Automatic verification method for automobile electronic diagnosis matrix

Abstract

The invention discloses an automatic verification method for an automobile electronic diagnosis matrix, and the method comprises the steps: selecting and checking the format integrity of a matrix table which comprises a manually configured demand table and a configuration table exported by a diagnosis tool; acquiring manually configured demand table data and generating a first matrix table; acquiring configuration table data exported by the diagnostic tool and generating a second matrix table; and comparing the generated first matrix table with the second matrix table, and outputting a difference report. According to the method, a set of double-source data automatic comparison method is constructed to replace a traditional manual verification mode, so that the problems of low efficiency, high error rate and poor consistency of manual verification of the FIM / DEM matrix in the field of automobile electronic diagnosis are solved, and the effects of remarkably improving the automation degree, accuracy and reliability of diagnosis configuration verification are achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automobile electronic diagnosis, and particularly relates to an automobile electronic diagnosis matrix automatic verification method. BACKGROUND

[0002] With the continuous improvement of the complexity of automobile electronic systems, the role of the diagnosis function module FIM (function inhibition management) / DEM (diagnostic event management) integrated in the ECU in fault identification, diagnostic triggering, DTC management and other aspects is increasingly critical. The traditional diagnosis configuration relies on engineers to manually sort out the correspondence between fault flag bits and FIM items, and to configure them one by one.

[0003] However, due to the large number of FIM items in multiple sub-modules, it is necessary to repeatedly consult the corresponding table and manually match the corresponding fault flag bits, which not only consumes time, but also is prone to errors. At the same time, when the diagnosis configuration table is updated, it is difficult for manual synchronization of the levels, strategies and enabling conditions and other information in different work tables, which can easily lead to version deviation and low diagnosis data reusability.

[0004] Therefore, an effective solution needs to be developed to alleviate the problem of relying on engineers to manually sort out the correspondence, thereby alleviating the above problems. SUMMARY

[0005] The purpose of the present application is to provide an automobile electronic diagnosis matrix automatic verification method to solve the problems in the prior art.

[0006] To this end, the present application provides an automobile electronic diagnosis matrix automatic verification method, comprising: selecting and verifying the format integrity of the matrix table, wherein the matrix table includes a manually configured requirement table and a configuration table derived by a diagnosis tool; collecting the manually configured requirement table data and generating a first matrix table; collecting the configuration table data derived by the diagnosis tool and generating a second matrix table; comparing the generated first matrix table and the second matrix table, and outputting a difference report.

[0007] In one embodiment, the selecting and verifying the format integrity of the matrix table, wherein the matrix table includes a manually configured requirement table and a configuration table derived by a diagnosis tool comprises: selecting the manually configured requirement table or the configuration table derived by the diagnosis tool; determining whether the list items of the manually configured requirement table or the configuration table derived by the diagnosis tool meet the preset rules; If it is met, the verification is passed, and if it is not met, an error report is output and the running is stopped.

[0008] In one embodiment, the list items include at least column names and data formats; The preset rules include at least FIM entries, fault flag bits, and whether the ECU mode exists.

[0009] In one embodiment, collecting the manually configured demand table and generating the first matrix table includes: Based on the preset configuration, multiple field information is extracted from the requirement table, and a first matrix table is generated to store FIM entries and fault flag bits; and in the first matrix table, a preset identifier is set in the cell corresponding to the fault flag bits and FIM entries that have a mapping relationship.

[0010] In one embodiment, the preset configuration includes extracting the project name, fault level, fault flag, fault classification number, and FIM name.

[0011] In one embodiment, the preset identifier is a visual graphic mark or character.

[0012] In one embodiment, the process of collecting configuration table data exported by the diagnostic tool and generating a second matrix table includes: Based on preset fields, one configuration file is identified as the main table in the configuration table, and the remaining configuration files that do not contain the preset fields are designated as sub-tables. The data in the sub-table is merged with the data in the main table, and based on the preset configuration, multiple field information is extracted from the requirement table to generate a second matrix table that stores FIM entries and fault flag bits; and in the second matrix table, a preset identifier is set in the cell corresponding to the fault flag bits and FIM entries that have a mapping relationship.

[0013] In one embodiment, the preset field is a column name used to identify the function suppression configuration.

[0014] In one embodiment, comparing the generated first matrix table with the second matrix table and outputting a difference report includes: Receive the selected first matrix table and second matrix table; Validate and load the data from the first matrix table and the second matrix table, using the data in the first column as the row index; Align the column name sequences of the first matrix table and the second matrix table; Compare cell data with overlapping row indices, identify and mark cells with discrepancies; Generate and output a difference report file.

[0015] In one embodiment, identifying and marking cells with discrepancies includes: Compare cell data for items with the same row index in two files; If the data content of the corresponding cell is inconsistent, a preset identifier will be entered in the cell position of the difference report file; If they match, leave the cell empty.

[0016] Beneficial effects: This invention solves the problems of low efficiency, high error rate and poor consistency of manual verification of FIM / DEM matrices in the field of automotive electronic diagnostics by constructing a dual-source data automated comparison method to replace the traditional manual verification method. It achieves the effect of significantly improving the automation, accuracy and reliability of diagnostic configuration verification. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 Flowchart of the automated verification method for automotive electronic diagnostic matrix provided by the present invention Figure 2 The flowchart illustrates the automated verification method for automotive electronic diagnostic matrix provided by this invention. Detailed Implementation

[0019] The invention will be more readily understood by referring to the following detailed description of preferred embodiments and included examples. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In case of conflict, the definitions in this specification shall prevail.

[0020] like Figure 1 As shown, an automated verification method for automotive electronic diagnostic matrices includes: Select and verify the format integrity of the matrix tables, which include manually configured requirement tables and configuration tables exported from diagnostic tools. Perform integrity verification on the manually configured requirement tables and configuration tables exported from diagnostic tools to ensure the structural compliance and field integrity of the data source.

[0021] Collect manually configured demand table data and generate the first matrix table; Collect configuration table data exported from the diagnostic tool and generate a second matrix table; By analyzing manually configured requirement table data and configuration table data exported from diagnostic tools, fault flag bits and FIM entries are extracted, and structured first and second matrix tables are automatically generated. The fault flag bits are unique identifiers representing specific fault events. FIM entries are function suppression management items, defining the function suppression strategies that the ECU must adopt for specific faults.

[0022] The generated first matrix table is compared with the second matrix table, and a difference report is output. By comparing the two matrix tables, differences in the mapping relationship are accurately identified, and an intuitive difference report is automatically generated.

[0023] This method fundamentally solves the problems of low efficiency, error-proneness, and poor consistency in manual comparison, and achieves standardization, automation, and high reliability in the diagnostic matrix verification process, significantly improving the accuracy and engineering efficiency of functional safety verification of automotive electronic systems.

[0024] In one embodiment, the format integrity of the matrix table is selected and verified, wherein the matrix table includes a manually configured requirements table and a configuration table exported by the diagnostic tool, including: Choose between a manually configured requirements table or a configuration table exported by a diagnostic tool; for example, through a pop-up selection box, the user can choose either a matrix table exported by the ETAS project or a manually configured internal diagnostic matrix table.

[0025] The system determines whether the list items in the manually configured requirements table or the configuration table exported by the diagnostic tool meet preset rules. These preset rules include at least the existence of key fields such as FIM entries, fault flags, and ECU modes. Each list item must include at least column names and data formats. For example, a structural integrity check is performed on the selected Excel file to verify whether the column names and data formats in the worksheet of the manually configured internal diagnostic table meet predefined rules, such as the existence of key fields like "FIM entries," "fault flags," and "ECU modes."

[0026] If the conditions are met, the verification passes, the file content is loaded and used for subsequent parsing and processing; if the conditions are not met, a detailed error report is output and the process stops.

[0027] By mandating verification of the input source—either a manually configured requirement table or a configuration table exported from a diagnostic tool—to ensure that it contains predefined key fields such as FIM entries, fault flags, and ECU modes, the subsequent parsing, processing, and comparison modules receive only structurally complete and formatted qualified data. This fundamentally solves the core pain point of interruptions, errors, and even incorrect output results in complex subsequent processing due to incorrect input file formats, missing fields, or version inconsistencies. The effect is a significant improvement in the reliability and stability of the entire verification system, avoiding errors caused by incorrect formats, saving engineers considerable time manually troubleshooting original data format errors, and guaranteeing the smooth execution and high reliability of automated processes.

[0028] Among them, ECU mode refers to the current working state of the electronic control unit (ECU) (such as operating mode, sleep mode, diagnostic mode, etc.), and different modes will affect the fault diagnosis and function suppression strategies.

[0029] In one embodiment, collecting the manually configured demand table and generating the first matrix table includes: Based on the preset configuration, multiple field information is extracted from the requirement table, and a first matrix table is generated to store FIM entries and fault flag bits; and in the first matrix table, a preset identifier is set in the cell corresponding to the fault flag bits and FIM entries that have a mapping relationship.

[0030] The preset configuration includes at least the following fields: project name, fault level, fault flag, fault category number, and FIM name. The preset identifiers are visual graphic marks or characters, such as "√", a solid dot, "1", or "Y".

[0031] For example, extract field information such as project name, fault level, fault flag, fault category number, and FIM name from the loaded Excel file, generate a matrix table to store FIM entries and fault flags, and mark the FIM entries contained in the fault flags with a "√" in the table. The above technical solution, through an automated, rule-driven data extraction and transformation process, transforms unstructured or semi-structured manual configuration requirement tables into a standardized, machine-readable, and intuitive diagnostic relationship matrix. This method accurately extracts key field information based on preset configurations and automatically constructs a clearly structured matrix table using built-in logic. Finally, preset identifiers (such as "√") visually mark the mapping relationship between fault flags and FIM entries. This fundamentally solves the pain points of low efficiency, strong subjectivity, and high error rate associated with manually interpreting raw tables.

[0032] In one embodiment, the process of collecting configuration table data exported by the diagnostic tool and generating a second matrix table includes: Based on preset fields, one configuration file is identified as the main table in the configuration table, and the remaining configuration files that do not contain the preset fields are designated as sub-tables. The data from the sub-table is merged with the data from the main table. Based on a preset configuration, multiple fields are extracted from the requirement table to generate a second matrix table storing FIM entries and fault flag bits. In the second matrix table, a preset identifier is set in the cell corresponding to the fault flag bit and FIM entry with a mapping relationship. The preset field is a column name used to identify the function suppression configuration.

[0033] For example, when judging multiple Excel files imported from ETAS, a unique master table is first determined based on whether there is a column name named FiMInhibitionConfiguration. Then, the data of the sub-table is merged with the data of the master table, and finally, the information of FIM entries and fault flag bits is extracted to generate a matrix table storing FIM entries and fault flag bits, and the FIM entries contained in the fault flag bits are marked with "√" in the table.

[0034] Through intelligent data integration and extraction, this system efficiently and accurately processes complex configuration data exported from diagnostic tools (such as ETAS), which is typically scattered across multiple files. Specifically, by utilizing preset fields (such as specific column names that identify configuration suppression features) as key information, it automatically identifies the main configuration file containing the core framework from numerous files and merges the remaining files as subordinate sub-tables. This process perfectly solves the integration challenges caused by the scattered and inconsistent structures of data exported from tools.

[0035] After data merging, key fields (such as FIM entries and fault flags) are extracted based on preset configurations, and a structured second matrix table is automatically generated. Preset identifiers clearly represent the mapping relationship between faults and function suppression strategies. The final result is an automated, high-fidelity reconstruction of the configuration data exported by the tool, generating a standardized comparison object with a structure completely consistent with the manually configured first matrix table. This not only avoids the inevitable omissions and errors when manually integrating massive amounts of scattered files, but also greatly improves the automation and reliability of the entire verification process, providing a solid and accurate data foundation for subsequent automated difference comparisons.

[0036] The main table refers to the configuration file containing the core framework and main information of the diagnostic configuration. It is the basis and main body for data merging. The sub-table refers to the configuration file containing some supplementary, subordinate or detailed configuration information. Its data needs to be integrated into the framework of the main table.

[0037] In one embodiment, comparing the generated first matrix table with the second matrix table and outputting a difference report includes: Receive the selected first matrix table and second matrix table; Validate and load the data from the first matrix table and the second matrix table, using the data in the first column as the row index; Align the column name sequences of the first matrix table and the second matrix table; Compare cell data with overlapping row indices, identify and mark cells with differences; specifically, compare cell data for items with the same row index in two files. If the data content of the corresponding cell is inconsistent, a preset identifier will be entered in the cell position of the difference report file; If they match, leave the cell empty; Generate and output a difference report file.

[0038] For example, a file selection dialog box pops up, and the user selects two Excel files to be compared (supporting xlsx and xlsm formats), namely the first matrix table and the second matrix table; Check if the file path is valid, and use openpyxl to verify if the file can be opened normally; Load data, using the first column as the row index, and fill empty values ​​with empty strings; Align column names; if the column name order is inconsistent, force the column names to be unified and pop up a warning. Compare the differences, mark the cells with overlapping row indices with a "√" if they differ, and leave them blank if they match. Generate a report and output the difference results to the configuration difference report.xlsx file, preserving the original table structure. This highly automated and precise cell-level comparison algorithm enables a fast and accurate comparison of the mapping relationship between the first and second matrix tables. Firstly, a data loading and alignment mechanism ensures the comparability of the two matrix tables originating from different sources (e.g., using the first column as the row index and aligning column name sequences). Then, the core comparison logic only compares the cell content of items with overlapping row indices (i.e., the same fault flag bits that both sides are concerned with), and intelligently fills in preset identifiers only at the positions of the differing cells in a new difference report file.

[0039] It effectively solves the problems of visual fatigue, omissions, and misjudgments that are unavoidable when manually comparing massive amounts of data. The resulting discrepancy report has an extremely high signal-to-noise ratio, eliminating the need for engineers to scrutinize thousands of identical cells. They can directly focus their attention on the marked, specific points of configuration inconsistency. The ultimate result is precise location of discrepancies, significantly reducing troubleshooting time and improving the efficiency and accuracy of diagnostic configuration verification.

[0040] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automotive electronic diagnostic matrix automated verification method, characterized by, The method comprises: selecting and checking the format integrity of a matrix table, wherein the matrix table comprises a manually configured requirement table and a configuration table derived by a diagnostic tool; collecting data of the manually configured requirement table and generating a first matrix table; collecting data of the configuration table derived by the diagnostic tool and generating a second matrix table; comparing the generated first matrix table and the second matrix table, and outputting a difference report.

2. The authentication method of claim 1, wherein, The selecting and checking the format integrity of a matrix table, wherein the matrix table comprises a manually configured requirement table and a configuration table derived by a diagnostic tool comprises: selecting the manually configured requirement table or the configuration table derived by the diagnostic tool; judging whether the list items of the manually configured requirement table or the configuration table derived by the diagnostic tool meet preset rules; if yes, the checking is passed, and if no, an error report is outputted and the running is stopped.

3. The authentication method of claim 2, wherein, The list items at least comprise column names and data formats; The preset rules at least comprise FIM entries, fault flag bits, and whether ECU modes exist.

4. The authentication method of claim 1, wherein, The collecting data of the manually configured requirement table and generating a first matrix table comprises: based on preset configurations, extracting multiple field information from the requirement table, and generating a first matrix table storing FIM entries and fault flag bits; and in the first matrix table, setting a preset identifier in cells corresponding to fault flag bits and FIM entries that exist in a mapping relationship.

5. The authentication method of claim 4, wherein, The preset configurations comprise extracting item names, fault levels, fault flag bits, fault classification serial numbers, and FIM names.

6. The authentication method of claim 4, wherein, The preset identifier is a visual graphical mark or character.

7. The method of claim 1, wherein, The collecting data of the configuration table derived by the diagnostic tool and generating a second matrix table comprises: based on preset fields, identifying a configuration file as a main table in the configuration table, and identifying other configuration files not containing the preset fields as sub-tables; merging data of the sub-tables with data of the main table, and based on preset configurations, extracting multiple field information from the requirement table, and generating a second matrix table storing FIM entries and fault flag bits; and in the second matrix table, setting a preset identifier in cells corresponding to fault flag bits and FIM entries that exist in a mapping relationship.

8. The authentication method of claim 7, wherein, The preset fields are column names used for identifying function inhibition configurations.

9. The method of claim 1, wherein, The comparing the generated first matrix table and the second matrix table, and outputting a difference report comprises: receiving the selected first matrix table and the second matrix table; checking and loading data of the first matrix table and the second matrix table, taking first column data as row indexes; aligning column name sequences of the first matrix table and the second matrix table; comparing cell data of overlapping rows, identifying and marking cells with differences; generating and outputting a difference report file.

10. The authentication method of claim 9, wherein, The identifying and marking cells with differences comprises: comparing cell data of items with the same row indexes in the two files; if data contents of corresponding cells are inconsistent, filling a preset identifier in the cell position of the difference report file; if consistent, keeping the cell empty.

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