Vehicle charging prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle charging prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining charging label data and to-be-charged prediction information of a target vehicle, and if a navigation target position is matched with a common charging place, and to-be-compared information is matched with a charging behavior label of the common charging place, determining that the target vehicle is charged; if yes, determining that a charging behavior exists in the charging prediction identifier of the target vehicle, and if the charging prediction identifier exists in the charging behavior, updating the predicted residual electric quantity to a residual electric quantity threshold value so as to perform energy planning on fuel and electric energy of the target vehicle; according to the method, the prediction accuracy is improved through the frequently-used charging places and the charging behavior tags which are associated in multiple dimensions, the enough endurance mileage is kept through energy planning, unnecessary energy waste is avoided, and the personalized use requirements of users are met.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle charging, and particularly relates to a vehicle charging prediction method, device, electronic device and storage medium. Background Art

[0002] With the popularization of new energy vehicles, especially range-extended electric vehicles, how to optimize the energy planning of the range extender to meet the personalized charging needs of different users has become an important issue. At present, most methods for identifying users' daily charging patterns are based on single-dimensional data analysis, resulting in low energy utilization efficiency and affecting user experience. Moreover, users' charging behaviors are actually affected by multiple factors, and there are also associated effects among multiple factors. Therefore, how to more accurately identify users' charging behaviors and predict users' charging plans in advance is crucial for the energy planning of the range extender. Summary of the Invention

[0003] The present application provides a vehicle charging prediction method, device, electronic device and storage medium to solve the above technical problem that energy planning cannot be carried out based on users' personalized charging behaviors.

[0004] In some embodiments of the present application, a vehicle charging prediction method is provided, including: obtaining charging label data of a target vehicle and charging prediction information to be predicted, where the charging prediction information to be predicted includes a navigation destination location and comparison information corresponding to the navigation destination location, and the comparison information includes at least one of an expected charging period, an expected arrival time, and an expected remaining power; the charging label data includes charging behavior labels of multiple common charging locations, and the charging behavior labels include at least one of a common charging period, a common charging time period, and a segmented remaining power before common charging; the charging label data is determined based on the historical charging data of the target vehicle; if the navigation destination location matches a common charging location, and the comparison information matches the charging behavior label of the common charging location, then determine the charging prediction identifier of the target vehicle as having a charging behavior; if the charging prediction identifier is determined as having a charging behavior, then update the expected remaining power to a remaining power threshold to perform energy planning on the fuel and electric energy of the target vehicle, and the remaining power threshold is obtained based on the segmented remaining power before common charging or a preset power threshold corresponding to the probability level of having a charging behavior.

[0005] In some embodiments of the present application, the determination of the probability level of the charging behavior includes: if the type of the common charging location is a home charging pile, determining the probability level of the charging behavior as the first level; if the type of the common charging location is a public charging pile and the common charging type is fast charging, determining the probability level of the charging behavior as the second level; if the type of the common charging location is a public charging pile and the common charging type is slow charging, determining the probability level of the charging behavior as the third level; wherein, the charging behavior label further includes the type of the common charging location and the common charging type, and the first level, the second level and the third level are probability levels that decrease in sequence, and the preset power thresholds corresponding to the probability levels from high to low increase from low to high.

[0006] In some embodiments of the present application, the determination of each common charging location includes: converting the charging position location points in the historical charging data into region of interest data, where the region of interest data includes the region of interest name and the region of interest type; counting the first charging times of charging at the same region of interest name in the historical charging data; and determining a plurality of common charging locations according to the sorting of the first charging times.

[0007] In some embodiments of the present application, the determination of the common charging cycle includes: determining the first charging time interval for each charging at the common charging location, where the first charging time interval is obtained based on the charging start time of the subsequent charging and the charging end time of the previous charging at the common charging location; determining the upper and lower limits of the recommended charging cycle according to the endurance and usage scenario of the target vehicle, and dividing to obtain a plurality of initial charging cycles; if a first charging time interval is greater than or equal to the lower limit of an initial charging cycle and the first charging time interval is less than or equal to the upper limit of an initial charging cycle, increasing the charging frequency of the initial charging cycle; and determining the initial charging cycle with the highest charging frequency as the common charging cycle of the common charging location.

[0008] In some embodiments of the present application, the determination of the common charging period includes: dividing a plurality of initial charging periods; if the charging start time of a charging record is greater than or equal to the lower limit of an initial charging period and the charging start time is less than or equal to the upper limit of the initial charging period, increasing the second charging times corresponding to the initial charging period, and accumulating the historical charging duration of the charging record to the cumulative charging duration corresponding to the initial charging period; if the second charging times of an initial charging period is greater than a preset first number threshold and the average charging duration of the initial charging period is greater than a preset first duration threshold, determining the initial charging period as a common charging period, where the average charging duration is obtained based on the cumulative charging duration and the second charging times; wherein, the charging record is the record corresponding to a common charging location in the historical charging data.

[0009] In some embodiments of the present application, the determination of the remaining power segmentation before common charging includes: dividing a plurality of initial power segments; if the remaining power before charging in a charging record is greater than or equal to the lower limit of an initial power segment and the remaining power before charging is less than or equal to the upper limit of the initial power segment, increasing the power frequency of the initial power segment; determining the initial power segment with the highest power frequency as the remaining power segment before common charging; wherein, the charging record is the record corresponding to a common charging location in the historical charging data.

[0010] In some embodiments of the present application, before counting the historical charging data, the method further includes: obtaining the historical charging data of the target vehicle, where the historical charging data includes a plurality of charging records; if the historical charging duration of a charging record is less than a preset second duration threshold, discarding the charging record, where the historical charging duration is obtained based on the charging end time and the charging start time of the charging record; if the second charging time interval between two charging records is less than a preset interval threshold, merging the two charging records into one charging record, where the second charging time interval is obtained based on the charging start time of the latter charging record and the end charging time of the former charging record; if there are multiple reported historical position positioning points in a charging record, counting the number of positioning times that the historical position positioning points fall within the same positioning accuracy area, and determining the positioning accuracy area with the highest number of positioning times as the charging position positioning point of the charging record; determining the charging label data according to the updated charging record.

[0011] In some embodiments of the present application, the present application provides a vehicle charging prediction device, including: a data acquisition module, configured to acquire charging label data and charging prediction information to be predicted of a target vehicle, where the charging prediction information to be predicted includes a navigation destination location and comparison information corresponding to the navigation destination location, and the comparison information includes at least one of an expected charging period, an expected arrival time, and an expected remaining power; the charging label data includes charging behavior labels of multiple common charging locations, and the charging behavior labels include at least one of a common charging period, a common charging time period, and a segmented remaining power before common charging; the charging label data is determined based on the historical charging data of the target vehicle; a charging prediction module, configured to determine that there is a charging behavior for the charging prediction identifier of the target vehicle if the navigation destination location matches a common charging location and the comparison information matches the charging behavior label of the common charging location; a remaining power update module, configured to update the expected remaining power to a remaining power threshold if the charging prediction identifier indicates that there is a charging behavior, so as to perform energy planning on the fuel and electric energy of the target vehicle, where the remaining power threshold is obtained based on the segmented remaining power before common charging or a preset power threshold corresponding to the probability level of the charging behavior.

[0012] In some embodiments of the present application, the present application provides an electronic device, where the electronic device includes: one or more processors; a storage device, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device is caused to implement the steps of the vehicle charging prediction method as described in any one of the above.

[0013] In some embodiments of the present application, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor of a computer, the computer is caused to execute the steps of the vehicle charging prediction method as described in any one of the above.

[0014] Advantages of the embodiments of the present application: The present application provides a vehicle charging prediction method, device, electronic device, and storage medium. The embodiments of the present application obtain charging behavior labels of multiple common charging locations through historical charging data, so that it is possible to predict in advance whether there is a charging behavior at the navigation destination location through the multi-dimensionally associated common charging locations and charging behavior labels, improving the accuracy of charging behavior prediction; moreover, by updating the expected remaining power according to the probability level of the charging behavior, energy planning is performed on the fuel and electric energy of the target vehicle, which can ensure that the target vehicle can maintain sufficient cruising range under different charging conditions, avoid unnecessary energy waste, meet the personalized usage needs of users, and provide a more convenient and efficient charging and driving experience.

[0015] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. Brief Description of the Drawings

[0016] The drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application. Obviously, the drawings in the following description are only some embodiments of this application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts. In the drawings:

[0017] Figure 1 A schematic diagram of an exemplary system architecture to which the technical solution of the embodiment of this application can be applied is shown;

[0018] Figure 2 A schematic flowchart of a vehicle charging prediction method according to an embodiment of this application is shown;

[0019] Figure 3 A schematic flowchart of determining common charging locations and charging behavior tags according to an embodiment of this application is shown;

[0020] Figure 4 A schematic flowchart of determining a common charging cycle according to an embodiment of this application is shown;

[0021] Figure 5 A schematic flowchart of determining common charging time periods according to an embodiment of this application is shown;

[0022] Figure 6 A schematic flowchart of implementing the vehicle charging prediction method according to an embodiment of this application is shown;

[0023] Figure 7 A block diagram of a vehicle charging prediction device according to an embodiment of this application is shown;

[0024] Figure 8 A schematic diagram of the structure of a computer system of an electronic device suitable for implementing the embodiment of this application is shown. Detailed Embodiments

[0025] The following uses specific specific examples to illustrate the implementation manners of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific implementation manners, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0026] The illustrations provided in the following embodiments only schematically illustrate the basic concept of the present application. Therefore, only the components related to the present application are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The form, number, and ratio of each component in actual implementation can be arbitrarily changed, and the layout form of its components may also be more complex.

[0027] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0028] User charging behavior is complex and affected by multi-dimensional factors such as the location, type, and cycle of charging, and there are also associated effects among multiple factors. The present application can form a multi-dimensional composite charging behavior portrait by establishing charging behavior tags for multiple common charging locations, predict in advance the possibility of users having charging behavior, and thus perform energy planning for the fuel and electric energy of the target vehicle.

[0029] To solve the above technical problems, the present application provides a vehicle charging prediction method, device, electronic device, and storage medium. The following elaborates in detail on the implementation details of the technical solutions of the embodiments of the present application.

[0030] Please refer to Figure 1 , Figure 1 which shows a schematic diagram of an exemplary system architecture to which the technical solutions of the embodiments of the present application can be applied. As Figure 1 shown, the system architecture includes a cloud server 101 and a target vehicle 102. After the cloud server 101 obtains the historical charging data and the to-be-charged prediction information of the target vehicle 102, it updates the expected remaining power to the remaining power threshold to perform energy planning for the fuel and electric energy of the target vehicle 102.

[0031] In some embodiments of the present application, the target vehicle includes a range-extended vehicle or a plug-in hybrid vehicle.

[0032] In some embodiments of the present application, the update of the expected remaining power can be directly implemented at the target vehicle 102 end, and energy planning for fuel and electric energy is performed according to the new expected remaining power.

[0033] Please refer to Figure 2 , Figure 2 which shows a flowchart of a vehicle charging prediction method according to an embodiment of the present application. As Figure 2As shown, in an exemplary embodiment, the vehicle charging prediction method at least includes steps S220 to S230, which are introduced in detail as follows:

[0034] Step S210, obtain the charging label data of the target vehicle and the information to be predicted for charging

[0035] Among them, the information to be predicted for charging includes the navigation destination location and the information to be compared corresponding to the navigation destination location. The information to be compared includes at least one of the expected charging cycle, the expected arrival time, and the expected remaining power. The charging label data includes the charging behavior labels of multiple common charging locations. The charging behavior labels include at least one of the common charging cycle, the common charging time period, and the segmented remaining power before common charging. The charging label data is determined based on the historical charging data of the target vehicle.

[0036] Step S220, if the navigation destination location matches a common charging location and the information to be compared matches the charging behavior label of the common charging location, then determine the charging prediction identifier of the target vehicle as having a charging behavior.

[0037] Step S230, if the charging prediction identifier is determined as having a charging behavior, then update the expected remaining power to the remaining power threshold to perform energy planning for the fuel and electric energy of the target vehicle.

[0038] Among them, the remaining power threshold is obtained based on the segmented remaining power before common charging or the preset power threshold corresponding to the probability level of having a charging behavior.

[0039] In some embodiments of the present application, the historical charging data is collected through the charging data collection fields. The charging data collection fields are shown as follows:

[0040] Table 1 Charging Data Collection Fields

[0041]

[0042] In some embodiments of the present application, the data reported for each charging is collected through the charging data collection fields to form a charging record, thereby obtaining the historical charging data.

[0043] In some embodiments of the present application, the statistical period of the historical charging data can be set to 180 days.

[0044] In some embodiments of the present application, the navigation destination location is used to represent the destination set by the user at the current time.

[0045] In some embodiments of the present application, the expected charging cycle is used to represent the third charging time interval between the current time and the last charging at the navigation destination location.

[0046] In some embodiments of the present application, the predicted remaining power is used to characterize the predicted state of charge (SOC) of the battery when the target vehicle reaches the navigation destination under the current vehicle condition and the predicted driving road condition. The predicted remaining power is obtained based on the current power and the predicted power consumption between the current vehicle position and the navigation destination.

[0047] In some embodiments of the present application, the current vehicle condition at least includes the driving mode, the external environmental temperature, the tire condition, etc. The predicted driving road condition at least includes the flatness of the road, the traffic flow, the degree of sharpness of the curve, etc.

[0048] In some embodiments of the present application, the charging behavior label further includes the VIN.

[0049] In some embodiments of the present application, the charging behavior label further includes the type of common charging location and the type of common charging.

[0050] In some embodiments of the present application, the common charging location is also included in the charging behavior label.

[0051] In some embodiments of the present application, in step S220, the matching of the information to be compared with the charging behavior label of the common charging location includes at least one of: the predicted charging cycle is within the range of the common charging cycle, the predicted arrival time is within the common charging period, and the predicted remaining power is within the remaining power segment before common charging. The present application improves the accuracy of charging behavior prediction through multi-dimensional matching of the common charging location and the charging behavior label.

[0052] In some embodiments of the present application, the fuel at least includes at least one of gasoline, diesel, natural gas, biofuel, hydrogen, and synthetic fuel.

[0053] In some embodiments of the present application, after the cloud server completes the update of the predicted remaining power in step S230, it sends the new predicted remaining power to the target vehicle, thereby realizing the energy planning of the fuel and electric energy of the target vehicle, and avoiding unnecessary energy waste while maintaining sufficient cruising range, meeting the personalized charging needs of users.

[0054] In some embodiments of the present application, the energy planning of the fuel and electric energy is used to characterize the balance of the consumption of the fuel and electric energy during driving, so that the predicted remaining power when the target vehicle reaches the navigation destination is the corresponding remaining power threshold.

[0055] In some embodiments of the present application, when the target vehicle is an extended-range vehicle, the energy planning of the fuel and electric energy of the target vehicle is also the energy planning of the range extender to plan the consumption of the fuel to achieve electric energy replenishment.

[0056] In some embodiments of the present application, the charging label data is determined based on the historical charging data of the target vehicle, including: obtaining the historical charging data of the target vehicle, where the historical charging data includes multiple charging records; if the historical charging duration of a charging record is less than a preset second duration threshold, the charging record is discarded, and the historical charging duration is obtained based on the charging end time and the charging start time of the charging record; if the second charging time interval between two charging records is less than a preset interval threshold, the two charging records are combined into one charging record, and the second charging time interval is obtained based on the charging start time of the latter charging record and the charging end time of the former charging record; if there are multiple reported historical position positioning points in a charging record, the number of positioning times that the historical position positioning points fall within the same positioning accuracy area is counted, and the positioning accuracy area with the highest number of positioning times is determined as the charging position positioning point of the charging record; the charging label data is determined according to the updated charging records.

[0057] In some embodiments of the present application, the charging records are stored after ETL (Extract, Transform, Load) data cleaning.

[0058] In some embodiments of the present application, the data cleaning includes cleaning up invalid charging records. For example, the preset second duration threshold can be set to 5 minutes. When the historical charging duration of a charging record is less than 5 minutes, the charging record is discarded and not recorded as a valid charge.

[0059] In some embodiments of the present application, the data cleaning also includes integrating charging records with a short second charging time interval. For example, the preset interval threshold can be set to 30 minutes. When the second charging time interval between two charging records is less than 30 minutes, the two charging records are combined into one charging record.

[0060] In some embodiments of the present application, the data cleaning also includes finding the most accurate charging position positioning point. For example, 10 historical position positioning points reported after the start of charging are obtained. The 10 historical position positioning points may fall on different positioning accuracy areas, and the positioning accuracy area with the most occurrences is obtained from the 10 historical position positioning points as the charging position positioning point.

[0061] In some embodiments of the present application, the positioning accuracy area can be obtained by one of clustering analysis, weighted average, and majority voting on multiple historical position positioning points.

[0062] In some embodiments of the present application, the determination of each common charging location includes: converting the charging position location points in the historical charging data into region of interest data, where the region of interest data includes the region of interest name and the region of interest type; counting the first charging times for charging at the same region of interest name in the historical charging data; and determining multiple common charging locations according to the sorting of the first charging times.

[0063] In some embodiments of the present application, the charging position location points in each charging record are converted into region of interest data through a map interface, such as the region of interest identifier (AOI_Id), the region of interest type (AOI_Type), and the region of interest name (AOI_Name).

[0064] In some embodiments of the present application, the AOI_Name and the AOI_Id are uniquely corresponding. Counting the first charging times for charging at the same region of interest name in the historical charging data includes: grouping and summing the AOI_Id to obtain the first charging times corresponding to each region of interest name.

[0065] In some embodiments of the present application, determining multiple common charging locations according to the sorting of the first charging times includes: if the ranking of a first charging time meets a preset ranking threshold, determining the region of interest name corresponding to the first charging time as a common charging location; or, if the ranking of a first charging time meets a preset ranking threshold and the first charging time is greater than or equal to a preset second time threshold, determining the region of interest name corresponding to the first charging time as a common charging location.

[0066] In some embodiments of the present application, the preset ranking threshold is set to 2, and the preset second time threshold is set to 5. That is, the top two region of interest names with the first charging times exceeding 5 times and the most first charging times within the statistical period are determined as common charging locations, obtaining AOI_Name_1 and AOI_Name_2. The embodiments of the present application are explained by taking two common charging locations as examples.

[0067] In some embodiments of the present application, the determination of the common charging location type and the common charging type includes: determining the common charging location type of the common charging location according to the region of interest type corresponding to the common charging location; counting the first charging times of the same historical charging type corresponding to the common charging location, and determining the historical charging type with the highest first charging times as the common charging type of the common charging location.

[0068] In some embodiments of the present application, calculate the most frequently used charging types with the highest number of the first charging times on AOI_Name_1 and AOI_Name_2 respectively, and record (AOI_Name_1, Charge_Type_1) and (AOI_Name_2, Charge_Type_2).

[0069] In some embodiments of the present application, determine the type of the common charging location through the AOI_Type corresponding to AOI_Name. For example, if AOI_Type = Commercial and Residential - Residential Area, it means that type is a home charging pile; if AOI_type = Commercial and Residential - Office Building, or AOI_type = Charging Station - Charging Station, it means that type is a public charging pile.

[0070] In some embodiments of the present application, record the VIN, the type of the common charging location, and the most frequently used charging type of two common charging locations of the target vehicle, such as (VIN, AOI_Name_1, Charge_Type_1, type_1) and (VIN, AOI_Name_2, Charge_Type_2, type_2).

[0071] In some embodiments of the present application, please refer to Figure 3 , Figure 3 which shows a schematic flow chart of determining the common charging location and the charging behavior label according to an embodiment of the present application. As Figure 3As shown in the figure, obtain the AOI_Id, AOI_Type, and AOI_Name of the GPS of each charging location through the map interface: convert the charging location positioning points in each charging record into region of interest data; group by AOI_Id and calculate the first charging times of the same group of AOI_Id: count the first charging times of charging in the same region of interest name through the grouping statistics of AOI_Id; whether there is an AOI_Id with the first charging times exceeding 5 times: if there is an AOI_Id with the first charging times exceeding 5 times, enter the step of determining the two region of interest names with the top 2 first charging times as the common charging locations, if there is no AOI_Id with the first charging times exceeding 5 times, continue to convert the region of interest data of the next charging record; determine the two region of interest names with the top 2 first charging times as the common charging locations: determine the two region of interest names with the highest first charging times as the common charging locations; calculate the historical charging type with the most first charging times at each common charging location: count the first charging times of the same historical charging type corresponding to the common charging location, and determine the historical charging type with the highest first charging times as the common charging type of the common charging location; identify the common charging location type according to AOI_Type. The region of interest data comes with building boundaries. After converting the charging location positioning points into region of interest data in this application, it can aggregate the scattered charging location positioning points in all charging records accurately in combination with the building boundaries of the AOI, improving the accuracy of identifying common charging locations.

[0072] In some embodiments of the present application, the determination of the common charging cycle includes: determining the first charging time interval for each charging at the common charging location, where the first charging time interval is obtained based on the charging start time of the subsequent charging and the charging end time of the previous charging at the common charging location; determining the upper and lower limits of the recommended charging cycle according to the battery life and usage scenario of the target vehicle, and dividing to obtain multiple initial charging cycles; if a first charging time interval is greater than or equal to the lower limit of an initial charging cycle and less than or equal to the upper limit of an initial charging cycle, increase the charging frequency of the initial charging cycle; determine the initial charging cycle with the highest charging frequency as the common charging cycle of the common charging location.

[0073] In some embodiments of the present application, for two common charging locations, calculate the first charging time interval for each charging at each common charging location respectively, as shown in the following example:

[0074] Table 2 Determination of the first charging time interval

[0075] VIN Common charging locations Charging start time Charging end time First charging time interval xx Common charging point_1 time1 time2 xx Common charging point_1 time3 time4 time3 - time2 xx Common charging point_1 time5 time6 time5 - time4

[0076] In some embodiments of the present application, the distribution of all the first charging time intervals is statistically analyzed. According to the endurance of current mainstream range-extended vehicles, such as 200 - 300 km, in the usage scenario where the user's daily driving mileage is 50 km, charging is required once every 3 - 5 days (d). Moreover, considering usage scenarios such as longer-range models, longer commutes, or traffic congestion during commuting, a recommended charging interval of 2 days can be set to cover as many usage scenarios as possible. For example, multiple initial charging cycles can be respectively set as (0, 3d], (3d, 5d], (5d, 7d], (7d, 10d], etc.

[0077] In some embodiments of the present application, please refer to Figure 4 , Figure 4 which shows a schematic flowchart for determining a common charging cycle according to an embodiment of the present application. As Figure 4 shown, the first charging time interval for each charging at two common charging locations is calculated respectively: the first charging time interval is the difference between the charging start time of the subsequent charging and the charging end time of the previous charging at a common charging location; the charging frequency falling within each initial charging cycle is calculated: if a first charging time interval is greater than or equal to the lower limit of an initial charging cycle and the first charging time interval is less than or equal to the upper limit of the initial charging cycle, then the charging frequency of the initial charging cycle is increased, that is, when it is characterized that the first charging time interval falls within the initial charging cycle, the corresponding charging frequency is increased at this time; the initial charging cycle with the highest occurrence frequency at the two common charging locations is found, and the common charging cycles of the two common charging locations are recorded: the initial charging cycle with the highest charging frequency is determined as the common charging cycle corresponding to the common charging location and recorded, such as (VIN, AOI_Name_1, common charging cycle_1), (VIN, AOI_Name_2, common charging cycle_2). The above method realizes the aggregation of the initial charging cycles and improves the accuracy of identifying the common charging cycle.

[0078] In some embodiments of the present application, the determination of the common charging period includes: dividing multiple initial charging periods; if the charging start time of a charging record is greater than or equal to the lower limit of an initial charging period and the charging start time is less than or equal to the upper limit of the initial charging period, then the second charging count corresponding to the initial charging period is increased, and the historical charging duration of the charging record is accumulated to the cumulative charging duration corresponding to the initial charging period; if the second charging count of an initial charging period is greater than a preset first count threshold and the average charging duration of the initial charging period is greater than a preset first duration threshold, then the initial charging period is determined as the common charging period, and the average charging duration is obtained based on the cumulative charging duration and the second charging count; where the charging record is the record corresponding to a common charging location in the historical charging data.

[0079] In some embodiments of the present application, a day is divided into multiple initial charging periods. For example, the interval of each initial charging period is 2 hours. For the convenience of subsequent statistics and analysis, a unique number can be assigned to each initial charging period.

[0080] In some embodiments of the present application, all charging records at two common charging locations are traversed. For each charging record, the initial charging period in which the charging start time is located is determined, the second charging count of the initial charging period where it is located is incremented by one, and the historical charging duration of each charging record is accumulated to the corresponding initial charging period to obtain the cumulative charging duration. Dividing the cumulative charging duration of each initial charging period by the second charging count of the corresponding initial charging period gives the average charging duration of the initial charging period. The data records are as follows:

[0081] Table 3 Data records during the determination of common charging periods

[0082]

[0083] In some embodiments of the present application, the preset first count threshold can be set to a preset ratio of the total second charging count, such as 30%.

[0084] In some embodiments of the present application, the preset first duration threshold can be set to 0.5 hours.

[0085] In some embodiments of the present application, all initial charging periods corresponding to the second charging count being greater than the preset first count threshold and the average charging duration being greater than the preset first duration threshold are determined as common charging periods. The recorded common charging periods are as follows:

[0086] Table 4 Common charging periods

[0087] Common charging locations Common charging periods Common charging point_1 20:00-22:00 Common charging point_1 22:00-00:00 Common charging point_2 10:00-12:00 Common charging point_2 14:00-16:00

[0088] In some embodiments of the present application, please refer to Figure 5 , Figure 5 shows a schematic flowchart of determining common charging periods according to an embodiment of the present application. As Figure 5As shown, the time is discretely segmented, with an initial charging period every 2 hours; multiple initial charging periods are obtained; the charging start times corresponding to the common charging locations are assigned to each initial charging period: the charging start times of each common charging location are respectively assigned to the corresponding initial charging period; the second charging times on the initial charging period are counted: there are respective corresponding second charging times for multiple common charging locations in an initial charging period; the second charging times are greater than 30% of the total number of second charging times: if the second charging times of this initial charging period are greater than 30% of the total number of second charging times, then proceed to the step of calculating the average charging duration on the initial charging period. If the second charging times of this initial charging period are less than or equal to 30% of the total number of second charging times, then continue to count the second charging times of the next initial charging period; calculate the average charging duration on the initial charging period: determine the average charging duration of the initial charging period based on the cumulative charging duration and the second charging times of the initial charging period; the average charging duration is greater than 0.5 hours: if the average charging duration of this initial charging period is greater than 0.5 hours, then record this initial charging period as a common charging period. If the average charging duration of this initial charging period is less than or equal to 0.5 hours, then continue to calculate the average charging duration of the next initial charging period. By aggregating the charging start times through the initial charging period, the accuracy of identifying common charging periods is improved.

[0089] In some embodiments of the present application, the determination of the remaining power segmentation before common charging includes: dividing multiple initial power segments; if the remaining power before charging in a charging record is greater than or equal to the lower limit of an initial power segment and less than or equal to the upper limit of an initial power segment, then increase the power frequency of the initial power segment; determine the initial power segment with the highest power frequency as the remaining power segment before common charging; wherein, the charging record is the record corresponding to a common charging location in the historical charging data.

[0090] In some embodiments of the present application, with an interval of 10% for each initial power segment, multiple initial power segments are such as: (0, 10%], (10%, 20%], (20%, 30%], (30%, 40%], (40%, 50%], etc. By aggregating the remaining power before charging through the above initial power segments, the accuracy of the remaining power segment before common charging is improved.

[0091] In some embodiments of the present application, the remaining power segment before common charging is as follows:

[0092] Table 5 Remaining Power Segment before Common Charging

[0093] Common charging locations Remaining power segments before common charging Common charging point_1 (10％,20％] Common charging point_2 (30％,40％]

[0094] In some embodiments of the present application, the overall charging behavior label is as follows:

[0095] Table 6 Overall charging behavior label

[0096]

[0097] In some embodiments of the present application, the charging behavior label is updated daily. Taking a statistical period of 180 days as an example, the charging records on the first day in the historical charging data are discarded, and the charging records from the second day to the 181st day are included in the scope of the historical charging data, realizing the rolling update of the charging behavior label, improving the matching degree of the recognition of the user's charging behavior, and thus improving the accuracy of the charging behavior prediction.

[0098] In some embodiments of the present application, the determination of the probability level of the charging behavior includes: if the type of the common charging location is a home charging pile, the probability level of the existence of the charging behavior is determined as the first level; if the type of the common charging location is a public charging pile and the common charging type is fast charging, the probability level of the existence of the charging behavior is determined as the second level; if the type of the common charging location is a public charging pile and the common charging type is slow charging, the probability level of the existence of the charging behavior is determined as the third level; wherein, the charging behavior label also includes the type of the common charging location and the common charging type, and the first level, the second level and the third level are probability levels that decrease in sequence, and the preset power thresholds corresponding to the probability levels from high to low increase from low to high.

[0099] In some embodiments of the present application, on the basis of predicting the existence of the charging behavior, the probability level of the existence of the charging behavior is determined according to the type of the common charging location and the common charging type, so as to set different new predicted remaining powers and perform energy planning on the fuel and electric energy of the target vehicle.

[0100] In some embodiments of the present application, the first level is used to represent a very high probability, the second level is used to represent a relatively high probability, and the third level is used to represent a general probability.

[0101] In some embodiments of the present application, if the type of the common charging location is a home charging pile, it can be predicted that the user will charge after arriving at the navigation destination location, and there is a very high probability level. Therefore, with the goal of user energy conservation, it is planned that the user uses more electric energy and less fuel. For example, the fuel and electric energy of the target vehicle are energy-planned according to a predicted remaining power of 10%.

[0102] In some embodiments of the present application, if the type of the common charging location is a public charging pile, although it is predicted that the user has a charging behavior, but because it is a public charging pile, there is still a certain probability that the user can finally charge, and the common charging type (such as fast charging, slow charging) determines the waiting time of the user's charging, so both will affect the possibility of the user's charging.

[0103] In some embodiments of the present application, if the type of common charging location is a public charging pile and the common charging type is fast charging, it can be predicted that the user will charge after arriving at the navigation destination location, and there is a relatively high probability level. Therefore, the goal is to save energy for the user and reduce the user's anxiety. For example, the fuel and electric energy of the target vehicle are energy-planned according to 15% of the estimated remaining power.

[0104] In some embodiments of the present application, if the type of common charging location is a public charging pile and the common charging type is slow charging, it can be predicted that the user will charge after arriving at the navigation destination location, and there is a general probability level. Therefore, the main focus is on reducing the user's anxiety and ensuring that there is still available power for the user after reaching the navigation destination location. For example, the fuel and electric energy of the target vehicle are energy-planned according to 20% of the estimated remaining power.

[0105] In some embodiments of the present application, 10%, 15%, and 20% are preset power thresholds corresponding to different probability levels.

[0106] In some embodiments of the present application, if the remaining power segment before common charging corresponding to the navigation destination location is (30%, 40%], the type of common charging location is a public charging pile, and the common charging type is fast charging, the energy can be planned according to the estimated remaining power = 35%. This is only an example of updating the estimated remaining power based on the remaining power segment before common charging to achieve the energy planning of fuel and electric energy.

[0107] In some embodiments of the present application, please refer to Figure 6 , Figure 6 which shows a schematic flowchart of an implementation vehicle charging prediction method according to an embodiment of the present application. As Figure 6As shown, the reported navigation destination location and the estimated arrival time are provided; it is determined whether the navigation destination location is a common charging location: if the navigation destination location is a common charging location, then proceed to the step of calculating the charging time interval between the current time and the last charging time at the navigation destination location; if the navigation destination location is not a common charging location, then end; calculate the third charging time interval between the current time and the last charging time at the navigation destination location: calculate the estimated charging cycle; is it within the range of the common charging cycle: if the estimated charging cycle is within the range of the common charging cycle, then proceed to the step of determining whether the estimated arrival time is within the common charging period; if the estimated charging cycle is not within the range of the common charging cycle, then end; determine whether the estimated arrival time is within the common charging period: if the estimated arrival time is within the common charging period, then proceed to the step of determining whether the estimated remaining battery power is within the range of the remaining battery power segments before common charging; if the estimated arrival time is not within the common charging period, then end; determine whether the estimated remaining battery power is within the range of the remaining battery power segments before common charging: if the estimated remaining battery power is within the range of the remaining battery power segments before common charging, then predict that there is a charging behavior at the navigation destination location; if the estimated remaining battery power is not within the range of the remaining battery power segments before common charging, then end; determine the type of the common charging location: if the type of the common charging location corresponding to the navigation destination location is a home charging pile, then it is considered that the probability level of there being a charging behavior is very high, and the estimated remaining battery power can be set to 10%; if the type of the common charging location corresponding to the navigation destination location is a public charging pile and the common charging type is fast charging, then it is considered that the probability level of there being a charging behavior is relatively high, and the estimated remaining battery power can be set to 15%; if the type of the common charging location corresponding to the navigation destination location is a public charging pile and the common charging type is slow charging, then it is considered that the probability level of there being a charging behavior is average, and the estimated remaining battery power can be set to 20%. Based on the above method, the present application improves the accuracy of charging behavior prediction through multi-dimensional associated charging behavior tags, and avoids unnecessary energy waste by formulating personalized energy planning strategies for fuel and electric energy; moreover, reasonably controls the consumption of fuel and electric energy, ensures that the target vehicle can maintain sufficient cruising range under different charging conditions, meets the personalized usage needs of users, and provides a more convenient and efficient charging and driving experience.

[0108] Please refer to Figure 7 , Figure 7 shows a block diagram of a vehicle charging prediction device according to an embodiment of the present application. The device can be applied to Figure 1 the shown implementation environment and is specifically configured in the cloud server 101. The device can also be applicable to other exemplary implementation environments and is specifically configured in other devices. This embodiment does not limit the implementation environment to which the device applies.

[0109] As Figure 7As shown in the figure, a vehicle charging prediction device 700 according to an embodiment of the present application includes: a data acquisition module 701, a charging prediction module 702, and a remaining power update module 703.

[0110] Among them, the data acquisition module 701 is configured to acquire charging label data of a target vehicle and charging prediction information to be predicted. The charging prediction information to be predicted includes a navigation destination location and comparison information corresponding to the navigation destination location. The comparison information includes at least one of an estimated charging cycle, an estimated arrival time, and an estimated remaining power. The charging label data includes charging behavior labels of multiple common charging locations. The charging behavior labels include at least one of a common charging cycle, a common charging time period, and a segmented remaining power before common charging. The charging label data is determined based on the historical charging data of the target vehicle;

[0111] The charging prediction module 702 is configured to, if the navigation destination location matches a common charging location and the comparison information matches the charging behavior label of the common charging location, determine the charging prediction identifier of the target vehicle as having a charging behavior;

[0112] The remaining power update module 703 is configured to, if the charging prediction identifier is determined as having a charging behavior, update the estimated remaining power to a remaining power threshold to perform energy planning on the fuel and electric energy of the target vehicle. The remaining power threshold is obtained based on the segmented remaining power before common charging or a preset power threshold corresponding to the probability level of having a charging behavior.

[0113] The vehicle charging prediction device provided in the above embodiment and the vehicle charging prediction method provided in the above embodiment belong to the same concept. The specific manners in which each module and unit perform operations have been described in detail in the method embodiment and will not be elaborated here. In practical applications, the vehicle charging prediction device provided in the above embodiment can, as needed, allocate the above functions to different functional modules, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here either.

[0114] An embodiment of the present application further provides an electronic device, including: one or more processors; a storage device configured to store one or more programs. When the one or more programs are executed by the one or more processors, the electronic device implements the vehicle charging prediction method provided in each of the above embodiments.

[0115] Please refer to Figure 8 , Figure 8 which shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. Figure 8 The computer system 800 of the electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0116] As Figure 8 shown, computer system 800 includes a central processing unit 801, which can perform various appropriate actions and processes according to a program stored in the read-only memory 802 or a program loaded from the storage section 808 into the random access memory 803, such as executing the method in the above embodiments. In the random access memory 803, various programs and data required for system operation are also stored. The central processing unit 801, the read-only memory 802, and the random access memory 803 are connected to each other via a bus 804. The input / output interface 805 is also connected to the bus 804.

[0117] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read from it can be installed into the storage section 808 as needed.

[0118] Specifically, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit (CPU) 801, various functions defined in the system of the present application are executed.

[0119] The computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted by any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in an order different from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0121] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the units themselves. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of this application.

[0122] On the other hand, this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor of the computer, the computer is enabled to execute the vehicle charging prediction method provided in each of the above embodiments. The computer-readable storage medium can be included in the electronic device described in the above embodiments, or can exist alone without being assembled into the electronic device.

[0123] In the above embodiments, unless otherwise specified, when using serial numbers such as "first" and "second" to describe a common object, it only represents different instances referring to the same object, rather than indicating that the object to be described must be in a given order, whether in terms of time, space, sorting, or any other way.

[0124] The above embodiments are only used to exemplarily illustrate the principles and effects of this application, rather than to limit this application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in this application should still be covered by the claims of this application.

Claims

1. A vehicle charging prediction method, characterized in that: The method comprises: Acquire charging tag data and to-be-charged prediction information of a target vehicle, wherein the to-be-charged prediction information includes a navigation destination location and information to be compared corresponding to the navigation destination location, wherein the information to be compared includes at least one of an estimated charging cycle, an estimated arrival time, and an estimated remaining power, wherein the charging tag data includes charging behavior tags of a plurality of commonly used charging locations, wherein the charging behavior tags include at least one of a commonly used charging cycle, a commonly used charging time period, and a commonly used remaining power segment before charging, and wherein the charging tag data is determined based on historical charging data of the target vehicle; If the navigation destination matches a common charging location, and the information to be compared matches the charging behavior tag of the common charging location, the charging prediction mark of the target vehicle is determined as the presence of charging behavior; If the charging prediction indicates that charging behavior exists, the estimated remaining power is updated to the remaining power threshold to perform energy planning for the fuel and electricity of the target vehicle. The remaining power threshold is obtained based on the preset power threshold corresponding to the commonly used remaining power segment before charging or the probability level of the charging behavior.

2. The vehicle charging prediction method according to claim 1, characterized in that: The determination of the probability level of the existence of the charging behavior includes: If the common charging location type is a home charging pile, the probability level of the existence of the charging behavior is determined to be the first level; If the common charging location type is a public charging pile and the common charging type is fast charging, the probability level of the existence of the charging behavior is determined to be the second level; If the common charging location type is a public charging pile, and the common charging type is slow charging, the probability level of the existence of the charging behavior is determined to be the third level; Among them, the charging behavior label also includes the commonly used charging location type and the commonly used charging type, the first level, the second level and the third level are probability levels that decrease in sequence, and the preset power thresholds corresponding to the probability levels from high to low are from low to high.

3. The vehicle charging prediction method according to claim 1, characterized in that: Determination of each of the commonly used charging locations includes: Converting the charging position location points in the historical charging data into region of interest data, wherein the region of interest data includes a region of interest name and a region of interest type; Counting the number of first charging times of charging in the same area of ​​interest name in the historical charging data; A plurality of frequently used charging locations are determined according to the ranking of the first charging times.

4. The vehicle charging prediction method according to any one of claims 1 to 3, characterized in that: The determination of the commonly used charging cycle includes: Determine a first charging time interval for each charging at the common charging location, wherein the first charging time interval is obtained based on a charging start time of a subsequent charging and a charging end time of a previous charging at the common charging location; Determine the upper and lower limits of the recommended charging cycle according to the endurance and usage scenario of the target vehicle, and divide them to obtain multiple initial charging cycles; If a first charging time interval is greater than or equal to a lower limit of an initial charging cycle, and the first charging time interval is less than or equal to an upper limit of an initial charging cycle, then increasing the charging frequency of the initial charging cycle; The initial charging cycle with the highest charging frequency is determined as the common charging cycle of the common charging location.

5. The vehicle charging prediction method according to any one of claims 1 to 3, characterized in that: The determination of the commonly used charging period includes: Dividing the initial charging period into multiple periods; If the charging start time of a charging record is greater than or equal to the lower limit of an initial charging period, and the charging start time is less than or equal to the upper limit of an initial charging period, then the second charging number corresponding to the initial charging period is increased, and the historical charging duration of the charging record is accumulated to the cumulative charging duration corresponding to the initial charging period; If the second charging number of an initial charging period is greater than the preset first number threshold, and the average charging duration of the initial charging period is greater than the preset first duration threshold, the initial charging period is determined as a common charging period, and the average charging duration is obtained based on the accumulated charging duration and the second charging number; The charging record is a record corresponding to a commonly used charging location in the historical charging data.

6. The vehicle charging prediction method according to any one of claims 1 to 3, characterized in that: The determination of the conventional remaining power segmentation before charging includes: Divide the initial power into multiple segments; If the remaining power before charging in a charging record is greater than or equal to the lower limit of an initial power segment, and the remaining power before charging is less than or equal to the upper limit of an initial power segment, then increase the power frequency of the initial power segment; The initial power segment with the highest power frequency is determined as the commonly used remaining power segment before charging; The charging record is a record corresponding to a commonly used charging location in the historical charging data.

7. The vehicle charging prediction method according to any one of claims 1 to 3, characterized in that: The charging tag data is determined based on the historical charging data of the target vehicle, including: Acquire historical charging data of the target vehicle, wherein the historical charging data includes a plurality of charging records; If the historical charging duration of a charging record is less than a preset second duration threshold, the charging record is discarded, and the historical charging duration is obtained based on the charging end time and the charging start time of the charging record; If the second charging time interval between the two charging records is less than the preset interval threshold, the two charging records are merged into one charging record, wherein the second charging time interval is obtained based on the charging start time of the latter charging record and the charging end time of the previous charging record; If there are multiple reported historical location positioning points in a charging record, the number of times the historical location positioning point falls within the same positioning accuracy area is counted, and the positioning accuracy area with the highest number of positioning times is determined as the charging location positioning point of the charging record; The charging tag data is determined according to the updated charging record.

8. A vehicle charging prediction device, characterized in that: The device comprises: A data acquisition module, used to acquire charging tag data and to-be-charged prediction information of a target vehicle, wherein the to-be-charged prediction information includes a navigation destination location and information to be compared corresponding to the navigation destination location, wherein the information to be compared includes at least one of an estimated charging cycle, an estimated arrival time, and an estimated remaining power, wherein the charging tag data includes charging behavior tags of a plurality of commonly used charging locations, wherein the charging behavior tags include at least one of a commonly used charging cycle, a commonly used charging time period, and a commonly used remaining power segment before charging, and wherein the charging tag data is determined based on historical charging data of the target vehicle; A charging prediction module, configured to determine the charging prediction flag of the target vehicle as the presence of charging behavior if the navigation destination matches a common charging location and the information to be compared matches the charging behavior tag of the common charging location; The remaining power update module is used to update the estimated remaining power to a remaining power threshold if the charging prediction indicates that charging behavior exists, so as to plan the fuel and electricity of the target vehicle. The remaining power threshold is obtained based on the preset power threshold corresponding to the commonly used remaining power segmentation before charging or the probability level of the existence of charging behavior.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enables the electronic device to implement the vehicle charging prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the vehicle charging prediction method according to any one of claims 1 to 7.

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