Vehicle driving data processing method and device, storage medium and program product

Abstract

The application relates to a vehicle driving data processing method and device, a storage medium and a program product, and relates to the technical field of automobile data analysis. The method comprises the following steps: acquiring vehicle driving data; the vehicle driving data is used for indicating speed change information of a target vehicle in a driving process; the vehicle driving data at least comprises speed information, pedal depth information and acceleration information; a reference coefficient is acquired; the reference coefficient is used for indicating weight values of the speed information, the pedal depth information and the acceleration information; a user driving characteristic value of the target vehicle is acquired according to the vehicle driving data and the reference coefficient; the vehicle driving characteristic value is used for indicating a speed control characteristic when the user drives the vehicle; an analysis result is acquired according to the user driving characteristic value of the target vehicle; the analysis result is used for indicating a behavior characteristic type when the user drives the target vehicle. The application can improve the accuracy of analyzing the driving behavior habit of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile data analysis, and particularly relates to a vehicle driving data processing method and device, a storage medium and a program product. BACKGROUND

[0002] The driving behavior habit of a user can affect the service life of a vehicle, and even the life safety of the user and passengers.

[0003] In related technologies, with the continuous improvement of the intelligent degree of an automobile, a vehicle manufacturer can understand the driving state of a vehicle by collecting and analyzing driving data of the vehicle, and then infer the driving behavior habit of a user.

[0004] However, the method for analyzing the driving data of the vehicle in the related technologies is relatively single, and thus the accuracy of the driving behavior habit of the user is affected. SUMMARY

[0005] Embodiments of the present application provide a vehicle driving data processing method and device, a storage medium and a program product, which can improve the accuracy of analyzing the driving behavior habit of a user. The technical solutions are as follows:

[0006] In one aspect, a vehicle driving data processing method is provided, the method is executed by a mobile device, the mobile device is bound to a vehicle, and the method comprises the following steps.

[0007] Obtaining vehicle driving data; the vehicle driving data is used to indicate speed change information of a target vehicle in a driving process; the vehicle driving data at least comprises vehicle speed information, pedal depth information and acceleration information;

[0008] Obtaining a reference coefficient; the reference coefficient is used to indicate the weight values of the vehicle speed information, the pedal depth information and the acceleration information;

[0009] According to the vehicle driving data and the reference coefficient, a user driving feature value of the target vehicle is obtained; the vehicle driving feature value is used to indicate the vehicle speed control feature when the user drives the vehicle;

[0010] According to the user driving feature value of the target vehicle, an analysis result is obtained; the analysis result is used to indicate the behavior feature type when the user drives the target vehicle.

[0011] In another aspect, a vehicle driving data processing device is provided, the device comprises the following.

[0012] The data acquisition module is used to acquire vehicle driving data; the vehicle driving data is used to indicate the speed change information of the target vehicle during driving; the vehicle driving data includes at least vehicle speed information, pedal depth information, and acceleration information;

[0013] A coefficient acquisition module is used to acquire reference coefficients; the reference coefficients are used to indicate the weight values ​​of the vehicle speed information, the pedal depth information, and the acceleration information.

[0014] The feature value acquisition module is used to acquire the user driving feature value of the target vehicle based on the vehicle driving data and the reference coefficient; the vehicle driving feature value is used to indicate the speed control characteristics of the user when driving the vehicle.

[0015] The result acquisition module is used to acquire analysis results based on the user driving feature values ​​of the target vehicle; the analysis results are used to indicate the type of behavioral characteristics of the user when driving the target vehicle.

[0016] In one possible implementation, the vehicle driving data also includes driving time information and driving mileage information;

[0017] The coefficient acquisition module is used to query the first reference coefficient correspondence table based on the vehicle speed information, the driving time information and the driving mileage information to obtain the reference coefficient;

[0018] The first reference coefficient correspondence table contains the reference coefficients and their correspondence with the vehicle speed information, the driving time information, and the driving mileage information.

[0019] In one possible implementation, the vehicle driving data also includes geographic location information;

[0020] The coefficient acquisition module is used to obtain the road type of the road where the target vehicle is located based on the geographical location information;

[0021] The coefficient acquisition module is used to query the second reference coefficient correspondence table according to the road type to obtain the reference coefficient;

[0022] The second reference coefficient correspondence table contains the correspondence between the road type and the reference coefficient.

[0023] In one possible implementation, the result acquisition module is used to acquire a user driving feature dataset; the user driving feature dataset contains the user driving feature values ​​corresponding to N vehicles respectively; the target vehicle is included among the N vehicles;

[0024] The result acquisition module is used to filter out the user driving feature values ​​that meet the set conditions in the user driving feature dataset, and obtain the filtered user driving feature dataset.

[0025] The result acquisition module is used to obtain the analysis results based on the filtered user driving feature dataset.

[0026] In one possible implementation, the result acquisition module is used to acquire a reference value; the reference value is the average value of each user driving feature value in the filtered user driving feature dataset;

[0027] The result acquisition module is used to acquire a reference set; the reference set is a sorted set of the differences between each user driving feature value and the reference value in the filtered user driving feature dataset, from high to low.

[0028] The result acquisition module is used to identify the user as a first type of user when the difference corresponding to the target vehicle is among the first P differences in the reference set. The first type of user refers to a user with aggressive driving habits.

[0029] The result acquisition module is used to determine the user as a second type of user when the difference corresponding to the target vehicle is among the last Q differences in the reference set. The second type of user refers to a user with conservative driving habits.

[0030] In one possible implementation, the result acquisition module is used to identify the user as a first type of user when the user driving characteristic value of the target vehicle is not less than a first threshold. The first type of user refers to a user with aggressive driving habits.

[0031] The result acquisition module is used to identify the user as a second type of user when the user driving characteristic value of the target vehicle is not greater than a second threshold. The second type of user refers to a user with conservative driving habits.

[0032] Wherein, the first threshold is greater than the second threshold.

[0033] In another aspect, a computer device is provided, the computer device comprising a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the vehicle driving data processing method as described above.

[0034] In another aspect, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the vehicle driving data processing method described above.

[0035] In another aspect, a computer program product is provided, comprising a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium and executes the computer program, causing the computer device to perform the vehicle driving data processing method provided in the various optional implementations described above.

[0036] The technical solution provided in this application may include the following beneficial effects:

[0037] By using vehicle speed, pedal depth, and acceleration data as references to analyze user driving behavior, and by configuring appropriate reference coefficients for these metrics, the vehicle's speed control characteristics can be accurately captured. This allows for better analysis and evaluation of user driving behavior, improving the accuracy of analyzing user driving habits. The resulting user driving habits can then be used for services related to user driving safety, such as providing reminders and alerts to users regarding their driving behavior.

[0038] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0040] Figure 1 This is a schematic diagram of the application environment of a vehicle driving data processing method according to an exemplary embodiment of this application;

[0041] Figure 2 This is a flowchart of a vehicle driving data processing method provided in an exemplary embodiment of this application;

[0042] Figure 3 This is a flowchart of a driving behavior tendency screening method based on a multi-parameter dynamic weighting algorithm provided in an exemplary embodiment of this application;

[0043] Figure 4This is a block diagram of a vehicle driving data processing apparatus provided in an exemplary embodiment of this application;

[0044] Figure 5 This is a schematic diagram of the structure of a computer device provided in an exemplary embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0046] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0047] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0048] It should be understood that although the terms first, second, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, a first parameter may also be referred to as a second parameter without departing from the scope of this disclosure, and similarly, a second parameter may also be referred to as a first parameter. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0049] It should be noted that in the steps related to user data collection in this application (such as collecting the aforementioned vehicle driving data), the control system may display prompt interfaces or pop-ups on the vehicle's instrument panel, user app, etc., and may also broadcast voice prompts. These prompt interfaces, pop-ups, and voice prompts are used to inform / notify the user that user data is being collected. Only after obtaining the user's consent instruction (such as a confirmation operation by the user to the prompt interface or pop-up) can the control device begin to execute the relevant steps for acquiring user data; otherwise, the steps for acquiring user data are terminated, i.e., no user data is acquired. In other words, all user data collected in this application is collected with the user's full knowledge, consent, and authorization, and the collection, use, analysis, and processing of the relevant user data comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0050] Furthermore, the user's driving behavior habits analyzed in the various embodiments of this application can be used for services related to ensuring user driving safety, such as reminding users of their driving habits.

[0051] To facilitate understanding, some concepts involved in this application are explained below.

[0052] 1) Telematics Box (TBOX): Also known as a vehicle networking box, it is an intelligent communication terminal device installed in a vehicle and an important component of the vehicle networking system. The TBOX uses telecommunication technology and information processing capabilities to connect the vehicle to the internet, providing a wealth of services and functions for users, vehicle managers, and vehicle manufacturers. The TBOX includes a microprocessor, a Subscriber Identification Module (SIM) card, a positioning module, a wireless communication module, a Controller Area Network (CAN) bus interface, and memory. Some TBOXes can also integrate near-field communication technologies such as Bluetooth.

[0053] The vehicle-mounted TBOX features data acquisition and transmission capabilities. On one hand, by connecting to the CAN bus, it can collect various vehicle data, such as engine status, mileage, fuel consumption, and vehicle diagnostic information. On the other hand, the TBOX can transmit the collected data to a server via a wireless network at preset frequencies (e.g., timed transmission, event-triggered transmission) or under specific conditions, supporting remote vehicle monitoring, data analysis, and value-added services. Furthermore, encryption measures can be implemented during data transmission to ensure data security and privacy.

[0054] For example, the data transmitted back by the vehicle TBOX includes, but is not limited to, the following:

[0055] Basic vehicle information includes static information such as Vehicle Identification Number (VIN), model, and year, as well as vehicle status (such as door lock status, window status, engine status, etc.).

[0056] Location information: Through the integrated positioning module, the vehicle TBOX can transmit the vehicle's geographical location information in real time, such as longitude, latitude, altitude, and driving direction.

[0057] Driving data includes mileage, average speed, instantaneous speed, acceleration, deceleration behavior, and driving path.

[0058] Vehicle health status: Fault codes (DTCs), battery status, fluid levels (such as engine oil and coolant), tire pressure, and braking system status read via the CAN bus can help detect and prevent potential vehicle malfunctions in a timely manner.

[0059] Environmental and status information: outside temperature, interior temperature, rain sensor readings, and lighting status, etc. This data helps the vehicle automatically adjust comfort settings or provide early warning information.

[0060] 2) Vehicle-to-everything (V2X) system: This is a comprehensive system that integrates advanced information technology. Its core purpose is to connect vehicles with the Internet, other vehicles, traffic infrastructure, and service centers to achieve real-time data exchange and processing, thereby improving performance in multiple aspects such as driving safety, traffic efficiency, vehicle management, and personalized services.

[0061] The working principle of the vehicle-to-everything (V2X) system includes:

[0062] Data collection: Various on-board terminal devices installed on the vehicle (such as locators, cameras, electronic control units, various sensors, driving recorders, etc.) are responsible for collecting vehicle status information, driving parameters, environmental data, and driving behavior data.

[0063] Information transmission: The collected data is uploaded to the cloud platform or data center via the vehicle communication module;

[0064] Data analysis and processing: Cloud platforms or data centers use big data and cloud computing technologies to analyze and process the received data and extract valuable information, such as driving habit analysis, vehicle health status assessment, and traffic flow prediction.

[0065] Service provision: Based on the analysis results, we provide users with a variety of services, including but not limited to navigation optimization, remote vehicle diagnostics, emergency rescue services, customized insurance, smart parking, fleet management, etc.

[0066] For example, the components of a vehicle-to-everything (V2X) system are as follows:

[0067] Vehicle-mounted terminal: Hardware devices installed in vehicles, responsible for data collection and preliminary processing, such as vehicle-mounted TBOX;

[0068] Computing and processing platform: Receives and processes data uploaded by the vehicle terminal, providing powerful computing capabilities and storage space;

[0069] Data analytics platform: Further analyzes the processed data to uncover its value and support decision-making;

[0070] User terminal applications: mobile applications (APP), web interfaces, etc., enable users to interact with the vehicle, obtain service information, and control some functions of the vehicle;

[0071] Communication network: a bridge connecting the vehicle terminal and the server, ensuring real-time data transmission.

[0072] 3) Data tracking technology based on in-vehicle TBOX: This refers to the use of in-vehicle TBOX as the core device for data collection and transmission in vehicle networking systems, enabling real-time monitoring and remote management of vehicle operation data. Data tracking technology based on TBOX is a crucial foundation for the intelligence of vehicle networking. It not only concerns data collection and transmission but also encompasses multiple aspects such as data security, data analysis, and strategy iteration. It is of great significance for improving vehicle management efficiency, enhancing user experience, and driving industry innovation.

[0073] As data tracking technology is increasingly applied in the new energy vehicle sector, driving behavior analysis based on tracked data is becoming increasingly important. Currently, the development of tracking data is still in its early stages. To address the issue of low efficiency in using tracking data, the solutions illustrated in the following embodiments of this application can be adopted to improve the efficiency of tracking data utilization, thereby enabling better driving behavior analysis.

[0074] Please refer to Figure 1 This diagram illustrates an implementation environment for a vehicle driving data processing method provided in an exemplary embodiment of this application. Figure 1 As shown, the implementation environment may include: terminal device 110 and server 120.

[0075] Terminal device 110 may include in-vehicle terminal devices and mobile terminal devices. In-vehicle terminal devices may include, but are not limited to, one or more of the following device types: in-vehicle TBOX, Telematics Control Unit (TCU), in-vehicle navigation system, in-vehicle video surveillance system, dashcam, and smart center console / vehicle infotainment system. Specifically, the in-vehicle TBOX provides remote communication capabilities, supporting functions such as remote vehicle control, data upload to the cloud, remote diagnostics, and over-the-air (OTA) software upgrades; the TCU is used for vehicle monitoring and dispatch management, integrating functions such as satellite positioning, data communication, and vehicle status monitoring; the in-vehicle navigation system provides map navigation services and can plan driving routes for vehicles based on traffic information; the in-vehicle video surveillance system includes an in-vehicle video server, cameras, etc., for video monitoring inside and outside the vehicle, supporting video recording, remote viewing, and downloading; the dashcam records video images and sound during vehicle operation; and the smart center console / vehicle infotainment system is a touchscreen device integrating infotainment, navigation, vehicle settings, and mobile phone connectivity (such as Apple CarPlay and Android Auto). Mobile terminal devices may include, but are not limited to, one or more of the following device types: smartphones, tablets, e-book readers, MP3 players, MP4 players, laptops, and desktop computers.

[0076] In this embodiment, a client application for the target application can be installed and run on the terminal device 110. The target application can be an application with data acquisition and data transmission functions, such as an in-vehicle TBOX. This application does not limit the form of the target application.

[0077] Server 120 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services such as cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and basic cloud computing services such as big data and artificial intelligence platforms. Among these, the cloud server for big data and artificial intelligence platforms can provide artificial intelligence cloud services. Server 120 can be a backend server for the aforementioned target application, used to provide backend services to the clients of the target application.

[0078] The terminal device 110 and the server 120 can be directly or indirectly connected via a communication network 130 (wired or wireless network), which is not limited herein. Optionally, the aforementioned wireless or wired network uses vehicle communication technology and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to any of the following:

[0079] 1) Controller Area Network (CAN): A common in-vehicle communication network used to connect various Electronic Control Units (ECUs) within the vehicle, such as the engine control unit, braking system control unit, and air conditioning control unit. CAN bus offers high transmission speed and reliability, and is used to transmit real-time data and commands within the vehicle.

[0080] 2) Local Area Network (LAN): A LAN is a local area network used to connect various electronic devices and systems within a vehicle, such as multimedia systems, navigation systems, and in-vehicle entertainment systems. LANs are typically based on Ethernet technology, providing high-speed data transmission and multi-device connectivity.

[0081] 3) Vehicle-to-Infrastructure (V2I) communication: V2I refers to communication between vehicles and road infrastructure (such as traffic lights, road signs, road facilities, etc.). V2I can be used in traffic management, intelligent transportation systems, vehicle navigation, and other applications to improve traffic efficiency and safety.

[0082] 4) Vehicle-to-Vehicle (V2V) communication: V2V refers to direct communication between vehicles to exchange information such as vehicle position, speed, and direction, thereby improving traffic safety and efficiency. V2V communication can be used for applications such as collision prevention and traffic congestion avoidance.

[0083] 5) Wireless Local Area Networks (WLAN): Wireless local area networks refer to the ability to provide wireless network connectivity within a vehicle, enabling drivers and passengers to connect to the internet, download data, and use online services.

[0084] 6) In-vehicle mobile communication network: In-vehicle mobile communication network refers to the mobile communication module integrated inside the vehicle, which is used to connect to the mobile communication network and provide communication functions such as in-vehicle Internet, voice calls, and text messages.

[0085] 7) Vehicle-mounted satellite communication network: Vehicle-mounted satellite communication refers to communication between vehicles and satellites, which is used to provide communication services in remote areas or places without terrestrial mobile communication network coverage.

[0086] The aforementioned communication networks can be used individually or in combination to provide vehicles with various data transmission, communication, and internet connectivity functions, thereby enabling a more intelligent and convenient driving and riding experience.

[0087] For example, in Figure 1 In the system shown, terminal device 110 acquires vehicle driving data, which is used to indicate the speed change information of the target vehicle during driving. The vehicle driving data includes at least vehicle speed information, pedal depth information, and acceleration information. Correspondingly, terminal device 110 can send the vehicle driving data to server 120 through communication network 130. Server 120 acquires reference coefficients, which are used to indicate the weight values ​​of vehicle speed information, pedal depth information, and acceleration information. Then, server 120 acquires user driving characteristic values ​​of the target vehicle based on the vehicle driving data and reference coefficients. The vehicle driving characteristic values ​​are used to indicate the speed control characteristics of the user when driving the vehicle. Then, server 120 acquires analysis results based on the user driving characteristic values ​​of the target vehicle. The analysis results are used to indicate the behavioral characteristic type of the user when driving the target vehicle.

[0088] Please refer to Figure 2 The present application provides a flowchart of a method for processing vehicle driving data according to one embodiment. This method can be executed by a computer device; optionally, the computer device may be... Figure 1 The terminal device 110 in the system shown; or, it could be server 120, or both terminal device 110 and server 120. For example... Figure 2 As shown, the method may include steps 210, 220, 230 and 240.

[0089] Step 210: Obtain vehicle driving data; vehicle driving data is used to indicate the speed change information of the target vehicle during driving; vehicle driving data includes at least vehicle speed information, pedal depth information, and acceleration information.

[0090] The aforementioned vehicle driving data can be obtained by the server calculating the data transmitted back from the vehicle's TBOX. For example, the data transmitted back from the vehicle's TBOX may include, but is not limited to, one or more of the following: vehicle speed, driving time, mileage, accelerator pedal depth, and brake pedal depth. Specifically, the data transmitted back from the vehicle's TBOX can be in units of days (calendar days) or weeks.

[0091] Accordingly, after receiving the data transmitted back from the vehicle TBOX, the server can obtain the average vehicle speed within a specified time period and speed range based on the vehicle's driving speed; it can also obtain the average maximum pedal depth for each acceleration / deceleration based on the accelerator pedal depth and brake pedal depth; and it can also obtain the average forward acceleration of the vehicle based on the vehicle's driving speed and accelerator pedal depth.

[0092] In this embodiment, the vehicle speed information can reflect whether the user tends to drive the target vehicle at high speed; the pedal depth information includes accelerator pedal depth information and brake pedal depth information. The accelerator pedal depth information and acceleration information can reflect whether the user tends to accelerate rapidly and frequently, and the brake pedal depth information can reflect whether the user tends to brake suddenly and frequently.

[0093] Step 220: Obtain reference coefficients; reference coefficients are used to indicate the weight values ​​of vehicle speed information, pedal depth information, and acceleration information.

[0094] In this system, the sum of the weights for vehicle speed, pedal depth, and acceleration is equal to 1. Depending on different objective conditions, the weights for these three information values ​​can vary. For example, under favorable conditions such as high speeds, the weight for vehicle speed can be greater than the weights for pedal depth and acceleration.

[0095] For example, developers can pre-set a reference coefficient mapping table, which includes the mapping relationship between vehicle speed information, pedal depth information, acceleration information and reference coefficients. The computer device can then query the reference coefficients corresponding to the vehicle speed information, pedal depth information, and acceleration information respectively in the aforementioned reference coefficient mapping table based on the vehicle driving data obtained in step 210. For example, developers can model the data in a simulation tool and use a lookup table to calibrate the weight values.

[0096] The reference coefficient can be related to factors such as vehicle type, road conditions, and safety standards, allowing it to match objective conditions and improve its rationality. For example, the reference coefficient for vehicle speed information can be different when vehicles of the same type are traveling on roads with different road conditions; similarly, the reference coefficient for vehicle speed information can also be different when vehicles of different types are traveling on roads with the same road conditions.

[0097] For example, when the target vehicle is driving on a congested road, the reference coefficients for vehicle speed, pedal depth, and acceleration are 0.3, 0.3, and 0.4, respectively; when the target vehicle is driving on a highway, the reference coefficients for vehicle speed, pedal depth, and acceleration are 0.5, 0.25, and 0.25, respectively.

[0098] Step 230: Based on the vehicle driving data and reference coefficients, obtain the user driving characteristic value of the target vehicle; the vehicle driving characteristic value is used to indicate the speed control characteristics of the user when driving the vehicle.

[0099] In this embodiment of the application, the computer device can calculate the user driving characteristic value of the target vehicle based on the vehicle driving data and reference coefficients of the target vehicle according to the preset rules (such as the set formula).

[0100] Among them, the above-mentioned vehicle driving characteristic values ​​are used to indicate the speed control characteristics of the user when driving the vehicle. This means that the above-mentioned vehicle driving characteristic values ​​can reflect the user's driving habits, such as the force and frequency of acceleration and deceleration.

[0101] Step 240: Obtain the analysis results based on the user driving characteristic values ​​of the target vehicle; the analysis results are used to indicate the type of behavioral characteristics of the user when driving the target vehicle.

[0102] The aforementioned user driving behavior characteristics when driving the target vehicle can include aggressive driving, conservative driving, and so on. For example, if the user driving characteristic value of the target vehicle indicates that the user accelerates and decelerates frequently when driving the target vehicle, the corresponding user behavior characteristic type can be aggressive driving; conversely, if the user driving characteristic value of the target vehicle indicates that the user accelerates and decelerates less frequently when driving the target vehicle, the corresponding user behavior characteristic type can be conservative driving.

[0103] In this application embodiment, the larger the user driving characteristic value, the more aggressive the user is; the smaller the user driving characteristic value, the more conservative the user is.

[0104] For example, developers can pre-set the value range of user driving characteristics corresponding to each behavioral characteristic type. The computer device can then look up the value range corresponding to the user driving characteristics of the target vehicle, thereby obtaining the behavioral characteristic type of the user driving the target vehicle.

[0105] For example, the computing device can compare the user driving characteristic value of the target vehicle with the user driving characteristic value of the reference vehicle to obtain the degree of deviation between the user driving characteristic value of the target vehicle and the user driving characteristic value of the reference vehicle, and determine the type of behavioral characteristics of the user when driving the target vehicle.

[0106] In one possible implementation, the vehicle driving data also includes the rate of change of speed during turning, which reflects the user's smoothness when turning, making U-turns, or changing lanes. Correspondingly, a reference coefficient indicates the weight of the rate of change of speed. Then, the computer device can obtain the user's driving characteristic values ​​for the target vehicle based on the vehicle driving data including the rate of change of speed and the reference coefficient, thereby obtaining the analysis results.

[0107] In one possible implementation, the vehicle driving data also includes a driving time distribution, which can reflect user preferences for daytime or nighttime driving, and long-distance or short-distance driving. Correspondingly, a reference coefficient can indicate the weight value of the driving time distribution. Then, the computer device can obtain the user driving characteristic value of the target vehicle based on the vehicle driving data including the driving time distribution and the reference coefficient, thereby obtaining the analysis results.

[0108] In summary, the solution presented in this application uses vehicle speed information, pedal depth information, acceleration information, and other vehicle driving data as references for analyzing user driving behavior. Furthermore, by configuring reasonable reference coefficients for vehicle speed information, pedal depth information, and acceleration information, the solution can accurately obtain the vehicle speed control characteristics of the user while driving, thereby better realizing the analysis and evaluation of user driving behavior and improving the accuracy of analyzing user driving habits. The user driving habits obtained from the above analysis can be used for services related to ensuring user driving safety; for example, the user driving habits can be used for services that remind users of their driving behavior.

[0109] Based on the above Figure 2 In one possible implementation of the scheme shown in the embodiment, the vehicle driving data further includes driving time information and driving mileage information; step 220 above can be implemented as follows:

[0110] Based on the vehicle speed information, travel time information, and mileage information, the reference coefficient is obtained by querying the first reference coefficient correspondence table; the first reference coefficient correspondence table contains the correspondence between the reference coefficient and the vehicle speed information, travel time information, and mileage information.

[0111] The aforementioned driving time information reflects the duration of each trip a user takes while driving the target vehicle, as well as the distribution of driving time. For example, by combining driving time information with vehicle speed information, it is possible to analyze whether the driver is engaging in prolonged high-speed driving or driving while fatigued.

[0112] The mileage information mentioned above can reflect the distance a user travels while driving the target vehicle; for example, by combining mileage information with fuel consumption data, the vehicle's average fuel consumption can be calculated.

[0113] In this embodiment, by combining vehicle speed information, travel time information, and mileage information, the road conditions traversed by the user driving the target vehicle can be obtained. Then, based on different road conditions, reasonable reference coefficients are configured for the vehicle speed information, pedal depth information, and acceleration information.

[0114] The aforementioned first reference coefficient correspondence table can be pre-set by the developers to indicate the correspondence between vehicle speed information, driving time information, and driving mileage information and reference coefficients. For example, vehicle speed information x in range 1, driving time information y in range 2, and driving mileage information z in range 3 can correspond to a fixed set of reference coefficients (X, Y, Z), where X, Y, and Z are the weight values ​​of vehicle speed information, pedal depth information, and acceleration information, respectively. Specifically, the aforementioned first reference coefficient correspondence table is shown in Table 1.

[0115] Table 1

[0116]

[0117] Based on the above embodiments, this embodiment illustrates a feasible solution for obtaining reference coefficients, which may include: a computer device acquiring vehicle speed information, driving time information, and driving mileage information, and querying reference coefficients in a first reference coefficient correspondence table; this solution can configure reasonable reference coefficients for vehicle speed information, pedal depth information, and acceleration information according to different vehicle speed information, driving time information, and driving mileage information, thereby improving the accuracy of user driving characteristic values ​​of the target vehicle.

[0118] Based on the above Figure 2 In one possible implementation of the scheme shown in the embodiment, the vehicle driving data further includes geographic location information; step 220 above can be implemented as follows:

[0119] Based on the geographical location information, obtain the road type of the road where the target vehicle is located; query the second reference coefficient correspondence table based on the road type to obtain the reference coefficient; the second reference coefficient correspondence table contains the correspondence between road type and reference coefficient.

[0120] The aforementioned geographic location information can reflect the terrain, altitude, and area traversed by the user while driving the target vehicle. For example, by combining it with a high-precision map of the corresponding area, the road type of the road where the target vehicle is located can be determined.

[0121] For example, road types can be divided into three levels: Level 1, Level 2, and Level 3. Level 1 roads correspond to flat or highway sections, which are favorable for driving. In this case, the weight of vehicle speed information can be increased, while the weight of pedal depth and acceleration information can be decreased. Level 2 roads correspond to urban or city roads, taking into account congestion and elevated road conditions. In this case, the weight of various factors should be balanced as much as possible to better assess driving behavior. Level 3 roads correspond to hilly, mountainous, or congested sections with poor driving conditions. In this case, the weight of pedal depth and acceleration information can be increased, while the weight of vehicle speed information can be decreased. In other words, road types can be divided into three levels from best to worst.

[0122] The aforementioned second reference coefficient correspondence table can be pre-set by the developers to indicate the correspondence between road types and reference coefficients. For example, a primary road type can correspond to a fixed set of reference coefficients, and a secondary road type can correspond to another fixed set of reference coefficients. Specifically, the aforementioned second reference coefficient correspondence table is shown in Table 2.

[0123] Table 2

[0124] Road type Vehicle speed information Pedal depth information Acceleration information Primary 0.5 0.25 0.25 Secondary 0.3 0.3 0.4 Tertiary 0.2 0.4 0.4 …… …… …… ……

[0125] Based on the above embodiments, this embodiment illustrates a feasible solution for obtaining reference coefficients, which may include: a computer device acquiring geographical location information and querying reference coefficients in a second reference coefficient correspondence table; this solution can configure reasonable reference coefficients for vehicle speed information, pedal depth information, and acceleration information according to different geographical location information, thereby improving the accuracy of user driving characteristic values ​​of the target vehicle.

[0126] Based on the solutions shown in the above embodiments of this application, in one possible implementation, step 240 can be implemented as follows:

[0127] Step 240a: Obtain the user driving feature dataset; the user driving feature dataset contains the user driving feature values ​​corresponding to N vehicles; the N vehicles include the target vehicle;

[0128] Step 240b: Filter out user driving feature values ​​in the user driving feature dataset that meet the set conditions to obtain the filtered user driving feature dataset;

[0129] Step 240c: Obtain the analysis results based on the filtered user driving feature dataset.

[0130] The aforementioned user driving characteristic value can be the user driving characteristic value corresponding to a single vehicle (i.e., the target vehicle), and the aforementioned N vehicles can be multiple vehicles located in the same country or region as the target vehicle. For example, the vehicle driving data of the aforementioned N vehicles and the vehicle driving data of the target vehicle are obtained under the same standard, such as the vehicle driving data of the N vehicles and the vehicle driving data of the target vehicle being obtained within the same time period.

[0131] For example, the user driving characteristic values ​​that meet the set conditions mentioned above can be user driving characteristic values ​​with the same value. That is, the computer device can filter out user driving characteristic values ​​with duplicate values.

[0132] For example, the user driving feature values ​​that meet the set conditions mentioned above can be user driving feature values ​​whose values ​​are too close. For instance, by calculating the variance of the user driving feature dataset, user driving feature values ​​with a variance less than a specified value can be eliminated.

[0133] Therefore, the user driving feature dataset after the above screening does not contain user driving feature values ​​that are too close, so as to avoid the user driving feature values ​​that are too close to affect the accuracy of data analysis. The computer equipment can obtain the analysis results based on the user driving feature dataset after the screening.

[0134] Based on the above embodiments, this embodiment illustrates a feasible solution for obtaining analysis results, which may specifically include: obtaining user driving feature values ​​corresponding to multiple vehicles containing the target vehicle (i.e., the aforementioned user driving feature dataset), and performing filtering processing on the user driving feature dataset; this solution can compare the user driving feature value of the target vehicle with the user driving feature values ​​of multiple vehicles, and can obtain the differences between the user driving feature value of the target vehicle and the user driving feature values ​​of other vehicles, thereby improving the accuracy of the analysis results.

[0135] Based on the solutions shown in the above embodiments of this application, in one possible implementation, step 240c can be implemented as follows:

[0136] Obtain reference values; the reference values ​​are the average values ​​of each user's driving feature in the filtered user driving feature dataset. Obtain a reference set; the reference set is a sorted set of differences between each user's driving feature value and the reference value in the filtered user driving feature dataset, ranked from highest to lowest. If the difference corresponding to the target vehicle is among the top P differences in the reference set, the user is identified as a first-type user, which refers to users with aggressive driving habits. If the difference corresponding to the target vehicle is among the bottom Q differences in the reference set, the user is identified as a second-type user, which refers to users with conservative driving habits.

[0137] In this embodiment of the application, taking n as an example of the number of user driving feature values ​​that are filtered out, the computer device calculates the average value of the Nn user driving feature values ​​(i.e., the reference value mentioned above); then, it obtains the difference between the Nn user driving feature values ​​and the average value, and sorts the Nn differences from largest to smallest to correspond to the reference set mentioned above.

[0138] For example, if the larger the user's driving characteristic value, the user is considered to be an aggressive driver. The first P vehicles (i.e., the vehicles with larger differences) correspond to aggressive drivers (i.e., the first type of users mentioned above), while the last Q vehicles (i.e., the vehicles with smaller differences) correspond to conservative drivers (i.e., the second type of users mentioned above).

[0139] In one possible implementation, the reference set is a sorted set of user driving feature values ​​from low to high, representing the differences between each user's driving feature value and the reference value in the filtered user driving feature dataset. If the difference corresponding to the target vehicle is among the first Q differences in the reference set, the user is identified as a second-type user, which refers to a user with conservative driving habits. If the difference corresponding to the target vehicle is among the last P differences in the reference set, the user is identified as a first-type user, which refers to a user with aggressive driving habits.

[0140] Based on the above embodiments, this embodiment demonstrates a feasible solution for obtaining analysis results from a filtered user driving feature dataset. Specifically, it may include: obtaining the average value of each user driving feature value in the filtered user driving feature dataset; and determining whether the user corresponding to the target vehicle belongs to a first type of user or a second type of user, or neither, based on the degree of difference between each user driving feature value and the average value. This solution determines the type of user of the target vehicle based on the degree of difference between the user driving feature value of the target vehicle and the user driving feature values ​​of multiple vehicles, which can reflect the characteristics (convergence or divergence) of the user of the target vehicle among multiple users, thereby reducing the error in improving the user behavior feature type.

[0141] Based on the solutions shown in the above embodiments of this application, in one possible implementation, step 240 can be implemented as follows:

[0142] If the user's driving characteristic value of the target vehicle is not less than the first threshold, the user is identified as a first type of user, which refers to a user with aggressive driving habits; if the user's driving characteristic value of the target vehicle is not greater than the second threshold, the user is identified as a second type of user, which refers to a user with conservative driving habits; wherein, the first threshold is greater than the second threshold.

[0143] The first and second conditions mentioned above can be numerical values ​​preset by the developers to determine the user type. For example, the developers can derive the first and second conditions based on the user's driving characteristic values ​​for multiple vehicles and the user's behavioral characteristics while driving.

[0144] For example, the first and second conditions mentioned above can be related to factors such as vehicle type, road conditions, and safety standards, so that the first and second conditions can match objective conditions and improve the rationality of the analysis results. For instance, when vehicles of the same type travel on roads with different road conditions, the first and second conditions mentioned above can be different; similarly, when vehicles of different types travel on roads with the same road conditions, the first and second conditions mentioned above can also be different.

[0145] In one possible implementation, taking the example that a smaller user driving characteristic value represents an aggressive driving user and a larger user driving characteristic value represents a conservative driving user, step 240 above can be implemented as follows:

[0146] If the user's driving characteristic value of the target vehicle is not less than the first threshold, the user is identified as a second type of user, which refers to a user with conservative driving habits; if the user's driving characteristic value of the target vehicle is not greater than the second threshold, the user is identified as a first type of user, which refers to a user with aggressive driving habits; wherein, the first threshold is greater than the second threshold.

[0147] Based on the above embodiments, this embodiment illustrates a feasible solution for obtaining analysis results. This solution obtains the behavioral characteristic type of a user driving the target vehicle by determining whether the user's driving characteristic value of the target vehicle meets the first condition / second condition. This solution can improve the efficiency of obtaining analysis results.

[0148] For example, in response to the problems of difficulty in screening driving behavior and low efficiency in utilizing embedded data, this application proposes a driving behavior tendency screening method based on a multi-parameter dynamic weighting algorithm, based on any one or more of the above embodiments. This method belongs to a driving behavior tendency screening strategy, which generates driving tendency indicators by transmitting vehicle data back through vehicle networking devices and by designing formulas and algorithms.

[0149] The rapid popularization of new energy vehicles has greatly promoted the process of automotive intelligence, and data tracking technology based on TBOX is widely used in vehicle operation. The significance of data tracking in new energy vehicles lies in the fact that by recording and analyzing various vehicle data in real time, vehicle manufacturers can gain a deeper understanding of the vehicle's operating status. This not only helps manufacturers to promptly identify and resolve potential problems, improving vehicle safety and efficiency, but also provides strong support for the optimized design and technological advancement of new energy vehicles. The large amount of data analysis accumulated over time will also help drive the sustainable development of the new energy vehicle industry, ensuring its continued competitiveness and innovative vitality.

[0150] Please refer to Figure 3 This illustrates a flowchart of a driving behavior tendency screening method based on a multi-parameter dynamic weighting algorithm provided in an exemplary embodiment of this application. Figure 3 As shown, the method includes the following steps.

[0151] Step 31, determine the sample data:

[0152] To control variables as accurately as possible, the following sample range can be determined based on three aspects: time, space, and work schedule. This range includes data on N vehicles in the same area during four consecutive weekdays (e.g., Monday to Friday) between 7:00 and 9:00 and between 17:00 and 21:00, where the effective driving time (time with a speed greater than 25 km / h) is greater than 0.5 hours.

[0153] Step 32, Configure weight values:

[0154] Based on the data transmitted from the TBOX, such as vehicle speed and travel time, the road conditions for different vehicles can be determined. If the average vehicle speed is >80km / h, the travel time is greater than 1 hour, and the travel distance is greater than 70km, the road conditions are close to those of highways or intercity expressways. In this case, the weight value X for the average vehicle speed is set to 0.6, while Y and Z are 0.2. If the average vehicle speed is greater than 25km / h but less than 70km / h and the travel distance is less than 40km, it is considered to be an urban (suburban) road. In this case, the weight value X for vehicle speed is reduced to 0.2, while the weight values ​​for the more decisive pedal depth and average acceleration are increased, to Y = 0.4 and Z = 0.4, respectively.

[0155] Step 33, Data Calculation:

[0156] The selection of TBOX back-transmitted embedded data is based on a daily unit, combining vehicle speed, single pedal depth, and brake pedal depth to calculate the user's driving characteristic value S for the day:

[0157]

[0158] Where Vavg is the average driving speed when the vehicle speed is between 25-120km / h, and X is the weight of Vavg; Y is the average maximum pedal depth during each acceleration and deceleration. The weights are: Aavg is the mean of positive acceleration, and Z is the weight of Aavg; where X+Y+Z=1, X>0, Y>0, Z>0.

[0159] Step 34, Data Filtering:

[0160] The user driving characteristic value S calculated above can be used for data filtering to exclude data groups with values ​​that are too close and affect the overall selection. For example, the data filtering strategy is as follows: On a daily basis, take the variance Y of the daily S value for N vehicles. n The data is then sorted from largest to smallest, and the bottom 20% of the data with the smallest variance are removed. Variance Y n The calculation formula is as follows:

[0161]

[0162] Step 35, Analysis Results:

[0163] Through the above steps, the daily user driving characteristic value S for each vehicle has been determined. The average daily S value of a single vehicle is taken to obtain the average driving trend value B of the single vehicle over four weeks, and the total average value Bavg of all vehicles is calculated.

[0164] Finally, calculate the deviation value C = (B - Baavg) for each vehicle, sort C from high to low, and select the top 5% and bottom 5% of vehicles. The specific allocation can be selected according to actual requirements. These two sets of data will be used as the target groups with a more aggressive driving tendency and the target groups with a mild driving tendency, respectively.

[0165] Step 36, Application of Results:

[0166] Using this data, vehicle manufacturers can filter out consumers with distinct personalities from their user base and provide a range of personalized services, including maintenance, surveys, and parameter calibration. For example, vehicle manufacturers can invite users with extremely aggressive driving habits to experience new cars and collect their opinions. The strategy of this application embodiment greatly enriches the usability and economic value of in-vehicle TBOX embedded data, while also enhancing the user experience.

[0167] In addition, understanding drivers' driving intensity helps vehicle manufacturers better optimize vehicle performance and design. For example, if analysis shows that drivers generally drive aggressively, manufacturers can consider strengthening the design of components such as the suspension and braking systems to improve vehicle stability and safety. If a region has unique road conditions, such as uneven terrain, manufacturers can design specific models accordingly.

[0168] In summary, the embodiments of this application address the challenges of driving behavior analysis. By calculating feature values ​​from the returned data, the behavioral trends of different drivers can be deduced. Vehicle manufacturers can then provide targeted software calibration and personalized support, enhancing the user experience and satisfaction. Furthermore, this solution maximizes the use of data returned from the in-vehicle TBOX, demonstrating the value and significance of data tracking.

[0169] The solutions described in the above embodiments of this application can be applied to the following scenarios:

[0170] Precise Risk Assessment and Insurance Pricing: By collecting information on vehicle speed, pedal depth (usually referring to the pressure applied to the accelerator or brake pedal), and acceleration, insurance companies can more accurately assess a driver's risk level. Combined with reference coefficients (weighting values), insurance companies can personalize insurance products, offering lower premiums to users with good driving habits while encouraging safe driving.

[0171] Driving behavior improvement and training: The analysis results can clearly identify the types of driver behavior characteristics, such as whether they tend to accelerate rapidly, brake suddenly, or speed. Based on this information, drivers can receive personalized feedback and improvement suggestions, which helps improve driving skills and road safety awareness, thus ensuring traffic safety.

[0172] Vehicle maintenance and performance optimization: Vehicle driving data can also reflect the vehicle's operating status. For example, frequent rapid acceleration and braking may increase vehicle wear and tear. Data analysis can remind car owners to perform timely vehicle maintenance and extend the vehicle's service life.

[0173] Fleet Management and Efficiency Improvement: For fleet operations, this solution helps managers identify efficient and inefficient driving behaviors, optimize route planning, reduce fuel consumption, and improve transportation efficiency.

[0174] Legal and accident liability determination: In the event of a traffic accident, detailed driving data can serve as evidence to help determine the attribution of liability and provide an objective basis for legal decisions.

[0175] Intelligent driver assistance system optimization: This data is crucial for the development of intelligent driver assistance systems, as it can help algorithms learn and simulate human driving habits, thereby improving the safety and comfort of autonomous driving.

[0176] Environmental protection and energy conservation: By analyzing the impact of driving behavior on vehicle speed control, we can promote energy conservation and emission reduction. For example, we can encourage smooth driving to reduce fuel consumption and emissions.

[0177] In other words, by comprehensively analyzing vehicle driving data and reference coefficients, this application can not only provide personalized feedback to drivers, but also promote traffic safety, improve industry efficiency, and provide data support for the advancement of automotive technology.

[0178] Please refer to Figure 4 The diagram illustrates a block diagram of a vehicle driving data processing apparatus provided in an exemplary embodiment of this application. This apparatus can be implemented as all or part of a computer device through hardware or a combination of hardware and software, to achieve the above-described... Figure 2 All or part of the steps in the illustrated embodiments. For example... Figure 4 As shown, the data processing device includes:

[0179] The data acquisition module 401 is used to acquire vehicle driving data; the vehicle driving data is used to indicate the speed change information of the target vehicle during driving; the vehicle driving data includes at least vehicle speed information, pedal depth information and acceleration information;

[0180] The coefficient acquisition module 402 is used to acquire reference coefficients; the reference coefficients are used to indicate the weight values ​​of vehicle speed information, pedal depth information, and acceleration information.

[0181] The feature value acquisition module 403 is used to acquire the user driving feature value of the target vehicle based on the vehicle driving data and reference coefficients; the vehicle driving feature value is used to indicate the vehicle speed control characteristics when the user drives the vehicle.

[0182] The result acquisition module 404 is used to acquire analysis results based on the user driving characteristic values ​​of the target vehicle; the analysis results are used to indicate the type of behavioral characteristics of the user when driving the target vehicle.

[0183] In one possible implementation, vehicle driving data also includes driving time information and driving mileage information;

[0184] The coefficient acquisition module 402 is used to query the first reference coefficient correspondence table based on vehicle speed information, driving time information and driving mileage information to obtain the reference coefficient;

[0185] The first reference coefficient correspondence table contains the reference coefficients and their correspondence with vehicle speed information, driving time information, and driving mileage information.

[0186] In one possible implementation, vehicle driving data also includes geographic location information;

[0187] The coefficient acquisition module 402 is used to obtain the road type of the road where the target vehicle is located based on the geographical location information;

[0188] The coefficient acquisition module 402 is used to query the second reference coefficient correspondence table according to the road type to obtain the reference coefficient;

[0189] The second reference coefficient correspondence table contains the correspondence between road types and reference coefficients.

[0190] In one possible implementation, the result acquisition module 404 is used to acquire a user driving feature dataset; the user driving feature dataset contains user driving feature values ​​corresponding to N vehicles; the N vehicles include the target vehicle;

[0191] The result acquisition module 404 is used to filter out user driving feature values ​​that meet the set conditions in the user driving feature dataset, and obtain the filtered user driving feature dataset.

[0192] The result acquisition module 404 is used to obtain analysis results based on the filtered user driving feature dataset.

[0193] In one possible implementation, the result acquisition module 404 is used to acquire a reference value; the reference value is the average value of each user driving feature value in the filtered user driving feature dataset;

[0194] The result acquisition module 404 is used to acquire the reference set; the reference set is a sorted set of the differences between each user driving feature value and the reference value in the filtered user driving feature dataset, from high to low.

[0195] The result acquisition module 404 is used to identify the user as a first type of user when the difference corresponding to the target vehicle is among the first P differences in the reference set. The first type of user refers to a user with aggressive driving habits.

[0196] The result acquisition module 404 is used to identify the user as a second type of user when the difference corresponding to the target vehicle is among the last Q differences in the reference set. The second type of user refers to a user with conservative driving habits.

[0197] In one possible implementation, the result acquisition module 404 is used to identify the user as a first type of user if the user driving characteristic value of the target vehicle is not less than a first threshold. The first type of user refers to a user with aggressive driving habits.

[0198] The result acquisition module 404 is used to identify the user as a second type of user if the user driving characteristic value of the target vehicle is not greater than the second threshold. The second type of user refers to a user with conservative driving habits; wherein, the first threshold is greater than the second threshold.

[0199] It should be noted that the device provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules according to actual needs, that is, the content structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0200] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments of the relevant method; the technical effects achieved by each module performing its operation are the same as the technical effects in the embodiments of the relevant method, and will not be elaborated here.

[0201] Please refer to Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an exemplary embodiment of this application. The computer device 500 includes a Central Processing Unit (CPU) 501, a system memory 504 including Random Access Memory (RAM) 502 and Read-Only Memory (ROM) 503, and a system bus 505 connecting the system memory 504 and the CPU 501. The computer device 500 also includes a Basic Input / Output System (I / O System) 506 that facilitates the transfer of information between various devices within the computer, and a mass storage device 507 for storing the operating system 513, application programs 514, and other program modules 515.

[0202] The basic input / output system 506 includes a display 508 for displaying information and an input device 509 for user input, such as a mouse or keyboard. Both the display 508 and the input device 509 are connected to the central processing unit 501 via an input / output controller 510 connected to the system bus 505. The basic input / output system 506 may also include the input / output controller 510 for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 510 also provides output to a display screen, printer, or other types of output devices.

[0203] Mass storage device 507 is connected to central processing unit 501 via a mass storage controller (not shown) connected to system bus 505. Mass storage device 507 and its associated computer-readable media provide non-volatile storage for computer device 500. That is, mass storage device 507 may include computer-readable media (not shown) such as hard disk or CD-ROM (Compact Disc Read-Only Memory) drive.

[0204] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes RAM (Random Access Memory), ROM (Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other solid-state storage technologies, CD-ROM, DVD (Digital Video Disc) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The system memory 504 and mass storage device 507 described above can be collectively referred to as memory.

[0205] Computer device 500 can be connected to the Internet or other network devices through network interface unit 511 connected to system bus 505.

[0206] The memory also includes one or more programs, which are stored in the memory. The central processing unit 501 implements these programs by executing them. Figure 2 All or some of the steps in the method shown.

[0207] In an exemplary embodiment, a chip is also provided, the chip including programmable logic circuitry and / or program instructions, which, when the chip is run on a computer device, are used to implement all or part of the steps of the methods shown in the above embodiments of this application.

[0208] In an exemplary embodiment, a computer program product is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions to implement all or part of the steps of the methods shown in the above embodiments of this application.

[0209] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores a computer program that is loaded and executed by a processor to implement all or part of the steps of the methods shown in the above embodiments of this application.

[0210] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0211] Those skilled in the art will recognize that the functions described in the embodiments of this application in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0212] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of processing vehicle travel data, characterized by, The method comprises: obtaining vehicle driving data; the vehicle driving data is used to indicate speed change information of a target vehicle during driving; the vehicle driving data at least comprises vehicle speed information, pedal depth information and acceleration information; obtaining a reference coefficient; the reference coefficient is used to indicate weight values of the vehicle speed information, the pedal depth information and the acceleration information; obtaining a user driving characteristic value of the target vehicle according to the vehicle driving data and the reference coefficient; the vehicle driving characteristic value is used to indicate a vehicle speed control characteristic when a user drives the vehicle; obtaining a user driving characteristic data set; the user driving characteristic data set comprises user driving characteristic values corresponding to N vehicles respectively; the N vehicles comprise the target vehicle; screening out the user driving characteristic values in the user driving characteristic data set that meet a set condition to obtain a screened user driving characteristic data set; obtaining a reference value; the reference value is an average value of each user driving characteristic value in the screened user driving characteristic data set; obtaining a reference set; the reference set is a sorting set of difference values between each user driving characteristic value in the screened user driving characteristic data set and the reference value from high to low; in a case where the difference value corresponding to the target vehicle is in the front P difference values in the reference set, determining the user as a first type user; the first type user refers to a user with aggressive driving habits; in a case where the difference value corresponding to the target vehicle is in the rear Q difference values in the reference set, determining the user as a second type user; the second type user refers to a user with conservative driving habits.

2. The method of claim 1, wherein, The vehicle driving data further comprises driving time information and driving mileage information; The reference coefficient is obtained by: querying a first reference coefficient corresponding table according to the vehicle speed information, the driving time information and the driving mileage information to obtain the reference coefficient; wherein the first reference coefficient corresponding table comprises a corresponding relationship between the reference coefficient and the vehicle speed information, the driving time information and the driving mileage information.

3. The method of claim 1, wherein, The vehicle driving data further comprises geographical position information; The reference coefficient is obtained by: obtaining a road type of a road where the target vehicle is located according to the geographical position information; querying a second reference coefficient corresponding table according to the road type to obtain the reference coefficient; wherein the second reference coefficient corresponding table comprises a corresponding relationship between the road type and the reference coefficient.

4. The method according to any one of claims 1 to 3, characterized in that, The analysis result is obtained according to the user driving characteristic value of the target vehicle, comprising: in a case where the user driving characteristic value of the target vehicle is not less than a first threshold value, determining the user as a first type user; the first type user refers to a user with aggressive driving habits; in a case where the user driving characteristic value of the target vehicle is not greater than a second threshold value, determining the user as a second type user; the second type user refers to a user with conservative driving habits; wherein the first threshold value is greater than the second threshold value.

5. A processing device of vehicle travel data, characterized by, The device comprises: a data acquisition module configured to acquire vehicle driving data, wherein the vehicle driving data is used to indicate speed change information of a target vehicle during driving, and the vehicle driving data comprises at least vehicle speed information, pedal depth information and acceleration information; a coefficient acquisition module configured to acquire a reference coefficient, wherein the reference coefficient is used to indicate weight values of the vehicle speed information, the pedal depth information and the acceleration information; a characteristic value acquisition module configured to acquire a user driving characteristic value of the target vehicle according to the vehicle driving data and the reference coefficient, wherein the vehicle driving characteristic value is used to indicate a vehicle speed control characteristic when a user drives the vehicle; a result acquisition module configured to acquire a user driving characteristic data set, wherein the user driving characteristic data set comprises user driving characteristic values corresponding to N vehicles respectively, the N vehicles comprise the target vehicle, the user driving characteristic data set is filtered to obtain a filtered user driving characteristic data set, a reference value is acquired, the reference value is an average value of each user driving characteristic value in the filtered user driving characteristic data set, a reference set is acquired, the reference set is an ordered set of difference values from high to low between each user driving characteristic value in the filtered user driving characteristic data set and the reference value, in a case where the difference value corresponding to the target vehicle is in the first P difference values in the reference set, the user is determined as a first type user, and the first type user refers to a user with aggressive driving habits, and in a case where the difference value corresponding to the target vehicle is in the last Q difference values in the reference set, the user is determined as a second type user, and the second type user refers to a user with conservative driving habits.

6. A computer device, comprising: The computer device comprises a processor and a memory, and the memory stores at least one computer instruction, and the at least one computer instruction is loaded and executed by the processor to implement the vehicle driving data processing method according to any one of claims 1 to 4.

7. A computer readable storage medium characterized by The computer readable storage medium stores at least one computer instruction, and the computer instruction is loaded and executed by the processor to implement the vehicle driving data processing method according to any one of claims 1 to 4.

8. A computer program product, characterised in that, The computer program product comprises computer instructions stored in a computer readable storage medium, and the computer instructions are read and executed by the processor of the computer device to implement the vehicle driving data processing method according to any one of claims 1 to 4.

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