Industrial instruction data construction method and system based on large model

Abstract

The invention relates to the technical field of artificial intelligence, in particular to an industry instruction data construction method and system based on a large model, and the method comprises the following steps: industry demand investigation, task demand definition and target setting. The method has the beneficial effects that through in-depth industry demand investigation and strict evaluation criteria, the instruction data generated by the method is superior to an existing general instruction data set in the aspects of grammar correctness, logic leakproofness and content industry applicability. For example, in the government affair field, the generated instruction can accurately reflect the policy execution process, instruction execution errors caused by insufficient data specialty in the prior art are avoided, and the application effect of the model in the professional field is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and particularly to a method and system for constructing industry instruction data based on large models. Background Art

[0002] With the rapid development of artificial intelligence technology, especially the application of large-scale pre-trained language models (LLMs), the demand for intelligent solutions in various industries is increasing day by day. LLMs can generate natural and fluent language outputs by learning a large amount of text data and have shown strong application potential in multiple fields. However, when applying these models in specific industry scenarios, the quality of instruction data has become a key factor restricting their performance.

[0003] Existing instruction datasets have deficiencies in many aspects: on the one hand, most datasets lack sufficient professionalism. Since they are usually constructed based on general corpora, they may not fully reflect the complex business logics, terms, and operation specifications of specific industries. This results in inaccurate or impractical instructions output by the model when performing some highly specialized tasks, making it difficult to meet actual needs. On the other hand, the complexity of existing datasets is limited. In many cases, the instruction design is too simple and fails to cover all possible task scenarios, especially in industries that need to handle emergencies or non-standard processes, such as emergency response, traffic control, etc. In addition, existing datasets often have a low update frequency and cannot timely reflect industry development trends and technological progress, making it difficult for the model to adapt to a rapidly changing working environment.

[0004] Another significant problem is the limited amount of data. Although some industries have accumulated a considerable amount of data resources, the quantity of data truly suitable for training high-quality instruction data is still insufficient. This is because effective instruction data not only requires correct grammar but also a rigorous logical structure and a high degree of fit with specific business scenarios. To ensure that the model can provide reliable guidance under various complex conditions, a large number of carefully planned and verified instruction samples are required as support. However, the current data collection and construction methods are inefficient and costly, making it difficult to meet this demand. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for constructing industry instruction data based on large models to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A method for constructing industry instruction data based on large models, comprising the following steps:

[0007] Industry Requirement Research: Collect the business processes, operation specifications, terms, and special requirements of the target industry through literature research, industry reports, expert interviews, etc.; classify the collected requirements according to business modules, operation types, scenario complexities, etc. to form a structured requirement document;

[0008] Task Requirement Definition: Determine the required instruction types according to different business modules, such as operation instructions, decision-making instructions, and emergency instructions; define specific application scenarios for each instruction type, including daily operations and emergency event handling; set the expected effects of instruction data generation, such as instruction accuracy, adaptability, and execution efficiency indicators;

[0009] Goal Setting: Set quality goals for instruction data in terms of syntactic correctness, logical rigor, and content relevance; determine the quantity requirements of instruction data, such as the number of samples generated for each instruction type; set the time nodes for instruction data generation and review.

[0010] Preferably, it includes the following steps for establishing evaluation criteria:

[0011] Syntactic Specification: Define the syntactic rules of instructions according to industry characteristics, including sentence structure and keyword usage; use a syntactic analysis tool to automatically check the generated instructions and eliminate instructions with syntactic errors;

[0012] Logical Consistency: Define the logical relationship rules of instruction content to ensure the consistency and reasonableness of the content before and after the instruction; develop or use existing logical review tools to automatically evaluate the logical relationship of the instructions;

[0013] Industry Applicability: Establish matching criteria for instruction content with industry operation specifications and business logics; ensure that the terms used in the instructions are consistent with industry standard terms to avoid term confusion;

[0014] Innovation and Diversity: Set diversity indicators for instruction content, such as the coverage rate of different scenarios and different task types; evaluate the novelty of instruction content through expert review or automated tools to encourage the generation of innovative instructions.

[0015] Preferably, it includes the following steps for designing a prompt template:

[0016] Template Structure Design: Design a template structure that includes task descriptions, scenario settings, and operation steps; divide the template into multiple modules for easy combination and adjustment according to different requirements;

[0017] Template Parameterization: Parameterize the key elements of instruction generation, such as operation objects, condition restrictions, and expected results, to enhance the flexibility of the template; define variable placeholders to allow dynamic replacement of specific content during generation;

[0018] The example instructions provide: embedding typical industry instruction examples in the template to guide the model to understand the direction and content of instruction generation; providing multiple example instructions to cover different scenarios and task types, and enhancing the diversity of generated instructions.

[0019] Preferably, it includes the following instruction data generation steps:

[0020] Model selection and initialization: Select a suitable pre-trained large language model and evaluate the model according to requirements; load the pre-trained model and perform necessary initialization configurations, such as loading the vocabulary of a specific industry.

[0021] Parameter adjustment: Adjust the generation temperature, maximum generation length, and repetition penalty parameter to control the quality and diversity of instruction generation; set the scale of batch generation to improve generation efficiency while ensuring the diversity of generated content.

[0022] Instruction generation: Input the designed prompt template into the model to guide the model to generate instruction content that meets the requirements; adopt a combination of step-by-step generation and batch generation strategies to ensure the quality and efficiency of generated instructions.

[0023] Preferably, it includes the following instruction dataset construction and application steps:

[0024] Dataset integration: Classify and label the filtered instruction data according to industry and task categories to form a structured dataset; eliminate duplicate or highly similar instructions to ensure the diversity and uniqueness of the dataset.

[0025] Data format standardization: Convert the instruction data into a unified format standard, such as JSON or CSV, for subsequent processing and application; add metadata to each instruction, such as the industry it belongs to, task type, generation time, etc., to enhance data manageability.

[0026] Model training and fine-tuning: Divide the dataset into training set, validation set, and test set to ensure the effectiveness of model training and the accuracy of evaluation; adopt transfer learning or incremental learning strategies to train or fine-tune the language model of the target industry using high-quality instruction data; evaluate the performance of the model on specific tasks through the validation set and adjust the training parameters to optimize model performance.

[0027] Application deployment: Integrate the trained industry large model into actual application systems, such as government affairs Q&A systems and emergency response platforms; establish a real-time update mechanism for instruction data to ensure that the model can adapt to industry dynamic changes and technological progress; collect user feedback in actual applications and continuously optimize instruction data and model performance.

[0028] A system for a method of constructing industry instruction data based on a large model includes a requirement analysis and target setting module, specifically including:

[0029] Industry Requirements Research Unit: Through pre-set data collection interfaces, call data collection methods such as literature research, industry report acquisition, and expert interviews to collect the business processes, operation specifications, terms, and special requirements of the target industry, and classify the collected requirements according to business modules, operation types, and scenario complexities to generate a structured requirements document;

[0030] Task Requirements Definition Unit: Determine the required instruction types according to different business modules, such as operation instructions, decision-making instructions, and emergency instructions; Define specific application scenarios for each instruction type, including daily operations and emergency event handling; Set the expected effects of instruction data generation, such as instruction accuracy, adaptability, and execution efficiency indicators;

[0031] Goal Setting Unit: Formulate specific quality standards for instruction data in terms of syntactic correctness, logical rigor, and content relevance; Determine the quantity requirements of instruction data, such as the number of samples generated for each instruction type; Set the time nodes for instruction data generation and review to ensure the project progresses on schedule.

[0032] Preferably, it includes an Evaluation Criteria Establishment Module, which contains the following sub-modules:

[0033] Syntax Specification Sub-module: Define the syntax rules of instructions according to industry characteristics, including sentence structure and keyword usage; Call a syntax analysis tool to automatically check the generated instructions and eliminate instructions with syntax errors;

[0034] Logical Consistency Sub-module: Define the logical relationship rules of instruction content to ensure the consistency and reasonableness of the content before and after the instruction; Develop or use existing logical review tools to automatically evaluate the logical relationship of the instructions;

[0035] Industry Applicability Sub-module: Formulate the matching criteria for instruction content with industry operation specifications and business logics; Ensure that the terms used in the instructions are consistent with industry standard terms to avoid term confusion;

[0036] Innovation and Diversity Sub-module: Set the diversity indicators of instruction content, such as the coverage rate of different scenarios and different task types; Evaluate the novelty of instruction content through expert review or automated tools to encourage the generation of innovative instructions.

[0037] Preferably, it includes a Prompt Template Design Module, which has the following functions:

[0038] Template Structure Design Unit: Design a template structure that includes parts such as task description, situation setting, and operation steps; Divide the template into multiple modules for easy combination and adjustment according to different requirements;

[0039] Template parameterization unit: Parameterize key elements of instruction generation, such as operation objects, conditional restrictions, and expected results, to enhance the flexibility of the template; Define variable placeholders to allow dynamic replacement of specific content during generation;

[0040] Example instruction providing unit: Embed typical industry instruction examples in the template to guide the model to understand the direction and content of instruction generation; Provide multiple example instructions covering different scenarios and task types to enhance the diversity of generated instructions.

[0041] Preferably, it includes an instruction data generation and optimization module, which contains the following functional units:

[0042] Instruction data generation unit: Model selection and initialization subunit: Select a suitable pre-trained large language model and evaluate the model according to requirements; Load the pre-trained model and perform necessary initialization configurations, such as loading a vocabulary for a specific industry;

[0043] Parameter adjustment subunit: Adjust the generation temperature, maximum generation length, and repetition penalty parameters to control the quality and diversity of instruction generation; Set the scale of batch generation to improve generation efficiency while ensuring the diversity of generated content;

[0044] Instruction generation subunit: Input the designed prompt template into the model to guide the model to generate instruction content that meets requirements; Adopt a combination of step-by-step generation and batch generation strategies to ensure the quality and efficiency of generated instructions;

[0045] Instruction data screening and optimization unit: Automatic screening subunit: Use an automatic grammar checking tool to eliminate instructions with grammar errors; Use a logical review tool to filter out instructions with inconsistent logic; Screen instructions that meet industry requirements through key term matching and applicability criteria;

[0046] Manual verification subunit: Organize industry experts to review the instructions after automatic screening to ensure the professionalism and practicality of the instructions; Record expert feedback to identify common problems and improvement directions;

[0047] Feedback optimization subunit: Adjust the prompt template according to the review feedback to optimize the direction and content of instruction generation; Adjust the model generation parameters, such as temperature and generation length, to improve the quality of generated instructions; Repeat the instruction generation and screening process to gradually improve the overall quality of instruction data.

[0048] Preferably, it includes an instruction dataset construction and application module, which has the following functions:

[0049] Dataset integration unit: Classify and label the screened instruction data according to industry and task categories to form a structured dataset; Eliminate duplicate or highly similar instructions to ensure the diversity and uniqueness of the dataset;

[0050] Data Format Standardization Unit: Convert instruction data into a unified format standard, such as JSON, CSV, etc., for subsequent processing and applications; Add metadata to each instruction, such as the industry it belongs to, task type, generation time, to enhance data manageability;

[0051] Model Training and Fine-tuning Unit: Divide the dataset into a training set, a validation set, and a test set to ensure the effectiveness of model training and the accuracy of evaluation; Adopt transfer learning or incremental learning strategies to train or fine-tune the language model of the target industry using high-quality instruction data; Evaluate the performance of the model on specific tasks through the validation set and adjust the training parameters to optimize the model performance;

[0052] Application Deployment Unit: Integrate the trained industry large model into actual application systems, such as government affairs Q&A systems, emergency response platforms; Establish a real-time update mechanism for instruction data to ensure that the model can adapt to industry dynamic changes and technological progress; Collect user feedback in actual applications and continuously optimize instruction data and model performance.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] The method and system for constructing industry instruction data based on a large model proposed by the present invention, through in-depth industry demand research and strict evaluation criteria, the instruction data generated by the present invention is superior to the existing general instruction datasets in terms of grammar correctness, logical rigor, and industry applicability of content. For example, in the government affairs field, the instructions generated by the present invention can accurately reflect the policy execution process, avoid instruction execution errors caused by insufficient data professionalism in the prior art, and improve the application effect of the model in the professional field. Brief Description of the Drawings

[0055] Figure 1 It is the flowchart of the method of the present invention;

[0056] Figure 2 It is the flowchart of instruction data generation and screening of the present invention;

[0057] Figure 3 It is the flowchart of instruction dataset construction and model training application of the present invention. Detailed Embodiments

[0058] In order to clearly and completely describe the objectives, technical solutions of the present invention, and make the advantages more clearly understood, the following further details the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0059] Example 1, please refer to Figures 1 to 3 , the present invention provides a technical solution: a method for constructing industry instruction data based on a large model, including the following steps:

[0060] I: Task Requirement Analysis and Goal Setting

[0061] 1. Industry Requirement Research

[0062] Data collection: Collect the business processes, operation specifications, terms, and special requirements of the target industry through literature research, industry reports, expert interviews, etc.

[0063] Requirement classification: Classify the collected requirements according to business modules, operation types, scenario complexities, etc. to form a structured requirement document.

[0064] 2. Task Requirement Definition

[0065] Instruction type determination: Determine the required instruction types according to different business modules, such as operation instructions, decision-making instructions, emergency instructions, etc.

[0066] Application scenario description: Define specific application scenarios for each instruction type, including daily operations, emergency event handling, etc.

[0067] Expected effect setting: Set the expected effects of instruction data generation, such as indicators of instruction accuracy, adaptability, execution efficiency, etc.

[0068] 3. Goal Setting

[0069] Quality goal: Formulate specific standards for instruction data in terms of syntactic correctness, logical rigor, content compliance, etc.

[0070] Quantitative goal: Determine the quantity requirements of instruction data, such as the number of samples generated for each instruction type.

[0071] Time goal: Set the time nodes for instruction data generation and review to ensure the project progresses on schedule.

[0072] II: Establishment of Evaluation Criteria

[0073] 1. Syntax Specification

[0074] Syntax rule definition: Define the syntax rules of the instructions according to the industry characteristics, including sentence structure, keyword usage, etc.

[0075] Automatic syntax check: Use a syntax analysis tool to automatically check the generated instructions and eliminate instructions with syntax errors.

[0076] 2. Logical consistency

[0077] Logical rule formulation: Define the logical relationship rules of the instruction content to ensure the consistency and reasonableness of the content before and after the instruction.

[0078] Logical review tool: Develop or use existing logical review tools to automatically evaluate the logical relationship of the instructions.

[0079] 3. Industry applicability

[0080] Applicability standard: Establish the matching standards between the instruction content and industry operation specifications and business logic.

[0081] Key term matching: Ensure that the terms used in the instructions are consistent with industry standard terms to avoid term confusion.

[0082] 4. Innovation and diversity

[0083] Diversity index: Set the diversity index of the instruction content, such as the coverage rate of different scenarios and different task types.

[0084] Innovation evaluation: Evaluate the novelty of the instruction content through expert review or automated tools to encourage the generation of innovative instructions.

[0085] III: Prompt template design

[0086] 1. Template structure design

[0087] Template composition: Design a template structure that includes parts such as task description, scenario setting, and operation steps.

[0088] Modular design: Divide the template into multiple modules to facilitate combination and adjustment according to different needs.

[0089] 2. Template parameterization

[0090] Parameterization of key elements: Parameterize the key elements of instruction generation, such as the operation object, condition restrictions, expected results, etc., to enhance the flexibility of the template.

[0091] Variable setting: Define variable placeholders to allow dynamic replacement of specific content during generation.

[0092] 3. Provision of example instructions

[0093] Typical Instruction Examples: Embed typical industry instruction examples in the template to guide the model to understand the direction and content of instruction generation.

[0094] Diverse Examples: Provide multiple example instructions covering different scenarios and task types to enhance the diversity of generated instructions.

[0095] IV. Instruction Data Generation

[0096] 1. Model Selection and Initialization

[0097] Model Selection: Select a suitable pre-trained large language model (such as GPT-4, Qwen), and evaluate the model according to requirements.

[0098] Model Initialization: Load the pre-trained model and perform necessary initialization configurations, such as loading the vocabulary of a specific industry.

[0099] 2. Parameter Adjustment

[0100] Generation Parameter Settings: Adjust parameters such as generation temperature, maximum generation length, and repetition penalty to control the quality and diversity of instruction generation.

[0101] Batch Generation Configuration: Set the scale of batch generation to improve generation efficiency while ensuring the diversity of generated content.

[0102] 3. Instruction Generation

[0103] Template Input: Input the designed prompt template into the model to guide the model to generate instruction content that meets the requirements.

[0104] Generation Strategy: Adopt a combination of step-by-step generation and batch generation to ensure the quality and efficiency of generated instructions.

[0105] V. Instruction Data Screening and Optimization

[0106] 1. Automatic Screening

[0107] Syntax Check: Use an automatic syntax check tool to eliminate instructions with syntax errors.

[0108] Logical Evaluation: Use a logical review tool to filter instructions with inconsistent logic.

[0109] Applicability Matching: Screen instructions that meet industry requirements through keyword matching and applicability criteria.

[0110] 2. Manual Verification

[0111] Expert Review: Organize industry experts to review the instructions after automatic screening to ensure the professionalism and practicality of the instructions.

[0112] Feedback Collection: Record expert feedback to identify common problems and improvement directions.

[0113] 3. Feedback optimization

[0114] Template adjustment: Adjust the prompt template based on review feedback and optimize the direction and content of instruction generation.

[0115] Parameter optimization: Adjust model generation parameters, such as temperature and generation length, to improve the quality of generation instructions.

[0116] Iterative generation: Repeat the instruction generation and screening process to gradually improve the overall quality of instruction data.

[0117] 6: Construction and application of instruction data set

[0118] 1. Dataset Integration

[0119] Data classification: Classify and label the filtered instruction data according to industry and task categories to form a structured data set.

[0120] Data deduplication: Remove duplicate or highly similar instructions to ensure the diversity and uniqueness of the data set.

[0121] 2. Data format standardization

[0122] Unified format: Convert command data into a unified format standard, such as JSON, CSV, etc., to facilitate subsequent processing and application.

[0123] Metadata addition: Add metadata to each instruction, such as industry, task type, generation time, etc., to enhance data manageability.

[0124] 3. Model training and fine-tuning

[0125] Data partitioning: Divide the dataset into training set, validation set, and test set to ensure the effectiveness of model training and the accuracy of evaluation.

[0126] Training strategy: Use transfer learning or incremental learning strategies to train or fine-tune the language model for the target industry using high-quality instruction data.

[0127] Performance evaluation: Evaluate the model's performance on a specific task using a validation set and adjust training parameters to optimize model performance.

[0128] 4. Application Deployment

[0129] System integration: Integrate trained industry models into actual application systems, such as government question-and-answer systems and emergency response platforms.

[0130] Real-time update: Establish a real-time update mechanism for instruction data to ensure that the model can adapt to dynamic changes in the industry and technological advances.

[0131] User feedback mechanism: Collect user feedback in actual applications and continuously optimize instruction data and model performance.

[0132] VII: Additional implementation methods:

[0133] 1. Data augmentation techniques

[0134] Synonym replacement: During the instruction generation process, adopt synonym replacement technology to increase the diversity of instructions.

[0135] Context expansion: Generate diverse instructions applicable to different scenarios by expanding the application context of instructions.

[0136] Dynamic template adjustment

[0137] Real-time feedback mechanism: Dynamically adjust the prompt template according to user feedback and application effects, and optimize the instruction generation strategy.

[0138] 2. Security and privacy protection

[0139] Data desensitization processing: During the generation and processing of instruction data, desensitize sensitive information to ensure data security.

[0140] Access control: Establish a strict access control mechanism to ensure the secure storage and use of the instruction dataset.

[0141] 3. Cross-industry instruction sharing

[0142] Instruction library sharing mechanism: Establish a cross-industry instruction library sharing mechanism to promote knowledge exchange and instruction data sharing between different fields.

[0143] Standardized interface: Design a standardized data interface to facilitate the exchange and integration of instruction data between different systems and platforms.

[0144] Example 2. On the basis of Example 1, a system for constructing industry instruction data based on a large model is proposed, including a requirements analysis and goal setting module, which specifically includes: Industry requirements research unit: Through a preset data collection interface, call data collection methods such as literature research, industry report acquisition, and expert interviews to collect the business processes, operation specifications, terms, and special requirements of the target industry, and classify the collected requirements according to business modules, operation types, and scenario complexities to generate a structured requirements document; Task requirements definition unit: Determine the required instruction types according to different business modules, such as operation instructions, decision-making instructions, and emergency instructions; Define specific application scenarios for each instruction type, including daily operations and emergency event handling; Set the expected effects of instruction data generation, such as instruction accuracy, adaptability, and execution efficiency indicators; Goal setting unit: Develop specific quality standards for instruction data in terms of syntactic correctness, logical rigor, and content relevance; Determine the quantity requirements of instruction data, such as the number of samples generated for each instruction type; Set the time nodes for instruction data generation and review to ensure the timely progress of the project.

[0145] It includes an evaluation criterion setting module, which contains the following sub-modules: Syntax specification sub-module: Define the syntax rules of instructions according to industry characteristics, including sentence structure and keyword usage; Call a syntax analysis tool to automatically check the generated instructions and eliminate instructions with syntax errors; Logical consistency sub-module: Define the logical relationship rules of instruction content to ensure the consistency and reasonableness of the content before and after the instructions; Develop or use existing logical review tools to automatically evaluate the logical relationships of instructions; Industry applicability sub-module: Develop matching criteria for instruction content with industry operation specifications and business logics; Ensure that the terms used in the instructions are consistent with industry standard terms to avoid term confusion; Innovation and diversity sub-module: Set diversity indicators for instruction content, such as coverage of different scenarios and different task types; Evaluate the novelty of instruction content through expert review or automated tools to encourage the generation of innovative instructions.

[0146] It includes a prompt template design module, which has the following functions: Template structure design unit: Design a template structure including parts such as task description, situation setting, and operation steps; Divide the template into multiple modules for easy combination and adjustment according to different needs; Template parameterization unit: Parameterize the key elements of instruction generation, such as operation objects, condition restrictions, and expected results, to enhance the flexibility of the template; Define variable placeholders to allow dynamic replacement of specific content during generation; Example instruction providing unit: Embed typical industry instruction examples in the template to guide the model to understand the direction and content of instruction generation; Provide multiple example instructions covering different scenarios and task types to enhance the diversity of generated instructions.

[0147] It includes an instruction data generation and optimization module, which contains the following functional units: Instruction data generation unit: Model selection and initialization subunit: Select a suitable pre-trained large language model, evaluate the model according to requirements; Load the pre-trained model and perform necessary initialization configurations, such as loading the vocabulary of a specific industry; Parameter adjustment subunit: Adjust the generation temperature, maximum generation length, and repetition penalty parameter to control the quality and diversity of instruction generation; Set the scale of batch generation to improve generation efficiency while ensuring the diversity of generated content; Instruction generation subunit: Input the designed prompt template into the model to guide the model to generate instruction content that meets the requirements; Adopt a combination of step-by-step generation and batch generation strategies to ensure the quality and efficiency of generated instructions; Instruction data screening and optimization unit: Automatic screening subunit: Use an automatic grammar checking tool to eliminate instructions with grammar errors; Use a logical review tool to filter instructions with inconsistent logic; Screen instructions that meet industry requirements through key term matching and applicability criteria; Manual verification subunit: Organize industry experts to review the instructions after automatic screening to ensure the professionalism and practicality of the instructions; Record expert feedback, identify common problems and improvement directions; Feedback optimization subunit: Adjust the prompt template according to the review feedback to optimize the direction and content of instruction generation; Adjust model generation parameters, such as temperature and generation length, to improve the quality of generated instructions; Repeat the instruction generation and screening process to gradually improve the overall quality of instruction data.

[0148] It includes an instruction dataset construction and application module, which has the following functions: Dataset integration unit: Classify and label the screened instruction data according to industry and task categories to form a structured dataset; Eliminate duplicate or highly similar instructions to ensure the diversity and uniqueness of the dataset; Data format standardization unit: Convert the instruction data into a unified format standard, such as JSON, CSV, etc., for subsequent processing and application; Add metadata to each instruction, such as the industry it belongs to, task type, and generation time, to enhance data manageability; Model training and fine-tuning unit: Divide the dataset into a training set, a validation set, and a test set to ensure the effectiveness of model training and the accuracy of evaluation; Adopt transfer learning or incremental learning strategies to train or fine-tune the language model of the target industry using high-quality instruction data; Evaluate the performance of the model on specific tasks through the validation set and adjust the training parameters to optimize model performance; Application deployment unit: Integrate the trained industry large model into actual application systems, such as government affairs Q&A systems and emergency response platforms; Establish a real-time update mechanism for instruction data to ensure that the model can adapt to industry dynamic changes and technological progress; Collect user feedback in actual applications and continuously optimize instruction data and model performance.

[0149] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing industry instruction data based on a large model, characterized in that: It includes the following steps: Industry demand research: Collect the business processes, operation specifications, terms, and special requirements of the target industry through literature research, industry reports, and expert interviews; classify the collected requirements according to business modules, operation types, and scenario complexities to form a structured requirement document; Task requirement definition: Determine the required instruction types according to different business modules, such as operation instructions, decision-making instructions, and emergency instructions; define specific application scenarios for each instruction type, including daily operations and emergency event handling; set the expected effects of instruction data generation, such as instruction accuracy, adaptability, and execution efficiency indicators; Goal setting: Set quality goals for instruction data in terms of syntactic correctness, logical rigor, and content relevance; determine the quantity requirements of instruction data, such as the number of samples generated for each instruction type; set the time nodes for instruction data generation and review.

2. The method for constructing industry instruction data based on a large model according to claim 1, wherein: It includes the following steps for establishing evaluation criteria: Syntactic specification: Define the syntactic rules of instructions according to industry characteristics, including sentence structure and keyword usage; use a syntactic analysis tool to automatically check the generated instructions and eliminate instructions with syntactic errors; Logical consistency: Define the logical relationship rules of instruction content to ensure the consistency and reasonableness of the content before and after the instruction; Develop or use existing logical review tools to automatically evaluate the logical relationship of instructions; Industry applicability: Establish matching criteria for instruction content with industry operation specifications and business logic; ensure that the terms used in the instructions are consistent with industry standard terms to avoid term confusion; Innovativeness and diversity: Set diversity indicators for instruction content, such as the coverage of different scenarios and task types; evaluate the novelty of instruction content through expert review or automated tools to encourage the generation of innovative instructions.

3. A method for constructing industry instruction data based on a large model according to claim 2, characterized in that: It includes the following steps for designing prompt templates: Template structure design: Design a template structure that includes task descriptions, scenario settings, and operation steps; divide the template into multiple modules for easy combination and adjustment according to different requirements; Template parameterization: Parameterize the key elements of instruction generation, such as operation objects, condition restrictions, and expected results, to enhance the flexibility of the template; Define variable placeholders to allow dynamic replacement of specific content during generation; Provide example instructions: Embed typical industry instruction examples in the template to guide the model to understand the direction and content of instruction generation; provide multiple example instructions covering different scenarios and task types to enhance the diversity of generated instructions.

4. A method for constructing industry instruction data based on a large model according to claim 3, characterized in that: It includes the following steps for generating instruction data: Model selection and initialization: Select a suitable pre-trained large language model and evaluate the model according to requirements; load the pre-trained model and perform necessary initialization configurations, such as loading a specific industry vocabulary; Parameter adjustment: Adjust the generation temperature, maximum generation length, and repetition penalty parameters to control the quality and diversity of instruction generation; set the scale of batch generation to improve generation efficiency while ensuring the diversity of generated content; Instruction generation: Input the designed prompt template into the model to guide the model to generate instruction content that meets the requirements; adopt a combination of step-by-step generation and batch generation strategies to ensure the quality and efficiency of generated instructions.

5. A method for constructing industry instruction data based on a large model according to claim 4, characterized in that: It includes the following steps for constructing and applying an instruction dataset: Dataset Integration: Classify and label the filtered instruction data according to industry and task categories to form a structured dataset; Remove duplicate or highly similar instructions to ensure the diversity and uniqueness of the dataset; Data Format Standardization: Convert the instruction data into a unified format standard, such as JSON or CSV, for subsequent processing and application; Add metadata to each instruction, such as the industry it belongs to, task type, and generation time, to enhance data manageability; Model Training and Fine-tuning: Divide the dataset into training set, validation set, and test set to ensure the effectiveness of model training and the accuracy of evaluation; Adopt transfer learning or incremental learning strategies to train or fine-tune the language model for the target industry using high-quality instruction data; Evaluate the performance of the model on specific tasks through the validation set and adjust the training parameters to optimize the model performance; Application Deployment: Integrate the trained industry large model into actual application systems, such as government affairs Q&A systems and emergency response platforms; Establish a real-time update mechanism for instruction data to ensure that the model can adapt to industry dynamic changes and technological progress; Collect user feedback in actual applications and continuously optimize the instruction data and model performance.

6. A system for the method of constructing industry instruction data based on a large model according to claim 5, characterized in that: Include a requirements analysis and goal setting module, which specifically includes: Industry Requirements Research Unit: Through preset data collection interfaces, call data collection methods such as literature research, industry report acquisition, and expert interviews to collect the business processes, operation specifications, terms, and special requirements of the target industry, and classify the collected requirements according to business modules, operation types, and scenario complexities to generate a structured requirements document; Task Requirements Definition Unit: Determine the required instruction types according to different business modules, such as operation instructions, decision-making instructions, and emergency instructions; Define specific application scenarios for each instruction type, including daily operations and emergency event handling; Set the expected effects of instruction data generation, such as accuracy, adaptability, and execution efficiency indicators of instructions; Goal Setting Unit: Formulate specific quality standards for instruction data in terms of grammar correctness, logical rigor, and content relevance; Determine the quantity requirements of instruction data, such as the number of samples generated for each instruction type; Set the time nodes for instruction data generation and review to ensure the project progresses on schedule.

7. A system according to claim 6, wherein: Include an evaluation criterion setting module, which contains the following sub-modules: Grammar Specification Sub-module: Define the grammar rules of instructions according to industry characteristics, including sentence structure and keyword usage; Call grammar analysis tools to automatically check the generated instructions and remove instructions with grammar errors; Logical Consistency Sub-module: Define the logical relationship rules of instruction content to ensure the consistency and reasonableness of the content before and after the instruction; Develop or use existing logical review tools to automatically evaluate the logical relationship of instructions; Industry Applicability Sub-module: Formulate the matching standards between instruction content and industry operation specifications and business logic; Ensure that the terms used in the instructions are consistent with industry standard terms to avoid term confusion; Innovation and Diversity Sub-module: Set diversity indicators for instruction content, such as the coverage of different scenarios and different task types; Evaluate the novelty of instruction content through expert review or automated tools to encourage the generation of innovative instructions.

8. A system according to claim 7, characterized in that: It includes a prompt template design module, which has the following functions: Template structure design unit: Design a template structure that includes task descriptions, scenario settings, and operation steps; Divide the template into multiple modules to facilitate combination and adjustment according to different requirements; Template parameterization unit: Parameterize the key elements generated by the instructions, such as operation objects, condition restrictions, and expected results, to enhance the flexibility of the template; Define variable placeholders to allow dynamic replacement of specific content during the generation process; Example instruction providing unit: Embed industry-typical instruction examples in the template to guide the model to understand the direction and content of instruction generation; Provide multiple example instructions covering different scenarios and task types to enhance the diversity of generated instructions.

9. A system according to claim 8, wherein: It includes an instruction data generation and optimization module, which contains the following functional units: Instruction data generation unit: Model selection and initialization subunit: Select a suitable pre-trained large-scale language model and evaluate the model according to requirements; Load the pre-trained model and perform necessary initialization configurations, such as loading a vocabulary for a specific industry; Parameter adjustment subunit: Adjust the generation temperature, maximum generation length, and repetition penalty parameters to control the quality and diversity of instruction generation; Set the scale of batch generation to improve generation efficiency while ensuring the diversity of generated content; Instruction generation subunit: Input the designed prompt template into the model to guide the model to generate instruction content that meets the requirements; Adopt a strategy that combines step-by-step generation and batch generation to ensure the quality and efficiency of generated instructions; Instruction data screening and optimization unit: Automatic screening subunit: Use an automatic grammar checking tool to eliminate instructions with grammar errors; Use a logical review tool to filter instructions with inconsistent logic; Screen instructions that meet industry requirements through key term matching and applicability criteria; Manual verification subunit: Organize industry experts to review the instructions after automatic screening to ensure the professionalism and practicality of the instructions; Record expert feedback to identify common problems and improvement directions; Feedback optimization subunit: Adjust the prompt template according to the review feedback to optimize the direction and content of instruction generation; Adjust model generation parameters, such as temperature and generation length, to improve the quality of generated instructions; Repeat the instruction generation and screening process to gradually improve the overall quality of instruction data.

10. A system according to claim 9, characterized in that: It includes an instruction dataset construction and application module, which has the following functions: Dataset integration unit: Classify and label the screened instruction data according to industry and task categories to form a structured dataset; Eliminate duplicate or highly similar instructions to ensure the diversity and uniqueness of the dataset; Data format standardization unit: Convert the instruction data into a unified format standard, such as JSON or CSV, for subsequent processing and application; Add metadata to each instruction, such as the industry it belongs to, task type, and generation time, to enhance data manageability; Model training and fine-tuning unit: Divide the dataset into a training set, a validation set, and a test set to ensure the effectiveness of model training and the accuracy of evaluation; Adopt transfer learning or incremental learning strategies to train or fine-tune the language model of the target industry using high-quality instruction data. Evaluate the performance of the model on specific tasks through the validation set, and adjust the training parameters to optimize the model performance; Application deployment unit: Integrate the trained industry large model into actual application systems, such as government affairs Q&A systems and emergency response platforms; Establish a real-time update mechanism for instruction data to ensure that the model can adapt to industry dynamic changes and technological progress; Collect user feedback in actual applications and continuously optimize the instruction data and model performance.

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