Vehicle diagnosis method, system, equipment and product

Abstract

The invention discloses a vehicle diagnosis method, system, device and product. The method comprises the following steps: acquiring a vehicle diagnosis request; performing task analysis and queue management processing on the vehicle diagnosis request to obtain a task queue; performing identity authentication and data integrity verification processing on the task queue to obtain a security verification result; and performing protocol adaptation and diagnosis processing on the vehicle diagnosis request according to the safety verification result to obtain a diagnosis processing result. The embodiment of the invention can improve the vehicle diagnosis efficiency, and can be widely applied to the technical field of vehicle diagnosis.

Description

Technical Field

[0001] This invention relates to the field of vehicle diagnostic technology, and in particular to a vehicle diagnostic method, system, device and product. Background Technology

[0002] As automotive electronic and electrical architectures evolve from distributed to centralized, the demand for in-vehicle network communication bandwidth has increased dramatically. In related technologies, gateways typically process diagnostic requests from the same session or multiple sessions sequentially. When faced with scenarios requiring parallel diagnostics or batch flashing of multiple ECUs, gateways cannot intelligently schedule diagnostic tasks and network resources, resulting in inefficient use of high-bandwidth Ethernet channels.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to provide a vehicle diagnostic method, system, device, and product that can improve the efficiency of vehicle diagnostics.

[0005] To achieve the above objectives, one aspect of this application provides a vehicle diagnostic method, the method comprising: Obtain vehicle diagnostic request; The vehicle diagnostic requests are processed through task analysis and queue management to obtain a task queue; The task queue is subjected to identity authentication and data integrity verification to obtain security verification results; Based on the security verification results, the vehicle diagnostic request is adapted and processed for protocol adaptation to obtain the diagnostic processing results.

[0006] In some embodiments, the step of performing task analysis and queue management processing on the vehicle diagnostic request to obtain a task queue includes: The vehicle diagnostic request is analyzed and processed to obtain the request parameters; Based on the request parameters, perform resource monitoring to obtain status data; Based on the status data, the vehicle diagnostic request is processed for decision scheduling and queue allocation to obtain a task queue.

[0007] In some embodiments, the step of performing decision scheduling and queue allocation processing on the vehicle diagnostic request based on the status data to obtain a task queue includes the following steps: The target electronic control unit is obtained based on the vehicle diagnostic request; The target electronic control unit is subjected to predictive processing based on a pre-trained predictive model to obtain the request response time; Based on the request response time and the shortest job first algorithm, the vehicle diagnostic request is processed to generate a sequence, resulting in an execution sequence. The execution sequence is allocated corresponding threads through a thread pool management system, and a task queue is generated.

[0008] In some embodiments, the process of performing identity authentication and data integrity verification on the task queue to obtain a security verification result includes the following steps: The task queue is subjected to two-way authentication to obtain the authentication result; Based on the authentication result, perform hash value calculation on the data packets in the task queue to obtain the hash value; The hash value is compressed to obtain a compressed hash value, and the compressed hash value is stored in the service message field; Submit the compressed hash value to the blockchain network; The task queue is processed by the blockchain network to verify data integrity and obtain a security verification result.

[0009] In some embodiments, the step of performing data integrity verification on the task queue through the blockchain network to obtain a security verification result includes the following steps: The service message is processed to extract fields, resulting in verification fields; The security verification result is obtained by comparing the hash values ​​of the verification field through the blockchain network.

[0010] In some embodiments, the step of performing protocol adaptation and diagnostic processing on the vehicle diagnostic request based on the security verification result to obtain a diagnostic processing result includes the following steps: Based on the security verification results, the vehicle diagnostic request is decomposed into multiple independent services. Dynamically configure the protocols of multiple independent services to obtain a configuration service; The diagnostic process is performed based on the configuration service to obtain the diagnostic results.

[0011] In some embodiments, the dynamic configuration processing of the protocols of the multiple independent services to obtain a configuration task includes the following steps: The independent service is subjected to interface exposure processing to obtain the service interface; The service interface is parsed to obtain the service protocol; The independent service is configured according to the service agreement to obtain the configuration service.

[0012] To achieve the above objectives, another aspect of this application provides a vehicle diagnostic system, the system comprising: The request retrieval module is used to retrieve vehicle diagnostic requests; The queue management module is used to perform task analysis and queue management processing on the vehicle diagnostic requests to obtain a task queue; The security verification module is used to perform identity authentication and data integrity verification on the task queue to obtain the security verification result; The safety diagnostic module is used to perform protocol adaptation and diagnostic processing on the vehicle diagnostic request based on the safety verification results, and obtain the diagnostic processing results.

[0013] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0014] To achieve the above objectives, another aspect of this application provides a computer program product that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a vehicle diagnostic method, system, device, and product. This solution obtains vehicle diagnostic requests, performs task analysis and queue management on these requests to obtain a task queue, and enables parallel processing of multiple tasks through an intelligent scheduling algorithm, solving the efficiency bottleneck caused by the serial mechanism. Furthermore, this solution performs identity authentication and data integrity verification on the task queue to obtain security verification results. It can use blockchain technology to ensure data integrity and strengthen the identity authentication mechanism, improving data security. In addition, this solution performs protocol adaptation and diagnosis on vehicle diagnostic requests, enabling flexible adjustment of system capabilities through protocol adaptation, thus improving vehicle diagnostic efficiency. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an implementation environment provided in the embodiments of this application; Figure 2 This is a flowchart of a vehicle diagnostic method provided in an embodiment of this application; Figure 3 This is an overall architecture diagram of an intelligent diagnostic system provided in an embodiment of this application; Figure 4 This is a flowchart of a diagnostic request processing method provided in an embodiment of this application; Figure 5 This is a flowchart of an intelligent scheduling algorithm provided in an embodiment of this application; Figure 6This is a schematic diagram of the structure of a vehicle diagnostic system provided in an embodiment of this application; Figure 7 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] As automotive electronic and electrical architectures evolve from distributed to centralized systems, the demand for in-vehicle network communication bandwidth has increased dramatically. This has led to the emergence of DoIP (Diagnostic over Internet Protocol) technology based on in-vehicle Ethernet, which has become an important diagnostic communication method in the AUTOSAR Adaptive Platform (AP) standard. DoIP technology follows the ISO 13400 series standards, and its core advantage lies in its ability to leverage the high bandwidth of Ethernet to significantly improve diagnostic data throughput, making it particularly suitable for scenarios such as software over-the-air (SOTA / FOTA) upgrades and large-scale data diagnostics.

[0022] In related technologies, gateways typically process diagnostic requests from the same or multiple sessions sequentially. When faced with scenarios requiring parallel diagnostics or batch flashing of multiple ECUs, the gateway cannot intelligently schedule diagnostic tasks and network resources, resulting in inefficient use of high-bandwidth Ethernet channels.

[0023] For example, related technologies lack defined parallel processing rules in standard protocols and lack optimization for multi-task scheduling and resource management. This results in overall diagnostic time increasing linearly with the number of electronic control units (ECUs), leading to inefficiency. While related technologies introduce multithreading to improve concurrency, the significant differences in response times among different ECUs make it easy for threads to become blocked while waiting for slower-responding ECUs, creating a "weakest link" effect that may actually increase system complexity and instability.

[0024] Because the standard DoIP protocol primarily relies on the reliability of the TCP protocol to ensure that data is not corrupted by network transmission errors, it cannot defend against tampering attacks by malicious nodes along the transmission path. For example, during the transmission of upgrade packages (service 0x36 as defined in ISO 13400-2), related technologies typically only perform basic verification and checks, with insufficient verification of the authenticity of the upgrade package content. Furthermore, the authentication mechanisms of these technologies are relatively weak, making it difficult to effectively prevent unauthorized diagnostic device access and attacks. Solving these security problems is challenging because adding complex encryption and signature verification at the application layer would significantly increase message length and processing latency, contradicting the efficiency requirements of diagnostics. Additionally, the related technical solutions lack flexibility and scalability, making it difficult to adapt to diverse automotive application scenarios.

[0025] In view of this, this application provides a vehicle diagnostic method, system, device, and product. This solution obtains a task queue by performing task analysis and queue management on vehicle diagnostic requests, and achieves parallel processing of multiple tasks through an intelligent scheduling algorithm, solving the efficiency bottleneck caused by the serial mechanism. This application uses blockchain technology to ensure data integrity and strengthens the identity authentication mechanism. Furthermore, this application adapts the vehicle diagnostic requests to protocols and performs diagnostic processing based on security verification results to obtain diagnostic processing results. A modular design can be used to adapt the requests to protocols, giving the system customizable and scalable characteristics. This application can interact with data through clearly defined interfaces, collaboratively completing efficient and secure diagnostic communication.

[0026] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0027] Figure 1 This is a schematic diagram illustrating the implementation environment of a method provided in an embodiment of this application. (Refer to...) Figure 1 The main hardware and software components of this implementation environment include a cloud service platform 101, an edge computing unit 102, and a vehicle-side system 103. The cloud service platform 101 is communicatively connected to the edge computing unit 102, and the edge computing unit 102 is communicatively connected to the vehicle-side system 103. This method can be executed based on the interaction between the cloud service platform 101, the edge computing unit 102, and the vehicle-side system 103.

[0028] Figure 2 This is an optional flowchart of a vehicle diagnostic method provided in an embodiment of this application. Figure 2 The method may include, but is not limited to, steps S201 to S204.

[0029] Step S201: Obtain a vehicle diagnostic request; Step S202: Perform task analysis and queue management processing on the vehicle diagnostic request to obtain a task queue; Step S203: Perform identity authentication and data integrity verification on the task queue to obtain security verification results; Step S204: Based on the security verification result, perform protocol adaptation and diagnostic processing on the vehicle diagnostic request to obtain the diagnostic processing result.

[0030] In steps S201 to S204 of this embodiment, the edge computing unit receives a vehicle diagnostic request sent by the cloud service platform. This vehicle diagnostic request is structured vehicle diagnostic information, which may include a specific task description, data collection requirements, triggering conditions, and upload strategies. By performing task analysis and queue management on the vehicle diagnostic request, a corresponding task queue can be obtained through an AI dynamic scheduling algorithm, enabling parallel diagnosis of multiple ECUs. The edge computing unit performs identity authentication and data integrity verification on the task queue, employing blockchain technology to ensure data integrity and strengthen the identity authentication mechanism. This embodiment performs protocol adaptation and diagnostic processing on the vehicle diagnostic request based on the security verification results, obtaining the diagnostic processing result. A modular design can be used to adapt the vehicle diagnostic request to the protocol, thereby performing vehicle diagnostics and giving the system customizable and scalable characteristics.

[0031] In step S201 of some embodiments, a vehicle diagnostic request is obtained; Specifically, a vehicle diagnostic request refers to a data message containing specific instructions and parameters initiated by the vehicle system, external diagnostic equipment, or remote service platform. This message triggers status detection, fault analysis, and performance evaluation of specific vehicle components or systems. In a vehicle intelligent diagnostic system, when the vehicle ECU detects a persistent abnormality in the engine oxygen sensor voltage or receives a specific PID query instruction from a diagnostic tool via the CAN bus, the system generates a structured vehicle diagnostic request. This request is encapsulated into a standardized UDS protocol message by the gateway controller, containing the target ECU address, diagnostic service code, and relevant parameters, and transmitted to the diagnostic service module via the in-vehicle network. The system simultaneously records the request's timestamp, trigger source, and priority tag for subsequent diagnostic process scheduling and result traceability, ultimately completing the status acquisition and fault analysis of the specified vehicle system.

[0032] In step S202 of some embodiments, the process of performing task analysis and queue management on the vehicle diagnostic request to obtain a task queue includes: The vehicle diagnostic request is analyzed and processed to obtain the request parameters; Based on the request parameters, perform resource monitoring to obtain status data; Based on the status data, the vehicle diagnostic request is processed for decision scheduling and queue allocation to obtain a task queue.

[0033] Specifically, a vehicle diagnostic request refers to a data message containing specific diagnostic instructions and parameters initiated by a vehicle system, diagnostic equipment, or management platform. This application embodiment parses and classifies diagnostic requests, extracts key operational parameters, and then processes these parameters to obtain request parameters. The request parameters refer to key information extracted from the diagnostic request, such as specific operational instructions, target system, and diagnostic requirements. This application embodiment monitors the status of relevant components and resource availability of the vehicle system in real time to obtain status data. Status data refers to a set of monitoring data reflecting the real-time operating status and resource usage of various vehicle systems. This application embodiment also performs intelligent decision-making on task priority ranking and resource allocation based on system status and request characteristics, resulting in an optimized diagnostic task execution sequence, thus obtaining a task queue.

[0034] In one feasible embodiment, when the system receives a vehicle diagnostic request, it first performs task analysis processing to extract request parameters including the target ECU address, diagnostic service identifier, and parameter range; then, it performs resource monitoring processing on the relevant vehicle system based on these parameters to obtain status data including CPU load, memory usage, and network bandwidth; finally, based on the status data, it performs priority evaluation and queue allocation processing on the diagnostic request through a decision scheduling algorithm to generate a task queue optimized according to urgency and resource requirements, ensuring that high-priority diagnostic tasks are processed in a timely manner.

[0035] One of the above technical solutions has the following advantages or beneficial effects: This application embodiment achieves accurate parsing and classification of diagnostic requests through systematic task analysis and parameter extraction, laying the foundation for subsequent processing. Furthermore, based on real-time status data monitoring and evaluation, it ensures optimal matching between diagnostic tasks and system resources, avoiding resource conflicts and overload. This application embodiment employs an intelligent decision-making mechanism to achieve reasonable task prioritization, ensuring that critical diagnostic tasks are executed first and improving system response efficiency. Moreover, through a scientific queue allocation strategy, it improves the processing efficiency of diagnostic tasks and system throughput, ensuring service quality and timeliness.

[0036] In some embodiments, the step of performing decision scheduling and queue allocation processing on the vehicle diagnostic request based on the status data to obtain a task queue includes the following steps: The target electronic control unit is obtained based on the vehicle diagnostic request; The target electronic control unit is subjected to predictive processing based on a pre-trained predictive model to obtain the request response time; Based on the request response time and the shortest job first algorithm, the vehicle diagnostic request is processed to generate a sequence, resulting in an execution sequence. The execution sequence is allocated corresponding threads through a thread pool management system, and a task queue is generated.

[0037] Specifically, the target electronic control unit (ECU) refers to a specific control module in a vehicle, such as the engine control module or the braking system control module, responsible for managing and monitoring the operation of the corresponding vehicle subsystems. The predictive model is a machine learning model trained based on historical diagnostic data and ECU performance characteristics, used to estimate the time required to complete a specific diagnostic task. The request response time refers to the estimated time required to complete a specific vehicle diagnostic request, as output by the predictive model. The shortest job first algorithm is a scheduling algorithm that prioritizes tasks with the shortest estimated completion time to reduce average waiting time. The execution sequence refers to the order in which diagnostic tasks are executed according to the scheduling algorithm. Thread pool management refers to the set of reusable threads maintained by the system for efficiently handling concurrent diagnostic tasks.

[0038] In this embodiment, when the system receives multiple vehicle diagnostic requests, it first identifies the target electronic control unit (such as engine ECU, transmission ECU, etc.) corresponding to each request; then, it analyzes the processing capacity and current load of each ECU through a pre-trained prediction model to predict the request response time of each diagnostic request; then, it sorts these requests using the shortest job first algorithm to generate the optimal execution sequence; finally, it allocates appropriate worker threads to each diagnostic task in the execution sequence through a thread pool management mechanism to form the final task queue, ensuring that the diagnostic tasks are executed efficiently.

[0039] One of the above technical solutions has the following advantages or beneficial effects: The embodiments of this application accurately estimate job time through a predictive model and optimize the execution order by combining it with the shortest job first algorithm, significantly shortening the average completion time of diagnostic tasks. Furthermore, the embodiments of this application avoid the overhead of frequently creating and destroying threads through intelligent thread pool management, improving system resource utilization and ensuring the smooth and efficient execution of diagnostic tasks.

[0040] In some embodiments, the process of performing identity authentication and data integrity verification on the task queue to obtain a security verification result includes the following steps: The task queue is subjected to two-way authentication to obtain the authentication result; Based on the authentication result, perform hash value calculation on the data packets in the task queue to obtain the hash value; The hash value is compressed to obtain a compressed hash value, and the compressed hash value is stored in the service message field; Submit the compressed hash value to the blockchain network; The task queue is processed by the blockchain network to verify data integrity and obtain a security verification result.

[0041] Specifically, this application embodiment ensures the legitimacy and trustworthiness of both communicating parties through a two-way authentication process between the client and server identities. This application embodiment also uses a hash algorithm to convert data packets into fixed-length unique digital fingerprints for encryption. Compressed hash values ​​refer to simplified digital fingerprints obtained by further compressing the original hash value. The service message field refers to a specific data area reserved in the communication protocol for carrying verification information. A blockchain network refers to a distributed ledger system composed of multiple nodes, providing tamper-proof data storage and verification services. This application embodiment performs data integrity verification by comparing hash values ​​to verify whether data has been tampered with during transmission or storage.

[0042] In this embodiment, the system first performs two-way authentication on the task queue, confirms the operation permissions, and then performs SHA-256 hash calculation on the data packet; then it uses Base64 encoding to compress the hash value and stores the resulting compressed hash value in the authentication field of the service message; then it submits the value to the blockchain network for notarization; finally, it completes the data integrity verification through the consensus mechanism of the blockchain to ensure that the diagnostic task queue is not tampered with during the entire execution process.

[0043] One of the above technical solutions has the following advantages or beneficial effects: The embodiments of this application effectively prevent unauthorized access and man-in-the-middle attacks through a two-way verification mechanism, establishing a trusted communication environment. The embodiments of this application also eliminate the risk of single points of failure based on the distributed verification mechanism of blockchain, enhancing the system's fault tolerance.

[0044] In some embodiments, the step of performing data integrity verification on the task queue through the blockchain network to obtain a security verification result includes the following steps: The service message is processed to extract fields, resulting in verification fields; The security verification result is obtained by comparing the hash values ​​of the verification field through the blockchain network.

[0045] Specifically, a service message refers to a standardized data unit transmitted in network communication, containing structured fields such as headers, payloads, and verification information. This application embodiment parses and separates specific data segments from the service message to obtain verification fields used for verification. Verification fields are specific data segments in the service message specifically used for integrity verification, typically containing verification information such as hash values ​​or digital signatures. This application embodiment compares the extracted verification fields with the original hash values ​​stored in the blockchain network to obtain a security verification result. The security verification result refers to the data integrity verification conclusion obtained through hash comparison, confirming whether the data has been tampered with.

[0046] In this embodiment, the system first extracts a specific verification field from the received service message, and then obtains the original hash value stored in the corresponding data block through the blockchain network interface. Next, the extracted verification field is precisely compared with the hash value in the blockchain. If the two are completely consistent, a security verification result of "verification passed" is generated; otherwise, it is marked as "data abnormal". Finally, the verification result is returned to the business system as the basis for subsequent data processing decisions.

[0047] One of the above technical solutions has the following advantages or beneficial effects: This application embodiment achieves near real-time data integrity verification through rapid field extraction and hash comparison. Furthermore, this application embodiment relies on the immutability of blockchain to ensure the reliability of the verification benchmark.

[0048] In some embodiments, the step of performing protocol adaptation and diagnostic processing on the vehicle diagnostic request based on the security verification result to obtain a diagnostic processing result includes the following steps: Based on the security verification results, the vehicle diagnostic request is decomposed into multiple independent services. Dynamically configure the protocols of multiple independent services to obtain a configuration service; The diagnostic process is performed based on the configuration service to obtain the diagnostic results.

[0049] Specifically, the security verification result refers to the conclusion after verifying the integrity of the vehicle diagnostic request, including status indicators such as "verification passed" or "verification failed". This application embodiment decomposes the complex vehicle diagnostic request into multiple functionally independent sub-service units, resulting in multiple independent services. Each independent service possesses complete functional boundaries and independently executable diagnostic sub-tasks, such as "fault code reading service" and "sensor data acquisition service". This application embodiment dynamically adjusts communication protocol parameters and interaction rules in real time based on service characteristics and environmental requirements, resulting in a configuration service. The configuration service refers to an independent service instance with complete execution parameters after protocol optimization and configuration. The diagnostic processing result refers to the set of diagnostic data generated after the execution of each independent service, including fault information, performance indicators, and status reports.

[0050] In this embodiment, based on the security verification result, the system decomposes the verified vehicle diagnostic request into multiple independent services according to functional modules; for the characteristics of each independent service, the most suitable communication protocol parameters (such as CAN bus rate, UDS service identifier, transmission priority, etc.) are dynamically configured to form a configuration service that can be executed immediately; finally, by executing these configuration services in parallel, real-time data from each ECU is collected and comprehensively analyzed to generate a diagnostic processing result containing complete information such as fault codes, sensor readings, and system status.

[0051] One of the above technical solutions has the following advantages or beneficial effects: This application embodiment achieves refined processing of diagnostic tasks through service decomposition, improving system flexibility and maintainability. Furthermore, this application embodiment ensures that each service uses optimal communication parameters through a dynamic configuration mechanism, improving diagnostic efficiency and accuracy.

[0052] In some embodiments, the dynamic configuration processing of the protocols of the multiple independent services to obtain a configuration task includes the following steps: The independent service is subjected to interface exposure processing to obtain the service interface; The service interface is parsed to obtain the service protocol; The independent service is configured according to the service agreement to obtain the configuration service.

[0053] Specifically, this application embodiment exposes the functionality of an independent service to the outside world through a standardized interface. A service interface refers to a standardized access endpoint provided by an independent service, including input parameters, output results, and calling specifications. This application embodiment performs in-depth analysis and structural parsing of the communication protocol used by the service interface, achieving protocol parsing. A service protocol refers to the specific communication rules and data structure specifications used by the service interface, such as RESTful API, SOAP, or a dedicated binary protocol. This application embodiment performs parameter configuration and protocol adaptation for the independent service based on the parsed protocol specifications, resulting in a standardized service instance with complete communication capabilities and execution parameters after protocol configuration.

[0054] In this embodiment, each independent service is exposed to generate a service interface containing endpoint addresses, calling methods, and parameter specifications. Then, these service interfaces are parsed to analyze the communication protocol type, data format, and interaction process used, resulting in detailed service protocol specifications. Finally, based on the service protocols, each independent service is precisely configured, including setting communication parameters, configuring serialization rules, and defining timeout policies, ultimately generating a configuration service that can be executed efficiently, establishing a standardized communication foundation for subsequent vehicle diagnostic processing.

[0055] One of the above technical solutions has the following advantages or beneficial effects: This application embodiment establishes a standardized service access mechanism through unified interface exposure processing, improving system integration efficiency. Furthermore, this application embodiment, through a modular interface and protocol processing architecture, enables the system to quickly adapt to new service types and protocol standards, improving the system's scalability and flexibility.

[0056] The following is a detailed description and explanation of the solutions in the embodiments of this application, using specific application examples: The embodiments of this application can be applied to vehicle diagnostic scenarios. Please refer to [link / reference]. Figure 3 This application's embodiments can be applied to an AI- and blockchain-based intelligent DoIP diagnostic system. The system mainly consists of three modules: an intelligent scheduling module, a security verification module, and a protocol adaptation module. Each module interacts with data through clearly defined interfaces, collaborating to achieve efficient and secure diagnostic communication. Please refer to... Figure 4In this embodiment, when a diagnostic request arrives, it first enters the intelligent scheduling module for task analysis and queue management; then, the security verification module performs identity authentication and data integrity verification; finally, the protocol adaptation module converts it into a specific DoIP message for transmission. The diagnostic response from each ECU returns along the reverse path and is ultimately aggregated and sent to the diagnostic client. The security verification module consists of a blockchain node, an encryption engine, and an authentication manager. When an upgrade package is uploaded, the encryption engine first calculates its SHA-256 hash value; then, it intelligently reuses the "memory address" field in the DoIP protocol 0x34 service message to store a compressed representation of this hash value (using CRC32 algorithm compression); next, it submits the full hash value to the blockchain network; during transmission, the receiver compares the verification field in the 0x34 message with the hash value stored on the blockchain to achieve integrity verification. The authentication manager is responsible for performing two-way authentication: using a TLS handshake protocol based on X.509 certificates, and adding a custom challenge-response mechanism at the application layer to ensure the legitimate identities of the client and server.

[0057] This application embodiment receives the original diagnostic request and parses parameters such as the target ECU address, service type, and data volume. It also collects real-time status data for each ECU, including CPU load, network latency, and memory usage, which can be obtained through periodic heartbeat messages. Please refer to... Figure 5 This application's embodiment performs dynamic scheduling: First, a prediction model trained on historical data is used to estimate the response time of each ECU to the current request. This prediction model can be constructed based on the gradient boosting decision tree algorithm. Then, considering factors such as task priority and resource dependencies, safety-critical tasks are prioritized, and resource conflicts are avoided. An improved shortest job first algorithm is used to generate the optimal execution sequence. Finally, thread pool management technology is used to allocate tasks to pre-initialized worker threads, avoiding the overhead of frequently creating and destroying threads. This module communicates with other modules through a message queue to ensure the reliability of asynchronous data transmission.

[0058] This application adopts a microservice architecture, decomposing the traditional monolithic diagnostic stack into multiple independent services. These independent services may include communication services, diagnostic services, and logging services. Each service exposes its functionality through a gRPC interface and is dynamically configured and managed through a unified configuration center. For example, the communication service is responsible for underlying Socket management; the diagnostic service implements the UDS protocol logic; and the logging service is responsible for diagnostic data storage. This design allows for flexible adjustment of system capabilities by adding or removing service instances; for example, communication service instances can be expanded separately for high-concurrency scenarios.

[0059] Please see Figure 6This application also provides a vehicle diagnostic system that can implement the above-mentioned vehicle diagnostic method. The system includes: Request retrieval module 601 is used to retrieve vehicle diagnostic requests; The queue management module 602 is used to perform task analysis and queue management processing on the vehicle diagnostic request to obtain a task queue; The security verification module 603 is used to perform identity authentication and data integrity verification on the task queue to obtain the security verification result. The safety diagnostic module 604 is used to perform protocol adaptation and diagnostic processing on the vehicle diagnostic request based on the safety verification result, and obtain the diagnostic processing result.

[0060] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0061] Please see Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 702 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701. The input / output interface 703 is used to implement information input and output; The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 705 transmits information between various components of the device (e.g., processor 701, memory 702, input / output interface 703, and communication interface 704); The processor 701, memory 702, input / output interface 703, and communication interface 704 are connected to each other within the device via bus 705.

[0062] The cloud-based data processing device provided in this embodiment of the invention includes a memory, a processor, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the vehicle diagnostic method of the above embodiment.

[0063] Taking the example of a processor and memory in a vehicle controller being connected via a bus, the memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the control processor, and these remote memories can be connected to the control device via a network.

[0064] The non-transitory software program and instructions required to implement the data processing method of the above embodiments are stored in memory. When executed by the processor, the vehicle diagnostic method in the above embodiments is executed.

[0065] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0066] This invention also provides a vehicle that includes the cloud-based data processing device described in the above embodiments.

[0067] The vehicle can be a private car, such as a sedan, SUV, MPV, or pickup truck. It can also be a commercial vehicle, such as a van, bus, small truck, or large semi-trailer. The vehicle must have an electric motor capable of outputting power or acting as a generator to store mechanical energy. When the vehicle is a new energy vehicle, it can be a hybrid or a pure electric vehicle.

[0068] Since the vehicle applies all the technical solutions of the above-mentioned vehicle fuel consumption management device or vehicle fuel consumption management controller, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0069] Furthermore, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions for performing the control method described above. Exemplarily, the method steps described above are performed.

[0070] It is worth noting that, since the computer-readable storage medium of the present invention is capable of executing the control method of any of the above embodiments, the specific implementation and technical effects of the computer-readable storage medium of the present invention can be referred to the specific implementation and technical effects of the control method of any of the above embodiments.

[0071] Furthermore, one embodiment of the present invention provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the robot control method described above. Exemplarily, the method steps described above are performed.

[0072] It is worth noting that, since the computer program product of the present invention can execute the control method of any of the above embodiments, the specific implementation method and technical effect of the computer program product of the present invention can be referred to the specific implementation method and technical effect of the control method of any of the above embodiments.

[0073] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0074] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A vehicle diagnostic method, characterized in that, The method includes: Obtain vehicle diagnostic request; The vehicle diagnostic requests are processed through task analysis and queue management to obtain a task queue; The task queue is subjected to identity authentication and data integrity verification to obtain security verification results; Based on the security verification results, the vehicle diagnostic request is adapted and processed for protocol adaptation to obtain the diagnostic processing results.

2. The method according to claim 1, characterized in that, The process of performing task analysis and queue management on the vehicle diagnostic request to obtain a task queue includes: The vehicle diagnostic request is analyzed and processed to obtain the request parameters; Based on the request parameters, perform resource monitoring to obtain status data; Based on the status data, the vehicle diagnostic request is processed for decision scheduling and queue allocation to obtain a task queue.

3. The method according to claim 2, characterized in that, The step of performing decision-making scheduling and queue allocation processing on the vehicle diagnostic request based on the status data to obtain a task queue includes the following steps: The target electronic control unit is obtained based on the vehicle diagnostic request; The target electronic control unit is subjected to predictive processing based on a pre-trained predictive model to obtain the request response time; Based on the request response time and the shortest job first algorithm, the vehicle diagnostic request is processed to generate a sequence, resulting in an execution sequence. The execution sequence is allocated corresponding threads through a thread pool management system, and a task queue is generated.

4. The method according to claim 1, characterized in that, The process of performing identity authentication and data integrity verification on the task queue to obtain a security verification result includes the following steps: The task queue is subjected to two-way authentication to obtain the authentication result; Based on the authentication result, perform hash value calculation on the data packets in the task queue to obtain the hash value; The hash value is compressed to obtain a compressed hash value, and the compressed hash value is stored in the service message field; Submit the compressed hash value to the blockchain network; The task queue is processed by the blockchain network to verify data integrity and obtain a security verification result.

5. The method according to claim 4, characterized in that, The process of performing data integrity verification on the task queue through the blockchain network to obtain a security verification result includes the following steps: The service message is processed to extract fields, resulting in verification fields; The security verification result is obtained by comparing the hash values ​​of the verification field through the blockchain network.

6. The method according to claim 1, characterized in that, The process of performing protocol adaptation and diagnostic processing on the vehicle diagnostic request based on the security verification result to obtain the diagnostic processing result includes the following steps: Based on the security verification results, the vehicle diagnostic request is decomposed into multiple independent services. Dynamically configure the protocols of multiple independent services to obtain a configuration service; The diagnostic process is performed based on the configuration service to obtain the diagnostic results.

7. The method according to claim 6, characterized in that, The dynamic configuration process for the protocols of multiple independent services to obtain a configuration task includes the following steps: The independent service is subjected to interface exposure processing to obtain the service interface; The service interface is parsed to obtain the service protocol; The independent service is configured according to the service agreement to obtain the configuration service.

8. A vehicle diagnostic system, characterized in that, The system includes: The request retrieval module is used to retrieve vehicle diagnostic requests; The queue management module is used to perform task analysis and queue management processing on the vehicle diagnostic requests to obtain a task queue; The security verification module is used to perform identity authentication and data integrity verification on the task queue to obtain the security verification result; The safety diagnostic module is used to perform protocol adaptation and diagnostic processing on the vehicle diagnostic request based on the safety verification results, and obtain the diagnostic processing results.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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