Methods, systems, equipment, and storage media for revising engineering documents

Abstract

This invention discloses a method, system, device, and storage medium for correcting engineering files. The method includes: parsing a set of engineering files to be verified and constructing a unified data graph model based on the parsed engineering data; performing multi-dimensional rule detection and data augmentation analysis on the unified data graph model to obtain file anomaly detection results; analyzing the file anomaly detection results based on a standard rule library for engineering files to generate error correction suggestions; and correcting the set of engineering files to be verified according to a preset authorization strategy using the error correction suggestions. This invention identifies various errors, conflicts, and inconsistencies in files through multi-dimensional rule detection and data augmentation analysis, and can provide intelligent error correction suggestions or automatic repair based on predefined rules and artificial intelligence technology, thereby significantly improving the quality and efficiency of automotive electronic system design.

Description

Technical Field

[0001] This invention relates to the field of document processing technology, and in particular to a method, system, device, and storage medium for correcting engineering documents. Background Technology

[0002] In modern automotive electronic system development, the Automotive Open System Architecture (AUTOSAR) standard has become mainstream. System designers and software engineers use various tools to generate numerous ARXML files to describe complex information such as software components (SWCs), system configurations, electronic control unit (ECU) resources, and bus communication matrices. Simultaneously, data forms such as Excel are widely used during development to define requirements, record signal lists, and configure parameters. The correctness, consistency, and completeness of these files (ARXML and Excel) are crucial, directly impacting the functionality, performance, and safety of the onboard software.

[0003] Currently, there are significant challenges in verifying these files: 1. High dependence on toolchain basic checks: Mainstream AUTOSAR design tools can only perform basic syntax and schema checks, and have limited ability to check for deep logical errors, cross-file consistency, and design specification compliance.

[0004] 2. Difficult to review manually: ARXML files are machine-readable XML files with large amounts of data and complex structures, making them almost impossible for humans to read and review directly. Checking the consistency between Excel data and ARXML files is also tedious and error-prone.

[0005] 3. Complex file relationships: A system project typically contains tens of thousands of ARXML files, which are linked through references, forming a complex network. It is difficult to manually ensure that all file references and configuration items are completely consistent.

[0006] 4. Insufficient standard compliance verification: It is difficult to automatically verify whether the design conforms to the company's internal customization specifications, design guidelines or industry best practices.

[0007] Therefore, how to deeply analyze the project files to be verified and perform auxiliary error correction has become an urgent problem to be solved.

[0008] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention The main objective of this invention is to provide a method, system, device, and storage medium for correcting engineering files, addressing the technical problem of how to deeply analyze engineering files to be verified and perform auxiliary error correction.

[0009] To achieve the above objectives, the present invention provides a method for correcting engineering documents, the method comprising: S1, parse the set of project files to be verified, and construct a unified data graph model based on the parsed project data; S2, perform multi-dimensional rule detection and data augmentation analysis on the unified data graph model to obtain file anomaly detection results; S3, Analyze the anomaly detection results of the file based on the standard rule base of the engineering file, and generate error correction suggestions; S4. Correct the set of project files to be verified according to the preset authorization strategy and the error correction suggestions.

[0010] Optionally, S1 includes: S1.1 Extract the ARXML file set, data form and requirement document from the set of project files to be verified; S1.2, extract the structured metadata and data relationships within the ARXML file set using an ARXML parser; S1.3, Extract the table information of the data form using a table parser; S1.4, Extract key information from the requirements document using a text parsing tool; S1.5, construct a unified data graph model based on the structured metadata, the data relationships, the table information, and the key information.

[0011] Optionally, S2 includes: S2.1, Construct sub-libraries for syntax and model rules, semantic logic rules, consistency rules, and compliance and best practice rules; S2.2, Construct a multi-dimensional rule library based on the syntax and model rule sub-library, the semantic logic rule sub-library, the consistency rule sub-library, and the compliance and best practice rule sub-library; S2.3, Perform multi-dimensional rule detection on the unified data graph model using the multi-dimensional rule base to obtain rule detection results; S2.4, Perform data augmentation analysis on the unified data graph model to obtain the augmentation analysis results; S2.5, Generate file anomaly detection results based on the rule detection results and the enhanced analysis results.

[0012] Optionally, S2.4 includes: S2.4.1, Anomaly registration detection is performed on the relevant data in the unified data graph model using the anomaly pattern detection model to obtain an anomaly configuration report; S2.4.2, A bidirectional traceability chain analysis is performed on the requirements, design models, and implementation data in the unified data graph model using the correlation traceability analysis model to obtain a traceability report; S2.4.3, The requirement documents and element descriptions in the unified data graph model are verified using a natural language processing model to obtain a text verification report; S2.4.4, Generate enhanced analysis results based on the abnormal configuration report, the traceability report, and the text verification report.

[0013] Optionally, S3 includes: S3.1, Analyze the abnormal issues based on the file anomaly detection results to obtain an error list; S3.2, Determine the error type and context information of the exception problem based on the error list; S3.2, Generate error correction suggestions based on the error type and the context information using the standard rule base of the project file.

[0014] Optionally, after S4, the following steps are included: S5. Generate a correction feedback log based on the corrected result and the file anomaly detection result, so that the user can view the correction feedback log.

[0015] Optionally, after S5, the following steps are included: S6, obtain the corrected set of project files to be verified; S7. Generate a verification report based on the file anomaly detection results, the correction feedback log, and the corrected set of project files to be verified.

[0016] Furthermore, to achieve the above objectives, the present invention also proposes an engineering document correction system, the engineering document correction system comprising: The building module is used to parse the set of project files to be verified and to build a unified data graph model based on the parsed project data; The analysis module is used to perform multi-dimensional rule detection and data augmentation analysis on the unified data graph model to obtain file anomaly detection results; The generation module is used to analyze the anomaly detection results of the file based on the standard rule library of engineering files and generate error correction suggestions; The correction module is used to correct the set of project files to be verified according to the error correction suggestions based on the preset authorization strategy.

[0017] Furthermore, to achieve the above objectives, the present invention also proposes an apparatus for correcting engineering documents, the apparatus comprising: a memory, a processor, and a correction program for the engineering documents stored in the memory and executable on the processor, the correction program for the engineering documents being configured to implement the steps of the correction method for the engineering documents as described above.

[0018] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a correction program for an engineering file, wherein when the correction program for the engineering file is executed by a processor, it implements the steps of the engineering file correction method described above.

[0019] This invention first parses the set of engineering files to be verified and constructs a unified data graph model based on the parsed engineering data. Then, it performs multi-dimensional rule detection and data augmentation analysis on the unified data graph model to obtain file anomaly detection results. Next, it analyzes the file anomaly detection results based on a standard rule library for engineering files, generating error correction suggestions. Finally, it corrects the set of engineering files to be verified according to the preset authorization strategy using the error correction suggestions. This invention identifies various errors, conflicts, and inconsistencies in files through multi-dimensional rule detection and data augmentation analysis, and can provide intelligent error correction suggestions or automatic repair based on predefined rules and artificial intelligence technology, thereby significantly improving the quality and efficiency of automotive electronic system design. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the structure of the device for correcting engineering files of the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the method for correcting engineering documents according to the present invention; Figure 3 This is a structural block diagram of the first embodiment of the engineering document correction system of the present invention.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0023] Reference Figure 1 , Figure 1 This is a schematic diagram of the modified device structure of the engineering files of the hardware operating environment involved in the embodiments of the present invention.

[0024] like Figure 1As shown, the modification device for this engineering document may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage system independent of the aforementioned processor 1001.

[0025] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the modification device for the engineering document and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0026] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a program for correcting engineering files.

[0027] exist Figure 1 In the shown engineering file correction device, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the engineering file correction device of the present invention can be set in the engineering file correction device, and the engineering file correction device calls the engineering file correction program stored in the memory 1005 through the processor 1001 and executes the engineering file correction method provided in the embodiment of the present invention.

[0028] This invention provides a method for correcting engineering files, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the method for correcting engineering documents according to the present invention.

[0029] In this embodiment, the method for correcting the project file includes the following steps: S1 parses the set of project files to be verified and constructs a unified data graph model based on the parsed project data.

[0030] It is easy to understand that the execution subject of this embodiment can be a system for correcting engineering files with functions such as data processing, network communication and program execution, or other computer devices with similar functions. This embodiment does not limit it.

[0031] The system framework for correcting engineering documents includes: a user interface layer, a business logic layer, and a data layer.

[0032] User Interface Layer: Provides a web interface or integrated plugins (such as integration with common design tools like Vector Prevision and ETASISOLAR) for users to upload files, configure validation rules, view 3D relationship diagram results, and interactively correct errors.

[0033] Business logic layer: This is the core, including a multi-format file parsing module, a rules engine, an AI analysis engine, an error correction decision module, and a report generation module.

[0034] Data layer: Stores AUTOSAR meta-model rule base, vendor-specific specification base, AI models, historical verification data, etc.

[0035] It should be noted that the system accepts a set of project files to be verified uploaded by the user. The set of project files to be verified includes, but is not limited to: ARXML file sets (system description, software component description, ECU configuration, etc.), Excel / CSV data forms, requirements documents, etc.

[0036] Furthermore, ARXML file sets, data forms, and requirement documents are extracted from the project file set to be verified; structured metadata and data relationships within the ARXML file set are extracted using an ARXML parser; table information from the data forms is extracted using a table parser; key information from the requirement documents is extracted using a text parsing tool; and a unified data graph model is constructed based on the structured metadata, data relationships, table information, and key information.

[0037] In the specific implementation, a set of project files to be verified is uploaded through the user interface layer. An ARXML parser (such as the parsing tools in the AUTOSAR toolchain) is used to parse the uploaded ARXML files, extracting structured metadata, including software components (SWC), ports, runtime entities, connectors, ECU instances, signals, and their complex relationships. A table parser (such as the Pandas library or other table processing tools) is used to parse the uploaded Excel or CSV files, extracting configuration data, signal lists, parameter values, and other information. Text parsing tools (such as regular expressions and natural language processing tools) are used to parse the requirements document, extracting key information such as functional requirements and interface requirements. All data extracted from the ARXML files, Excel / CSV files, and requirements documents is converted into a unified data graph model using a graph database (such as Neo4j) or a custom data structure.

[0038] In this embodiment, we take the verification of a communication matrix design as an example: The user uploads a system architecture ARXML file, multiple SWC ARXML files, and an Excel matrix table defining communication signals. The system parses all files and constructs a global data diagram containing all signals, ECUs, SWCs, and their connections.

[0039] S2, perform multi-dimensional rule detection and data augmentation analysis on the unified data graph model to obtain file anomaly detection results.

[0040] Furthermore, a sub-library of syntax and model rules, a sub-library of semantic logic rules, a sub-library of consistency rules, and a sub-library of compliance and best practice rules are constructed. A multi-dimensional rule library is then built based on these sub-libraries. Multi-dimensional rule detection is performed on the unified data graph model using this multi-dimensional rule library to obtain rule detection results. Data augmentation analysis is then performed on the unified data graph model to obtain augmentation analysis results. Finally, file anomaly detection results are generated based on the rule detection results and the augmentation analysis results.

[0041] In its implementation, the multi-dimensional rule base includes sub-libraries for syntax and model rules, semantic logic rules, consistency rules, and compliance and best practice rules.

[0042] Syntax and Model Rules Sublime: Checks whether ARXML files conform to the AUTOSAR XSD Schema definition and whether Excel spreadsheets are formatted correctly.

[0043] Semantic logic rules sub-library: Based on the semantics and design logic of the AUTOSAR meta-model, checks are performed, such as: port data type matching, reasonableness of the runnable event cycle, whether the hardware resource allocation (such as RAM / ROM) exceeds the limit, and evaluation of communication signal length and bus load.

[0044] Consistency rules sub-library: checks the consistency of cross-file references (such as ARXML references) and the consistency of ARXML configuration and Excel data mapping (such as the signal ID defined in Excel must be consistent with the one defined in ARXML).

[0045] Compliance and Best Practice Rules Sub-library: Checks whether the design complies with specific OEM specifications, supplier internal standards, or safety standards (such as element traceability for ISO 26262 functional safety requirements).

[0046] It should also be noted that users can also perform rule validation by calling multiple rule sub-libraries within the multi-dimensional rule base according to their own preferences.

[0047] For example, when a user selects "Communication Consistency Check", the system calls consistency rules (such as "the Signal_ID corresponding to the Signal_Name in Excel must be consistent with the UUID mapping value of the signal in ARXML") and semantic rules (such as "the signal length cannot exceed the remaining space of the PDU").

[0048] Furthermore, data augmentation analysis is performed on the unified data graph model. The processing method for obtaining the augmentation analysis results is as follows: an anomaly registration detection model is used to detect anomalies in the relevant data in the unified data graph model to obtain an anomaly configuration report; a bidirectional traceability chain analysis is performed on the requirements, design models, and implementation data in the unified data graph model using an association traceability analysis model to obtain a traceability report; a text verification is performed on the requirements documents and element descriptions in the unified data graph model using a natural language processing model to obtain a text verification report; and the augmentation analysis results are generated based on the anomaly configuration report, traceability report, and text verification report.

[0049] In the specific implementation, a pre-trained anomaly pattern detection model (a neural network model built based on machine learning or deep learning frameworks such as TensorFlow and PyTorch) is loaded. Relevant data from the data graph model (such as stack allocation size, bus load rate, signal period, etc.) are input into the anomaly pattern detection model to identify abnormal configurations that do not conform to the normal pattern. The model outputs a detailed anomaly configuration report, which includes clear anomaly configuration information.

[0050] Load a pre-trained correlation traceability analysis model (a graph neural network built on machine learning or deep learning frameworks such as TensorFlow and PyTorch), input the requirements, design models and implementation data from the data graph model into the correlation traceability analysis model, automatically establish and verify a bidirectional traceability chain between requirements, design models and implementation, discover missing or broken links, and output a detailed traceability report, which includes clear traceability chain information.

[0051] Load the pre-trained Natural Language Processing (NLP) model (a model built on NLP frameworks such as spaCy, NLTK, BERT), and combine the requirement document and element description (such as...). <desc>The model takes a field as input, parses the text content, verifies whether the text description matches the actual design elements, and outputs clear text verification information.

[0052] S3. Analyze the anomaly detection results of the file based on the standard rule base of the project file, and generate error correction suggestions.

[0053] Furthermore, based on the file anomaly detection results, the anomalies are analyzed to obtain an error list; the error types and context information of the anomalies are determined according to the error list; and error correction suggestions are generated through the project file standard rule base based on the error types and context information.

[0054] In the specific implementation, anomalies are analyzed based on file anomaly detection results. Identified errors are categorized and recorded to generate an error list. Then, an error classification tool (such as a custom script or rule engine) clarifies the type and context of each error, providing a basis for generating subsequent error correction suggestions. Based on the error type and context, correction suggestions are generated using correction rules from the rule base or machine learning models (such as decision trees or neural networks).

[0055] For example: for errors of data type mismatch, it is recommended to modify the data type to match the port interface; for errors of abnormal signal period, it is recommended to adjust the signal period to optimize bus load; for errors of inconsistent signal names in Excel and ARXML, it is recommended to synchronize the signal names.

[0056] S4. Correct the set of project files to be verified according to the preset authorization strategy and the error correction suggestions.

[0057] It should be noted that the default authorization policy allows users to customize certain issues, which can be automatically modified based on error correction suggestions.

[0058] In practical implementation, a user-friendly interface can be designed using front-end development frameworks (such as React and Vue.js) to present errors and suggestions in a visual format (such as lists or graph structures with highlighting), facilitating user interaction. Users can view the error list and correction suggestions on the interface and select "accept" or modify a suggestion.

[0059] Furthermore, a correction feedback log is generated based on the corrected results and the file anomaly detection results, allowing the user to view the correction feedback log. The corrected set of project files to be verified is obtained; a verification report is generated based on the file anomaly detection results, the correction feedback log, and the corrected set of project files to be verified.

[0060] In this embodiment, the set of engineering files to be verified is first parsed, and a unified data graph model is constructed based on the parsed engineering data. Then, multi-dimensional rule detection and data augmentation analysis are performed on the unified data graph model to obtain file anomaly detection results. Subsequently, the file anomaly detection results are analyzed based on the standard rule library of engineering files to generate error correction suggestions. Finally, the set of engineering files to be verified is corrected according to the preset authorization strategy using the error correction suggestions. This embodiment identifies various errors, conflicts, and inconsistencies in files through multi-dimensional rule detection and data augmentation analysis, and can provide intelligent error correction suggestions or automatic repair based on predefined rules and artificial intelligence technology, thereby significantly improving the quality and efficiency of automotive electronic system design.

[0061] Reference Figure 3 , Figure 3 This is a structural block diagram of the first embodiment of the engineering document correction system of the present invention.

[0062] like Figure 3 As shown, the engineering document correction system proposed in this embodiment of the invention includes: Module 3001 is used to parse the set of project files to be verified and to build a unified data graph model based on the parsed project data. Analysis module 3002 is used to perform multi-dimensional rule detection and data augmentation analysis on the unified data graph model to obtain file anomaly detection results; The generation module 3003 is used to analyze the anomaly detection results of the file based on the standard rule library of engineering files and generate error correction suggestions; The correction module 3004 is used to correct the set of project files to be verified according to the error correction suggestions based on the preset authorization strategy.

[0063] Other embodiments or specific implementations of the engineering document correction system of this invention can be found in the above-described method embodiments, and will not be repeated here.

[0064] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0065] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0067] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.< / desc>

Claims

1. A method for correcting engineering documents, characterized in that, The method includes the following steps: S1, parse the set of project files to be verified, and construct a unified data graph model based on the parsed project data; S2, perform multi-dimensional rule detection and data augmentation analysis on the unified data graph model to obtain file anomaly detection results; S3, Analyze the anomaly detection results of the file based on the standard rule base of the engineering file, and generate error correction suggestions; S4, Correct the set of project files to be verified according to the preset authorization strategy and the error correction suggestions; S2 includes: S2.1, Construct sub-libraries for syntax and model rules, semantic logic rules, consistency rules, and compliance and best practice rules; S2.2, Construct a multi-dimensional rule library based on the syntax and model rule sub-library, the semantic logic rule sub-library, the consistency rule sub-library, and the compliance and best practice rule sub-library; S2.3, Perform multi-dimensional rule detection on the unified data graph model using the multi-dimensional rule base to obtain rule detection results; S2.4, Perform data augmentation analysis on the unified data graph model to obtain the augmentation analysis results; S2.5, Generate file anomaly detection results based on the rule detection results and the enhanced analysis results; S2.4 includes: S2.4.1, Anomaly registration detection is performed on the relevant data in the unified data graph model using the anomaly pattern detection model to obtain an anomaly configuration report; S2.4.2, A bidirectional traceability chain analysis is performed on the requirements, design models, and implementation data in the unified data graph model using the correlation traceability analysis model to obtain a traceability report; S2.4.3, The requirement documents and element descriptions in the unified data graph model are verified using a natural language processing model to obtain a text verification report; S2.4.4, Generate enhanced analysis results based on the abnormal configuration report, the traceability report, and the text verification report.

2. The method as described in claim 1, characterized in that, S1 includes: S1.1 Extract the ARXML file set, data form and requirement document from the set of project files to be verified; S1.2, extract the structured metadata and data relationships within the ARXML file set using an ARXML parser; S1.3, Extract the table information of the data form using a table parser; S1.4, Extract key information from the requirements document using a text parsing tool; S1.5, construct a unified data graph model based on the structured metadata, the data relationships, the table information, and the key information.

3. The method as described in claim 1, characterized in that, The S3 includes: S3.1, Analyze the abnormal issues based on the file anomaly detection results to obtain an error list; S3.2, Determine the error type and context information of the exception problem based on the error list; S3.3, Generate error correction suggestions based on the error type and the context information using the standard rule base of the project file.

4. The method as described in claim 3, characterized in that, Following S4, the following is included: S5. Generate a correction feedback log based on the corrected result and the file anomaly detection result, so that the user can view the correction feedback log.

5. The method as described in claim 4, characterized in that, Following S5, the following is included: S6, obtain the corrected set of project files to be verified; S7. Generate a verification report based on the file anomaly detection results, the correction feedback log, and the corrected set of project files to be verified.

6. A system for correcting engineering documents, applied to the method for correcting engineering documents as described in claim 1, characterized in that, The system includes: The building module is used to parse the set of project files to be verified and to build a unified data graph model based on the parsed project data; The analysis module is used to perform multi-dimensional rule detection and data augmentation analysis on the unified data graph model to obtain file anomaly detection results; The generation module is used to analyze the anomaly detection results of the file based on the standard rule library of engineering files and generate error correction suggestions; The correction module is used to correct the set of project files to be verified according to the error correction suggestions based on the preset authorization strategy.

7. A device for correcting engineering documents, characterized in that, The device includes: a memory, a processor, and a correction program for a project file stored in the memory and executable on the processor, the correction program for the project file being configured to implement the steps of the correction method for the project file as claimed in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a correction program for the project file, which, when executed by a processor, implements the steps of the correction method for the project file as described in any one of claims 1 to 5.

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