Cloud-based vehicle infotainment end-to-end log recording and fault location interaction method and system

Abstract

This invention discloses a cloud-based vehicle infotainment system (V2X) end-to-end log recording and fault location interaction method and system. The method includes real-time acquisition of front-end interaction logs through a front-end interaction layer and real-time acquisition of V2X execution logs through a V2X execution layer. Based on preset rules, a cloud service layer processes the front-end interaction logs and V2X execution logs in real time to obtain end-to-end logs. The end-to-end logs are then processed to obtain an end-to-end traceability diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback. This invention generates end-to-end logs through the full-link cooperation of the front-end interaction layer, V2X execution layer, and cloud service layer, achieving data traceability and fault location, ensuring stability and efficient problem-solving.

Description

Technical Field

[0001] This invention relates to the field of automotive fault location technology, specifically to a cloud-based vehicle-machine end-to-end log recording and fault location interaction method and system. Background Technology

[0002] The vehicle-to-everything (V2X) system has already taken over the core functions of vehicle intelligence. Traditional V2X systems rely on local data storage, but due to the small memory of the local storage, it is impossible to store abnormal data for the entire V2X system. When the V2X system experiences intermittent failures, it is difficult to locate the root cause in a timely manner. Therefore, there is a need for a lightweight method to record abnormal data throughout the entire process when the V2X system interacts with people and the cloud. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a method and system for full-link log recording and fault location in a cloud-based vehicle-machine interaction system. Through the full-link cooperation of the front-end interaction layer, the vehicle-machine execution layer, and the cloud service layer, full-link logs are generated, enabling data traceability and fault location, and ensuring stability and efficiency in troubleshooting.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to a first aspect of this application, a cloud-based vehicle infotainment system full-link log recording and fault location interaction method is provided, characterized in that: Get front-end interaction logs in real time through the front-end interaction layer; The vehicle's execution logs are obtained in real time through the vehicle's execution layer. Based on preset rules, the front-end interaction logs and vehicle system execution logs are processed in real time through the cloud service layer to obtain full-link logs. The full-link logs are then processed to obtain a full-link traceability diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback.

[0006] In some embodiments of this application, based on the foregoing scheme, the step of obtaining front-end interaction logs in real time through the front-end interaction layer includes: Real-time collection of front-end interaction information, including face verification, permission operations, and configuration distribution, and real-time acquisition of basic device information; The front-end interaction information is converted into standardized front-end interaction logs, which are in JSON format and include log ID, request ID, user ID, vehicle system ID, and operation feedback result. Output standardized front-end interaction logs.

[0007] In some embodiments of this application, based on the foregoing solution, the step of obtaining front-end interaction logs in real time through the front-end interaction layer further includes: Users initiate specific interactive operations in the front-end interaction layer, including but not limited to face verification, permission request, and configuration distribution; The front-end interaction layer initiates a local pre-verification process, which includes: operation legality verification, parameter integrity verification, and network status verification. If the local pre-verification process fails, the front-end interaction layer intercepts the specific interaction operation, outputs an interception prompt to the user, and records and stores the interception log locally. If the local pre-verification process passes, the front-end interaction information is collected in real time. The front-end interaction layer automatically generates a log ID locally. The log ID is a unique link tracing identifier. The unique link tracing identifier adopts a combination format of timestamp, device fingerprint, and random code. At the same time, it is bound to the core information of the specific interaction operation, including request ID, user ID, vehicle ID, device fingerprint, operation type, operation initiation timestamp, and operation feedback result. After binding, it is stored locally. The front-end interaction layer records the detailed results of the local pre-verification process and generates standardized front-end interaction logs.

[0008] In some embodiments of this application, based on the aforementioned scheme, the cloud service layer aggregates full-link logs in real time according to a unique link tracing identifier, and simultaneously establishes a three-level index: The primary index serves as a unique link tracing identifier; The secondary index is a time slice, and the node position of the full-link log is located by millisecond-level time slice. The node position is the front-end interaction layer, the vehicle execution layer and the cloud service layer. The third-level index is the anomaly feature code. When the full-link log is generated, anomalies are automatically matched and a corresponding unique anomaly feature code is assigned. For each new end-to-end log entry added to the cloud service layer, the third-level index is updated synchronously.

[0009] In some embodiments of this application, based on the foregoing scheme, the step of obtaining the vehicle system execution log in real time through the vehicle system execution layer includes: Real-time collection of vehicle information, including configuration execution logs, environment adaptation logs, driving record logs, and fault warning logs issued by the cloud service layer; The vehicle information is converted into standardized vehicle execution logs, which are in JSON format and include log ID, request ID, user ID, vehicle ID, configuration execution result, and fault warning information. Output standardized vehicle system execution logs.

[0010] In some embodiments of this application, based on the aforementioned scheme, the step of processing the front-end interaction logs and vehicle system execution logs in real time through the cloud service layer based on preset rules to obtain full-link logs, processing the full-link logs to obtain a full-link source tracing diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback includes: The system receives front-end interaction logs, vehicle system execution logs, and preset rules, including permission verification logs, configuration synchronization logs, conflict handling logs, and fault determination rules. Based on preset rules, the front-end interaction logs and vehicle system execution logs are processed in real time to complete permission verification, conflict handling and fault determination. Based on the permission verification results, configuration synchronization results, conflict handling results and fault determination results, standardized full-link logs and fault alarm information with fault IDs are generated. Based on the fault ID, the full-link log is processed to obtain the full-link source tracing diagram, fault root cause analysis results, and repair suggestions; The fault alarm information containing the fault ID and the repair suggestions are sent to the vehicle's execution layer, and feedback on the fault handling status is obtained.

[0011] In some embodiments of this application, based on the aforementioned scheme, both the front-end interaction layer and the vehicle-mounted execution layer upload the front-end interaction log and the vehicle-mounted execution log respectively through streaming fragmented upload. The cloud service layer uses a queue to receive the front-end interaction log and the vehicle-mounted execution log in real time, marks the receiving timestamp after receiving, and pushes them to the streaming processing link for sequential storage.

[0012] According to a second aspect of this application, a cloud-based vehicle infotainment system for end-to-end log recording and fault location interaction is provided, comprising: The front-end interaction layer is used to obtain front-end interaction logs in real time. The vehicle system execution layer is used to obtain vehicle system execution logs in real time. The cloud service layer is used to process the front-end interaction logs and vehicle system execution logs in real time based on preset rules, obtain the full-link logs, process the full-link logs, and obtain the full-link traceability link diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback.

[0013] According to a third aspect of this application, a computer-readable storage medium is provided that stores a computer program thereon, the computer program including executable instructions that, when executed by a processor, implement the method described above.

[0014] According to a fourth aspect of this application, an electronic device is provided, comprising: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to implement the method described above.

[0015] The beneficial effects of this application are as follows: (1) The cloud-based vehicle-machine full-link log recording and fault location interaction method and system provided in this application covers three-dimensional correlation logic through three-level log collection of the front-end interaction layer, cloud service layer and vehicle-machine execution layer, realizes full-process data traceability without dead angles, and can quickly connect the context after the fault occurs, avoiding the inefficiency of traditional troubleshooting interruption point data finding problem, and shortening the average fault location time from hours to seconds.

[0016] (2) The cloud-based vehicle-mounted system full-link log recording and fault location interaction method and system provided in this application are designed with a storage layer architecture. High-frequency short-term logs are stored locally to reduce cloud transmission pressure. Unstructured data and structured data are classified and stored to optimize resource usage. Compared with the full cloud storage solution, the storage cost is significantly reduced.

[0017] (3) The cloud-based vehicle-machine full-link log recording and fault location interaction method and system provided in this application, with a multi-level association scheme of request ID, vehicle-machine ID, user ID and log ID, breaks through the limitations of traditional single ID association. It can accurately connect the full-process log of the same operation, and trace the historical operation context of the same user and vehicle, realizing the dual value of single operation closed-loop tracing and historical association analysis.

[0018] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and are intended to explain the invention, but do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the cloud-based vehicle infotainment system's end-to-end log recording and fault location interaction method of the present invention; Figure 2 This is a schematic diagram of the cloud-based vehicle infotainment end-to-end log recording and fault location interaction system of the present invention; Figure 3 This is a schematic diagram of an electronic device according to the present invention. Detailed Implementation

[0020] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] It should be understood that the terms "comprising" and other similar expressions in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, or apparatus that includes a series of steps or units and is not limited to the listed steps or units. Furthermore, "first" and "second" are used to distinguish different objects and are not intended to describe a specific order.

[0022] According to the first aspect of this application, Figure 1 As shown, this embodiment provides a cloud-based vehicle infotainment system full-link log recording and fault location interaction method, including: Step S1: Obtain front-end interaction logs in real time through the front-end interaction layer.

[0023] In some implementations of this embodiment, such as Figure 1 As shown, the step of obtaining front-end interaction logs in real time through the front-end interaction layer includes: Real-time collection of front-end interaction information, including face verification, permission operations, and configuration distribution, and real-time acquisition of basic device information; The front-end interaction information is converted into standardized front-end interaction logs, which are in JSON format and include log ID, request ID, user ID, vehicle system ID, and operation feedback result. Output standardized front-end interaction logs.

[0024] In this embodiment, log collection is completed within a first preset time after the front-end interactive information operation is triggered. Logs are cached locally when there is a network anomaly. The front-end interactive information is reported immediately after the cache reaches the first preset threshold or the network is restored. Logs that fail format verification are discarded and verification error logs are recorded.

[0025] Specifically, the first preset time can be 100ms, but this embodiment does not limit it.

[0026] The first preset threshold can be 100 items, but this embodiment does not limit it.

[0027] The method of obtaining front-end interaction logs in real time through the front-end interaction layer also includes: Users initiate specific interactive operations in the front-end interaction layer, including but not limited to face verification, permission request, and configuration distribution; The front-end interaction layer initiates a local pre-verification process, which includes: operation legality verification, parameter integrity verification, and network status verification. If the local pre-verification process fails, the front-end interaction layer intercepts the specific interaction operation, outputs an interception prompt to the user, and records and stores the interception log locally. If the local pre-verification process passes, the front-end interaction information is collected in real time. The front-end interaction layer automatically generates a log ID locally. The log ID is a unique link tracing identifier. The unique link tracing identifier adopts a combination format of timestamp, device fingerprint, and random code. At the same time, it is bound to the core information of the specific interaction operation, including request ID, user ID, vehicle ID, device fingerprint, operation type, operation initiation timestamp, and operation feedback result. After binding, it is stored locally. The front-end interaction layer records the detailed results of the local pre-verification process and generates standardized front-end interaction logs.

[0028] Table 1 Front-end Interaction Information Table

[0029] In some implementations of this embodiment, an incremental acquisition mode is used to collect front-end interaction information, recording only the changed fields in each operation. For example, if the same user initiates the same configuration distribution operation multiple times, only the first complete parameters are recorded, and subsequent operations only record the parameter changes, without repeatedly uploading redundant information, thus reducing network transmission pressure. The acquisition frequency is completed within 100ms after the operation is triggered to ensure the real-time nature of log acquisition.

[0030] Step S2: Obtain the vehicle's execution log in real time through the vehicle's execution layer.

[0031] In some embodiments of this example, the step of obtaining the vehicle system execution log in real time through the vehicle system execution layer includes: Real-time collection of vehicle information, including configuration execution logs, environment adaptation logs, driving record logs, and fault warning logs issued by the cloud service layer; The vehicle information is converted into standardized vehicle execution logs, which are in JSON format and include log ID, request ID, user ID, vehicle ID, configuration execution result, and fault warning information. Output standardized vehicle system execution logs.

[0032] In this embodiment, the vehicle system execution log is reported to the cloud service layer in real time, the ordinary log is summarized and reported within a second preset time, the vehicle system execution log is cached locally for a third preset time and automatically overwritten after the expiration time, and the receiving status is fed back within a fourth preset time after the cloud service layer issues the instruction.

[0033] Specifically, the second preset time is 5 minutes, but this embodiment does not limit it.

[0034] The third preset time is 7 days, but this embodiment does not limit it.

[0035] The fourth preset time is 3 seconds, but this embodiment does not limit it.

[0036] Table 1 Vehicle Controller Execution Log

[0037] Step S3: Based on preset rules, the front-end interaction logs and vehicle system execution logs are processed in real time through the cloud service layer to obtain the full-link logs. The full-link logs are then processed to obtain the full-link traceability diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback.

[0038] In some implementations of this embodiment, the step of processing the front-end interaction logs and vehicle system execution logs in real time through the cloud service layer based on preset rules to obtain full-link logs, processing the full-link logs to obtain a full-link source tracing diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback includes: The system receives front-end interaction logs, vehicle system execution logs, and preset rules, including permission verification logs, configuration synchronization logs, conflict handling logs, and fault determination rules. Based on preset rules, the front-end interaction logs and vehicle system execution logs are processed in real time to complete permission verification, conflict handling and fault determination. Based on the permission verification results, configuration synchronization results, conflict handling results and fault determination results, standardized full-link logs and fault alarm information with fault IDs are generated. Based on the fault ID, the full-link log is processed to obtain the full-link source tracing diagram, fault root cause analysis results, and repair suggestions; The fault alarm information containing the fault ID and the repair suggestions are sent to the vehicle's execution layer, and feedback on the fault handling status is obtained.

[0039] In some embodiments of this example, both the front-end interaction layer and the vehicle-mounted execution layer upload the front-end interaction log and the vehicle-mounted execution log respectively via streaming fragmented upload. The cloud service layer uses a queue to receive the front-end interaction log and the vehicle-mounted execution log in real time, marks the receiving timestamp after receiving, and pushes them to the streaming processing link for sequential storage.

[0040] Real-time collection of front-end interaction logs and vehicle system execution logs; as soon as each logical processing step is completed, the cloud service layer immediately generates a corresponding full-link log to ensure that the full-link log is synchronized with the processing flow; the node time consumption is marked with millisecond precision to ensure the accuracy of link bottleneck location.

[0041] Table 1 Preset Rules Table

[0042] The cloud service layer aggregates full-link logs in real time based on a unique link tracing identifier, and simultaneously establishes a three-level index: The primary index serves as a unique link tracing identifier; The secondary index is a time slice, and the node position of the full-link log is located by millisecond-level time slice. The node position is the front-end interaction layer, the vehicle execution layer and the cloud service layer. The third-level index is the anomaly feature code. When the full-link log is generated, anomalies are automatically matched and a corresponding unique anomaly feature code is assigned. For each new end-to-end log entry added to the cloud service layer, the third-level index is updated synchronously.

[0043] In this embodiment, the core field ID that the cloud service layer primarily binds to is the request ID. The automotive cloud service layer provides vehicles with key capabilities for data management, computing support, service delivery, and full lifecycle management, integrating multi-dimensional data to achieve integrated collaboration between vehicle, cloud, road, and people, supporting the full-scenario application of intelligent connected vehicles. The cloud service layer can manage infrastructure resources downwards based on the request ID and establish secure communication with the vehicle, enabling remote management and diagnosis of the vehicle, configuration distribution, and other functions.

[0044] In this embodiment, the core field ID primarily bound to the front-end interaction layer (mobile terminal) is user_id (user ID); the core field ID primarily bound to the vehicle execution layer (vehicle terminal) is car_id (vehicle ID). The front-end interaction layer and the vehicle execution layer are core components of the intelligent vehicle's on-board architecture. The front-end interaction layer manages "user operations and result viewing," focusing on user experience and interactivity, and records the user ID; the vehicle execution layer manages "converting user operations into vehicle-recognizable instructions, execution, and status feedback," focusing on real-time performance, reliability, and compatibility, and records the vehicle ID.

[0045] In this embodiment, log_id (log ID) is recorded for any operation performed at any of the three levels. Log recording is a core means for troubleshooting, functional testing, fault tracing, and performance optimization in the entire intelligent vehicle architecture. It is also a key basis for verifying instruction closed loops, locating bus communication problems, and reproducing test scenarios.

[0046] In this embodiment, a full-link log concatenation rule is set up, with request_id (request ID) as the first-level concatenation field to associate the full-process logs of the same operation; car_id+user_id (vehicle ID and user ID) as the second-level association field to supplement the association of the historical operation context of the same user on the same vehicle; and log_id (log ID) as the third-level association field to associate the dependencies between logs (such as configuring the distribution log ID to associate with the subsequent configuration execution log log_id).

[0047] Furthermore, this embodiment also sets up anomaly node marking rules. When a log with status=error is retrieved, it is automatically marked as an anomaly node, and key anomaly fields such as error_code and fail_reason are highlighted. At the same time, the system queries the two logs before and after the anomaly node to form an anomaly context fragment, which helps to analyze the cause of the anomaly.

[0048] Furthermore, this embodiment also sets up cross-layer log matching verification. When the logs of the front-end interaction layer, cloud service layer, and vehicle execution layer are connected in series, the timestamp time difference is verified (under normal circumstances, the time difference between the front-end operation log and the cloud received log is ≤100ms, and the time difference between the cloud-issued log and the vehicle received log is ≤3s). If the time difference exceeds the threshold, it is marked as a link timing abnormality and included in the source tracing result prompt.

[0049] This embodiment sets up process interruption handling. If an abnormality occurs in the log filtering, chain rule execution, or other stages (such as missing log fields causing chain failure), the system will output that the traceability process is abnormal, some logs cannot be associated, and the abnormal information has been recorded. At the same time, the matched log data will be returned to avoid no output at all.

[0050] This embodiment sets up the display and output control of the traceability results, using a dual-dimensional display of timeline and service layer. The timeline is arranged in the order of log generation time, and the service layer is divided into three columns: front-end interaction layer, cloud service layer, and vehicle system execution layer. Log nodes are displayed according to their layer of origin, with abnormal nodes marked in red and time-series abnormalities marked in yellow. When the mouse hovers over a log node, the core log fields (log_id, request_id, timestamp, status, and core content) are displayed. Three output methods are supported: page visualization, Excel export, and JSON format download. The output file naming convention is: full-link traceability result_search criteria_search time.xlsx / json.

[0051] By using a three-level log collection coverage of front-end interaction layer, cloud service layer and vehicle execution layer, and a three-dimensional association logic of request ID, log ID and vehicle ID, the system achieves full-process data traceability without blind spots. After a fault occurs, the context can be quickly linked together, avoiding the inefficiency of traditional troubleshooting interruption point data search, and reducing the average fault location time from hours to seconds.

[0052] In some implementations of this embodiment, the following are also included: The system uses a tiered storage system, which includes local storage and cloud storage. The local storage system stores high-frequency, short-term front-end interaction logs and vehicle system execution logs locally, reducing the pressure on cloud transmission. The cloud storage system stores end-to-end logs and unstructured data. This tiered storage system optimizes resource usage and significantly reduces storage costs compared to a full cloud storage solution.

[0053] In some implementations of this embodiment, a downgraded cache is also included in weak network / network outage environments.

[0054] Cache activation conditions: When the front-end interaction layer or vehicle system execution layer detects that the network signal strength is lower than the preset threshold (such as 4G signal strength < -100dBm) or the network is interrupted, the local degradation caching mechanism is immediately activated and streaming upload is stopped.

[0055] Vehicle Execution Layer Cache: The vehicle execution layer uses a fixed-size circular buffer (cache capacity is configurable, default is 1000 entries) to store vehicle execution logs. The vehicle execution logs are classified according to their importance (critical fault logs > ordinary execution logs > redundant logs). When the cache is full, the old ordinary vehicle execution logs are overwritten with new critical fault vehicle execution logs to avoid cache overflow. The cached vehicle execution logs are stored in chronological order of generation and the vehicle execution log generation timestamp is recorded.

[0056] Front-end interaction layer caching: The front-end interaction layer uses a local lightweight cache, which only caches the most recent 100 front-end interaction logs. Once the network is restored, the logs are uploaded first, and the oldest front-end interaction logs are automatically overwritten when the cache is full.

[0057] In some implementations of this embodiment, orderly retransmission after network recovery is also included.

[0058] Network detection: The front-end interaction layer and the vehicle system execution layer detect the network status in real time. When the network is detected to have recovered (signal strength meets the standard and normal communication with the cloud is possible), the log resending process is immediately initiated.

[0059] Ordered resending: Based on the log generation time sequence, starting with the earliest cached log, resend the logs in segments to the cloud in sequence. The resending status is marked during the resending process to avoid duplicate log uploads. After the resending is completed, the local cache is automatically cleared to release storage space.

[0060] Cloud service layer verification: After receiving the resent full-link logs, the cloud service layer verifies the timestamp and TraceID of the full-link logs. If duplicate full-link logs are found (logs with the same TraceID and the same log_id), the duplicates are automatically removed, and only the earliest received version is retained.

[0061] After receiving the logs, the cloud service layer enters the streaming processing chain and completes three processing steps in sequence: full-chain log cleaning: removing invalid full-chain logs (such as logs with incorrect format or missing fields) and correcting abnormal fields; full-chain log deduplication: removing duplicate full-chain logs based on TraceID + log_id; field completion: supplementing missing core fields in the full-chain logs (such as receiving timestamp and cloud processing node ID) to ensure the standardization of the full-chain log data. After processing, the logs are pushed to the associated aggregation stage.

[0062] In some implementations of this embodiment, real-time anomaly detection at the cloud service layer is also included. The detection scope covers the entire real-time monitoring link at the cloud service layer, with a focus on monitoring four types of anomalies. A preset anomaly threshold is used, and triggering the threshold determines an anomaly. Sudden increase in latency: time difference between front-end and cloud logs > 100ms, time difference between cloud and vehicle system logs > 3s; Log chain break: Logs for the same TraceID are missing (e.g., the front-end logs have been received, but the vehicle system execution logs have not been received). Execution result mismatch: The configuration command issued by the front end is inconsistent with the execution result fed back by the vehicle system; Vehicle infotainment system resource limits exceeded: CPU usage > 80% and memory usage > 90% for more than 5 seconds.

[0063] Detection frequency: Millisecond-level detection, scanning the entire link log and link status every 10ms to ensure timely detection of anomalies.

[0064] In some implementations of this embodiment, truncated tracing at the cloud service layer is also included. Once an anomaly is detected, truncated tracing is immediately initiated, and all logs for the same TraceID are no longer fully retrieved, thus avoiding IO consumption caused by full retrieval and improving tracing efficiency.

[0065] Minimal Fault Segment Generation: Automatically extract two full-link logs before and after the anomaly point (a total of five full-link logs) to generate the minimum analyzable fault segment. The segment includes the anomaly node, the time of the anomaly, the anomaly signature code, and the related full-link logs before and after it, ensuring that the root cause of the anomaly can be quickly located through the segment.

[0066] Anomaly labeling: The system automatically labels the smallest fault segment, clearly indicating the node where the anomaly occurred (front-end / cloud / vehicle system), the possible root cause level, the anomaly signature code, the error code, and the fail_reason, while also providing preliminary troubleshooting directions (such as checking the vehicle system's resource usage if the vehicle system times out).

[0067] In some implementations of this embodiment, fault alarms and feedback are also included.

[0068] Fault ID Generation: Within 1 second after an anomaly is triggered, a unique fault ID is automatically generated in the cloud, which is bound to TraceID, anomaly type, anomaly node, and fault occurrence time, facilitating fault management and tracing.

[0069] Fault Alarm: Push the fault ID, minimum fault segment, and anomaly labeling information to relevant maintenance personnel (via APP, SMS, email, etc.) to facilitate timely intervention and troubleshooting.

[0070] Status feedback: After maintenance personnel handle a fault, they update the fault handling status (unhandled / handling / handled) in the system. The system then synchronously feeds back the handling status to the front end and the vehicle's infotainment system, forming a closed loop.

[0071] In some implementations of this embodiment, cloud-based cold, hot, and temperature three-tier storage is also included.

[0072] Hot storage (1-7 days): Stores the full structured link snapshot and full logs for the most recent 7 days. It uses high-performance storage media, supports fast retrieval and traceability, and is used for rapid troubleshooting of recent faults to ensure troubleshooting efficiency. Warm storage (7-30 days): Stores logs for 7-30 days, retaining only the third-level index and critical logs (abnormal logs, core execution logs), discarding redundant logs (such as duplicate environment status logs), using medium-performance storage media, and optimizing storage resource usage; Cold storage (30 days or more): Stores logs older than 30 days, retaining only the TraceID index and exception logs. Regular logs are automatically compressed and cleaned up (compression ratio of 10:1). Low-cost storage media are used to further reduce storage costs.

[0073] According to the second aspect of this application, such as Figure 2 As shown in the figure, this embodiment provides a cloud-based vehicle infotainment system for end-to-end log recording and fault location interaction, comprising: The front-end interaction layer is used to obtain front-end interaction logs in real time. The vehicle system execution layer is used to obtain vehicle system execution logs in real time. The cloud service layer is used to process the front-end interaction logs and vehicle system execution logs in real time based on preset rules, obtain the full-link logs, process the full-link logs, and obtain the full-link traceability link diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback.

[0074] In this embodiment, a hierarchical storage system is also included, comprising a local storage system and a cloud storage system. The local storage system stores high-frequency, short-term front-end interaction logs and vehicle system execution logs locally to reduce cloud transmission pressure. The cloud storage system stores end-to-end logs and unstructured data.

[0075] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0076] According to a third aspect of this application, this embodiment provides a computer-readable storage medium having a computer program stored thereon, the computer program including executable instructions that, when executed by a processor, implement the method described above.

[0077] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0078] According to the fourth aspect of this application, such as Figure 3 As shown, an electronic device is provided, comprising: One or more processors; Memory is used to store executable instructions for the processor, which, when executed by one or more processors, cause one or more processors to implement the methods described above.

[0079] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0080] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of a computer system, connecting all parts of the computer system through various interfaces and lines.

[0081] Memory can be used to store computer programs and / or modules. The processor implements various functions of the computer system by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0082] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and memory) containing computer-usable program code.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0087] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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. Such 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 cloud-based vehicle infotainment system end-to-end log recording and fault location interaction method, characterized in that: Get front-end interaction logs in real time through the front-end interaction layer; The vehicle's execution logs are obtained in real time through the vehicle's execution layer. Based on preset rules, the front-end interaction logs and vehicle system execution logs are processed in real time through the cloud service layer to obtain full-link logs. The full-link logs are then processed to obtain a full-link traceability diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback.

2. The method of claim 1, wherein, The method of obtaining front-end interaction logs in real time through the front-end interaction layer includes: Real-time collection of front-end interaction information, including face verification, permission operations, and configuration distribution, and real-time acquisition of basic device information; The front-end interaction information is converted into standardized front-end interaction logs, which are in JSON format and include log ID, request ID, user ID, vehicle system ID, and operation feedback result. Output standardized front-end interaction logs.

3. The method of claim 2, wherein, The method of obtaining front-end interaction logs in real time through the front-end interaction layer also includes: Users initiate specific interactive operations in the front-end interaction layer, including but not limited to face verification, permission request, and configuration distribution; The front-end interaction layer initiates a local pre-verification process, which includes: operation legality verification, parameter integrity verification, and network status verification. If the local pre-verification process fails, the front-end interaction layer intercepts the specific interaction operation, outputs an interception prompt to the user, and records and stores the interception log locally. If the local pre-verification process passes, the front-end interaction information is collected in real time. The front-end interaction layer automatically generates a log ID locally. The log ID is a unique link tracing identifier. The unique link tracing identifier adopts a combination format of timestamp, device fingerprint, and random code. At the same time, it is bound to the core information of the specific interaction operation, including request ID, user ID, vehicle ID, device fingerprint, operation type, operation initiation timestamp, and operation feedback result. After binding, it is stored locally. The front-end interaction layer records the detailed results of the local pre-verification process and generates standardized front-end interaction logs.

4. The method according to claim 3, characterized in that, The cloud service layer aggregates full-link logs in real time based on a unique link tracing identifier, and simultaneously establishes a three-level index: The primary index serves as a unique link tracing identifier; The secondary index is a time slice, and the node position of the full-link log is located by millisecond-level time slice. The node position is the front-end interaction layer, the vehicle execution layer and the cloud service layer. The third-level index is the anomaly feature code. When the full-link log is generated, anomalies are automatically matched and a corresponding unique anomaly feature code is assigned. For each new end-to-end log entry added to the cloud service layer, the third-level index is updated synchronously.

5. The method of claim 1, wherein, The method of obtaining vehicle system execution logs in real time through the vehicle system execution layer includes: Real-time collection of vehicle information, including configuration execution logs, environment adaptation logs, driving record logs, and fault warning logs issued by the cloud service layer; The vehicle information is converted into standardized vehicle execution logs, which are in JSON format and include log ID, request ID, user ID, vehicle ID, configuration execution result, and fault warning information. Based on the fault ID, the full-link log is processed to obtain the full-link source tracing diagram, fault root cause analysis results, and repair suggestions; The fault alarm information containing the fault ID and the repair suggestions are sent to the vehicle's execution layer, and feedback on the fault handling status is obtained.

6. The method according to claim 1, characterized in that, Based on preset rules, the system processes front-end interaction logs and vehicle system execution logs in real time through a cloud service layer to obtain full-link logs. These full-link logs are then processed to obtain a full-link source tracing diagram, root cause analysis results, repair suggestions, and fault handling status feedback, including: The system receives front-end interaction logs, vehicle system execution logs, and preset rules, including permission verification logs, configuration synchronization logs, conflict handling logs, and fault determination rules. Based on preset rules, the front-end interaction logs and vehicle system execution logs are processed in real time to complete permission verification, conflict handling and fault determination. Based on the permission verification results, configuration synchronization results, conflict handling results and fault determination results, standardized full-link logs and fault alarm information with fault IDs are generated. The fault alarm information containing the fault ID is sent to the vehicle's execution layer.

7. The method according to claim 1, characterized in that: Both the front-end interaction layer and the vehicle system execution layer upload the front-end interaction log and the vehicle system execution log respectively through streaming and segmented upload. The cloud service layer uses a queue to receive the front-end interaction log and the vehicle system execution log in real time. After receiving, it marks the receiving timestamp and pushes it to the streaming processing link for sequential storage.

8. A cloud-based vehicle infotainment system for end-to-end log recording and fault location interaction, characterized in that, include: The front-end interaction layer is used to obtain front-end interaction logs in real time. The vehicle system execution layer is used to obtain vehicle system execution logs in real time. The cloud service layer is used to process the front-end interaction logs and vehicle system execution logs in real time based on preset rules, obtain the full-link logs, process the full-link logs, and obtain the full-link traceability link diagram, fault root cause analysis results, repair suggestions, and fault handling status feedback.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program includes executable instructions that, when executed by a processor, implement the method of any one of claims 1-7.

10. An electronic device, characterized in that, include: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1-7.

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