Vehicle service data management method and system based on block chain

Abstract

The invention relates to the field of data management, in particular to a block chain-based vehicle service data management method and system. The method comprises the following steps: acquiring full-cycle vehicle service behavior data and a vehicle service protocol log; performing vehicle digital identifier identification and one-by-one service behavior type analysis on the full-period vehicle service behavior data to obtain a service behavior module of each vehicle; service-driven intelligent contract editing is carried out on the vehicle service protocol log, distributed block chain storage processing is carried out on a service behavior module of each vehicle, and a vehicle service block chain network is constructed; identifying real-time vehicle service behavior data; and carrying out block chain module positioning on the vehicle service block chain network according to the real-time vehicle service behavior data, and carrying out instant automatic updating execution to obtain an instant updated block chain network. According to the invention, efficient and safe vehicle service data management is realized, and more personalized and intelligent vehicle services are provided for vehicle owners.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to a vehicle service data management method and system based on blockchain. Background Art

[0002] With the rapid development of intelligent technology and the continuous innovation of the automobile industry, vehicle service management has gradually shifted from the traditional manual management mode to digital and intelligent management. Vehicle services include regular maintenance, fault repair, insurance claims, roadside assistance and many other contents. These services involve information interaction and data sharing between multiple entities such as car owners, maintenance service providers, insurance companies, and automobile manufacturers. The traditional vehicle service data management method relies on centralized databases and manual records, and often has problems such as information islands, data tampering, and service tracking difficulties. In addition, the vehicle's service data is usually stored in a decentralized manner, making it difficult to achieve cross-platform and cross-organizational data sharing and integration, resulting in the vehicle's historical service records and real-time maintenance status cannot be efficiently queried and updated by all parties.

[0003] As blockchain technology matures, it is a decentralized, distributed storage technology that can provide tamper-proof and transparent data records with high credibility and security. Therefore, the application of blockchain technology in vehicle service data management can effectively solve the problems existing in traditional methods. Blockchain can not only realize the secure storage and sharing of vehicle service data, but also automatically manage various business logics in the vehicle service process through smart contracts, such as service agreement, payment settlement, service fulfillment, etc., which improves the transparency and efficiency of the entire management process.

[0004] However, although blockchain technology has brought many advantages to vehicle service data management, it still faces some challenges in practical applications. First, since vehicle services involve multiple stakeholders, how to ensure data mutual trust and collaboration among different service entities is a complex issue. Secondly, how to achieve real-time updates and monitoring, and timely handle abnormal situations in the service process, such as vehicle failures or service deviations, is also an important problem faced by blockchain in vehicle service management. Therefore, it is urgent to develop a more intelligent vehicle service data management method. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a vehicle service data management method and system based on blockchain to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a vehicle service data management method based on blockchain, comprising the following steps: Step S1: Acquire full-cycle vehicle service behavior data and vehicle service agreement log; perform vehicle digital identification and service behavior type analysis on the full-cycle vehicle service behavior data to obtain a service behavior module for each vehicle; Step S2: Edit the vehicle service agreement log using a service-driven smart contract, and perform distributed blockchain storage processing on the service behavior module of each vehicle to build a vehicle service blockchain network; Step S3: Identify real-time vehicle service behavior data; locate the blockchain module of the vehicle service blockchain network according to the real-time vehicle service behavior data, and perform instant automatic update execution to obtain an instant updated blockchain network; Step S4: Perform real-time user access detection on the instant update blockchain network, analyze abnormal access behavior, and generate abnormal blockchain access warning signals; Step S5: based on the abnormal blockchain access warning signal, the abnormal vehicle service is traced back on the instant update blockchain network, and the abnormal node is repaired adaptively, thereby generating an abnormal service behavior repair strategy; Step S6: Based on the real-time update blockchain network, personalized service behavior recommendations are made, and based on the abnormal service behavior repair strategy, intelligent collaborative management optimization is performed to build an intelligent vehicle service management model.

[0007] The present invention can provide comprehensive basic information for subsequent data processing by comprehensively collecting the full-cycle service data of the vehicle, ensuring that in-depth analysis can be performed on the specific service behavior of each vehicle. Vehicle digital identification and service behavior analysis help to clarify the service requirements and service content of each vehicle, thereby laying a solid foundation for subsequent smart contracts and blockchain storage. Through the editing of smart contracts and blockchain storage, the automatic execution and non-tamperability of service agreements and vehicle service behaviors are ensured. Smart contracts can automatically execute service behaviors according to service agreements, improve service efficiency, and ensure data security and transparency through distributed storage of blockchain to prevent data tampering. The blockchain network provides an efficient and secure management framework for subsequent vehicle service behaviors. By identifying vehicle service behavior data in real time and updating the blockchain network in real time, the timeliness and accuracy of vehicle service data can be maintained. Blockchain module positioning enables each service behavior to be quickly confirmed and processed, ensuring the real-time and traceability of service data, and avoiding potential problems caused by data lag. Real-time access detection and abnormal behavior analysis can help to timely discover potential problems in the blockchain network, such as data security threats or non-compliant behaviors. The generated early warning signals help to intervene before the problem expands, protecting the security of the blockchain network and vehicle service data. Abnormal tracing and adaptive repair strategies can quickly locate and resolve abnormalities in service behaviors and prevent abnormal situations from spreading. Through the automated abnormal repair mechanism, the normal operation of the system can be restored in a timely manner, reducing manual intervention and improving the stability and reliability of the system. Through personalized recommendations, customized services can be provided based on the service history and user needs of each vehicle to improve service quality and user satisfaction. Intelligent collaborative management optimization helps to improve the overall operational efficiency and management level of vehicle services. At the same time, intelligent optimization based on abnormal repair strategies can continuously improve the system's self-regulation capabilities in the service and build a more intelligent and personalized service management model.

[0008] Preferably, step S1 comprises the following steps: Step S11: Collect all full-cycle vehicle service behavior data and vehicle service agreement logs in the vehicle service information system; Step S12: performing vehicle digital identification on the full-cycle vehicle service behavior data to extract the digital identification of each vehicle; Step S13: extracting each service behavior timestamp of the full-cycle vehicle service behavior data; Step S14: Analyzing the service behavior types of the full-cycle vehicle service behavior data one by one to generate a feature of each service behavior type; Step S15: performing vehicle service time series fitting on each service behavior type feature according to each service behavior timestamp to generate a service behavior time series sequence; Step S16: Based on the digital identification of each vehicle, the service behavior time sequence is subjected to service identification binding processing to obtain the service behavior module of each vehicle.

[0009] The present invention accurately associates all service behavior data with specific vehicles by assigning a unique digital identifier (such as a license plate number, vehicle identification code, etc.) to each vehicle. This ensures that in subsequent data analysis and blockchain storage, the service record of each vehicle can be accurately identified and tracked, avoiding data confusion and improving the accuracy and traceability of data management. By extracting the timestamp of each service behavior, the service behavior is accurately managed in time series. The timestamp data provides a basis for subsequent time series fitting, service behavior analysis and anomaly detection. This allows subsequent analysis to take into account the time dimension of service behavior, ensure the timeliness and continuity of data, and facilitate the analysis of the service mode of each vehicle. By analyzing the service behavior type of the vehicle, different service categories (such as repair, maintenance, insurance, etc.) are identified, and specific characteristics are given to each service behavior. This classification helps to manage and analyze service data more carefully, and provides a clear service structure for subsequent intelligent decision-making, personalized recommendation and blockchain network management. By fitting the service behavior data in time series, the service behavior change trend of each vehicle is obtained, which helps to identify the regularity and periodicity of vehicle services. The generation of service behavior time series provides data support for subsequent data prediction, anomaly detection and optimization. In addition, time-series fitting can also make the management of vehicle services more forward-looking and intelligent, providing a basis for data-driven service management decisions. By binding the service behavior time series with the digital identification of each vehicle, a service behavior module for each vehicle can be generated, so that the service data of each vehicle can be managed accurately and independently. This modular service behavior management method facilitates subsequent blockchain storage, data analysis and intelligent optimization, and improves the operability and personalized management capabilities of service data.

[0010] Preferably, step S2 specifically comprises the following steps: Step S21: parsing the vehicle service agreement log according to the general service data standard to obtain the vehicle service general standard; Step S22: defining a service interaction protocol based on the vehicle service universal standard to obtain a vehicle service standardized interaction protocol; Step S23: setting the rights and responsibilities of multiple service entities in the vehicle service agreement log, and generating the service rights and responsibilities of each entity; Step S24: Editing the service-driven smart contract based on the standardized interaction protocol of vehicle services and the service rights and responsibilities of each subject, thereby generating a vehicle service-driven smart contract; Step S25: Perform distributed blockchain storage processing on the service behavior module of each vehicle according to the vehicle service driving smart contract to build a vehicle service blockchain network.

[0011] The present invention can unify service agreements from different sources and in different formats into a common standard by standardizing and parsing the vehicle service agreement log, thereby ensuring data compatibility and interoperability between different service entities. This lays a unified normative foundation for subsequent data processing, smart contract writing and blockchain storage, and avoids the risk of data confusion or inconsistent operations due to inconsistent protocols. Based on the general service standard, a unified vehicle service interaction protocol is defined, which helps to standardize the interaction mode between different service entities. Through a standardized service interaction protocol, it is ensured that all aspects of vehicle services are carried out in accordance with a unified process, thereby improving the efficiency and consistency of the service. In addition, the standardized protocol can reduce service disputes caused by poor communication or unclear agreements, and enhance the transparency and trust of the entire service system. It clarifies the rights and responsibilities that each service entity (such as car owners, repair shops, insurance companies, etc.) should bear during the service process to prevent legal risks or disputes caused by unclear responsibilities. By setting the rights and responsibilities of each entity, it is ensured that all participants abide by the rules and there is a clear responsibility tracing mechanism. This provides a basis for the subsequent execution of smart contracts and ensures the fairness and legality of contract execution. By editing service-driven smart contracts, the service behaviors, task allocation, and responsibility traceability between the parties are automatically defined according to the standardized interaction protocols and the rights and responsibilities of the service subject. The automatic execution of smart contracts eliminates manual intervention and human errors, and improves the efficiency and transparency of services. Smart contracts also have the characteristics of immutability and automatic execution, which ensures the strict implementation of contract terms and improves the credibility and compliance of services. By storing the service behavior module of each vehicle on the blockchain, the immutability, transparency, and security of all vehicle service data are guaranteed. The distributed storage method effectively avoids single point failures and data loss, and each service behavior is traced and verified to ensure the authenticity and reliability of service data. The introduction of the blockchain network also improves the sharing of data. All participants collaborate and make decisions based on the same decentralized data platform, thereby improving service efficiency and collaboration.

[0012] Preferably, step S3 specifically comprises the following steps: Step S31: identifying real-time vehicle service behavior data; Step S32: performing multi-party identity authentication on the real-time vehicle service behavior data to obtain service behavior identity authentication information; Step S33: performing data authorization analysis according to the service behavior identity authentication information to obtain the real-time service behavior authorization scope; Step S34: positioning the blockchain module of the vehicle service blockchain network based on the authorization scope of the real-time service behavior to obtain blockchain positioning data of the real-time behavior; Step S35: The real-time vehicle service behavior data is automatically updated on the vehicle service blockchain network according to the blockchain positioning data of the real-time behavior to obtain a real-time updated blockchain network.

[0013] The present invention enables the system to dynamically monitor and record the service behavior of each vehicle by identifying vehicle service behavior data in real time. This process ensures the timeliness of vehicle service data, can capture every detail, and improves the real-time and accuracy of the data. In addition, the acquisition of real-time data provides basic support for subsequent blockchain storage, smart contract execution and anomaly detection. Multi-party identity authentication can ensure the authenticity and legitimacy of the identities of all parties involved in the service behavior (such as car owners, repair shops, insurance companies, etc.), and prevent forged identities or unauthorized personnel from accessing or operating data. By ensuring the security and accuracy of identity authentication, the trust of the system is enhanced, and the data is protected from unauthorized access or tampering. Authorization analysis based on identity authentication information can ensure that each service subject can only access and process the relevant data for which it is authorized. By setting a strict authorization scope, data leakage or abuse can be effectively avoided, ensuring that only legally authorized parties can perform corresponding operations. This authorization mechanism improves the security of the system and ensures data privacy protection. The module of the blockchain network is positioned according to the authorization scope to ensure that only authorized parties can access and modify relevant data blocks. This process further enhances the security and controllability of data management by ensuring legal access and operation of data. Real-time blockchain positioning data provides an accurate path for subsequent automated updates and data verification, improving the efficiency and accuracy of the blockchain network. Through real-time automated update execution, every vehicle service behavior is ensured to be instantly recorded and verified in the blockchain network. The automatic update execution of the blockchain network not only improves the efficiency of data processing, but also ensures the immutability and real-time nature of the data. The update of each service behavior is reflected in real time in the network, ensuring the consistency and transparency of the data, further improving the reliability of blockchain as a data management tool.

[0014] Preferably, the specific steps of step S4 are: Step S41: Perform real-time user access detection on the instant update blockchain network and extract multiple user access behavior data; Step S42: performing access compliance verification on the access behavior data of multiple users to obtain a compliance evaluation value of each access behavior; Step S43: Analyze abnormal access behaviors based on the compliance evaluation value of each access behavior, and mark abnormal user access behaviors; Step S44: Generate an abnormal blockchain access warning signal based on abnormal user access behavior.

[0015] The present invention can continuously monitor and record all user access behaviors in the blockchain network through real-time user access detection. This process provides a key data basis for subsequent access behavior analysis and compliance verification. Real-time monitoring ensures that any suspicious or abnormal access behavior can be captured in time, improving the security and operability of the blockchain network. At the same time, it also provides rich historical access records for data analysis and decision-making. By verifying the compliance of user access behavior, it is ensured that each access meets the established security and compliance standards. This process helps identify any unauthorized access, violations or non-compliant operations, thereby providing support for subsequent data protection and service management. By evaluating the compliance of each access behavior, it is ensured that all access behaviors comply with the requirements of laws, regulations and service agreements, thereby improving the legitimacy and compliance of the system. Abnormal access behavior analysis based on compliance evaluation values ​​helps the system automatically identify access behaviors that do not conform to normal patterns, such as unauthorized operations, potential malicious behaviors or abnormal access frequencies. This process helps to quickly discover potential security threats or system vulnerabilities and prevent data leakage or tampering. By marking abnormal access behaviors, the system automatically takes necessary measures to handle them, such as further review, alarm or access restriction, thereby enhancing the protection capabilities of the system. By generating early warning signals based on abnormal user access behaviors, administrators or related personnel can be reminded of potential security risks in a timely manner. This early warning mechanism helps the system respond to problems before they occur or in the early stages to prevent them from further expanding. By generating early warning signals and enabling automated response measures, the system's emergency response capabilities are improved, manual intervention is reduced, and response speed is increased. This process effectively enhances the monitoring and risk response capabilities of the vehicle service data management system.

[0016] Preferably, the specific steps of step S5 are: Step S51: based on the abnormal blockchain access warning signal, the instant update blockchain network is used to trace abnormal vehicle services and extract all abnormal vehicle service record information; Step S52: Tracing and locating abnormal nodes based on all abnormal vehicle service record information, and marking abnormal blockchain nodes; Step S53: Isolate the abnormal blockchain nodes and perform adaptive node abnormality repair to generate an abnormal service behavior repair strategy.

[0017] The present invention triggers the tracing of abnormal service behaviors of the blockchain network based on abnormal access warning signals, ensuring that all vehicle service records involving abnormal behaviors can be quickly found. This provides key data for subsequent abnormal analysis and processing, and helps to immediately understand the specific details and scope of the abnormality. By tracing the relevant service records, it is ensured that the problem can be fully diagnosed to prevent any potential abnormal behavior from being missed. By tracing the source of all abnormal vehicle service records, the abnormal blockchain nodes can be located, that is, which nodes or data points have problems. The beneficial effect of this step is that it can help the system to clarify the root cause of abnormal behavior and provide a basis for subsequent node isolation and repair. After marking the abnormal nodes, the system can further narrow the scope of the problem and improve the accuracy of abnormal detection. After locating the abnormal nodes, targeted measures are taken to repair them to avoid the expansion of the problem. By isolating the abnormal nodes, the interference of the abnormal nodes is limited before the problem affects the entire system, ensuring that the rest of the blockchain network can operate normally. Adaptive node abnormal repair means that the system can automatically adjust and repair damaged nodes without relying too much on manual operations, which improves the response speed and system stability. The resulting abnormal service behavior repair strategy provides a sustainable solution for subsequent service optimization and system repair, ensuring the long-term healthy operation of vehicle service data management.

[0018] Preferably, the specific steps of step S6 are: Step S61: Evolving the personalized behavior habits of the car owner based on the real-time updated blockchain network, thereby generating a personalized behavior preference portrait of the car owner; Step S62: Recommending personalized service behaviors based on the personalized behavior preference portrait of the car owner to generate a personalized service behavior recommendation strategy; Step S63: Intelligent collaborative management optimization of personalized service behavior recommendation strategies and abnormal service behavior repair strategies is performed to build an intelligent vehicle service management model.

[0019] The present invention analyzes the evolution of the owner's behavior habits based on a real-time updated blockchain network, and tracks the owner's service needs and behavior patterns at different times and in different scenarios. By analyzing these behavior data, a personalized behavior preference portrait of the owner is generated to reveal the owner's preferences, habits and changes in needs. By combining the owner's personalized behavior preference portrait with the service recommendation algorithm, tailored service recommendations are provided to the owner. These recommendations include regular maintenance, repair services, insurance renewal, etc., and personalized recommendations are made based on the owner's needs, driving habits, past service history and other data. Intelligent collaborative management optimizes personalized service recommendation strategies and abnormal service behavior repair strategies, making vehicle service management more intelligent and flexible. In terms of service recommendation, intelligent collaborative management can adjust the recommendation strategy in time according to the owner's behavioral changes to cope with changes in the owner's needs. In terms of abnormal repair, the repair measures can be automatically adjusted according to the abnormal detection and repair strategy of the service behavior to avoid the occurrence of repeated problems. Overall, this optimization process can maximize the system's adaptive ability and realize accurate management and intelligent operation of services. Through this collaborative optimization, the efficiency of the system is improved, manual intervention is reduced, and a sustainable solution is provided for future vehicle service management.

[0020] Preferably, the specific steps of step S61 are: Analyze various types of vehicle service behaviors on the real-time updated blockchain network to generate various types of vehicle service behavior characteristics; Calculate the behavior frequency of each type of service behavior characteristics of the vehicle to obtain the frequency of each service behavior of the vehicle; Carry out vehicle service cycle trend mining on various types of vehicle service behavior characteristics to generate service behavior cycle trend characteristics; The frequency of each service behavior of the vehicle and the trend characteristics of the service behavior cycle are used to evolve the personalized behavior habits of the owner, thereby generating a personalized behavior preference portrait of the owner.

[0021] The present invention analyzes vehicle service behaviors on the blockchain network that is updated in real time, and the system can extract different service behavior characteristics (repair, maintenance, refueling, insurance, etc.) from each service operation of the vehicle. The core advantage of this step is that it can analyze the service data of the vehicle in real time and accurately identify the characteristics of different service behaviors, providing basic data for subsequent personalized service recommendations and owner behavior analysis. At the same time, the data immutability of the blockchain network ensures the authenticity and integrity of the service behavior characteristics and enhances the credibility of the analysis results. By calculating the frequency of various types of vehicle service behaviors, the system helps quantify the frequency of each service behavior, such as how many times a vehicle is maintained per year and how often it is repaired. Frequency analysis can provide detailed information about the owner's use of the vehicle, revealing the owner's daily usage pattern and the intensity of demand for different services. This analysis process helps the system understand the owner's preferences and demand changes, and is helpful for the design of subsequent personalized services. Blockchain-based data ensures the accuracy of frequency calculation and the reliability of the data source, avoiding the risk of human modification. By mining the periodic trend of vehicle service behavior, the owner's periodic service needs are captured, such as some services (such as oil change, maintenance, tire inspection) are repeated every few months or a year. Periodic trend mining helps the system identify the periodic behavior patterns of car owners and further improve the accuracy of personalized service recommendations. By combining the frequency of service behavior and periodic trend characteristics, the system can generate a personalized behavior preference portrait of the car owner, which comprehensively describes the car owner's service needs, habits and preferences. This portrait can accurately reflect the car owner's service preferences, help the system better understand the car owner's needs, and thus provide tailored services in daily management.

[0022] In this specification, a vehicle service data management system based on blockchain is provided, which is used to execute the vehicle service data management method based on blockchain as described above, including: A behavior type analysis module is used to obtain full-cycle vehicle service behavior data and vehicle service agreement logs; perform vehicle digital identification and service behavior type analysis on the full-cycle vehicle service behavior data to obtain a service behavior module for each vehicle; A blockchain network module is used to perform service-driven smart contract editing on the vehicle service agreement log, and to perform distributed blockchain storage processing on the service behavior module of each vehicle to build a vehicle service blockchain network; An instant update module is used to identify real-time vehicle service behavior data; locate the blockchain module of the vehicle service blockchain network according to the real-time vehicle service behavior data, and perform instant automatic update execution to obtain an instant updated blockchain network; Abnormal access module, used to detect real-time user access to the instant update blockchain network, analyze abnormal access behavior, and generate abnormal blockchain access warning signals; The adaptive repair module is used to trace abnormal vehicle services on the instant update blockchain network based on abnormal blockchain access warning signals, and to perform adaptive node abnormal repair, thereby generating abnormal service behavior repair strategies; The collaborative management module is used to recommend personalized service behaviors based on the instantly updated blockchain network, and to optimize intelligent collaborative management based on abnormal service behavior repair strategies, thereby building an intelligent vehicle service management model.

[0023] The present invention ensures a complete record of vehicle service behavior by fully acquiring service data and protocol logs. Through digital identification and behavior type analysis, a detailed service behavior module can be established for each vehicle, laying the foundation for subsequent personalized services and intelligent management. The authenticity and transparency of service protocol logs and service behavior modules are guaranteed by blockchain technology to ensure that data cannot be tampered with. Through the editing of service-driven smart contracts, the automatic management and execution of vehicle services are realized, and service efficiency and transparency are improved. Through real-time updates, vehicle service data is always kept up to date, supporting dynamic management and decision-making. The relevant blockchain modules can be quickly located and accurately updated to improve the efficiency of data management. Automated update execution reduces manual intervention, improves work efficiency and system response speed. By detecting user access in real time, potential security threats can be quickly identified and responded to. In-depth analysis of abnormal access behavior helps to timely discover system vulnerabilities, unauthorized access and other malicious activities, and ensure data security. By generating early warning signals, actions can be taken before security threats expand to prevent data leakage or tampering. Rapid tracing and repair of abnormal nodes and services reduces service interruption time and improves system stability. The adaptive repair mechanism automatically repairs anomalies without human intervention, improving the self-healing ability of the system. The repair strategy prevents the recurrence of abnormal problems and ensures the long-term stable operation of the system. Through real-time updated personalized recommendations, the owner experience is improved, the specific needs of car owners are met, and the efficiency and accuracy of vehicle services are improved. Through collaborative management, combined with abnormal repair strategies and personalized service recommendations, intelligent service management is achieved, service quality is improved, and unnecessary waste of resources is reduced. This module makes vehicle service management not only rely on human intervention, but also autonomously optimized by intelligent algorithms, improving the system's adaptability and the overall efficiency of service management. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a vehicle service data management method based on blockchain in the present invention; Figure 2Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0025] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0026] The example of this application provides a vehicle service data management method and system based on blockchain. The execution subject of the vehicle service data management method and system based on blockchain includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of audio and image management system, information management system, and cloud data management system.

[0027] See also Figures 1 to 4 The present invention provides a vehicle service data management method based on blockchain, and the vehicle service data management method based on blockchain includes the following steps: Step S1: Acquire full-cycle vehicle service behavior data and vehicle service agreement log; perform vehicle digital identification and service behavior type analysis on the full-cycle vehicle service behavior data to obtain a service behavior module for each vehicle; Step S2: Edit the vehicle service agreement log using a service-driven smart contract, and perform distributed blockchain storage processing on the service behavior module of each vehicle to build a vehicle service blockchain network; Step S3: Identify real-time vehicle service behavior data; locate the blockchain module of the vehicle service blockchain network according to the real-time vehicle service behavior data, and perform instant automatic update execution to obtain an instant updated blockchain network; Step S4: Perform real-time user access detection on the instant update blockchain network, analyze abnormal access behavior, and generate abnormal blockchain access warning signals; Step S5: based on the abnormal blockchain access warning signal, the abnormal vehicle service is traced back on the instant update blockchain network, and the abnormal node is repaired adaptively, thereby generating an abnormal service behavior repair strategy; Step S6: Based on the real-time update blockchain network, personalized service behavior recommendations are made, and based on the abnormal service behavior repair strategy, intelligent collaborative management optimization is performed to build an intelligent vehicle service management model.

[0028] The present invention can provide comprehensive basic information for subsequent data processing by comprehensively collecting the full-cycle service data of the vehicle, ensuring that in-depth analysis can be performed on the specific service behavior of each vehicle. Vehicle digital identification and service behavior analysis help to clarify the service requirements and service content of each vehicle, thereby laying a solid foundation for subsequent smart contracts and blockchain storage. Through the editing of smart contracts and blockchain storage, the automatic execution and non-tamperability of service agreements and vehicle service behaviors are ensured. Smart contracts can automatically execute service behaviors according to service agreements, improve service efficiency, and ensure data security and transparency through distributed storage of blockchain to prevent data tampering. The blockchain network provides an efficient and secure management framework for subsequent vehicle service behaviors. By identifying vehicle service behavior data in real time and updating the blockchain network in real time, the timeliness and accuracy of vehicle service data can be maintained. Blockchain module positioning enables each service behavior to be quickly confirmed and processed, ensuring the real-time and traceability of service data, and avoiding potential problems caused by data lag. Real-time access detection and abnormal behavior analysis can help to timely discover potential problems in the blockchain network, such as data security threats or non-compliant behaviors. The generated early warning signals help to intervene before the problem expands, protecting the security of the blockchain network and vehicle service data. Abnormal tracing and adaptive repair strategies can quickly locate and resolve abnormalities in service behaviors and prevent abnormal situations from spreading. Through the automated abnormal repair mechanism, the normal operation of the system can be restored in a timely manner, reducing manual intervention and improving the stability and reliability of the system. Through personalized recommendations, customized services can be provided based on the service history and user needs of each vehicle to improve service quality and user satisfaction. Intelligent collaborative management optimization helps to improve the overall operational efficiency and management level of vehicle services. At the same time, intelligent optimization based on abnormal repair strategies can continuously improve the system's self-regulation capabilities in the service and build a more intelligent and personalized service management model.

[0029] In the embodiment of the present invention, refer to Figure 1 , is a flowchart of a vehicle service data management method based on blockchain of the present invention. In this example, the steps of the method include: Step S1: Obtaining full-cycle vehicle service behavior data and vehicle service agreement log; performing vehicle digital identification and service behavior type analysis on the full-cycle vehicle service behavior data to obtain a service behavior module for each vehicle; In this embodiment, the data sources are determined, including the vehicle service management system, vehicle-mounted equipment, blockchain network, and third-party service platform, to ensure that the vehicle's service behavior data and service agreement logs can be fully obtained, and the time range for data extraction is set, such as all service records in the past year, to ensure the integrity and comprehensiveness of the data. The API interface and database query language (such as SQL) are used to extract data from different data sources, and the SQL query statement SELECT * FROMservice_records WHERE date BETWEEN '2022-01-01' AND '2022-12-31' to extract service records, store the extracted data in a data warehouse or data lake for subsequent processing and analysis, clean the acquired full-cycle vehicle service behavior data, remove redundant or invalid data (such as duplicate records, wrong formats, etc.), use data cleaning tools (such as Python's Pandas library) for processing, format the service agreement log, and ensure that all records follow the same format, including timestamp, vehicle ID, service type and status, etc., integrate the service behavior data with the service agreement log to form a comprehensive data set, and ensure that each record contains the vehicle's digital identification, service type and related information. During the integration process, generate a metadata description of the data, including the source, format, time range, etc. of the data for subsequent analysis, determine the vehicle's digital identification rules, including how to extract the vehicle's unique identification (such as VIN code or owner ID) from the service behavior data, set identification recognition standards, and ensure that each vehicle is uniquely identified. The identification is unique in the entire data set. Regular expressions and string processing functions are used to extract digital identification from service behavior data. The VIN code is extracted using Python's re library. The extracted identification is verified to ensure its accuracy and completeness. Any missing or erroneous identification information is recorded to facilitate subsequent data correction. Classification standards for service behavior types are established. Service types include maintenance, repair, cleaning, inspection, etc. The hierarchical structure of classification, such as main categories and subcategories, is determined for in-depth analysis. Appropriate data analysis tools (such as Python's Pandas and NumPy) are selected to analyze service behavior types. Classification algorithms (such as decision trees or rule engines) are run to classify each service record and identify its corresponding service behavior type. The analysis results are organized into a structured data table. The service behavior module of each vehicle is recorded, including vehicle ID, service type, service time, service status, etc. The results are stored in a database for subsequent query and analysis.

[0030] Step S2: Edit the vehicle service agreement log using a service-driven smart contract, and perform distributed blockchain storage processing on the service behavior module of each vehicle to build a vehicle service blockchain network; In this embodiment, the extracted vehicle service agreement log is analyzed to identify key fields, such as service type, service time, fee, participants (car owner, service provider), etc., and the input and output parameters required by the smart contract are determined so that the terms of the service agreement can be automatically executed, such as the start, end, payment and confirmation of the service, etc. The logical structure of the smart contract is formulated, including state variables (such as service status, timestamp, fee) and functions (such as creating a service request, confirming the service, paying the fee, etc.), and the execution conditions and triggering events of the contract are set. When the service is completed, the payment function is automatically triggered. The smart contract code is written using a smart contract development language (such as Solidity) to ensure that the contract can be automatically executed according to the terms of the service agreement. A function completeService() public is written to update the service status and trigger payment after the service is completed. In a local development environment (such as Remix 1. Perform unit tests on the written smart contracts in the IDE or Truffle framework to ensure the correctness and security of the contract logic, simulate the execution of different scenarios (such as normal service, service failure), check whether the contract responds as expected, select the appropriate blockchain network (such as Ethereum, Hyperledger Fabric) according to the needs, and set up the corresponding network environment, consider the network throughput and transaction fees, select the appropriate chain to support high-frequency service requests, use development tools (such as Truffle or Hardhat) to deploy the tested smart contracts to the blockchain network, generate the contract address, record the contract address and related information for subsequent queries and calls, define the data structure according to the vehicle's service behavior module, including vehicle ID, service type, service time, fee and other information, ensure that the data structure can interact with the smart contract, support the input and output of the contract, write a function in the smart contract to store the data of the vehicle service behavior module to the blockchain, use arrays or mappings to store the service records of each vehicle, and write a function function storeServiceRecord(uint vehicleId, ServiceRecord memory record) to store service records, ensure that the data of the vehicle service behavior module is stored synchronously on multiple nodes, ensure the integrity and consistency of the data, set up a data verification mechanism, ensure that each data written to the blockchain is verified, and avoid data tampering.

[0031] Step S3: Identify real-time vehicle service behavior data; locate the blockchain module of the vehicle service blockchain network according to the real-time vehicle service behavior data, and perform instant automatic update execution to obtain an instant updated blockchain network; In this embodiment, a real-time data collection architecture is constructed to collect real-time service behavior data of vehicles using on-board sensors, mobile applications and API interfaces. These data include vehicle status, mileage, service requests, etc. The frequency of data collection is set, for example, vehicle status information is collected every 5 seconds to ensure the timeliness and accuracy of the data. The collected real-time service behavior data is parsed to extract key fields (such as vehicle ID, timestamp, service type, service parameters). Data processing tools (such as Python's Pandas library) are used to format the data into a standardized format (such as JSON) for subsequent processing and analysis. The architecture of the blockchain module is designed to ensure that each service behavior can correspond to a specific blockchain module. Each module represents a vehicle service type (such as maintenance, repair, etc.). A unique identifier for the module is set to facilitate rapid positioning in the blockchain network. According to the vehicle ID and service type in the real-time service behavior data, the module is located using the smart contract method. Call the contract function get Service Module (vehicleId, service Type) to obtain the corresponding service module. Use the on-chain data identifier to confirm the status of the module and its relationship with other modules to ensure that the service module of the current vehicle can be quickly located. Design the logic of automatic update to ensure that after locating the service module, the corresponding actions can be automatically performed according to the real-time data. Update the service status, record the service time, execute the payment, etc. Determine the trigger conditions, such as automatically calling the update function when the service status changes (for example, from "in progress" to "complete"). Use the functions in the smart contract to update the data. Write a contract function function update Service Status(uint vehicleId, string memory new Status) to automatically update the service status of the vehicle. Ensure that each update operation is verified to prevent data tampering and inconsistency. Build a real-time monitoring system that can monitor changes in vehicle service behavior data and feedback to management personnel. Use dashboards (such as Grafana) to display real-time data of service behavior. Set up an alarm mechanism to automatically send an alarm when abnormal service behavior (such as frequent fault requests) is detected. Ensure that the real-time updated data is synchronized in the blockchain network, and all nodes can obtain the latest service behavior data in a timely manner. Ensure that all nodes can verify the legitimacy of the latest data through a consensus mechanism. Set data verification rules to ensure that each updated data meets the security standards of the blockchain.

[0032] Step S4: Perform real-time user access detection on the instant update blockchain network, analyze abnormal access behavior, and generate abnormal blockchain access warning signals; In this embodiment, an efficient access log recording system is designed to record the access behavior of all users in real time, including user ID, access time, access type, IP address, accessed blockchain module and other information. Set the frequency of log recording, such as recording immediately each time a user accesses to ensure that no access behavior is missed. Use a stream processing framework (such as Apache Kafka or Apache Flink) to collect real-time access logs and store them in a high-performance database (such as MongoDB or Elasticsearch). Set the time window for data storage, such as retaining access logs for the past 30 days for subsequent analysis. Clarify what constitutes abnormal access behavior, such as frequent failed login attempts, unusual access time, access requests beyond the normal range (such as too high request frequency), etc. Set the threshold for anomaly detection. If a user makes more than 10 requests in one minute, it will be considered suspicious behavior. Select a suitable anomaly detection algorithm, such as rule-based detection, cluster analysis (such as K-means), or machine learning algorithm (such as Isolation Forest or LOF - Local OutlierFactor). Use historical access data to train the model to improve the accuracy and sensitivity of anomaly detection. Use stream processing technology to analyze real-time access data and calculate the statistical characteristics of access behavior in real time, such as access frequency and request type distribution. Set the window size for real-time analysis and analyze the access behavior in the past 5 minutes every 5 minutes to quickly identify anomalies. Run the selected anomaly detection algorithm to analyze the real-time access data and identify suspicious access behaviors. Use the Isolation Forest algorithm to analyze the user's access frequency and mark abnormal behaviors. When the amount reaches a certain threshold (such as 5 times). Set the content of the warning signal, including abnormal user ID, abnormal behavior description, occurrence time and recommended response measures. Once abnormal behavior is detected, immediately generate a warning signal and notify relevant personnel through multiple channels (such as email, SMS, system notification). Set the processing flow of the warning signal to ensure that quick action can be taken after receiving the warning, such as locking suspicious accounts and reviewing access logs. Set a feedback mechanism to record the response to each warning, including processing time, processing results and subsequent measures. Continuously adjust the parameters and models of the anomaly detection algorithm based on feedback data to improve future detection accuracy.

[0033] Step S5: based on the abnormal blockchain access warning signal, the abnormal vehicle service is traced back on the instant update blockchain network, and the abnormal node is repaired adaptively, thereby generating an abnormal service behavior repair strategy; In this embodiment, a traceability model for abnormal service behavior is designed, and the key steps and data sources of traceability are clarified, including service records, vehicle status, and user access logs. The time window for traceability is determined, such as all service behaviors in the past 30 minutes after the warning signal is triggered, so as to quickly locate the anomaly. The warning signal is combined with the service record on the blockchain, and the relevant data sets are integrated. Data analysis tools (such as Python's Pandas) are used for data integration to ensure that each abnormal behavior has detailed context information. Standards for data integration are set, such as ensuring that each service record contains information such as vehicle ID, service time, service type, and related user ID. According to the warning signal, specific abnormal service behavior categories are identified, such as repeated service requests, abnormally high-frequency service calls, unauthorized access, etc. Thresholds and standards for abnormal behavior are set. If the same vehicle initiates multiple identical service requests in a short period of time, it is considered abnormal. A query is executed on the blockchain, and the corresponding service records are queried using smart contracts to identify all records related to abnormal behavior. The smart contract function getServiceRecords(vehicl eId), query the service record of a specific vehicle, analyze it one by one, set up a node status monitoring mechanism, monitor the health status of each node in the blockchain network in real time, including the node's response time, processing power and data consistency, use monitoring tools (such as Prometheus) to collect node performance indicators, and set corresponding thresholds, design an adaptive node repair strategy based on monitoring data and abnormal behavior analysis results, and automatically start the repair process if a node is found to have frequent delays or errors. The repair strategy includes restarting the node, updating the node software or resynchronizing the data. According to the identified abnormal service behavior and node status, implement the corresponding repair measures. If a service request is found to be unauthorized, the request is automatically revoked and the relevant users are notified. Record detailed logs of the repair process, including the time, executor, repair measures and results of the repair, so as to facilitate subsequent audit and analysis, set evaluation criteria for the repair strategy, monitor the overall performance and stability of the system after the repair, and evaluate by analyzing indicators such as service recovery time and user feedback. According to the evaluation results, continuously optimize the repair strategy, adjust the algorithm parameters and repair process to improve the efficiency and accuracy of future responses.

[0034] Step S6: Based on the real-time update blockchain network, personalized service behavior recommendations are made, and based on the abnormal service behavior repair strategy, intelligent collaborative management optimization is performed to build an intelligent vehicle service management model.

[0035] In this embodiment, based on the collected user behavior data (including service frequency, service type, feedback information, etc.), a profile of each user is constructed. The user profile should include the user's basic information, historical service records, preferred service types, etc. The time range of data collection is set, such as service behavior data in the past year, to ensure the accuracy and comprehensiveness of the user profile, and a suitable recommendation algorithm is selected, such as collaborative filtering, content-based recommendation or hybrid recommendation system. The collaborative filtering method uses the behavior of similar users to recommend services, while the content-based method focuses on the service types selected by the user in the past. In actual applications, the parameters of the recommendation algorithm are set, such as the threshold for similarity calculation, the length of the recommendation list (such as the top 5 recommendations), etc. The user data is processed using a machine learning framework (such as TensorFlow or Scikit-learn), the user service behavior data is converted into a format that can be used by the model, the recommendation algorithm is trained, the historical service data is used to optimize the model, the user's historical service records are used for training, and the model parameters are adjusted to improve the accuracy of the recommendation. When the user initiates a service request, a personalized recommendation list is calculated in real time, and a service recommendation suitable for the user is quickly generated by calling a smart contract and combining the service records on the blockchain. The smart contract function function is used recommendServices(uintuserId), returns a list of recommended services based on user portraits and real-time service data, designs a feedback mechanism, collects user feedback on recommended services, including acceptance rate, satisfaction, and subsequent service behavior, sets a time window for feedback collection, such as conducting a survey after each service is completed, and adjusts the repair strategy based on user feedback results. If the acceptance rate of a recommended service is low, consider optimizing the recommendation method for the service. In the management system, integrate the abnormal service behavior repair strategy with the personalized recommendation system to achieve intelligent collaborative management. By analyzing the relationship between abnormal behavior and user preferences, optimize service recommendations. If a certain service type has a high frequency of abnormal behavior, it will be recommended at the time of recommendation. Reduce the weight of this type of service, give priority to recommending other services preferred by users, combine the personalized recommendation system with the abnormal service behavior repair strategy, build a complete intelligent vehicle service management model, ensure that the system can respond to user needs in real time and automatically repair under abnormal circumstances, conduct system integration testing, verify the effectiveness and stability of the recommendation system and repair strategy in different scenarios, simulate different user behaviors and abnormal service scenarios, observe the system's response and adjustment capabilities, set performance evaluation indicators, such as recommendation accuracy, user satisfaction, abnormal service processing time, etc., evaluate through user feedback and system logs, continuously optimize the model according to the evaluation results, and adjust the parameters of the recommendation algorithm and repair strategy to improve the overall service management efficiency.

[0036] In this embodiment, refer to Figure 2, is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Collect all full-cycle vehicle service behavior data and vehicle service agreement logs in the vehicle service information system; Step S12: performing vehicle digital identification recognition on the full-cycle vehicle service behavior data, and extracting the digital identification of each vehicle; Step S13: extracting each service behavior timestamp of the full-cycle vehicle service behavior data; Step S14: Analyzing the service behavior types of the full-cycle vehicle service behavior data one by one to generate a feature of each service behavior type; Step S15: performing vehicle service time series fitting on each service behavior type feature according to each service behavior timestamp to generate a service behavior time series sequence; Step S16: Based on the digital identification of each vehicle, the service behavior time sequence is subjected to service identification binding processing to obtain the service behavior module of each vehicle.

[0037] In this embodiment, the various modules of the vehicle service information system are determined, such as repair records, maintenance records, accident handling records, vehicle inspection records, and service agreements. Set the time range for data collection, such as all service behavior data in the past year, to ensure the comprehensiveness and timeliness of the data. Select appropriate data extraction tools, such as SQL database queries, API calls, or data crawler tools, to extract service behavior data and protocol logs from relevant systems. Set extraction parameters, Data format: The extracted data should be in a structured format (such as CSV, JSON) for subsequent processing. Field selection: including vehicle ID, service behavior type, service time, service content, etc. Store the extracted data in a centralized database for subsequent analysis and processing. Use a relational database (such as MySQL) or a non-relational database (such as MongoDB) for storage. Set data storage specifications, such as: Table structure: Each service behavior data should contain fields such as timestamp, vehicle ID, and service type. Determine the digital identification format of the vehicle, such as VIN (Vehicle Identification Number), to ensure its uniqueness and standardization. VIN usually consists of 17 characters, including letters and numbers. Clean the collected vehicle service behavior data, remove duplicate or erroneous entries, and ensure the validity of each record. Use regular expressions or string matching algorithms to extract characters that conform to the VIN format from the data. Store the extracted digital identifiers in the database and associate them with the corresponding service behavior data for subsequent analysis. Determine the standard format of timestamps, such as ISO 8601 format (YYYY-MM-DDTHH:MM:SS), to ensure uniformity and parsability. Use data processing tools (such as Pandas) to process the service behavior data and extract the timestamp field of each record. Set extraction conditions to ensure the correlation between the timestamp and other fields of the service behavior data. Define service behavior types, such as repair, maintenance, accident handling, and inspection. Ensure that each type has clear standards and classification basis. Use data analysis tools (such as Python's Pandas library) to map the service behavior data and classify it into the corresponding type according to the description of the service behavior. Set classification rules, such as using keyword matching or machine learning algorithms for automatic classification. Extract feature data for each service behavior type, such as the number of services, service intervals, and average service time. Store the extracted feature data in a new data frame for subsequent analysis. Define the time series features of service behavior, such as service time interval, service frequency, etc. Ensure that these features can reflect the changing trend of service behavior. Select a time series analysis tool (such as Statsmodels or TensorFlow) to perform time series fitting of service behavior. Use an ARIMA model or LSTM network for time series forecasting. Set model parameters, such as the selected time window size and prediction step size.Associate the fitting results with the original service behavior data to form a complete service behavior time series sequence, and store it in the database. Select a time series analysis tool (such as Statsmodels or TensorFlow) to perform time series fitting of service behaviors. Use an ARIMA model or LSTM network for time series prediction. Set model parameters, such as the selected time window size and prediction step size. Associate the fitting results with the original service behavior data to form a complete service behavior time series sequence, and store it in the database. Define service ID binding rules to ensure that each service behavior sequence can be associated with the corresponding vehicle digital ID. Use data processing tools (such as Pandas) to merge the service behavior time series sequence with the vehicle digital ID to ensure the integrity of the service behavior module. Set the merging conditions to ensure that each service behavior sequence can find the corresponding vehicle ID. Store the service behavior module of each vehicle generated in the database for subsequent query and analysis.

[0038] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: parsing the vehicle service agreement log according to the general service data standard to obtain the vehicle service general standard; Step S22: defining a service interaction protocol based on the vehicle service universal standard to obtain a vehicle service standardized interaction protocol; Step S23: setting the rights and responsibilities of multiple service entities in the vehicle service agreement log, and generating the service rights and responsibilities of each entity; Step S24: Editing the service-driven smart contract based on the standardized interaction protocol of vehicle services and the service rights and responsibilities of each subject, thereby generating a vehicle service-driven smart contract; Step S25: Perform distributed blockchain storage processing on the service behavior module of each vehicle according to the vehicle service driving smart contract to build a vehicle service blockchain network.

[0039] In this embodiment, vehicle service protocol logs are collected, which include vehicle service requests, responses, error reports and other information. The integrity and accuracy of the logs are ensured. The time range for data collection is set, such as all service interaction records in the past month. The collected log data is preprocessed, including denoising, formatting and cleaning. Regular expressions are used to clean up unnecessary characters and inconsistent format parts. A standard format for log data is set, such as JSON or XML, for subsequent parsing. Appropriate data parsing methods are selected, such as data mining, natural language processing (NLP), etc., to extract valuable information. NLP tools (such as NLTK, spaCy) are used to perform word segmentation, part-of-speech tagging and entity recognition on the log text to identify key fields such as service type, timestamp and participant. Based on the parsing results, general standards for service data are defined, including fields such as service type, time, participant, service status, etc., to ensure that standardized data can be applied to different service scenarios. A general service data standard document is output as the basis for subsequent steps. The design principles of the service interaction protocol, such as simplicity, scalability, compatibility and security, are determined to ensure that the protocol can adapt to future service needs and technological changes. A protocol version control mechanism is set. , in order to facilitate subsequent updates and maintenance, according to the general service data standard, design the basic structure of the service interaction protocol, including request format, response format, error handling mechanism, etc., determine the field name and data type of the request and response, the service request should contain service ID, user ID, timestamp, request parameters, etc., write a detailed protocol specification document, describe the meaning of each field, data format and usage examples, through expert review and multi-party discussion, ensure the practicality and effectiveness of the agreement, identify the participants in the service agreement, including vehicle owners, service providers, third-party platforms, etc., use NLP technology to extract relevant subject information, set the standard for subject identification, such as classification based on their roles and functions in the agreement, design the rights and responsibilities model of the service subject, clarify the rights and obligations of each subject in the service process, the service provider is responsible for ensuring the quality of service, and the vehicle owner has the right to request service feedback, use decision trees or graph models to visualize the rights and responsibilities between the subjects, ensure the clarity of the rights and responsibilities setting, store the generated rights and responsibilities setting results in the database, and ensure that the subsequent steps can access and use this information, choose a suitable smart contract development framework, such as Ethereum's Solidity, Hyperledger Fabric, etc., to ensure support for the required functions and performance, determine the version control and management mechanism of the contract, write smart contract code according to the service interaction agreement and rights and responsibilities, define the basic structure, state variables and necessary functions of the contract, set the trigger conditions and execution logic of the contract, and automatically record the service status and update the rights and responsibilities of the corresponding subject when a vehicle service request is received. Use a test network (such as Ropsten) to test the smart contract to verify the correctness and security of its functions.Conduct code audits to ensure that there are no security vulnerabilities and logical errors in the contract, determine the architecture of the blockchain network, such as public chain, private chain or alliance chain, make choices based on the specific needs of the service, set consensus mechanisms (such as PoW, PoS or BFT) to ensure the security and efficiency of the network, store each vehicle's service behavior records (such as service requests, responses, status updates, etc.) on the blockchain through smart contracts, use on-chain data structures (such as Merkl trees) to improve the efficiency and traceability of data storage, set up network monitoring mechanisms, monitor the operating status and performance indicators of the blockchain network in real time, such as transaction confirmation time, network latency, etc., and regularly perform network maintenance and updates to ensure the stability and security of the system.

[0040] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: identifying real-time vehicle service behavior data; Step S32: performing multi-party identity authentication on the real-time vehicle service behavior data to obtain service behavior identity authentication information; Step S33: performing data authorization analysis according to the service behavior identity authentication information to obtain the real-time service behavior authorization scope; Step S34: positioning the blockchain module of the vehicle service blockchain network based on the authorization scope of the real-time service behavior to obtain blockchain positioning data of the real-time behavior; Step S35: The real-time vehicle service behavior data is automatically updated on the vehicle service blockchain network according to the blockchain positioning data of the real-time behavior to obtain a real-time updated blockchain network.

[0041] In this embodiment, vehicle-mounted sensors (such as GPS, accelerometer, OBD interface) are used to collect vehicle operation data in real time, including location, speed, driving status and service requests. Set the data collection frequency, such as collecting data once per second, to ensure the real-time nature of the data. Format the collected raw data and convert it into a standardized data format (such as JSON or XML) for subsequent processing. Record the timestamp of each data to ensure that the data is analyzed according to the time series. Apply data processing algorithms (such as time series analysis, anomaly detection, etc.) to analyze the real-time vehicle service behavior data and identify effective service behaviors. Set a threshold value, and when the vehicle speed reaches a certain value or the vehicle position changes, it is identified as a "driving" or "parking" service behavior. Select a suitable identity authentication mechanism, such as a public key infrastructure (PKI), OAuth2.0 or blockchain identity authentication system to ensure the authenticity of the identities of all parties. Set an authentication process, including identity authentication request, verification response and result confirmation. Identity authentication implementation collects identity information of all parties involved in the service, including vehicle owners, service providers, third-party platforms, etc. Ensure the integrity and accuracy of the information. Use digital signatures or encryption technology to protect identity information to prevent information from being tampered with. Perform identity authentication requests on real-time vehicle service behavior data to verify the validity of the identity information of each party. Generate service behavior identity authentication information, including authentication status, identity information of participating entities, and authentication timestamp. Select a suitable authorization analysis model, and use role-based access control (RBAC) or attribute-based access control (ABAC) models to authorize according to the identities and attributes of different users. Set access control policies to clarify which identities access which service data. Define the access rights and operation scope of each identity based on the identity authentication information. Vehicle owners view vehicle status and history records, and service providers access real-time service data. Set permission levels, such as read, write, modify, etc., to ensure that the permissions of each identity are consistent with their roles. Based on the identity authentication information and the defined authorization rules, perform authorization analysis on real-time vehicle service behavior data to confirm the authorization scope of service behaviors of each party. Generate real-time service behavior authorization results, including authorization status and permission scope. Design the module structure of the blockchain network, including service request module, service response module, and data storage module, to adapt to different service behaviors. Determine the functions and interfaces of each module to facilitate interaction and data transmission between modules. Select an appropriate positioning algorithm, such as content-based addressing or location-based addressing, to ensure that the corresponding blockchain module can be found quickly according to the authorization scope. Set the conditions for module positioning so that only service behaviors within the authorization scope can access the corresponding module. According to the authorization scope of real-time service behaviors, locate each module in the blockchain network and obtain the corresponding blockchain positioning data. Record the access rights and current status of the module to provide a basis for subsequent data updates.Design a real-time data update mechanism, including data submission, confirmation, and rollback mechanisms, to ensure the reliability and security of data updates. Determine the triggering conditions for data updates, and automatically perform data updates when service behaviors are authenticated and authorized. Submit real-time vehicle service behavior data to the corresponding module of the blockchain network based on blockchain positioning data. Use smart contracts or on-chain transactions to perform data updates to ensure the immutability and transparency of data on the blockchain. After the data is updated, verify the results to ensure that the updated data is consistent with the service behavior. Generate an update log to record the time, content, and participants of each update for subsequent auditing and tracing.

[0042] In this embodiment, step S4 includes the following steps: Step S41: Perform real-time user access detection on the instant update blockchain network and extract multiple user access behavior data; Step S42: performing access compliance verification on the access behavior data of multiple users to obtain a compliance evaluation value of each access behavior; Step S43: Analyze abnormal access behaviors based on the compliance evaluation value of each access behavior, and mark abnormal user access behaviors; Step S44: Generate an abnormal blockchain access warning signal based on abnormal user access behavior.

[0043] In this embodiment, the data collection frequency is set, for example, a user access event is recorded once per second, including access time, user ID, access module and operation type, etc. The API and smart contract of the blockchain network are used to automatically record the user's access behavior. Each time a user performs a blockchain operation, the system will generate a corresponding access record. The recorded access behavior data includes: user identity, access timestamp, access type (such as read, write), access module, and the collected access behavior data is formatted and converted into a standardized data format (such as JSON) for subsequent analysis. The formatted data is stored in the database to form a user access behavior data set, and compliance verification standards are formulated, including access rights. , operation rules and data protection requirements. Only authorized users can access specific modules or perform specific operations. Set compliance assessment indicators, such as compliance behavior scores, violation behavior scores, etc. Select appropriate compliance verification algorithms, such as rule engines or machine learning models (such as decision trees), to perform compliance assessments on access behaviors. Input compliance standards into the algorithm to calculate the compliance assessment value of each access behavior in real time. Perform compliance verification on the access behavior of each user and generate a compliance assessment value. If the user accesses an unauthorized module, the assessment value is negative, otherwise it is positive. Record the assessment results, including user ID, access behavior and its compliance assessment value. Establish identification standards for abnormal access behaviors and set compliance assessments. The threshold of the value is set. When the evaluation value is lower than a certain value (such as -0.5), it is marked as abnormal. The behavior frequency limit is set. If the same user frequently accesses the same module in a short period of time, it is regarded as abnormal behavior. Statistical analysis methods (such as Z-score analysis, IQR method) or machine learning algorithms (such as cluster analysis) are used to identify abnormal access behaviors. Analysis parameters are set, such as time window (access records in the past 10 minutes) and threshold (such as the minimum limit of compliance evaluation value). Based on the compliance evaluation value, each user's access behavior is analyzed for abnormalities. If a user's behavior is outside the set threshold range, it is marked as abnormal behavior. The access behavior data of abnormal users is recorded, including user ID, access time, Access modules and compliance assessment values, design abnormal access warning signal mechanism, including signal type (such as email, SMS, system alarm) and trigger conditions (such as sending immediately after abnormal behavior is identified), determine the content of the warning signal, including abnormal user information, access behavior description, compliance assessment value, etc. When abnormal user access behavior is detected, a warning signal is automatically generated. The signal should contain detailed information so that relevant personnel can handle it in time. Use a templated approach to generate warning signals to ensure the accuracy and consistency of information. Send warning signals to relevant managers or system administrators through set communication channels (such as email, SMS or system notifications), record the sending log, and ensure that the sending of each warning signal can be traced.

[0044] In this embodiment, step S5 includes the following steps: Step S51: based on the abnormal blockchain access warning signal, the instant update blockchain network is used to trace abnormal vehicle services and extract all abnormal vehicle service record information; Step S52: Tracing and locating abnormal nodes based on all abnormal vehicle service record information, and marking abnormal blockchain nodes; Step S53: Isolate the abnormal blockchain nodes and perform adaptive node abnormality repair to generate an abnormal service behavior repair strategy.

[0045] In this embodiment, the collected abnormal blockchain access warning signals are parsed to extract relevant information, including abnormal user ID, access time, abnormal behavior description, etc. The signal processing priority is set to give priority to abnormal access records of high-risk users. In the blockchain network, the smart contract query function is used to extract all relevant vehicle service record information according to the time and user ID of the abnormal access record. The query time range is set to all abnormal vehicle service records in the past 24 hours to ensure the timeliness of the data. The extracted abnormal vehicle service records are sorted to generate structured data, including service requests, responses, involved blockchain nodes and their status. The sorted abnormal service records are stored in a database to form an abnormal service record database for subsequent analysis and positioning. The standards for tracing and locating abnormal nodes are formulated to clarify the characteristics of abnormal behavior, the types of nodes involved and their roles in the blockchain (such as verification nodes, etc.). The parameters of the traceability analysis, the frequency of abnormal behavior, the scope of impact and the historical behavior of users are set. Graph theory analysis and network topology structure analysis methods are used to conduct in-depth analysis of abnormal service records. A graph database (such as Neo4j) is used to build a relationship diagram between blockchain nodes. An analysis threshold is set to identify nodes that are frequently related to abnormal service records. According to the analysis results, abnormal blockchain nodes are marked. The information of abnormal nodes is recorded in the database, including node ID, abnormal behavior description and impact scope. A list of abnormal nodes is generated to facilitate subsequent isolation and repair. Establish isolation standards for abnormal nodes. Clarify what types of abnormal behaviors need to be isolated, such as frequent illegal access, abnormal operation requests, etc. Set isolation time and conditions. If a node has three abnormal behaviors within an hour, it will be temporarily isolated. According to the set standards, isolate abnormal nodes. Disconnect abnormal nodes from the blockchain network to prevent them from continuing to affect other nodes. Record the time of isolation operation, node ID and operator information to ensure transparency and traceability. Develop adaptive repair strategies based on the type and abnormal behavior of abnormal nodes. If the abnormality is caused by software failure, it can be repaired by upgrading the node software. Set the repair process, including problem identification, repair implementation, effect verification and feedback adjustment. Execute the repair strategy to restart the node, update the software or adjust the configuration. Record detailed operation logs for each repair step. Perform effect verification to ensure that the repaired node can resume normal function and reconnect to the blockchain network.

[0046] In this embodiment, step S6 includes the following steps: Step S61: Evolving the personalized behavior habits of the car owner based on the real-time updated blockchain network, thereby generating a personalized behavior preference portrait of the car owner; Step S62: Recommending personalized service behaviors based on the personalized behavior preference portrait of the car owner to generate a personalized service behavior recommendation strategy; Step S63: Intelligent collaborative management optimization of personalized service behavior recommendation strategies and abnormal service behavior repair strategies is performed to build an intelligent vehicle service management model.

[0047] In this embodiment, the real-time behavior data of the car owner, including service requests, driving records, feedback and evaluation, etc., is extracted from the blockchain network. The smart contract and API interface are used to obtain this data in a timely manner. The time range for data collection is set, such as the past month, to ensure the representativeness and integrity of the data. The collected data is cleaned to remove redundant information and outliers (such as wrong timestamps, invalid service requests, etc.), and the data is converted into a standardized format (such as a table or JSON) for subsequent analysis. Data mining and machine learning algorithms (such as cluster analysis and decision trees) are applied to analyze the car owner's behavior. These algorithms can identify the owner's usage habits and Preferences, set analysis parameters, such as the number of clusters in the clustering algorithm (such as the K value in K-means) to ensure the accuracy of the analysis, and generate a personalized behavioral preference portrait of the car owner through the interpretation of the analysis results. This portrait includes the car owner's commonly used service types, preferred service time, feedback satisfaction, etc. The generated portrait is stored in the database to facilitate subsequent service recommendations and management. Select a suitable recommendation algorithm, such as collaborative filtering, content-based recommendation, or hybrid recommendation system, so as to generate personalized service recommendations based on the car owner's behavioral preferences. Determine the input and output of the recommendation system. The input is the car owner's behavioral preference portrait, and the output is the recommended service list. The owner's preference profile is used to build a personalized service recommendation strategy. If the owner prefers a certain type of maintenance service, the system will recommend related maintenance packages, set the recommendation priority, give priority to the services that the owner has used recently, or recommend specific services based on seasonal needs, run the recommendation algorithm, generate a personalized service behavior recommendation list, and match it with the owner's preference profile to ensure the relevance and accuracy of the recommendation. The recommendation results are stored in the user profile for subsequent tracking and adjustment. An intelligent collaborative management framework is designed to integrate personalized service recommendations with abnormal service repair strategies. The framework should have the functions of real-time monitoring, data feedback, and automatic adjustment. Determine the management framework The core components of the framework, such as the data analysis module, decision support module and feedback mechanism, analyze the effectiveness of personalized service recommendations and the repair effect of abnormal service behaviors by monitoring the usage feedback of car owners in real time, set key performance indicators (KPIs) for each recommendation strategy and repair strategy, such as user satisfaction, service utilization rate and abnormal behavior reduction rate, and perform intelligent collaborative optimization based on the data analysis results. If it is found that a certain recommendation strategy is not effective, it will be adjusted, or the repair strategy will be updated according to the frequency of abnormal service behaviors. The machine learning algorithm is implemented to enable the management model to continuously learn and optimize according to historical data, thereby improving the accuracy of service recommendations and exception handling.

[0048] In this embodiment, the specific steps of step S61 are: Analyze various types of vehicle service behaviors on the real-time updated blockchain network to generate various types of vehicle service behavior characteristics; Calculate the behavior frequency of each type of service behavior characteristics of the vehicle to obtain the frequency of each service behavior of the vehicle; Carry out vehicle service cycle trend mining on various types of vehicle service behavior characteristics to generate service behavior cycle trend characteristics; The frequency of each service behavior of the vehicle and the trend characteristics of the service behavior cycle are used to evolve the personalized behavior habits of the owner, thereby generating a personalized behavior preference portrait of the owner.

[0049] In this embodiment, vehicle service records are extracted from the blockchain network, including service requests, responses, usage time, service types, etc., and the smart contract interface is used to obtain these data. The data collection time range is set, such as all service records in the past three months, to ensure the representativeness of the data. The collected service records are cleaned to remove invalid data (such as duplicate records, wrong formats, etc.) to ensure the consistency and integrity of the data. The data is formatted into a standardized format (such as CSV or JSON) to facilitate subsequent analysis and processing. The vehicle service behavior characteristics are defined according to the service type, including service category (such as maintenance, repair, cleaning, etc.), service time, service frequency, etc., and the dimensions of feature extraction are set, such as: service type, Service time period, service status (completed, in progress, failed), etc. Use data analysis tools (such as Pandas and NumPy in Python) to analyze the data, extract the defined service behavior characteristics, run statistical analysis (such as mean, standard deviation, etc.), conduct in-depth analysis of the service behavior characteristics to generate a service behavior characteristic report, determine the standard for behavior frequency calculation, calculate the frequency of each service behavior in months, set the type of service behavior that needs attention, such as maintenance, repair, cleaning, etc., use data processing tools (such as SQL or Python) to group and count the service records, calculate the frequency of each service behavior within the set time range, and use the SQL query statement SELECT service_type,COUNT(*) FROM service_records GROUP BY service_type to obtain frequency data, organize the calculated frequency results into tables or charts for visualization and subsequent analysis, record the frequency value of each service behavior, and mark its proportion in the overall service, select appropriate time series analysis methods (such as moving average method, seasonal decomposition) to identify the periodicity of service behavior, set the time span of analysis, such as weekly or monthly trend analysis, organize service frequency data by time series, build a time series data set, use Python's statsmodels library for time series analysis, identify periodic trends, run trend analysis algorithms, extract periodic characteristics of service behavior (such as service peak season, off-season, etc.), and generate The report on periodic trend characteristics is used to describe in detail the distribution of different service behaviors over time, set the standard and model for the evolution of personalized behavior habits, use cluster analysis or machine learning algorithms (such as K-means or decision trees) to determine the factors that affect behavior habits, such as service type frequency, service cycle, user feedback, etc., integrate vehicle service behavior characteristics, frequency data and periodic trend characteristics to form a comprehensive data set, run cluster analysis algorithms, identify the behavior patterns of different car owners, and generate personalized behavior preference portraits of car owners based on the analysis results, describing their preferences in terms of service type, service frequency, service time, etc. The generated portraits are stored in the database for subsequent service recommendations and management.

[0050] In this embodiment, a vehicle service data management system based on blockchain is provided, which is used to execute the vehicle service data management method based on blockchain as described above, including: A behavior type analysis module is used to obtain full-cycle vehicle service behavior data and vehicle service agreement logs; perform vehicle digital identification and service behavior type analysis on the full-cycle vehicle service behavior data to obtain a service behavior module for each vehicle; A blockchain network module is used to perform service-driven smart contract editing on the vehicle service agreement log, and to perform distributed blockchain storage processing on the service behavior module of each vehicle to build a vehicle service blockchain network; An instant update module is used to identify real-time vehicle service behavior data; locate the blockchain module of the vehicle service blockchain network according to the real-time vehicle service behavior data, and perform instant automatic update execution to obtain an instant updated blockchain network; Abnormal access module, used to detect real-time user access to the instant update blockchain network, analyze abnormal access behavior, and generate abnormal blockchain access warning signals; The adaptive repair module is used to trace abnormal vehicle services on the instant update blockchain network based on abnormal blockchain access warning signals, and to perform adaptive node abnormal repair, thereby generating abnormal service behavior repair strategies; The collaborative management module is used to recommend personalized service behaviors based on the instantly updated blockchain network, and to optimize intelligent collaborative management based on abnormal service behavior repair strategies, thereby building an intelligent vehicle service management model.

[0051] The present invention ensures a complete record of vehicle service behavior by fully acquiring service data and protocol logs. Through digital identification and behavior type analysis, a detailed service behavior module can be established for each vehicle, laying the foundation for subsequent personalized services and intelligent management. The authenticity and transparency of service protocol logs and service behavior modules are guaranteed by blockchain technology to ensure that data cannot be tampered with. Through the editing of service-driven smart contracts, the automatic management and execution of vehicle services are realized, and service efficiency and transparency are improved. Through real-time updates, vehicle service data is always kept up to date, supporting dynamic management and decision-making. The relevant blockchain modules can be quickly located and accurately updated to improve the efficiency of data management. Automated update execution reduces manual intervention, improves work efficiency and system response speed. By detecting user access in real time, potential security threats can be quickly identified and responded to. In-depth analysis of abnormal access behavior helps to timely discover system vulnerabilities, unauthorized access and other malicious activities, and ensure data security. By generating early warning signals, actions can be taken before security threats expand to prevent data leakage or tampering. Rapid tracing and repair of abnormal nodes and services reduces service interruption time and improves system stability. The adaptive repair mechanism automatically repairs anomalies without human intervention, improving the self-healing ability of the system. The repair strategy prevents the recurrence of abnormal problems and ensures the long-term stable operation of the system. Through real-time updated personalized recommendations, the owner experience is improved, the specific needs of car owners are met, and the efficiency and accuracy of vehicle services are improved. Through collaborative management, combined with abnormal repair strategies and personalized service recommendations, intelligent service management is achieved, service quality is improved, and unnecessary waste of resources is reduced. This module makes vehicle service management not only rely on human intervention, but also autonomously optimized by intelligent algorithms, improving the system's adaptability and the overall efficiency of service management.

[0052] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0053] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A vehicle service data management method based on blockchain, characterized in that: The following steps are involved: Step S1: Acquire full-cycle vehicle service behavior data and vehicle service agreement log; perform vehicle digital identification and service behavior type analysis on the full-cycle vehicle service behavior data to obtain a service behavior module for each vehicle; Step S2: Edit the vehicle service agreement log using a service-driven smart contract, and perform distributed blockchain storage processing on the service behavior module of each vehicle to build a vehicle service blockchain network; Step S3: Identify real-time vehicle service behavior data; The vehicle service blockchain network locates the blockchain module according to the real-time vehicle service behavior data, and performs instant automatic update execution to obtain an instant updated blockchain network; Step S4: Perform real-time user access detection on the instant update blockchain network, analyze abnormal access behavior, and generate abnormal blockchain access warning signals; Step S5: based on the abnormal blockchain access warning signal, the abnormal vehicle service is traced back on the instant update blockchain network, and the abnormal node is repaired adaptively, thereby generating an abnormal service behavior repair strategy; Step S6: Based on the real-time update blockchain network, personalized service behavior recommendations are made, and based on the abnormal service behavior repair strategy, intelligent collaborative management optimization is performed to build an intelligent vehicle service management model.

2. The vehicle service data management method based on blockchain according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: Collect all full-cycle vehicle service behavior data and vehicle service agreement logs in the vehicle service information system; Step S12: performing vehicle digital identification recognition on the full-cycle vehicle service behavior data, and extracting the digital identification of each vehicle; Step S13: extracting each service behavior timestamp of the full-cycle vehicle service behavior data; Step S14: Analyzing the service behavior types of the full-cycle vehicle service behavior data one by one to generate a feature of each service behavior type; Step S15: performing vehicle service time series fitting on each service behavior type feature according to each service behavior timestamp to generate a service behavior time series sequence; Step S16: Based on the digital identification of each vehicle, the service behavior time sequence is subjected to service identification binding processing to obtain the service behavior module of each vehicle.

3. The vehicle service data management method based on blockchain according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: parsing the vehicle service agreement log according to the general service data standard to obtain the vehicle service general standard; Step S22: defining a service interaction protocol based on the vehicle service universal standard to obtain a vehicle service standardized interaction protocol; Step S23: setting the rights and responsibilities of multiple service entities in the vehicle service agreement log, and generating the service rights and responsibilities of each entity; Step S24: Editing the service-driven smart contract based on the standardized interaction protocol of vehicle services and the service rights and responsibilities of each subject, thereby generating a vehicle service-driven smart contract; Step S25: Perform distributed blockchain storage processing on the service behavior module of each vehicle according to the vehicle service driving smart contract to build a vehicle service blockchain network.

4. The vehicle service data management method based on blockchain according to claim 1 is characterized in that: The specific steps of step S3 are: Step S31: identifying real-time vehicle service behavior data; Step S32: performing multi-party identity authentication on the real-time vehicle service behavior data to obtain service behavior identity authentication information; Step S33: performing data authorization analysis according to the service behavior identity authentication information to obtain the real-time service behavior authorization scope; Step S34: positioning the blockchain module of the vehicle service blockchain network based on the authorization scope of the real-time service behavior to obtain blockchain positioning data of the real-time behavior; Step S35: The real-time vehicle service behavior data is automatically updated on the vehicle service blockchain network according to the blockchain positioning data of the real-time behavior to obtain a real-time updated blockchain network.

5. The vehicle service data management method based on blockchain according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41: Perform real-time user access detection on the instant update blockchain network and extract multiple user access behavior data; Step S42: performing access compliance verification on the access behavior data of multiple users to obtain a compliance evaluation value of each access behavior; Step S43: Analyze abnormal access behaviors based on the compliance evaluation value of each access behavior, and mark abnormal user access behaviors; Step S44: Generate an abnormal blockchain access warning signal based on abnormal user access behavior.

6. The vehicle service data management method based on blockchain according to claim 1 is characterized in that: The specific steps of step S5 are: Step S51: based on the abnormal blockchain access warning signal, the instant update blockchain network is used to trace abnormal vehicle services and extract all abnormal vehicle service record information; Step S52: Tracing and locating abnormal nodes based on all abnormal vehicle service record information, and marking abnormal blockchain nodes; Step S53: Isolate the abnormal blockchain nodes and perform adaptive node abnormality repair to generate an abnormal service behavior repair strategy.

7. The vehicle service data management method based on blockchain according to claim 1 is characterized in that: The specific steps of step S6 are: Step S61: Evolving the personalized behavior habits of the car owner based on the real-time updated blockchain network, thereby generating a personalized behavior preference portrait of the car owner; Step S62: Recommending personalized service behaviors based on the personalized behavior preference portrait of the car owner to generate a personalized service behavior recommendation strategy; Step S63: Intelligent collaborative management optimization of personalized service behavior recommendation strategies and abnormal service behavior repair strategies is performed to build an intelligent vehicle service management model.

8. The vehicle service data management method based on blockchain according to claim 1 is characterized in that: The specific steps of step S61 are: Analyze various types of vehicle service behaviors on the real-time updated blockchain network to generate various types of vehicle service behavior characteristics; Calculate the behavior frequency of each type of service behavior characteristics of the vehicle to obtain the frequency of each service behavior of the vehicle; Carry out vehicle service cycle trend mining on various types of vehicle service behavior characteristics to generate service behavior cycle trend characteristics; The frequency of each service behavior of the vehicle and the trend characteristics of the service behavior cycle are used to evolve the personalized behavior habits of the owner, thereby generating a personalized behavior preference portrait of the owner.

9. A vehicle service data management system based on blockchain, characterized in that: The method for managing vehicle service data based on blockchain according to claim 1 comprises: A behavior type analysis module is used to obtain full-cycle vehicle service behavior data and vehicle service agreement logs; perform vehicle digital identification and service behavior type analysis on the full-cycle vehicle service behavior data to obtain a service behavior module for each vehicle; A blockchain network module is used to perform service-driven smart contract editing on the vehicle service agreement log, and to perform distributed blockchain storage processing on the service behavior module of each vehicle to build a vehicle service blockchain network; An instant update module is used to identify real-time vehicle service behavior data; locate the blockchain module of the vehicle service blockchain network according to the real-time vehicle service behavior data, and perform instant automatic update execution to obtain an instant updated blockchain network; Abnormal access module, used to detect real-time user access to the instant update blockchain network, analyze abnormal access behavior, and generate abnormal blockchain access warning signals; The adaptive repair module is used to trace abnormal vehicle services on the instant update blockchain network based on abnormal blockchain access warning signals, and to perform adaptive node abnormal repair, thereby generating abnormal service behavior repair strategies; The collaborative management module is used to recommend personalized service behaviors based on the instantly updated blockchain network, and to optimize intelligent collaborative management based on abnormal service behavior repair strategies, thereby building an intelligent vehicle service management model.

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