obtained by:

[0017] Obtain the historical charging order data of users at the charging station;

[0018] Extracting features from the user's historical charging order data to obtain user charging order features, wherein the user charging order features include at least one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charging power, charging frequency, charging cost, and charging pile type;

[0019] Based on the characteristics of the user's charging order, feature clustering processing is performed on the electric vehicle user to obtain the user's charging behavior portrait.

[0020] According to some embodiments of the embodiments of the present application, the determining of the day-ahead invitation price and the day-initiative guide price according to the preset station load forecasting model and the station load analysis information includes:

[0021] Obtaining the next day's station load forecast information according to the station load forecast model and the station load analysis information;

[0022] Determine the day-ahead invitation price according to a preset price-sensitive model, the next-day station load forecast information and the auxiliary forecast data information;

[0023] The intraday guidance price is determined according to the price sensitivity model, the station load analysis information and the auxiliary forecast data information.

[0024] According to some embodiments of the embodiments of the present application, after the day-ahead invitation processing is performed on the electric vehicle user according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait, the method further includes:

[0025] Obtaining the electric vehicle user's electricity consumption start time, charging end time, and charging amount, and determining the cumulative charging invitation amount according to the electricity consumption start time, the charging end time, and the charging amount;

[0026] When the cumulative charging invitation amount is not greater than the total invitation load of the charging station, the invitation to the electric vehicle user is successful.

[0027] According to some embodiments of the embodiments of the present application, the intraday invitation processing for electric vehicle users according to the station load analysis information and the intraday guide price includes:

[0028] Determine a short-term prediction value of the charging station according to the station load analysis information;

[0029] The electric vehicle user is invited to make an appointment within the day according to the short-term forecast value of the charging station and the intra-day guide price.

[0030] According to some embodiments of the embodiments of the present application, the day-ahead invitation price is less than the day-ahead guide price.

[0031] According to the second aspect of the embodiment of the present application, the electric vehicle charging guidance device comprises:

[0032] A first processing module is used to obtain charging behavior information of electric vehicle users, wherein the charging behavior information includes station load analysis information and user charging behavior portrait;

[0033] A second processing module is used to determine the day-ahead invitation price and the intraday guidance price according to a preset station load forecasting model and the station load analysis information;

[0034] The third processing module is used to perform day-ahead invitation processing on electric vehicle users based on the station load analysis information, the day-ahead invitation price and the user charging behavior portrait; and to perform intraday invitation processing on electric vehicle users based on the station load analysis information and the intraday guide price.

[0035] According to the electronic device of the third aspect of the embodiment of the present application, the electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the electric vehicle charging guidance method as described above when executing the computer program.

[0036] According to a computer-readable storage medium of the fourth aspect of an embodiment of the present application, the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a control processor, the electric vehicle charging guidance method as described above is implemented.

[0037] According to the electric vehicle charging guidance method of the embodiment of the present application, at least the following beneficial effects are achieved: in the process of guiding electric vehicle charging, firstly, the charging behavior information of the electric vehicle user is obtained, wherein the charging behavior information includes the station load analysis information and the user charging behavior portrait; then, the day-ahead invitation price and the intraday guidance price are determined according to the preset station load prediction model and the station load analysis information; finally, the electric vehicle user is invited according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait; and the electric vehicle user is invited according to the station load analysis information and the intraday guidance price. Through the above technical scheme, the day-ahead invitation price is set for the user in the day-ahead stage to invite the electric vehicle user to charge; in the intraday stage, the intraday guidance price is set for the user to guide the charging behavior of the electric vehicle user, thereby reducing the deviation cost of the charging station.

[0038] Other features and advantages of the embodiments of the present application will be described in the subsequent description, and partly become apparent from the description, or can be understood by implementing the embodiments of the present application. The purpose and other advantages of the embodiments of the present application can be achieved and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to provide further understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation on the technical solution of the present disclosure.

[0040] Figure 1 is a flow chart of an electric vehicle charging guidance method provided by an embodiment of the present application;

[0041] Figure 2 It is a specific flow chart of building a station load prediction model provided by an embodiment of the present application;

[0042] Figure 3 It is a specific flow chart of constructing a user charging behavior portrait provided by an embodiment of the present application;

[0043] Figure 4 is a specific flow chart of S200 provided in one embodiment of the present application;

[0044] Figure 5is a flow chart of an electric vehicle charging guidance method provided by another embodiment of the present application;

[0045] Figure 6 is a specific flow chart of S300 provided in another embodiment of the present application;

[0046] Figure 7 It is a schematic diagram of the structure of an electric vehicle charging guidance device provided by an embodiment of the present application;

[0047] Figure 8 It is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application more clear, the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the embodiments of the present application and are not used to limit the embodiments of the present application.

[0049] In the description of the embodiments of the present application, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0050] In the description of the embodiments of the present application, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the embodiments of the present application based on the specific content of the technical solution.

[0051] The embodiment of the present application provides an electric vehicle charging guidance method, device, electronic device and computer-readable storage medium, the method comprising: in the process of guiding electric vehicle charging, firstly obtaining the charging behavior information of the electric vehicle user, wherein the charging behavior information includes the station load analysis information and the user charging behavior portrait; then determining the day-ahead invitation price and the intraday guidance price according to the preset station load prediction model and the station load analysis information; finally, performing day-ahead invitation processing on the electric vehicle user according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait; and performing intraday invitation processing on the electric vehicle user according to the station load analysis information and the intraday guidance price. Through the above technical scheme, the day-ahead invitation price is set for the user in the day-ahead stage to invite the electric vehicle user to charge; in the intraday stage, the intraday guidance price is set for the user to guide the charging behavior of the electric vehicle user, thereby reducing the deviation cost of the charging station.

[0052] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0053] like Figure 1 As shown, Figure 1 1 is a flow chart of an electric vehicle charging guidance method provided by an embodiment of the present application. The method includes but is not limited to step S100, step S200 and step S300.

[0054] Step S100, obtaining charging behavior information of electric vehicle users, wherein the charging behavior information includes station load analysis information and user charging behavior portrait;

[0055] Step S200, determining the day-ahead invitation price and the intraday guidance price according to the preset station load forecasting model and station load analysis information;

[0056] Step S300, performing day-ahead invitation processing on electric vehicle users according to station load analysis information, day-ahead invitation prices and user charging behavior portraits; and performing day-ahead invitation processing on electric vehicle users according to station load analysis information and day-ahead guidance prices.

[0057] It should be noted that in the process of guiding electric vehicle charging, the charging behavior information of electric vehicle users is first obtained, wherein the charging behavior information includes the station load analysis information and the user charging behavior portrait; then the day-ahead invitation price and the intraday guidance price are determined according to the preset station load prediction model and the station load analysis information; finally, the day-ahead invitation processing is performed on the electric vehicle users according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait; and the intraday invitation processing is performed on the electric vehicle users according to the station load analysis information and the intraday guidance price. Through the above technical solution, the day-ahead invitation price is set for the user in the day-ahead stage to invite the electric vehicle user to charge; in the intraday stage, the intraday guidance price is set for the user to guide the charging behavior of the electric vehicle user, thereby reducing the deviation cost of the charging station.

[0058] It is worth noting that the number of electric vehicle users in the embodiment of the present application can be multiple, and the number of charging stations can also be multiple; multiple electric vehicle users and multiple charging stations are combined for unified analysis and processing, so that the charging station can meet the charging needs of multiple electric vehicle users while reducing the deviation cost of the charging station.

[0059] It can be understood that the station load analysis information can be the charging load information of the charging station; the user charging behavior portrait can be constructed based on some previous charging behavior information of the electric vehicle user, and the portrait can be understood as a charging behavior prediction model for the electric vehicle user.

[0060] It is worth noting that the preset station load forecasting model is the station load forecasting model that has been trained. The day-ahead invitation price is the price for inviting electric vehicle users to charge the day before, and the day-introduction price is the price for guiding electric vehicle users to charge on the day.

[0061] It should be noted that in the process of processing day-ahead invitations for electric vehicle users, it is necessary to combine the station load analysis information, the day-ahead invitation price and the user's charging behavior profile; and in the process of processing intraday invitations for electric vehicle users, it is necessary to base it on the station load analysis information and the intraday guidance price.

[0062] In addition, in one embodiment, if Figure 2 As shown, constructing a station load prediction model may include but is not limited to step S410 and step S420.

[0063] Step S410, obtaining historical charging record information and auxiliary prediction data information of the charging station;

[0064] Step S420, training a preset station load prediction initial model according to historical charging record information and auxiliary prediction data information to obtain a station load prediction model; wherein the auxiliary prediction data information includes at least one of the following: temperature data, meteorological data, seasonal data and date type data.

[0065] It should be noted that in the process of constructing the station load prediction model, the historical charging record information and auxiliary prediction data information of the charging station are first obtained; then the preset station load prediction initial model is trained according to the historical charging record information and the auxiliary prediction data information to obtain the station load prediction model, wherein the auxiliary prediction data information includes at least one of the following: temperature data, meteorological data, seasonal data and date type data.

[0066] It can be understood that the temperature data is the temperature data of the charging station; the meteorological data is information such as whether the weather is sunny, rainy or cloudy; the season data is season information, namely spring, summer, autumn and winter; the date type data is information such as whether it is a holiday.

[0067] It is worth noting that the initial model for station load prediction is trained in combination with historical charging record information and related auxiliary prediction data information, so that the obtained station load prediction model can more accurately predict subsequent station loads.

[0068] For example, the algorithm for constructing a station load prediction model may be as follows:

[0069] Import historical load data and auxiliary forecast data, and perform data preprocessing, including but not limited to: data cleaning, data fusion, feature selection and extraction, data standardization and normalization, data division into training sets and test sets. Create a neural network model and initialize model parameters;

[0070]

[0071] Output the day-ahead load forecast results based on the load forecast values ​​for each time period.

[0072] In addition, in one embodiment, if Figure 3 As shown, constructing a user charging behavior portrait may include but is not limited to step S510, step S520 and step S530.

[0073] Step S510, obtaining historical charging order data of users at the charging station;

[0074] Step S520, extracting features from the user's historical charging order data to obtain user charging order features, wherein the user charging order features include at least one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charging power, charging frequency, charging cost, and charging pile type;

[0075] Step S530, performing feature clustering processing on electric vehicle users based on the user charging order characteristics to obtain a user charging behavior portrait.

[0076] It should be noted that in the process of constructing a user charging behavior portrait, the user's historical charging order data at the charging station is first obtained; then, the user's charging order features are extracted to obtain the user's charging order features, wherein the user's charging order features include at least one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charging power, charging frequency, charging cost and charging pile type; finally, based on the user's charging order features, feature clustering processing is performed on the electric vehicle users to obtain the user's charging behavior portrait.

[0077] It is worth noting that the user's historical charging order data contains a variety of user charging behavior characteristics. Feature extraction is performed on the charging order data, including but not limited to charging start time, charging end time, charging start state of charge, charging end state of charge, charging power, charging frequency, charging fee, and charging pile type. Based on the user's charging order characteristics, feature clustering is performed on electric vehicle charging users to achieve electric vehicle charging user behavior portraits, and multiple categories of electric vehicle charging users and the charging frequency, charging location, charging time, and charging power preferences of corresponding categories of users are obtained.

[0078] It is worth noting that based on the logic of "invitation-user feedback-real data correction", the user's real charging behavior is regularly collected, the user behavior database is updated, and user behavior analysis is performed. The validity of the user behavior analysis database is guaranteed by updating, iterating and adjusting it in real time. Among them, the user behavior database includes the user's historical charging order data.

[0079] Exemplarily, the algorithm for constructing a user charging behavior profile may be as follows:

[0080] Initialize and import user charging feature data, determine the number of clusters k and randomly initialize the centroid

[0081] for i=1: number of users

[0082] Calculate the Euclidean distance between the data point and each centroid

[0083] Assign the user to the closest cluster

[0084] end

[0085] for i=1: number of clusters k

[0086] for j=1: the number of data in the kth cluster

[0087] Take this data point as the new centroid

[0088] Calculate the Euclidean distance between all data points in the cluster and the new centroid

[0089] Select the data point with the smallest distance as the new centroid

[0090] end

[0091] end

[0092] The above cycle is repeated until the centroid no longer changes or the maximum number of iterations is reached.

[0093] In addition, in one embodiment, if Figure 4 As shown, the above step S200 may include but is not limited to step S210, step S220 and step S230.

[0094] Step S210, obtaining the next day's station load forecast information according to the station load forecast model and the station load analysis information;

[0095] Step S220, determining the day-ahead invitation price according to a preset price-sensitivity model, the next day's station load forecast information and the auxiliary forecast data information;

[0096] Step S230, determining the intraday guidance price according to the price sensitivity model, the station load analysis information and the auxiliary forecast data information.

[0097] It should be noted that in the process of determining the day-ahead invitation price and the intraday guidance price, the next day's station load forecast information is first obtained based on the station load forecast model and the station load analysis information; then the day-ahead invitation price is determined based on the preset price-sensitive model, the next day's station load forecast information and the auxiliary forecast data information; finally, the intraday guidance price is determined based on the price-sensitive model, the station load analysis information and the auxiliary forecast data information.

[0098] It is worth noting that in the process of formulating the price of charging stations, a user anti-learning mechanism is first constructed, following the principle of "price for invitation the day before < price for guidance within the day < price for not participating in invitation guidance", to construct an optimal charging pricing mechanism with the goal of maximizing user benefits. The user's interpretation of historical invitation information and use of rules are also aimed at maximizing their own interests, which is consistent with the goal of the pricing mechanism in the embodiment of the present application. Therefore, the optimal charging pricing strategy based on the user anti-learning mechanism constructed in the embodiment of the present application can ensure the effect of the invitation. Formulate the invitation price of the charging station in the day-ahead stage and construct a price sensitivity model: D = RF (w t ,Q t ,H t ,P t DA ), where w t Indicates whether it is raining, Q t Indicates temperature, H t Indicates whether it is a holiday, P t DA represents the day-ahead invitation electricity price. All this information is used as input to a price sensitivity model built based on a deep learning (RF()) method to generate the EV charging demand at that time. In the day-ahead stage, the charging station purchases electricity based on the next day's load forecast information, and determines the day-ahead invitation electricity price based on the charging load price sensitivity model and the power station benchmark electricity price. Features include time period, minimum temperature, maximum temperature, date, and weather type.

[0099] For example, the algorithm for determining the day-ahead invitation price may be as follows:

[0100] Initialize the base electricity price P of the charging station t 0 , the maximum price fluctuation range allowed is d max ,

[0101]

[0102] In addition, in the process of determining the day-ahead invitation price, the charging station operator makes pricing decisions based on the day-ahead power purchase plan, the number of invitations accepted by users, and the observed ultra-short-term charging load. The charging station operator decides the direction and magnitude of price adjustment based on this information. The pricing strategy for guiding prices in the day-ahead phase is similar to the algorithm for determining the day-ahead invitation price mentioned above, with the difference being the maximum price fluctuation range allowed (d max ) by d max =P t 0 -P t DA This is to make the intraday guide price P t RT Higher than the invitation price in the previous stage.

[0103] In addition, in one embodiment, if Figure 5 As shown, after executing the above step S300, it may include but not be limited to step S310 and step S320.

[0104] Step S310, obtaining the electric vehicle user's electricity consumption start time, charging end time and charging amount, and determining the cumulative charging invitation amount according to the electricity consumption start time, charging end time and charging amount;

[0105] Step S320: When the cumulative charging invitation quantity is not greater than the total invitation load of the charging station, the invitation to the electric vehicle user is successful.

[0106] It should be noted that when an electric car user receives an invitation before the charging date, the first step is to obtain the electric car user's electricity start time, charging end time and charging amount, and determine the cumulative charging invitation amount based on the electricity start time, charging end time and charging amount; then, when the cumulative charging invitation amount is not greater than the total invitation load of the charging station, the invitation to the electric car user is successful.

[0107] It is worth noting that based on the day-ahead invitation price of each charging station, electric vehicle users can select and process the invitation information on the application; obtain the charging start time, charging end time, and charging amount of each electric vehicle user. When the cumulative invitation amount in this period is less than the total invitation load of the station in this period, the invitation is successful, otherwise the invitation fails.

[0108] For example, the algorithm for charging invitation based on the day-ahead invitation price may be as follows:

[0109] Initialize the invitation quantity of each time period of the i-th charging station Base electricity price P t 0 , recently invited electricity price P t DA, cumulative invitations in each period

[0110]

[0111]

[0112] In addition, in one embodiment, if Figure 6 As shown, the above step S300 may include but is not limited to step S330 and step S340.

[0113] Step S330, determining a short-term prediction value of the charging station according to the station load analysis information;

[0114] Step S340, making an intraday invitation to the electric vehicle user based on the short-term forecast value of the charging station and the intraday guidance price.

[0115] It should be noted that in the process of intraday invitations, the short-term forecast value of the charging station is first determined based on the station load analysis information; then the electric vehicle users can be invited intraday based on the short-term forecast value of the charging station and the intraday guidance price, and subsequent electric vehicle users can make choices based on the intraday invitations.

[0116] For example, the algorithm for charging invitation based on the intraday invitation price can be as follows:

[0117] Initialize the maximum price fluctuation range allowed max =P t 0 -P t DA ,

[0118]

[0119] In addition, if electric vehicle users choose not to participate in the day-ahead invitation and intraday guidance, they will pay the default price P t 0 Under this pricing strategy, the day-ahead invitation price P t DA Always lower than the intraday guide price P t RT , intraday guide price P t RT Always lower than the benchmark electricity price P of the charging station t 0 Therefore, users who respond to day-ahead invitations and day-intraday guidance will receive lower charging costs.

[0120] In some embodiments of the present application, the day-ahead invitation price is less than the intraday guidance price, and the intraday guidance price is less than the electric vehicle users who do not participate in the invitation and guidance; in the day-ahead stage, the day-ahead invitation price is set for the user, and the electric vehicle users are invited to charge according to the invitation volume of each station in each time period; in the intraday stage, the intraday guidance price is set for the user to guide the charging behavior of the electric vehicle users, thereby reducing the deviation cost of the charging station.

[0121] like Figure 7 As shown, an embodiment of the present application further provides an electric vehicle charging guidance device 10, the device comprising:

[0122] The first processing module 100 is used to obtain the charging behavior information of the electric vehicle user, wherein the charging behavior information includes the station load analysis information and the user charging behavior portrait;

[0123] The second processing module 200 is used to determine the day-ahead invitation price and the intraday guidance price according to the preset station load forecasting model and station load analysis information;

[0124] The third processing module 300 is used to perform day-ahead invitation processing on electric vehicle users based on station load analysis information, day-ahead invitation prices and user charging behavior portraits; and to perform intraday invitation processing on electric vehicle users based on station load analysis information and intraday guidance prices.

[0125] The specific implementation of the electric vehicle charging guidance device 10 is substantially the same as the specific implementation of the electric vehicle charging guidance method described above, and will not be described in detail herein.

[0126] like Figure 8 As shown, an embodiment of the present application also provides an electronic device 700, including: a memory 720, a processor 710, and a computer program stored in the memory 720 and executable on the processor 710. When the processor 710 executes the computer program, the electric vehicle charging guidance method in the above embodiment is implemented, for example, the above described Figure 1 Steps S100 to S300 of the method, Figure 2 Steps S410 to S420 of the method, Figure 3 Steps S510 to S530 of the method, Figure 4 Steps S210 to S230 of the method, Figure 5 Steps S310 to S320 of the method and Figure 6 Method steps S330 to S340.

[0127] An embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions are executed by a processor or a controller, for example, by a processor in the above-mentioned device embodiment, so that the above-mentioned processor can execute the electric vehicle charging guidance method in the above-mentioned embodiment, for example, execute the above-mentioned Figure 1 Steps S100 to S300 of the method, Figure 2 Steps S410 to S420 of the method, Figure 3 Steps S510 to S530 of the method, Figure 4 Steps S210 to S230 of the method, Figure 5 Steps S310 to S320 of the method and Figure 6 Method steps S330 to S340.

[0128] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0129] The above is a specific description of the preferred implementation of the embodiments of the present application, but the embodiments of the present application are not limited to the above-mentioned implementation methods. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the embodiments of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the embodiments of the present application.

Claims

1. A method for guiding charging of an electric vehicle, characterized in that: The method comprises: Acquiring charging behavior information of electric vehicle users, wherein the charging behavior information includes station load analysis information and user charging behavior portrait; Determine the day-ahead invitation price and the intraday guidance price according to the preset station load forecasting model and the station load analysis information; Performing day-ahead invitation processing on electric vehicle users according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait; and performing day-ahead invitation processing on electric vehicle users according to the station load analysis information and the day-ahead guidance price; The user charging behavior profile is obtained in the following way: Obtain the historical charging order data of users at the charging station; Extracting features from the user's historical charging order data to obtain user charging order features, wherein the user charging order features include at least one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charging power, charging frequency, charging cost, and charging pile type; Performing feature clustering processing on electric vehicle users based on the user charging order features to obtain the user charging behavior portrait; The station load prediction model is obtained by the following method: Obtain historical charging record information and auxiliary prediction data information of charging stations; Training a preset station load prediction initial model according to the historical charging record information and the auxiliary prediction data information to obtain the station load prediction model; Wherein, the auxiliary prediction data information includes at least one of the following: temperature data, meteorological data, season data and date type data; The determining of the day-ahead invitation price and the day-initiative guidance price according to the preset station load forecasting model and the station load analysis information includes: Obtaining the next day's station load forecast information according to the station load forecast model and the station load analysis information; Determine the day-ahead invitation price according to a preset price-sensitive model, the next-day station load forecast information and the auxiliary forecast data information; Determine the intraday guidance price according to the price sensitivity model, the station load analysis information and the auxiliary forecast data information; After the day-ahead invitation processing is performed on the electric vehicle user according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait, the method further includes: Obtaining the electric vehicle user's electricity consumption start time, charging end time, and charging amount, and determining the cumulative charging invitation amount according to the electricity consumption start time, the charging end time, and the charging amount; In the case where the cumulative charging invitation amount is not greater than the total invitation load of the charging station, the invitation to the electric vehicle user is successful; The intraday invitation processing for electric vehicle users according to the station load analysis information and the intraday guide price includes: Determine a short-term prediction value of the charging station according to the station load analysis information; The electric vehicle user is invited to make an appointment within the day according to the short-term forecast value of the charging station and the intra-day guide price.

2. The electric vehicle charging guidance method according to claim 1, characterized in that: The day-ahead invitation price is less than the day-ahead guide price.

3. An electric vehicle charging guidance device, characterized in that: The device comprises: A first processing module is used to obtain charging behavior information of electric vehicle users, wherein the charging behavior information includes station load analysis information and user charging behavior portrait; A second processing module is used to determine the day-ahead invitation price and the intraday guidance price according to a preset station load forecasting model and the station load analysis information; A third processing module is used to perform day-ahead invitation processing on electric vehicle users according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait; and to perform day-ahead invitation processing on electric vehicle users according to the station load analysis information and the day-ahead guidance price; The user charging behavior profile is obtained in the following way: Obtain the historical charging order data of users at the charging station; Extracting features from the user's historical charging order data to obtain user charging order features, wherein the user charging order features include at least one of the following: charging start time, charging end time, charging start state of charge, charging end state of charge, charging power, charging frequency, charging cost, and charging pile type; Performing feature clustering processing on electric vehicle users based on the user charging order features to obtain the user charging behavior portrait; The station load prediction model is obtained by the following method: Obtain historical charging record information and auxiliary prediction data information of charging stations; Training a preset station load prediction initial model according to the historical charging record information and the auxiliary prediction data information to obtain the station load prediction model; Wherein, the auxiliary prediction data information includes at least one of the following: temperature data, meteorological data, season data and date type data; The determining of the day-ahead invitation price and the day-initiative guidance price according to the preset station load forecasting model and the station load analysis information includes: Obtaining the next day's station load forecast information according to the station load forecast model and the station load analysis information; Determine the day-ahead invitation price according to a preset price-sensitive model, the next-day station load forecast information and the auxiliary forecast data information; Determine the intraday guidance price according to the price sensitivity model, the station load analysis information and the auxiliary forecast data information; After the day-ahead invitation processing is performed on the electric vehicle user according to the station load analysis information, the day-ahead invitation price and the user charging behavior portrait, the method further includes: Obtaining the electric vehicle user's electricity consumption start time, charging end time, and charging amount, and determining the cumulative charging invitation amount according to the electricity consumption start time, the charging end time, and the charging amount; In the case where the cumulative charging invitation amount is not greater than the total invitation load of the charging station, the invitation to the electric vehicle user is successful; The intraday invitation processing for electric vehicle users according to the station load analysis information and the intraday guide price includes: Determine a short-term prediction value of the charging station according to the station load analysis information; The electric vehicle user is invited to make an appointment within the day according to the short-term forecast value of the charging station and the intra-day guide price.

4. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the electric vehicle charging guidance method as claimed in any one of claims 1 to 2 when executing the computer program.

5. A computer-readable storage medium storing computer-executable instructions, characterized in that: When the computer executable instructions are executed by the control processor, the electric vehicle charging guidance method according to any one of claims 1 to 2 is implemented.

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