TBOX-supported vehicle full life cycle data management method and system

Abstract

The invention discloses a TBOX-supported vehicle full life cycle data management method and system, and relates to the technical field of data management, and the method comprises the steps: carrying out the connection with a cloud platform through a TBOX, binding a user identity, carrying out the qualification verification, carrying out the matching of an optimal rental vehicle, monitoring a driving behavior and a vehicle state in real time through the TBOX in a vehicle rental process, and carrying out the real-time monitoring of the vehicle state through the TBOX. Driving behavior scoring and vehicle condition state scoring are carried out, scoring results are archived to a TBOX shared database, and closed-loop two-way rental matching and vehicle health management are realized through the shared database. The technical problems that in the prior art, in a shared automobile and time-sharing leasing mode, the health management ability of the automobile is insufficient, and the personalized requirement of a user cannot be met are solved, and closed-loop two-way leasing matching and automobile health management are carried out based on a shared database supported by TBOX. The vehicle health management capability is improved; and the personalized requirements of the user are met.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a vehicle full life cycle data management method and system supported by TBOX. Background Art

[0003] At present, most shared car systems focus on vehicle positioning and status monitoring, but lack active health prediction and personalized service recommendation functions, and cannot assess driving risks in real time or provide preventive maintenance. How to use intelligent technology to analyze user behavior and vehicle conditions in real time to achieve more accurate health management and rental matching has become an urgent problem to be solved in the industry. Summary of the invention

[0004] The present application provides a vehicle life cycle data management method and system supported by TBOX, which is used to solve the technical problems that the existing technology has insufficient vehicle health management capabilities and cannot meet the personalized needs of users under the shared car and time-sharing rental models.

[0005] In a first aspect of the present application, a vehicle life cycle data management method supported by TBOX is provided, the method comprising: connecting to a cloud platform via TBOX, binding the identity information of the current user in real time, and performing qualification verification to generate a qualification verification result; if the qualification verification result is passed, obtaining the historical rental data of the current user, and accessing the TBOX shared database to match and obtain the optimal rental vehicle; during the rental use of the optimal rental vehicle, using TBOX to monitor the user's driving behavior and vehicle status in real time, and obtaining full-cycle rental driving behavior data and full-cycle vehicle working status data; based on the full-cycle rental driving behavior data, performing driving behavior scoring, generating a user driving behavior score and archiving it to the TBOX shared database; based on the full-cycle vehicle working status data, performing vehicle condition scoring, generating a vehicle performance scoring result and a vehicle health analysis report, and archiving them to the TBOX shared database; based on the TBOX shared database, performing closed-loop two-way rental matching and vehicle health management.

[0006] The second aspect of the present application provides a vehicle life cycle data management system supported by TBOX, the system comprising: a qualification verification module, the qualification verification module is used to connect to the cloud platform through TBOX, bind the identity information of the current user in real time, perform qualification verification, and generate a qualification verification result; a rental vehicle matching module, the rental vehicle matching module is used to obtain the historical rental data of the current user if the qualification verification result is passed, and access the TBOX shared database to match the optimal rental vehicle; a TBOX real-time monitoring module, the TBOX real-time monitoring module is used to use TBOX to monitor the user's driving behavior and vehicle status in real time during the rental of the optimal rental vehicle. , obtaining full-cycle rental driving behavior data and full-cycle vehicle working status data; a driving behavior scoring module, the driving behavior scoring module is used to perform driving behavior scoring based on the full-cycle rental driving behavior data, generate a user driving behavior score and archive it to the TBOX shared database; a vehicle condition scoring module, the vehicle condition scoring module is used to perform vehicle condition scoring based on the full-cycle vehicle working status data, generate a vehicle performance scoring result and a vehicle health analysis report, and archive them to the TBOX shared database; a closed-loop rental management module, the closed-loop rental management module is used to perform closed-loop two-way rental matching and vehicle health management based on the TBOX shared database.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The vehicle life cycle data management method and system supported by TBOX provided in the present application relate to the field of data management technology. By connecting TBOX with a cloud platform, the user identity is verified in real time and the optimal rental vehicle is matched. During the vehicle rental process, TBOX monitors the user's driving behavior and vehicle status, and performs driving behavior scoring and vehicle condition scoring. The scoring results are archived to a shared database, and closed-loop two-way rental matching and vehicle health management are performed based on the shared database. This solves the technical problem that the prior art has insufficient vehicle health management capabilities and cannot meet the personalized needs of users under the shared car and time-sharing rental modes, and realizes closed-loop two-way rental matching and vehicle health management based on a shared database supported by TBOX, improves vehicle health management capabilities, and meets the personalized needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of the process flow of a vehicle life cycle data management method supported by TBOX provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a vehicle life cycle data management system supported by TBOX provided in an embodiment of the present application.

[0010] Explanation of reference numerals: qualification verification module 11 , rental vehicle matching module 12 , TBOX real-time monitoring module 13 , driving behavior scoring module 14 , vehicle condition scoring module 15 , closed-loop rental management module 16 . DETAILED DESCRIPTION

[0011] The present application provides a vehicle life cycle data management method and system supported by TBOX, which is used to solve the technical problems that the existing technology has insufficient vehicle health management capabilities and cannot meet the personalized needs of users under the shared car and time-sharing rental models.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a vehicle life cycle data management method supported by TBOX, the method comprising: P10: Connect with the cloud platform through TBOX, bind the current user's identity information in real time, perform qualification verification, and generate qualification verification results.

[0015] Specifically, when a user initiates a vehicle rental request through a mobile application or other terminal device, a secure communication connection will be quickly established between TBOX and the cloud platform. As the core device on the vehicle side, TBOX has the ability to integrate with the vehicle's electronic system and can receive and process various data signals inside and outside the vehicle. When a user initiates a rental request, he or she must submit his or her identity information through a mobile application. This information usually includes the user's name, ID number, driver's license information, mobile phone number, and payment account information. This identity information is sent from the user's terminal device to the cloud platform through encrypted transmission to ensure the security and privacy of the information.

[0016] After the identity information is successfully submitted, TBOX pairs with the user's terminal device through its built-in user identification module (such as Bluetooth low energy module BLE or near field communication module NFC), thereby binding the current user's identity information in real time. Qualification verification is a key link to ensure that the user has legal rental qualifications. The cloud platform will verify the user's identity information according to the preset rules. The verification content includes but is not limited to whether the user's age meets the legal driving requirements, whether the driver's license is valid, whether the credit record is good, etc. The cloud platform obtains the user's detailed information by exchanging data with external databases (such as the driver's license database of relevant departments, the credit record database of credit reporting agencies, etc.), and makes judgments based on the preset qualification verification rules. For example, check whether the user's age reaches the legal driving age (usually over 18 years old), whether the driver's license is valid and has not been revoked or temporarily withheld, and whether the user's credit score meets the minimum standard set by the operator (such as the credit score must be above 600 points).

[0017] After a series of verification processes, the cloud platform will generate a qualification verification result. The qualification verification result is usually in two states: "pass" or "fail". If the user's identity information meets all the preset qualification conditions, a "pass" verification result is generated, allowing the user to continue with the subsequent rental operation; conversely, if the user's identity information does not meet any of the qualification conditions, a "fail" verification result is generated, and a corresponding prompt message is sent to the user to inform him / her of the reason why he / she cannot rent a vehicle. The qualification verification results will be stored in the cloud platform's database for archiving.

[0018] Through the above steps, TBOX not only ensures the accurate transmission of identity information, but also ensures the timeliness and effectiveness of qualification verification. The efficient implementation of this process relies on the seamless data transmission and rapid response mechanism between TBOX and the cloud platform, thereby ensuring that the vehicle is always in compliance with regulations during the rental process, reducing potential safety risks.

[0019] P20: If the qualification verification result is passed, the historical rental data of the current user is obtained, and the TBOX shared database is accessed to match the best rental vehicle.

[0020] Optionally, if the qualification verification result is passed, the system will further obtain the current user's historical rental data and access the TBOX shared database to match the most suitable rental vehicle.

[0021] First, the system obtains the current user's historical rental records through the interface with the cloud platform, including the user's rental frequency, preferred car model, past usage habits and other data. This data can help the system understand the user's specific needs. For example, some users may prefer more energy-efficient vehicles, or have a higher preference for certain brands and models. This historical data provides the system with a comprehensive view of the user's rental habits and is an important basis for achieving accurate matching.

[0022] After obtaining historical rental data, the system will combine all the information of available rental vehicles in the TBOX shared database (such as vehicle model, vehicle condition, availability, etc.), match data through intelligent algorithms, and select the best rental vehicles. The intelligent algorithm will automatically recommend the vehicle that best meets the user's needs based on the user's historical data and current needs, such as vehicle model, function preference, rental time, etc. The system will also consider the current availability of the vehicle (for example, whether the vehicle is under repair) to ensure that the recommended vehicle meets the actual availability.

[0023] To achieve this goal, the vehicle data in the TBOX shared database needs to be managed through real-time updates and synchronization to ensure that the status information of each vehicle (such as location, vehicle condition, availability, etc.) is always up to date. This process relies on the efficient data interaction and real-time synchronization mechanism between the TBOX system and the cloud platform to ensure that historical data and real-time vehicle information can be seamlessly connected, so as to accurately match the best vehicle.

[0024] Through this process, TBOX can not only provide personalized recommendations based on the user's historical rental records, but also make the most reasonable rental options based on the real-time status of the vehicle, thereby improving user experience and optimizing vehicle operation management.

[0025] P30: During the leasing of the best leasing vehicle, TBOX is used to monitor the user's driving behavior and vehicle status in real time, and obtain the full-cycle leasing driving behavior data and the full-cycle vehicle working status data.

[0026] It should be understood that during the leasing process of the optimal leasing vehicle, the TBOX system will monitor the user's driving behavior and the vehicle's working status in real time, and comprehensively obtain the full-cycle leasing driving behavior data and vehicle working status data.

[0027] Among them, driving behavior data can be obtained through TBOX's built-in accelerometer, GPS, and on-board diagnostic system (OBD) and other devices. These devices can accurately record various actions of users during driving, such as sudden braking, sudden acceleration, speeding, etc. These data not only help to evaluate the driver's driving habits and risky behaviors, but also provide a basis for subsequent vehicle maintenance. Through real-time monitoring and analysis, the TBOX system can promptly detect irregular driving behaviors and issue alarms or reminders according to preset rules to ensure driving safety.

[0028] The vehicle operating status data includes engine operating conditions, fuel or battery remaining, tire pressure, brake system status, etc. These data can be obtained in real time through various sensors and on-board diagnostic interfaces of the vehicle, and uploaded to the cloud platform by TBOX for processing in real time. The system can detect any potential faults or abnormal conditions in a timely manner and issue early warnings to avoid vehicle failures or damage during the rental process and ensure the normal operation of the vehicle.

[0029] During the rental period, TBOX will upload the collected driving behavior data and vehicle status data to the cloud platform at set time intervals (e.g. no more than 30 seconds). These data will be stored in the cloud platform's database to form complete operating data for the vehicle's entire life cycle. Even in the case of unstable signals or unavailable networks, TBOX will temporarily store the data in the local storage module and upload it again when conditions are met.

[0030] Through this process, TBOX can not only fully record the user's driving behavior, but also monitor the health status of the vehicle in real time, providing all-round protection for the safe use of the vehicle. All collected data will constitute the driving behavior data and vehicle working status data during the full-cycle rental process, which will be used for subsequent scoring, analysis and health management to further optimize the management and safety of the vehicle.

[0031] P40: Based on the full-cycle rental driving behavior data, a driving behavior score is performed, and a user driving behavior score is generated and archived in the TBOX shared database.

[0032] Furthermore, step P40 of the embodiment of the present application also includes: P41: According to the safe driving standards and vehicle maintenance standards, high-risk driving assessment indicators and high-consumption driving assessment indicators are obtained; P42: According to the high-risk driving assessment indicators and high-consumption driving assessment indicators, based on the full-cycle rental driving behavior data, high-risk feature sets and high-consumption feature sets are extracted; P43: Based on the high-risk feature set and high-consumption feature set, a multi-dimensional driving behavior score is performed on the current user to generate a personalized driving behavior score report; P44: The personalized driving behavior score report is combined with the user's historical rental data to form a long-term driving behavior profile of the current user, and archived to the TBOX shared database.

[0033] Optionally, based on the full-cycle rental driving behavior data, the user's driving behavior is scored and a user driving behavior score is generated, which is eventually archived in the TBOX shared database.

[0034] First, based on the safe driving standards and vehicle maintenance standards, obtain high-risk driving assessment indicators and high-consumption driving assessment indicators. These indicators are key parameters for evaluating user driving behavior, and are used to determine whether the driving behavior is safe, economical, and whether it causes potential damage to the vehicle. For example, high-risk driving assessment indicators may include the number of speeding, frequency of sudden braking, frequency of sudden acceleration, etc.; high-consumption driving assessment indicators may include excessive fuel consumption, unnecessary idling time, etc. Use these standards as an assessment benchmark to help identify high-risk behaviors and high-consumption behaviors that may affect driving safety and vehicle health during driving.

[0035] On this basis, according to the high-risk driving assessment indicators and high-consumption driving assessment indicators, high-risk feature sets and high-consumption feature sets are extracted from the full-cycle rental driving behavior data. This process uses data mining and pattern recognition technology to automatically identify high-risk behavior characteristics (such as frequent speeding, sudden braking, etc.) and high-consumption behavior characteristics (such as continuous high speed, long-term idling, etc.) that exist in the rental process. These characteristics can help the system accurately locate the driver's potential driving risks and vehicle loss risks.

[0036] Furthermore, based on the extracted high-risk feature set and high-consumption feature set, a multi-dimensional driving behavior score is performed. The multi-dimensional scoring method can score the driver's performance according to different dimensions (such as safety, economy, stability, etc.). For example, according to the user's multi-dimensional data such as the number of speeding, emergency braking frequency, fuel consumption, etc., different weights are assigned to calculate a comprehensive score, and a personalized driving behavior score report is generated. The report records the user's driving behavior score and its corresponding high-risk and high-consumption characteristics in detail, which can provide users with detailed driving behavior feedback and help operators assess the user's risk level.

[0037] Finally, the personalized driving behavior score report is combined with the user's historical rental data to form a long-term driving behavior profile of the current user, which is then archived in the TBOX shared database. By accumulating user driving behavior data over a long period of time, operators can obtain a more comprehensive user profile in order to optimize vehicle allocation, formulate rental rules, and provide personalized recommendations based on the user's historical behavior. In addition, the long-term archives archived in the shared database also provide strong data support for future risk assessments and improvement measures.

[0038] Through the above steps, each user's driving behavior can be accurately evaluated and recorded, helping operators to effectively manage drivers' behavior, reduce safety risks, and ensure the healthy operation of vehicles.

[0039] P50: Based on the full-cycle vehicle working status data, the vehicle condition is scored, a vehicle performance scoring result and a vehicle health analysis report are generated, and the results are archived in the TBOX shared database.

[0040] Specifically, based on the full-cycle vehicle working status data, the vehicle's working status is evaluated, and the vehicle condition score, vehicle performance score results and vehicle health analysis report are generated. These results are archived in the TBOX shared database, thereby providing real-time vehicle management support for operators.

[0041] First, the vehicle operating status data includes a variety of vehicle operating parameters, such as engine temperature, fuel or battery level, tire pressure, brake system condition, vehicle speed, transmission system operating status, etc. The TBOX system collects these key data in real time through on-board sensors, OBD interfaces and other diagnostic tools. During the rental period, these data are continuously monitored and uploaded to the cloud platform to ensure that all vehicle operating indicators are within a reasonable range.

[0042] Based on these real-time collected data, the health status of the vehicle is analyzed and a vehicle condition score is generated. The scoring dimensions may include engine performance, braking system, drive system, fuel efficiency and other aspects, and the corresponding score can be given according to the data performance of each dimension. For example, if the engine temperature of the vehicle is too high or the tire pressure is too low, the system will mark these problems and reduce the vehicle score accordingly. Through these scores, operators can quickly understand the performance status of the vehicle and determine whether maintenance or repair is needed in a timely manner.

[0043] In addition, TBOX will also generate a vehicle health analysis report, which includes not only the vehicle's performance score, but also a detailed analysis of the vehicle's health status. For example, based on historical data and real-time monitoring results, it can predict the risk of possible vehicle failures and help operators take preventive maintenance measures in advance. For example, if the report shows that the vehicle's brake system is abnormal after a long period of high-load operation, the system will remind the operator to check or replace the brake system.

[0044] Finally, all generated vehicle performance scoring results and health analysis reports will be archived in the TBOX shared database. The key to this process is the real-time update and synchronization of the shared database, which facilitates operators to track and manage vehicles over the long term. By archiving this data, TBOX not only provides a basis for real-time management of vehicles, but also provides an important reference for subsequent fault tracing and historical data analysis.

[0045] In general, TBOX helps operators optimize vehicle management and improve vehicle utilization efficiency by comprehensively monitoring the working status of vehicles and generating scores and health reports, while ensuring the safety and reliability of vehicles during the rental process.

[0046] P60: Based on the TBOX shared database, closed-loop two-way rental matching and vehicle health management are performed.

[0047] Furthermore, closed-loop two-way leasing matching is performed, and step P60 of the embodiment of the present application further includes: P61: The TBOX shared database is associated with a qualification verification unit and a two-way matching unit; P62: After the qualification verification is passed based on the qualification verification unit, the verified user identity information is extracted; P63: The user identity information is used as input, and the two-way matching unit traverses the TBOX shared database to perform two-way rental selection matching to obtain the optimal rental vehicle. Among them, the TBOX shared database performs data storage and extraction operations based on blockchain.

[0048] It should be understood that based on the TBOX shared database, closed-loop two-way rental matching and vehicle health management are further realized. Closed-loop two-way rental matching involves not only the best match between users and vehicles, but also the continuous monitoring and management of the vehicle's health status.

[0049] First of all, the TBOX shared database serves as the data storage and management center of the entire system, and includes a qualification verification unit and a two-way matching unit. The qualification verification unit verifies the user's identity information to ensure that only qualified users can participate in the rental operation; while the two-way matching unit is used to match the optimal rental options based on the user's needs and the vehicle's status. Through the collaboration of these two units, the system can ensure that the matching of users and vehicles during the rental process is more accurate and efficient.

[0050] When a user applies for a lease, the system first calls the qualification verification unit for identity verification. Through the aforementioned steps (such as driver's license scanning, APP login, etc.), it confirms whether the user has legal qualifications. If the qualification verification passes, the system will extract the verified user identity information as input data in the subsequent two-way matching process. This step ensures that only qualified users can enter the lease process, ensuring the compliance and security of the lease process.

[0051] After verification, the user's identity information is used as input to select and match the rental vehicle through the two-way matching unit. The two-way matching unit will traverse all vehicle information stored in the TBOX shared database, and select the most suitable vehicle based on multi-dimensional data such as the user's historical rental data, preferences, current needs, and the real-time status of the vehicle. The real-time status information of the vehicle, such as health status and availability, is matched with the user's needs to ensure that the selected vehicle can meet the user's expectations and also meet the health management requirements of the vehicle.

[0052] In this process, the TBOX shared database uses blockchain-based technology for data storage and retrieval operations. The application of this technology ensures data security, immutability and transparency. Through blockchain, all rental data and vehicle health status data can be encrypted, stored and traced, and each operation will be recorded in the blockchain to ensure the authenticity and integrity of the data. In addition, blockchain technology can also ensure that the shared database can ensure efficient data sharing and secure management in multi-party collaborative operations.

[0053] Furthermore, based on the qualification verification unit performing qualification verification, the embodiment of the present application further includes step P10a, and step P10a further includes: P11a: Connect with the cloud platform through TBOX, bind the identity information of the current user in real time, and transmit it to the qualification verification unit as input; P12a: Perform basic user qualification verification based on the qualification verification unit, and generate a basic qualification verification result, wherein the basic qualification verification includes user driving qualification verification and credit record check; P13a: When the basic qualification verification result is passed, retrieve the user driving behavior score of the current user based on the TBOX shared database, perform secondary rental qualification verification, and generate the qualification verification result.

[0054] In a possible embodiment of the present application, in order to further improve the qualification verification process and ensure that the user has legal and safe rental qualifications, the present application also includes step P10a, which describes in detail the user identity verification process.

[0055] First, the TBOX system establishes a stable connection with the cloud platform through the vehicle communication module to obtain the current user's identity information in real time. This identity information can be collected through driver's license scanning, APP login authentication, etc., and transmitted as input to the qualification verification unit to start the subsequent qualification verification process. In this way, it is ensured that the user's identity information can be transmitted to the system in real time and accurately as the basis for subsequent operations.

[0056] Next, the qualification verification unit will perform basic qualification verification based on the obtained user identity information. Basic qualification verification includes two aspects: user driving qualification verification and credit record check. First, the system will connect with the public data of relevant departments to verify whether the user holds a valid driver's license and ensure that he meets the basic requirements for leasing vehicles, such as driving age and driving experience. In addition, check whether the user's driver's license is expired, revoked, etc., to ensure that it complies with traffic regulations. At the same time, check the user's credit record and ensure that the user has no bad records in the past leasing or transaction process by connecting with the data of a third-party credit rating agency, such as failure to return the vehicle on time or failure to pay the rental fee. If the user's basic qualification verification fails, the system will prohibit him from performing the leasing operation and provide corresponding feedback on the reason for failure.

[0057] Once the user's basic qualification verification is passed, the secondary rental qualification verification stage will begin. In this stage, the user's historical driving behavior score data is retrieved from the shared database to further determine the user's rental qualifications. The user's driving behavior score is generated based on the user's driving behavior data during the historical rental period, reflecting the user's driving habits and risk level. For example, if the user frequently engages in unsafe driving behaviors such as speeding and sudden braking in historical rentals, his driving behavior score may be low.

[0058] In the second rental qualification verification, the system will evaluate the user according to the preset scoring standards (for example, the driving behavior score must reach 70 points or more). If the user's driving behavior score meets the standard, the final qualification verification result is generated as "passed", and the user can continue to rent the vehicle; if it does not meet the standard, the qualification verification result of "failed" is generated, and the corresponding prompt information is sent to the user.

[0059] Through the above verification steps, the system can ensure that only qualified users can perform leasing operations, while effectively managing and controlling leasing risks. The multi-level verification of this process not only ensures the safety and compliance of the leasing process, but also optimizes leasing decisions based on the user's driving behavior and reduces operational risks.

[0060] Furthermore, step P63 of the embodiment of the present application also includes: P63-1: Based on the user identity information, the two-way matching unit traverses the TBOX shared database, calls historical car rental data, and obtains the user's historical car rental data; P63-2: Based on the user's historical car rental data, retrieve the user's historical car rental preference data and driving habit data; P63-3: Based on the TBOX shared database, retrieve the most recent vehicle health analysis report and vehicle performance score results of the available vehicles; P63-4: Using the user's historical car rental preference data and driving habit data as a benchmark, combined with the most recent vehicle health analysis report and vehicle performance score results of the available vehicles, perform multi-dimensional rental matching, and generate an optional vehicle sequence according to the degree of matching; P63-5: Push the optional vehicle sequence to the user end, and the user makes a reverse selection.

[0061] It should be understood that the TBOX system conducts multi-dimensional rental matching for users through a two-way matching unit to achieve more accurate and personalized closed-loop two-way rental matching.

[0062] First, based on the acquired user identity information, the two-way matching unit traverses the TBOX shared database to call historical car rental data, thereby obtaining the user's historical car rental data. This data includes the user's past car rental records, usage frequency, preferred car models, common vehicle locations, rental duration, etc. Through these historical data, the system can initially understand the user's car rental habits and preferences.

[0063] Next, based on these historical car rental data, the user's historical car rental preference data and driving habit data are extracted. For example, the system will identify the user's preferred car model (such as SUV, sedan, etc.), the color or configuration of the commonly used vehicle, and driving habits (such as whether they frequently speed or brake suddenly). This information provides the system with the user's personalized needs and can help the system match vehicles more accurately.

[0064] Then, the latest health analysis report and vehicle performance score results of currently available vehicles are retrieved through the TBOX shared database. The vehicle health analysis report contains various technical conditions of the vehicle, such as engine status, brake system, tire pressure, battery power, etc., reflecting the working status of the vehicle and potential failure risks. At the same time, the vehicle performance score results evaluate the overall performance of the vehicle to ensure that the recommended vehicle meets the technical requirements and can provide a good driving experience.

[0065] Furthermore, the user's historical car rental preference data and driving habit data are used as a benchmark, combined with the health analysis report and performance score results of the available vehicles, to perform multi-dimensional rental matching. The matching process considers multiple factors, such as the proximity of the vehicle location to the user's current location, whether the vehicle's health status meets the user's needs, and whether the vehicle's performance meets the user's driving habits. The system will combine these factors, generate an optional vehicle sequence according to the degree of matching, and arrange the vehicles that meet the user's needs in order according to the degree of matching.

[0066] Finally, the system pushes this optional vehicle sequence to the user end, and the user makes a reverse selection. The user can choose from the optional vehicle sequence according to personal needs and preferences. This reverse selection process not only improves user satisfaction, but also ensures that users can make the best choice based on their actual situation. Through this series of steps, the TBOX system can provide users with a more personalized and accurate rental experience, while optimizing the configuration of vehicle resources and ensuring the efficiency and safety of the rental process.

[0067] Furthermore, step P63-4 of the embodiment of the present application also includes: P63-41: The user's historical car rental preference data and driving habit data are used to extract the user's vehicle usage environment characteristics and driving habit characteristics; P63-42: Based on the vehicle usage environment characteristics and driving habit characteristics, the vehicle hardware conditions are matched respectively to generate recommended vehicle hardware requirements; P63-43: Based on the most recent vehicle health analysis report and vehicle performance scoring results of the available vehicles, the hardware performance score of each vehicle is extracted; P63-44: Based on the vehicle hardware requirements, the hardware performance scores of each vehicle are combined to perform a step-by-step matching arrangement to generate the optional vehicle sequence.

[0068] Optionally, the multi-dimensional rental matching process can be further refined by extracting the user's historical rental preference data and driving habit data to accurately generate a recommended vehicle sequence.

[0069] First, the user's vehicle usage environment characteristics and driving habit characteristics are extracted from the user's historical car rental preference data and driving habit data. Vehicle usage environment characteristics refer to the environments in which users usually drive their vehicles (such as urban roads, highways, mountain roads, etc.), which affect the performance requirements of the vehicle. Driving habit characteristics refer to the behavioral patterns that users often exhibit during driving, such as whether they often make sudden brakes or speeding. These characteristics help the system understand the user's actual usage, thereby providing data support for recommending suitable vehicles.

[0070] Next, based on the vehicle usage environment characteristics and driving habit characteristics extracted by the user, the system matches the vehicle hardware conditions respectively. For example, for the vehicle usage environment characteristics, the system selects the appropriate hardware configuration according to the different requirements of the environment. For example, when driving on mountain roads, you may be more inclined to choose a vehicle with better traction and stability; on urban roads, you may pay more attention to fuel efficiency and in-car comfort. For driving habit characteristics, the system selects the appropriate hardware configuration according to the user's driving style. For example, users who frequently brake suddenly may need a more efficient braking system, while users who often speed may need a higher-performance engine and tires.

[0071] Next, based on the most recent vehicle health analysis report and vehicle performance score results of the available vehicles, the hardware performance scores of each vehicle are extracted. These hardware performance scores include key indicators such as the vehicle's engine performance, brake system, tire performance, fuel efficiency, etc., reflecting the performance of each vehicle in various hardware conditions. Through these hardware performance scores, the system can quantitatively evaluate the technical status and performance of each vehicle, providing a basis for the matching process.

[0072] Finally, based on the user's vehicle hardware requirements and the hardware performance scores of each vehicle, a ladder matching arrangement is performed to generate a sequence of optional vehicles. The ladder matching arrangement means that the system will sort according to the level of hardware performance to ensure that each vehicle can meet the user's needs to the greatest extent in terms of technical configuration. For example, vehicles that fully meet the user's hardware requirements and have a higher performance score are ranked in the highest level; vehicles that partially meet the requirements but have slightly lower performance are ranked in the second highest level; vehicles that do not fully meet the requirements are ranked in a lower level. The system generates a sequence of optional vehicles based on these levels and pushes the sequence to the user end for the user to make a reverse selection.

[0073] Through these steps, the user's historical car rental data is deeply matched with the vehicle's hardware performance, which optimizes the vehicle recommendation process and improves the accuracy of vehicle matching and user satisfaction.

[0074] Furthermore, to perform vehicle health management, step P60 of the embodiment of the present application further includes: P64: Based on the TBOX shared database, multiple car rental usage records of multiple users of the current vehicle are extracted; P65: Based on the multiple car rental usage records, user usage behavior data are extracted respectively for clustering, and high-risk usage behaviors are extracted according to the clustering results; P66: Based on the multiple car rental usage records, performance scoring results and vehicle health analysis reports are extracted respectively, vehicle performance trend analysis is performed, and potential health conditions of the vehicle are identified; P67: Using the high-risk usage behaviors and the potential health conditions of the vehicle, a joint vehicle health analysis and prediction is performed to generate a vehicle health prediction result.

[0075] In a possible embodiment of the present application, the vehicle health management process is further refined. By comprehensively analyzing the usage records, driving behaviors and vehicle performance data of multiple users, the system can effectively identify and predict the health status of the vehicle, thereby achieving preventive maintenance and optimized management of the vehicle.

[0076] First, based on the TBOX shared database, multiple rental records of multiple users of the current vehicle are extracted. By analyzing these multiple rental records, the system can collect information about each user's use of the vehicle in different time periods, including driving behavior, rental duration, usage environment, etc. By accumulating this data, the system can fully understand the performance and status of the vehicle under different usage conditions.

[0077] Next, based on the extracted multiple car rental usage records, the user's usage behavior data is extracted and clustered. Through cluster analysis, the system classifies records with similar driving behaviors, thereby identifying the common driving mode of the current vehicle, and then extracts high-risk usage behaviors based on the frequency of occurrence of each behavior, such as frequent emergency braking, speeding, excessive acceleration, etc. These high-risk behaviors may have a negative impact on the health of the vehicle, so they need special attention.

[0078] Furthermore, based on multiple rental car usage records, the vehicle performance scoring results and vehicle health analysis reports in different usage records are extracted, and the vehicle performance trend analysis is performed using the vehicle performance scoring results of different time periods according to the time stamp. By analyzing the historical performance data and health status of the vehicle, the performance changes of the vehicle during different rental periods are identified, and potential health problems are discovered. For example, engine performance decline, brake system wear, battery life reduction, etc. These potential problems may not have caused vehicle failures yet, but early intervention is required to avoid further deterioration.

[0079] Finally, high-risk usage behaviors and the potential health status of the vehicle are used to perform joint vehicle health analysis and prediction. This process combines user behavior data and vehicle status data to predict the health status of the vehicle over a period of time in the future through machine learning algorithms or statistical models. For example, if a vehicle frequently exhibits high-risk driving behaviors and its performance score is on a downward trend, it is predicted that the vehicle has a high probability of failure in the future. The generated vehicle health prediction results will provide important decision support for operators, helping them to arrange vehicle maintenance in advance, optimize vehicle scheduling, improve vehicle reliability and service life, and user driving safety.

[0080] Furthermore, step P67 of the embodiment of the present application also includes: P67-1: Based on the high-risk usage behavior, the vehicle health indicators are associated to generate multiple behavior indicator association combinations; P67-2: Based on the high-risk usage behavior, multiple behavior divergence matrices are generated, and the behavior divergence matrices include behavior type, behavior frequency and behavior degree value; P67-3: Based on the multiple behavior divergence matrices, the vehicle wear and tear is evaluated in combination with the multiple behavior indicator association combinations to generate a vehicle wear and tear evaluation result; P67-4: Using the vehicle wear and tear evaluation result, the potential health status of the vehicle is proofread and corrected to generate the vehicle health prediction result.

[0081] Specifically, a more accurate and detailed vehicle health prediction can be achieved by analyzing in detail the relationship between high-risk usage behaviors and vehicle health conditions.

[0082] First, based on high-risk usage behaviors, the vehicle health indicators are associated to generate multiple behavioral indicator association combinations. High-risk usage behaviors, such as frequent emergency braking, speeding, and excessive acceleration, will have varying degrees of impact on the vehicle's various hardware indicators (such as the brake system, engine, tires, etc.). Based on the characteristics of these behaviors, the system will associate relevant health indicators (such as brake wear, engine load, tire wear, etc.) and generate multiple behavioral indicator association combinations. In this way, the system can more comprehensively evaluate how the user's driving behavior affects the health of the vehicle and establish a direct connection between behavior and vehicle wear.

[0083] Next, multiple behavior divergence matrices are generated based on high-risk usage behaviors. The behavior divergence matrix includes three main dimensions: behavior type, behavior frequency, and behavior severity value. The behavior type represents different types of high-risk behaviors (such as sudden braking, sudden acceleration, etc.); the behavior frequency represents the number of times these behaviors occur in a specific time period; and the behavior severity value quantifies the intensity or impact of these behaviors (such as the severity of sudden braking). Through these three dimensions, the system can comprehensively quantify the user's high-risk driving behavior and provide detailed data support for subsequent wear assessment.

[0084] Furthermore, based on multiple behavior divergence matrices and in combination with multiple behavior index association combinations, vehicle wear assessment is performed. Exemplarily, the degree of wear of each component of the vehicle is quantitatively calculated based on the behavior frequency and behavior degree value in the behavior divergence matrix and the health index in the behavior index association combination. For example, if a vehicle has a high frequency of emergency braking and a large degree of deceleration each time it is emergency braked, the degree of wear of the brake system is assessed based on these data, and a corresponding wear assessment result is generated.

[0085] Finally, the vehicle wear assessment results are used to calibrate and correct the potential health status of the vehicle in the vehicle health analysis report. For example, if the wear assessment shows that the brake system has a lot of wear, a wear calibration coefficient can be generated to correct the health prediction results, thereby issuing an early warning of possible brake failure in the vehicle. Through this dynamic calibration and correction, a more accurate health prediction is provided to help operators perform preventive maintenance before problems occur and avoid possible failures. This process not only improves the accuracy of vehicle health predictions, but also provides operators with the opportunity to intervene in advance, which helps to extend the service life of the vehicle, reduce operating costs, and improve the reliability and safety of the vehicle.

[0086] In summary, the embodiments of the present application have at least the following technical effects: This application connects to the cloud platform through TBOX, binds user identity in real time and verifies qualifications, and matches the best rental vehicle. During the vehicle rental process, TBOX monitors driving behavior and vehicle status in real time, generates full-cycle driving behavior data and vehicle working status data, and performs driving behavior scoring and vehicle condition scoring. The scoring results are archived in the TBOX shared database, and closed-loop two-way rental matching and vehicle health management are achieved through the shared database.

[0087] The technical effect of conducting closed-loop two-way rental matching and vehicle health management based on a shared database supported by TBOX has been achieved, improving vehicle health management capabilities and meeting users' personalized needs.

[0088] Embodiment 2 is based on the same inventive concept as the vehicle life cycle data management method supported by TBOX in the above embodiment. Figure 2 As shown, the present application provides a vehicle life cycle data management system supported by TBOX, and the system and method embodiments in the present application embodiments are based on the same inventive concept. Among them, the system includes:

[0089] The qualification verification module 11 is used to connect to the cloud platform through TBOX, bind the identity information of the current user in real time, perform qualification verification, and generate a qualification verification result.

[0090] The rental vehicle matching module 12 is used to obtain the historical rental data of the current user and access the TBOX shared database to match and obtain the best rental vehicle if the qualification verification result is passed.

[0091] The TBOX real-time monitoring module 13 is used to use TBOX to monitor the user's driving behavior and vehicle status in real time during the rental use of the optimal rental vehicle, and obtain the full-cycle rental driving behavior data and the full-cycle vehicle working status data.

[0092] The driving behavior scoring module 14 is used to perform driving behavior scoring based on the full-cycle rental driving behavior data, generate a user driving behavior score and archive it to the TBOX shared database.

[0093] The vehicle condition scoring module 15 is used to score the vehicle condition based on the full-cycle vehicle working status data, generate a vehicle performance scoring result and a vehicle health analysis report, and archive them to the TBOX shared database.

[0094] The closed-loop rental management module 16 is used to perform closed-loop two-way rental matching and vehicle health management based on the TBOX shared database.

[0095] Furthermore, the qualification verification module 11 is further configured to perform the following steps: Through the connection between TBOX and the cloud platform, the identity information of the current user is bound in real time and transmitted to the qualification verification unit as input; based on the qualification verification unit, the user's basic qualifications are verified and a basic qualification verification result is generated, wherein the basic qualification verification includes user driving qualification verification and credit record check; when the basic qualification verification result is passed, the user driving behavior score of the current user is retrieved based on the TBOX shared database, and a secondary rental qualification verification is performed to generate the qualification verification result.

[0096] Furthermore, the driving behavior scoring module 14 is also used to perform the following steps: According to the safe driving standards and vehicle maintenance standards, high-risk driving assessment indicators and high-consumption driving assessment indicators are obtained; according to the high-risk driving assessment indicators and high-consumption driving assessment indicators, based on the full-cycle rental driving behavior data, high-risk feature sets and high-consumption feature sets are extracted; based on the high-risk feature set and high-consumption feature set, a multi-dimensional driving behavior score is performed on the current user to generate a personalized driving behavior score report; the personalized driving behavior score report is combined with the user's historical rental data to form a long-term driving behavior profile of the current user, and archived to the TBOX shared database.

[0097] Furthermore, the closed-loop leasing management module 16 is further configured to perform the following steps: The TBOX shared database is associated with a qualification verification unit and a two-way matching unit; after the qualification verification is passed based on the qualification verification unit, the verified user identity information is extracted; the user identity information is used as input, and the two-way matching unit traverses the TBOX shared database to perform two-way rental selection matching to obtain the optimal rental vehicle. The TBOX shared database performs data storage and extraction operations based on blockchain.

[0098] Furthermore, the closed-loop leasing management module 16 is further configured to perform the following steps: Based on the user identity information, the two-way matching unit traverses the TBOX shared database, calls historical car rental data, and obtains the user's historical car rental data; based on the user's historical car rental data, retrieves the user's historical car rental preference data and driving habit data; based on the TBOX shared database, retrieves the most recent vehicle health analysis report and vehicle performance score results of the available vehicles; uses the user's historical car rental preference data and driving habit data as a benchmark, combined with the most recent vehicle health analysis report and vehicle performance score results of the available vehicles, performs multi-dimensional rental matching, and generates an optional vehicle sequence according to the degree of matching; pushes the optional vehicle sequence to the user end, and the user makes a reverse selection.

[0099] Furthermore, the closed-loop leasing management module 16 is further configured to perform the following steps: The user's historical car rental preference data and driving habit data are used to extract the user's vehicle usage environment characteristics and driving habit characteristics; based on the vehicle usage environment characteristics and driving habit characteristics, the vehicle hardware conditions are matched respectively to generate recommended vehicle hardware requirements; based on the most recent vehicle health analysis report and vehicle performance scoring results of the available vehicles, the hardware performance score of each vehicle is extracted; based on the vehicle hardware requirements, the hardware performance scores of each vehicle are combined to perform step-by-step matching and arrangement to generate the optional vehicle sequence.

[0100] Furthermore, the closed-loop leasing management module 16 is further configured to perform the following steps: Based on the TBOX shared database, multiple car rental usage records of multiple users of the current vehicle are extracted; based on the multiple car rental usage records, user usage behavior data are extracted respectively for clustering, and high-risk usage behaviors are extracted based on the clustering results; based on the multiple car rental usage records, performance scoring results and vehicle health analysis reports are extracted respectively, vehicle performance trend analysis is performed, and potential health conditions of the vehicle are identified; using the high-risk usage behaviors and the potential health conditions of the vehicle, a joint vehicle health analysis and prediction is performed to generate a vehicle health prediction result.

[0101] Furthermore, the closed-loop leasing management module 16 is further configured to perform the following steps: Based on the high-risk usage behavior, vehicle health indicators are associated to generate multiple behavior indicator association combinations; based on the high-risk usage behavior, multiple behavior divergence matrices are generated, and the behavior divergence matrices include behavior type, behavior frequency and behavior degree value; based on the multiple behavior divergence matrices, vehicle wear assessment is performed in combination with the multiple behavior indicator association combinations to generate a vehicle wear assessment result; using the vehicle wear assessment result, the potential health status of the vehicle is proofread and corrected to generate the vehicle health prediction result.

[0102] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0104] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The vehicle life cycle data management method supported by TBOX is characterized by: The method comprises: Through TBOX's connection with the cloud platform, the current user's identity information is bound in real time, and qualification verification is performed to generate qualification verification results; If the qualification verification result is passed, the historical rental data of the current user is obtained, and the TBOX shared database is accessed to match the best rental vehicle; During the leasing of the best leasing vehicles, TBOX is used to monitor the user's driving behavior and vehicle status in real time, and obtain the full-cycle leasing driving behavior data and full-cycle vehicle working status data; Based on the full-cycle rental driving behavior data, a driving behavior score is performed, a user driving behavior score is generated and archived in the TBOX shared database; Based on the full-cycle vehicle working status data, the vehicle condition is scored, a vehicle performance scoring result and a vehicle health analysis report are generated, and the results are archived in the TBOX shared database; Based on the TBOX shared database, closed-loop two-way rental matching and vehicle health management are performed.

2. The vehicle life cycle data management method supported by TBOX as claimed in claim 1, characterized in that: Based on the TBOX shared database, two-way leasing selection matching is performed, including: The TBOX shared database is associated with an eligibility verification unit and a two-way matching unit; After the qualification verification is passed based on the qualification verification unit, extracting the verified user identity information; The user identity information is used as input, and the two-way matching unit traverses the TBOX shared database to perform two-way rental selection matching to obtain the optimal rental vehicle.

3. The vehicle life cycle data management method supported by TBOX as claimed in claim 2, characterized in that: The TBOX shared database performs data storage and retrieval operations based on blockchain.

4. The vehicle life cycle data management method supported by TBOX as claimed in claim 3, characterized in that: Taking the user identity information as input, the two-way matching unit traverses the TBOX shared database to perform two-way rental selection matching, including: Based on the user identity information, the two-way matching unit traverses the TBOX shared database, calls historical car rental data, and obtains the user's historical car rental data; According to the user's historical car rental data, retrieve the user's historical car rental preference data and driving habit data; Based on the TBOX shared database, retrieve the most recent vehicle health analysis report and vehicle performance score results of the available vehicles; Using the user's historical car rental preference data and driving habit data as a benchmark, combined with the most recent vehicle health analysis report and vehicle performance score results of the available vehicles, multi-dimensional rental matching is performed, and a sequence of optional vehicles is generated according to the degree of matching; The optional vehicle sequence is pushed to the user end, and the user makes a reverse selection.

5. The vehicle life cycle data management method supported by TBOX as claimed in claim 4, characterized in that: Perform multi-dimensional rental matching and generate optional vehicle sequences according to the matching degree, including: The user's historical car rental preference data and driving habit data are used to extract the user's vehicle usage environment characteristics and driving habit characteristics; Based on the vehicle usage environment characteristics and driving habit characteristics, vehicle hardware conditions are matched respectively to generate recommended vehicle hardware requirements; Extracting the hardware performance score of each vehicle according to the most recent vehicle health analysis report and vehicle performance score result of the available vehicles; Based on the vehicle hardware requirements and in combination with the hardware performance scores of the various vehicles, a step-by-step matching arrangement is performed to generate the optional vehicle sequence.

6. The vehicle life cycle data management method supported by TBOX as claimed in claim 3, characterized in that: Through TBOX’s connection with the cloud platform, the current user’s identity information is bound in real time, and eligibility verification is performed, including: Connecting TBOX with the cloud platform, binding the current user's identity information in real time, and transmitting it to the qualification verification unit as input; Performing basic qualification verification of the user based on the qualification verification unit to generate a basic qualification verification result, wherein the basic qualification verification includes a driving qualification verification and a credit record check of the user; When the basic qualification verification result is passed, the user driving behavior score of the current user is retrieved based on the TBOX shared database, and a secondary rental qualification verification is performed to generate the qualification verification result.

7. The vehicle life cycle data management method supported by TBOX as claimed in claim 1, characterized in that: Based on the full-cycle rental driving behavior data, a driving behavior score is performed, and a user driving behavior score is generated and archived in the TBOX shared database, including: According to the safe driving standards and vehicle maintenance standards, obtain high-risk driving assessment indicators and high-consumption driving assessment indicators; According to the high-risk driving evaluation index and the high-consumption driving evaluation index, based on the full-cycle rental driving behavior data, extract a high-risk feature set and a high-consumption feature set; Based on the high-risk feature set and the high-consumption feature set, a multi-dimensional driving behavior score is performed on the current user to generate a personalized driving behavior score report; The personalized driving behavior score report is combined with the user's historical rental data to form a long-term driving behavior profile of the current user, and is archived in the TBOX shared database.

8. The vehicle life cycle data management method supported by TBOX as claimed in claim 1, characterized in that: Based on the TBOX shared database, vehicle health management is performed, including: Based on the TBOX shared database, extract multiple car rental usage records of multiple users of the current vehicle; According to the plurality of car rental usage records, respectively extracting user usage behavior data for clustering, and extracting high-risk usage behaviors according to the clustering results; Extracting performance scoring results and vehicle health analysis reports respectively according to the plurality of vehicle rental usage records, performing vehicle performance trend analysis, and identifying potential vehicle health conditions; Using the high-risk usage behavior and the potential health status of the vehicle, a joint vehicle health analysis and prediction is performed to generate a vehicle health prediction result.

9. The vehicle life cycle data management method supported by TBOX as claimed in claim 8, characterized in that: Using the high-risk usage behavior and the potential health status of the vehicle, a joint vehicle health analysis prediction is performed, including: Based on the high-risk usage behavior, the vehicle health index is associated to generate multiple behavior index association combinations; Generate a plurality of behavior divergence matrices according to the high-risk usage behaviors, wherein the behavior divergence matrices include behavior types, behavior frequencies, and behavior degree values; Based on the multiple behavior divergence matrices, the vehicle wear assessment is performed in combination with the multiple behavior indicator association combinations to generate a vehicle wear assessment result; The vehicle wear and tear assessment result is used to calibrate and correct the potential health status of the vehicle to generate the vehicle health prediction result.

10. The vehicle life cycle data management system supported by TBOX is characterized by: The system comprises: A qualification verification module, which is used to connect to the cloud platform through TBOX, bind the identity information of the current user in real time, perform qualification verification, and generate a qualification verification result; A rental vehicle matching module, wherein if the qualification verification result is passed, the rental vehicle matching module is used to obtain the historical rental data of the current user and access the TBOX shared database to match and obtain the best rental vehicle; TBOX real-time monitoring module, which is used to use TBOX to monitor the user's driving behavior and vehicle status in real time during the rental use of the optimal rental vehicle, and obtain full-cycle rental driving behavior data and full-cycle vehicle working status data; A driving behavior scoring module, which is used to perform driving behavior scoring based on the full-cycle rental driving behavior data, generate a user driving behavior score and archive it to the TBOX shared database; A vehicle condition scoring module, which is used to score the vehicle condition based on the full-cycle vehicle working status data, generate a vehicle performance scoring result and a vehicle health analysis report, and archive them to the TBOX shared database; A closed-loop rental management module is used to perform closed-loop two-way rental matching and vehicle health management based on the TBOX shared database.

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