Method and device for determining charging station of electric vehicle and electronic equipment

Abstract

The invention discloses a method and device for determining a charging station of an electric vehicle and electronic equipment. The method comprises the steps that station information corresponding to a plurality of charging stations in a target area at the current moment is acquired, and the station information comprises station distance information, charging speed information, charging price information and station type information; determining user preference information of a target user corresponding to the target electric vehicle; based on the station information corresponding to the plurality of charging stations and the user preference information, station scores corresponding to the plurality of charging stations are determined; determining predicted queuing durations respectively corresponding to the plurality of charging stations; and determining a target charging station for charging the target electric vehicle based on the station scores corresponding to the plurality of charging stations and the predicted queuing duration corresponding to the plurality of charging stations. According to the method and the device, the technical problem that the determination result of the charging station for charging the electric vehicle is inaccurate in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of electric vehicle management, and more specifically, to a method, apparatus, and electronic device for determining charging stations for electric vehicles. Background Technology

[0002] With the surge in electric vehicle ownership, charging demand exhibits high volatility and randomness, especially during peak hours. Charging stations often face severe resource congestion, leading to longer waiting times and a degraded charging experience for users. Accurate charging station recommendations can effectively shorten charging wait times, meet personalized user needs, and improve charging convenience and efficiency. Current technologies typically determine charging stations for electric vehicles based on distance, ignoring individual user differences and dynamic changes in spatiotemporal resources. This results in recommendations that do not match actual user needs, reducing the utilization efficiency of charging stations and user satisfaction. Therefore, current technologies suffer from inaccurate charging station selection for electric vehicles.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for determining charging stations for electric vehicles, in order to at least solve the technical problem of inaccurate determination results of charging stations for electric vehicles in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for determining charging stations for electric vehicles is provided, comprising: acquiring station information corresponding to multiple charging stations in a target area at the current time, wherein the station information includes station distance information, charging speed information, charging price information, and station type information; determining user preference information of a target user corresponding to a target electric vehicle; determining station scores corresponding to multiple charging stations based on the station information and user preference information; determining predicted queuing times corresponding to multiple charging stations, wherein the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station; and determining a target charging station for charging the target electric vehicle based on the station scores and the predicted queuing times corresponding to multiple charging stations.

[0006] According to another aspect of the embodiments of this application, a charging station determination device for electric vehicles is provided, comprising: an information acquisition module, configured to acquire station information corresponding to multiple charging stations in a target area at the current time, wherein the station information includes station distance information, charging speed information, charging price information, and station type information; a first determination module, configured to determine user preference information of a target user corresponding to a target electric vehicle; a second determination module, configured to determine station scores corresponding to multiple charging stations based on the station information and user preference information corresponding to the multiple charging stations; a third determination module, configured to determine the predicted queuing time corresponding to the multiple charging stations, wherein the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station; and a fourth determination module, configured to determine the target charging station for charging the target electric vehicle based on the station scores and the predicted queuing times corresponding to the multiple charging stations.

[0007] According to another aspect of the embodiments of this application, a non-volatile storage medium is provided, which stores a plurality of instructions adapted for a method for determining charging stations for electric vehicles, any one of which is loaded by a processor.

[0008] According to another aspect of the embodiments of this application, an electronic device is provided, including: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the following methods for determining charging stations for electric vehicles.

[0009] According to another aspect of the embodiments of this application, a computer program product is provided, which, when executed on a data processing device, is adapted to perform the steps of a method for determining charging stations for electric vehicles.

[0010] In this embodiment, by acquiring station information corresponding to multiple charging stations within the target area at the current time, including station distance information, charging speed information, charging price information, and station type information; determining the user preference information of the target user corresponding to the target electric vehicle; determining the station score corresponding to each of the multiple charging stations based on the station information and user preference information; determining the predicted queuing time corresponding to each of the multiple charging stations, where the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station; and determining the target charging station for charging the target electric vehicle based on the station scores and the predicted queuing times of the multiple charging stations. This achieves the goal of determining the station score of multiple charging stations by acquiring station information and user preference information, and combining this with the predicted queuing time of the multiple charging stations to determine the target charging station for charging the target electric vehicle. This improves the accuracy of the result of determining the target charging station for charging the target electric vehicle, thereby solving the technical problem of inaccurate determination of charging stations for electric vehicles in related technologies. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0012] Figure 1 This is a flowchart of a method for determining charging stations for electric vehicles according to an embodiment of this application;

[0013] Figure 2 This is a flowchart of an optional method for determining charging stations for electric vehicles according to an embodiment of this application;

[0014] Figure 3 This is a flowchart of an optional user profile determination method provided according to an embodiment of this application;

[0015] Figure 4 This is a flowchart of an optional station score determination method provided according to an embodiment of this application;

[0016] Figure 5 This is a schematic diagram of an optional electric vehicle charging station determination device according to an embodiment of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] According to an embodiment of this application, a method embodiment for determining charging stations for electric vehicles is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0020] Figure 1 This is a flowchart of a method for determining charging stations for electric vehicles according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0021] Step S102: Obtain the station information corresponding to multiple charging stations in the target area at the current time. The station information includes station distance information, charging speed information, charging price information, and station type information.

[0022] It is understandable that this involves acquiring information on multiple charging stations within a target area (e.g., a 5km radius around the target electric vehicle) at the current moment. This information may include, but is not limited to, station distance, charging speed, charging price, and station type. By collecting and updating charging station information within the target area in real time, it ensures that the charging resource status obtained by the user is up-to-date, avoiding charging decision errors due to information lag and improving the accuracy of target charging station identification.

[0023] Step S104: Determine the user preference information of the target user corresponding to the target electric vehicle;

[0024] It's understandable that this involves acquiring user preference information for the target electric vehicle's target users, such as spatial preferences, time preferences, price preferences, and speed preferences. By determining user preference information, highly personalized and intelligent charging station recommendations can be achieved, thereby improving the accuracy of target charging station determination and the user charging experience.

[0025] Optionally, the above spatial preference refers to the preference for charging station type (home, workplace, public station, shopping mall, etc.), time preference refers to the preference for charging speed (slow charging, fast charging), price preference refers to the preference for charging cost, that is, the choice of different charging prices during peak and off-peak periods, and speed preference refers to the preference for how fast to reach the charging station.

[0026] In one optional embodiment, determining the user preference information of the target user corresponding to the target electric vehicle includes: if the target user has preset preference information, determining the preset preference information as user preference information; or, if the target user has not preset preference information, determining the user preference information based on the historical charging information of the target electric vehicle in a predetermined historical time period.

[0027] It is understandable that if the target user has already set preset preference information, then that preset preference information will be used as the target user's user preference information; if the target user has not set preset preference information, then the target user's user preference information will be determined based on the historical charging information of the target electric vehicle within a predetermined historical time period (e.g., the last 90 days). By directly using the target user's preset preference information or analyzing its historical charging information to determine the target user's user preference information, highly personalized charging station recommendations can be provided, ensuring that the recommended charging station results closely match the target user's needs and improving the user experience.

[0028] In one optional embodiment, user preference information is determined based on the historical charging information of the target electric vehicle within a predetermined historical time period, including: determining the total number of charging sessions, the first preferred charging session, the second preferred charging session, the third preferred charging session, and the fourth preferred charging session of the target electric vehicle within the predetermined historical time period based on the historical charging information, wherein the first preferred charging session is the number of charging sessions selected due to charging station distance from the total number of charging sessions, the second preferred charging session is the number of charging sessions selected due to charging speed from the total number of charging sessions, the third preferred charging session is the number of charging sessions selected due to charging price from the total number of charging sessions, and the fourth preferred charging session is the number of charging sessions selected due to charging station type from the total number of charging sessions; determining a charging station distance weight based on the total number of charging sessions and the first preferred charging session; determining a charging speed weight based on the total number of charging sessions and the second preferred charging session; determining a charging price weight based on the total number of charging sessions and the third preferred charging session; determining a charging station type weight based on the total number of charging sessions and the fourth preferred charging session; and determining the charging station distance weight, the charging speed weight, the charging speed weight, and the charging station type weight as user preference information.

[0029] It is understandable that the target user's preference information is determined based on historical charging information in the following manner. The total number of charges, the number of first-preference charges, the number of second-preference charges, the number of third-preference charges, and the number of fourth-preference charges for the target electric vehicle within a predetermined historical period are determined from the historical charging information. The ratio of the first-preference charges to the total number of charges is used to determine the charging station distance weight; the ratio of the second-preference charges to the total number of charges is used to determine the charging speed weight; the ratio of the third-preference charges to the total number of charges is used to determine the charging price weight; and the ratio of the fourth-preference charges to the total number of charges is used to determine the charging station type weight. Based on these weights—charging station distance weight, charging speed weight, and charging station type weight—the target user's preference information is determined. User preference information determined based on the target user's historical charging information more accurately reflects the target user's true needs, thereby improving the accuracy of the target charging station determination results and user satisfaction.

[0030] Optionally, the weights for station type, charging speed, charging price, and station distance can be determined in the following ways:

[0031] The distance weight of the charging station = the number of times the charging station was selected due to the distance in the last 90 days / the total number of times the charging station was selected in the last 90 days;

[0032] Charging speed weight = Number of times the charging station was selected based on charging speed in the last 90 days / Total number of times the charging station was selected in the last 90 days;

[0033] Charging price weight = Number of times charging at charging stations selected based on charging price in the last 90 days / Total number of times charging in the last 90 days;

[0034] Site type weight = Number of times the charging station was selected based on site type in the last 90 days / Total number of times the charging station was selected in the last 90 days.

[0035] Step S106: Based on the station information and user preference information corresponding to the multiple charging stations, determine the station score corresponding to each of the multiple charging stations.

[0036] It is understandable that a station score is calculated for each charging station based on its information and user preferences. The calculation of these station scores fully considers the preferences of the target users, ensuring that the recommended charging stations are highly compatible with their charging habits and preferences, thereby improving user satisfaction.

[0037] In one optional embodiment, based on the site information and user preference information corresponding to multiple charging stations, a site score is determined for each of the multiple charging stations, including: for any one of the multiple charging stations, a first preference score is determined based on the site distance information in the site information and the site distance weight in the user preference information; a second preference score is determined based on the charging speed information in the site information and the charging speed weight in the user preference information; a third preference score is determined based on the charging price information in the site information and the charging price weight in the user preference information; a fourth preference score is determined based on the site type information in the site information and the site type weight in the user preference information; a site score for any one charging station is determined based on the first preference score, the second preference score, the third preference score, and the fourth preference score; and the site scores for each of the multiple charging stations are determined by using the method of determining the site score for any one charging station.

[0038] It is understandable that for any charging station among multiple charging stations, a first preference score is determined based on the station distance information in the station information and the station distance weight in the user preference information; a second preference score is determined based on the charging speed information in the station information and the charging speed weight in the user preference information; a third preference score is determined based on the charging price information in the station information and the charging price weight in the user preference information; and a fourth preference score is determined based on the station type information in the station information and the station type weight in the user preference information. The sum of the first, second, third, and fourth preference scores is determined as the station score for any charging station. By using this method to determine the station score for any single charging station, station scores are determined for each of the multiple charging stations. By integrating complex station information and user preference information into a station score, the decision-making process for target users among multiple charging stations can be simplified, reducing the difficulty for target users in selecting a target charging station and improving the user experience.

[0039] Optionally, the first preference score, second preference score, third preference score, and fourth preference score can be determined as follows: A station distance score is determined based on the station distance information in the station information of any charging station, and the product of the station distance score and the station distance weight is determined as the first preference score. A charging speed score is determined based on the charging speed information in the station information of any charging station, and the product of the charging speed score and the charging speed weight is determined as the second preference score. A charging price score is determined based on the charging price information in the station information of any charging station, and the product of the charging price score and the charging price weight is determined as the third preference score. A station type score is determined based on the station type information in the station information of any charging station, and the product of the station type score and the station type weight is determined as the fourth preference score.

[0040] Optionally, the station distance score, charging speed score, charging price score, and station type score can be determined based on the following principles: The closer the station is to the target user's current location, the higher the station distance score. The more fast charging stations there are in the station, the faster the charging speed, and the higher the charging speed score. The lower the charging price, especially during off-peak hours, the higher the charging price score. Stations whose station type matches the user's preferences have higher station type scores.

[0041] Step S108: Determine the predicted queuing time for each of the multiple charging stations, where the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station.

[0042] This involves determining the predicted queuing time for each of the multiple charging stations—that is, the time between the arrival time of the target electric vehicle at the corresponding charging station and the start of charging. By accurately predicting the queuing time at charging stations, users can choose the station with the shortest queue, thereby reducing unnecessary waiting and improving charging efficiency and experience.

[0043] In one optional embodiment, determining the predicted queuing time corresponding to each of the multiple charging stations includes: for any one of the multiple charging stations, determining the arrival time of the target electric vehicle at that charging station, the number of charging piles in that charging station, the current state of each charging pile in that charging station, and the initial queuing list of that charging station, wherein the initial queuing list is used to record, at the current time, the multiple electric vehicles waiting to be charged at that charging station, the charging order of the multiple electric vehicles waiting to be charged, and the charging time corresponding to each of the multiple electric vehicles waiting to be charged; based on the arrival time and the current state of each charging pile, determining the predicted queuing time corresponding to each of the multiple charging piles. The queuing prediction model calculates the predicted queuing time for any charging station by determining the end time of the charging station's charging position and adding the target electric vehicle to the initial queuing list. It then inputs the arrival time, the number of charging stations, the current status of each charging station, the end time of the charging position, and the target queuing list into the queuing prediction model to obtain the predicted queuing time for any charging station. The queuing prediction model pre-learns the relationships between the arrival time, the number of charging stations, the current status of each charging station, the end time of the charging position, the target queuing list, and the predicted queuing time for the target electric vehicle. Finally, it determines the predicted queuing time for multiple charging stations by using the same method as for any charging station.

[0044] The predicted queuing time for any one of multiple charging stations can be determined as follows: First, determine the arrival time of the target electric vehicle at any charging station, the number of charging piles at that station, the current status (e.g., idle or occupied) of each charging pile, and the initial queue list for that station. Second, based on the arrival time and the current status of each charging pile, determine the end time of each charging position—the time when the charging pile can charge the next electric vehicle. Next, add the target electric vehicle to the initial queue list, which records the current number of electric vehicles waiting to be charged at any given charging station, their charging order, and their respective charging times, thus obtaining the target queue list for that charging station. Finally, input the arrival time, the number of charging piles, the current status of each charging pile, the end time of each charging pile, and the target queue list into the queuing prediction model to output the predicted queuing time for any one charging station. This method of determining the predicted queuing time for any one charging station is used to determine the predicted queuing time for multiple charging stations. The queuing prediction model can predict the queuing time of a target electric vehicle at different charging stations in real time. Even when the network load changes or the charging pile status is updated, the prediction results can be quickly adjusted to ensure the timeliness and accuracy of the recommended target charging stations.

[0045] Optionally, the charging station's end time can be determined as follows: If the charging station is currently idle, an electric vehicle is assigned to it. After assignment, the charging station's end time is determined based on the assigned electric vehicle's charging time, the current time, and the target electric vehicle's arrival time. If the end time is less than the arrival time, electric vehicles are continuously assigned to the charging station, and the end time is updated until the end time equals or exceeds the arrival time. If the end time equals the arrival time, the arrival time is determined as the charging station's end time. If the charging station is currently occupied, the charging station's end time is determined based on the end time of the occupied electric vehicle. If the end time is less than the arrival time, electric vehicles are continuously assigned to the charging station until the end time equals or exceeds the arrival time. If the end time equals the arrival time, the arrival time is determined as the charging station's end time.

[0046] Step S110: Based on the station scores corresponding to multiple charging stations and the predicted queuing times corresponding to multiple charging stations, determine the target charging station for the target electric vehicle.

[0047] It is understandable that by determining the overall score of multiple charging stations based on their respective station scores and predicted queue times, a target charging station for the desired electric vehicle can be identified. By comprehensively considering both the static station scores and the dynamic predicted queue times, it is possible to more accurately determine which charging station best suits the current needs of the target user, thereby providing the optimal charging station for that user.

[0048] In one optional embodiment, the target charging station for charging the target electric vehicle is determined based on the station scores and the predicted queuing times of the multiple charging stations, including: determining the comprehensive score of the multiple charging stations based on the station scores and the predicted queuing times of the multiple charging stations; and determining the charging station with the highest comprehensive score among the multiple charging stations as the target charging station for charging the target electric vehicle.

[0049] Understandably, based on the site scores and predicted queuing times of multiple charging stations, a comprehensive score is determined for each charging station. The charging station with the highest comprehensive score is then identified as the target charging station for the target electric vehicle. This comprehensive scoring mechanism ensures that the recommended target charging station achieves an optimal balance between user preference and charging efficiency, avoiding the problem of excessively long charging wait times that may result from recommendations based on a single factor, thus improving the accuracy of the target charging station determination.

[0050] In one optional embodiment, a comprehensive score is determined based on the station scores and predicted queuing times for each of the multiple charging stations, including: determining the reciprocal of the queuing time for each of the multiple charging stations based on the predicted queuing times for each of the multiple charging stations; and determining the comprehensive score for each of the multiple charging stations based on the reciprocal of the queuing time for each of the multiple charging stations and the station scores for each of the multiple charging stations.

[0051] It is understandable that for any charging station among multiple charging stations, the reciprocal of the predicted queuing time for that station is taken to obtain the reciprocal of the queuing time. Multiplying this reciprocal by the station's score yields the overall score for that station. The overall scores for multiple charging stations are determined by using the same method as determining the overall score for any single charging station. By calculating the reciprocal of the predicted queuing time and multiplying it by the station score, the predicted queuing time can be effectively combined with the target user's preference for charging stations, thus balancing the personalized needs of the target user with charging efficiency.

[0052] Through the above steps S102 to S110, the goal of determining the station scores corresponding to multiple charging stations by acquiring station information and user preference information, and combining the predicted queuing time corresponding to multiple charging stations, can be achieved to determine the target charging station for the target electric vehicle. This improves the accuracy of the result of determining the target charging station for the target electric vehicle, thereby solving the technical problem of inaccurate determination of charging stations for electric vehicles in related technologies.

[0053] Based on the above embodiments and optional embodiments, this application proposes an implementation method for an optional method of determining charging stations for electric vehicles, which is used to accurately determine the target charging stations for a target electric vehicle.

[0054] Figure 2 This is a flowchart of an optional method for determining charging stations for electric vehicles according to an embodiment of this application, such as... Figure 2 As shown, the steps for determining the target charging station for the target electric vehicle include:

[0055] Step S1: Receive the user charging request from the target user.

[0056] Step S2: Obtain the target user's user preference information.

[0057] If the target is to pre-set preset preference information, the preset preference information is determined as the target user's user preference information; otherwise, the target user's historical charging information is obtained, and a target user profile of the target user is constructed by analyzing the historical charging information, which is used as the target user's user preference information.

[0058] User preferences include spatial preferences, time preferences, price preferences, and speed preferences. Spatial preferences refer to preferences for charging station type (home, workplace, public stations, shopping malls, etc.), time preferences refer to preferences for charging speed (slow charging, fast charging), price preferences refer to preferences for charging costs, i.e., the choice of different charging prices during peak and off-peak hours, and speed preferences refer to preferences for how quickly one can reach a charging station.

[0059] Figure 3 This is a flowchart of an optional user profile determination method provided according to an embodiment of this application, such as... Figure 3 As shown, the historical charging information of the target electric vehicle in the past 90 days is obtained. Based on the above historical charging information, the charging preference analysis of the target user is performed, and then the user's spatial preference weight (i.e., station type weight), time preference weight (i.e., charging speed weight), price preference weight (i.e., charging price weight), and speed preference weight (i.e., station distance weight) are determined. The above spatial preference weight, time preference weight, price preference weight, and speed preference weight are determined as the target user profile of the target user. The target user profile is used as the user preference information of the target user and stored.

[0060] Among them, the charging station distance weight = the number of times the charging station was selected due to the distance in the last 90 days / the total number of times the charging was performed in the last 90 days;

[0061] Charging speed weight = Number of times the charging station was selected based on charging speed in the last 90 days / Total number of times the charging station was selected in the last 90 days;

[0062] Charging price weight = Number of times charging at charging stations selected based on charging price in the last 90 days / Total number of times charging in the last 90 days;

[0063] Site type weight = Number of times the charging station was selected based on site type in the last 90 days / Total number of times the charging station was selected in the last 90 days.

[0064] The aforementioned spatial preference refers to the preference for charging station type (home, workplace, public station, shopping mall, etc.), time preference refers to the preference for charging speed (slow charging, fast charging), price preference refers to the preference for charging cost, that is, the choice of different charging prices during peak and off-peak periods, and speed preference refers to the preference for how quickly one can reach the charging station.

[0065] Step S3: Determine the station score corresponding to each of the multiple charging stations.

[0066] Figure 4 This is a flowchart of an optional site score determination method provided according to an embodiment of this application, such as... Figure 4As shown, firstly, multiple charging stations within a 5km radius of the target electric vehicle are selected. Secondly, based on the station information for each station, spatial preference scores (fourth preference score), time preference scores (second preference score), price preference scores (third preference score), and speed preference scores (first preference score) are determined for each station. For each charging station, the spatial preference score, time preference score, price preference score, and speed preference score are summed to obtain the station score for each station.

[0067] Step S4: Execute the queuing prediction model to obtain the predicted queuing time for each of the multiple stations.

[0068] Step S5: Determine the overall score ranking of multiple charging stations and identify the target charging station.

[0069] For any charging station among multiple charging stations, take the reciprocal of the predicted queuing time to obtain the reciprocal of the queuing time for that charging station. Multiply the reciprocal of the queuing time for any charging station by its station score to obtain its overall score. The overall scores for each of the multiple charging stations are determined using the same method as for any single charging station. The overall scores for each of the multiple charging stations are then sorted in descending order to obtain a ranking of overall scores. The charging station ranked first in the ranking is then selected as the target charging station for the target electric vehicle.

[0070] Step S6: After the target electric vehicle finishes charging at the target charging station, the charging record is added to the target user's historical charging information and stored for subsequent determination and updating of the target user's target user profile.

[0071] The above optional implementation methods achieve at least the following effects: by directly using the target user's preset preference information or analyzing their historical charging information to determine the target user's user preference information, highly personalized charging station recommendations can be provided, ensuring that the target charging station recommendation results closely match the target user's needs and improving the user experience; the comprehensive scoring mechanism ensures that the recommended target charging station achieves the optimal balance between user preference and charging efficiency, avoiding the problem of excessively long charging waiting time that may be caused by recommending based on only a single factor, and improving the accuracy of the target charging station determination results.

[0072] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0073] This embodiment also provides a charging station determination device for electric vehicles, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0074] According to an embodiment of this application, an apparatus embodiment for implementing a method for determining charging stations for electric vehicles is also provided. Figure 5 This is a schematic diagram of a charging station determination device for electric vehicles according to an embodiment of this application, as shown below. Figure 5 As shown, the above-mentioned electric vehicle charging station determination device includes an information acquisition module 502, a first determination module 504, a second determination module 506, a third determination module 508, and a fourth determination module 510. The device will be described below.

[0075] The information acquisition module 502 is used to acquire the station information corresponding to multiple charging stations in the target area at the current time. The station information includes station distance information, charging speed information, charging price information and station type information.

[0076] The first determining module 504, connected to the information acquisition module 502, is used to determine the user preference information of the target user corresponding to the target electric vehicle;

[0077] The second determining module 506, connected to the first determining module 504, is used to determine the station score corresponding to each of the multiple charging stations based on the station information and user preference information corresponding to each of the multiple charging stations.

[0078] The third determining module 508, connected to the second determining module 506, is used to determine the predicted queuing time corresponding to multiple charging stations, wherein the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station.

[0079] The fourth determining module 510, connected to the third determining module 508, is used to determine the target charging station for the target electric vehicle based on the station scores corresponding to the multiple charging stations and the predicted queuing time corresponding to the multiple charging stations.

[0080] This application provides an electric vehicle charging station determination device. By setting up an information acquisition module 502, a first determination module 504, a second determination module 506, a third determination module 508, and a fourth determination module 510, the device achieves the purpose of determining the station scores corresponding to multiple charging stations by acquiring station information and user preference information, and combining the predicted queuing time corresponding to multiple charging stations to determine the target charging station for the target electric vehicle. This improves the accuracy of the target charging station determination result for the target electric vehicle, thereby solving the technical problem of inaccurate charging station determination results for electric vehicles in related technologies.

[0081] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0082] It should be noted that the information acquisition module 502, the first determining module 504, the second determining module 506, the third determining module 508, and the fourth determining module 510 mentioned above correspond to steps S102 to S110 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0083] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0084] The aforementioned electric vehicle charging station determination device may further include a processor and a memory. The information acquisition module 502, the first determination module 504, the second determination module 506, the third determination module 508, the fourth determination module 510, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0085] The processor contains a core that retrieves the corresponding program unit from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0086] This application provides a non-volatile storage medium storing a program that, when executed by a processor, implements a method for determining charging stations for electric vehicles.

[0087] This application provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring station information corresponding to multiple charging stations within a target area at the current time, wherein the station information includes station distance information, charging speed information, charging price information, and station type information; determining user preference information of the target user corresponding to the target electric vehicle; determining station scores corresponding to the multiple charging stations based on the station information and user preference information; determining the predicted queuing time corresponding to the multiple charging stations, wherein the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station; and determining the target charging station for charging the target electric vehicle based on the station scores and the predicted queuing times of the multiple charging stations. The device in this document may be a server, PC, etc.

[0088] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: obtaining station information corresponding to multiple charging stations in a target area at the current time, wherein the station information includes station distance information, charging speed information, charging price information, and station type information; determining user preference information of the target user corresponding to the target electric vehicle; determining station scores corresponding to the multiple charging stations based on the station information and user preference information; determining the predicted queuing time corresponding to the multiple charging stations, wherein the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station; and determining the target charging station for charging the target electric vehicle based on the station scores and the predicted queuing times corresponding to the multiple charging stations.

[0089] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0093] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0094] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0095] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0096] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0097] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining charging stations for electric vehicles, characterized in that, include: Obtain the station information corresponding to multiple charging stations in the target area at the current time, wherein the station information includes station distance information, charging speed information, charging price information and station type information; Determine the user preference information of the target users corresponding to the target electric vehicle; Based on the site information corresponding to the multiple charging stations and the user preference information, the site score corresponding to the multiple charging stations is determined. Determine the predicted queuing time for each of the multiple charging stations, wherein the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station; Based on the station scores corresponding to the multiple charging stations and the predicted queuing times corresponding to the multiple charging stations, the target charging station for the target electric vehicle is determined.

2. The method according to claim 1, characterized in that, The user preference information for determining the target user corresponding to the target electric vehicle includes: If the target user has pre-set preset preference information, the preset preference information will be determined as the user preference information; or, If the target user has not pre-set preset preference information, the user preference information is determined based on the historical charging information of the target electric vehicle in a predetermined historical time period.

3. The method according to claim 2, characterized in that, The step of determining the user preference information based on the historical charging information of the target electric vehicle within a predetermined historical time period includes: Based on the historical charging information, the total number of times the target electric vehicle is charged, the first preferred number of times, the second preferred number of times, the third preferred number of times, and the fourth preferred number of times are determined within the predetermined historical time period. The first preferred number of times is the number of times the charging station is selected due to the distance to the charging station in the total number of times; the second preferred number of times is the number of times the charging station is selected due to the charging speed in the total number of times; the third preferred number of times is the number of times the charging station is selected due to the charging price in the total number of times; and the fourth preferred number of times is the number of times the charging station is selected due to the type of the charging station in the total number of times. The station distance weight is determined based on the total number of charging times and the first preferred number of charging times; The charging speed weight is determined based on the total number of charging attempts and the second preferred number of charging attempts. The charging price weight is determined based on the total number of charging attempts and the number of third-preference charging attempts. The site type weight is determined based on the total number of charging times and the fourth preferred charging times; The station distance weight, the charging speed weight, the charging speed weight, and the station type weight are determined as the user preference information.

4. The method according to claim 1, characterized in that, The step of determining the station score for each of the multiple charging stations based on the station information and the user preference information includes: For any one of the plurality of charging stations, a first preference score is determined based on the station distance information in the station information of the charging station and the station distance weight in the user preference information. Based on the charging speed information in the station information of any one of the charging stations and the charging speed weight in the user preference information, a second preference score for any one of the charging stations is determined. Based on the charging price information in the site information of any charging station and the charging price weight in the user preference information, a third preference score is determined for any charging station. Based on the station type information in the station information of any one of the charging stations and the station type weight in the user preference information, a fourth preference score for any one of the charging stations is determined. Based on the first preference score, the second preference score, the third preference score, and the fourth preference score, determine the station score for any charging station; The station scores for each of the multiple charging stations are determined by using a method that determines the station score for any one of the charging stations.

5. The method according to claim 1, characterized in that, The determination of the predicted queuing time for each of the multiple charging stations includes: For any one of the plurality of charging stations, determine the arrival time of the target electric vehicle at the charging station, the number of charging piles in the charging station, the current status of each charging pile in the charging station, and the initial queue list of the charging station. The initial queue list is used to record the plurality of electric vehicles waiting to be charged in the charging station at the current time, the charging order of the plurality of electric vehicles waiting to be charged, and the charging time of each of the plurality of electric vehicles waiting to be charged. Based on the arrival time and the current state of each of the multiple charging piles, the end time of each charging pile location is determined. Add the target electric vehicle to the initial queue list to obtain the target queue list for any charging station; The arrival time, the number of charging piles, the current status of each of the multiple charging piles, the end time of each charging pile position, and the target queue list are input into the queue prediction model to obtain the predicted queue time for any charging station. The queue prediction model has pre-learned the correlation between the arrival time, the number of charging piles, the current status of each of the multiple charging piles, the end time of each charging pile position, the target queue list, and the predicted queue time of the target electric vehicle. The predicted queuing time for each of the multiple charging stations is determined by using the method of determining the predicted queuing time for any one of the charging stations.

6. The method according to any one of claims 1 to 5, characterized in that, The step of determining the target charging station for the target electric vehicle based on the station scores corresponding to the plurality of charging stations and the predicted queuing times corresponding to the plurality of charging stations includes: Based on the station scores corresponding to the multiple charging stations and the predicted queuing times corresponding to the multiple charging stations, the comprehensive scores corresponding to the multiple charging stations are determined. The charging station with the highest score among the comprehensive scores of the multiple charging stations is determined as the target charging station for the target electric vehicle.

7. The method according to claim 6, characterized in that, The determination of the comprehensive score for each of the multiple charging stations based on their respective station scores and predicted queuing times includes: Based on the predicted queuing time corresponding to each of the multiple charging stations, the reciprocal of the queuing time corresponding to each of the multiple charging stations is determined. Based on the reciprocal of the duration corresponding to each of the multiple charging stations and the station score corresponding to each of the multiple charging stations, the comprehensive score corresponding to each of the multiple charging stations is determined.

8. A device for determining charging stations for electric vehicles, characterized in that, include: The information acquisition module is used to acquire the station information corresponding to multiple charging stations in the target area at the current time. The station information includes station distance information, charging speed information, charging price information and station type information. The first determining module is used to determine the user preference information of the target user corresponding to the target electric vehicle; The second determining module is used to determine the station score corresponding to each of the multiple charging stations based on the station information corresponding to each of the multiple charging stations and the user preference information. The third determining module is used to determine the predicted queuing time corresponding to multiple charging stations, wherein the predicted queuing time is the waiting time required for the target electric vehicle to start charging at the corresponding charging station; The fourth determining module is used to determine the target charging station for charging the target electric vehicle based on the station scores corresponding to the plurality of charging stations and the predicted queuing time corresponding to the plurality of charging stations.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions adapted for loading and execution by a processor of the electric vehicle charging station determination method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for determining charging stations for electric vehicles according to any one of claims 1 to 7.

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