Work order task analysis and processing method based on large model

Abstract

The invention discloses a work order task analysis and processing method based on a large model, and relates to the technical field of business process management. Comprising the following steps of: performing cue word management based on process links: configuring specific cue words by taking process names and process links as basic dimensions, setting different cue words for the cue words of each process link according to different business characteristics, and analyzing work order contents: performing deep analysis on dominant contents of a work order by utilizing natural language processing capability of a large model, the method comprises the following steps: acquiring problems, demands and backgrounds related to a work order, extracting key information, providing basic data support for subsequent processing, and mining implicit meanings: mining implicit deep meanings in the work order through a large model, so as to help processing personnel to grasp core key points of the work order and make preparation in advance; the processing results of handling personnel in each process link are tracked in real time, the processing results are analyzed and sorted through the large model and displayed to related personnel, decision support is provided, and decision suggestions are provided for the handling personnel based on comprehensive analysis of the work order content and the processing results of the large model.

Description

Technical Field

[0001] The invention discloses a work order task analysis and processing method based on a large model, and relates to the technical field of business process management. Background Art

[0002] With the rapid development of the communications industry, the volume of work order tasks continues to increase. Existing work order processing methods primarily rely on manual operations, which suffer from low efficiency, insufficient accuracy, and difficulty in tracking results. For example, parsing work order content requires manual reading and understanding of each item, which is prone to misunderstandings. The results of each step need to be manually recorded and summarized, increasing the probability of errors. Furthermore, when faced with complex work order tasks, manual decision-making is often limited by experience, making it difficult to quickly reach the optimal decision, which affects the overall operational efficiency of the business process. Summary of the Invention

[0003] This invention addresses the challenges of existing technologies by providing a large-scale model-based work order task analysis and processing method. This method applies large-scale model technology to the production management process of the communications industry, providing comprehensive intelligent support for the processing of work order tasks. This method enables users to deeply analyze work order details, gain precise insights into the core tasks, and make informed decisions. Furthermore, with its efficient execution capabilities, this invention significantly improves work efficiency, optimizes work quality, and elevates decision-making to a whole new level, injecting powerful momentum into intelligent production management in the communications industry.

[0004] The specific scheme proposed by the present invention is:

[0005] The present invention provides a work order task analysis and processing method based on a large model, comprising:

[0006] Prompt word management based on process links: Configure specific prompt words based on process name and process link. Different prompt words are set for each process link according to different business characteristics.

[0007] Parsing the content of work orders: Using the natural language processing capabilities of the large model, we can deeply analyze the explicit content of the work order, obtain the issues, requirements and background involved in the work order, extract key information, and provide basic data support for subsequent processing.

[0008] Mining hidden meanings: Using large models to mine the deep meanings hidden in work orders, we can help processing personnel grasp the core points of the work orders and make preparations in advance.

[0009] Presentation of processing results: real-time tracking of the processing results of the personnel in each process link, and analysis and organization through the big model, and presentation to relevant personnel,

[0010] Provide decision support: Based on the comprehensive analysis of work order content and processing results based on the big model, provide decision suggestions for processing personnel.

[0011] Furthermore, the mining of implicit meanings in the work order task analysis and processing method based on a large model includes: mining the correlation, urgency, and priority of the problems implicit in the work order, discovering the common root cause behind multiple seemingly independent problems by mining the correlation, and jointly processing the problems; judging the urgency of the problems implicit in the work order by mining the urgency, and giving priority to those problems that have the greatest impact on the normal operation of the business; formulating a reasonable problem-solving plan by mining the priority, and clarifying the processing order of each problem.

[0012] Furthermore, the decision support provided by the work order task analysis and processing method based on a large model includes problem solution, resource allocation suggestions, and risk warning. According to the problem solution decision, all information related to the problem is collected, the problem is accurately defined, the surface phenomenon and the root cause of the problem are distinguished, and the appropriate solution is selected for implementation; according to the resource allocation suggestion decision, the human resources, material resources, and financial resources of various departments within the enterprise are sorted out, and the problem needs are analyzed, and resources are allocated according to the needs; according to the risk warning decision, risk identification is carried out, and risk assessment is carried out at the same time, warning indicators and thresholds are set, and response warnings are issued according to the indicators and thresholds.

[0013] Furthermore, the described method for analyzing and processing work order tasks based on a large model realizes the automated processing of some work order tasks according to preset rules and recommendations of the large model, wherein the automated processing includes automatic classification, automatic transfer, and automatic reply.

[0014] The present invention also provides a work order task analysis and processing device based on a large model, comprising a prompt word management module, a parsing module, a mining module, a display module and a decision module.

[0015] The prompt word management module manages prompt words based on process links: specific prompt words are configured based on process name and process link. Different prompt words are set for each process link according to different business characteristics.

[0016] The parsing module parses the work order content: using the natural language processing capabilities of the large model, it deeply analyzes the explicit content of the work order, obtains the issues, requirements and background involved in the work order, extracts key information, and provides basic data support for subsequent processing.

[0017] The mining module mines the hidden meaning: It uses a large model to mine the deep meaning hidden in the work order, which is used to help the processing personnel grasp the core points of the work order and make preparations in advance.

[0018] The display module presents the processing results: real-time tracking of the processing results of the personnel in each process link, and analysis and organization through the big model, and display to relevant personnel,

[0019] The decision-making module provides decision support: based on the comprehensive analysis of the work order content and processing results of the big model, it provides decision-making suggestions for processing personnel.

[0020] Furthermore, the mining module of the work order task analysis and processing device based on a large model mines implicit meanings, including: mining the correlation, urgency, and priority of the problems implicit in the work order, discovering the common root cause behind multiple seemingly independent problems by mining the correlation, and jointly processing the problems; judging the urgency of the problems implicit in the work order by mining the urgency, and giving priority to those problems that have the greatest impact on the normal operation of the business; formulating a reasonable problem-solving plan by mining the priority, and clarifying the processing order of each problem.

[0021] Furthermore, the decision support provided by the decision module of the work order task analysis and processing device based on a large model includes problem solution, resource allocation suggestion, and risk warning. According to the problem solution decision, all information related to the problem is collected, the problem is accurately defined, the surface phenomenon and the root cause of the problem are distinguished, and the appropriate solution is selected for implementation; according to the resource allocation suggestion decision, the human resources, material resources, and financial resources of various departments within the enterprise are sorted out, and the problem requirements are analyzed, and resources are allocated according to the requirements; according to the risk warning decision, risk identification is carried out, and risk assessment is carried out at the same time, warning indicators and thresholds are set, and response warnings are issued according to the indicators and thresholds.

[0022] Furthermore, the work order task analysis and processing device based on a large model also includes an automation module, which realizes the automated processing of some work order tasks according to preset rules and large model recommendations, wherein the automated processing includes automatic classification, automatic transfer, and automatic reply.

[0023] The benefits of the present invention are:

[0024] Improved Efficiency: Through the rapid parsing and automated processing capabilities of large models, work order processing time has been significantly shortened, improving work efficiency. For example, work order content parsing speed is several times faster than manual processing, and automated processing tasks reduce manual operations, resulting in an overall processing efficiency increase of over 50%.

[0025] Improved Accuracy: The large model accurately understands work order content, eliminating human misunderstandings. It also performs real-time analysis and verification of processing results, ensuring their accuracy. In practical application testing, the accuracy of work order processing has increased from approximately 80% with traditional methods to over 95%.

[0026] Decision optimization: Based on the comprehensive analysis and decision-making recommendations provided by the big model, processing personnel can make better decisions quickly, reduce incorrect decisions caused by lack of experience or incomplete information, improve decision-making level, and reduce business risks.

[0027] Improved work quality: The overall work order processing process has been optimized, reducing manual operation errors, improving work efficiency and accuracy, thereby improving work quality and providing stronger support for production management in the communications industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 It is a schematic diagram of the interaction of the application of the method of the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0031] Example 1

[0032] The present invention provides a work order task analysis and processing method based on a large model, comprising:

[0033] Manage prompts based on process steps: Configure specific prompts based on process name and process step. Each process step has different prompts based on different business characteristics. This allows the big model to summarize, analyze, or output solutions based on the work order content and contextual processing results.

[0034] Parsing work order content: Leveraging the natural language processing capabilities of large models, we conduct in-depth analysis of the explicit content of work orders, obtain the issues, requirements, and background involved in the work orders, extract key information, and provide basic data support for subsequent processing.

[0035] Uncovering hidden meanings: Using large models to uncover the deeper meanings hidden in work orders, we can help processing personnel grasp the core points of work orders and prepare for responses in advance, such as the relevance, urgency, and priority of potential issues. This helps processing personnel quickly grasp the core points of work orders and prepare for responses in advance.

[0036] The mining of implicit meaning includes: mining the relevance, urgency, and priority of the problems implied in the work order, discovering the common root cause behind multiple seemingly independent problems by mining the relevance, and jointly handling the problems; judging the urgency of the problems implied in the work order by mining the urgency, and giving priority to those problems that have the greatest impact on the normal operation of the business; and formulating a reasonable problem-solving plan by mining the priority, and clarifying the order in which each problem is handled.

[0037] Presenting processing results: Track the processing results of personnel in each process link in real time, analyze and organize them through large models, and display them to relevant personnel to facilitate timely identification of problems, adjustment of strategies, and ensure the smooth operation of business processes.

[0038] Provide decision support: Based on the comprehensive analysis of work order content and processing results based on the big model, provide decision suggestions for processing personnel.

[0039] The decision support provided includes problem-solving solutions, resource allocation suggestions, and risk warnings. Based on problem-solving solution decisions, all information related to the problem is collected, the problem is accurately defined, the surface phenomena and root causes of the problem are distinguished, and appropriate solutions are selected for implementation; based on resource allocation suggestion decisions, the human resources, material resources, and financial resources of various departments within the enterprise are sorted out, and problem needs are analyzed, and resources are allocated according to needs; based on risk warning decisions, risks are identified and assessed at the same time, warning indicators and thresholds are set, and response warnings are issued based on the indicators and thresholds.

[0040] The method of the present invention also realizes the automated processing of some work order tasks based on preset rules and large model recommendations, wherein the automated processing includes automatic classification, automatic transfer, and automatic reply.

[0041] Example 2

[0042] The present invention also provides a work order task analysis and processing device based on a large model, comprising a prompt word management module, a parsing module, a mining module, a display module and a decision module.

[0043] The prompt word management module manages prompt words based on process links: specific prompt words are configured based on process name and process link. Different prompt words are set for each process link according to different business characteristics.

[0044] The parsing module parses the work order content: using the natural language processing capabilities of the large model, it deeply analyzes the explicit content of the work order, obtains the issues, requirements and background involved in the work order, extracts key information, and provides basic data support for subsequent processing.

[0045] The mining module mines the hidden meaning: It uses a large model to mine the deep meaning hidden in the work order, which is used to help the processing personnel grasp the core points of the work order and make preparations in advance.

[0046] The display module presents the processing results: real-time tracking of the processing results of the personnel in each process link, and analysis and organization through the big model, and display to relevant personnel,

[0047] The decision-making module provides decision support: based on the comprehensive analysis of the work order content and processing results of the big model, it provides decision-making suggestions for processing personnel.

[0048] Since the information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention, the specific contents can be found in the description of the embodiment of the method of the present invention and will not be repeated here.

[0049] Likewise, the benefits of the device of the present invention are:

[0050] Improved Efficiency: Through the rapid parsing and automated processing capabilities of large models, work order processing time has been significantly shortened, improving work efficiency. For example, work order content parsing speed is several times faster than manual processing, and automated processing tasks reduce manual operations, resulting in an overall processing efficiency increase of over 50%.

[0051] Improved Accuracy: The large model accurately understands work order content, eliminating human misunderstandings. It also performs real-time analysis and verification of processing results, ensuring their accuracy. In practical application testing, the accuracy of work order processing has increased from approximately 80% with traditional methods to over 95%.

[0052] Decision optimization: Based on the comprehensive analysis and decision-making recommendations provided by the big model, processing personnel can make better decisions quickly, reduce incorrect decisions caused by lack of experience or incomplete information, improve decision-making level, and reduce business risks.

[0053] Improved work quality: The overall work order processing process has been optimized, reducing manual operation errors, improving work efficiency and accuracy, thereby improving work quality and providing stronger support for production management in the communications industry.

[0054] It should be noted that not all steps and modules in the above-mentioned processes and device structures are required, and certain steps or modules can be omitted according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or may be implemented by certain components in multiple independent devices.

[0055] The above embodiments are merely preferred embodiments for the purpose of fully illustrating the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are within the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

Claims

1. A method for analyzing and processing work orders based on a large model, characterized by include: Prompt word management based on process links: Configure specific prompt words based on process name and process link. Different prompt words are set for each process link according to different business characteristics. Parsing the content of work orders: Using the natural language processing capabilities of the large model, we can deeply analyze the explicit content of the work order, obtain the issues, requirements and background involved in the work order, extract key information, and provide basic data support for subsequent processing. Mining hidden meanings: Using large models to mine the deep meanings hidden in work orders, we can help processing personnel grasp the core points of the work orders and make preparations in advance. Presentation of processing results: real-time tracking of the processing results of the personnel in each process link, and analysis and organization through the big model, and presentation to relevant personnel, Provide decision support: Based on the comprehensive analysis of work order content and processing results based on the big model, provide decision suggestions for processing personnel.

2. A method for analyzing and processing work orders based on a large model according to claim 1, characterized in that The mining of implicit meaning includes: mining the relevance, urgency, and priority of the problems implied in the work order, discovering the common root cause behind multiple seemingly independent problems by mining the relevance, and jointly handling the problems; judging the urgency of the problems implied in the work order by mining the urgency, and giving priority to those problems that have the greatest impact on the normal operation of the business; and formulating a reasonable problem-solving plan by mining the priority, and clarifying the order in which each problem is handled.

3. The method for analyzing and processing work orders based on a large model according to claim 1 is characterized in that The decision support provided includes problem-solving solutions, resource allocation suggestions, and risk warnings. Based on problem-solving decisions, all relevant information is collected, the problem is accurately defined, the surface symptoms and root causes are distinguished, and appropriate solutions are selected for implementation. Based on resource allocation suggestions, the human, material, and financial resources of various departments within the enterprise are sorted out, and problem requirements are analyzed and resources are allocated according to the requirements. Based on risk warning decisions, risk identification is carried out, risk assessment is conducted, warning indicators and thresholds are set, and response warnings are issued based on the indicators and thresholds.

4. The method for analyzing and processing work orders based on a large model according to claim 1 is characterized in that Based on preset rules and large model recommendations, some work order tasks are automatically processed, including automatic classification, automatic transfer, and automatic reply.

5. A work order task analysis and processing device based on a large model, characterized by It includes prompt word management module, analysis module, mining module, display module and decision module. The prompt word management module manages prompt words based on process links: specific prompt words are configured based on process name and process link. Different prompt words are set for each process link according to different business characteristics. The parsing module parses the work order content: using the natural language processing capabilities of the large model, it deeply analyzes the explicit content of the work order, obtains the issues, requirements and background involved in the work order, extracts key information, and provides basic data support for subsequent processing. The mining module mines the hidden meaning: It uses a large model to mine the deep meaning hidden in the work order, which is used to help the processing personnel grasp the core points of the work order and make preparations in advance. The display module presents the processing results: real-time tracking of the processing results of the personnel in each process link, and analysis and organization through the big model, and display to relevant personnel, The decision-making module provides decision support: based on the comprehensive analysis of the work order content and processing results of the big model, it provides decision-making suggestions for processing personnel.

6. The work order task analysis and processing device based on a large model according to claim 5 is characterized by The mining module mines implicit meanings, including: mining the relevance, urgency, and priority of the problems implied in the work order, discovering the common root cause behind multiple seemingly independent problems by mining the relevance, and jointly handling the problems; judging the urgency of the problems implied in the work order by mining the urgency, and giving priority to those problems that have the greatest impact on the normal operation of the business; and formulating a reasonable problem-solving plan by mining the priority, and clarifying the order in which each problem is handled.

7. The work order task analysis and processing device based on a large model according to claim 5 is characterized by The decision-making module provides decision support, including problem-solving solutions, resource allocation suggestions, and risk warnings. Based on problem-solving decisions, all information related to the problem is collected, the problem is accurately defined, the surface symptoms and root causes are distinguished, and appropriate solutions are selected for implementation. Based on resource allocation suggestions, the human, material, and financial resources of various departments within the enterprise are sorted out, and problem requirements are analyzed and resources are allocated according to the requirements. Based on risk warning decisions, risk identification is carried out, risk assessment is conducted, warning indicators and thresholds are set, and response warnings are issued based on the indicators and thresholds.

8. The work order task analysis and processing device based on a large model according to claim 5 is characterized by It also includes an automation module, which realizes the automated processing of some work order tasks based on preset rules and large model recommendations. The automated processing includes automatic classification, automatic transfer, and automatic reply.

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