Human-machine cooperation dialogue strategy and dynamic modeling system based on AI large model

Abstract

The invention discloses a man-machine cooperation dialogue strategy and dynamic modeling system based on an AI large model, which supports a domain expert mode and a cross-domain adaptation function through prompt words, guide words, a role simulation mechanism, a dynamic feedback module and logic process design, effectively calls thinking resources of the AI large model, and realizes intelligent solution of complex problems. The innovation point of the method is that the improvement of the man-machine cooperation efficiency is promoted by utilizing a logic process of a management idea, a professional term semantic anchor point and an AI dynamic guide mechanism and combining efficient adaptation of a natural language and a software language. Due to the high flexibility and replicability, the user can quickly generate an accurate solution according to the requirements of a specific field, and the social brainwork productivity is remarkably improved. The method is widely applied to the fields of education, medical treatment, enterprise management and analysis and the like, efficient support is provided for intelligent decision making, and the social production mode is promoted to change to a more efficient and more economical direction.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence technology, and specifically relates to a human-machine collaborative dialogue strategy and dynamic modeling system based on an AI big model. Through the collaborative work of multiple modules, hierarchical analysis and dynamic optimization of complex problems are achieved, which is applicable to a wide range of technical and management scenarios. Background Art

[0002] The current AI large-model dialogue technology has limitations in terms of accuracy, logic, and adaptability. It is highly dependent on user input and lacks dynamic adjustment capabilities. Especially in professional field problems, existing technologies are difficult to provide efficient cross-domain solutions. For example, vague or inaccurate descriptions may cause AI-generated results to deviate from user expectations. Summary of the invention

[0003] The purpose of the present invention is to address the shortcomings of the existing technology and propose a human-computer collaborative dialogue strategy and dynamic modeling system based on an AI large model, which realizes full-process closed-loop optimization from user input to optimization solution output through dynamic adaptation of natural language and software language, prompt module, guide word module, dynamic feedback mechanism, simulation role mechanism and domain expert mode.

[0004] The objective of the present invention is to achieve the following technical solution: a human-machine collaborative dialogue strategy and dynamic modeling system based on an AI big model, the system comprising the following modules: Natural language and software language dynamic adaptation module: used to combine the natural language input by the user with the software language to generate a logical operation process template, and generate a reasoning path consistent with user needs through dynamic modeling; Prompt module: used to parse the subject, content description, target requirements and background information input by the user, generate relevant prompts respectively, and obtain a structured problem description; Guide word module: It is used to dynamically generate guide words based on the prompts generated by the prompt module, stimulate the AI ​​big model to respond through the prompts, and dynamically adjust the reasoning path through the guide words; Simulation role module: used to dynamically generate simulation roles in corresponding technical fields according to user needs, realize multiple rounds of interaction through role collaboration, and complete complex problem analysis and solution optimization; Dynamic feedback module: used to adjust the guide word logic and reasoning path in real time based on multiple rounds of interaction; Domain Expert Mode Module: used to generate specialized dynamic solutions in related technical fields based on keyword triggers in the technical field; Multi-version adaptation module: used to dynamically enable and adjust the prompt module, guide word module and feedback module functions, combined with the operation process template of the logic flow, to provide adaptation version solutions corresponding to the needs of various users.

[0005] Furthermore, the natural language and software language dynamic adaptation module generates a logical operation process template through semantic analysis and logical modeling technology, uses natural language processing technology to analyze user input, and based on management logic, intelligently adjusts resource allocation according to task priority, complexity and resource requirements, and optimizes reasoning paths and computing efficiency.

[0006] Furthermore, the prompt module extracts key entities, logical relations and contextual semantics through semantic parsing, providing high-quality input for the guide word module.

[0007] Furthermore, the guide word module combines the prompt + guide word steering technology to generate multi-level guide words in real time according to the natural language or task description input by the user, and optimize the reasoning path of the AI ​​large model in real time; the guide words are dynamically adjusted based on the internal reasoning logic of the model and external user needs to ensure that the generated output minimizes deviation from user expectations.

[0008] Furthermore, the simulation role module supports the generation of technical analysts, optimization consultants and market analysts, and shares the tasks through collaborative logic.

[0009] Furthermore, the dynamic feedback module can generate optimization suggestions based on user feedback, including user input deviation detection, output quality optimization and dynamic adjustment of reasoning paths, and support multiple rounds of iterations until the target requirements are met.

[0010] Furthermore, the domain expert mode module integrates keyword triggering and resource call logic as well as role collaboration mechanism to generate specialized dynamic solutions for specific fields and continuously optimize resource paths and logic.

[0011] Furthermore, the multi-version adaptation module supports dynamic adjustment of functional modules to meet the corresponding needs of ordinary users, technical users and enterprise users.

[0012] Furthermore, it supports open function expansion and further optimizes module performance through customized prompts and guide word logic.

[0013] Furthermore, the combination of the prompt module and the guide word module realizes the efficient calling of AI large model resources through the logic optimization algorithm. 1. Combination of prompts and guide words: For the first time, the dynamic interactive transformation from "stimulating response" to "guiding reasoning" is realized, and the semantic focus is improved through the term anchor function.

[0014] 2. Dynamic feedback and interactive optimization: Realize multiple rounds of solution optimization through real-time adjustments, and gradually approach user goals.

[0015] 3. Simulation role mechanism: Complete multi-dimensional analysis tasks through virtual role division of labor and collaboration, significantly improving the ability to solve complex problems.

[0016] 4. Domain expert mode: Through keyword triggering and resource calling, it realizes deep domain support and dynamic optimization, and supports cross-domain collaboration.

[0017] 5. Combination of logical process and semantic anchor points: ensure the logic and operability of complex problems and improve the efficiency of AI thinking.

[0018] 6. Combination of natural language and software language: forming a unified framework to meet the needs of diverse scenarios. The detailed description of the dynamic modeling method based on natural language + programming language + professional terminology demonstrates the technical advantages of the technology of the present invention in lowering user thresholds, improving the effectiveness of AI models and its wide application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a principle architecture diagram, showing the functional interaction between prompts, guide words, simulated roles and dynamic feedback modules.

[0021] Figure 2 It is an application logic diagram, showing the logical path from user input to AI output.

[0022] Figure 3 To use a flowchart, describe the complete process of user input, system analysis, dynamic feedback and final output.

[0023] Figure 4 This is a UAV drawing generated by the AI ​​big model in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific implementation examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] like Figure 1 As shown, the present invention provides a human-machine collaborative dialogue strategy and dynamic modeling system based on an AI big model, the system includes the following modules: Natural language and software language dynamic adaptation module: used to combine the natural language input by the user with the software language, generate a logical operation process template through semantic analysis and logical modeling technology, use natural language processing technology to analyze user input, and generate a reasoning path that is highly consistent with user needs through dynamic modeling. Based on management logic, it intelligently adjusts resource allocation according to task priority, complexity and resource requirements, optimizes reasoning paths and computing efficiency; this method minimizes the deviation between output and user expectations and ensures the accuracy and integrity of the results; the application of management logic ensures that the AI ​​large model always maintains efficient resource scheduling in complex task environments, and improves its adaptability and efficiency in multi-tasking; integrates natural language and software language characteristics, supports efficient adaptation under a unified framework, and improves the efficiency of solving complex problems. This module uses a combination of natural language and programming language to guide the AI ​​large model with the help of natural language and achieves precise control through programming language. This combination ensures: 1. Natural language simplifies user input methods, allowing users to interact with large AI models without having programming skills.

[0026] 2. The programming language ensures precise control and customization of the reasoning path, thereby improving the reasoning efficiency and accuracy of the model.

[0027] 3. Professional terminology is used for domain-specific modeling, which further improves the application effect and reasoning quality of the model in professional fields. Prompt module: It is used to parse the subject, content description, target requirements and background information input by the user, generate relevant prompts respectively, obtain structured problem description, and generate preliminary solution direction; and extract key entities, logical relationships and contextual semantics through semantic analysis to provide high-quality input for the guide word module.

[0028] Guide word module: used for prompts generated based on the prompt module. It combines the prompt + guide word technology to dynamically generate multi-level guide words in real time according to the natural language or task description input by the user. The prompts stimulate the reaction of the AI ​​large model, adjust the prompt logic and guide word weights in real time according to user feedback, and dynamically optimize and adjust the reasoning path through guide words to ensure the adaptability and flexibility of the reasoning process. The guide words are dynamically adjusted based on the internal reasoning logic of the model and external user needs to ensure that the generated output minimizes the deviation from user expectations. The adjustment process can cope with multiple rounds of dialogues, complex problems or professional field needs, and provide accurate answers and solutions. The guide word generation process is dynamic, that is, the system generates suitable guide words in real time according to the current input content, and dynamically adjusts the reasoning path according to user feedback or task requirements. This mechanism allows the AI ​​model to optimize its reasoning direction in real time according to the progress of the dialogue or task, improving the adaptability and efficiency of problem solving; the combination of the prompt module and the guide word module realizes the efficient call of AI large model resources through the logic optimization algorithm; through the dynamic adjustment of the guide words, based on the natural language optimization reasoning results, the AI ​​system can generate reasoning paths and outputs that are consistent with user expectations based on the input natural language description, reducing the deviation between the output and user needs. For example, the system analyzes the user's input and automatically generates the corresponding reasoning path in combination with domain knowledge, thereby obtaining an accurate solution that meets user needs. Through the application of natural language analysis capabilities, dynamically generated guide words and adjusted reasoning paths not only improve the convenience of user interaction, but also fully mobilize the computing and reasoning capabilities of the AI ​​large model. The traditional AI model reasoning path is fixed and it is difficult to flexibly respond to complex problems. The present invention can greatly improve the performance of the model in complex scenarios through dynamic modeling.

[0029] Simulation role module: It is used to dynamically generate a variety of simulation roles including technical analysts, optimization consultants and market analysts in the corresponding technical fields according to user needs, and share tasks through collaborative logic to support multi-dimensional analysis and optimization of cross-domain problems; realize multiple rounds of interaction through role collaboration to complete complex problem analysis and solution optimization; simulate the discussion method of a real team and significantly improve the ability to solve complex problems. For example, in the problem of enterprise digital transformation, technical experts provide technical advice, financial consultants evaluate investment returns, and market analysts analyze user needs.

[0030] Dynamic feedback module: It is used to generate optimization suggestions based on user feedback based on multiple rounds of interactions, including user input deviation detection (calculating the semantic similarity between user input and generated content through a semantic embedding model), output quality optimization, and real-time dynamic adjustment of guide word logic and reasoning paths. It also supports multiple rounds of iterations and updates the generation logic through real-time feedback to ensure that the content gradually approaches the user's goals until the target requirements are met.

[0031] Domain Expert Mode Module: It is used to integrate keyword triggering, resource calling logic and role collaboration mechanism, generate specialized dynamic solutions for specific technical fields, and continuously optimize resource paths and logic. For example, when you enter "tumor treatment", it automatically retrieves the relevant medical research and treatment plan database to generate accurate medical advice.

[0032] Modular multi-version adaptation architecture: used to dynamically enable and adjust the functions of the prompt module, guide word module and feedback module, combined with the operation process template of the logical flow, to provide adaptation version solutions corresponding to the needs of ordinary users, technical users and enterprise users. Through the step-by-step implementation mechanism, complex problems are broken down into logically clear multi-step subtasks, such as demand analysis, solution design and feedback optimization. Combined with dynamic feedback, the intermediate results generated by each task can be adjusted in real time, and the optimal solution is finally generated.

[0033] The present invention supports open function expansion and further optimizes module performance through custom prompts and guide word logic. Figure 2 and Figure 3 As shown, based on the above module functions, the present invention provides different versions of application examples for three application scenarios of ordinary users, technical users and enterprise users, as follows: 1. Ordinary user scenarios: Through the prompt module and the guide word module, based on the standardized guide word logic, a simplified operation path and an intelligent dynamic feedback mechanism are provided to provide ordinary users with a "one-click" solution, generate a standardized logic path, and simplify interaction requirements. Based on the natural language and software language dynamic adaptation module, combined with natural language interaction and Python logic processing, it aims to provide ordinary users with efficient and intelligent solution generation support. This version significantly improves dialogue efficiency and solution quality through the guide word module, dynamic feedback module, optimization and role configuration. The specific implementation steps are as follows: Step 1: Enter basic information (required for users) Today's topic (user required content) Brief description (Required by the user) Purpose requirements (user required content) Background information (required for users) The embodiments of the present invention are: Today's topic ("How to choose a subject for the first year of high school?") Simple description ("My child is in the first year of high school now. He has very good grades. His grades in all subjects are relatively balanced, but he is not sure about his current interests or career. Given this situation, I am very confused about his current choice of subjects in high school because I don't know his future career direction. I don't know what to do.") Purpose and requirements ("My child is in the first year of high school now. His academic performance is very good. His grades in all subjects are relatively balanced. He is not sure about his current interests and career. In view of this situation, I am very confused about his current high school subject selection because I don't know his future career direction. I don't know what to do.") Background information: ("unclear") Technical terminology association: Tip: User natural language input triggers Python logic parsing, generates structured data and stimulates the AI ​​big model reasoning path.

[0034] Python logic trigger point annotation: Input parsing → Extract core target and background.

[0035] Dynamically generate prompts → Improve the accuracy of solution generation.

[0036] Step 2: Set discussion parameters (selected by the user or obtained by the system based on analysis) Question Type Select from the following options and fill in your specific needs: Task-based (such as data collation, task scheduling).

[0037] Creative (e.g. generating ideas, designing proposals).

[0038] Analytical (e.g., problem assessment, strategy optimization).

[0039] Problem complexity User selection: Single area issues (e.g. planning a travel itinerary).

[0040] Multi-domain issues (e.g. travel budget and schedule integration).

[0041] Expected output type User selection: A brief summary (good for quickly understanding the results).

[0042] Detailed plans (suitable for in-depth implementation and reference).

[0043] Technical terminology association: Prompt + guide words: Python logic dynamically adjusts the prompt generation strategy based on the question type and complexity.

[0044] Python logic trigger point annotation: Optimize discussion parameters based on user selections.

[0045] Dynamically adjust prompt logic → Improve response accuracy.

[0046] Step 3: Role configuration (the system simplifies the division of labor and users can adjust it) Basic role (automatically generated by the system) Mission Planner: Generates preliminary mission plans or solutions.

[0047] Optimization Advisor: Provides improvement suggestions based on goals.

[0048] Extended roles (users can choose whether to enable or not) Data Analyst: Perform rapid analysis and visualization of data input.

[0049] User representative: simulate end-user needs and put forward usage suggestions.

[0050] Technical terminology association: Guiding words: User feedback triggers Python logic to adjust the division of role tasks and module calling paths.

[0051] Python logic trigger point annotation: Dynamic role configuration → Assign responsibilities based on requirements.

[0052] Improve discussion efficiency → Adjust module task logic.

[0053] Step 4: Discussion process (the system guides users to complete multiple rounds of dialogue) First round of discussion: generating preliminary proposals Based on user input, the system generates a preliminary plan, including key steps, task lists, or brief descriptions.

[0054] Second round of discussion: Feedback and optimization Users can make adjustment suggestions based on the generated content, and the system will dynamically optimize the solution.

[0055] Final round confirmation When the user completes all feedback and has no tasks to complete, the system will automatically pop up a final round confirmation prompt: The discussion process has been completed. Please select the next step: [Export final report now] [Continue to optimize the current plan] [Integrate the optimized content into the final solution and output it] [End of discussion process] If the user does not take any action, the system will automatically export the analysis report after 10 seconds and prompt: "The system has automatically exported the final plan for you, please check it." to ensure a closed-loop process.

[0056] Process progress guide: Current step: Step 4 / 5 - Discuss process.

[0057] Next step: Complete the final round of confirmation and generate the final plan. The system outputs the optimized complete plan, and the user confirms whether it needs to be adjusted again.

[0058] Technical terminology association: Feedback Loop: User feedback triggers prompt optimization and adjusts the system reasoning path.

[0059] Python logic trigger point annotation: Analysis of differences → Adjustment of solution generation strategy.

[0060] Dynamically optimize discussion content → Improve adaptability.

[0061] Step 5: Output and precipitation Final solution output System generated: Concise execution steps: suitable for direct operation.

[0062] In-depth analysis and recommendations: including logical paths and priority assignments.

[0063] Knowledge accumulation Automatically save users' discussion records and final solutions to form a standardized knowledge list for easy subsequent reuse.

[0064] Technical terminology association: Contextual Memory: Enhances the ability to remember conversation contexts through Python logic to ensure efficient access to long-term user data.

[0065] Python logic trigger point annotation: Save user interaction records → Dynamically update the knowledge base.

[0066] Provide long-term optimization suggestions → Strengthen personalized support.

[0067] The results of the embodiment of the present invention based on the above process are as follows. The following is the content output by the large model: Based on the content you provided, the following is the complete implementation process and preliminary suggestions: Step 1: Enter basic information Today's Topic How to choose the subject direction in the first year of high school? Brief description My child is in the first year of high school. His academic performance is excellent and balanced, but his interests and career direction are not yet clear, and he is currently confused about his choice of subjects.

[0068] Purpose Clarify the direction of subject selection in the first year of high school and provide the best support for future development.

[0069] Step 2: Set discussion parameters Question Type Analytical (problem assessment and strategy optimization) Problem complexity Multi-sector issues (comprehensive educational planning and career development) Expected output type Detailed plan (suitable for in-depth implementation and reference) Step 3: Role Configuration Basic Characters 1) Task planner: Generate subject selection strategies based on the characteristics of the education system and student conditions.

[0070] 2) Optimization advisor: Provides adjustment suggestions that match the goals.

[0071] Extended Roles 1) Data Analyst: Analyze career trends and subject connections.

[0072] 2) User representative: simulate students’ future needs and optimize subject selection strategies.

[0073] Step 4: Discuss the process First round of discussion: generating preliminary proposals Subject Assessment: 1) Choice of arts and science subjects: Give priority to subjects that are a balance of arts and sciences, such as mathematics, English, etc., to retain more possibilities for future choices.

[0074] 2) Core subject foundation: Focus on basic subjects such as mathematics, Chinese, and English, which play an important role in any field.

[0075] 3) Interest stimulation and expansion: Add a variety of elective courses, such as physics, biology, history, art, etc., to help discover interests.

[0076] Strategies for choosing a career when career direction is unclear: 1) Maintain balance: choose a balanced course combination to avoid premature orientation.

[0077] 2) Participate in career experience activities: help students gradually clarify their interests.

[0078] Second round of discussion: Feedback and optimization Future career trend analysis can be introduced to check the demand for disciplines in current popular fields (such as AI, medical care, engineering, etc.).

[0079] If it is found that students are more interested in certain subjects, the course selection plan can be adjusted in the second semester.

[0080] Step 5: Output and precipitation Final Solution 1) Discipline selection strategy Core subjects: Mathematics, English, Chinese Exploration subjects: Physics, Biology, History (adjusted according to specific interests) 2) Execution plan Step 1: Discuss initial interests with your child and prioritize subjects.

[0081] Step 2: Consult school teachers or education experts to ensure that your choice is in line with your overall plan for the college entrance examination and career development.

[0082] Step 3: Regularly review subject performance and changes in interests, and make timely adjustments.

[0083] 3) Long-term planning Use summer and winter vacations or weekends to participate in career experience programs to gradually clarify your future direction.

[0084] Summary and suggestions 1) Maintain balanced selection so that there are more possibilities in the future.

[0085] 2) Explore diverse interests and gradually discover your passion through elective courses and extracurricular activities.

[0086] 3) Dynamically adjust strategies to optimize choices based on changes in subject performance and interests.

[0087] The ultimate goal is to provide children with a more comprehensive development path through a reasonable combination of subjects and flexible adjustments, while avoiding the pressure caused by premature orientation.

[0088] If you need a more detailed personalized plan, please continue to provide relevant background information or feedback! Round 2 You said: Please output the final complete plan.

[0089] AI Big Model says: How to choose a subject for the first year of high school: the final plan 1. Project Overview For first-year high school students whose academic performance is balanced but whose interests and career directions are not yet clear, a set of scientific and reasonable subject selection and planning strategies is provided, with the goal of maintaining subject balance, stimulating potential interests, and retaining multiple possibilities for future development.

[0090] 2. Discipline Selection Strategy Core disciplines first Mathematics: As a basic subject, mathematical and logical thinking skills are essential regardless of your future career direction.

[0091] English: An international language tool and an important guarantee for further study and career development.

[0092] Language: Language and expression skills are basic skills in all fields.

[0093] Explore subject combinations Balance of arts and sciences: Choose subjects such as physics, biology, and history to broaden your knowledge.

[0094] Interest-based: If students have a specific interest in a subject, such as art or programming, they can choose it as a supplementary elective.

[0095] Retain flexibility Avoid premature orientation and give priority to a diverse combination of disciplines to provide room for future changes in direction.

[0096] III. Action Plan Short-term plan: subject selection and adaptation Communicate deeply with children: understand their interest in various subjects and their learning experience.

[0097] Consult teachers and experts: Listen to the school’s suggestions on students’ subject choices and make a comprehensive assessment based on their subject strengths.

[0098] First round of subject selection: Determine core and exploratory subjects based on the principles of balance and breadth.

[0099] Mid-term plan: interest discovery and optimization Interest cultivation: Discover children’s potential interests through extracurricular activities, reading and practice.

[0100] Regular review and adjustment: At the end of each semester, review subject performance and changes in interests with teachers and children, and adjust subject direction.

[0101] Long-term plan: career experience and positioning Career experience activities: Participate in career experience programs during summer and winter vacations or on weekends, such as company visits, practical activities, etc.

[0102] Future trend analysis: Understand the subject requirements of current hot fields (such as artificial intelligence, healthcare, engineering, etc.).

[0103] IV. Specific Implementation Plan

[0104] 5. Suggestions and precautions 1) Focus on balanced selection and gradually clarify the direction. Too early orientation may limit future choices. Balanced subjects can help provide children with more possibilities.

[0105] 2) Actively participate in extracurricular activities and stimulate interest through elective courses, clubs, career experience, etc. to help children discover areas of interest more quickly.

[0106] 3) Make dynamic adjustments and respond flexibly to changes. Communicate with your child regularly, pay attention to academic performance, changes in interests, and psychological state, and adjust direction in a timely manner when necessary.

[0107] VI. Conclusion The first year of high school is a critical period for subject selection and interest cultivation. A steady and flexible strategy should be adopted to maintain balanced development, gradually stimulate interest, explore directions, and gradually identify future directions through career experience activities. Through dynamic adjustments and multi-party support, we can help children lay a solid foundation for the future.

[0108] If you would like more in-depth personalized coaching or career planning advice, please feel free to provide further background information and we will optimize the solution! The technical features of the general user version are as follows: Core Features 1. Prompt + guide word technology The system uses Python logic to parse the user's natural language input, extract key information (such as goals, background, and restrictions), and generate standardized prompts to ensure efficient interaction with the AI ​​model.

[0109] 1) Functional highlights: Prompts are generated dynamically to adapt to the task type and problem complexity.

[0110] User feedback drives the guide word module to optimize the reasoning path in real time.

[0111] 2) Differences: The general user version provides fixed input templates, which are suitable for quick task startup, and is distinguished from the domain adaptation of the technical user version and the dynamic completion function of the enterprise user version.

[0112] 2. Fixed role configuration The system assigns roles by default, such as task planner and optimization advisor, to automatically complete the division of tasks.

[0113] 1) Functional highlights: The role configuration is simple and efficient, suitable for non-professional users to get started quickly.

[0114] 2) Differences: Unlike the technical user version that supports extended roles and the dynamic collaboration of the enterprise user version, the general user version emphasizes simplicity and fixed division of labor for basic roles.

[0115] 3. Linear discussion process Users complete the discussion process step by step, and each link has clear task instructions.

[0116] 1) Functional highlights: The system gradually generates solutions based on user input and supports multiple rounds of feedback adjustments.

[0117] 2) Differences: The discussion process of the general user version is designed to be linear, while the technical user version emphasizes difference point analysis, and the enterprise user version provides non-linear jump support.

[0118] 4. Final round confirmation and automatic export After the user completes the discussion, the system automatically enters the final round of confirmation and prompts the user to select the next step.

[0119] 1) Functional highlights: Supports users to choose to export the final report or continue optimization.

[0120] When the user does not perform any operation, the system automatically generates standardized output documents.

[0121] 2) Differences: The general user version provides a final round confirmation prompt in a fixed format, which is clearly distinguished from the field recommendation prompts of the technical user version and the dynamic recommendation function of the enterprise user version.

[0122] 5. Fixed output template and knowledge accumulation The output includes problem description, solution, resource constraints and follow-up recommendations, using a fixed structure to ensure consistency.

[0123] 1) Functional highlights: Automatically save discussion records to form a standardized knowledge list for easy reuse.

[0124] 2) Differences: The output structure of the general user version is standardized and concise, which complements the domain extension content of the technical user version and the visualization template of the enterprise user version.

[0125] Technical value of the general user version 1) Versatility and ease of use: Through fixed templates and linear process design, the general user version is suitable for general users to quickly get started.

[0126] 2) Efficiency and stability: Provide clear task paths and dynamic feedback support to ensure efficient completion of tasks.

[0127] 3) Long-term support: Through the knowledge sedimentation function, help users record discussion results and reuse solutions.

[0128] 2. Technical user scenarios: Open the custom function of the prompt module to support technical users to edit prompts and guide word logic based on domain requirements and dynamically call resources. This version is optimized based on the "Domain Expert Mode" technical module. It is based on the natural language and software language dynamic adaptation module, combined with natural language interaction and Python logic processing, to provide precise domain support and efficient dynamic feedback mechanism, and provide intelligent and efficient dialogue strategy support. It is especially suitable for technical users and industry users, and meets the multi-level needs of technical users and industry users. Generate professional solutions through keyword triggering of the domain expert mode module, authoritative resource calling, dynamic feedback mechanism of the dynamic feedback module, and role collaboration of the simulation role module. The specific steps are as follows: Step 1: Enter basic information (required for users) 1. Today’s topic: (Please briefly describe the problem that needs to be solved.) Example: 1) How to improve product recommendation algorithms? 2) How to optimize market growth strategy? 2. Brief description: (Please describe the problem background and requirements in detail.) Example: 1) The current algorithm has performance bottlenecks and cannot meet the needs of real-time recommendation.

[0129] 2) It is necessary to deeply analyze market growth points and formulate strategies.

[0130] 3. Purpose: (Please specify the goal and expected results.) Example: 1) Improve the performance of recommendation algorithms and increase user click-through rates.

[0131] 2) Improve the accuracy of market analysis and support decision making.

[0132] Background Information: (Please provide necessary background) (e.g. time frame, resource constraints, etc.). Example: 1) The project budget is limited and an efficient solution needs to be selected.

[0133] 2) It involves collaboration among multiple departments and requires balancing different objectives.

[0134] The embodiments of the present invention are: Today's topic ({Design a future home drone}); Brief description ({Design requirements: Future household drone products should be suitable for use by urban families or remote rural families. Fly within 100 kilometers. Solve the congestion problem in cities and the inconvenience of transportation in remote rural areas. The drone can hover on the roof or balcony of the home to facilitate getting on and off, etc.}); Purpose requirements ({It is required to be able to carry three to five people. The mobile phone can determine the route. The price is within the threshold x. The appearance design of the drone and related design parameters must be provided so that it can be submitted to the factory for production}); Technical terminology association: Tip: User natural language input triggers Python logic parsing, generates structured data and stimulates the AI ​​big model reasoning path.

[0135] Python logic trigger point annotation: User input parsing → Generate domain-specific prompts.

[0136] Adjust module calling path → Improve adaptability.

[0137] Step 2: Set discussion parameters (system guidance and partial automatic generation) Field keyword trigger: The user enters field keywords (such as "academic", "industry", "technical").

[0138] The system automatically switches to the corresponding professional mode through the domain expert mode, loading the guide word module and resource call logic.

[0139] Theme type (user selected or system generated boot parameter): Academic type (such as cutting-edge technology exploration, theoretical support analysis).

[0140] Industry type (such as market trend insights, industry report analysis).

[0141] Technical type (such as patent evaluation, technology application optimization).

[0142] Problem complexity (user-filled or system-assigned): Single field problems (such as data cleaning and optimization).

[0143] Cross-cutting issues (e.g. combining industry analysis with technology implementation).

[0144] Objectives and expected results: The user selects one of the following goals and specifies the specific content: Innovation goal: Generate original technical solutions.

[0145] Market suitability goal: Improve user conversion rate.

[0146] Authoritative goal: Generate highly credible solutions based on academic resources.

[0147] Technical terminology association: Domain expert mode: Dynamically call professional domain modules through Python logic to load key resource support.

[0148] Python logic trigger point annotation: Match the module based on domain keywords → call authoritative resources.

[0149] Dynamically optimize discussion parameters → Improve the accuracy of generated solutions.

[0150] Step 3: Role configuration (the system automatically assigns and allows users to adjust) Basic role (generated by system default): Technical idea generator: Proposes core ideas or initial concepts for themes.

[0151] Technical evaluator: Analyzes the feasibility of the solution and the technical implementation path.

[0152] Optimization feedback provider: Propose improvement suggestions from an application perspective.

[0153] Applied predictor: predicts the applicability of solutions in future scenarios.

[0154] Extended roles (user addable): Academic advisor: Provide theoretical support and key literature analysis.

[0155] Data Analyst: Supports multidimensional data analysis and structured output.

[0156] Industry consultant: Provide market trend insights and strategy optimization suggestions.

[0157] Technical terminology association: Guiding words: User feedback triggers Python logic to adjust the division of role tasks and module calling paths.

[0158] Python logic trigger point annotation: Dynamic role configuration → Assign responsibilities based on user needs.

[0159] Module task optimization → Improve discussion efficiency.

[0160] Step 4: Discussion process (automatically guided and dynamically adjusted by the system) First round of discussion: generating preliminary proposals The system generates preliminary plans and analysis results based on the role tasks. Example: Idea Generator: Produces initial ideas.

[0161] Technology Evaluator: Analyzes the feasibility of technology implementation.

[0162] Optimization feedback: Make specific suggestions for improvement.

[0163] Application Forecaster: Predict the future applicability of solutions.

[0164] Multiple rounds of iteration and feedback optimization: After the user provides feedback, the system adjusts the solution content through Python logic, including resource call logic and role division.

[0165] Final round confirmation: When the user has completed all feedback and has no tasks to be completed, the system will automatically pop up a final round confirmation prompt: The discussion process has been completed, please select the next step: [Export final report now] [Continue to optimize the current plan] [Integrate the optimized content into the final solution and output it] [Exit and save current progress] Dynamic prompts: Based on the field or task type selected by the user, the system can recommend the next step: Academic tasks: It is recommended that users export analysis reports and add references.

[0166] Technical tasks: Suggest users to add technical parameters and move on to the next round of discussion.

[0167] Technical terminology association: Feedback loop: User feedback triggers prompt optimization and adjusts the system reasoning path.

[0168] Python logic trigger point annotation: Analysis of differences → Adjustment of solution generation strategy.

[0169] Dynamically optimize discussion content → Improve adaptability.

[0170] Step 5: Output and precipitation Final solution output: The system generates a complete document, including: Authoritative argument support: A list of theoretical support from academic or industry background.

[0171] Solution roadmap: detailed implementation steps and priorities.

[0172] Recommended field trends: Recommendations for the latest field trends and cutting-edge achievements.

[0173] Knowledge accumulation: Automatically save discussion records, dynamically adjust content and output documents to form a user-specific knowledge base.

[0174] Technical terminology association: Background memory: Enhance the ability to remember conversation context through Python logic to ensure efficient access to long-term user data.

[0175] Python logic trigger point annotation: Save user interaction records → Dynamically update the knowledge base.

[0176] Provide long-term optimization suggestions → Strengthen personalized support.

[0177] The results of the embodiment of the present invention based on the above process are as follows. The following is the content output by the large model: The following is a discussion plan framework design based on the content you provided: Step 1: Input basic information Today's topic: Designing a future home drone Brief description: Future household drones need to be suitable for urban households and remote rural areas, and have the following features: 100 km flight distance.

[0178] Alleviate urban traffic congestion.

[0179] Serve the transportation needs of remote areas.

[0180] It can be rotated and stopped on the roof or balcony of the home, making it easy to get on and off.

[0181] Purpose requirements: It can carry 3-5 people and supports route setting on smartphones.

[0182] The cost is controlled within the threshold x.

[0183] Provide appearance design and design parameters to provide a basis for factory production.

[0184] Step 2: Set discussion parameters Field keyword trigger The user enters keywords: technical type (UAV design, parameter optimization).

[0185] System switching mode: Domain expert mode is activated and drone related resources are loaded.

[0186] Theme Type: Technology-based: Focus on patent evaluation and technology application optimization.

[0187] Problem complexity: Cross-cutting issues (combining transportation and technology).

[0188] Objectives and expected results: Innovation Goal: Generate original drone design solutions to meet urban and rural needs.

[0189] Step 3: Role Configuration Basic roles: Technical idea generator: design the appearance and functions of drones.

[0190] Technical evaluator: evaluates the feasibility of flight distance, carrying capacity and cost control.

[0191] Optimizers: Provide suggestions for improvements, such as energy efficiency and ease of use.

[0192] Application Forecaster: Predict future market applicability and potential application scenarios.

[0193] Extended roles: Academic advisor: Analyze flight principles and cutting-edge technology trends.

[0194] Industry consultant: Provide market demand and pricing strategy advice.

[0195] Step 4: Discuss the process First round of discussion: generating preliminary proposals System guide role tasks: Idea Generator: Propose the appearance and docking design of the drone.

[0196] Technical Evaluator: Preliminary analysis of UAV performance parameters (flight speed, energy type).

[0197] Optimization Feedback: Evaluate user experience (usability and security).

[0198] Applied Forecaster: Predicting market demand.

[0199] Multiple rounds of iteration and feedback optimization: 1) After users provide feedback, the system optimizes the design: Improve flight stability.

[0200] Optimize cost and energy configuration.

[0201] 2) Dynamic adjustment plan: Add solar charging or hybrid energy mode.

[0202] Improve the rotation stop mechanism and safety measures.

[0203] Final round of confirmation: Generate the final optimization plan.

[0204] Step 5: Output and precipitation Final solution output: 1) Supported by authoritative arguments: Including technical parameters and market trend analysis.

[0205] 2) Solution roadmap: Drone design process and production priorities.

[0206] 3) Field dynamic recommendation: The latest UAV technology trends and research results.

[0207] Knowledge accumulation: Automatically save complete discussion records, establish a unique knowledge base for drone design, and support subsequent optimization and R&D.

[0208] Technical terminology associations and Python logic triggers 1) Prompt analysis: Analyze user needs and generate technical design prompts.

[0209] 2) Feedback loop: Optimize technical paths and design solutions based on feedback.

[0210] 3) Background memory: save interaction records, update the knowledge base, and improve future support capabilities.

[0211] Hopefully this design framework will help you quickly generate high-quality drone designs! If you would like to discuss each step in detail, please let me know! You said: Please provide detailed and specific plans.

[0212] AI Big Model says: AI Big Model The following is a detailed design plan for future home drones, including appearance, functional design and parameter configuration: Future home drone design 1. Project Overview Topic: Home drone design Objective: To meet the needs of urban and rural families and provide convenient and safe short-distance transportation.

[0213] 2. Core content of drone design 1. Design Appearance: Streamlined design, similar to a flying saucer or a small helicopter, to reduce air resistance and enhance aesthetics.

[0214] Material: Lightweight and high-strength composite materials (such as carbon fiber or aluminum alloy).

[0215] Color: Customizable, multiple color schemes available.

[0216] Iconic Design: Equipped with foldable rotors for easy docking and storage.

[0217] 2. Functional design 1) Carrying capacity: Loading capacity: 3-5 persons, total weight about 300-500 kg.

[0218] Seat layout: Compact cockpit, ergonomically designed, equipped with seat belts.

[0219] 2) Flight capability: Flight distance: 100 kilometers on a single charge.

[0220] Flight altitude: 200-500 meters, avoid conflict with buildings.

[0221] Flight speed: Maximum speed can reach 100 km / h, cruising speed is 60-80 km / h.

[0222] 3) Power system: Active electric drive with lithium-ion or solid-state batteries.

[0223] Equipped with solar-assisted charging panels (can be automatically charged when parked on the roof).

[0224] 4) Intelligent control: The mobile phone application sets the route and supports both automatic driving and manual driving modes.

[0225] GPS real-time navigation system, combined with geo-fencing technology, to avoid no-fly zones.

[0226] Equipped with LIDAR (laser radar) and ultrasonic sensors for obstacle avoidance and precise landing.

[0227] 5) Take-off and landing system: Vertical take-off and landing technology (VTOL) can take off and land directly on the roof of a home, balcony or parking lot.

[0228] Equipped with a safety locking mechanism to ensure a secure docking.

[0229] 6) Safety measures: Multiple redundant flight control systems to prevent single point failure.

[0230] Emergency parachute system.

[0231] Windproof and rainproof design, adaptable to complex weather conditions.

[0232] 7) Intelligent assistance function: Voice recognition and intelligent assistant for easy operation.

[0233] Real-time flight status monitoring (such as battery level, position, wind speed).

[0234] 3. Technical parameters

[0235] 4. Market Applicability Analysis 1) Urban families Main uses: daily commuting, avoiding traffic jams.

[0236] Features and advantages: convenient docking (rooftop or balcony), intelligent navigation.

[0237] 2) Rural households Main purpose: To solve the transportation inconvenience in remote areas and shorten the travel time.

[0238] Special advantages: long endurance and adaptability to various complex terrains.

[0239] 5. Production Cost and Market Price 1) Production costs Key components (such as batteries, radars, and composite materials) account for about 70% of the total cost.

[0240] The manufacturing cost per unit is expected to be no more than the threshold x price.

[0241] 2) Market price Pricing strategy: Below threshold x price to meet household consumption needs.

[0242] Value-added services: Customized color matching and maintenance services are available at an additional charge.

[0243] VI. Project Implementation Path 1) Technology R&D Phase 1: Verify core technologies (such as power systems and vertical take-off and landing technologies).

[0244] Phase 2: Develop a prototype and conduct small-scale testing.

[0245] 2) Testing and Optimization Safety testing: verification of emergency system and obstacle avoidance capabilities.

[0246] Performance optimization: Improve battery life and smart navigation.

[0247] 3) Mass production and sales Produce in small batches to test market response.

[0248] Promote through e-commerce platforms and offline channels.

[0249] 7. Additional Features and Future Upgrades Add a detachable cargo hold to support logistics transportation.

[0250] Develop a shared drone model for short-term rental services in cities.

[0251] Equipped with an unmanned driving AI assistant to further improve the level of automation.

[0252] You can further adjust the appearance design or parameter configuration according to actual needs. If you need to submit for production, please further improve the technical drawings and cost assessment report! You said: Provide the final complete plan, including drawings.

[0253] AI Big Model says: AI large model generates drawings, such as Figure 4 shown.

[0254] The final drone design has been generated, with detailed design drawings attached. The drawings show a domestic drone suitable for urban and rural use, with the following features: 1) Appearance: Streamlined design, foldable rotors, convenient for storage and docking.

[0255] 2) Function: Can carry 3-5 people, supports electric and solar hybrid power.

[0256] 3) Docking: Vertical take-off and landing, suitable for docking on roofs and balconies.

[0257] 4) Intelligent control: combining mobile phone APP route planning with autonomous driving.

[0258] You can click on the image to view and download for further reference or to submit for factory production. If you need any more details, please let me know! The technical features of the technical user version are as follows: Core Features 1. Field keyword triggering and professional module loading The system activates the domain expert mode through keyword input (such as "technology type" and "industry type") and loads professional field modules and resources.

[0259] 1) Functional highlights: Automatically match keywords and task templates to improve adaptation efficiency.

[0260] Supports multi-domain switching to meet cross-domain collaboration needs.

[0261] 2) Differences: The technical user version supports more fine-grained domain module loading than the normal user version.

[0262] Unlike the enterprise user version, the technical user version focuses more on the user's explicit domain input rather than automatic scenario prediction.

[0263] 2. Customized role configuration Users can dynamically adjust role tasks based on the basic roles automatically generated by the system (such as technology idea generator and technology evaluator).

[0264] 1) Functional highlights: Provide extended roles (e.g. industry consultant, academic consultant) to provide professional support for specific tasks.

[0265] The division of roles is clearer, supporting refined operations of complex tasks.

[0266] 2) Differences: The roles of ordinary users are fixed, the roles of enterprise users are dynamically collaborative, and the technical users' version focuses on role expansion in professional fields.

[0267] 3. Multiple rounds of discussion and feedback optimization It supports multiple rounds of feedback adjustments from users, and the system optimizes the solution logic based on the feedback.

[0268] 1) Functional highlights: Provide difference point analysis and resource call adjustment to gradually improve the plan.

[0269] User feedback triggers role reallocation and parameter adjustments.

[0270] 2) Differences: Compared with the linear discussion process of the ordinary user version, the technical user version incorporates difference point analysis logic into the feedback.

[0271] The enterprise user version focuses more on real-time optimization, while the optimization of the technical user version is based on the call and adjustment of domain resources.

[0272] 4. Professional final round confirmation and dynamic prompts The system provides clear final round confirmation prompts after the discussion is over and recommends dynamic operations based on the task type.

[0273] 1) Functional highlights: Dynamically prompt users to export analysis reports or proceed to the next round of optimization.

[0274] Provide customized output suggestions based on academic or technical tasks.

[0275] 2) Differences: Compared with the fixed confirmation logic of the general user version, the technical user version dynamically generates prompts based on domain characteristics.

[0276] The enterprise user version provides a more flexible jump mechanism, while the technical user version emphasizes domain-specific final-round recommendations.

[0277] 5. Output structure and knowledge accumulation The system supports the generation of structured documents and the saving of user discussion records.

[0278] 1) Functional highlights: Outputs include solution roadmaps, theoretical support checklists, and domain dynamic recommendations.

[0279] The knowledge sedimentation function automatically saves the adjusted content and updates it to the user knowledge base.

[0280] 2) Differences: It provides structured outputs consistent with the general user version, but the outputs of the technical user version focus on domain extension content.

[0281] The output of the enterprise user version has added visualization support, while the technical user version tends to be more specialized templates.

[0282] Technical Value of Technical User Versions 1) Professional field support: quickly locate task requirements through field keyword triggering and module loading in field expert mode.

[0283] 2) Refined role division: Supports dynamic configuration of extended roles to meet the professional needs of complex tasks.

[0284] 3) Domain optimization logic: Combine user feedback and resource call adjustment to gradually generate efficient and accurate solutions.

[0285] 3. Enterprise user scenarios: Through the domain expert mode module, it provides enterprises with integrated solutions for complex processes and supports the complex logical requirements of multi-department collaboration. This version is optimized based on the "prompt + guide words" and "domain expert mode" technical modules, combined with natural language interaction, dynamic feedback optimization and long-term memory functions, to provide highly intelligent and efficient solution generation support. Users can obtain accurate and efficient solutions in multi-domain and multi-level tasks. At the same time, it supports multi-round interactions and long-term knowledge management to meet the complex needs of technical users and industry users. The specific steps are as follows: Step 1: Enter basic information (required for users) 1. Today's topic: Please briefly describe the problem that needs to be solved. Example: 1) How to improve the recommendation system algorithm? 2) How to formulate a marketing strategy? 2. Brief description: Please describe the problem background and related requirements in detail. Example: 1) The current algorithm has bottlenecks and cannot meet the needs of real-time recommendation.

[0286] 2) An efficient marketing plan needs to be developed to increase quarterly sales.

[0287] 3. Purpose: Please describe your expected results. Example: 1) Improve the click-through rate and conversion rate of the recommendation system.

[0288] 2) Optimize market analysis and increase coverage of target users.

[0289] 4. Background information: The system will automatically identify and complete the missing parts of the information. Example: 1) Project budget constraints.

[0290] 2) It involves collaboration among multiple departments.

[0291] The embodiments of the present invention are: Today's topic: "[Feasibility analysis of cultural tourism AI model project]" Simple description: "[The company is preparing to invest in a cultural tourism AI model project]" Objective: "[Determine whether the project has prospects. How big is the market space? How big is the investment risk? And give a specific marketing plan for the project. ]" Background information: "[See the attachment for the white paper on the local cultural tourism AI model project. ]" New features: intelligent input completion and scene prediction The system automatically identifies missing information in the input and prompts the user to supplement it.

[0292] Recommend task scenarios (such as technical and industry-based) based on keyword analysis.

[0293] Technical terminology association: Dynamic generation of prompts: User input is parsed through Python logic to generate domain-adaptive prompts.

[0294] Scenario prediction: The system intelligently activates domain templates and related resources based on input content.

[0295] Step 2: Set discussion parameters (system guidance and dynamic adjustment) Field keyword trigger: When the user enters keywords (such as "technology-based" or "industry-based"), the system automatically activates the domain expert mode.

[0296] Problem complexity (user selection or system recommendation): Single-area problem: single task, focus on specific execution.

[0297] Cross-domain issues: multi-departmental collaboration or technical integration is required.

[0298] Goals and expected results (system recommendations): Innovation goal: Propose original technical solutions.

[0299] Market suitability goal: optimize market coverage and user conversion.

[0300] Authoritative goal: Produce high-confidence analysis and reports.

[0301] Step 3: Role configuration (dynamic adjustment and collaboration support) Basic roles: Idea Generator: Comes up with core ideas or topic concepts.

[0302] Technical evaluator: Analyze the feasibility of the solution and provide suggestions for improvement.

[0303] Optimizing feedback: Optimizing solutions from an application perspective.

[0304] Application Forecaster: Predicts the market applicability of solutions.

[0305] Dynamic role adjustment: After the user provides feedback, the system dynamically adjusts the role tasks according to the new requirements. Example: The user adds "Industry Adaptation Analysis", and the system automatically enables "Industry Consultant".

[0306] Cross-role collaboration: Supports synchronous collaboration between role modules to improve discussion efficiency. Example: Technical evaluators and data analysts collaborate to generate a feasibility report.

[0307] Step 4: Discuss the process (non-linear jump and real-time optimization) First round of discussion: generating preliminary proposals The system generates a preliminary plan based on user input and role tasks.

[0308] Multiple rounds of feedback and optimization: Users provide feedback, and the system uses Python logic to adjust the plan and division of labor in real time. Example: adding "market segmentation analysis" or adjusting technical parameters.

[0309] Final round confirmation: After the discussion process is completed, the system pops up a final round confirmation prompt: [Export final report now] [Continue to optimize the current plan] [Integrate the optimized content into the final solution and output it] [Exit and save current progress] Dynamic recommendation: Technical tasks: It is recommended to export an execution plan that includes technical parameters.

[0310] Marketing tasks: Recommend generating user segmentation reports.

[0311] Non-linear jump support: Users can jump to other discussion steps at any time (such as directly generating a solution).

[0312] Technical terminology association: Feedback loop: User feedback triggers prompt adjustments and optimization solutions.

[0313] Step 5: Output and knowledge accumulation Diverse output templates: Solution roadmap: clarify steps and priorities.

[0314] Dynamic analysis charts: such as market trend charts and technical parameter comparison charts.

[0315] Field recommendation report: Generates extended reading based on the latest developments in the field.

[0316] Knowledge accumulation and long-term memory management: The system saves discussion records and dynamically updates the user knowledge base.

[0317] Personalized optimization: Generate user preference solutions based on long-term memory.

[0318] The results of the embodiment of the present invention based on the above process are as follows. The following is the content output by the large model: Based on the input you provided and the background information in the Local Culture and Tourism AI Big Model White Paper, I suggest the following steps to conduct project analysis and solution design: Today's topic: "Feasibility analysis of the cultural tourism AI model project" Simple description: "The company is preparing to invest in the cultural tourism AI model project" Purpose: "Determine whether the project has prospects, how big the market space is, the investment risks, and propose a specific marketing plan" Background information: "Industry analysis and technology trends based on the white paper of the local cultural tourism AI model project" Step 2: Recommend intelligent completion and scene prediction 1) Input completion suggestions: If the user does not provide complete market demand or goal setting, it can be supplemented based on the market analysis in the white paper (such as the innovative application of AI technology in the cultural and tourism industry).

[0319] 2) Scenario prediction and recommendation: Based on the keywords "technology" and "industry", we recommend technology scenarios (such as smart guide systems and data analysis support) and industry scenarios (such as the creation and marketing of local IP).

[0320] Step 3: Discuss parameter settings 1. Field keyword trigger: activate the field expert mode, focusing on "digitalization of cultural and tourism industry", "local IP innovation" and "data-driven precise insights".

[0321] 2. Problem complexity selection: Select "cross-domain problem" and comprehensively evaluate multiple factors such as technology and market.

[0322] 3. Objectives and expected results: 1) Innovative goal: Propose a new cultural and tourism AI model architecture and functional design.

[0323] 2) Market applicability goal: clarify the project's market positioning and user coverage.

[0324] 3) Authoritative goal: Provide highly credible market forecasts and industry reports.

[0325] Step 4: Role configuration and discussion process 1. Role configuration: 1) Creative Generator: Design innovative products such as cultural tourism IP digital humans and metaverse virtual scenic spots.

[0326] 2) Technical evaluator: Analyze the technical architecture and security of local cultural and tourism AI models.

[0327] 3) Optimize feedback: optimize the match between products and user needs.

[0328] 4) Applied Forecasters: Evaluate the market potential and operational risks of the model.

[0329] 2. Discussion process: 1) Round 1: Generate a preliminary plan based on the white paper content, focusing on market analysis.

[0330] 2) Multiple rounds of optimization: Refine the technical architecture and marketing strategy based on user feedback.

[0331] 3) Final round confirmation: Output a complete report including technical solutions, market analysis, risk assessment and marketing plan.

[0332] Step 5: Output and knowledge accumulation 1. Output template: 1) Solution roadmap: Identify the key steps in technology development and market promotion.

[0333] 2) Dynamic analysis charts: such as market demand trend charts and technology application scenario comparison tables.

[0334] 3) Field recommendation report: Combined with the cases and data in the white paper, it provides extended reading on the application of cultural and tourism AI models in the industry.

[0335] 2. Knowledge accumulation: The system saves analysis records, updates the user knowledge base, and provides data support for subsequent decision-making.

[0336] If you need to further refine specific technical solutions or marketing strategies, you can provide more information or requirements at any time! You said: Continue to optimize The AI ​​big model says: No.

[0337] Based on the current analysis and your needs for further optimization, the following is a plan to further deepen and optimize the feasibility analysis and solution design of the cultural tourism AI model project: Optimize content structure and flow 1. Intelligent optimization of input and scene Information completion mechanism optimization: the system automatically detects whether the input contains the following key factors: 1) Market size and demand: target customer groups, consumption preferences, and market capacity.

[0338] 2) Technical feasibility: the technical basis and implementation path of the project.

[0339] 3) Risk factors: technical risk, market volatility, and capital planning.

[0340] Smart scene recommendation: Introducing scene simulation function: 1) Recommend technical paths for “technical” tasks, such as AI tour guides and digital content generation.

[0341] 2) For “industry-type” tasks, such as cultural and tourism IP operations, generate industry trend analysis and profit models.

[0342] 2. Deepening of dynamic role configuration Basic role mission enhancement: Creative Generator: Further refine the image design and content production process of digital people in cultural and tourism IP, such as personalized recommendations and applications integrated with the metaverse.

[0343] Technology Evaluators: Use white paper-based model architecture analysis to evaluate the computing requirements, data security, and scalability of AI technologies.

[0344] Optimize feedback: Optimize recommendation algorithms and interactive experience through user data simulation.

[0345] Application Forecaster: Based on market trend modeling, predict the project's revenue and market share within three years.

[0346] Dynamic character adjustments and new features: Risk management experts: specialize in analyzing the data security and market volatility risks that a project may face.

[0347] Cultural consultant: Provide in-depth exploration of local cultural content and IP creation suggestions.

[0348] 3. Technical solutions and architecture optimization Model architecture recommendations: Use RouteLLM as the core architecture, combined with the following components: Natural language processing module: supports multi-language interaction and personalized recommendations.

[0349] Large-scale knowledge graph: integrating local culture, scenic spot information and tourist behavior data.

[0350] Low-computing-power lightweight model: optimize costs while ensuring model operation efficiency.

[0351] Multi-model collaborative solution: Combine general models (such as AI big models) with local customized models to enhance the ability to mine cultural connotations.

[0352] Platform function expansion: Digital content generation tools: support the efficient production of cultural and tourism promotional content.

[0353] Interactive experience module: Develop AR / VR-based scene experience to attract tourists to participate deeply.

[0354] Data analysis and management platform: monitor market data in real time and provide support for strategy optimization.

[0355] 4. Market applicability and marketing strategy optimization Market segmentation and positioning: Based on the market data analyzed in the white paper, customer groups are segmented, such as parent-child families, the elderly, and cultural enthusiasts.

[0356] Identify the core needs of market segments and design targeted products and services.

[0357] Marketing strategy: Launch pilot projects for the application of AI models in culture and tourism (such as digital human tour guide services in famous local scenic spots).

[0358] Promote through social media, based on visitor-generated content (UGC) content, to stimulate user engagement.

[0359] Introduce cross-platform cooperation, such as cooperating with online travel (OTA) platforms to achieve the linkage between online booking and offline experience.

[0360] 5. Risk assessment and dynamic feedback mechanism risk assessment: Market volatility risk: Analyze the impact of tourism peak and off-seasons and external factors through scenario simulation.

[0361] Technical risks: Evaluate the operating efficiency, failure rate, and data privacy protection capabilities of AI models.

[0362] Dynamic feedback mechanism: User feedback is integrated into the optimization process in real time, creating a closed loop of “suggestion → adjustment → testing → feedback”.

[0363] Provide an interactive feedback interface for users to optimize the model's personalized service capabilities.

[0364] 6. Output optimization Report structure optimization: Cover: A brief description of the project background and objectives.

[0365] Technical solution: architecture design and functional highlights.

[0366] Market analysis: target market size, customer profile, and trend forecast.

[0367] Risks and countermeasures: including detailed analysis at the technical and market levels.

[0368] Implementation plan: phased objectives and resource allocation.

[0369] Appendix: Summary of literature citations.

[0370] Diversified output formats: Visualized roadmap: A flowchart is used to show the key nodes of a project from R&D to commercialization.

[0371] Market dynamics charts: including market trends, user growth forecasts, and investment return analysis.

[0372] Product demonstration video: simulates the application scenarios of AI models, such as a video of a digital human explaining a tourist attraction.

[0373] Through these optimizations, you can more comprehensively judge the feasibility of the cultural tourism AI model project and generate more systematic and reliable reports. If you need specific implementation steps or tool support, please continue to provide requirements! You said: Generate and output more systematic and reliable complete reports AI Big Model says: Feasibility analysis report of cultural tourism AI model project 1. Project Background This project aims to combine the innovative technology and industry needs of local cultural tourism AI models, empower the cultural tourism industry through AI, promote local cultural dissemination and economic development, and enhance tourists' digital tourism experience. With the rapid development and digital transformation of the cultural tourism industry, the company plans to invest in the development of cultural tourism AI model projects, using artificial intelligence technology and big data analysis to realize cultural tourism IP digital people, smart guides and metaverse experience. This report aims to comprehensively analyze the feasibility of the project from the aspects of industry status, technical analysis, market potential, investment planning and risk assessment.

[0374] 2. Industry and Market Analysis 1. Current status of cultural tourism industry In recent years, the cultural tourism industry has flourished. At the same time, technologies such as AI and Metaverse are widely used in the cultural tourism industry, providing tourists with personalized and immersive experiences and helping the industry transform and upgrade.

[0375] 2. Market demand and characteristics Diversified demands: Tourists pay more attention to personalized, high-quality tourism services, and parent-child families and the elderly are important market segments.

[0376] Digital trends: The demand for smart tour guides, digital content generation and interactive experiences has increased significantly.

[0377] Competition landscape: The local cultural tourism market presents differentiated competition, and digital IP building has become a core competitive advantage.

[0378] 3. Potential market of the project Cultural and tourism IP digital humans, smart guides and metaverse experience projects will become an important driving force for the upgrading of the cultural and tourism industry. By deeply exploring local cultural characteristics and combining AI technology, the projects will have strong market competitiveness and sustainable development potential.

[0379] 3. Technology and Architecture Analysis 1. Technical architecture The project's technical architecture is based on multi-model collaboration, using the RouteLLM framework, combining a general large language model with a local customized model to achieve a deep integration of technology and culture. The main features include: Modular design: With the LangChain knowledge base as the core, it supports flexible expansion and low computing power operation.

[0380] Natural language processing module: multi-language interaction and personalized recommendations to enhance tourist experience.

[0381] Cultural and tourism data analysis platform: integrates local cultural and tourism data to provide accurate decision-making support.

[0382] Enhanced interactive experience: Develop immersive scenes based on AR / VR technology to innovate user interactive experience.

[0383] 2. Technical functions and innovation Cultural and tourism IP digital human: Develop digital human images with local cultural characteristics for use in smart guidance and cultural communication.

[0384] Metaverse virtual scene: Create a digital scenic spot through virtual reality technology to achieve an immersive experience.

[0385] Data-driven decision making: Analyze visitor behavior data in real time to optimize operational strategies.

[0386] 4. Marketing strategy and implementation plan 1. Market positioning and goals Target customers: domestic and foreign tourists, especially families with children and cultural enthusiasts, as well as cultural and tourism enterprises.

[0387] Market target: To cover major domestic tourist cities within three years and achieve a market share of 20%.

[0388] 2. Marketing strategy Brand building: Create local IP digital people, deeply integrate with local culture, and enhance brand influence.

[0389] Channel promotion: Promote cultural and tourism AI applications through social media, online travel platforms and short videos.

[0390] User participation: Encourage visitors to generate content to enhance interactivity and communication effectiveness.

[0391] 3. Implementation plan Phase 1 (0-6 months): Build the technical architecture, complete the development of digital human guides and content generation tools, and pilot applications in local iconic scenic spots.

[0392] Phase 2 (6-18 months): Expand to regional cooperative scenic spots, improve marketing strategies, and optimize technology and services through feedback.

[0393] The third phase (18-36 months): Fully promote to major cities across the country, deepen functional modules, such as the metaverse virtual scene, and form a standardized operation model.

[0394] V. Investment and Risk Assessment 1. Investment budget Technology research and development: determined based on model development and data management platform construction.

[0395] Marketing promotion: determined based on brand building and channel promotion.

[0396] Operation and maintenance: determined based on server and labor costs.

[0397] 2. Risk Analysis Market risk: Tourists’ acceptance of digital products may fluctuate.

[0398] Technical risks: The operating efficiency of AI models and their matching with user needs need to be continuously optimized.

[0399] 3. Risk Countermeasures Strengthen market research and optimize product functions and user experience.

[0400] Adopt modular architecture to flexibly respond to technology upgrade needs.

[0401] 6. Expected benefits and social value of the project 1. Economic Benefits Revenue forecast: The return on investment is expected to be approximately 50% within three years.

[0402] Cost optimization: Significantly reduce operating costs through modular design and low computing power model.

[0403] 2. Social benefits Promote the digital transformation of local culture and tourism and enhance the tourist experience.

[0404] Promote the dissemination and inheritance of local culture and enhance cultural confidence.

[0405] Create employment opportunities and drive regional economic development.

[0406] VII. Conclusion and Recommendations The cultural tourism AI model project combines local cultural characteristics and AI technology advantages, has significant market potential and technical feasibility, and can achieve higher economic and social benefits through reasonable planning and implementation.

[0407] suggestion: 1. Prioritize pilot projects of local cultural and tourism IP digital human projects to verify technology and market potential.

[0408] 2. Deepen the technical architecture to ensure the deep integration of culture and technology.

[0409] 3. Establish a dynamic feedback mechanism to continuously optimize product functions and user experience.

[0410] This project is in line with the current trend of digital transformation in the cultural and tourism industry and is an important innovative measure to promote the development of the local cultural and tourism industry.

[0411] The technical features of the enterprise user version are as follows: Core Features 1. Intelligent input completion and scene prediction The system can intelligently identify and complete missing information based on the user's natural language input, and automatically recommend task scenarios based on keyword analysis.

[0412] 1) Functional highlights: Automatically complete input content to lower the user operation threshold.

[0413] The scenario prediction function activates the corresponding field template and provides highly customized task support.

[0414] 2) Differences: Compared with the fixed input templates of the ordinary user version, the enterprise user version has dynamic completion capabilities and scenario recommendation functions.

[0415] Different from the domain adaptation of the technical user version, the input completion of the enterprise user version is more automated and multi-level.

[0416] 2. Dynamic role configuration and cross-role collaboration After users provide feedback, the system can dynamically adjust role tasks and support synchronous collaboration between modules.

[0417] 1) Functional highlights: Adjust the division of roles and tasks according to user needs, such as adding industry consultant or academic analysis roles.

[0418] Collaboration between roles supports the generation of more precise and comprehensive solutions.

[0419] 2) Differences: The enterprise user version enables cross-role collaboration and real-time dynamic adjustment, which is different from the relatively fixed role configuration in the technical user version.

[0420] 3. Nonlinear discussion process and dynamic optimization Allow users to jump or adjust in the discussion flow without having to follow strict linear steps.

[0421] 1) Functional highlights: Users can jump to any step as needed, such as going directly to solution output from the feedback stage.

[0422] The system optimizes the current solution or generates a new solution path through real-time feedback.

[0423] 2) Differences: The discussion process of the general user version is mainly linear, while the enterprise user version provides flexible non-linear jumps and real-time optimization functions.

[0424] The enterprise user version supports more complex logic adjustments in multiple rounds of optimization to meet advanced needs.

[0425] 4. Dynamic recommendation and final round confirmation The system recommends appropriate output formats or next steps based on the task type and domain characteristics.

[0426] 1) Functional highlights: Dynamically prompt users whether to export technical parameter plans, generate market segmentation reports, or continue optimization.

[0427] Final round confirmation ensures the integrity of discussion results and automatically exports reports when no operation is performed.

[0428] 2) Differences: The final round of confirmation of the enterprise user version is combined with the field dynamic recommendation function, which is more intelligent than the ordinary user version and technical user version.

[0429] 5. Diversified output templates and long-term memory support The output content includes not only path maps and dynamic charts, but also dynamically generates recommendation reports based on the field.

[0430] The system saves user discussion records to support knowledge accumulation and long-term optimization.

[0431] 1) Functional highlights: The output results are highly visualized, such as generating market trend charts, parameter comparison charts, etc.

[0432] Combined with long-term memory, it intelligently generates optimized solutions based on user preferences.

[0433] 2) Differences: The diversified output and long-term memory functions of the enterprise user version are extended capabilities that cannot be fully covered by the ordinary user version and the technical user version.

[0434] Technical value of the enterprise user version 1) Highly intelligent: automatic input completion, dynamic role adjustment and non-linear processes provide accurate and efficient solutions to complex problems.

[0435] 2) Scalability and visualization: Supports graphical presentation of complex tasks and diversified template output to meet the needs of users at multiple levels.

[0436] 3) Domain adaptation and memory support: Provides unique personalized optimization capabilities through long-term memory and domain dynamic recommendations.

[0437] The main differences between the three application versions of the present invention are as follows:

[0438] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modification and change made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A human-machine collaborative dialogue strategy and dynamic modeling system based on AI big model, characterized in that: The system includes the following modules: Natural language and software language dynamic adaptation module: used to combine the natural language input by the user with the software language to generate a logical operation process template, and generate a reasoning path consistent with user needs through dynamic modeling; Prompt module: used to parse the subject, content description, target requirements and background information input by the user, generate relevant prompts respectively, and obtain a structured problem description; Guide word module: It is used to dynamically generate guide words based on the prompts generated by the prompt module, stimulate the AI ​​big model to respond through the prompts, and dynamically adjust the reasoning path through the guide words; Simulation role module: used to dynamically generate simulation roles in corresponding technical fields according to user needs, realize multiple rounds of interaction through role collaboration, and complete complex problem analysis and solution optimization; Dynamic feedback module: used to adjust the guide word logic and reasoning path in real time based on multiple rounds of interaction; Domain Expert Mode Module: used to generate specialized dynamic solutions in related technical fields based on keyword triggers in the technical field; Multi-version adaptation module: used to dynamically enable and adjust the prompt module, guide word module and feedback module functions, combined with the operation process template of the logic flow, to provide adaptation version solutions corresponding to the needs of various users.

2. According to the human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model in claim 1, it is characterized in that: The natural language and software language dynamic adaptation module generates a logical operation process template through semantic analysis and logical modeling technology, uses natural language processing technology to analyze user input, and based on management logic, intelligently adjusts resource allocation according to task priority, complexity and resource requirements, and optimizes reasoning paths and computing efficiency.

3. According to claim 1, a human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model is characterized in that: The prompt module extracts key entities, logical relations and contextual semantics through semantic analysis, providing high-quality input for the guide word module.

4. According to claim 1, a human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model is characterized in that: The guide word module combines the prompt + guide word steering technology to generate multi-level guide words in real time according to the natural language or task description input by the user, and optimize the reasoning path of the large AI model in real time; the guide words are dynamically adjusted based on the internal reasoning logic of the model and external user needs to ensure that the generated output minimizes deviation from user expectations.

5. According to claim 1, a human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model is characterized in that: The simulation role module supports the generation of technical analysts, optimization consultants and market analysts, and shares tasks through collaborative logic.

6. According to the human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model in claim 1, it is characterized in that: The dynamic feedback module can generate optimization suggestions based on user feedback, including user input deviation detection, output quality optimization and dynamic adjustment of reasoning paths, and supports multiple rounds of iterations until the target requirements are met.

7. According to claim 1, a human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model is characterized in that: The domain expert mode module integrates keyword triggering, resource calling logic and role collaboration mechanism to generate specialized dynamic solutions for specific fields and continuously optimize resource paths and logic.

8. According to claim 1, a human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model is characterized in that: The multi-version adaptation module supports dynamic adjustment of functional modules to meet the corresponding needs of ordinary users, technical users and enterprise users.

9. According to claim 1, a human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model is characterized in that: Supports open function expansion and further optimizes module performance through customized prompts and guide word logic.

10. The human-computer collaborative dialogue strategy and dynamic modeling system based on AI big model according to claim 1 is characterized in that: The combination of the prompt module and the guide word module realizes the efficient calling of AI large model resources through the logic optimization algorithm.

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