Mileage strategy recommendation method and apparatus, electronic device, and storage medium

Abstract

The present application provides a mileage strategy recommendation method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring historical feature information of a vehicle, the historical feature information being used for indicating usage of the vehicle; on the basis of the historical feature information, determining, from among at least one preset recommendation category, a target recommendation category to which the vehicle belongs, each recommendation category corresponding to at least one mileage strategy; and recommending at least one mileage strategy corresponding to the target recommendation category to a user. In the technical solution, starting from daily usage of vehicles, and incorporating related features of recommendation categories, mileage strategies suitable for users can be more accurately recommended to the users, thereby enhancing recommendation feedback.

Description

Recommendation methods, devices, electronic equipment, and storage media for mileage strategies

[0001] This application claims priority to Chinese Patent Application No. 202411044218.7, filed on July 31, 2024, entitled “Method, Apparatus, Electronic Device and Storage Medium for Recommending Mileage Strategies”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of vehicle technology, and more particularly to a method, apparatus, electronic device, and storage medium for recommending a mileage strategy. Background Technology

[0003] With the continuous development of battery-powered vehicle technology, the market share of electric vehicles is increasing. In order to reduce the burden of purchasing a car for users, some manufacturers deduct the battery cost from the car price. Users do not need to buy the battery, but only need to rent the battery for daily use.

[0004] In battery rental services, a large number of rental plans are usually displayed on the client side. However, due to the wide variety of plans, users find it difficult to easily find the appropriate mileage purchase strategy, which leads to a negative user experience.

[0005] Therefore, how to recommend appropriate mileage strategies to different users has become a pressing technical problem that needs to be solved. Summary of the Invention

[0006] This application provides a method, apparatus, electronic device, and storage medium for recommending mileage strategies to solve the current technical problem of being unable to recommend suitable mileage strategies to current users.

[0007] In a first aspect, embodiments of this application provide a method for recommending mileage strategies, including:

[0008] Obtain historical feature information of the vehicle, which is used to indicate the usage status of the vehicle;

[0009] Based on the historical feature information, the target recommendation category to which the vehicle belongs is determined from at least one preset recommendation category, and each recommendation category corresponds to at least one mileage strategy;

[0010] Recommend at least one mileage strategy corresponding to the target recommendation category to the user.

[0011] In one or more embodiments, before determining the target recommendation category to which the user belongs from at least one preset recommendation category based on the historical feature information, the method includes:

[0012] Obtain multiple mileage policies;

[0013] Clustering is performed on the multiple mileage strategies to obtain recommendation categories corresponding to different mileage ranges.

[0014] In one or more embodiments, the historical feature information includes: mileage data of the user's vehicle driven within a first preset time period;

[0015] Accordingly, determining the target recommendation category to which the vehicle belongs from at least one preset recommendation category based on the historical feature information includes:

[0016] Based on the vehicle's mileage data within the first preset time period, determine the vehicle's average driving mileage over all preset time periods.

[0017] The target recommendation category to which the vehicle belongs is determined based on the average driving mileage and the mileage range corresponding to the at least one recommendation category.

[0018] In one or more embodiments, the historical feature information further includes: vehicle charging data within a second preset time period;

[0019] Accordingly, determining the target recommendation category to which the vehicle belongs from at least one preset recommendation category based on the historical feature information includes:

[0020] Based on the vehicle's charging data within the second preset time period, determine the average number of times the vehicle is charged across all preset time periods;

[0021] The target recommendation category to which the vehicle belongs is determined based on the average number of charging cycles, the average driving mileage, and the mileage range corresponding to at least one recommendation category.

[0022] In one or more embodiments, the method further includes:

[0023] If the vehicle's free driving mileage is greater than the average driving mileage, or greater than the first preset value, or greater than the sum of the average driving mileage and the first preset value, then the target recommendation category to which the vehicle belongs is determined to be empty.

[0024] In one or more embodiments, the method further includes:

[0025] In response to a user's request, order information is generated according to a target mileage strategy, wherein the target mileage strategy is a mileage strategy selected by the user from at least one mileage strategy.

[0026] Send the order information corresponding to the target mileage strategy to the user.

[0027] Secondly, embodiments of this application provide a mileage strategy recommendation device, comprising:

[0028] The acquisition module is used to acquire historical feature information of the vehicle, which is used to indicate the usage status of the vehicle.

[0029] The determination module is used to determine the target recommendation category to which the vehicle belongs from at least one preset recommendation category based on the historical feature information, and each recommendation category corresponds to at least one mileage strategy;

[0030] The push module is used to recommend at least one mileage strategy corresponding to the target recommendation category to the user.

[0031] In one or more embodiments, before determining the target recommendation category to which the user belongs from at least one preset recommendation category based on the historical feature information, the determining module is further configured to:

[0032] Obtain multiple mileage policies;

[0033] Clustering is performed on the multiple mileage strategies to obtain recommendation categories corresponding to different mileage ranges.

[0034] In one or more embodiments, the historical feature information includes: mileage data of the user's vehicle driven within a first preset time period;

[0035] Accordingly, the determining module is specifically used for:

[0036] Based on the vehicle's mileage data within the first preset time period, determine the vehicle's average driving mileage over all preset time periods.

[0037] The target recommendation category to which the vehicle belongs is determined based on the average driving mileage and the mileage range corresponding to the at least one recommendation category.

[0038] In one or more embodiments, the historical feature information further includes: vehicle charging data within a second preset time period;

[0039] Accordingly, the determining module is specifically used for:

[0040] Based on the vehicle's charging data within the second preset time period, determine the average number of times the vehicle is charged across all preset time periods;

[0041] The target recommendation category to which the vehicle belongs is determined based on the average number of charging cycles, the average driving mileage, and the mileage range corresponding to at least one recommendation category.

[0042] In one or more embodiments, the determining module is further configured to:

[0043] If the vehicle's free driving mileage is greater than the average driving mileage, or greater than the first preset value, or greater than the sum of the average driving mileage and the first preset value, then the target recommendation category to which the vehicle belongs is determined to be empty.

[0044] In one or more embodiments, the determining module is further configured to:

[0045] In response to a user's request, order information is generated according to a target mileage strategy, wherein the target mileage strategy is a mileage strategy selected by the user from at least one mileage strategy.

[0046] Send the order information corresponding to the target mileage strategy to the user.

[0047] Thirdly, embodiments of this application provide an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0048] The memory stores computer-executed instructions;

[0049] The processor executes computer execution instructions stored in the memory to implement the method as described in the first aspect or any of the above methods.

[0050] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in the first aspect or any of the above-described methods.

[0051] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods described in the first aspect and any one of the above.

[0052] This application provides a method, apparatus, electronic device, and storage medium for recommending mileage strategies. The method acquires historical feature information of a vehicle, which indicates its usage. Based on this historical feature information, it determines a target recommendation category for the vehicle from at least one preset recommendation category, with each recommendation category corresponding to at least one mileage strategy. Finally, it recommends at least one mileage strategy corresponding to the target recommendation category to the user. This technical solution, starting from daily vehicle usage and combining relevant features of the recommendation category, can more accurately recommend mileage strategies suitable for the user, thereby improving the feedback of the recommendations. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0054] Figure 1 is a flowchart illustrating the mileage strategy recommendation method provided in an embodiment of this application.

[0055] Figure 2 is a flowchart illustrating the mileage strategy recommendation method provided in this embodiment of the application.

[0056] Figure 3 is a flowchart illustrating the mileage strategy recommendation method provided in this embodiment of the application.

[0057] Figure 4 is a flowchart illustrating the mileage strategy recommendation method provided in the embodiments of this application.

[0058] Figure 5 is a schematic diagram of the structure of the mileage strategy recommendation device provided in the embodiment of this application;

[0059] Figure 6 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application.

[0060] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0062] Before introducing the embodiments of this application, the application background of the embodiments of this application will be explained first:

[0063] With the continuous development of battery-powered vehicle technology, the market share of electric vehicles is increasing. In order to reduce the burden of purchasing a car for users, some manufacturers deduct the battery cost from the car price. Users do not need to buy the battery, but only need to rent the battery for daily use.

[0064] In battery rental services, a large number of rental plans are usually displayed on the client side. However, due to the wide variety of plans, users find it difficult to easily find the appropriate mileage purchase strategy, which leads to a negative user experience.

[0065] The present application aims to address the following existing problem: how to recommend suitable mileage strategies to different users in order to improve user feedback and user experience.

[0066] To address the existing technical problems, the inventors of this application propose the following concept: Currently, vehicle applications typically recommend numerous mileage strategies (such as mileage products, packages, etc.) to users. Users need to click through or browse the various mileage strategies one by one to select the one that suits them. Since the purchase of mileage strategies is often linked to vehicle usage, if historical vehicle usage data, such as battery charging status and daily mileage, could be obtained, the user's category could be determined. For each category, mileage strategies could be categorized. When two categories match, a recommendation can be made directly, which not only makes it easier for users to find a suitable mileage strategy but also increases the feedback on mileage strategies.

[0067] The technical solution of this application will now be described in detail through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0068] It is worth noting that the application areas of the recommended methods, devices, electronic devices and storage media for mileage strategies provided in this disclosure are not limited.

[0069] In this application, the executing entity is an electronic device, which may specifically be a server, etc.

[0070] Figure 1 is a flowchart illustrating the mileage strategy recommendation method provided in this embodiment of the application. As shown in Figure 1, the method may include the following steps:

[0071] Step 11: Obtain the vehicle's historical feature information;

[0072] Among them, historical feature information is used to indicate the vehicle's usage status;

[0073] In this step, in order to recommend a suitable mileage strategy for the user's driving, we can first obtain some historical data of the user while driving, that is, the historical characteristic information of the vehicle.

[0074] Optionally, the vehicle's driving status can be the mileage data of the user driving the vehicle within a first preset time period, such as how many miles the user drove the vehicle in several days. This driving status can be obtained based on the following methods:

[0075] 1. The vehicle's driving data is recorded in real time in the cloud, which can be read directly (the electronic device is the cloud server itself), or it can be obtained after obtaining authorization from the server (the electronic device is not the cloud server).

[0076] 2. Obtain from the vehicle's internal system, such as the vehicle start time, driving route, distance, and vehicle shutdown time recorded in each driving cycle;

[0077] The driving cycle can be determined based on the vehicle's state or driving mode. For example, it can be determined by the vehicle's three states: starting, driving, and stopping; or by switching driving modes; or by performing a power-on and power-off operation; or by changing the vehicle's gear.

[0078] 3. The vehicle's fleet management system records data such as vehicle mileage and corresponding time in real time.

[0079] Optionally, the battery usage information in the vehicle can be the vehicle's charging data over a second preset period. For example, the number of charges over several days. The battery usage information can be obtained based on the following methods:

[0080] 1. Obtained from the vehicle's built-in system or application, for example, electric vehicles or plug-in hybrid vehicles provide statistics on the number of charges;

[0081] 2. Obtain information from charging equipment, such as charging piles or charging stations, which usually record the vehicle's charging status, including charging time, power consumption, and other information. If public charging equipment is used, charging history can be viewed through the relevant charging service provider or application, which usually includes statistics on the number of charging sessions.

[0082] 3. The vehicle's fleet management system records data such as the number of times the vehicle is charged and the charging time in real time.

[0083] The above does not impose any restrictions on the methods for obtaining relevant historical feature information; these are merely examples.

[0084] It should be understood that, in one possible implementation, the acquisition of data and information involved in the embodiments of this application requires authorization from the corresponding user or the corresponding software manufacturer.

[0085] Step 12: Based on historical feature information, determine the target recommendation category to which the vehicle belongs from at least one preset recommendation category;

[0086] Each recommendation category corresponds to at least one mileage strategy;

[0087] In this step, different recommendation categories are pre-configured. Based on the historical feature information of the vehicle, the recommendation category to which the vehicle belongs can be determined first, thereby obtaining at least one mileage strategy corresponding to the recommendation category.

[0088] Optionally, the recommended categories are associated with the vehicle's feature information, meaning that different recommended categories correspond to a certain range of vehicle feature information.

[0089] For example, a recommendation category may contain multiple mileage strategies, each corresponding to a different mileage range. Thus, the recommendation category may include mileage strategies within a certain mileage range. Based on the vehicle's historical characteristics, the user's driving behavior and battery usage can be determined and compared with the mileage ranges corresponding to different recommendation categories to determine the target recommendation category to which the vehicle belongs.

[0090] In one possible implementation, the mileage strategy could be: mileage packages, for example, a package that allows you to drive 100 kilometers.

[0091] Step 13: Recommend at least one mileage strategy corresponding to the target recommendation category to the user.

[0092] In this step, after obtaining the target recommendation category, at least one mileage strategy corresponding to that target recommendation category can be recommended to the user.

[0093] For example, at least one mileage strategy corresponding to the target recommendation category can be displayed in the vehicle's human-machine interface.

[0094] For example, at least one mileage strategy corresponding to the target recommendation category can be displayed on the terminal of the vehicle owner.

[0095] Furthermore, the method may also include the following implementation:

[0096] Step 1: In response to the user's request, generate order information according to the target mileage strategy;

[0097] The target mileage strategy is the mileage strategy selected by the user from at least one mileage strategy.

[0098] Under this implementation, users can select a mileage strategy from at least one mileage strategy corresponding to the target recommendation category shown above, as the mileage strategy to be purchased for the vehicle, i.e., the target mileage strategy. Based on the target mileage strategy, order information for purchasing the target mileage strategy can be generated.

[0099] Step 2: Send the order information corresponding to the target mileage strategy to the user.

[0100] In this implementation, after generating the order information for the target mileage strategy, the order information is pushed to the user so that the user can successfully obtain the corresponding target mileage strategy.

[0101] The mileage strategy recommendation method provided in this application involves acquiring historical feature information of a vehicle, which indicates the vehicle's usage; determining a target recommendation category for the vehicle from at least one preset recommendation category based on the historical feature information, with each recommendation category corresponding to at least one mileage strategy; and recommending at least one mileage strategy corresponding to the target recommendation category to the user. This technical solution, starting from daily vehicle usage and combining relevant features of the recommendation category, can more accurately recommend mileage strategies suitable for the user, thereby improving the feedback of the recommendations.

[0102] Based on the above embodiments, Figure 2 is a second flowchart illustrating the mileage strategy recommendation method provided in this application embodiment. As shown in Figure 2, the method may further include the following steps:

[0103] Step 21: Obtain multiple mileage policies;

[0104] In this step, different mileage strategies can be obtained, and the implementation of this acquisition can be as follows:

[0105] 1. Obtained from online platforms, online stores, and / or servers;

[0106] For example, an online platform can be an OTA (Over-The-Air Update Container), and an online store can be an OTA (Over-The-Air Store).

[0107] 2. Export from the vehicle's infotainment system;

[0108] For example, the vehicle's infotainment system obtains data from mileage packages available on relevant platforms, online stores, and / or servers;

[0109] 3. Obtain from the vehicle's corresponding terminal equipment.

[0110] Step 22: Cluster the multiple mileage strategies to obtain the recommendation categories corresponding to different mileage ranges.

[0111] In this step, after obtaining multiple mileage strategies, these mileage strategies are categorized to obtain different recommendation categories.

[0112] In one implementation of this application, the mileage can be classified based on the mileage corresponding to the mileage strategy to obtain recommended categories corresponding to different mileage ranges.

[0113] For example, Table 1 is an example of the recommended category classification provided in the embodiments of this application, as shown in Table 1:

[0114] Table 1:

[0115] For example, Package 1 is 50km, Package 2 is 60km, Package 3 is 70km, Package 4 is 100km, Package 5 is 150km, Package 6 is 170km, Package 7 is 200km, and Package 8 is 240km.

[0116] The mileage strategy recommendation method provided in this application involves acquiring multiple mileage strategies and performing clustering processing on these strategies to obtain recommendation categories corresponding to different mileage ranges. This technical solution categorizes different mileage strategies according to mileage conditions, enabling simple and accurate classification of mileage strategies and facilitating subsequent recommendations to users.

[0117] Based on the above embodiments, the historical feature information includes: mileage data of the user's vehicle driven within a first preset time period, which is as follows:

[0118] Figure 3 is a flowchart illustrating the mileage strategy recommendation method provided in this embodiment of the application. As shown in Figure 3, step 12 of this method may include the following steps:

[0119] Step 31: Based on the vehicle's mileage data within the first preset time period, determine the vehicle's average driving mileage over all preset time periods.

[0120] In this step, after obtaining the mileage data of the user's vehicle within the first preset time period, the mileage data of the user's vehicle within the first preset time period can be processed.

[0121] Optionally, the first preset duration can be a relatively long period of time, such as one month or 15 days; the preset time period can be a relatively short period of time, such as one day.

[0122] In one possible implementation, the average daily driving distance could be determined to be 100km; 50km, etc.

[0123] Step 32: Determine the target recommended category to which the vehicle belongs based on the average driving mileage and the mileage range corresponding to at least one recommended category.

[0124] In this step, based on the average driving mileage determined above and the mileage range corresponding to different recommendation categories, the average driving mileage can be compared with different mileage ranges to find the target mileage range that matches the vehicle's driving conditions, thereby obtaining the recommendation category corresponding to the target mileage range, which is then recorded as the target recommendation category to which the vehicle belongs.

[0125] In one possible implementation, based on Table 1, if the daily driving distance is 100km, then the target recommendation category is determined to be Category 2, and the selectable mileage strategies are Package 4 and Package 5.

[0126] In one possible implementation, based on Table 1, if the daily driving distance is 100km, the target recommendation categories are determined to be Category 2 and Category 3, and the selectable mileage strategies are Package 4, Package 5, Package 8, Package 6, and Package 7.

[0127] The mileage strategy recommendation method provided in this application determines the vehicle's average driving mileage over all preset time periods based on the vehicle's mileage data within a first preset time period; and determines the target recommendation category to which the vehicle belongs based on the average driving mileage and the mileage range corresponding to at least one recommendation category. This technical solution can utilize the vehicle's actual mileage to determine the target recommendation category to which the vehicle belongs.

[0128] Based on the above embodiments, the historical feature information also includes: vehicle charging data within a second preset time period, which is as follows:

[0129] Figure 4 is a flowchart illustrating the mileage strategy recommendation method provided in this embodiment of the application. As shown in Figure 4, step 12 of this method may include the following steps:

[0130] Step 41: Based on the vehicle's charging data within the second preset time period, determine the average number of times the vehicle is charged within all preset time periods.

[0131] In this step, after obtaining the vehicle's charging data within the second preset time period, the number of times the user drives the vehicle for charging within the second preset time period can be processed.

[0132] Optionally, the second preset duration can be a relatively long period of time, such as one month or 15 days; the preset time period can be a relatively short period of time, such as one day.

[0133] In one possible implementation, the average number of charging cycles can be determined to be once a day, once every two days, etc.

[0134] Step 42: Determine the target recommended category to which the vehicle belongs based on the average number of charging cycles, average driving mileage, and the mileage range corresponding to at least one recommended category.

[0135] In this step, based on the average driving mileage, average number of charging times, and the mileage range corresponding to different recommendation categories determined above, the average driving mileage can be compared with different mileage ranges to find the target mileage range that matches the user's driving and charging conditions, thereby obtaining the recommendation category corresponding to the target mileage range, which is then recorded as the target recommendation category to which the vehicle belongs.

[0136] Optionally, by combining average driving mileage and average number of charging sessions, the minimum mileage that a user can drive the vehicle after each charge can be obtained.

[0137] Example 1: If you drive 100km per day and charge once a day, you can determine that you need to charge once every 100km. Example 2: If you drive 100km per day and charge once every two days, you can determine that you need to charge once every 200km.

[0138] Based on the data above, it can be seen that in Example 1, the user needs a mileage strategy of at least 100km; while in Example 2, the user needs a mileage strategy of at least 200km. Therefore:

[0139] In one possible implementation, based on Table 1, if the daily driving distance is 100km and the charging frequency is once a day, then the target recommended category is determined to be Package 8, Package 6, or Package 7 in Category 3, or Package 5 or Package 4 in Category 2.

[0140] In one possible implementation, based on Table 1, if the daily driving distance is 100km and the charging frequency is once every 2 days, then the target recommended category is determined to be Package 8, Package 6, Package 7 in Category 3, or Package 5 in Category 2.

[0141] Furthermore, the method also includes: if the vehicle's free driving mileage is greater than the average driving mileage, or greater than a first preset value, or greater than the sum of the average driving mileage and the first preset value, then the target recommendation category to which the vehicle belongs is determined to be empty.

[0142] In this implementation, since the vehicle has available free driving mileage, if it is determined that the vehicle's free driving mileage is greater than the average driving mileage, or a first preset value, or the sum of the average driving mileage and the first preset value, then it can be assumed that the user does not need to use the mileage strategy, and the target recommendation category to which the vehicle belongs is determined to be empty.

[0143] In one possible implementation, if the vehicle travels 30km per day and the free driving mileage is 40km, then it is determined that the user does not need to obtain the mileage policy.

[0144] In one possible implementation, if the vehicle travels 50km per day and the free driving mileage is 40km, then it is determined that the user needs to obtain the mileage policy.

[0145] The first preset value can be 10, 12, etc., and can be set based on the actual situation.

[0146] In other implementations, the mileage strategy can also be determined based on the average number of times the vehicle is charged. If the average number of times the vehicle is charged (i.e., the charging frequency) is high, such as twice a day, then the mileage during each discharge period can be used to determine a higher mileage strategy.

[0147] For example, if you charge twice a day and drive 50km during each discharge, a mileage strategy of 100km or more can be recommended to avoid the poor driving experience caused by frequent charging and to protect the lifespan of the vehicle's battery.

[0148] The mileage strategy recommendation method provided in this application determines the average number of times a vehicle is charged over all preset time periods based on the vehicle's charging data within a second preset time period; and determines the target recommendation category to which the vehicle belongs based on the average number of charging times, average driving mileage, and the mileage range corresponding to at least one recommendation category. This technical solution also considers the impact of the vehicle's charging status on mileage to more accurately determine the target recommendation category to which the vehicle belongs.

[0149] The above is a detailed description of the preferred embodiments of this application, but this application is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

[0150] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0151] Figure 5 is a schematic diagram of the mileage strategy recommendation device provided in an embodiment of this application. As shown in Figure 5, the device includes:

[0152] The acquisition module 51 is used to acquire historical feature information of the vehicle, which is used to indicate the usage status of the vehicle.

[0153] The determination module 52 is used to determine the target recommendation category to which the vehicle belongs in at least one preset recommendation category based on historical feature information, and each recommendation category corresponds to at least one mileage strategy;

[0154] The push module 53 is used to recommend at least one mileage strategy corresponding to the target recommendation category to the user.

[0155] In one or more embodiments, before determining the target recommendation category to which the user belongs from at least one preset recommendation category based on historical feature information, the determining module 52 is further configured to:

[0156] Obtain multiple mileage policies;

[0157] Clustering is performed on multiple mileage strategies to obtain recommendation categories corresponding to different mileage ranges.

[0158] In one or more embodiments, historical feature information includes: mileage data of the user's vehicle driven within a first preset time period;

[0159] Accordingly, module 52 is specifically used for:

[0160] Based on the vehicle's mileage data within the first preset time period, determine the vehicle's average driving mileage over all preset time periods.

[0161] The target recommendation category to which the vehicle belongs is determined based on the average driving mileage and the mileage range corresponding to at least one recommendation category.

[0162] In one or more embodiments, the historical feature information further includes: vehicle charging data within a second preset time period;

[0163] Accordingly, module 52 is specifically used for:

[0164] Based on the vehicle's charging data within the second preset time period, determine the average number of times the vehicle charges within all preset time periods.

[0165] The target recommendation category to which the vehicle belongs is determined based on the average number of charging cycles, the average driving mileage, and the mileage range corresponding to at least one recommendation category.

[0166] In one or more embodiments, the determining module 52 is further configured to:

[0167] If the vehicle's free driving mileage is greater than the average driving mileage, or greater than the first preset value, or greater than the sum of the average driving mileage and the first preset value, the target recommendation category to which the vehicle belongs is determined to be empty.

[0168] In one or more embodiments, the determining module 52 is further configured to:

[0169] In response to the user's request, order information is generated based on the target mileage strategy, which is the mileage strategy selected by the user from at least one mileage strategy.

[0170] Send order information corresponding to the target mileage strategy to the user.

[0171] The mileage strategy recommendation device provided in this application embodiment can be used to execute the mileage strategy recommendation method in any of the above embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0172] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. Additionally, these modules can be fully or partially integrated together, or implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0173] Figure 6 is a schematic diagram of the structure of the electronic device provided in the embodiment of this application. As shown in Figure 6, the electronic device can be a server for a vehicle.

[0174] The electronic device may include: a processor 61, a memory 62, and computer program instructions stored in the memory 62 and executable on the processor 61, wherein the processor 61 executes the computer program instructions to implement the method provided in any of the foregoing embodiments.

[0175] Optionally, the various components of the electronic device can be connected via a system bus.

[0176] The memory 62 can be a separate memory unit or a memory unit integrated into the processor 61. The number of processors 61 can be one or more.

[0177] It should be understood that processor 61 can be a Central Processing Unit (CPU), or other general-purpose processor 61, digital signal processor 61 (DSP), application-specific integrated circuit (ASIC), etc. The general-purpose processor 61 can be a microprocessor 61, or any conventional processor 61. The steps of the method disclosed in this application can be directly manifested as being executed by the hardware processor 61, or being executed by a combination of hardware and software modules within the processor 61.

[0178] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Memory 62 may include random access memory (RAM) 62, and may also include non-volatile memory (NVM) 62, such as at least one disk storage device 62.

[0179] All or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory 62. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory 62 (storage medium) includes: read-only memory 62 (ROM), RAM, flash memory 62, hard disk, solid-state hard disk, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0180] The electronic device provided in this application embodiment can be used to execute the method provided in any of the above method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0181] This application provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the above-described method.

[0182] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0183] Optionally, a readable storage medium can be coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. Both the processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components within the device.

[0184] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and the at least one processor can implement the above-described method when executing the computer program.

[0185] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method of recommending a mileage strategy, wherein, The method comprises: obtaining historical feature information of a vehicle, the historical feature information being used to indicate a use condition of the vehicle; determining a target recommendation category to which the vehicle belongs among at least one preset recommendation category according to the historical feature information, each recommendation category corresponding to at least one mileage strategy; recommending at least one mileage strategy corresponding to the target recommendation category to the user.

2. The method of claim 1, wherein, Before the step of determining the target recommendation category to which the user belongs among at least one preset recommendation category according to the historical feature information, the method comprises: obtaining a plurality of mileage strategies; performing clustering processing on the plurality of mileage strategies to obtain recommendation categories corresponding to different mileage ranges.

3. The method of claim 1 or 2, wherein, The historical feature information comprises mileage data of the vehicle driven by the user within a first preset time period; Correspondingly, the step of determining the target recommendation category to which the vehicle belongs among at least one preset recommendation category according to the historical feature information comprises: determining an average driving mileage of the vehicle in all preset time periods according to the mileage data of the vehicle within the first preset time period; determining the target recommendation category to which the vehicle belongs according to the average driving mileage and mileage ranges corresponding to the at least one recommendation category.

4. The method of claim 3, wherein, The historical feature information further comprises charging data of the vehicle within a second preset time period; Correspondingly, the step of determining the target recommendation category to which the vehicle belongs among at least one preset recommendation category according to the historical feature information comprises: determining an average charging frequency of the vehicle in all preset time periods according to the charging data of the vehicle within the second preset time period; determining the target recommendation category to which the vehicle belongs according to the average charging frequency, the average driving mileage, and mileage ranges corresponding to the at least one recommendation category.

5. The method of claim 3, wherein, The method further comprises: if the free driving mileage of the vehicle is greater than the average driving mileage, or greater than a first preset value, or greater than a sum of the average driving mileage and the first preset value, determining that the target recommendation category to which the vehicle belongs is empty.

6. The method of claim 1 or 2, wherein, The method further comprises: in response to a user's acquisition request, generating order information according to a target mileage strategy, the target mileage strategy being a mileage strategy selected by the user from the at least one mileage strategy; sending the order information corresponding to the target mileage strategy to the user.

7. A device for recommending a mileage strategy, wherein, The method comprises: an obtaining module configured to obtain historical feature information of a vehicle, the historical feature information being used to indicate a use condition of the vehicle; a determining module configured to determine a target recommendation category to which the vehicle belongs among at least one preset recommendation category according to the historical feature information, each recommendation category corresponding to at least one mileage strategy; a pushing module configured to recommend at least one mileage strategy corresponding to the target recommendation category to the user.

8. An electronic device, comprising: The method comprises: a processor, and a memory connected with the processor in communication; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer readable storage medium, wherein, The computer readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the method of any one of claims 1-6.

10. A computer program product, wherein, The computer program product comprises a computer program stored in a computer readable storage medium, from which the computer program can be read by at least one processor, and the at least one processor, when executing the computer program, can implement the method of any one of claims 1-6.

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