Energy supplement station recommendation method, electronic equipment and computer readable storage medium

Abstract

The invention discloses an energy supplement station recommendation method, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining target data of a target driver; the target data comprises shift-over time and shift-over geographic position; according to the target data of the target driver, predicting an energy complementing station recommendation list for the target driver based on the trained energy complementing station recommendation model; the energy complementing station recommendation model is obtained by training based on historical target data and historical energy complementing data of different drivers, and the historical energy complementing data comprises energy complementing station information of which energy is removed and complemented; and recommending the energy complementing station to the target driver based on the energy complementing station recommendation list. Therefore, the prediction accuracy of the energy supplement demand of the driver can be improved, the calculation speed is high, and the real-time performance is good.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicles, and particularly to a method for recommending energy replenishment stations, an electronic device, and a computer-readable storage medium. Background Art

[0002] With the rapid development of the electric vehicle market, the demand for battery swapping and charging of electric vehicles is increasing day by day. How to recommend personalized energy replenishment stations such as battery swapping stations and charging piles to users has become an important research topic. The existing related technologies mainly recommend energy replenishment stations to users by collecting and analyzing the user's own historical behavior data, such as battery swapping and charging habits. However, the above methods consider relatively single factors and cannot accurately predict the energy replenishment stations required by users, that is, the accuracy of predicting the energy replenishment needs of users is low. Summary of the Invention

[0003] The purpose of this application is to provide a method for recommending energy replenishment stations, an electronic device, and a computer-readable storage medium, which can improve the prediction accuracy of the energy replenishment needs of drivers, and has a fast calculation speed and good real-time performance.

[0004] To achieve the above object:

[0005] In a first aspect, an embodiment of this application provides a method for recommending energy replenishment stations, including:

[0006] Obtain the target data of the target driver; the target data includes the shift handover time and the shift handover geographical location;

[0007] Based on the target data of the target driver, predict a list of recommended energy replenishment stations for the target driver based on a trained energy replenishment station recommendation model; the energy replenishment station recommendation model is trained based on the historical target data and historical energy replenishment data of different drivers, and the historical energy replenishment data includes information on energy replenishment stations that have been visited and replenished;

[0008] Recommend energy replenishment stations to the target driver based on the list of recommended energy replenishment stations.

[0009] Optionally, the step of predicting a list of recommended energy replenishment stations for the target driver based on the trained energy replenishment station recommendation model according to the target data of the target driver includes:

[0010] Determine the target handover type of the target driver according to the target data of the target driver;

[0011] Input the target handover type of the target driver into the trained energy replenishment station recommendation model to obtain a list of recommended energy replenishment stations predicted by the energy replenishment station recommendation model for the target driver.

[0012] Optionally, the training process of the energy replenishment station recommendation model includes:

[0013] Classify the handover of different drivers based on their historical target data to obtain the handover type of each driver among the different drivers.

[0014] Train the constructed charging station recommendation model according to the handover type of each driver among the different drivers and the corresponding historical charging data to obtain the trained charging station recommendation model.

[0015] Optionally, before training the constructed charging station recommendation model according to the handover type of each driver among the different drivers and the corresponding historical charging data to obtain the trained charging station recommendation model, it includes:

[0016] Preprocess the historical charging data corresponding to each driver among the different drivers, and the preprocessing includes data cleaning and / or data processing.

[0017] Optionally, the historical charging data further includes information about charging stations that have been visited but not charged.

[0018] Optionally, recommending a charging station to the target driver based on the charging station recommendation list includes:

[0019] Sort at least one charging station in the charging station recommendation list according to the target information; the target information includes the historical charging data of the target driver.

[0020] Based on the sorting result, determine the charging stations whose rankings meet the preset conditions as the target charging stations, and output a recommendation message including the target charging station information to the target driver.

[0021] Optionally, the target information further includes at least one of the following: the current status data of each charging station in the charging station recommendation list, the remaining battery power information of the vehicle driven by the target driver, the traffic congestion status from the current location of the target driver to each charging station in the charging station recommendation list, and the distance from the current location of the target driver to each charging station in the charging station recommendation list.

[0022] Optionally, before obtaining the target data of the target driver, it includes:

[0023] Detect whether a charging operation needs to be performed according to the destination to be traveled of the target driver and the remaining battery power information of the vehicle driven by the target driver.

[0024] If so, perform the step of obtaining the target data of the target driver.

[0025] In a second aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing a computer program. When the computer program runs on the processor, the above-mentioned charging station recommendation method is implemented.

[0026] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned charging station recommendation method is implemented.

[0027] The charging station recommendation method, electronic device and computer-readable storage medium provided by the embodiments of the present application, the method includes: obtaining target data of a target driver; the target data includes shift handover time and shift handover geographical location; based on the target data of the target driver, predicting a charging station recommendation list for the target driver based on a trained charging station recommendation model; the charging station recommendation model is trained based on historical target data and historical charging data of different drivers, and the historical charging data includes information of charging stations that have been visited and charged; recommending a charging station to the target driver based on the charging station recommendation list. In this way, according to the target data of the target driver including the shift handover time and the shift handover geographical location, recommending a charging station to the target driver based on the charging station recommendation model can improve the prediction accuracy of the charging demand of the driver, and has a fast calculation speed and good real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic flowchart of the charging station recommendation method provided by an embodiment of the present invention;

[0029] Figure 2 It is a schematic structural diagram of the electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] Here, exemplary embodiments will be described in detail, and examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0031] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising such element. In addition, components, features, and elements with the same name in different embodiments of this application may have the same meaning or may have different meanings, and their specific meanings need to be determined by their interpretation in the specific embodiment or further in combination with the context in the specific embodiment.

[0032] It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this text, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining". Furthermore, as used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprise", "include" indicate the presence of the stated features, steps, operations, elements, components, items, kinds, and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or meaning any one or any combination. Thus, "A, B or C" or "A, B and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C". An exception to this definition occurs only when the combination of elements, functions, steps or operations is inherently mutually exclusive in some way.

[0033] It should be understood that although the steps in the flowchart in the embodiments of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order restriction, and they can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0034] It should be noted that in this article, step codes such as S101 and S102 are used. The purpose is to more clearly and briefly express the corresponding content and do not constitute a substantial limitation in order. Those skilled in the art may execute S102 first and then S101 during specific implementation, etc., but these should all be within the protection scope of the present application.

[0035] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] In the subsequent description, suffixes such as "module", "component", or "unit" used to represent components are only for the convenience of explaining the present application, and they have no specific meaning in themselves. Therefore, "module", "component", or "unit" can be used interchangeably.

[0037] Refer to Figure 1 , a charging station recommendation method provided by an embodiment of the present application. This charging station recommendation method can be executed by a charging station recommendation device provided by an embodiment of the present application. The charging station recommendation device can be implemented in a software and / or hardware manner, such as electronic devices such as in-vehicle terminals and servers. In this embodiment, taking the execution entity of this charging station recommendation method as a server as an example, the charging station recommendation method provided in this embodiment includes:

[0038] Step S101: Obtain the target data of the target driver; the target data includes the shift handover time and the shift handover geographical location.

[0039] Among them, the target driver is the driver who needs to be recommended a charging station. In one implementation, obtaining the target data of the target driver may be to obtain the target data corresponding to the identity identifier of the target driver from a database according to the identity identifier of the target driver. In one implementation, obtaining the target data of the target driver may be to obtain the target data of the target driver corresponding to the vehicle identifier from a database according to the vehicle identifier of the vehicle driven by the target driver. The target data can be used to indicate the driving characteristics of the driver, including the current shift handover time and the shift handover geographical location. The shift handover time is used to indicate the time when the target driver takes over the vehicle from the previous driver or a preset location, such as morning, afternoon, or evening, etc. The shift handover geographical location is used to indicate the location where the target driver takes over the vehicle from the previous driver or the location where the target driver takes over the vehicle and reaches a preset location, such as area A, area B, etc. It can be understood that after the target driver takes over the vehicle from the previous driver or a preset location, due to the driving operations of the previous driver on the vehicle, the target driver may need to charge the vehicle first, such as charging or swapping the battery, etc., to ensure that the vehicle can be driven normally for a period of time later.

[0040] It should be noted that the application scenario of the charging station recommendation method provided in this embodiment can be a scenario where different drivers take turns driving the same vehicle, such as different drivers taking turns driving the same bus or operating vehicle, etc.

[0041] In one implementation, before obtaining the target data of the target driver, it includes:

[0042] Detect whether a charging operation needs to be performed according to the target driver's destination to be traveled or historical driving records and the remaining battery power information of the vehicle driven by the target driver;

[0043] If so, perform the step of obtaining the target data of the target driver.

[0044] Among them, information such as the average power consumption per kilometer of the vehicle can be obtained in advance according to the historical driving data of the vehicle. According to the destination to be traveled by the target driver and combined with information such as the average power consumption per kilometer of the vehicle, the power required for the vehicle to travel to the destination to be traveled can be known. Furthermore, based on the remaining battery power information of the vehicle driven by the target driver, it can be detected whether a charging operation needs to be performed. For example, when it is detected that the remaining battery power of the vehicle driven by the target driver is not sufficient for the vehicle to travel to the destination to be traveled, it is determined that a charging operation needs to be performed. In one embodiment, the power consumption required for the target driver to drive the vehicle can also be obtained according to the historical driving record of the target driver. When it is detected that the remaining battery power of the vehicle driven by the target driver is lower than the required power consumption, it is determined that a charging operation needs to be performed. In this way, by detecting whether a charging operation needs to be performed on the vehicle and then triggering the acquisition of the target data of the target driver for charging station recommendation, the timeliness and accuracy of charging station recommendation can be improved.

[0045] Step S102: Based on the target data of the target driver, predict a charging station recommendation list for the target driver using the trained charging station recommendation model; the charging station recommendation model is trained based on the historical target data and historical charging data of different drivers, and the historical charging data includes the information of the charging stations that have been visited and charged.

[0046] Among them, the charging station is used to charge the vehicle, including but not limited to charging stations, battery swapping stations, etc. The constructed charging station recommendation model can be trained in advance based on the historical target data and historical charging data of different drivers to obtain the trained charging station recommendation model. The charging station recommendation model can predict a charging station recommendation list composed of the charging stations that the target driver may need according to the target data of the target driver. The charging station recommendation model can be constructed based on machine learning algorithms such as support vector machines and XGBoost, which are not specifically limited here. The historical charging data includes the information of the charging stations that have been visited and charged, that is, the historical charging data includes the information of the charging stations that the driver has driven the vehicle to and the vehicle has completed charging operations such as battery swapping or charging. The charging station information includes at least the name and geographical location of the charging station. In addition, the charging station information may also include the number of charging devices and / or the number of queuing vehicles at the charging station. The charging devices can be specifically charging piles, battery cabinets, etc. The number of queuing vehicles refers to the number of vehicles queuing for charging at the charging station when the vehicle is charging at the charging station. Of course, the historical charging data may also include information such as the time when the vehicle arrives at the charging station and / or the charging consumption duration.

[0047] In one embodiment, the historical energy replenishment data further includes information about the energy replenishment stations that the driver has visited but where the vehicle did not replenish energy. That is, the historical energy replenishment data may also include information about the energy replenishment stations that the driver has driven the vehicle to but where the vehicle did not complete energy replenishment operations such as battery swapping or charging. The energy replenishment station information includes at least the name and geographical location of the energy replenishment station. It can be understood that when the vehicle needs energy replenishment, the driver may go to a certain energy replenishment station based on personal needs, but the vehicle may not be able to replenish energy at that energy replenishment station due to reasons such as too many queuing vehicles at the station. At this time, it is necessary to record the information about the energy replenishment stations that the driver has visited but where the vehicle did not replenish energy. In this way, by considering the information about the energy replenishment stations that the driver has visited but where the vehicle did not replenish energy, the prediction accuracy of the driver's energy replenishment needs can be further improved, that is, the recommended energy replenishment stations can better meet the driver's personalized needs.

[0048] In one embodiment, based on the target data of the target driver, predicting a list of recommended energy replenishment stations for the target driver using a trained energy replenishment station recommendation model includes: inputting the target data of the target driver into the trained energy replenishment station recommendation model to obtain the list of recommended energy replenishment stations for the target driver output by the energy replenishment station recommendation model. Among them, when the input variable of the energy replenishment station recommendation model is the target data of the driver, the target data of the target driver can be directly used as the input variable of the energy replenishment station recommendation model to obtain the output variable of the energy replenishment station recommendation model, that is, the list of recommended energy replenishment stations.

[0049] In one embodiment, based on the target data of the target driver, predicting a list of recommended energy replenishment stations for the target driver using a trained energy replenishment station recommendation model includes:

[0050] Determining the target handover type of the target driver according to the target data of the target driver;

[0051] Inputting the target handover type of the target driver into the trained energy replenishment station recommendation model to obtain the list of recommended energy replenishment stations for the target driver predicted by the energy replenishment station recommendation model.

[0052] Among them, according to the driver's shift handover time and shift handover geographical location, the drivers can be classified into different shift handover types, that is, the drivers belonging to the same shift handover type need to have the same shift handover time and shift handover geographical location. Based on the preset correspondence between the shift handover time and geographical location and different shift handover types, the target shift handover type of the target driver can be determined according to the target data of the target driver. For example, when there are a total of 3 shift handover times and 2 shift handover geographical locations, 6 shift handover types can be determined. When the input variable of the charging station recommendation model is the driver's shift handover type, the target shift handover type of the target driver can be directly used as the input variable of the charging station recommendation model to obtain the output variable of the charging station recommendation model, that is, the charging station recommendation list. In this way, by determining the driver's shift handover type and then having the charging station recommendation model recommend charging stations based on the driver's shift handover type, the prediction efficiency of the charging demand of the driver can be improved, and the calculation speed and real-time performance can be further improved.

[0053] In one embodiment, the training process of the charging station recommendation model includes:

[0054] Perform shift handover classification on different drivers respectively according to the historical target data of different drivers to obtain the shift handover type of each driver among different drivers;

[0055] Train the constructed charging station recommendation model according to the shift handover type of each driver among different drivers and the corresponding historical charging data to obtain the trained charging station recommendation model.

[0056] Among them, based on the preset correspondence between the shift handover time and geographical location and different shift handover types, different drivers can be classified into different shift handover types respectively according to the historical shift handover time and historical shift handover geographical location and other historical target data of different drivers, so as to obtain the shift handover type of each driver among different drivers. Then, the constructed charging station recommendation model can be trained according to the shift handover type of each driver among different drivers and the corresponding historical charging data to obtain the trained charging station recommendation model. It can be understood that when training the constructed charging station recommendation model, the data of different drivers can be divided into a training set and a validation set. After initially training the constructed charging station recommendation model with the training set, then use the initially trained charging station recommendation model to predict the validation set, and adjust the initially trained charging station recommendation model in combination with indicators for evaluating model performance such as root mean square error, mean absolute error or accuracy to obtain the trained charging station recommendation model. In this way, it is possible to quickly and accurately obtain the trained charging station recommendation model and further improve the prediction accuracy of the charging demand of the driver.

[0057] In one embodiment, before training the constructed charging station recommendation model according to the handover type of each driver among different drivers and the corresponding historical charging data, the following steps are included:

[0058] Preprocess the historical charging data corresponding to each driver among different drivers. The preprocessing includes data cleaning and / or data processing.

[0059] It can be understood that due to reasons such as incomplete data collection, there may be problems such as missing values, error values, or inconsistent formats of the same type of data in the historical charging data corresponding to some drivers. Therefore, the historical charging data corresponding to each driver among different drivers can be first subjected to data cleaning and / or data processing to delete obvious errors or abnormal data and format the data according to a unified standard, so as to facilitate subsequent processing and further improve the calculation speed and real-time performance.

[0060] Step S103: Recommend a charging station to the target driver based on the charging station recommendation list.

[0061] Among them, recommending a charging station to the target driver based on the charging station recommendation list can be to recommend the charging station recommendation list to the target driver, or to recommend some charging stations in the charging station recommendation list to the target driver. For example, recommend the charging stations that the target driver has not visited in the charging station recommendation list to the target driver.

[0062] In one embodiment, recommending a charging station to the target driver based on the charging station recommendation list includes:

[0063] Sort at least one charging station in the charging station recommendation list according to the target information to obtain a sorting result; the target information includes the historical charging data of the target driver.

[0064] Based on the sorting result, determine the charging stations whose rankings meet the preset conditions as the target charging stations, and output a recommendation message including the target charging station information to the target driver.

[0065] It can be understood that since the charging station recommendation list may include multiple charging stations, or the target driver may have preferences for charging stations, or the time required for the driver to reach each charging station may be different, etc., at least one charging station in the charging station recommendation list can be sorted according to the target information to obtain a sorting result, and then based on the sorting result, determine the charging stations whose rankings meet the preset conditions as the target charging stations, and output a recommendation message including the target charging station information to the target driver.

[0066] Among them, when the target information includes the historical energy replenishment data of the target driver, sorting at least one charging station in the charging station recommendation list according to the target information may be arranging the charging stations that the target driver has visited but not replenished energy in the charging station recommendation list at the front, and / or arranging the charging stations that the target driver has visited and replenished energy in the charging station recommendation list at the back, etc. Meeting the preset conditions for ranking may be determining the top N charging stations as the target charging stations. N can be set in advance based on requirements, or can be set according to the number of charging stations included in the charging station recommendation list. For example, N can be set to one-half of the number of charging stations included in the charging station recommendation list, etc. Among them, outputting a recommendation message including the target charging station information to the target driver may be sending a recommendation message including the target charging station information to the vehicle driven by the target driver or the mobile terminal bound to the target driver, so that the vehicle driven by the target driver or the mobile terminal bound to the target driver outputs the recommendation message in the form of text or sound, etc. The target charging station information may include information such as the name and geographical location of the target charging station. In addition, the target charging station information may further include the vehicle queuing situation and / or idle time period of the target charging station, etc. In this way, sorting at least one charging station in the charging station recommendation list according to the target information and determining the target charging station recommended to the target driver based on the sorting result can improve the recommendation accuracy of the charging station required by the driver and enhance the user experience.

[0067] In an embodiment, the target information further includes at least one of the following: the current status data of each charging station in the charging station recommendation list, the remaining battery power information of the vehicle driven by the target driver, the traffic congestion status from the current location of the target driver to each charging station in the charging station recommendation list, and the distance from the current location of the target driver to each charging station in the charging station recommendation list.

[0068] Among them, if the target information includes multiple types of information, corresponding weights can be set in advance for each type of information. When sorting at least one charging station in the charging station recommendation list according to the target information, the scores of each charging station in the charging station recommendation list can be obtained by scoring each charging station in the charging station recommendation list based on the weights corresponding to each type of information respectively, and then sorting according to the high and low scores of each charging station to obtain the sorting result. Among them, the current status data of the charging station may include the current vehicle queuing situation of the charging station, the number of idle charging devices, etc. The traffic congestion status from the current location of the target driver to each charging station in the charging station recommendation list may include data such as the length of the congested road and the duration of the congested road from the current location of the target driver to each charging station in the charging station recommendation list.

[0069] It can be understood that based on the current status data of the energy replenishment station, it can be known whether the energy replenishment station can currently replenish the vehicle normally after the vehicle arrives at the energy replenishment station. Based on the remaining battery power information of the vehicle driven by the target driver, the remaining driving mileage or duration of the vehicle can be known. Based on the traffic congestion status and distance from the current location to each energy replenishment station in the recommended list of energy replenishment stations, the estimated time required to reach each energy replenishment station from the current location can be known, etc. Therefore, multiple pieces of information among the above-mentioned information can be combined to sort at least one energy replenishment station in the recommended list of energy replenishment stations, so as to arrange the energy replenishment stations that better meet the needs of the target driver and / or the vehicle in the front. In this way, sorting at least one energy replenishment station in the recommended list of energy replenishment stations through multiple pieces of information will make the sorting result more in line with the needs of the target driver and / or the vehicle, and further improve the prediction accuracy of the energy replenishment needs of the driver.

[0070] In summary, in the energy replenishment station recommendation method provided by the above embodiment, based on the target data of the target driver including the shift handover time and the shift handover geographical location, an energy replenishment station is recommended to the target driver based on the energy replenishment station recommendation model, which can improve the prediction accuracy of the energy replenishment needs of the driver, and has a fast calculation speed and good real-time performance.

[0071] Based on the same inventive concept as the foregoing embodiments, an embodiment of the present invention provides an electronic device, as Figure 2 shown. The electronic device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 2 The processor 310 shown in does not refer to the number of processors 310 being one, but only refers to the positional relationship of the processor 310 relative to other devices. In actual applications, the number of processors 310 can be one or more; similarly, Figure 2 The memory 311 shown in has the same meaning, that is, it only refers to the positional relationship of the memory 311 relative to other devices. In actual applications, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the above-mentioned energy replenishment station recommendation method is implemented.

[0072] The electronic device may further include: at least one network interface 312. Each component in the electronic device is coupled together through a bus system 313. It can be understood that the bus system 313 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear illustration, in Figure 2 all kinds of buses are labeled as the bus system 313.

[0073] Among them, the memory 311 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory), an electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read-Only Memory), a ferromagnetic random access memory (FRAM, ferromagnetic random access memory), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM, Compact Disc Read-Only Memory); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM, Random Access Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM, Static Random Access Memory), synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory), dynamic random access memory (DRAM, Dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, Synchronous Dynamic Random Access Memory), double data rate synchronous dynamic random access memory (DDR SDRAM, Double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, Enhanced Synchronous Dynamic Random Access Memory), sync link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), direct rambus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 311 described in the embodiments of the present invention is intended to include but not limited to these and any other suitable types of memories.

[0074] The memory 311 in the embodiments of the present invention is used to store various types of data to support the operation of the electronic device. Examples of such data include: any computer programs for operating on the electronic device, such as operating systems and application programs; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system contains various system programs, such as the framework layer, the core library layer, the driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application programs can include various application programs, such as a Media Player, a Browser, etc., for implementing various application services. Here, the program for implementing the method of the embodiments of the present invention can be included in the application programs.

[0075] Based on the same inventive concept as the foregoing embodiments, this embodiment also provides a computer storage medium. The computer storage medium stores a computer program. The computer storage medium can be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer storage medium is run by a processor, the above-described energy replenishment station recommendation method is implemented. For the specific step flow implemented when the computer program is executed by the processor, please refer to Figure 1 the description of the illustrated embodiments, which will not be repeated here.

[0076] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0077] In this document, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, in addition to the listed elements, and may also include other elements not specifically listed.

[0078] As described above, this is only a specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for recommending a charging station, characterized in that, including: obtaining target data of a target driver; the target data includes shift handover time and shift handover geographical location; based on the target data of the target driver, predicting a list of recommended charging stations for the target driver by using a trained charging station recommendation model; the charging station recommendation model is trained based on historical target data and historical charging data of different drivers, and the historical charging data includes information of charging stations that have been visited and charged; recommending a charging station to the target driver based on the list of recommended charging stations.

2. The method according to claim 1, wherein The predicting, based on the trained charging station recommendation model, a list of recommended charging stations for the target driver according to the target data of the target driver includes: determining a target handover type of the target driver according to the target data of the target driver; inputting the target handover type of the target driver into the trained charging station recommendation model to obtain a list of recommended charging stations for the target driver predicted by the charging station recommendation model.

3. The method according to claim 2, characterized in that The training process of the charging station recommendation model includes: performing handover classification on each of the different drivers according to the historical target data of the different drivers to obtain the handover type of each driver among the different drivers; training a constructed charging station recommendation model according to the handover type of each driver among the different drivers and the corresponding historical charging data to obtain a trained charging station recommendation model.

4. The method according to claim 1, characterized in that Before the training of the constructed charging station recommendation model according to the handover type of each driver among the different drivers and the corresponding historical charging data to obtain a trained charging station recommendation model, it includes: performing preprocessing on the historical charging data corresponding to each driver among the different drivers, and the preprocessing includes data cleaning and / or data processing.

5. The method according to claim 1, wherein The historical charging data further includes information of charging stations that have been visited but not charged.

6. The method according to any one of claims 1 to 5, characterized in that, The recommending a charging station to the target driver based on the list of recommended charging stations includes: sorting at least one charging station in the list of recommended charging stations according to target information; the target information includes the historical charging data of the target driver; based on the sorting result, determining a target charging station whose ranking meets a preset condition, and outputting a recommendation message including information of the target charging station to the target driver.

7. The method according to claim 6, wherein The target information further includes at least one of the following: current status data of each charging station in the list of recommended charging stations, remaining battery power information of the vehicle driven by the target driver, traffic congestion status from the current location of the target driver to each charging station in the list of recommended charging stations, and distance from the current location of the target driver to each charging station in the list of recommended charging stations.

8. The method according to claim 1, characterized in that, Before the obtaining of the target data of the target driver, it includes: detecting whether a charging operation needs to be performed according to the destination to be traveled by the target driver and the remaining battery power information of the vehicle driven by the target driver; if so, performing the step of obtaining the target data of the target driver.

9. An electronic device, characterized in that, including: A processor and a memory storing a computer program, when the processor runs the computer program, implementing the charging station recommendation method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a processor, implementing the charging station recommendation method according to any one of claims 1 to 8.

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