Automatic analysis method, device and equipment for vehicle-mounted signal and medium

Abstract

The invention provides a vehicle-mounted signal automatic analysis method and device, equipment and a medium, and effectively solves the problems of low accuracy and low efficiency of manual vehicle-mounted signal analysis. The method comprises the following steps: according to abnormal test data generated when a vehicle-mounted system on a vehicle performs a performance test on a vehicle-mounted signal, a defect management system creates a defect bill of lading and configures log information corresponding to the defect bill of lading; uploading log information corresponding to the defective lading bill to an automatic analysis system, and extracting key log information corresponding to various vehicle-mounted modules from the log information; preprocessing the key log information to obtain target log information, serially connecting the target log information based on the signal link and a timestamp in the target log information to obtain log flow direction data, and verifying the log flow direction data to obtain abnormal log data; and controlling the automatic analysis system to visually display the log flow direction data and the abnormal log data according to a preset visual template, and completing the defect lading bill.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to an automated analysis method, apparatus, device, and medium for vehicle-mounted signals. Background Technology

[0002] As in-vehicle systems evolve from "single-function" to "multi-module collaborative intelligent systems," the complexity of the vehicle's electrical and electronic architecture (EE architecture) increases exponentially. The core objective of analyzing the in-vehicle signals generated by these systems is to address the pain points of "uncontrollable signal flow under multi-module interaction, difficulty in problem localization, and low functional reliability," thereby supporting the transformation of automobiles from traditional mechanical products to intelligent connected products.

[0003] Currently, vehicle signal analysis primarily relies on manual methods, where professionals analyze the signals sent and reported by the vehicle system one by one. This method requires analysis of each module from top to bottom to determine which module is experiencing a problem. Each module requires analysis by professionals before being passed on to other modules. This approach is not only highly complex and inefficient, but also susceptible to human error. The experience and skill level of the professionals can limit the accuracy and consistency of the analysis results, making it difficult to guarantee their precision and consistency. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide an automated method, apparatus, device and medium for analyzing vehicle signals, which effectively solves the problems of low accuracy and efficiency in manual analysis of vehicle signals.

[0005] In a first aspect, embodiments of this application provide an automated analysis method for vehicle-mounted signals, applicable to an automated analysis system connected to a defect management system, the defect management system connected to a vehicle-mounted system, the method comprising: Based on abnormal test data generated during performance testing of vehicle signals by the vehicle's onboard system, the defect management system creates a defect order and configures the corresponding log information for the defect order; the log information comes from various onboard modules. Upload the log information corresponding to the defect bill of lading to the automated analysis system, and extract the key log information corresponding to the various vehicle modules from the log information based on the predefined identifiers of the various vehicle modules; Preprocess the key log information to obtain target log information, concatenate the target log information based on the signal link and the timestamp in the target key log information to obtain log flow direction data, and verify the log flow direction data to obtain abnormal log data; The automated analysis system is controlled to visualize the log flow data and the abnormal log data according to a preset visualization template, and to complete the defect report.

[0006] In conjunction with the first aspect, this application provides a first possible implementation of the first aspect, wherein obtaining log flow direction data by concatenating the target log information based on the timestamps in the signal link and the target key log information includes: A mapping relationship is established based on the identifiers of the various vehicle modules, and the automated analysis system is configured based on the mapping relationship; The configured automated analysis system connects the target log information in series according to the signal link and the timestamp.

[0007] In conjunction with the first aspect, this application provides a second possible implementation of the first aspect, wherein the step of concatenating the target log information according to the signal link and the timestamp through the configured automated analysis system includes: Based on the signal flow direction of the vehicle signal, the signal links are classified to obtain uplink signal links and downlink signal links; In the uplink and downlink signal links, the target log information is sorted according to timestamps.

[0008] In conjunction with the first aspect, this application provides a third possible implementation of the first aspect, wherein the step of verifying the log flow direction data to obtain abnormal log data includes: The preset values ​​of the various vehicle modules are configured in advance based on the signal link, and the reported values ​​of the various vehicle modules are captured from the log flow data; The preset value is compared with the corresponding reported value to obtain a comparison result, and the abnormal log data is determined based on the comparison result.

[0009] In conjunction with the first aspect, this application provides a fourth possible implementation of the first aspect, wherein, after determining the abnormal log data based on the comparison result, the following steps are included: Extract the identifier contained in the abnormal log data to locate the abnormal vehicle module based on the identifier; Based on the signal link and the abnormal vehicle module, the source of the abnormal log data is determined.

[0010] In conjunction with the first aspect, this application provides a fifth possible implementation of the first aspect, wherein controlling the automated analysis system to visualize the log flow data and the abnormal log data according to a preset visualization template includes: Based on multiple standard fields in the visualization template, corresponding log data is filtered out from the log flow data and the abnormal log data; The log data is automatically populated into the visualization template according to the standard fields for visualization display.

[0011] In conjunction with the first aspect, this application provides a sixth possible implementation of the first aspect, wherein the step of extracting key log information corresponding to multiple vehicle modules from the log information based on predefined identifiers of the multiple vehicle modules includes: Based on the identifier, the log information is filtered to obtain the first log information corresponding to the various vehicle modules; Key information is extracted from the first log information corresponding to the various vehicle modules and then combined to form key log information.

[0012] Secondly, embodiments of this application provide an automated analysis device for vehicle-mounted signals, suitable for automated analysis systems. The automated analysis system is connected to a defect management system, and the defect management system is connected to a vehicle-mounted system. The device includes: The generation module is used to generate abnormal test data generated when the vehicle's onboard system performs performance testing on the vehicle's onboard signals. The defect management system creates a defect order and configures the log information corresponding to the defect order. The log information comes from various onboard modules. The upload module is used to upload the log information corresponding to the defect bill of lading to the automated analysis system, and extract the key log information corresponding to the various vehicle modules from the log information according to the predefined identifiers of the various vehicle modules; The processing module is used to preprocess the key log information to obtain target log information, concatenate the target log information based on the signal link and the timestamp in the target key log information to obtain log flow data, and verify the log flow data to obtain abnormal log data. The display module is used to control the automated analysis system to visualize the log flow data and the abnormal log data according to the preset visualization template, and to complete the defect report.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of any one of the automated analysis methods for vehicle signals described above.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of any one of the automated analysis methods for vehicle signals.

[0015] This application provides an automated analysis method for vehicle-mounted signals, applicable to an automated analysis system connected to a defect management system, which in turn is connected to a vehicle-mounted system. The method first generates abnormal test data during performance testing of the vehicle-mounted signals by the vehicle-mounted system. The defect management system creates a defect report and configures corresponding log information for the defect report. The log information originates from various vehicle-mounted modules. Next, the log information corresponding to the defect report is uploaded to the automated analysis system. Based on predefined identifiers for the various vehicle-mounted modules, key log information corresponding to each module is extracted from the log information. Then, the key log information is preprocessed to obtain target log information. Based on the signal link and the timestamp in the target key log information, the target log information is concatenated to obtain log flow data. The log flow data is verified to obtain abnormal log data. Finally, the automated analysis system is controlled to visualize the log flow data and the abnormal log data according to a preset visualization template, and the defect report is completed. Based on the above methods, not only is automatic analysis of vehicle signals achieved, but rapid analysis is also realized, improving analysis efficiency and ensuring analysis accuracy. This effectively solves the problems of low accuracy and efficiency in manual vehicle signal analysis, and avoids the inconsistency that cannot be achieved in manual analysis. Attached Figure Description To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating an automated analysis method for vehicle signals provided in an embodiment of this application is shown. Figure 2 This application provides a schematic diagram of the process for obtaining key log information according to an embodiment of the present application. Figure 3 A flowchart illustrating the process provided in an embodiment of this application is shown. Figure 4 This paper shows a structural block diagram of an automated analysis device for vehicle signals provided in an embodiment of this application; Figure 5 A structural block diagram of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0018] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0020] When analyzing vehicle signals manually, it is necessary to analyze each module from top to bottom to determine which module the problem occurs in. Each module requires analysis by professionals before being transferred to other modules for analysis. This not only makes the analysis difficult but also results in low efficiency. Furthermore, it is susceptible to human factors, and the experience and skill level of professionals may limit the accuracy and consistency of the analysis results.

[0021] Based on this, embodiments of this application provide an automated analysis method, apparatus, device, and medium for vehicle-mounted signals, which are described below through embodiments.

[0022] Example 1 To facilitate understanding of this embodiment, a detailed description of an automated analysis method for vehicle signals disclosed in this application will be provided first. For example... Figure 1The diagram illustrates an automated analysis method for vehicle-mounted signals. This application provides an automated analysis method for vehicle-mounted signals, applicable to an automated analysis system connected to a defect management system, which in turn is connected to the vehicle-mounted system. The method includes: S101. Based on the abnormal test data generated when the vehicle-mounted system performs performance testing on the vehicle-mounted signal, the defect management system creates a defect order and configures the log information corresponding to the defect order; the log information comes from various vehicle-mounted modules. S102. Upload the log information corresponding to the defect bill to the automated analysis system, and extract the key log information corresponding to the various vehicle modules from the log information according to the predefined identifiers of the various vehicle modules. S103. Preprocess the key log information to obtain target log information, concatenate the target log information based on the signal link and the timestamp in the target key log information to obtain log flow data, and verify the log flow data to obtain abnormal log data. S104. Control the automated analysis system to visualize the log flow data and the abnormal log data according to the preset visualization template, and complete the defect report.

[0023] In this application, the automated analysis system includes a log collection module, a preprocessing module, an automated analysis module, and a visualization module. The automated analysis system is connected to a defect management system. In actual use, the defect management system is generally the ZenTao system. The defect management system is connected to an in-vehicle system, which is the in-vehicle system on a vehicle and has various in-vehicle modules built in, such as windows, Bluetooth, displays, etc.

[0024] In step S101, when the tester discovers that the test data generated during the performance test of the vehicle's onboard system for the vehicle signal is abnormal after comparing it with standard test data, the present application controls the defect management system to create a defect report based on the abnormal test data. The abnormal test data is generated when there is a functional abnormality, performance problem, or error. The log information corresponding to the abnormal test data generated by the onboard system is captured using methods such as USB flash drive copying and message capture tools. Specifically, offline logs (such as vehicle system operation logs and MCU hardware interaction logs) are exported from the local storage of the vehicle's infotainment system and MCU devices using a USB flash drive. Professional message capture tools (such as CANoe, CANalyzer, and other onboard CAN bus analysis tools) are used to capture the CAN bus data at the time of the problem. The message, the log information comes from various vehicle modules, that is, it is generated by various vehicle modules when the vehicle system is being tested, such as vehicle logs generated by the vehicle head unit, MCU logs generated by the MCU, and CAN messages generated by the CAN bus. The log information corresponding to the defect proposal is configured, that is, the corresponding log information is captured according to the various standard defect fields existing in the defect proposal. The various standard defect fields include problem phenomenon, reproduction steps, test environment, module attribution, etc., thereby realizing the binding of "problem + data".

[0025] In step S102, after generating the defect order, the defect management system uploads the log information corresponding to the defect order to the log acquisition module of the automated analysis system, thereby enabling the automated analysis system to acquire the log information. The log acquisition module inputs the log information into the preprocessing module. The automated analysis system has pre-acquired various predefined identifiers of vehicle modules, such as AdapterAPI, CarService, and Vehicle on the vehicle system. Each of these has a defined ID and a CanID for the message defined by the CAN bus, used to identify the source of the log information. For example, for the air conditioning switch, the identifier defined by the app in the mobile terminal is: HVAC_POWER_ON_KEY; the identifier defined by AdapterAPI is: HVAC_POWER_ON; the identifier defined by CarService is: AC_POWER; the identifier defined by Vehicle is: 0x2140a625; the identifier defined by MCU is: the MCU protocol content, such as: 0c,01,01; and the identifier defined by CanID is MMI_AC_OnKey_reserved. If VCU_AC_OnState is selected, the preprocessing module extracts key log information corresponding to various vehicle modules from the log information. That is, the log information may contain various invalid characters or log information generated by other vehicle modules. Invalid characters include debugging prefixes and repeated printing status, thereby ensuring the accuracy of the log information that needs to be analyzed and processed.

[0026] In the specific implementation of step S102, one embodiment is as follows: Figure 2 As shown, the step of extracting key log information corresponding to various vehicle modules from the log information based on predefined identifiers of the various vehicle modules includes: S1021. Based on the identifier, filter the log information to obtain the first log information corresponding to the various vehicle modules; S1022. Extract key information from the first log information corresponding to the various vehicle modules respectively to combine key log information.

[0027] In steps S1021-S1022, the preprocessing module, based on the identifier, filters the log information to obtain the first log information corresponding to the various vehicle modules. For example, it extracts log information containing identifiers corresponding to AdapterAPI, CarService, Vehicle, and CAN ID from the log information to obtain log information corresponding to AdapterAPI, CarService, Vehicle, and CAN ID respectively. It filters out information from irrelevant modules (such as the entertainment system) or irrelevant signals (such as debug logs), or extracts records containing mcu_signal_id or associated CANID, filtering out log information from MCU internal self-tests and irrelevant hardware (such as the air conditioning module). This achieves filtering of log information. Furthermore, it extracts key information from the first log information corresponding to the various vehicle modules to assemble key log information. For example, key information extracted from the vehicle system log information includes: timestamp, module name, signal ID, signal value, and status (such as "sending" or "receiving successfully"); key information extracted from the MCU log information includes: timestamp, mcu_signal_id, execution result (such as "motor started" or "timeout"), and error code (if any); from the CAN... The key information extracted from the message information includes: timestamp, CanID, frame type, data field (hex to decimal / boolean), and sending / receiving direction. Based on the combination of the above key information, key log data is obtained to facilitate analysis and processing.

[0028] In step S103, the automated analysis system of this application further preprocesses the key log information based on the preprocessing module to obtain target log information, that is, unifies the format of the timestamps in the key log information to solve the time format differences of different vehicle modules (e.g., Vehicle uses milliseconds, MCU uses seconds + local offset). The preprocessing module also concatenates the target log information based on the signal link and the timestamps in the target key log information to obtain log flow data. The log flow is determined based on the flow of vehicle signals, which includes signal reporting and signal transmission. The signal transmission flow is: app in the mobile terminal → AdapterAPI → CarService → Vehicle → MCU → CAN, and the signal reporting flow is CAN → MCU → Vehicle → CarService → AdapterAPI → app display in the mobile terminal. The system outputs the log flow data to the automated analysis module, which calls pre-stored analysis rules and verification rules to analyze and verify the log flow data to obtain abnormal log data, thereby achieving accurate processing of the log data.

[0029] In a specific implementation of step S103, one embodiment is as follows: obtaining log flow data by concatenating the target log information based on the timestamps in the signal link and the target key log information includes: A1. Establish a mapping relationship based on the identifiers of the various vehicle modules, and configure the automated analysis system based on the mapping relationship; A2. The configured automated analysis system connects the target log information according to the signal link and the timestamp.

[0030] In steps A1-A2, the preprocessing module establishes a mapping relationship based on the identifiers of the various vehicle modules, and configures the preprocessing module of the automated analysis system based on the mapping relationship. This allows the automated analysis system to determine the identifiers of other vehicle modules by querying the mapping relationship based on the identifier of one vehicle module. The mapping relationship is a three-dimensional mapping table of "identifier-module-upstream / downstream association" constructed based on signal links (downlink / uplink) and module interaction rules, and is stored in the preprocessing module. By configuring a "mapping rule base," "link parsing logic," and "timestamp calibration rules," the automated analysis system has the ability to identify cross-vehicle module log associations and can also verify whether upstream and downstream signal values ​​match based on "value mapping rules," such as CarService. AC_POWER=1 should correspond to 0x2140a625=1 for the Vehicle. If they do not match, they are marked as abnormal. The preprocessing module of the configured automated analysis system concatenates the target log information according to the signal link and the timestamp. That is, the automated analysis system concatenates the target log information to obtain the log flow data according to the process of "log input → identifier matching → link association → timestamp sorting → abnormal marking". For example, the identifier of the air conditioner switch mentioned above. Based on the log flow data obtained by concatenating the target log information, the entire life cycle of the "air conditioner switch" signal from issuance to reporting can be clearly determined.

[0031] In a specific implementation of step A2, one embodiment is as follows: the configured automated analysis system concatenates the target log information according to the signal link and the timestamp, including: A21. Based on the signal flow direction of the vehicle signal, the signal links are classified to obtain the uplink signal link and the downlink signal link; A22. In the uplink and downlink signal links, the target log information is sorted according to the timestamp.

[0032] In steps A21-A22, the automated analysis system, based on the preprocessing module and the signal flow direction of the vehicle signal, classifies the signal links into uplink and downlink signal links, thus corresponding to the signal reporting and transmission of the vehicle signal. Based on a triple judgment of "module location + signal identifier + direction feature," the target log information is allocated to the corresponding uplink / downlink signal link. Within the uplink and downlink signal links, the target log information is sorted according to its timestamp, specifically in ascending order, to reconstruct the actual flow order of the vehicle signal corresponding to the target log information in each signal link. If sorted by timestamp from earliest to latest, it reflects "bottom state → App." The order of the "display" allows observation of the delay in the feedback signal link. For example, the CAN report takes 120ms to be displayed on the App. Based on this, the complex flow of vehicle signals is decomposed into two clear timelines: the "downlink command chain" and the "uplink feedback chain". This preserves the independence of each link (facilitating individual analysis of command / feedback issues) and establishes timeline correlation through timestamps (facilitating cross-link consistency verification). This lays a structured foundation for subsequent visualization and anomaly localization.

[0033] In a specific implementation of step S103, another embodiment exists where: verifying the log flow direction data to obtain abnormal log data includes: B1. Pre-configure the preset values ​​of the various vehicle modules based on the signal link, and capture the reported values ​​of the various vehicle modules in the log flow data; B2. Compare the preset value with the corresponding reported value to obtain the comparison result, and determine the abnormal log data based on the comparison result.

[0034] In steps B1-B2, the automated analysis module pre-configures preset values ​​for the various vehicle-mounted modules based on the signal link. The preset values ​​for the downlink signal must form a closed loop with the preset values ​​for the uplink signal. For example, if "Enable command = 1" is sent downlink, "Enable successful = 1" should be returned uplink. The preset value formats for different modules must match their data types; for example, App uses Boolean values ​​"1 / 0", MCU uses protocol frames "0c,01,01", and CAN uses hexadecimal "0x01". The preset values ​​are stored in the "Preset Value Database" of the automated analysis system according to the dimensions of "Function + Link + Module". This database supports quick searching by module name and identifier, such as searching for "MCU". The preset value of the "downlink" returns the content "0c,01,01". The automated analysis module captures the reported values ​​of various vehicle modules in the log flow data. That is, it captures not only the signal uplink, but also the "forwarding value" of each module in the signal downlink. This is used to verify whether the instruction transmission is correct. The capture can be performed according to the standard of "function identifier" or "time range". After the capture is completed, the preset value is compared with the corresponding reported value to obtain the comparison result. Based on the comparison result, it is determined whether the corresponding vehicle module has made a correct response. For example, if the App sends the preset value HVAC_POWER_ON_KEY=1 (1 represents "on"), it is expected that the App should eventually receive the reported value HVAC_POWER_ON_KEY=1 (feedback "successfully on"), but the captured reported value is also 1. If the reported value is 0, it can be determined that the corresponding vehicle module has not made a correct response, thereby identifying abnormal log data.

[0035] In a specific implementation of step B2, one embodiment is as follows: after determining the abnormal log data based on the comparison result, the following steps are included: B21. Extract the identifier contained in the abnormal log data to locate the abnormal vehicle module based on the identifier; B22. Based on the signal link and the abnormal vehicle module, determine the source of the abnormal log data.

[0036] In steps B21-B22, after identifying the abnormal log data, the automated analysis module extracts the identifiers contained in the abnormal log data to locate the abnormal vehicle module based on the identifiers. Then, based on the timestamp and the abnormal vehicle module, it finds the vehicle module whose "first reported value ≠ preset value," such as the CAN reporting module. This module is the source of the abnormal log data (subsequent module anomalies are mostly "affected by the source"). The type of abnormal log data must match the responsibilities of the vehicle module. For example, if the CAN module is responsible for transmission, the MCU is responsible for protocol conversion, and the Vehicle is responsible for signal mapping, then a protocol error is most likely an MCU problem. This method transforms the "cross-module, multi-stage" anomaly localization in the vehicle system from "experience-driven" to "data-driven," significantly improving the efficiency and accuracy of automated analysis of vehicle signals.

[0037] In step S104, after obtaining the abnormal log data, the automated analysis module calls the visualization module to control the visualization module in the automated analysis system to visualize the log flow data and the abnormal log data corresponding to the log data according to a preset visualization template. The visualization module provides an intuitive interface for displaying analysis results, specifically including the entire process of the signal from app→AdapterAPI→CarSservice→Mcu→CAN, and then forwarded from CAN to app. If the abnormal log data exists, it is highlighted and displayed to quickly locate which module the problem occurred in and the cause. Based on the abnormal log data and the log flow data, a defect request is submitted to the defect management system to complete the defect request.

[0038] In the specific implementation of step S104, one embodiment is as follows: Figure 3 As shown, the automated analysis system is controlled to visualize the log flow data and the abnormal log data according to a preset visualization template, including: S1041. Based on multiple standard fields in the visualization template, filter out the corresponding log data from the log flow data and the abnormal log data; S1042. Automatically populate the log data into the visualization template according to the standard fields for visualization display.

[0039] In steps S1041-S1042, the visualization template has three levels: "Full Link Flow," "Anomaly Focus," and "Detailed Source Tracing," thus taking into account both macro-level processes and micro-level analysis. The "Full Link Flow" visually presents the complete path of signals from the App to the CAN (transmission) and from the CAN to the App (reporting), using colors and symbols to distinguish between normal and normal signals. Abnormal Status; "Abnormal Focus" highlights detailed information about abnormal log data, including abnormal type, preset and reported values, and possible causes; "Detailed Source Tracing" provides raw data support for in-depth verification of the cause of the abnormality. Based on multiple standard fields in the visualization template, such as "timestamp, module, identifier, signal value, direction, expected value, status, and raw log fragment," corresponding log data is filtered from the log flow data and the abnormal log data. The log data is the collective name of the data corresponding to the standard fields filtered from the log flow data and the abnormal log data. The log data is automatically populated into the visualization template. During the filling process, the data can be arranged in the order of the signal link, and "module name, identifier, and signal value" can be read from the log flow data for filling. The "status" field (normal / abnormal) is automatically set, and an alert identifier is added to the first abnormal vehicle module. Alternatively, "abnormal module, time, identifier, expected value, and actual value" can be extracted from the abnormal log data and filled. Front-end visualization libraries (such as ECharts, D3.js) or professional vehicle diagnostic tools (such as Vector CANoe) are used. The visualization plugin enables the generation of an interactive graphical interface from the filled visualization template for visual display.

[0040] Example 2 This application also provides an automated analysis device for vehicle signals, such as... Figure 4 The diagram shows a block diagram of an automated vehicle signal analysis device. This device performs functions corresponding to the steps of executing an automated vehicle signal analysis method on a terminal device as described above. The device can be understood as a server component including a processor. The automated vehicle signal analysis device described in this application is suitable for an automated analysis system connected to a defect management system, which in turn is connected to a vehicle system. The device includes: The generation module 401 is used to generate a defect order based on the abnormal test data generated when the vehicle system performs performance testing on the vehicle signal. The defect management system creates a defect order and configures the log information corresponding to the defect order. The log information comes from various vehicle modules. Upload module 402 is used to upload the log information corresponding to the defect bill of lading to the automated analysis system, and extract the key log information corresponding to the various vehicle modules from the log information according to the predefined identifiers of the various vehicle modules; Processing module 403 is used to preprocess the key log information to obtain target log information, concatenate the target log information based on the signal link and the timestamp in the target key log information to obtain log flow data, and verify the log flow data to obtain abnormal log data; The display module 404 is used to control the automated analysis system to visualize the log flow data and the abnormal log data according to the preset visualization template, and to complete the defect report.

[0041] In one feasible implementation, the processing module includes: A module is established to create a mapping relationship based on the identifiers of the various vehicle-mounted modules, and to configure the automated analysis system based on the mapping relationship; The concatenation module is used to concatenate the target log information according to the signal link and the timestamp through the configured automated analysis system.

[0042] In one feasible implementation, the processing module further includes: The classification module is used to classify the signal links into uplink and downlink signal links based on the signal flow direction of the vehicle signal. The sorting module is used to sort the target log information according to timestamps in the uplink and downlink signal links, respectively.

[0043] In one feasible implementation, the processing module also includes: The capture module is used to pre-configure the preset values ​​of the various vehicle modules based on the signal link, and capture the reported values ​​of the various vehicle modules in the log flow data; The comparison module is used to compare the preset value with the corresponding reported value to obtain a comparison result, so as to determine the abnormal log data based on the comparison result.

[0044] In one feasible implementation, the processing module further includes: The positioning module is used to extract the identifier contained in the abnormal log data, so as to locate the abnormal vehicle module based on the identifier; The determination module is used to determine the source of the abnormal log data based on the signal link and the abnormal vehicle module.

[0045] In one feasible implementation, the display module includes: The filtering module is used to filter out corresponding log data from the log flow data and the abnormal log data based on multiple standard fields in the visualization template. The population module is used to automatically populate the log data into the visualization template for visualization display according to the standard fields.

[0046] In one feasible implementation, the upload module includes: A filtering module is used to filter the log information based on the identifier to obtain the first log information corresponding to the various vehicle modules; The splitting module is used to extract key information from the first log information corresponding to the various vehicle modules respectively, and then combine them to form key log information.

[0047] Example 3 This application also provides an electronic device, such as Figure 5 As shown, it includes: a processor 501, a memory 502, and a bus 503. The memory 502 stores machine-readable instructions that can be executed by the processor 501. When the electronic device is running, the processor 501 and the memory 502 communicate through the bus 503. When the machine-readable instructions are executed by the processor 501, the steps of any one of the automated analysis methods for vehicle signals described above are executed.

[0048] Example 4 This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of any one of the automated analysis methods for vehicle signals.

[0049] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0050] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0051] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0052] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0053] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An automated analysis method for vehicle-mounted signals, characterized in that, The method, applicable to an automated analysis system connected to a defect management system and connected to an in-vehicle system, includes: Based on abnormal test data generated during performance testing of vehicle signals by the vehicle's onboard system, the defect management system creates a defect order and configures the corresponding log information for the defect order; the log information comes from various onboard modules. Upload the log information corresponding to the defect bill of lading to the automated analysis system, and extract the key log information corresponding to the various vehicle modules from the log information based on the predefined identifiers of the various vehicle modules; Preprocessing the key log information yields target log information; based on the signal link and the timestamp in the target log information, and concatenating the target log information, log flow direction data is obtained; verifying the log flow direction data yields abnormal log data. The automated analysis system is controlled to visualize the log flow data and the abnormal log data according to a preset visualization template, and to complete the defect report.

2. The method according to claim 1, characterized in that, The log flow data is obtained by concatenating the timestamps from the signal link and the target key log information with the target log information, including: A mapping relationship is established based on the identifiers of the various vehicle modules, and the automated analysis system is configured based on the mapping relationship; The configured automated analysis system connects the target log information in series according to the signal link and the timestamp.

3. The method according to claim 2, characterized in that, The process of linking the target log information through the configured automated analysis system according to the signal link and the timestamp includes: Based on the signal flow direction of the vehicle signal, the signal links are classified to obtain uplink signal links and downlink signal links; In the uplink and downlink signal links, the target log information is sorted according to timestamps.

4. The method according to claim 1, characterized in that, The verification of the log flow data to obtain abnormal log data includes: The preset values ​​of the various vehicle modules are configured in advance based on the signal link, and the reported values ​​of the various vehicle modules are captured from the log flow data; The preset value is compared with the corresponding reported value to obtain a comparison result, and the abnormal log data is determined based on the comparison result.

5. The method according to claim 4, characterized in that, After determining the abnormal log data based on the comparison results, the process includes: Extract the identifier contained in the abnormal log data to locate the abnormal vehicle module based on the identifier; Based on the signal link and the abnormal vehicle module, the source of the abnormal log data is determined.

6. The method according to claim 1, characterized in that, The automated analysis system is controlled to visualize the log flow data and the abnormal log data according to a preset visualization template, including: Based on multiple standard fields in the visualization template, corresponding log data is filtered out from the log flow data and the abnormal log data; The log data is automatically populated into the visualization template according to the standard fields for visualization display.

7. The method according to claim 1, characterized in that, The step of extracting key log information corresponding to various vehicle modules from the log information based on predefined identifiers of the various vehicle modules includes: Based on the identifier, the log information is filtered to obtain the first log information corresponding to the various vehicle modules; Key information is extracted from the first log information corresponding to the various vehicle modules and then combined to form key log information.

8. An automated analysis device for vehicle-mounted signals, characterized in that, Suitable for automated analysis systems, the automated analysis system being connected to a defect management system, the defect management system being connected to an on-board system, the device comprising: The generation module is used to generate abnormal test data generated when the vehicle's onboard system performs performance testing on the vehicle's onboard signals. The defect management system creates a defect order and configures the log information corresponding to the defect order. The log information comes from various onboard modules. The upload module is used to upload the log information corresponding to the defect bill of lading to the automated analysis system, and extract the key log information corresponding to the various vehicle modules from the log information according to the predefined identifiers of the various vehicle modules; The processing module is used to preprocess the key log information to obtain target log information, concatenate the target log information based on the signal link and the timestamp in the target log information to obtain log flow direction data, and verify the log flow direction data to obtain abnormal log data; The display module is used to control the automated analysis system to visualize the log flow data and the abnormal log data according to the preset visualization template, and to complete the defect report.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of an automated analysis method for vehicle signals as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the automated analysis method for vehicle signals as described in any one of claims 1 to 7.

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