A method and system for integrating driving data and map data based on Internet of Vehicles

Abstract

The present invention discloses a method for fusing driving data and map data based on the Internet of Vehicles, which comprises: step S10, receiving real vehicle T-BOX data reported through the Internet of Vehicles; step S11, using a map to perform trajectory correction processing on the reported real vehicle trajectory data to obtain the user's real vehicle driving trajectory; step S12, projecting the user's real vehicle driving trajectory onto the map road network to obtain the user's driving road trajectory based on the map road network trajectory; step S13, associating the road attribute data in the map with the user's driving road trajectory; step S14, associating the real-time vehicle data in the real vehicle T-BOX data with the user's driving road trajectory; step S15, responding to custom selection information, performing cross-calculations, and realizing visual multi-dimensional display. The present invention also discloses a corresponding system. Implementation of the present invention can achieve low-cost, simple, and highly reliable acquisition and analysis of big data used by users.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition in the vehicle development process, and in particular to a method and system for fusing driving data and map data based on an Internet of Vehicles. Background Art

[0002] Currently, domestic car companies often refer to or directly quote foreign test methods when formulating vehicle durability test specifications. They rarely obtain actual user road attribute data and vehicle data for research. Due to limited human, material and financial resources, they usually use small samples for random sampling surveys in the field of vehicle testing, and use the results of small sample sizes as the basis for planning vehicle durability tests. The road attribute data and vehicle data obtained by this method are difficult to represent the general use of domestic users and have limited relevance.

[0003] Due to the wide distribution of actual users, on-site user surveys are difficult, and actual user usage data is difficult to quantify. Factors such as the distribution of actual roads driven by users in different regions, the selection of road grade ratios, road congestion conditions, user speed distribution, gear changes, etc. are relatively complex. Therefore, it is difficult to ensure the accuracy of the survey data. Obtaining more comprehensive information on actual user usage requires a large workload, high investment, and insufficient coverage.

[0004] In addition, changes in domestic road conditions, vehicle power parameters, driving habits, and regulations will affect the operating conditions of users' actual vehicles, thereby causing changes in user usage information. Traditional market research methods are unable to track and adapt to changes in user usage conditions.

[0005] Therefore, it is necessary to develop a system and method for intelligently identifying user-related road attribute data and vehicle information, analyze user information-related roads through road attribute data, analyze the actual usage characteristics of domestic users, and formulate a vehicle durability test plan based on user data. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method and system for fusing driving data and map data based on the Internet of Vehicles, which can provide objective and reliable data basis for the planning of vehicle durability tests.

[0007] To solve the above technical problems, as one aspect of the present invention, a method for fusing driving data and map data based on the Internet of Vehicles is provided, which comprises the following steps:

[0008] Step S10: receiving real vehicle T-BOX data reported by the selected vehicle via the Internet of Vehicles, wherein the real vehicle T-BOX data includes at least real-time vehicle data and original real vehicle trajectory data, wherein the real-time vehicle data includes at least vehicle speed, gear position, accelerator pedal opening, brake pedal opening, component temperature, and power data, and the original real vehicle trajectory data includes positioning point information sampled at a predetermined frequency;

[0009] Step S11, using a map to perform trajectory correction processing on the reported actual vehicle trajectory data to obtain the user's actual vehicle driving trajectory;

[0010] Step S12, projecting the user's actual vehicle driving trajectory onto the map road network, performing correlation fitting with the road network trajectory on the map, and obtaining the user's driving road trajectory based on the map road network trajectory;

[0011] Step S13: Associating the road attribute data stored in the map with the user's driving trajectory, wherein the road attribute data includes: region, road grade, slope, altitude, road surface smoothness, number of lanes, congestion, and weather conditions;

[0012] Step S14, based on the correspondence between the user's actual vehicle driving trajectory and the user's driving road trajectory, the real-time vehicle data in the actual vehicle T-BOX data is associated with the user's driving road trajectory;

[0013] Step S15 , performing a visual multi-dimensional display in response to at least part of the user's customized selected driving road trajectory, road attribute data, and vehicle real-time data.

[0014] Wherein, the step S10 further includes:

[0015] Receive the real vehicle T-BOX data reported by the selected vehicle through the vehicle network, and decode the real vehicle T-BOX data to obtain the real-time vehicle data corresponding to each positioning point information in the original real vehicle trajectory data.

[0016] Wherein, the step S11 further includes:

[0017] Selecting a map that covers all positioning point information in the original real vehicle trajectory data;

[0018] All the positioning point information in the original real vehicle trajectory data is normalized with the positioning network library in the map, the positioning point information with abnormal jumps is cleaned, and each abnormal jump positioning point information is replaced with the average value of the positioning point information before and after it, so as to obtain the user's actual vehicle driving trajectory.

[0019] Wherein, in the step S12, the user's driving road trajectory includes a plurality of map positioning point information, and the corresponding relationship between each positioning point in the user's actual vehicle driving trajectory and each map positioning point in the user's driving road trajectory.

[0020] Wherein, the step S13 further includes:

[0021] The road attribute data corresponding to each map location point of the user's driving road trajectory is obtained through the map data API interface, and the road attribute data is marked on the corresponding map location point.

[0022] Wherein, the step S14 further includes:

[0023] According to the correspondence between each positioning point in the user's actual vehicle driving trajectory and each map positioning point in the user's driving road trajectory, the real-time vehicle data of each positioning point in the actual vehicle T-BOX data is marked on the corresponding map positioning point in the user's driving road trajectory.

[0024] Accordingly, another aspect of the present invention further provides a system for integrating driving data and map data based on the Internet of Vehicles, comprising:

[0025] a raw data collection unit, configured to receive real-vehicle T-BOX data reported by a selected vehicle via the Internet of Vehicles, the real-vehicle T-BOX data comprising at least real-time vehicle data and raw real-vehicle trajectory data, wherein the real-time vehicle data comprises at least vehicle speed, gear position, accelerator pedal opening, brake pedal opening, component temperature, and power data, and the raw real-vehicle trajectory data comprises positioning point information sampled at a predetermined frequency;

[0026] A deviation correction processing unit, configured to perform a deviation correction process on the reported actual vehicle trajectory data using a map to obtain the actual vehicle driving trajectory of the user;

[0027] A user driving road trajectory obtaining unit is used to project the user's actual vehicle driving trajectory onto a map road network, perform correlation fitting with the road network trajectory on the map, and obtain the user's driving road trajectory based on the map road network trajectory;

[0028] A first data association unit is configured to associate pre-stored road attribute data in the map road network with the user's driving road trajectory, wherein the road attribute data includes: region, road grade, slope, altitude, road surface smoothness, number of lanes, congestion, and weather conditions;

[0029] a second data association unit, configured to associate the real-time vehicle data in the real vehicle T-BOX data with the user's driving road trajectory based on a correspondence between the user's real vehicle driving trajectory and the user's driving road trajectory;

[0030] The visual display unit is used to respond to at least part of the information in the user's customized selected driving road trajectory, road attribute data and vehicle real-time data, perform cross calculations, and realize visual multi-dimensional display.

[0031] Wherein, the correction processing unit further includes:

[0032] A map selection unit, configured to select a map having information covering all positioning points in the original real vehicle trajectory data;

[0033] The cleaning processing unit is used to normalize all the positioning point information in the original real vehicle trajectory data with the positioning network library in the map, clean the positioning point information with abnormal jumps, and replace each abnormal jump positioning point information with the average value of the positioning point information before and after it, so as to obtain the user's real vehicle driving trajectory.

[0034] Which further includes:

[0035] The test plan determination and adjustment unit is used to determine the test conditions, roads, and environmental factors in the vehicle durability test based on the real market user data of each vehicle model, which includes user driving road trajectories, road attribute data and real-time vehicle data, to form a vehicle durability test plan; and regularly update and adjust the vehicle durability test plan based on changes in real market user data.

[0036] The implementation of the embodiments of the present invention has the following beneficial effects:

[0037] The present invention provides a method and system for fusing driving data and map data based on the Internet of Vehicles. The method can analyze user information and roads based on road attribute data, thereby obtaining the most authentic and reliable user vehicle usage information and timely detecting changing trends in user usage information. The method can also realize low-cost, simple, and highly reliable acquisition and analysis of user usage big data.

[0038] In the embodiment of the present invention, the data obtained by the present invention can ensure that the vehicle manufacturer has accurate and objective user data as a reference when planning the vehicle durability test, so as to design the test planning scheme that best suits the user's use, thereby saving a lot of user research costs;

[0039] Based on the actual usage of automobiles by actual users obtained by the present invention, the automobile durability test planning can be adaptively adjusted regularly according to changes in customer usage conditions in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, other drawings obtained based on these drawings still fall within the scope of the present invention.

[0041] Figure 1 A schematic diagram of the main process of an embodiment of a method for fusing driving data and map data based on the Internet of Vehicles provided by the present invention;

[0042] Figure 2 A schematic structural diagram of an embodiment of a system for integrating driving data and map data based on the Internet of Vehicles provided by the present invention;

[0043] Figure 3 for Figure 2 Schematic diagram of the structure of the correction processing unit. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0045] like Figure 1 FIG. 1 is a schematic diagram showing the main process of an embodiment of a method for fusing driving data and map data based on the Internet of Vehicles provided by the present invention. In this embodiment, the method for fusing driving data and map data based on the Internet of Vehicles includes the following steps:

[0046] Step S10: receiving real vehicle T-BOX data reported by the selected vehicle via the Internet of Vehicles, wherein the real vehicle T-BOX data includes at least real-time vehicle data and original real vehicle trajectory data, wherein the real-time vehicle data includes at least vehicle speed, gear position, accelerator pedal opening, brake pedal opening, component temperature, and power data, and the original real vehicle trajectory data includes positioning point information sampled at a predetermined frequency; wherein the positioning point information can be, for example, GPS point information;

[0047] In a specific example, the step S10 further includes:

[0048] Receive the real vehicle T-BOX data reported by the selected vehicle through the vehicle network, and decode the real vehicle T-BOX data to obtain the real-time vehicle data corresponding to each positioning point information in the original real vehicle trajectory data.

[0049] Among them, the user's original real vehicle driving trajectory obtained based on T-BOX can be recorded as trajectory a. Trajectory a is composed of many connected GPS points, and each GPS point is recorded as X0, X1, X2, X3...

[0050] Step S11: performing trajectory correction processing on the reported actual vehicle trajectory data using a map to obtain the actual vehicle driving trajectory of the user, wherein the map is an intelligent map, which will be referred to as an intelligent map in the following description;

[0051] In a specific example, the step S11 further includes:

[0052] Selecting an intelligent map that covers all positioning point information in the original real vehicle trajectory data;

[0053] All the positioning point information in the original real-life vehicle trajectory data is normalized with the positioning network database in the smart map. The positioning point information with abnormal jumps is cleaned and each abnormal jump positioning point information is replaced with the average of the positioning point information before and after it to obtain the user's actual vehicle driving trajectory. Among them, the positioning point information with abnormal jumps can be understood as points that deviate from the main road;

[0054] The final actual driving trajectory of the user's vehicle can be recorded as trajectory b. Trajectory b is composed of many connected GPS points, and each GPS point is recorded as Y0, Y1, Y2, Y3...

[0055] Step S12: Projecting the user's actual vehicle driving trajectory onto the smart map road network, performing correlation fitting with the road network trajectory on the smart map, and obtaining the user's driving road trajectory based on the smart map road network trajectory;

[0056] Wherein, in the step S12, the user's driving road trajectory includes a plurality of map positioning point information, and the corresponding relationship between each positioning point in the user's actual vehicle driving trajectory and each map positioning point in the user's driving road trajectory.

[0057] It can be understood that the projection transformation method is used to project the trajectory b onto the smart map road network, and the trajectory b is associated with the road network trajectory on the smart map by associating roads. The road network trajectory on the smart map is composed of many connected map positioning points (GPS points), and each map positioning point (GPS point) is recorded as a0, a1, a2, a3...y0, y1, y2, y3..., and the user driving road trajectory based on the smart map road network trajectory output is obtained, which is recorded as trajectory c. Trajectory c is composed of many connected map positioning points (GPS points), and each map GPS point is recorded as Z0, Z1, Z2, Z3...

[0058] Step S13: Associating the road attribute data pre-stored in the smart map road network with the user's driving trajectory, wherein the road attribute data includes: region, road grade, slope, altitude, road surface smoothness, number of lanes, congestion, and weather conditions;

[0059] Wherein, the step S13 further includes:

[0060] The road attribute data corresponding to each map location point of the user's driving road trajectory is obtained through the map data API interface, and the road attribute data is marked on the corresponding map location point.

[0061] Specifically, through the map data API interface, information such as the region, road grade, slope, altitude, road surface flatness, number of lanes, congestion, weather conditions, etc. is marked on each map positioning point (GPS point) in the user's actual driving road trajectory c.

[0062] Step S14, based on the correspondence between the user's actual vehicle driving trajectory and the user's driving road trajectory, the real-time vehicle data in the actual vehicle T-BOX data is associated with the user's driving road trajectory;

[0063] Wherein, the step S14 further includes:

[0064] According to the correspondence between each positioning point in the user's actual vehicle driving trajectory and each map positioning point in the user's driving road trajectory, the real-time vehicle data of each positioning point in the actual vehicle T-BOX data is marked on the corresponding map positioning point in the user's driving road trajectory.

[0065] Specifically, in one example, the driving road attribute data and the driving vehicle data are first associated, and the vehicle data information marked by each GPS point Xm in trajectory a is marked on Zm (m∈N) in each map positioning point (GPS point) in trajectory c, such as: the vehicle data such as vehicle speed, gear position, accelerator pedal opening, brake pedal opening, component temperature, power, etc. marked on X2 are marked on Z2.

[0066] Step S15: In response to the user's customized selected driving trajectory, road attribute data, and at least part of the vehicle real-time data, cross-calculations are performed to achieve a visual multi-dimensional display. In a specific example, a corresponding analysis report can also be output to obtain a visual data display system.

[0067] Wherein, the step S15 further includes:

[0068] Based on the real market user data of each model, which includes user driving road trajectories, road attribute data and real-time vehicle data, the test conditions, roads and environmental factors in the vehicle durability test are determined to form a vehicle durability test plan. The vehicle durability test plan is regularly updated and adjusted based on changes in the real market user data.

[0069] like Figure 2 FIG2 is a structural diagram of an embodiment of a system for integrating driving data and map data based on the Internet of Vehicles provided by the present invention. Figure 3 As shown, the system 1 includes:

[0070] The raw data collection unit 10 is configured to receive real-vehicle T-BOX data reported by a selected vehicle via the Internet of Vehicles. The real-vehicle T-BOX data includes at least real-time vehicle data and raw real-vehicle trajectory data. The real-time vehicle data includes at least vehicle speed, gear position, accelerator pedal opening, brake pedal opening, component temperature, and power data. The raw real-vehicle trajectory data includes positioning point information sampled at a predetermined frequency.

[0071] A deviation correction processing unit 11 is used to perform a deviation correction process on the reported real vehicle trajectory data using an intelligent map to obtain the user's real vehicle driving trajectory;

[0072] The user driving road trajectory obtaining unit 12 is configured to project the user's actual vehicle driving trajectory onto the smart map road network, perform correlation fitting with the road network trajectory on the smart map, and obtain the user's driving road trajectory based on the smart map road network trajectory;

[0073] A first data association unit 13 is configured to associate road attribute data pre-stored in the intelligent map road network with the user's driving trajectory, wherein the road attribute data includes: region, road grade, slope, altitude, road surface smoothness, number of lanes, congestion, and weather conditions;

[0074] A second data association unit 14 is configured to associate the real-time vehicle data in the real vehicle T-BOX data with the user's driving road trajectory based on the correspondence between the user's real vehicle driving trajectory and the user's driving road trajectory;

[0075] A visualization display unit 15 is configured to perform cross-calculations in response to at least a portion of the user's customized selected driving road trajectory, road attribute data, and vehicle real-time data, and implement a visualized multi-dimensional display;

[0076] The test plan determination and adjustment unit 16 is used to determine the test conditions, roads, and environmental factors in the vehicle durability test based on the real market user data of each vehicle model, which includes user driving road trajectories, road attribute data, and real-time vehicle data, to form a vehicle durability test plan; and regularly update and adjust the vehicle durability test plan based on changes in the real market user data.

[0077] In a specific example, the correction processing unit 11 further includes:

[0078] A map selection unit 110 is configured to select an intelligent map having information covering all positioning points in the original real vehicle trajectory data;

[0079] The cleaning processing unit 111 is used to normalize all the positioning point information in the original real vehicle trajectory data with the positioning network library in the smart map, clean the positioning point information with abnormal jumps, and replace each abnormal jump positioning point information with the average value of the positioning point information before and after it, so as to obtain the user's real vehicle driving trajectory.

[0080] For more details, please refer to the above Figure 1 The description is not repeated here.

[0081] The implementation of the embodiments of the present invention has the following beneficial effects:

[0082] The present invention provides a method and system for fusing driving data and map data based on the Internet of Vehicles. The method can analyze user information and roads based on road attribute data, thereby obtaining the most authentic and reliable user vehicle usage information and timely detecting changing trends in user usage information. The method can also realize low-cost, simple, and highly reliable acquisition and analysis of user usage big data.

[0083] In the embodiment of the present invention, the data obtained by the present invention can ensure that the vehicle manufacturer has accurate and objective user data as a reference when planning the vehicle durability test, so as to design the test planning scheme that best suits the user's use, thereby saving a lot of user research costs;

[0084] Based on the actual usage of automobiles by actual users obtained by the present invention, the automobile durability test planning can be adaptively adjusted regularly according to changes in customer usage conditions in the market.

[0085] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] The above disclosure is only a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for fusing driving data and map data based on the Internet of Vehicles, characterized in that: The steps include: Step S10: receiving real vehicle T-BOX data reported by the selected vehicle via the Internet of Vehicles, wherein the real vehicle T-BOX data includes at least real-time vehicle data and original real vehicle trajectory data, wherein the real-time vehicle data includes at least vehicle speed, gear position, accelerator pedal opening, brake pedal opening, component temperature, and power data, and the original real vehicle trajectory data includes positioning point information sampled at a predetermined frequency; Step S11, using a map to perform trajectory correction processing on the reported actual vehicle trajectory data to obtain the user's actual vehicle driving trajectory; Step S12, projecting the user's actual vehicle driving trajectory onto the map road network, performing correlation fitting with the road network trajectory on the map, and obtaining the user's driving road trajectory based on the map road network trajectory, wherein the user's driving road trajectory includes information of multiple map positioning points; Step S13: Associating the road attribute data stored in the map with the user's driving road trajectory. The road attribute data includes information about the region, road grade, slope, altitude, road surface roughness, number of lanes, congestion, and weather conditions. Specifically, the road attribute data corresponding to each map location point of the user's driving road trajectory is obtained through a map data API interface, and the road attribute data is marked on the corresponding map location point. Step S14: Based on the correspondence between the user's actual vehicle driving trajectory and the user's driving road trajectory, the real-time vehicle data in the actual vehicle T-BOX data is associated with the user's driving road trajectory; specifically, based on the correspondence between each positioning point in the user's actual vehicle driving trajectory and each map positioning point in the user's driving road trajectory, the real-time vehicle data of each positioning point in the actual vehicle T-BOX data is marked with the corresponding map positioning point in the user's driving road trajectory; Step S15 , performing a visual multi-dimensional display in response to at least part of the user's customized selected driving road trajectory, road attribute data, and vehicle real-time data.

2. The method according to claim 1, wherein The step S10 further includes: Receive the real vehicle T-BOX data reported by the selected vehicle through the vehicle network, and decode the real vehicle T-BOX data to obtain the real-time vehicle data corresponding to each positioning point information in the original real vehicle trajectory data.

3. The method according to claim 2, wherein The step S11 further comprises: Selecting a map that covers all positioning point information in the original real vehicle trajectory data; All the positioning point information in the original real vehicle trajectory data is normalized with the positioning network library in the map, the positioning point information with abnormal jumps is cleaned, and each abnormal jump positioning point information is replaced with the average value of the positioning point information before and after it, so as to obtain the user's actual vehicle driving trajectory.

4. The method according to any one of claims 1 to 3, wherein In the step S12, the correspondence between each positioning point in the user's actual vehicle driving trajectory and each map positioning point in the user's driving road trajectory is obtained.

5. A system for integrating driving data and map data based on the Internet of Vehicles, characterized in that: include: a raw data collection unit, configured to receive real-vehicle T-BOX data reported by a selected vehicle via the Internet of Vehicles, the real-vehicle T-BOX data comprising at least real-time vehicle data and raw real-vehicle trajectory data, wherein the real-time vehicle data comprises at least vehicle speed, gear position, accelerator pedal opening, brake pedal opening, component temperature, and power data, and the raw real-vehicle trajectory data comprises positioning point information sampled at a predetermined frequency; A deviation correction processing unit, configured to perform a deviation correction process on the reported actual vehicle trajectory data using a map to obtain the actual vehicle driving trajectory of the user; A user driving road trajectory obtaining unit is configured to project the user's actual vehicle driving trajectory onto a map road network, perform correlation fitting with the road network trajectory on the map, and obtain the user's driving road trajectory based on the map road network trajectory, wherein the user's driving road trajectory includes information of multiple map positioning points; A first data association unit is configured to associate pre-stored road attribute data in the map road network with the user's driving road trajectory. The road attribute data includes information about the region, road grade, slope, altitude, road surface roughness, number of lanes, congestion, and weather conditions. Specifically, the first data association unit obtains the road attribute data corresponding to each map location point in the user's driving road trajectory through a map data API interface and marks the road attribute data at the corresponding map location point. a second data association unit configured to associate the real-time vehicle data in the real-vehicle T-BOX data with the user's driving road trajectory based on the correspondence between the user's real-vehicle driving trajectory and the user's driving road trajectory; specifically, based on the correspondence between each positioning point in the user's real-vehicle driving trajectory and each map positioning point in the user's driving road trajectory, mark the real-time vehicle data of each positioning point in the real-vehicle T-BOX data with the corresponding map positioning point in the user's driving road trajectory; The visual display unit is used to respond to at least part of the information in the user's customized selected driving road trajectory, road attribute data and vehicle real-time data, perform cross calculations, and realize visual multi-dimensional display.

6. The system according to claim 5, wherein: The correction processing unit further includes: A map selection unit, configured to select a map having information covering all positioning points in the original real vehicle trajectory data; The cleaning processing unit is used to normalize all the positioning point information in the original real vehicle trajectory data with the positioning network library in the map, clean the positioning point information with abnormal jumps, and replace each abnormal jump positioning point information with the average value of the positioning point information before and after it, so as to obtain the user's real vehicle driving trajectory.

7. The system according to claim 5 or 6, characterized in that Further including: The test plan determination and adjustment unit is used to determine the test conditions, roads, and environmental factors in the vehicle durability test based on the real market user data of each vehicle model, which includes user driving road trajectories, road attribute data and real-time vehicle data, to form a vehicle durability test plan; and regularly update and adjust the vehicle durability test plan based on changes in real market user data.

8. The system according to claim 7, wherein: The user's driving road trajectory includes a plurality of map positioning point information, and the corresponding relationship between each positioning point in the user's actual vehicle driving trajectory and each map positioning point in the user's driving road trajectory.

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