An intelligent cockpit vehicle control problem diagnosis method and diagnosis system

Abstract

The application provides a diagnosis method and diagnosis system for intelligent cockpit vehicle control problems, the diagnosis method comprising: collecting vehicle machine core data in real time, associating the vehicle machine core data according to vehicle control functions to generate associated information, automatically marking abnormalities and generating a signal link diagram; generating a visual log in real time according to monitoring configuration, and superimposing a functional state layer on a test picture to display in layers; classifying and storing the signal link diagram according to vehicle control functions, exporting log data packets, and generating a verification report based on the signal link diagram before and after repair. The application solves the technical problem of low diagnosis efficiency in the existing intelligent cockpit vehicle control function development and testing process.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics and smart cockpit technology, and more specifically, to a diagnostic method and a diagnostic system for smart cockpit vehicle control problems. Background Technology

[0002] Vehicle control functions are core functions that users use frequently, have extremely high security requirements, and provide a strong user experience. These functions include, but are not limited to, window control, door lock control, seat adjustment, and air conditioning control. Throughout the entire process of developing, debugging, testing, optimizing, and mass-producing these vehicle control functions, the efficiency of diagnosing abnormal problems, the accuracy of locating them, the success rate of reproducing them, and the speed of repair and verification directly determine the overall vehicle development cycle, product quality, and user satisfaction.

[0003] In the current development and testing of intelligent cockpit vehicle control functions, the diagnostic approach generally adopts a combination of traditional log collection, manual hierarchical investigation and static tool configuration. This type of solution relies on external tools such as CANoe and Wireshark to capture bus data, and then manually compares the HMI screen with the middleware debugging information. Problems are located layer by layer according to "HMI → middleware → bus". Static parameters are configured using tools such as GEM. The overall process is fragmented and relies on manual experience.

[0004] The aforementioned traditional solutions suffer from common drawbacks, including incomplete log collection, lengthy diagnostic links, lack of visibility into vehicle control status, fragmented tools, and low efficiency in problem closure. In particular, they cannot automatically collect key ECU initialization information during the boot-up phase, making it difficult to achieve full-link automatic association between HMI operations, middleware calls, and bus commands. They also lack real-time visual status monitoring and automatic anomaly marking capabilities, and cannot form an integrated closed loop of collection, diagnosis, export, comparison, and verification. This results in long vehicle control problem localization cycles, difficulty in reproduction, and high cross-departmental communication costs, and can no longer meet the needs of efficient diagnosis. Summary of the Invention

[0005] This invention addresses the technical problem of low diagnostic efficiency in the development and testing of existing intelligent cockpit vehicle control functions.

[0006] To address the aforementioned issues, this invention provides a diagnostic method for intelligent cockpit vehicle control problems, comprising: real-time acquisition of core vehicle data; association of the core vehicle data with vehicle control functions to generate associated information; automatic marking of anomalies and generation of signal link diagrams; real-time generation of visual logs based on monitoring configurations, and overlaying of functional status layers on the test screen for layered display; classification and storage of signal link diagrams according to vehicle control functions and export of log data packages; and generation of a verification report based on the signal link diagrams before and after repair.

[0007] Compared with existing technologies, the technical effects achieved by adopting this solution are as follows: Through a complete process of "real-time data collection - function association - anomaly marking - visualization display - classification and storage - export verification", it enables one-stop rapid and accurate diagnosis of vehicle control problems; it solves the problem of low anomaly localization efficiency caused by incomplete logs, long diagnostic links, invisible test environments, and fragmented tools in the development of intelligent cockpit vehicle control functions; it significantly improves diagnostic efficiency by eliminating the need for switching between multiple tools and manual layer-by-layer investigation, shortening the problem localization cycle from several days to hours.

[0008] In one embodiment of the present invention, the core data of the vehicle infotainment system includes: HMI operation signals, middleware call logs, and bus data. The core data of the vehicle infotainment system is collected in real time, and the core data of the vehicle infotainment system is associated with vehicle control functions to generate associated information. Anomalies are automatically marked and a signal link diagram is generated. This includes: collecting HMI operation signals, middleware call logs, and bus data in real time and automatically associating them with specific vehicle control functions to generate associated information; automatically recording ECU initialization data during the vehicle startup phase; and performing anomaly detection on the associated information based on preset anomaly rules, automatically marking abnormal nodes and generating a signal link diagram.

[0009] Compared with existing technologies, the technical effects achieved by this solution are as follows: It enables the synchronous acquisition and integrated association of HMI operation signals, middleware call logs, and bus data, ensuring the completeness and consistency of diagnostic data dimensions and timing; it automatically records ECU initialization information, version information, and communication status information during the vehicle startup phase, completely resolving the industry pain point of missing key logs during the startup phase; and it achieves automatic detection, automatic marking, and automatic location of abnormal signals through preset anomaly rules, forming a complete and traceable signal chain of "HMI operation intent → middleware call → bus command," thereby improving diagnostic completeness, accuracy, and reproducibility.

[0010] In one embodiment of the present invention, the signal link diagram includes: HMI operation intent, middleware call and bus command. HMI operation signals, middleware call logs and bus data are collected in real time and automatically associated with specific vehicle control functions to generate associated information, including: using a lightweight AI model to parse HMI operation intent in real time and matching and associating HMI operation intent with corresponding bus commands and middleware call behaviors one by one.

[0011] Compared with existing technologies, the technical effects achieved by this solution are as follows: a lightweight AI model is used to analyze HMI operation intentions in real time, and the operation intentions are automatically matched with corresponding bus instructions and middleware calls, and the timing and logic of the entire process are aligned and bound together. This achieves automatic association without human intervention, ensuring consistent timing and accurate data correspondence throughout the entire process, and significantly improving the efficiency and accuracy of association.

[0012] In one embodiment of the present invention, the preset abnormal rules include: signal value exceeding the threshold, ECU no response, ECU response timeout, bus message loss, message verification error, and communication interruption.

[0013] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: it automatically identifies anomalies based on a preset anomaly rule engine, clearly includes various standardized anomaly rules, and highlights anomaly nodes; after the rules are solidified, they can be stably reused, executed in batches, and run automatically, which greatly improves the consistency, accuracy, and coverage of anomaly identification, reduces differences in manual judgment, and improves the stability and repeatability of diagnosis.

[0014] In one embodiment of the present invention, a visual log is generated in real time according to the monitoring configuration, and a functional status layer is superimposed on the test screen for hierarchical display. This includes: dynamically selecting the vehicle control function to be monitored according to the user configuration command, generating a visual log in real time based on the associated information; supporting filtering of the visual log by function or level; and superimposing a functional status layer on the test screen based on the visual log.

[0015] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: it supports dynamic selection of monitoring objects based on user configuration instructions, flexibly adapting to different vehicle control function diagnostic needs; it automatically generates structured and visual logs based on associated information, and supports filtering by function type or level, greatly reducing interference from invalid information; it directly overlays function status layers on the test screen, allowing for intuitive judgment of status without switching tools, significantly reducing the threshold for use and improving diagnostic convenience and intuitiveness.

[0016] In one embodiment of the present invention, the visual log includes: timestamps, signal values, and status icons.

[0017] Compared with existing technologies, the technical effects achieved by adopting this solution are as follows: by integrating timestamps, signal values, and status icons into the visualized log, the log becomes time-traceable, numerically quantifiable, and its status can be intuitively judged, resulting in a clear structure and efficient reading. Testers can quickly locate issues such as timing errors, numerical anomalies, and status mismatches, and can understand the log content without a professional background, significantly reducing the difficulty of troubleshooting and improving the speed and accuracy of problem identification.

[0018] In one embodiment of the present invention, the signal link diagram is classified and stored according to the vehicle control function, and log data packets are exported. A verification report is generated based on the signal link diagrams before and after the repair. The process includes: classifying and storing the vehicle's core data, related information, and signal link diagrams according to the vehicle control function; exporting log data packets according to the vehicle control function or time range according to user instructions; and automatically comparing the signal link diagrams before and after the repair to generate a verification report containing quantitative indicators.

[0019] Compared with existing technologies, the technical effects achieved by adopting this solution are as follows: core vehicle data, related information, and signal link diagrams are automatically classified and stored according to vehicle control functions, facilitating rapid retrieval, management, and traceability; log data packages are flexibly exported according to function or time range, facilitating cross-departmental sharing and problem reproduction; the differences in signal links before and after repair are automatically compared, objectively presenting the problem improvement effect, forming a closed loop of fully automated diagnosis, significantly reducing communication costs, improving verification efficiency, and enhancing the credibility of conclusions.

[0020] In one embodiment of the present invention, the quantitative indicators include: ECU response time, message integrity rate, signal delay, and anomaly disappearance rate.

[0021] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: Through four core quantitative indicators—ECU response time, message integrity rate, signal delay, and anomaly disappearance rate—the repair effect is presented objectively, accurately, and data-driven, making the verification results measurable, comparable, archiveable, and acceptable. The quantitative indicators provide clear data support for version optimization, problem review, and performance improvement, enhancing the standardization, professionalism, and credibility of verification, and contributing to the improvement of vehicle development quality.

[0022] On the other hand, embodiments of the present invention also provide a diagnostic system for intelligent cockpit vehicle control problems, employing a diagnostic method for intelligent cockpit vehicle control problems as described in any embodiment of the present invention. The diagnostic system includes: a full-link dynamic acquisition module, which is used to collect core vehicle data in real time, automatically mark anomalies after associating the core vehicle data according to vehicle control functions, and generate a signal link diagram; an intelligent visualization configuration module, which is used to generate a visualization log in real time according to the monitoring configuration, and overlay a functional status layer on the test screen for layered display; and an automated closed-loop verification module, which is used to classify and store the signal link diagram according to vehicle control functions, export log data packets, and generate a verification report based on the signal link diagram before and after repair.

[0023] Compared with the prior art, the technical effects achieved by adopting this technical solution are as follows: The diagnostic system in this embodiment is used to implement the diagnostic method for intelligent cockpit vehicle control problems as in any embodiment of the present invention, and therefore it has all the beneficial effects of the diagnostic method for intelligent cockpit vehicle control problems as in any embodiment of the present invention, which will not be repeated here.

[0024] In one embodiment of the present invention, the intelligent visual configuration module adopts the GEM enhanced configuration tool, which supports selecting the vehicle control function to be monitored by dragging and dropping.

[0025] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: The GEM enhanced configuration tool supports intuitive selection of vehicle control functions to be monitored through drag and drop, eliminating the need for script writing and manual input of complex commands. The configuration process is simple, intuitive, and efficient; it significantly reduces the operating threshold, shortens the configuration time, and improves the flexibility of use. Ordinary testers can quickly get started, with low learning costs, thus improving the efficiency and convenience of vehicle control monitoring configuration.

[0026] By adopting the technical solution of the present invention, the following technical effects can be achieved: (1) Through the complete process of "real-time collection - function association - anomaly marking - visualization display - classification storage - export verification", the vehicle control problem can be diagnosed quickly and accurately in one stop; the problem of low efficiency in anomaly location caused by incomplete logs, long diagnostic links, invisible test environment and fragmented tools in the development of intelligent cockpit vehicle control function is solved; no need to switch between multiple tools or manually check layer by layer, significantly improving diagnostic efficiency and shortening the problem location cycle from several days to hours; (2) Automatically record ECU initialization information, version information, and communication status information during the vehicle startup phase, completely solving the industry pain point of missing key logs during the startup phase; (3) Transparent layers and layered logs lower the skill threshold for testers, allowing them to determine the status without analyzing the underlying data; (4) A complete closed loop is formed from log collection to verification, reducing cross-departmental communication costs. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a flowchart of a diagnostic method for intelligent cockpit vehicle control problems provided in Embodiment 1 of the present invention; Figure 2 This is a schematic block diagram of a diagnostic system for intelligent cockpit vehicle control problems provided in Embodiment 2 of the present invention.

[0028] Explanation of reference numerals in the attached figures: 100 - Diagnostic system; 101 - End-to-end dynamic acquisition module; 102 - Intelligent visual configuration module; 103 - Automated closed-loop verification module. Detailed Implementation

[0029] To make the above-mentioned objectives, features, and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example 1 See Figure 1 This is a flowchart of a diagnostic method for intelligent cockpit vehicle control problems provided in the first embodiment of the present invention. The diagnostic method includes: S100: Real-time acquisition of core vehicle data, association of core vehicle data with vehicle control functions to generate associated information, automatic marking of anomalies and generation of signal link diagrams; S200: Generates visual logs in real time based on monitoring configuration, and overlays a functional status layer on the test screen for layered display; S300: Based on the vehicle control function, the signal link diagram is classified and stored, and log data packets are exported. A verification report is generated based on the signal link diagram before and after the repair.

[0031] In one specific embodiment, this application relates to anomaly diagnosis, data acquisition, visualization analysis, and closed-loop verification technologies during the vehicle control function development phase. It is applicable to scenarios such as vehicle ECU function verification, bus communication debugging, and HMI interaction testing. Specifically, it executes S100 by adding a full-link dynamic acquisition module: The full-link dynamic acquisition module is integrated into the vehicle system, capturing HMI operation signals, middleware call logs, and bus data in real time, and automatically associating them with specific vehicle control functions (such as seat adjustment and window control) to generate associated information. It performs full-link acquisition at startup and anomaly node marking, generating "HMI operation → middleware call → The system provides a complete signal link diagram for "bus commands"; it employs an intelligent visual configuration module to execute S200: dynamically configures the vehicle control functions to be monitored, generates real-time visual logs containing timestamps, signal values, and status icons, and achieves layered display and transparent layer overlay; it employs an automated closed-loop verification module to execute S300: stores the signal link diagram and related information generated by the full-link dynamic acquisition module according to function to the designated path in the vehicle system, and testers can directly extract the compressed package and submit it to the development team, and automatically compare the link differences before and after the repair, generating a verification report (such as "ECU response time optimized from 500ms to 200ms").

[0032] This application achieves rapid and accurate one-stop diagnosis of vehicle control issues through a complete process of "real-time data collection, function association, anomaly marking, visualization, categorized storage, and export verification." It solves the problem of low anomaly localization efficiency caused by incomplete logs, long diagnostic links, invisible test environments, and fragmented tools in the development of intelligent cockpit vehicle control functions. It significantly improves diagnostic efficiency by eliminating the need for switching between multiple tools and manual layer-by-layer investigation, shortening the problem localization cycle from several days to hours.

[0033] Preferably, the end-to-end dynamic acquisition module can be dynamically set to start with the highest priority in the system (i.e., as the priority startup module), subscribe to / register callbacks to the relevant ECUs, and obtain relevant information during startup.

[0034] Furthermore, the core data of the vehicle's infotainment system includes: HMI operation signals, middleware call logs, and bus data. The S100 includes: S110: Real-time acquisition of HMI operation signals, middleware call logs and bus data, and automatic association with specific vehicle control functions to generate associated information; S120: Automatically records ECU initialization data during the vehicle startup phase; S130: Based on preset anomaly rules, perform anomaly detection on associated information, automatically mark abnormal nodes, and generate a signal link diagram.

[0035] Specifically, the S100 includes: real-time capture of HMI operations, middleware logs, and bus data, and automatic association with specific vehicle control functions; subscription to ECU callback functions at the moment the vehicle system boots up, automatic recording of ECU initialization data (such as version number information and communication status), completion of missing boot logs, complete resolution of missing boot logs, and prevention of missing critical information; automatic marking of abnormal nodes according to preset abnormal rules (such as data exceeding thresholds, ECU not responding), and a complete signal link diagram of "HMI operation intent → middleware call → bus command", ensuring data integrity and accurate diagnosis; for vehicle control functions, after marking abnormal nodes, the full-link dynamic acquisition module will record the log of the complete operation link on site, collect operation-related logs, and synchronously output a problem analysis result.

[0036] Preferably, the vehicle's core data and ECU initialization data are transmitted efficiently via in-vehicle Ethernet, reducing interference with the actual vehicle environment.

[0037] Preferably, HMI operation signals include: touch, click, swipe, voice, interface switching, etc.; middleware call logs include: service calls, APIs, parameters, time consumption, return values, etc.; bus data specifically refers to CAN / Ethernet bus data, including: CAN / CANFD / LIN / Ethernet message ID, data, cycle, packet loss, and errors.

[0038] Furthermore, the signal link diagram includes: HMI operation intent, middleware call and bus command. S110 includes: using a lightweight AI model to parse the HMI operation intent in real time, and matching and associating the HMI operation intent with the corresponding bus command and middleware call behavior one by one.

[0039] Specifically, in traditional vehicle control diagnostics, HMI interface operations are independent of underlying bus commands and middleware call logic. They can only be subjectively matched manually based on timestamps and function descriptions, making it impossible to automatically associate operation intentions with command behaviors. Manual matching is inefficient, prone to timing misalignment, and easily confused in complex scenarios. Especially when multiple commands are concurrent and multiple functions are running in parallel, association errors are very likely to occur, making it impossible to accurately reconstruct the complete transmission link from user operation to device execution, which greatly reduces the efficiency and accuracy of problem localization.

[0040] Lightweight AI models (such as PaddlePaddleLite) are used to analyze HMI operation intentions in real time, and the operation intentions are automatically matched with corresponding bus instructions and middleware calls, and the timing and logic of the entire process are aligned. This achieves automatic association without human intervention, ensuring consistent timing and accurate data correspondence throughout the entire process, and greatly improving the efficiency and accuracy of association.

[0041] Furthermore, the preset abnormal rules include: signal value exceeding the threshold, ECU no response, ECU response timeout, bus message loss, message verification error, and communication interruption.

[0042] Specifically, existing vehicle control anomaly diagnosis lacks unified and standardized anomaly judgment rules. Anomaly identification relies on the personal experience of engineers, which is prone to problems such as missed judgments, misjudgments, and untimely anomaly marking. The anomaly types are not fully covered and lack standardization. This application is based on a preset anomaly rule engine for automatic identification, and clearly includes various standardized anomaly rules, and highlights anomaly nodes. After the rules are solidified, they can be stably reused, batch executed, and run automatically, which greatly improves the consistency, accuracy, and coverage of anomaly identification, reduces the differences in human judgment, and improves the stability and repeatability of diagnosis.

[0043] Furthermore, the S200 includes: S210: Dynamically selects the vehicle control function to be monitored based on user configuration instructions, and generates a visual log in real time based on the associated information; S220: Supports filtering visual logs by function or level; S230: Based on visual logs, a functional status layer is overlaid on the test screen.

[0044] Specifically, traditional vehicle control monitoring requires manual configuration, manual activation, and manual log filtering, making the configuration process cumbersome and inflexible. The log information is large, unstructured, and lacks hierarchical distinction, making it difficult for testers to quickly locate target information. At the same time, the execution status of vehicle control functions is not visible, making it impossible to intuitively judge whether the current function is responding normally or whether there are any abnormalities on the test screen. It requires repeatedly switching tools, checking logs, and comparing data, resulting in low troubleshooting efficiency, high usage threshold, and unfriendly experience.

[0045] This application supports dynamic configuration of vehicle control functions to be monitored through the vehicle interface or external tools (such as GEM) based on user configuration instructions, flexibly adapting to different vehicle control function diagnostic needs; it generates structured and visualized logs in real time based on associated information, and supports filtering logs by function type (such as air conditioning, lights) or level (HMI / middleware / bus), significantly reducing interference from invalid information; it directly overlays function status layers (such as green for normal, red for abnormal, yellow for execution, and gray for inactive with a delay of <100ms) on the HMI interface, allowing for intuitive status judgment without switching tools, significantly lowering the usage threshold and improving diagnostic convenience and intuitiveness; transparent layers and layered logs lower the skill threshold for testers, allowing them to judge the status without analyzing underlying data; testers can quickly select monitoring functions by dragging and dropping, without writing complex scripts, and supports one-click drag-and-drop configuration.

[0046] The overlay function status layer works as follows: After setting the monitoring mode, a green screen will be displayed at the top of the screen. When an operation is clicked, a visual log sequence will be displayed, which will help testers quickly locate the problem. It can initially confirm whether the problem is a test environment issue (ECU problem / coding problem, etc.), which can improve the testing efficiency of testers and provide developers with a faster investigation speed. The function status layer is dynamically updated with bus data with a delay of less than 100ms, realizing real-time feedback.

[0047] Furthermore, the visual logs include timestamps, signal values, and status icons.

[0048] Specifically, by integrating timestamps, signal values, and status icons into the visual logs, the logs become time-traceable, numerically quantifiable, and their statuses intuitively judged, resulting in a clear structure and efficient reading. Testers can quickly locate issues such as timing errors, numerical anomalies, and status mismatches, and can understand the log content without requiring a professional background, significantly reducing the difficulty of troubleshooting and improving the speed and accuracy of problem identification.

[0049] Furthermore, the S300 includes: S310: Stores the vehicle's core data, related information, and signal link diagrams according to vehicle control functions; S320: Exports log data packages according to user instructions based on vehicle control functions or time ranges; S330: Automatically compares the signal link diagrams before and after the repair to generate a verification report containing quantitative indicators.

[0050] Specifically, this application enables the automatic classification and storage of core vehicle system data, related information, and signal link diagrams according to vehicle control functions, facilitating rapid retrieval, management, and traceability; it supports flexible export of log data packages by function or time range, enabling one-click export, facilitating cross-departmental sharing and problem reproduction, and avoiding errors in manual screening; developers can quickly reproduce problems using parsing tools (such as custom Python scripts) and trigger regression testing to automatically compare the differences in signal links before and after repair, generating verification reports (such as "ECU response time optimized from 500ms to 200ms"), objectively presenting the problem improvement effect, forming a closed loop of fully automated diagnosis, significantly reducing communication costs, improving verification efficiency, and enhancing the credibility of conclusions.

[0051] Further quantitative indicators include: ECU response time, message integrity rate, signal delay, and anomaly disappearance rate.

[0052] Specifically, the repair effect is presented objectively, accurately, and data-driven through four core quantitative indicators: ECU response time, message integrity rate, signal delay, and anomaly disappearance rate. This makes the verification results measurable, comparable, archiveable, and acceptable. The quantitative indicators provide clear data support for version optimization, problem review, and performance improvement, enhancing the standardization, professionalism, and credibility of verification, and helping to improve the overall vehicle development quality.

[0053]

Example 2

[0054] In one specific embodiment, the diagnostic system 100's end-to-end dynamic acquisition module 101, intelligent visualization configuration module 102, and automated closed-loop verification module 103 work together to implement the diagnostic method for intelligent cockpit vehicle control problems as described in the first embodiment above. Through the collaborative work of the three core modules—end-to-end dynamic acquisition module 101, intelligent visualization configuration module 102, and automated closed-loop verification module 103—data acquisition, anomaly marking, link generation, visualization display, status monitoring, data storage, log export, and comparative verification are all integrated into one, achieving one-stop, integrated, and automated diagnosis. The tools are unified, the data is interconnected, and the process is coherent, eliminating the need to switch between multiple tools, reducing learning costs and operational complexity, and significantly improving overall diagnostic efficiency.

[0055] Furthermore, the intelligent visualization configuration module adopts the GEM enhanced configuration tool, which supports selecting the vehicle control functions to be monitored by dragging and dropping.

[0056] Specifically, the GEM enhanced configuration tool supports intuitive selection of vehicle control functions to be monitored via drag and drop, eliminating the need for script writing or manual input of complex commands. The configuration process is simple, intuitive, and efficient, significantly reducing the operational threshold, shortening configuration time, and improving usability. Ordinary testers can quickly get started, with low learning costs, thus improving the efficiency and convenience of vehicle control monitoring configuration.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for diagnosing intelligent cabin vehicle control problems, characterized in that, The diagnostic method includes: Real-time acquisition of core vehicle system data; association of the core vehicle system data with vehicle control functions to generate associated information; automatic marking of anomalies and generation of signal link diagrams. Visual logs are generated in real time based on the monitoring configuration, and functional status layers are overlaid on the test screen for layered display. The signal link diagram is classified and stored according to the vehicle control function, and log data packets are exported. A verification report is generated based on the signal link diagram before and after the repair.

2. The diagnostic method of claim 1, the head unit core data comprising: HMI operation signals, middleware call logs, and bus data, characterized in that, The real-time acquisition of core vehicle system data, the association of this core data with vehicle control functions to generate association information, automatic marking of anomalies and generation of signal link diagrams, including: The system collects the HMI operation signals, the middleware call logs, and the bus data in real time, and automatically associates them with specific vehicle control functions to generate the associated information. Automatically record ECU initialization data during the vehicle startup phase; Based on preset anomaly rules, anomaly detection is performed on the associated information, and abnormal nodes are automatically marked and the signal link diagram is generated.

3. The diagnostic method of claim 2, the signal link map comprising: HMI operation intent, middleware calls, and bus commands, characterized in that, The real-time acquisition of HMI operation signals, middleware call logs, and bus data, and the automatic association with specific vehicle control functions to generate the associated information, includes: A lightweight AI model is used to analyze the HMI operation intent in real time, and the HMI operation intent is matched and associated with the corresponding bus instructions and middleware call behaviors one by one.

4. The diagnostic method according to claim 2, characterized in that, The preset abnormal rules include: signal value exceeding the threshold, ECU no response, ECU response timeout, bus message loss, message verification error, and communication interruption.

5. The diagnostic method according to claim 1, characterized in that, The process of generating real-time visual logs based on monitoring configurations and overlaying functional status layers on the test screen for layered display includes: The vehicle control function to be monitored is dynamically selected according to the user configuration instructions, and the visualized log is generated in real time based on the associated information; The visualization logs can be filtered by function or level; Based on the visualization log, the functional status layer is overlaid on the test screen.

6. The diagnostic method according to claim 5, characterized in that, The visual log includes: timestamps, signal values, and status icons.

7. The diagnostic method according to claim 1, characterized in that, The step of classifying and storing the signal link diagram according to the vehicle control function and exporting log data packets, and generating a verification report based on the signal link diagram before and after the repair, includes: The vehicle system core data, the associated information, and the signal link diagram are classified and stored according to the vehicle control functions; Export the log data package according to the vehicle control function or time range as instructed by the user; An automatic comparison is performed on the signal link diagrams before and after the repair to generate a verification report containing quantitative indicators.

8. The diagnostic method according to claim 7, characterized in that, The quantitative indicators include: ECU response time, message integrity rate, signal delay, and anomaly disappearance rate.

9. A diagnostic system for intelligent cockpit vehicle control problems, characterized in that, The diagnostic system employs the diagnostic method for intelligent cockpit vehicle control problems as described in any one of claims 1 to 8, and the diagnostic system includes: The full-link dynamic acquisition module is used to collect core data of the vehicle system in real time, and automatically mark anomalies and generate a signal link diagram after associating the core data of the vehicle system according to the vehicle control function. The intelligent visualization configuration module is used to generate visualization logs in real time based on the monitoring configuration, and to overlay a functional status layer on the test screen for layered display. An automated closed-loop verification module is used to classify and store the signal link diagram according to the vehicle control function, export log data packets, and generate a verification report based on the signal link diagram before and after repair.

10. The diagnostic system according to claim 9, characterized in that, The intelligent visualization configuration module uses the GEM enhanced configuration tool, which supports selecting the vehicle control function to be monitored by dragging and dropping.

Images