Special work ticket checking method and system based on large model

Abstract

The invention provides a special work ticket checking method and system based on a large model, and relates to the technical field of work ticket checking, and the method comprises the steps: carrying out the information extraction of a work ticket, and obtaining the information of the work ticket; determining check content based on the job ticket information; checking the checking content through a job knowledge base to obtain a checking result; and generating a rectification suggestion according to the checking result, and carrying out checking rectification. Through the method and the device, the technical problem of relatively poor checking quality caused by limited working ticket checking coverage and low manual processing efficiency in the prior art can be solved, the technical target of constructing an intelligent working ticket checking system capable of automatically executing efficient checking is achieved, and the working ticket checking efficiency is improved. The technical effects of improving the verification coverage rate and timeliness, guaranteeing the stability and accuracy of the verification quality, effectively reducing the artificial omission risk, and improving the safety risk management and control level of the special operation in the park are achieved.

Description

Technical Field

[0001] This application relates to the field of work ticket verification technology, and in particular to a method and system for verifying special work tickets based on a large model. Background Technology

[0002] With the increasing demands for operational safety management in power, chemical, coal mining, and other high-risk industrial production scenarios, work permits, as an important management tool for standardizing work behavior, implementing safety measures, and clarifying work responsibilities, are widely used in various work scenarios such as hot work, work at height, confined space work, and temporary power supply work.

[0003] Currently, the mainstream verification methods mainly rely on chemical safety experts to manually review and judge the completeness, compliance, and risk control measures of work permits through random sampling. However, the manual-led verification model has a series of inherent defects. First, in terms of coverage, limited by manpower and time costs, verification can usually only be sampled proportionally or by risk level, failing to achieve full coverage. This leaves a large number of unverified work permits in a regulatory blind spot, and their potential risks cannot be effectively identified and controlled, resulting in gaps in the overall safety defense. Second, in terms of verification quality, the results heavily depend on the experience and knowledge of individual experts. Different verifiers, and even the same person, may have fluctuating judgment standards and attention to detail depending on their energy levels, leading to unstable and highly subjective verification conclusions. Third, in terms of efficiency and stability, facing a massive number of work permits with varying formats, manual review requires checking text, data, and logical relationships item by item, which is time-consuming, labor-intensive, and inefficient. Furthermore, under fatigue, it is easy to make mistakes, omissions, or errors, making it difficult to guarantee continuous and stable high-quality output. Finally, this method struggles to systematically integrate and rapidly apply continuously updated regulatory standards and expert experience knowledge bases, resulting in a lag in knowledge iteration.

[0004] In summary, existing technologies suffer from several technical problems. Due to limited verification coverage, over-reliance on individual expert experience, low efficiency and unstable status of manual processing, a large number of special operation tickets are left in an uncontrolled risk state, resulting in inconsistent verification quality and delayed regulatory response. These issues further affect the controllability of the overall safety risks of special operations in chemical industrial parks and the continuous improvement of management efficiency. Summary of the Invention

[0005] The purpose of this application is to provide a special operation ticket verification method and system based on a large model, in order to solve the technical problems in the existing technology that, due to the limited scope of verification, over-reliance on individual expert experience, low efficiency of manual processing and unstable status, a large number of special operation tickets are in an uncontrolled risk state, the verification quality is uneven, and the regulatory response is delayed, which further affects the controllability of the overall special operation safety risk in chemical industrial parks and the continuous improvement of management efficiency.

[0006] In view of the above problems, this application provides a special operation ticket verification method and system based on a large model.

[0007] Firstly, this application provides a special work ticket verification method based on a large model, implemented through a special work ticket verification system based on a large model, including: extracting information from the work ticket to obtain work ticket information; determining the verification content based on the work ticket information; verifying the verification content through a work knowledge base to obtain verification results; generating rectification suggestions based on the verification results, and carrying out verification and rectification.

[0008] Preferably, the special work ticket verification method based on the large model further includes: receiving electronic work tickets and paper work tickets; extracting information from the electronic work tickets based on work ticket elements to obtain first work content; converting the paper work tickets into images and extracting text, and extracting information based on the work ticket elements to obtain second work content; formatting the first work content and the second work content to obtain the work ticket information.

[0009] Preferably, the special work ticket verification method based on the large model further includes: the work ticket elements include basic work information, work personnel information, work approval information, work risk assessment information, work safety measures information, and work completion report information, wherein the basic work information includes work name information, work type information, work time information, work location information, and work steps information.

[0010] Preferably, the special work ticket verification method based on the large model further includes: deconstructing the work ticket information based on the work type information, the work approval information, the work risk assessment information, and the work safety measures information to obtain verification elements; generating verification prompt words based on the verification elements; and extracting the verification content based on the verification prompt words.

[0011] Preferably, the special work ticket verification method based on the large model further includes: adjusting the verification focus of the verification prompt words based on the work type information, the work approval information, the work risk assessment information, and the work safety measures information; according to the adjusted verification focus, extending the verification prompt words with the verification prompt words as the central prompt words to obtain auxiliary verification prompt words; and adding the auxiliary verification prompt words to the verification prompt words.

[0012] Preferably, the special work ticket verification method based on the large model further includes: introducing a work knowledge base, storing the work knowledge base in a structured manner, and obtaining metadata tags; using the metadata tags as a query index, traversing and querying the verification prompt words in the metadata tags to obtain the search content; and performing a compliance assessment on the verification content based on the search content to obtain an initial verification result.

[0013] Preferably, the special operation ticket verification method based on the large model further includes: performing a membership evaluation of the standard execution dimension and the safety compliance dimension of the initial verification result to generate a verification membership degree; if the verification membership degree of the standard execution dimension is greater than or equal to the verification membership degree of the safety compliance dimension, the initial verification result is adjusted to obtain the verification result.

[0014] Preferably, the special work ticket verification method based on the large model further includes: marking the work problems based on the verification results of the failed items to obtain the verification problems; generating rectification suggestions based on the verification problems, wherein the rectification suggestions include rectification measures, rectification goals and rectification time limits.

[0015] Preferably, the special work ticket verification method based on the large model further includes: generating verification records based on the resolved rectification suggestions, and performing statistics to generate a statistical report; identifying the key verification points of the work ticket information according to the statistical report; and prioritizing the extraction of verification content from the work tickets to be received based on the key verification points.

[0016] Secondly, this application also provides a special work ticket verification system based on a large model, used to execute the special work ticket verification method based on a large model as described in the first aspect, including: a work ticket information acquisition module, used to extract information from the work ticket to acquire work ticket information; a verification content determination module, used to determine the verification content based on the work ticket information; a verification result acquisition module, used to verify the verification content through a work knowledge base to obtain the verification result; and a verification rectification module, used to generate rectification suggestions based on the verification result and perform verification rectification.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goal of building an intelligent work ticket verification system based on a large language model that can automatically perform efficient verification, the system can improve the verification coverage and timeliness, ensure the stability and accuracy of verification quality, effectively reduce the risk of human error, and thus improve the level of safety risk control for special operations in the park.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application 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 merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the special operation ticket verification method based on a large model in this application.

[0021] Figure 2 This is a schematic diagram of the structure of the special operation ticket verification system based on a large model in this application.

[0022] Attached diagram labels: Module 1 for obtaining work ticket information, Module 2 for determining verification content, Module 3 for obtaining verification results, and Module 4 for verification and rectification. Detailed Implementation

[0023] This application provides a special work permit verification method and system based on a large-scale model. It addresses the technical problems in existing technologies, such as limited verification coverage, over-reliance on individual expert experience, low efficiency and instability of manual processing, leading to a large number of special work permits being in an uncontrolled risk state, inconsistent verification quality, and delayed regulatory response. These issues further affect the controllability of overall special work safety risks in chemical industrial parks and the continuous improvement of management efficiency. The application aims to build an intelligent work permit verification system based on a large-scale language model that can automatically perform efficient verification. This system will improve verification coverage and timeliness, ensure the stability and accuracy of verification quality, effectively reduce the risk of human error, and thus enhance the level of special work safety risk management in the industrial park.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a special work ticket verification method based on a large model, which is applied to a special work ticket verification system based on a large model. The method includes the following steps:

[0026] Extract information from the work order to obtain the work order information.

[0027] Furthermore, this application also includes: receiving electronic work tickets and paper work tickets; extracting information from the electronic work tickets based on work ticket elements to obtain first work content; converting the paper work tickets into images and extracting text, and extracting information based on the work ticket elements to obtain second work content; formatting the first work content and the second work content to obtain the work ticket information.

[0028] Furthermore, this application also includes: the elements of the work ticket include basic work information, personnel information, work approval information, work risk assessment information, work safety measures information, and work completion report information, wherein the basic work information includes work name information, work type information, work time information, work location information, and work steps information.

[0029] Specifically, receiving electronic and paper work tickets refers to acquiring work ticket data, which represents special work application, approval, and safety control information, through information system interfaces, file uploads, or manual collection. Electronic work tickets are work ticket records stored in the information system in structured or semi-structured data form, while paper work tickets are image data carried on paper media and formed by scanning, photographing, etc. This is used to achieve unified input of work tickets from different sources, provide a data foundation for subsequent processing, and provide a basis for differentiated processing strategies by distinguishing the receiving objects.

[0030] Furthermore, extracting information from electronic work tickets based on their elements to obtain the first work content involves parsing and extracting existing text fields, form items, or structured data from the electronic work ticket according to a pre-defined work ticket element model. Work ticket elements serve as the basic units for work ticket content parsing, compliance verification, and risk assessment. Through the collaborative description of information from different dimensions, they comprehensively reflect the entire process of the work from application and implementation to completion, providing a structured basis for subsequent intelligent analysis and verification. Work ticket elements include basic work information, personnel information, work approval information, work risk assessment information, work safety measures information, and work completion report information. The basic work information includes work name information, work type information, work time information, work location information, and work step information. Basic work information is a set of information describing the basic attributes and execution background of the work. It clarifies the objective characteristics and scope of the work itself, serving as a fundamental condition for distinguishing different work scenarios and matching corresponding safety specifications, thereby providing an initial basis for work ticket type identification and verification focus determination. Personnel information describes the composition and qualifications of personnel involved in the operation. It includes at least the personnel's identity, job responsibilities, and qualifications, used to determine whether participants possess the necessary operational skills and safety awareness, thus providing a basis for personnel compliance verification and responsibility delineation. Work approval information describes the workflow and authorization process of work permits. By recording approval nodes, approvers, and approval results, it reflects whether the work has undergone review and confirmation at the prescribed levels and processes, thereby determining the legality and validity of the work permit in the management process. Work risk assessment information describes the identification and risk level determination of potential risks during the operation. By assessing the work environment, work methods, and potential hazards, it provides a risk basis for developing targeted control measures and identifying key areas for verification. Work safety measures information describes the control measures taken to address identified risks, including safety protection configurations, on-site control requirements, and emergency response arrangements. It verifies whether the work permit has implemented corresponding safety control measures based on the risk assessment results, thereby ensuring the safety of the operation process. The work completion report describes the work completion status and site recovery status. By recording work completion confirmation, on-site inspection results, and explanations of any abnormalities, it reflects the degree of completion of closed-loop work management, providing a basis for post-event traceability and continuous improvement. Basic work information includes work name, work type, work time, work location, and work steps. This refers to the fundamental data that specifically describes the work activity, clarifying the work's name, category, planned or actual implementation timeframe, specific location, and main operational procedures. This provides the necessary prerequisites for subsequent risk identification, standard matching, and verification logic construction.

[0031] Furthermore, the paper work order is converted into an image and the text is extracted. Information based on the elements of the work order is extracted to obtain the second work content. This involves converting the paper work order into digital image data using an image acquisition device, then using optical character recognition technology to identify and restore the text information in the image. Subsequently, combined with layout analysis and semantic recognition technology, the identified text content is extracted according to the elements of the work order to eliminate the unstructured influence of the paper carrier. Finally, a second work content that is semantically equivalent to the electronic work order is formed.

[0032] Then, formatting the first and second job contents and obtaining job ticket information means performing unified data structure reorganization, field standardization, and semantic standardization on job contents from electronic and paper job tickets respectively. This maps job contents from different sources and in different formats to a consistent job ticket information model, ensuring consistency in data structure, field meaning, and expression. This provides a unified data input format for subsequent automatic verification, risk analysis, and compliance judgment.

[0033] The content to be checked is determined based on the work ticket information.

[0034] Furthermore, this application also includes: deconstructing the work ticket information based on the work type information, the work approval information, the work risk assessment information, and the work safety measures information to obtain verification elements; generating verification prompts based on the verification elements; and extracting the verification content based on the verification prompts.

[0035] Furthermore, this application also includes: adjusting the verification focus of the verification prompt words based on the job type information, the job approval information, the job risk assessment information, and the job safety measures information; based on the adjusted verification focus, extending the verification prompt words with the verification prompt words as the central prompt words to obtain auxiliary verification prompt words; and adding the auxiliary verification prompt words to the verification prompt words.

[0036] Specifically, deconstructing the work ticket information based on the work type information, work approval information, work risk assessment information, and work safety measure information to obtain verification elements refers to splitting and recombining the formatted work ticket information according to preset verification dimensions. Among them, the work type information is used to limit the scope of applicable safety management rules, the work approval information is used to reflect the compliance status of the work in the management process, the work risk assessment information is used to characterize the degree of danger and risk category that the work may cause, and the work safety measure information is used to reflect the prevention and control measures configured for the risks. Through semantic deconstruction and element mapping, a set of verification elements that can be independently analyzed and compared is formed, thereby providing a foundation for the construction of subsequent verification logic.

[0037] Furthermore, adjusting the focus of verification based on job type information, job approval information, job risk assessment information, and job safety measures information means dynamically allocating the focus of verification based on the generated verification prompts, according to the legal requirements, approval completeness, risk level, and adequacy of safety measures corresponding to different job types. This makes the verification prompts more semantically focused on the elements with higher risks or stricter compliance requirements in the current job ticket, thereby avoiding ineffective verification of low-relevance content and improving the overall targeting of the verification.

[0038] Furthermore, based on the adjusted verification focus, and using the verification prompt words as the central prompt words, the extension of the verification prompt words is performed to obtain auxiliary verification prompt words. This means that, using the verification prompt words adjusted by the focus as the semantic core, supplementary prompt words that are related to the central prompt words in terms of legal provisions, operational requirements, or risk control are automatically generated through semantic association, rule expansion, or knowledge base association. This forms a set of auxiliary verification prompt words that covers a more comprehensive set of verification dimensions, which is used to make up for the semantic omissions that may exist in a single prompt word.

[0039] Then, auxiliary verification prompts are added to the verification prompts. The verification content is extracted based on the verification prompts. This means that the central prompts and the auxiliary verification prompts are combined to form a complete set of verification prompts. This set of prompts is used as a semantic constraint to extract the content corresponding to the verification target from the work ticket information and its associated standard specifications, in order to support subsequent large-scale model reasoning analysis and compliance judgment.

[0040] The verification content is checked using the task knowledge base to obtain the verification results.

[0041] Furthermore, this application also includes: introducing an assignment knowledge base, storing the assignment knowledge base in a structured manner, and obtaining metadata tags; using the metadata tags as a query index, traversing and querying the verification prompt words in the metadata tags to obtain search content; and performing a compliance assessment on the verification content based on the search content to obtain an initial verification result.

[0042] Furthermore, this application also includes: performing a membership assessment of the standard execution dimension and the security compliance dimension of the initial verification result to generate a verification membership degree; if the verification membership degree of the standard execution dimension is greater than or equal to the verification membership degree of the security compliance dimension, the initial verification result is adjusted to obtain the verification result.

[0043] Specifically, introducing a work knowledge base and storing it in a structured manner to obtain metadata tags involves incorporating knowledge resources related to specific work, such as laws and regulations, national standards, industry norms, safe operating procedures, expert experience, and historical cases, into the work knowledge base. This is achieved by structuring the text content through segmentation, annotation, and semantic abstraction, thereby generating corresponding metadata tags for each piece of knowledge. These metadata tags characterize the applicable work type, risk category, management process, and standard clause attributes of the knowledge content, enabling efficient and accurate retrieval and association in the future.

[0044] Furthermore, using metadata tags as a query index, and traversing the verification prompts within the metadata tags to obtain the search content, means using the generated verification prompts as semantic query conditions and the metadata tags as index entry points to perform one-by-one matching and traversal retrieval of structured knowledge in the work knowledge base. This allows for the selection of legal provisions, operational requirements, or guiding content that are highly relevant to the verification prompts in terms of semantics, scope of application, or management requirements. The search content is used to provide clear standard basis and reference for subsequent verification judgments.

[0045] Furthermore, a compliance assessment is conducted on the verification content based on the search results to obtain initial verification results. The membership assessment of the standard execution dimension and the safety compliance dimension of the initial verification results is then performed to generate verification membership. This involves comparing and analyzing each item of the verification content extracted from the work order with the search results, evaluating from two different perspectives: whether standard clauses are implemented and whether the work behavior meets safety baseline requirements. The standard execution dimension measures the degree to which the work order conforms to normative clauses and process requirements, while the safety compliance dimension measures whether the work implementation meets actual safety control and risk prevention requirements. By calculating the matching degree of the two dimensions in the verification results, a verification membership that quantifies the compliance status is generated.

[0046] Then, if the verification membership degree of the standard implementation dimension is greater than or equal to the verification membership degree of the safety compliance dimension, the initial verification results are adjusted based on factual descriptions to obtain the verification results. This means that when the work ticket's compliance with the formal standard implementation level is not lower than its compliance with the actual safety compliance level, the initial verification results are corrected and supplemented based on factual descriptions. This makes the verification results more objectively reflect the true state of the work ticket's standard implementation, while avoiding misjudgments of implemented standard requirements due to insufficient description of safety measures. This results in a final verification result that can be used for management decisions and accountability.

[0047] Based on the verification results, rectification suggestions are generated and verified and rectified.

[0048] Furthermore, this application also includes: marking the failed items based on the verification results as operational issues to obtain verification issues; generating rectification suggestions based on the verification issues, wherein the rectification suggestions include rectification measures, rectification goals, and rectification time limits.

[0049] Furthermore, this application also includes: generating verification records based on the resolved rectification suggestions, and performing statistics to generate a statistical report; identifying key verification points for work ticket information based on the statistical report; and prioritizing the extraction of verification content for work tickets to be received based on the key verification points.

[0050] Specifically, marking non-compliant items based on the verification results to identify verification issues involves analyzing each item in the verification results, filtering out items that do not meet standard implementation requirements or safety compliance requirements, and associating these items with the corresponding work ticket content. The work issue marking is used to clearly indicate the specific location, problem type, and involved work elements of the non-compliance, thereby forming verification issues that can be individually identified, recorded, and tracked, providing a clear basis for subsequent rectification and accountability.

[0051] Furthermore, rectification suggestions are generated based on the identified issues. These suggestions include rectification measures, rectification goals, and rectification timelines. This means that for the identified issues, targeted improvement plans are automatically generated in conjunction with relevant laws, regulations, industry standards, and safety management requirements. The rectification measures clarify the specific operational or management methods to be taken, the rectification goals define the compliance or safety status to be achieved after rectification, and the rectification timelines limit the time requirements for rectification implementation. This results in structured and executable rectification suggestions, ensuring that identified issues can be effectively addressed in a closed-loop manner.

[0052] The process of generating verification records based on resolved rectification suggestions and compiling statistics to produce statistical reports involves associating and archiving the corresponding rectification results with the original verification issues, verification conclusions, and handling processes after the rectification suggestions have been confirmed and passed review. This forms a complete verification record, which is then summarized and analyzed. Statistical processing is performed from multiple dimensions, such as the distribution of issue types, the completion status of rectification, and the correlation between work types, to generate a statistical report that reflects the overall status and trends of work ticket verification, supporting management analysis and decision-making.

[0053] Furthermore, the key identification of work ticket information based on statistical reports refers to the analysis of high-frequency, high-risk, and recurring issues in the statistical reports to identify work ticket elements that are more likely to cause non-compliance or safety hazards in different work types or scenarios. These elements are then highlighted in the work ticket information to emphasize their priority in subsequent verification processes, thereby concentrating verification resources on key issue areas.

[0054] Furthermore, prioritizing the extraction of verification content from received work orders based on key verification priorities means that when a new work order is received, relevant content in the work order information is analyzed and extracted first based on the identified verification priorities. This ensures that verification content related to historically high-risk or high-frequency issues enters the verification process before other content, thereby improving verification efficiency and the timeliness of risk identification while ensuring the comprehensiveness of the verification.

[0055] In summary, the special operation ticket verification method based on a large model provided in this application has the following technical effects: by achieving the technical goal of building an intelligent operation ticket verification system based on a large language model that can automatically perform efficient verification, it can improve the verification coverage and timeliness, ensure the stability and accuracy of verification quality, effectively reduce the risk of human error, and thus improve the level of safety risk control for special operations in the park.

[0056] Example 2: Based on the same inventive concept as the special work ticket verification method based on a large model in the foregoing examples, this application also provides a special work ticket verification system based on a large model. Please refer to the appendix. Figure 2 It includes: a work ticket information acquisition module 1, used to extract information from work tickets and acquire work ticket information; a verification content determination module 2, used to determine the verification content based on the work ticket information; a verification result acquisition module 3, used to verify the verification content through the work knowledge base and obtain the verification result; and a verification rectification module 4, used to generate rectification suggestions based on the verification result and carry out verification rectification.

[0057] Furthermore, the special work ticket verification system based on the large model is also used for: receiving electronic work tickets and paper work tickets; extracting information from the electronic work tickets based on the work ticket elements to obtain the first work content; converting the paper work tickets into images and extracting text, and extracting information based on the work ticket elements to obtain the second work content; formatting the first work content and the second work content to obtain the work ticket information.

[0058] Furthermore, the special work ticket verification system based on the large model is also used for: the work ticket elements include basic work information, worker information, work approval information, work risk assessment information, work safety measures information, and work completion report information, wherein the basic work information includes work name information, work type information, work time information, work location information, and work steps information.

[0059] Furthermore, the special work ticket verification system based on the large model is also used to: deconstruct the work ticket information based on the work type information, the work approval information, the work risk assessment information, and the work safety measures information to obtain verification elements; generate verification prompt words based on the verification elements; and extract the verification content based on the verification prompt words.

[0060] Furthermore, the special work ticket verification system based on the large model is also used to: adjust the verification focus of the verification prompt words based on the work type information, the work approval information, the work risk assessment information, and the work safety measures information; according to the adjusted verification focus, extend the verification prompt words with the verification prompt words as the central prompt words to obtain auxiliary verification prompt words; and add the auxiliary verification prompt words to the verification prompt words.

[0061] Furthermore, the special work ticket verification system based on the large model is also used for: introducing a work knowledge base, storing the work knowledge base in a structured manner, and obtaining metadata tags; using the metadata tags as a query index, traversing and querying the verification prompt words in the metadata tags to obtain the search content; and performing a compliance assessment on the verification content based on the search content to obtain an initial verification result.

[0062] Furthermore, the special operation ticket verification system based on the large model is also used to: perform a membership evaluation of the standard execution dimension and the safety compliance dimension of the initial verification result, and generate a verification membership degree; if the verification membership degree of the standard execution dimension is greater than or equal to the verification membership degree of the safety compliance dimension, the initial verification result is adjusted to obtain the verification result.

[0063] Furthermore, the special work ticket verification system based on the large model is also used for: marking work problems based on the verification results of items that failed the verification, thereby obtaining verification problems; and generating rectification suggestions based on the verification problems, wherein the rectification suggestions include rectification measures, rectification goals, and rectification time limits.

[0064] Furthermore, the special work ticket verification system based on the large model is also used to: generate verification records based on the resolved rectification suggestions, and perform statistics to generate a statistical report; identify the key points of the work ticket information verification based on the statistical report; and extract the verification content of the work tickets to be received with priority based on the key points of verification.

[0065] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The special operation ticket verification method and specific examples based on the large model in the aforementioned embodiment 1 are also applicable to the special operation ticket verification system based on the large model in this embodiment. Through the foregoing detailed description of the special operation ticket verification method based on the large model, those skilled in the art can clearly understand the special operation ticket verification system based on the large model in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0066] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0067] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A special operation ticket verification method based on a large model, characterized in that, include: Extract information from the work order to obtain the work order information; The content to be checked is determined based on the work ticket information; The verification content is checked using the task knowledge base to obtain the verification results; Based on the verification results, rectification suggestions are generated and verified and rectified.

2. The special operation ticket verification method based on a large model as described in claim 1, characterized in that, Extract information from the work order to obtain work order information, including: Accepting both electronic and paper work tickets; The electronic work ticket is used to extract information based on its elements to obtain the first work content; The paper work order is converted into an image and the text is extracted. Information based on the elements of the work order is extracted to obtain the second work content. The first and second job contents are formatted and processed to obtain the job ticket information.

3. The special operation ticket verification method based on a large model as described in claim 2, characterized in that, The elements of the work order include basic work information, personnel information, work approval information, work risk assessment information, work safety measures information, and work completion report information. The basic work information includes work name information, work type information, work time information, work location information, and work steps information.

4. The special operation ticket verification method based on a large model as described in claim 3, characterized in that, The content to be verified is determined based on the work ticket information, including: The work ticket information is deconstructed based on the work type information, work approval information, work risk assessment information, and work safety measures information to obtain verification elements; Based on the verification elements, verification prompts are generated, and the verification content is extracted based on the verification prompts.

5. The special operation ticket verification method based on a large model as described in claim 4, characterized in that, Based on the verification elements, verification prompts are generated, including: The focus of the verification is adjusted based on the job type information, job approval information, job risk assessment information, and job safety measures information; Based on the adjusted verification focus, with the verification prompt words as the central prompt words, the extension of the verification prompt words is performed to obtain auxiliary verification prompt words; Add the auxiliary verification prompts to the verification prompts.

6. The special operation ticket verification method based on a large model as described in claim 4, characterized in that, Before verifying the verification content through the task knowledge base and obtaining the verification results, the following steps are included: An assignment knowledge base is introduced, and the assignment knowledge base is stored in a structured manner to obtain metadata tags; Using the metadata tags as the query index, the search results are obtained by iterating through the verification prompts within the metadata tags. Based on the search results, a compliance assessment is performed on the content to be verified to obtain initial verification results.

7. The special operation ticket verification method based on a large model as described in claim 6, characterized in that, The verification content is checked using the task knowledge base to obtain the verification results, including: Perform a membership assessment of the standard execution dimension and security compliance dimension of the initial verification results to generate verification membership. If the verification membership degree of the standard execution dimension is greater than or equal to the verification membership degree of the security compliance dimension, the initial verification result is adjusted to obtain the verification result.

8. The special operation ticket verification method based on a large model as described in claim 1, characterized in that, Based on the verification results, rectification suggestions are generated, including: Based on the verification results, the items that failed the verification are marked as operational issues, thus obtaining the verification issues; Based on the issues identified, rectification recommendations are generated, which include rectification measures, rectification objectives, and rectification timelines.

9. The special operation ticket verification method based on a large model as described in claim 1, characterized in that, After verification and rectification, including: Based on the resolved rectification suggestions, verification records are generated, and statistics are compiled to generate a statistical report; Based on the statistical report, key points should be identified during the verification of work ticket information; Prioritize extracting verification information from received work orders based on key verification criteria.

10. A special operation ticket verification system based on a large model, characterized in that, The steps for implementing the special operation ticket verification method based on a large model according to any one of claims 1 to 9 include: The work ticket information acquisition module is used to extract information from work tickets and obtain work ticket information; The verification content determination module is used to determine the verification content based on the work ticket information; The verification result acquisition module is used to verify the verification content through the job knowledge base and obtain the verification result; The verification and rectification module is used to generate rectification suggestions based on the verification results and to carry out verification and rectification.

Images