Vehicle rental-extension information generation method and system based on vehicle positioning

Abstract

Provided in the embodiments of the present application are a vehicle rental-extension information generation method and system based on vehicle positioning. The method comprises: when the return time of a currently rented vehicle is less than a set time threshold, acquiring the return time, return location and positioning information of the rented vehicle during a rental period; on the basis of the positioning information, determining stay location points of the rented vehicle during the rental period, and the current real-time location point of the rented vehicle, and on the basis of building information within a preset range of the stay location points, predicting the vehicle-usage time interval of the rented vehicle; and on the basis of an association relationship between the return time and the vehicle-usage time interval and a positional relationship between the real-time location point and the return location, determining a vehicle rental-extension strategy, and on the basis of the vehicle rental-extension strategy and vehicle information of the rented vehicle, generating vehicle rental-extension information. The method improves the accuracy and rationality of vehicle rental-extension information.

Description

A method and system for generating vehicle lease renewal information based on vehicle location

[0001] This application claims priority to Chinese Patent Application No. 202510373906.6, filed with the Chinese Patent Office on March 27, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of data processing, and in particular to a method and system for generating vehicle rental renewal information based on vehicle location. Background Technology

[0003] In recent years, the car rental industry has shown a booming development trend. Whether it is for tourism, business activities or urban commuting, the demand for car rental is constantly growing. With the continuous expansion of the market size and the increase in the number of car rental companies, the industry competition is becoming increasingly fierce. In this environment, how to retain old users and improve users' willingness to renew their car rentals has become a key factor for car rental companies to maintain competitiveness and achieve sustainable development.

[0004] In related technologies, users' car rental needs are usually determined based on factors such as their historical car rental frequency and total rental duration. Corresponding renewal rewards or discounts are then generated based on these needs and sent to the renewal user's client. However, when determining car rental needs, only the impact of car usage demand within the historical rental period on vehicle renewal is considered, resulting in poor accuracy and rationality of the generated car rental information, which fails to truly meet actual car rental needs. Summary of the Invention

[0005] This application provides a method and system for generating vehicle rental renewal information based on vehicle location, solving the problem that the generated rental information is inaccurate and lacks reasonableness, failing to truly meet actual rental needs. By comprehensively analyzing the user's actual usage based on the actual return location, return time, and vehicle location information, vehicle rental renewal information containing corresponding vehicle rental renewal strategies is generated according to different usage situations, improving the accuracy and reasonableness of vehicle rental renewal information.

[0006] In a first aspect, embodiments of this application provide a method for generating vehicle rental extension information based on vehicle location, comprising:

[0007] If the current return time of a rental vehicle is less than a set time threshold, obtain the return time, return location, and location information of the rental vehicle during the rental period.

[0008] Based on the location information, the stopping point of the taxi during the rental period and its current real-time location are determined, and the usage time range of the taxi is predicted based on the building information within a preset range of the stopping point.

[0009] Based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location, a vehicle renewal strategy is determined, and vehicle renewal information is generated based on the vehicle renewal strategy and the vehicle information of the rental vehicle.

[0010] Optionally, determining the stopping location of the taxi vehicle during the rental period based on the location information includes:

[0011] According to a preset time interval, multiple vehicle markers of the taxi during the rental period are collected, and it is identified whether there are traffic light signs in the area to which each vehicle marker belongs. The vehicle markers corresponding to the areas where there are no traffic light signs are determined as target vehicle markers.

[0012] The target vehicle markers are aggregated based on their coordinates, and the stopping location of the taxi during the rental period is determined based on the aggregation result.

[0013] Optionally, the step of aggregating the target vehicle marker points based on their coordinates and determining the stopping location of the taxi vehicle during the rental period based on the aggregation result includes:

[0014] The target vehicle markers are aggregated based on their coordinates to obtain overlapping markers with the same location. The number of times the overlapping markers overlap is determined, and the overlapping markers with the number of overlaps greater than a preset threshold are identified as the stopping locations of the taxi during the rental period.

[0015] Optionally, the step of predicting the rental time interval of the taxi based on building information within a preset range of the stopping location includes:

[0016] Based on the building information within the preset range of the stop location, the number of building signs is counted, the building sign with the most signs is determined as the target building sign, and the time interval associated with the target building sign is determined as the rental time interval of the taxi.

[0017] Optionally, determining the vehicle rental extension strategy based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location, includes:

[0018] A first renewal probability is determined based on the correlation between the return time and the usage time interval. A second renewal probability is determined based on the positional relationship between the real-time location and the return location. A target renewal probability is calculated based on the first renewal probability, the second renewal probability, and a vehicle renewal strategy corresponding to the target renewal probability is determined.

[0019] Optionally, determining the first renewal probability based on the correlation between the return time and the usage time interval includes:

[0020] If the rental time interval includes the return time, and the return time includes a continuous period after the return time, the first renewal probability is determined to be a first preset value.

[0021] If the rental time interval does not include the return time or the rental time interval does not include a continuous period after the return time, the first renewal probability is determined to be a second preset value, where the first preset value is greater than the second preset value.

[0022] Optionally, determining the second rental extension probability based on the location relationship between the real-time location and the return location includes:

[0023] Vehicle navigation information is generated based on the real-time location and the location of the return location, and the estimated arrival time is determined based on the navigation information.

[0024] Based on the estimated arrival time and the return time, it is determined whether the rental vehicle meets the return conditions. If the rental vehicle meets the return conditions, the second renewal probability is determined to be a second preset value. If the rental vehicle does not meet the return conditions, the second renewal probability is determined to be a first preset value, where the first preset value is greater than the second preset value.

[0025] In a second aspect, embodiments of this application provide a vehicle rental extension information generation system based on vehicle location, comprising:

[0026] The information acquisition module is used to acquire the return time, return location, and location information of the rental vehicle during the rental period when the current return time of the rental vehicle is less than a set time threshold.

[0027] The location determination module is used to determine the stopping location of the taxi vehicle during the rental period and its current real-time location based on the location information.

[0028] The vehicle usage time interval determination module is used to predict the vehicle usage time interval of the taxi based on building information within a preset range of the stop location.

[0029] The vehicle rental strategy determination module is used to determine the vehicle rental strategy based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location.

[0030] The vehicle rental renewal information generation module is used to generate vehicle rental renewal information based on the vehicle rental renewal strategy and the vehicle information of the rental vehicle.

[0031] In a third aspect, embodiments of this application provide an electronic device, the device comprising: one or more processors; and a storage device configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle rental information generation method based on vehicle location as described in the first aspect.

[0032] In a fourth aspect, embodiments of this application provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the vehicle rental information generation method based on vehicle location as described in the first aspect. Attached Figure Description

[0033] Figure 1 is a flowchart of a method for generating vehicle rental renewal information based on vehicle positioning according to an embodiment of this application;

[0034] Figure 2 is a schematic diagram of a preset range of a stopping position provided in an embodiment of this application;

[0035] Figure 3 is a flowchart of a method for determining a stopping point according to an embodiment of this application;

[0036] Figure 4 is a flowchart of a method for determining a first renewal probability provided in an embodiment of this application;

[0037] Figure 5 is a flowchart of a method for determining a second renewal probability provided in an embodiment of this application;

[0038] Figure 6 is a schematic diagram of a vehicle rental extension information generation system based on vehicle positioning provided in an embodiment of this application;

[0039] Figure 7 is a schematic diagram of a vehicle rental renewal information generation device based on vehicle positioning provided in an embodiment of this application. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0041] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0042] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0043] The following description, in conjunction with the accompanying drawings, details a method and system for generating vehicle rental renewal information based on vehicle location, through specific embodiments and application scenarios.

[0044] The vehicle rental renewal information generation method based on vehicle positioning provided in this application embodiment is used in scenarios for monitoring and managing rented vehicles. Based on the above application scenario, it can be understood that the execution subject of this solution can be a server.

[0045] Figure 1 is a flowchart of a method for generating vehicle rental extension information based on vehicle location according to an embodiment of this application. As shown in Figure 1, it includes:

[0046] Step S101: If the current return time of the rental vehicle is less than the set time threshold, obtain the return time, return location, and location information of the rental vehicle during the rental period.

[0047] In this context, "currently rented vehicles" refers to all vehicles currently rented out by the vehicle operator. The return time of each currently rented vehicle refers to the time pre-agreed between the vehicle operator and the renter before the rental. Similarly, the return location is also pre-agreed upon by both parties. Location information during the rental period is obtained through satellite positioning technology from the BeiDou Navigation Satellite System. In one embodiment, the return times of all currently rented vehicles are acquired, and the time difference between each return time and the current time is determined. Vehicles with a time difference less than a pre-set threshold are selected. The return time, return location, and location information during the rental period corresponding to the vehicle's identifier (such as license plate number or vehicle identification number) are then retrieved.

[0048] Step S102: Based on the location information, determine the stopping point of the taxi vehicle during the rental period and its current real-time location, and predict the rental time interval of the taxi vehicle based on the building information within a preset range of the stopping point.

[0049] The designated stopping point during the rental period can be any parking spot where the taxi stays for more than a preset time threshold during the rental period. The real-time location point refers to the current location of the vehicle. The preset range represents the area of ​​the building information identification region; the preset range for the stopping point can be the building information identification range centered on the stopping point and determined according to the area of ​​the preset identification region. Building information includes relevant data and descriptions of the building, such as its style, signage, and purpose. The rental time interval refers to the predicted time range with high rental frequency, such as holidays or weekdays.

[0050] In one embodiment, based on the location information of the rental vehicle's parking location during the rental period and its current location, Figure 2 is a schematic diagram of a preset range of stopping locations provided in this application embodiment. As shown in Figure 2, the target detection range, i.e., the preset range of stopping locations, is determined based on the area of ​​the preset detection range centered on the parking location. The uses of all buildings within the preset range of stopping locations are identified, and the use of the building with the highest frequency of occurrence is determined as the vehicle's use. The usage time interval of the rental vehicle associated with this vehicle use is then determined. For example, if a factory used for industrial production is repeatedly identified within the target detection range, it can be determined that the rental vehicle may be used for transporting goods to the factory or for factory employees to commute. Therefore, the pre-obtained factory working hours can be determined as the usage time of the rental vehicle, i.e., the usage time interval of the rental vehicle.

[0051] Optionally, the step of predicting the rental time interval of the taxi based on building information within a preset range of the stopping location includes:

[0052] Based on the building information within the preset range of the stop location, the number of building signs is counted, the building sign with the most signs is determined as the target building sign, and the time interval associated with the target building sign is determined as the rental time interval of the taxi.

[0053] Building signage refers to names or symbols used to identify, indicate, and explain building information in and around a building, such as scenic spots, schools, and office buildings. In one embodiment, the number of building signs within a preset range of each stop location is counted, along with the total number of all building signs. The building sign with the highest total number is then identified, and the time interval associated with that building sign is determined as the taxi's usage time interval. For example, if scenic spot signs exist within the preset range of each stop location, then the scenic spot sign is identified as the target building sign, and the time interval associated with the scenic spot sign is determined as the taxi's usage time interval, such as a holiday period.

[0054] This application embodiment counts the number of building signs within a preset range of the stop location, identifies the building sign with the most occurrences as the target building sign, and determines the time interval associated with the target building sign as the rental time interval for the taxi. This solution improves the accuracy of determining the rental time interval by statistically analyzing the frequency of building signs within the preset range of the stop location and determining the corresponding rental time interval based on the statistical results.

[0055] Step S103: Determine the vehicle rental extension strategy based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location, and generate vehicle rental extension information based on the vehicle rental extension strategy and the vehicle information of the rental vehicle.

[0056] The relationship between return time and usage time interval is used to indicate the connection between the two. This can be an inclusion relationship or an adjacent relationship. The vehicle renewal strategy indicates the incentives offered by the vehicle operator for vehicle renewal, which may include discounts or coupons to attract renters to extend their rentals. Vehicle information refers to basic vehicle details such as vehicle type, license plate number, color, and passenger capacity. Vehicle renewal information is used to remind renters to extend or return the vehicle, and may include vehicle information and renewal strategies.

[0057] In one embodiment, the probability of renewing the rental is determined based on the correlation between the return time and the usage time interval. If the correlation is that the return time and usage time interval do not overlap at all, the user is unlikely to renew the rental after returning the car. If the correlation is that the return time and usage time interval overlap, the user is likely to continue renting the car. The return distance between the real-time location and the return location is determined based on the coordinates of the real-time location and the coordinates of the return location. The return distance is compared with a preset distance threshold. If the return distance is greater than the preset distance threshold, the user is likely to continue renting the car. If the return distance is less than or equal to the preset distance threshold, the user is about to return the car and is unlikely to continue renting the car. Based on the above rental probability judgment results, the overall rental renewal probability is evaluated, and a corresponding vehicle rental renewal strategy is generated based on the evaluation results. For example, if the relationship is such that the return time and the usage time interval do not overlap at all, the corresponding renewal probability is 0. If the relationship is such that the return time and the usage time interval overlap, the corresponding renewal probability is 50%. If the return distance is greater than a preset distance threshold, the corresponding renewal probability is 50%. If the return distance is less than or equal to the preset distance threshold, the corresponding renewal probability is 0. The overall renewal probability is determined based on the relationship between the return time and the usage time interval, as well as the positional relationship between the real-time location and the return location. The corresponding vehicle renewal strategy is determined based on the relationship between the preset renewal probability and the vehicle renewal strategy. The vehicle renewal strategy is then bound to the vehicle information of the rental vehicle to generate vehicle renewal information.

[0058] In this embodiment, when the current return time of a rental vehicle is less than a set time threshold, the system obtains the return time, return location, and location information of the rental vehicle during the rental period. Based on the location information, it determines the stopping location and the current real-time location of the rental vehicle during the rental period, and predicts the usage time interval of the rental vehicle based on the building information within a preset range of the stopping location. Based on the correlation between the return time and the usage time interval, and the positional relationship between the real-time location and the return location, it determines the vehicle renewal strategy, and generates vehicle renewal information based on the vehicle renewal strategy and the vehicle information of the rental vehicle. In the above scheme, by determining the vehicle's stopping location and identifying the surrounding building information, the rental time interval of the taxi is determined based on the building information. This allows for analysis of the taxi's purpose and corresponding rental time interval based on the activity data of the taxi during the rental period, improving the accuracy of determining the rental time interval. Furthermore, by determining the vehicle renewal strategy based on the correlation between the return time and the rental time interval, as well as the positional relationship between the real-time location and the return location, the scheme comprehensively considers the impact of vehicle usage time and the positional relationship between the real-time location and the return location on generating the renewal strategy, improving the rationality of the vehicle renewal strategy. Consequently, this also improves the accuracy and rationality of the vehicle renewal information.

[0059] Optionally, determining the vehicle rental extension strategy based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location, includes:

[0060] A first renewal probability is determined based on the correlation between the return time and the usage time interval. A second renewal probability is determined based on the positional relationship between the real-time location and the return location. A target renewal probability is calculated based on the first renewal probability, the second renewal probability, and a vehicle renewal strategy corresponding to the target renewal probability is determined.

[0061] The first renewal probability represents the influence of the relationship between the return time and the usage time interval on the user's decision to renew the rental. The second renewal probability represents the influence of the location relationship between the real-time location and the return location on the user's decision to renew the rental. The target renewal probability represents the overall influence of the current status of the rental vehicle on the user's decision to renew the rental.

[0062] For example, if the rental time interval includes the return time, the first renewal probability is determined to be 60%; if the rental time interval does not include the return time, the first renewal probability is determined to be 40%. If the real-time location and the return location do not overlap, and the distance between the two points is greater than a preset distance threshold, the second renewal probability is determined to be 80%; if the real-time location and the return location do not overlap, the second renewal probability is 20%. If the current status of the rental vehicle is that the rental time interval includes the return time, the real-time location and the return location do not overlap, and the distance between the two points is greater than a preset distance threshold, the first renewal probability is determined to be 50%, the second renewal probability is determined to be 80%, and the preset weight ratio is 4:6. Then, based on the first renewal probability, the second renewal probability, and the preset weight ratio, the target renewal probability is calculated as: (60%*40%+80%*60%)100%=72%. The target renewal probability level is determined according to the preset target renewal probability level table, and the vehicle renewal strategy associated with that level is determined.

[0063] This application's embodiments determine a first renewal probability based on the correlation between the return time and the usage time interval, and a second renewal probability based on the location relationship between the real-time location and the return location. A target renewal probability is calculated based on the first and second renewal probabilities and a preset weighting ratio, and a vehicle renewal strategy corresponding to the target renewal probability is determined. In the above scheme, the vehicle renewal strategy is determined based on the influence of the correlation between the return time and the usage time interval and the location relationship between the real-time location and the return location on the target renewal probability, respectively, thus improving the accuracy of both the target renewal probability and the vehicle renewal strategy.

[0064] Figure 3 is a flowchart of a method for determining a stopping point according to an embodiment of this application. As shown in Figure 3, it includes:

[0065] Step S1021: Collect multiple vehicle markers of the taxi during the rental period according to a preset time interval, identify whether there are traffic light signs in the area to which each vehicle marker belongs, and determine the vehicle markers corresponding to the areas where there are no traffic light signs as target vehicle markers.

[0066] The vehicle marker is used to indicate the vehicle's position at a specific point in time and can be represented by location coordinates. The area to which the vehicle marker belongs refers to the traffic light recognition range determined centered on the vehicle marker, and the size of this range can be preset according to requirements. The target vehicle marker is a vehicle marker where there are no traffic lights nearby. In one embodiment, multiple vehicle markers of the taxi during the rental period are collected according to a preset time interval. For example, one vehicle marker is collected every hour, resulting in multiple vehicle markers. The area to which each vehicle marker belongs is determined based on the size of the preset traffic light recognition range, centered on each vehicle marker. It is then identified whether there are traffic light markers in the area to which each vehicle marker belongs. If there are traffic light markers, it can be determined that the taxi stopped because it was waiting for a traffic light. This stopping point has no impact on the determination of the rental time interval, and the vehicle markers corresponding to the area containing traffic light markers can be ignored. The vehicle markers corresponding to the area not containing traffic light markers are determined as the target vehicle markers.

[0067] Step S1022: Aggregate the target vehicle marker points according to their coordinates, and determine the stopping location of the taxi vehicle during the rental period based on the aggregation result.

[0068] Aggregation processing of target vehicle markers refers to the process of summarizing, statistically analyzing, or grouping target vehicle markers. In one embodiment, target vehicle markers are aggregated based on their coordinates to obtain overlapping target vehicle markers, and these overlapping target vehicle markers are identified as the stopping locations of the taxis during the rental period.

[0069] This application embodiment collects multiple vehicle markers of taxis during their rental period at preset time intervals, identifies whether traffic light signs exist in the area to which each vehicle marker belongs, and determines the vehicle markers corresponding to areas without traffic light signs as target vehicle markers. The target vehicle markers are then aggregated based on their coordinates, and the stopping location of the taxi during the rental period is determined based on the aggregation result. In the above scheme, by identifying traffic light signs within the areas to which multiple vehicle markers belong, the target vehicle markers are determined, reducing the impact of irrelevant vehicle markers on determining the rental time interval, improving the efficiency of the aggregation process, and increasing the accuracy of determining the rental time interval.

[0070] Optionally, the step of aggregating the target vehicle marker points based on their coordinates and determining the stopping location of the taxi vehicle during the rental period based on the aggregation result includes:

[0071] The target vehicle markers are aggregated based on their coordinates to obtain overlapping markers with the same location. The number of times the overlapping markers overlap is determined, and the overlapping markers with the number of overlaps greater than a preset threshold are identified as the stopping locations of the taxi during the rental period.

[0072] In one embodiment, target vehicle markers are aggregated based on their coordinates to determine overlapping locations. The number of overlapping markers at these locations is then determined, i.e., the overlap count. A higher overlap count indicates a longer stay at the target vehicle marker, and vice versa. Overlapping markers with an overlap count greater than a preset threshold are identified as the taxi's stops during the rental period. In another possible embodiment, a pre-defined range for overlap counts is set, and overlapping markers with overlap counts within this range are identified as the taxi's stops during the rental period.

[0073] This application embodiment aggregates the target vehicle markers based on their coordinates to obtain overlapping markers with matching locations. It then determines the number of times these overlapping markers match, and identifies the overlapping markers with a number of matches greater than a preset threshold as the stopping points of the taxi during the rental period. This aggregation process improves the accuracy of determining stopping points by identifying overlapping markers with a high number of matches.

[0074] Figure 4 is a flowchart of a method for determining a first renewal probability provided in an embodiment of this application. As shown in Figure 4, it includes:

[0075] Step S201: If the rental time interval includes the return time, and the return time includes a continuous period after the return time, determine the first renewal probability as a first preset value.

[0076] For example, if the car return time is Wednesday, and the car usage time range is Monday to Friday, the car usage time range includes the car return time, and also includes Thursday and Friday after the car return time on Wednesday. In this case, it can be assumed that the car rental user uses the car more frequently from Monday to Friday. Although Wednesday is the car return time, the probability that the user will continue to rent the vehicle on Thursday and Friday is relatively high. In this case, the first renewal probability can be determined to be 1.

[0077] Step S202: If the rental time interval does not include the return time or the rental time interval does not include a continuous period after the return time, determine the first renewal probability as a second preset value, wherein the first preset value is greater than the second preset value.

[0078] For example, if the car return time is Wednesday and the car usage time range is Saturday and Sunday, or if the car return time is Wednesday and the car usage time range is Monday to Wednesday, but the car usage time range does not include the continuous period after the car return time on Wednesday, it can be assumed that the car rental user uses the car more frequently from Monday to Wednesday. Although Wednesday is the car return time, which meets the car rental user's vehicle usage needs, the car rental user is unlikely to renew the lease. In this case, the first renewal probability can be determined to be 0.

[0079] In this embodiment, when the rental time interval includes the return time, and the return time includes a continuous period following the return time, the first renewal probability is determined to be a first preset value. When the rental time interval does not include the return time, or the rental time interval does not include a continuous period following the return time, the first renewal probability is determined to be a second preset value. By determining whether the rental time interval includes the return time and whether the return time includes a continuous period following the return time, the accuracy of the first renewal probability is improved.

[0080] Figure 5 is a flowchart of a method for determining a second renewal probability provided in an embodiment of this application. As shown in Figure 5, it includes:

[0081] Step S301: Generate vehicle navigation information based on the real-time location and the location of the return location, and determine the estimated arrival time based on the navigation information.

[0082] Step S302: Determine whether the rental vehicle meets the return conditions based on the estimated arrival time and the return time. If the rental vehicle meets the return conditions, determine the second renewal probability as a second preset value. If the rental vehicle does not meet the return conditions, determine the second renewal probability as a first preset value. The first preset value is greater than the second preset value.

[0083] Vehicle navigation information refers to providing vehicles with information such as location, driving route, and traffic conditions through technologies such as satellite positioning systems and geographic information systems to help drivers reach their destination accurately and efficiently. Optionally, the navigation information may also include information such as the estimated time of arrival at the destination and the vehicle's average speed. In one embodiment, the estimated time of arrival at the return location is determined based on the vehicle navigation information, and it is determined whether the estimated arrival time is before the agreed return time. If the estimated arrival time is before the agreed return time, it is determined that the car rental user intends to return the car, and the second renewal probability can be determined to be 0. If the estimated arrival time is after the agreed return time, it can be understood that the car rental user cannot return the car at the agreed return time and may intend to renew the rental, and the second renewal probability can be determined to be 1.

[0084] This application's embodiment generates vehicle navigation information based on the real-time location and the return location, and determines the estimated arrival time based on the navigation information. It then determines whether the rental vehicle meets the return conditions based on the estimated arrival time and the return time. If the rental vehicle meets the return conditions, a second extension probability is determined as a second preset value; otherwise, the second extension probability is determined as a first preset value. This method improves the accuracy of the second extension probability by determining whether the renter will return the vehicle within the agreed return time.

[0085] Figure 6 is a schematic diagram of a vehicle rental extension information generation system based on vehicle positioning provided in an embodiment of this application. As shown in Figure 6, it includes:

[0086] The information acquisition module 41 is configured to acquire the return time, return location, and location information of the rental vehicle during the rental period when the current return time of the rental vehicle is less than a set time threshold.

[0087] The location determination module 42 is configured to determine the stopping location of the taxi vehicle during the rental period and its current real-time location based on the location information.

[0088] The vehicle usage time interval determination module 43 is configured to predict the vehicle usage time interval of the taxi based on the building information within a preset range of the stop location.

[0089] The vehicle rental strategy determination module 44 is configured to determine the vehicle rental strategy based on the correlation between the return time and the usage time interval, and the positional relationship between the real-time location and the return location.

[0090] The vehicle rental renewal information generation module 45 is configured to generate vehicle rental renewal information based on the vehicle rental renewal strategy and the vehicle information of the rental vehicle.

[0091] In this embodiment, when the current return time of a rental vehicle is less than a set time threshold, the system obtains the return time, return location, and location information of the rental vehicle during the rental period. Based on the location information, it determines the stopping location and the current real-time location of the rental vehicle during the rental period, and predicts the usage time interval of the rental vehicle based on the building information within a preset range of the stopping location. Based on the correlation between the return time and the usage time interval, and the positional relationship between the real-time location and the return location, it determines the vehicle renewal strategy, and generates vehicle renewal information based on the vehicle renewal strategy and the vehicle information of the rental vehicle. In the above scheme, by determining the vehicle's stopping location and identifying the surrounding building information, the rental time interval of the taxi is determined based on the building information. This allows for analysis of the taxi's purpose and corresponding rental time interval based on the activity data of the taxi during the rental period, improving the accuracy of determining the rental time interval. Furthermore, by determining the vehicle renewal strategy based on the correlation between the return time and the rental time interval, as well as the positional relationship between the real-time location and the return location, the scheme comprehensively considers the impact of vehicle usage time and the positional relationship between the real-time location and the return location on generating the renewal strategy, improving the rationality of the vehicle renewal strategy. Consequently, this also improves the accuracy and rationality of the vehicle renewal information.

[0092] In one possible embodiment, the location point determination module 42 is specifically configured as follows:

[0093] According to a preset time interval, multiple vehicle markers of the taxi during the rental period are collected, and it is identified whether there are traffic light signs in the area to which each vehicle marker belongs. The vehicle markers corresponding to the areas where there are no traffic light signs are determined as target vehicle markers.

[0094] The target vehicle markers are aggregated based on their coordinates, and the stopping location of the taxi during the rental period is determined based on the aggregation result.

[0095] In one possible embodiment, the location point determination module 42 is further configured as follows:

[0096] The target vehicle markers are aggregated based on their coordinates to obtain overlapping markers with the same location. The number of times the overlapping markers overlap is determined, and the overlapping markers with the number of overlaps greater than a preset threshold are identified as the stopping locations of the taxi during the rental period.

[0097] In one possible embodiment, the vehicle usage time interval determination module 43 is specifically configured as follows:

[0098] Based on the building information within the preset range of the stop location, the number of building signs is counted, the building sign with the most signs is determined as the target building sign, and the time interval associated with the target building sign is determined as the rental time interval of the taxi.

[0099] In one possible embodiment, the vehicle lease renewal strategy determination module 44 is specifically configured as follows:

[0100] A first renewal probability is determined based on the correlation between the return time and the usage time interval. A second renewal probability is determined based on the positional relationship between the real-time location and the return location. A target renewal probability is calculated based on the first renewal probability, the second renewal probability, and a vehicle renewal strategy corresponding to the target renewal probability is determined.

[0101] In one possible embodiment, the vehicle lease renewal strategy determination module 44 is further configured as follows:

[0102] If the rental time interval includes the return time, and the return time includes a continuous period after the return time, the first renewal probability is determined to be a first preset value.

[0103] If the rental time interval does not include the return time or the rental time interval does not include a continuous period after the return time, the first renewal probability is determined to be a second preset value, where the first preset value is greater than the second preset value.

[0104] In one possible embodiment, the vehicle lease renewal strategy determination module 44 is further configured as follows:

[0105] Vehicle navigation information is generated based on the real-time location and the location of the return location, and the estimated arrival time is determined based on the navigation information.

[0106] Based on the estimated arrival time and the return time, it is determined whether the rental vehicle meets the return conditions. If the rental vehicle meets the return conditions, the second renewal probability is determined to be a second preset value. If the rental vehicle does not meet the return conditions, the second renewal probability is determined to be a first preset value, where the first preset value is greater than the second preset value.

[0107] This application also provides a vehicle location-based vehicle rental information generation device, which can be integrated with a vehicle location-based vehicle rental information generation system provided in this application. Figure 7 is a schematic diagram of the structure of a vehicle location-based vehicle rental information generation device provided in this application. Referring to Figure 7, the vehicle location-based vehicle rental information generation device includes: an input device 53, an output device 54, a memory 52, and one or more processors 51; the memory 52 is used to store one or more programs; when one or more programs are executed by one or more processors 51, the one or more processors 51 implement the vehicle location-based vehicle rental information generation method provided in the above embodiments. The input device 53, output device 54, memory 52, and processor 51 can be connected by a bus or other means, as shown in Figure 7, which is an example of connection via a bus. The memory 52, as a computing device readable storage medium, can be used to store software programs, computer executable programs, and modules, such as the program instructions / modules corresponding to the vehicle location-based vehicle rental information generation method provided in any embodiment of this application. The memory 52 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on the use of the device. Furthermore, the memory 52 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 52 may further include memory remotely located relative to the processor 51, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] Input device 53 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 54 may include display devices such as a display screen.

[0109] The processor 51 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 52, thereby realizing the above-mentioned method for generating vehicle rental information based on vehicle positioning.

[0110] The vehicle location-based vehicle rental information generation system, device, and computer provided above can be used to execute the vehicle location-based vehicle rental information generation method provided in any of the above embodiments, and have corresponding functions and beneficial effects.

[0111] This application embodiment also provides a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to perform the vehicle rental extension information generation method based on vehicle location provided in the above embodiment. The vehicle rental extension information generation method based on vehicle location includes:

[0112] If the current return time of a rental vehicle is less than a set time threshold, obtain the return time, return location, and location information of the rental vehicle during the rental period.

[0113] Based on the location information, the stopping point of the taxi during the rental period and its current real-time location are determined, and the usage time range of the taxi is predicted based on the building information within a preset range of the stopping point.

[0114] Based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location, a vehicle renewal strategy is determined, and vehicle renewal information is generated based on the vehicle renewal strategy and the vehicle information of the rental vehicle.

[0115] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROMs, floppy disks, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disks or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. Furthermore, storage media may reside in a first computer system in which a program is executed, or may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). Storage media may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0116] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the vehicle rental information generation method based on vehicle location as described above, but can also execute related operations in the vehicle rental information generation method based on vehicle location provided in any embodiment of this application.

[0117] The vehicle location-based vehicle rental information generation system, device, and storage medium provided in the above embodiments can execute the vehicle location-based vehicle rental information generation method provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the vehicle location-based vehicle rental information generation method provided in any embodiment of this application.

[0118] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method for generating vehicle rental renewal information based on vehicle location, wherein, include: If the current return time of a rental vehicle is less than a set time threshold, obtain the return time, return location, and location information of the rental vehicle during the rental period. Based on the location information, the stopping point of the taxi during the rental period and its current real-time location are determined, and the usage time range of the taxi is predicted based on the building information within a preset range of the stopping point. Based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location, a vehicle renewal strategy is determined, and vehicle renewal information is generated based on the vehicle renewal strategy and the vehicle information of the rental vehicle.

2. The method for generating vehicle rental extension information based on vehicle positioning according to claim 1, wherein, Determining the stopping location of the taxi vehicle during the rental period based on the location information includes: According to a preset time interval, multiple vehicle markers of the taxi during the rental period are collected, and it is identified whether there are traffic light signs in the area to which each vehicle marker belongs. The vehicle markers corresponding to the areas where there are no traffic light signs are determined as target vehicle markers. The target vehicle markers are aggregated based on their coordinates, and the stopping location of the taxi during the rental period is determined based on the aggregation result.

3. The method for generating vehicle rental renewal information based on vehicle positioning according to claim 2, wherein, The step of aggregating the target vehicle marker points based on their coordinates, and determining the stopping location of the taxi vehicle during the rental period based on the aggregation result, includes: The target vehicle markers are aggregated based on their coordinates to obtain overlapping markers with the same location. The number of times the overlapping markers overlap is determined, and the overlapping markers with the number of overlaps greater than a preset threshold are identified as the stopping locations of the taxi during the rental period.

4. The method for generating vehicle rental extension information based on vehicle location according to any one of claims 1-3, wherein, The step of predicting the rental time range of the taxi based on building information within a preset range of the stopping location includes: Based on the building information within the preset range of the stop location, the number of building signs is counted, the building sign with the most signs is determined as the target building sign, and the time interval associated with the target building sign is determined as the rental time interval of the taxi.

5. The method for generating vehicle rental extension information based on vehicle location according to any one of claims 1-4, wherein, The step of determining the vehicle rental extension strategy based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location, includes: A first renewal probability is determined based on the correlation between the return time and the usage time interval. A second renewal probability is determined based on the positional relationship between the real-time location and the return location. A target renewal probability is calculated based on the first renewal probability, the second renewal probability, and a vehicle renewal strategy corresponding to the target renewal probability is determined.

6. The method for generating vehicle rental extension information based on vehicle positioning according to claim 5, wherein, The step of determining the first renewal probability based on the correlation between the return time and the usage time interval includes: If the rental time interval includes the return time, and the return time includes a continuous period after the return time, the first renewal probability is determined to be a first preset value. If the rental time interval does not include the return time or the rental time interval does not include a continuous period after the return time, the first renewal probability is determined to be a second preset value, where the first preset value is greater than the second preset value.

7. The method for generating vehicle rental extension information based on vehicle location according to claim 5 or 6, wherein, The step of determining the second rental extension probability based on the location relationship between the real-time location and the return location includes: Vehicle navigation information is generated based on the real-time location and the location of the return location, and the estimated arrival time is determined based on the navigation information. Based on the estimated arrival time and the return time, it is determined whether the rental vehicle meets the return conditions. If the rental vehicle meets the return conditions, the second renewal probability is determined to be a second preset value. If the rental vehicle does not meet the return conditions, the second renewal probability is determined to be a first preset value, where the first preset value is greater than the second preset value.

8. A vehicle rental extension information generation system based on vehicle location, wherein, include: The information acquisition module is configured to acquire the return time, return location, and location information of the rental vehicle during the rental period when the current return time of the rental vehicle is less than a set time threshold. The location determination module is configured to determine the stopping location of the taxi vehicle during the rental period and its current real-time location based on the location information. The vehicle usage time interval determination module is configured to predict the vehicle usage time interval of the taxi based on building information within a preset range of the stop location. The vehicle rental strategy determination module is configured to determine the vehicle rental strategy based on the correlation between the return time and the usage time interval, and the location relationship between the real-time location and the return location. The vehicle rental renewal information generation module is configured to generate vehicle rental renewal information based on the vehicle rental renewal strategy and the vehicle information of the rental vehicle.

9. An electronic device, the device comprising: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the vehicle location-based vehicle rental information generation method as described in any one of claims 1-7.

10. A storage medium storing computer-executable instructions, which, when executed by a computer processor, are used to perform the vehicle rental extension information generation method based on vehicle location as described in any one of claims 1-7.

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