A debugging detection process approval method based on deep learning optimization

Abstract

This invention relates to the field of test and debugging process management technology, and provides a deep learning-optimized method for approving debugging and testing processes. The method includes the following steps: obtaining the report content and associated business information of the debugging and testing report to be approved, generating business approval and classification information; classifying the debugging and testing report and managing the pricing of miscellaneous business transactions according to the business approval and classification information; reviewing the debugging and testing report through a multi-level review process, automatically embedding electronic signatures during the review process; synchronizing the status of the completed debugging and testing report with associated production business documents and triggering downstream business processes; archiving the finally approved debugging and testing report and its entire process operation log to an electronic ledger system, and establishing an index supporting multi-dimensional combined retrieval. This invention enables automated triggering, standardized output, cross-system linkage, and structured archiving of the approval process.

Description

Technical Field

[0001] This invention relates to the field of test and debugging process management technology, and in particular to a debugging and testing process approval method based on deep learning optimization. Background Technology

[0002] In the current power engineering construction and operation and maintenance system, commissioning and testing refers to the systematic testing, verification, and performance evaluation activities conducted on power equipment, systems, and tools before or during commissioning, in accordance with national and industry standards. Its scope covers multiple professional fields, including substation equipment commissioning, insulating oil testing, safety tool testing, and electrical instrument calibration. It is a key technical link in ensuring the safe and stable operation of the power grid and guaranteeing equipment quality. This process generates a large number of legally valid and technically valuable test reports, experimental records, and process documents, forming the core basis for project quality traceability and safety responsibility determination. Therefore, the management level and standardization of the commissioning and testing process directly affect the reliability of project quality, the timeliness of service response, and the company's compliance operation capabilities.

[0003] Currently, the technological status and level in the field of commissioning and testing full-process management are still in a transitional period between traditional models and the exploration stage of digital transformation. From a technological foundation perspective, the current system mainly relies on existing construction management platforms as the basic support for information technology construction. While these platforms have basic functions for construction project management, process approval, and data storage, they have significant shortcomings in terms of professional adaptability to commissioning and testing operations. In the area of ​​commissioning and testing report management, current technology still relies primarily on manual offline operations. Core processes such as report preparation, approval, and archiving depend on paper document circulation and manual ledger management, and a digital closed loop covering the entire process has not yet been formed. Currently, the approval process for test reports requires multiple levels of signature confirmation, including the writer, tester, team leader, supervisor, and general manager. However, the existing system lacks online countersigning functionality, making the approval process susceptible to human interference and resulting in systemic defects such as version confusion and difficulty in information traceability. In terms of managing sporadic business, the institute still relies on offline operations for accepting external business, signing contracts, and maintaining price lists. There is a lack of management of the correlation between letters of entrustment and test reports, and the business classification (such as BJ, YH, and AQ categories) lacks a systematic classification mechanism, resulting in low efficiency in business allocation and unclear division of responsibilities. Summary of the Invention

[0004] This invention provides a deep learning-optimized approval method for debugging and testing processes, which can realize automated triggering, standardized output, cross-system linkage and structured archiving of approval processes. It transforms the traditional decentralized management mode that relies on paper circulation and manual operation into a collaborative management mode driven by data intelligence and with a closed-loop online process.

[0005] The first aspect of this invention provides a debugging and detection process approval method based on deep learning optimization, comprising the following steps: Obtain the report content and related business information of the debugging and testing reports pending approval; perform standardization processing and feature extraction on the report content and related business information to generate business approval and classification information; Based on business approval and classification information, commissioning and testing reports are categorized, and miscellaneous business pricing is managed in conjunction with the classification. The debugging and testing report is reviewed through a multi-level review process, and electronic signatures are automatically embedded during the review process. The report cover and signature page are automatically generated according to the preset standardized template library, and a unique standard report number is automatically generated based on the extracted features. The status of the completed and audited debugging and testing reports will be synchronized with the associated production business documents, and the downstream business processes will be triggered. The final approved debugging and testing report and its entire process operation log will be archived in the electronic ledger system, and an index supporting multi-dimensional combined retrieval will be established.

[0006] Furthermore, the standardization and feature extraction of the report content and related business information to generate business approval and classification information includes the following steps: Optical character recognition technology is used to recognize unstructured report text and attached images, and convert them into standard text format; Entity information is extracted from standardized text using a natural language processing model; the extracted entity information is analyzed based on a pre-trained deep learning classification model to generate structured classification information for business approval path decisions and business type determination.

[0007] Furthermore, the classification of commissioning and testing reports based on business approval and classification information, and the linkage management of prices for miscellaneous business, include matching structured classification information with a preset tagged business classification system to determine whether the commissioning and testing report belongs to at least one of the three business categories: substation (BJ), oil and chemical (YH), ​​and safety (AQ). Based on the determined business type, the system automatically links to the corresponding dynamic price list database and compares and verifies the testing items in the report with the latest prices and fluctuation coefficients in the database in real time. A unique business identification code is generated for each verified business, and this identification code, business type, and pricing information are then linked together.

[0008] Furthermore, the multi-level review process includes self-check by the writer, confirmation by the tester, initial review by the team leader, secondary review by the supervisor, and final review by the general manager; operator permissions are verified according to the role-based access control model, and operation logs containing timestamps, operation content, and electronic signature information are recorded.

[0009] Furthermore, the step of automatically generating a report cover and signature page based on a preset standardized template library, and automatically generating a unique standardized report number based on extracted features, includes the following steps: Based on the business type, the corresponding standard cover template and signature page template are automatically selected from the template library; Based on the preset numbering generation rules, a globally unique report number is generated by combining the abbreviation of the project name, the business type code, the date sequence, and the sequence number. The report number, extracted key entity information, and audit logs are automatically filled into the selected template to generate a standard format report file to be signed.

[0010] Furthermore, the process of synchronizing the status of the completed and audited debugging and testing reports with the associated production business documents and triggering downstream business processes includes the following steps: After the report is finalized, its unique business identifier will be automatically extracted. Using the business identification code as an index, search for the associated production business triplicate in the business system and update the report status; Task notifications are pushed to relevant responsible work teams via message queues, triggering subsequent production delivery or on-site operation processes.

[0011] Furthermore, the process of archiving the final approved debugging and testing report and its entire operation log to the electronic ledger system includes: The final version of the report, all historical versions, full-process operation logs, and associated business identification codes and pricing information are stored in a unified structured database. A composite index is established for archived data. The index should support rapid retrieval based on at least a combination of conditions, such as report name keywords, business type, review date range, commissioning unit, and responsible work team.

[0012] A second aspect of the present invention provides a debugging and testing process approval system based on deep learning optimization, including a first processing module for obtaining the report content and related business information of a debugging and testing report to be approved; performing standardization processing and feature extraction on the report content and related business information to generate business approval and classification information; The second processing module is used to classify debugging and testing reports and manage the price linkage of miscellaneous services based on business approval and classification information. The third processing module is used to review the debugging and testing report through a multi-level review process and automatically embed electronic signatures during the review process; it automatically generates a report cover and signature page according to a preset standardized template library, and automatically generates a unique standardized report number based on the extracted features; The fourth processing module is used to synchronize the status of the completed debugging and testing reports with the associated production business documents and trigger downstream business processes. The fifth processing module is used to archive the final approved debugging and testing report and its entire process operation log to the electronic ledger system, and to establish an index that supports multi-dimensional combined retrieval.

[0013] A third aspect of the present invention provides a computer device, comprising: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is used to execute a program in the memory, including executing the methods described above; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

[0014] A fourth aspect of the present invention provides a readable storage medium storing computer-readable instructions, characterized in that the computer-readable instructions, when executed by a processor, implement the steps of the method described above.

[0015] As can be seen from the above technical solutions, the present invention has the following advantages: This invention utilizes deep learning-based intelligent parsing technology to achieve highly accurate automated processing of unstructured report content and attachments, replacing the traditional manual data entry and verification method. This reduces the risk of human error and information omission, and also elevates the efficiency of initial data processing to a new level, laying an accurate and standardized data foundation for subsequent processes.

[0016] Secondly, this invention achieves accurate identification of business types and real-time verification of contract terms by setting up an intelligent classification and dynamic price linkage mechanism. On the one hand, it achieves standardization and consistency in business classification, eliminating management chaos caused by ambiguous classifications at the source; on the other hand, through automatic price comparison, it effectively prevents price disputes and compliance risks during contract execution, and improves the transparency and controllability of sporadic business management. Thirdly, this invention sets up a fully online and standardized multi-level electronic approval process, changing the traditional paper-based circulation method. Electronic signatures and access control ensure the legality and security of approval actions, while automatic process driving and visual tracking significantly shorten the approval cycle and eliminate process delays and human error. At the same time, the complete operation traceability constructs a clear and tamper-proof chain of responsibility, greatly enhancing the auditability of process management.

[0017] Finally, this invention achieves seamless integration between the approval and production processes by automatically linking them. Upon report approval, downstream tasks are automatically triggered, creating a closed-loop management system that seamlessly connects testing results with production execution. This effectively solves the problem of low collaboration efficiency caused by information gaps and accelerates overall business response speed.

[0018] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from an examination of the following, or may be learned from the practice of the invention. Attached Figure Description

[0019] Figure 1 The method flowchart provided by the present invention. Detailed Implementation

[0020] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Example 1 The implementation method in this embodiment can be implemented in a system, on a server, or on a terminal; no specific limitation is made. The method in this application will be described from the perspective of system implementation below. As shown in the figure, a debugging and detection process approval method based on deep learning optimization includes the following steps: Obtain the report content and related business information of the debugging and testing reports pending approval; perform standardization processing and feature extraction on the report content and related business information to generate business approval and classification information; Based on business approval and classification information, commissioning and testing reports are categorized, and miscellaneous business pricing is managed in conjunction with the classification. The debugging and testing report is reviewed through a multi-level review process, and electronic signatures are automatically embedded during the review process. The report cover and signature page are automatically generated according to the preset standardized template library, and a unique standard report number is automatically generated based on the extracted features. The status of the completed and audited debugging and testing reports will be synchronized with the associated production business documents, and the downstream business processes will be triggered. The final approved debugging and testing report and its entire process operation log will be archived in the electronic ledger system, and an index supporting multi-dimensional combined retrieval will be established.

[0022] First, the system receives pending debugging and testing reports and related business information. Its built-in standardization processing and feature extraction modules intelligently parse the report content, transforming it into structured business approval and classification information. Then, based on the generated classification information, it automatically performs business categorization and price-linked management. The report is assigned to a preset business category, such as substation, oil and chemical, or safety, and is simultaneously verified against a dynamic price list database to ensure consistency between business attributes and contract terms. A unique business identifier is also generated to guide all subsequent steps.

[0023] The process enters a multi-level review stage. The system drives the online circulation of reports according to the preset approval path. During this process, electronic signatures are automatically embedded to confirm the identity and operation of each node, and formatted report files conforming to standardized numbers and templates are generated in real time to ensure the consistency and traceability of the output results. When the entire review process is completed, the system automatically triggers downstream business collaboration. By extracting the unique business identifier code of the report, the system synchronizes its status with the associated production business documents, and uses a message notification mechanism to drive subsequent execution tasks, achieving data closure and process connection between the approval and production stages.

[0024] Finally, the system archives the approved reports, along with their complete process logs, version history, and related data, into an electronic ledger. By establishing an index structure that supports multi-dimensional combined retrieval, it enables efficient querying and management of historical information, forming auditable and reusable data assets, and completing the entire process from process processing to knowledge accumulation.

[0025] Example 2 The difference between this embodiment and Embodiment 1 is that the standardization and feature extraction of the report content and related business information to generate business approval and classification information includes the following steps: Optical character recognition technology is used to recognize unstructured report text and attached images, and convert them into standard text format; Entity information is extracted from standardized text using a natural language processing model; the extracted entity information is analyzed based on a pre-trained deep learning classification model to generate structured classification information for business approval path decisions and business type determination.

[0026] The system acquires the original report file and associated business attachments to be processed, which may include scanned images, photographs, or non-standard format electronic documents. The processing utilizes an optical character recognition (OCR) component to perform text recognition on image files, converting them into a standardized machine-readable text format. Subsequently, a natural language processing (NLP) module analyzes the recognized text, extracting key information based on a predefined entity recognition model. This information includes, but is not limited to, the project name, testing items, equipment number, testing data, and the name of the client.

[0027] After extracting basic information, the extracted entity set is further analyzed using a pre-trained deep learning classification model. The model, trained on historical business data, understands the business meaning represented by different entity combinations and outputs structured classification information. This information includes not only business type labels but also key attributes for determining the approval path, such as urgency, detection complexity, and the professional teams involved.

[0028] Example 3 The difference between this embodiment and embodiment two is that the classification of commissioning and testing reports based on business approval and classification information and the linkage management of sporadic business prices include matching structured classification information with a preset tagged business classification system to determine that the commissioning and testing report belongs to at least one of the three business categories: substation (BJ), oil and chemical (YH), ​​and safety (AQ). Based on the determined business type, the system automatically links to the corresponding dynamic price list database and compares and verifies the testing items in the report with the latest prices and fluctuation coefficients in the database in real time. A unique business identification code is generated for each verified business, and this identification code, business type, and pricing information are then linked together.

[0029] By receiving the structured classification information generated from Example 2 and matching it with the preset tagged business classification system, the business is divided into three major categories: substation (BJ), oil and chemical (YH), ​​and safety (AQ). Each category has specific sub-items. The system accurately classifies the reports to be approved into the corresponding categories through the rule engine and similarity calculation.

[0030] After classification, the system automatically links to the corresponding dynamic price list database based on the business type. The database centrally maintains the benchmark price, fluctuation factor, and effective date for various testing items. The system compares and verifies the testing items listed in the report against the latest standards in the database in real time, automatically calculates the contract amount, and identifies price anomalies to ensure consistency between business execution and pricing strategies. For verified businesses, the system generates a unique business identifier code and binds this code to the business type and the verified price snapshot.

[0031] Example 4 The difference between this embodiment and Embodiment 3 is that the multi-level review process includes self-check by the writer, confirmation by the tester, initial review by the team leader, secondary review by the supervisor, and final review by the general manager; the operator's permissions are verified according to the role-based access control model, and an operation log containing timestamps, operation content, and electronic signature information is recorded.

[0032] The multi-level review process is based on a preset sequence of nodes. The standard path includes five core steps: self-check by the writer, confirmation by the tester, preliminary review by the team leader, secondary review by the supervisor, and final review by the general manager.

[0033] During process execution, the system employs a role-based access control model for permission management. Each review node is bound to a specific user role. When a report reaches a node, the system only allows operators with the corresponding role to perform operations such as viewing, signing, rejecting, or forwarding. All operations require confirmation via electronic signature, and the system automatically records operation logs containing precise timestamps, operator identity, executed actions, and electronic signature hash values. This process design supports visual monitoring, allowing administrators to view the report's status and dwell time at each level in real time. Furthermore, the system allows reports to be returned to earlier nodes under specific rules (such as rejection).

[0034] Example 5 The difference between this embodiment and embodiment four is that the step of automatically generating a report cover and signature page based on a preset standardized template library, and automatically generating a unique standardized report number based on extracted features, includes the following steps: Based on the business type, the corresponding standard cover template and signature page template are automatically selected from the template library; Based on the preset numbering generation rules, a globally unique report number is generated by combining the abbreviation of the project name, the business type code, the date sequence, and the sequence number. The report number, extracted key entity information, and audit logs are automatically filled into the selected template to generate a standard format report file to be signed.

[0035] During the approval process, the system automatically selects the corresponding cover and signature page templates from a pre-set standardized template library based on the determined business type. This template library is established according to industry standards and internal management requirements, ensuring the authority and uniformity of the output file format.

[0036] When generating a report number, the system operates according to a predefined numbering rule engine. This engine combines the standardized abbreviation of the project name, the business type code, the system's current date sequence number, and the report serial number for that day to generate a globally unique and clearly structured report number. After selecting a template and generating the number, the system automatically populates the corresponding fields in the template with the key entity information extracted in the previous steps, the complete approval log, and the generated report number. The system then generates a final report file containing all standardized elements and awaiting signature. This file supports multi-version management; each major modification is saved as an independent version for easy traceability and comparison later.

[0037] Example 6 The difference between this embodiment and embodiment five is that the step of synchronizing the status of the completed and audited debugging and testing report with the associated production business documents and triggering the downstream business process includes the following steps: After the report is finalized, its unique business identifier will be automatically extracted. Using the business identification code as an index, search for the associated production business triplicate in the business system and update the report status; Task notifications are pushed to relevant responsible work teams via message queues, triggering subsequent production delivery or on-site operation processes.

[0038] Once the debugging and testing report has completed all final review stages, the system triggers the linkage engine to extract the unique business identifier code bound to the report, using this identifier code as the core index. Subsequently, the system queries the integrated production management business system to locate all production business documents associated with this business identifier code, such as production task triplicate forms. The system automatically updates the status of these documents to "report approved," recording the timestamp and trigger source of the status change. After the status update is complete, the system pushes task notifications to the responsible work team or personnel related to this business through an asynchronous message queue service.

[0039] Example 7 The difference between this embodiment and Embodiment Six is ​​that the step of archiving the final approved debugging and testing report and its entire process operation log to the electronic ledger system includes: The final version of the report, all historical versions, full-process operation logs, and associated business identification codes and pricing information are stored in a unified structured database. A composite index is established for archived data. The index should support rapid retrieval based on at least a combination of conditions, such as report name keywords, business type, review date range, commissioning unit, and responsible work team.

[0040] At the end of the entire test report execution process, the system starts the archiving process, storing the final approved version of the report, all historical version files, complete multi-level approval operation logs, associated business identification codes, and verified price snapshot information into a structured central database for persistent storage.

[0041] The composite index establishes inverted and combined indexes for commonly used query dimensions, supporting rapid combined retrieval based on multiple conditions such as report name keywords, business type, review date range, commissioning unit, and responsible team. Through this archiving and indexing system, the system transforms process data into enterprise knowledge assets. Users can quickly locate target reports and their entire lifecycle through a concise query interface, effectively supporting post-audit verification, statistical analysis, performance evaluation, and knowledge reuse, thereby achieving data-driven refined management goals.

[0042] Example 8 A debugging and testing process approval system based on deep learning optimization includes a first processing module, which is used to obtain the report content and related business information of the debugging and testing report to be approved; and to perform standardized processing and feature extraction on the report content and related business information to generate business approval and classification information. The second processing module is used to classify debugging and testing reports and manage the price linkage of miscellaneous services based on business approval and classification information. The third processing module is used to review the debugging and testing report through a multi-level review process and automatically embed electronic signatures during the review process; it automatically generates a report cover and signature page according to a preset standardized template library, and automatically generates a unique standardized report number based on the extracted features; The fourth processing module is used to synchronize the status of the completed debugging and testing reports with the associated production business documents and trigger downstream business processes. The fifth processing module is used to archive the final approved debugging and testing report and its entire process operation log to the electronic ledger system, and to establish an index that supports multi-dimensional combined retrieval.

[0043] Example 9 A computer device, comprising: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is used to execute a program in the memory, including executing the methods described above; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

[0044] Example 10 A readable storage medium storing computer-readable instructions, characterized in that the computer-readable instructions, when executed by a processor, implement the steps of the method described above.

[0045] In summary, this invention achieves high-precision automated processing of unstructured report content and attachments through deep learning-based intelligent parsing technology, replacing the traditional manual data entry and verification mode. This reduces the risk of human error and information omission, and also improves the efficiency of early data processing to a new level, laying an accurate and standardized data foundation for subsequent processes.

[0046] Secondly, this invention achieves accurate identification of business types and real-time verification of contract terms by setting up an intelligent classification and dynamic price linkage mechanism. On the one hand, it achieves standardization and consistency in business classification, eliminating management chaos caused by ambiguous classifications at the source; on the other hand, through automatic price comparison, it effectively prevents price disputes and compliance risks during contract execution, and improves the transparency and controllability of sporadic business management. Thirdly, this invention sets up a fully online and standardized multi-level electronic approval process, changing the traditional paper-based circulation method. Electronic signatures and access control ensure the legality and security of approval actions, while automatic process driving and visual tracking significantly shorten the approval cycle and eliminate process delays and human error. At the same time, the complete operation traceability constructs a clear and tamper-proof chain of responsibility, greatly enhancing the auditability of process management.

[0047] Finally, this invention achieves seamless integration between the approval and production processes by automatically linking them. Upon report approval, downstream tasks are automatically triggered, creating a closed-loop management system that seamlessly connects testing results with production execution. This effectively solves the problem of low collaboration efficiency caused by information gaps and accelerates overall business response speed.

[0048] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0049] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0050] Furthermore, the functional units in the various embodiments of the present invention 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0051] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part 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, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A debugging and inspection process approval method based on deep learning optimization, characterized in that, Includes the following steps: Obtain the report content and related business information of the pending debugging and testing report; Standardize and extract features from report content and related business information to generate business approval and classification information; Based on business approval and classification information, commissioning and testing reports are categorized, and miscellaneous business pricing is managed in conjunction with the classification. The debugging and testing report is reviewed through a multi-level review process, and electronic signatures are automatically embedded during the review process. The report cover and signature page are automatically generated according to the preset standardized template library, and a unique standard report number is automatically generated based on the extracted features. The status of the completed and audited debugging and testing reports will be synchronized with the associated production business documents, and the downstream business processes will be triggered. The final approved debugging and testing report and its entire process operation log will be archived in the electronic ledger system, and an index supporting multi-dimensional combined retrieval will be established.

2. The debugging and detection process approval method based on deep learning optimization according to claim 1, characterized in that, The process of standardizing and extracting features from report content and related business information to generate business approval and classification information includes the following steps: Optical character recognition technology is used to recognize unstructured report text and attached images, and convert them into standard text format; Entity information is extracted from standardized text using a natural language processing model; the extracted entity information is analyzed based on a pre-trained deep learning classification model to generate structured classification information for business approval path decisions and business type determination.

3. The debugging and detection process approval method based on deep learning optimization according to claim 1, characterized in that, The classification of commissioning and testing reports based on business approval and classification information, and the linkage management of prices for miscellaneous business, include matching structured classification information with a preset tagged business classification system to determine whether the commissioning and testing report belongs to at least one of the three business categories: substation, oil and chemical, and safety. Based on the determined business type, the system automatically links to the corresponding dynamic price list database and compares and verifies the testing items in the report with the latest prices and fluctuation coefficients in the database in real time. A unique business identification code is generated for each verified business, and this identification code, business type, and pricing information are then linked together.

4. The debugging and detection process approval method based on deep learning optimization according to claim 1, characterized in that, The multi-level review process includes self-check by the writer, confirmation by the tester, initial review by the team leader, secondary review by the supervisor, and final review by the general manager; the operator's permissions are verified according to the role-based access control model, and an operation log containing timestamps, operation content, and electronic signature information is recorded.

5. The debugging and detection process approval method based on deep learning optimization according to claim 1, characterized in that, The process of automatically generating a report cover and signature page based on a preset standardized template library, and automatically generating a unique standardized report number based on extracted features, includes the following steps: Based on the business type, the corresponding standard cover template and signature page template are automatically selected from the template library; Based on the preset numbering generation rules, a globally unique report number is generated by combining the abbreviation of the project name, the business type code, the date sequence, and the sequence number. The report number, extracted key entity information, and audit logs are automatically filled into the selected template to generate a standard format report file to be signed.

6. The debugging and detection process approval method based on deep learning optimization according to claim 1, characterized in that, The process of synchronizing the status of the completed and audited debugging and testing reports with the associated production business documents and triggering downstream business processes includes the following steps: After the report is finalized, its unique business identifier will be automatically extracted. Using the business identification code as an index, search for the associated production business triplicate in the business system and update the report status; Task notifications are pushed to relevant responsible work teams via message queues, triggering subsequent production delivery or on-site operation processes.

7. The debugging and detection process approval method based on deep learning optimization according to claim 1, characterized in that, The process of archiving the final approved debugging and testing report and its entire operation log to the electronic ledger system includes: The final version of the report, all historical versions, full-process operation logs, and associated business identification codes and pricing information are stored in a unified structured database. A composite index is established for archived data. The index should support rapid retrieval based on at least a combination of conditions, such as report name keywords, business type, review date range, commissioning unit, and responsible work team.

8. A debugging and detection process approval system based on deep learning optimization, characterized in that, It includes a first processing module, which is used to obtain the report content and related business information of the debugging and testing report to be approved; to perform standardization processing and feature extraction on the report content and related business information, and to generate business approval and classification information; The second processing module is used to classify debugging and testing reports and manage the price linkage of miscellaneous services based on business approval and classification information. The third processing module is used to review the debugging and testing report through a multi-level review process and automatically embed electronic signatures during the review process; it automatically generates a report cover and signature page according to a preset standardized template library, and automatically generates a unique standardized report number based on the extracted features; The fourth processing module is used to synchronize the status of the completed debugging and testing reports with the associated production business documents and trigger downstream business processes. The fifth processing module is used to archive the final approved debugging and testing report and its entire process operation log to the electronic ledger system, and to establish an index that supports multi-dimensional combined retrieval.

9. A computer device, characterized in that, include: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is configured to execute a program in the memory, including performing the method as described in any one of claims 1 to 7; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

10. A readable storage medium storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by a processor, they implement the steps of the method as described in any one of claims 1 to 7.

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