Human-Machine Collaboration Dialogue Strategy and Dynamic Modeling System Based on AI Large Model

Abstract

The present invention discloses a human-machine collaboration dialogue strategy and a dynamic modeling system based on an AI large model. Through prompts, guiding words, simulated role mechanisms, dynamic feedback modules, and logical process design, it supports the domain expert mode and cross-domain adaptation functions, effectively invokes the thinking power resources of the AI large model, and realizes the intelligent solution of complex problems. The innovation of the present invention lies in using the logical process of management thinking, semantic anchors of professional terms, and AI dynamic guidance mechanisms, combined with the efficient adaptation of natural language and software language, to promote the improvement of human-machine collaboration efficiency. Its high flexibility and replicability enable users to quickly generate accurate solutions for specific domain requirements, significantly improving social mental labor productivity. The present invention is widely applied in fields such as education, medical care, enterprise management, and analysis, providing efficient support for intelligent decision-making, and promoting the transformation of the social production mode towards a more efficient and inclusive direction.

Description

Technical Field

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

[0002] Currently, the AI large model dialogue technology has limitations in terms of accuracy, logic, and adaptability, has a high dependence on user input, and lacks the ability of dynamic adjustment. Especially in professional field problems, the existing technologies are difficult to provide efficient cross-domain solutions. For example, fuzzy or inaccurate descriptions may cause the results generated by the AI to deviate from the user's expectations. Summary of the Invention

[0003] The purpose of the present invention is to propose a human-computer collaborative dialogue strategy and dynamic modeling system based on an AI large model in view of the deficiencies of the existing technologies. Through the dynamic adaptation of natural language and software language, a prompt module, a guiding word module, a dynamic feedback mechanism, a simulated role mechanism, and a domain expert mode, a full-process closed-loop optimization from user input to the output of an optimized solution is achieved.

[0004] The object of the present invention is achieved by the following technical solutions: A human-computer collaborative dialogue strategy and dynamic modeling system based on an AI large model, the system includes the following modules:

[0005] Dynamic adaptation module of natural language and software language: used to combine the natural language and software language input by the user to generate a logicalized operation process template, and generate an inference path consistent with the user's needs through dynamic modeling;

[0006] Prompt module: used to parse the theme, content description, target requirements, and background information input by the user, respectively generate relevant prompts, and obtain a structured problem description;

[0007] Guiding word module: used to dynamically generate guiding words based on the prompts generated by the prompt module, stimulate the reaction of the AI large model through the prompts, and dynamically adjust the inference path through the guiding words;

[0008] Simulated role module: used to dynamically generate simulated roles in the corresponding technical field according to the user's needs, achieve multi-round interactions through role collaboration, and complete complex problem analysis and solution optimization;

[0009] Dynamic feedback module: used to adjust the guiding word logic and inference path in real time based on multi-round interactions;

[0010] Domain expert mode module: used to generate professional dynamic solutions in the relevant technical field triggered by technical field keywords;

[0011] Multi - version adaptation module: Used to dynamically enable and adjust the functions of the prompt module, guiding word module, and feedback module, and combine with the operation process template of the logic flow to provide adaptation version solutions corresponding to the needs of various users.

[0012] Furthermore, the natural language and software language dynamic adaptation module generates a logical operation process template through semantic analysis and logical modeling technologies, uses natural language processing technologies to analyze user inputs, and based on management logic, intelligently adjusts resource allocation according to the priority, complexity, and resource requirements of tasks, optimizing the inference path and calculation efficiency.

[0013] Furthermore, the prompt module extracts key entities, logical relationships, and context semantics through semantic parsing to provide high - quality inputs for the guiding word module.

[0014] Furthermore, the guiding word module combines the Prompt + Steering technologies of guiding words, generates multi - level guiding words in real - time according to the natural language or task description input by the user, and optimizes the inference path of the AI large model in real - time; the guiding words are dynamically adjusted based on the internal inference logic of the model and external user requirements to ensure that the generated output has a minimum deviation from the user's expectations.

[0015] Furthermore, the simulated role module supports the generation of including technical analysts, optimization consultants, and market analysts, and shares tasks through collaborative logic.

[0016] 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 the inference path, and supports multiple rounds of iteration until the target requirements are met.

[0017] Furthermore, the domain expert mode module integrates keyword trigger and resource call logics as well as role collaboration mechanisms to generate professional dynamic solutions for specific domains and continuously optimize the resource path and logic.

[0018] Furthermore, the multi - version adaptation module supports dynamically adjusting function modules to meet the corresponding needs of ordinary users, technical users, and enterprise users.

[0019] Furthermore, it supports open - ended function expansion to further optimize the module performance by customizing the prompt and guiding word logics.

[0020] Furthermore, the combination of the prompt module and the guiding word module realizes the efficient call of AI large - model resources through logical optimization algorithms. The beneficial effects of the present invention:

[0021] 1. The combined design of prompt + guiding word: For the first time, it realizes the dynamic interaction transformation from "stimulating reaction" to "guiding reasoning", and improves semantic focus through the term anchor function.

[0022] 2. Dynamic feedback and interaction optimization: Through real-time adjustment, multi-round scheme optimization is achieved, gradually approaching the user's goal.

[0023] 3. Simulated role mechanism: Through the division of labor and cooperation of virtual roles, multi-dimensional analysis tasks are completed, significantly improving the ability to solve complex problems.

[0024] 4. Domain expert mode: Through keyword triggering and resource invocation, in-depth domain support and dynamic optimization are achieved, supporting cross-domain collaboration.

[0025] 5. Combination of logical process and semantic anchor points: Ensure the logic and operability of complex problems, and improve the utilization efficiency of AI thinking ability.

[0026] 6. Combination of natural language and software language: A unified framework is formed to meet the needs of diverse scenarios. Based on the detailed description of the dynamic modeling method of natural language + programming language + professional terms, the technical advantages of the present invention in reducing the user threshold, improving the performance of AI models and their wide applications are demonstrated. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0028] Figure 1 It is a principle architecture diagram, showing the functional interaction relationship of the prompt, guiding word, simulated role and dynamic feedback modules.

[0029] Figure 2 It is an application logic diagram, showing the logical path from user input to AI output.

[0030] Figure 3 It is a usage flow chart, describing the complete process of user input, system parsing, dynamic feedback and final output.

[0031] Figure 4 It is the drone drawing generated by the AI large model in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] In order to make the purpose, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described below with reference to the drawings and implementation cases. It should be understood that the specific implementation cases described here are only used to explain the present invention and are not used to limit the present invention.

[0033] Such as Figure 1As shown in the figure, the present invention provides a human-machine collaborative dialogue strategy and dynamic modeling system based on an AI large model. The system includes the following modules:

[0034] Natural language and software language dynamic adaptation module: It is used to combine the natural language and software language input by the user, generate a logical operation process template through semantic analysis and logical modeling techniques, analyze the user input using natural language processing techniques, and generate an inference path highly consistent with the user's needs through dynamic modeling. Based on management logic, according to the priority, complexity, and resource requirements of the task, it intelligently adjusts resource allocation, optimizes the inference path and calculation efficiency; this method minimizes the deviation between the output and the user's expectations, ensuring the accuracy and integrity of the results; the application of management logic ensures that the AI large model always maintains efficient resource scheduling in a complex task environment and improves its adaptability and efficiency in multitasking processing; it integrates the characteristics of natural language and software language, supports efficient adaptation under a unified framework, and improves the efficiency of solving complex problems. This module combines natural language and programming language, uses natural language to guide the AI large model, and achieves precise control through programming language. This combination ensures that:

[0035] 1. Natural language simplifies the user input method, enabling users to interact with the AI large model without the need for programming skills.

[0036] 2. Programming language ensures precise control and customization of the inference path, thus improving the inference efficiency and accuracy of the model.

[0037] 3. Professional terms are used for domain-specific modeling, further improving the application effect and inference quality of the model in the professional field. Prompt module: It is used to parse the theme, content description, target requirements, and background information input by the user, generate relevant prompts respectively, obtain a structured problem description, and generate a preliminary solution direction; and extract key entities, logical relationships, and context semantics through semantic parsing to provide high-quality input for the guiding word module.

[0038] Prompt Module: It is used to generate multi-level prompts in real time and dynamically based on the prompts generated by the Prompt Generation Module, combining the prompt + prompt technology, according to the natural language or task description input by the user. It stimulates the reaction of the AI large model through prompts, adjusts the prompt logic and prompt weight in real time according to user feedback, and dynamically optimizes and adjusts the inference path through prompts to ensure the adaptability and flexibility of the inference process. The prompts are dynamically adjusted based on the internal inference logic of the model and external user needs to ensure that the generated output has a minimum deviation from the user's expectations; this adjustment process can handle the requirements of multi-round conversations, complex problems or professional fields, and provide accurate answers and solutions; the process of generating prompts is dynamic, that is, the system generates appropriate prompts in real time according to the current input content, and dynamically adjusts the inference path according to user feedback or task requirements. This mechanism allows the AI model to optimize its inference direction in real time according to the progress of the conversation or task, improving the adaptability and efficiency of problem-solving; the combination of the Prompt Generation Module and the Prompt Module realizes the efficient invocation of AI large model resources through logical optimization algorithms; through the dynamic adjustment of prompts and the optimization of inference results based on natural language, the AI system can generate an inference path and output that conform to the user's expectations according to the natural language description input, reducing the deviation between the output and the user's needs. For example, the system automatically generates the corresponding inference path by analyzing the user's input and combining domain knowledge, so as to obtain an accurate solution that meets the user's needs. Through the application of natural language analysis capabilities, dynamically generated prompts and adjusted inference paths not only improve the convenience of user interaction, but also fully mobilize the computing and inference capabilities of the AI large model. The inference path of traditional AI models is fixed and difficult to flexibly handle complex problems, while the present invention can significantly improve the performance of the model in complex scenarios through dynamic modeling methods.

[0039] Simulated Role Module: It is used to dynamically generate various simulated roles in the corresponding technical field according to user needs, including technical analysts, optimization consultants, and market analysts, and share tasks through collaboration logic to support multi-dimensional analysis and optimization of cross-domain problems; realize multi-round interaction through role collaboration to complete complex problem analysis and solution optimization; simulate the discussion method of a real team, significantly improving the ability to solve complex problems. For example, in the problem of enterprise digital transformation, technical experts provide technical advice, financial consultants evaluate the return on investment, and market analysts analyze user needs.

[0040] Dynamic Feedback Module: It is used to generate optimization suggestions based on multi-round interaction, including detecting user input deviation (calculating the semantic similarity between user input and generated content through a semantic embedding model), optimizing output quality, and dynamically adjusting the prompt logic and inference path in real time, and supporting multi-round iteration. Update the generation logic through real-time feedback to ensure that the content gradually approaches the user's goal until the target requirements are met.

[0041] Domain Expert Mode Module: It is used to integrate keyword triggering and resource invocation logic as well as role collaboration mechanisms, generate professional dynamic solutions for specific technical fields, and continuously optimize resource paths and logic. For example, when "tumor treatment" is input, it automatically retrieves relevant medical research and treatment plan databases and generates accurate medical advice.

[0042] Modular Multi-Version Adaptation Architecture: It is used to dynamically enable and adjust the functions of the prompt module, guiding word module, and feedback module, and combine the operation process template of the logic flow to provide adaptation version solutions for corresponding needs of ordinary users, technical users, and enterprise users. Through a step-by-step implementation mechanism, complex problems are decomposed into logically clear sub-tasks, such as requirement analysis, solution design, and feedback optimization. Combined with dynamic feedback, the intermediate results generated by each task can be adjusted in real time, and finally the optimal solution is generated.

[0043] The present invention supports open function expansion and further optimizes the module performance by customizing the prompt and guiding word logic. As Figure 2 and Figure 3 shown, based on the above module functions, the present invention provides application examples of different versions for three application scenarios of ordinary users, technical users, and enterprise users, as follows:

[0044] 1. Ordinary User Scenario: Through the prompt module and guiding word module, based on the standardized guiding word logic, it provides a simplified operation path and an intelligent dynamic feedback mechanism, provides a "one-key" solution for ordinary users, generates a standardized logic path, and simplifies 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 efficient and intelligent solution generation support for ordinary users. This version significantly improves the conversation efficiency and solution quality through the guiding word module, dynamic feedback module, optimization, and role configuration. The specific implementation steps are as follows:

[0045] Step 1: Input basic information (required content for users)

[0046] Today's topic (required content for users)

[0047] Brief description (required content for users)

[0048] Purpose requirements (required content for users)

[0049] Background information (required content for users)

[0050] An embodiment of the present invention is:

[0051] Today's topic ("How to choose the subject direction in the first year of high school?")

[0052] Brief description: ("My child is now in the first year of senior high school. He has excellent academic performance. His grades in all subjects are relatively balanced, and his current interests and hobbies as well as his future career are not yet clear. Given this situation, since I don't know his future career direction, I am very confused about the current subject selection in high school. I don't know what to do.")

[0053] Purpose and requirements: ("My child is now in the first year of senior high school. He has excellent academic performance. His grades in all subjects are relatively balanced, and his current interests and hobbies as well as his future career are not yet clear. Given this situation, since I don't know his future career direction, I am very confused about the current subject selection in high school. I don't know what to do.")

[0054] Background information: ("Not clear")

[0055] Technical term associations:

[0056] Prompt: User's natural language input triggers Python logic parsing, generates structured data, and activates the AI large model inference path.

[0057] Python logic trigger point annotation:

[0058] Input parsing → Extract the core goal and background.

[0059] Dynamically generate prompts → Improve the accuracy of solution generation.

[0060] Step 2: Set discussion parameters (selected by the user or obtained by the system based on analysis)

[0061] Question type

[0062] Select and fill in the specific requirements from the following options:

[0063] Task-based (e.g., data collation, task arrangement).

[0064] Creative (e.g., generating ideas, design proposals).

[0065] Analytical (e.g., problem assessment, strategy optimization).

[0066] Question complexity

[0067] User selection:

[0068] Single-domain problem (e.g., planning a travel itinerary).

[0069] Multi-domain problem (e.g., combining travel budget and schedule).

[0070] Expected output type

[0071] User selection:

[0072] Brief Summary (suitable for quickly understanding the results).

[0073] Detailed Solution (suitable for in-depth implementation and reference).

[0074] Technical Term Association:

[0075] Prompt + Guiding Word: The Python logic dynamically adjusts the prompt generation strategy according to the question type and complexity.

[0076] Python Logic Trigger Point Marking:

[0077] Optimize discussion parameters according to user selection.

[0078] Dynamically adjust the prompt logic → Improve response accuracy.

[0079] Step 3: Role Configuration (The system simplifies the division of labor and the user can adjust it)

[0080] Basic Roles (Automatically generated by the system)

[0081] Task Planner: Generate a preliminary task plan or solution.

[0082] Optimization Advisor: Provide improvement suggestions based on the goal.

[0083] Extended Roles (The user can choose whether to enable them)

[0084] Data Analyst: Perform quick analysis and visualization of data input.

[0085] User Representative: Simulate the needs of the end user and provide usage opinions.

[0086] Technical Term Association:

[0087] Guiding Word: User feedback triggers the Python logic to adjust the role task division and module call path.

[0088] Python Logic Trigger Point Marking:

[0089] Dynamic Role Configuration → Allocate responsibilities according to requirements.

[0090] Improve discussion efficiency → Adjust the module task logic.

[0091] Step 4: Discussion Process (The system guides the user to complete multiple rounds of conversations)

[0092] First Round of Discussion: Generate a preliminary solution

[0093] The system generates a preliminary solution based on the user input, including key steps, task lists, or brief descriptions.

[0094] Second Round of Discussion: Feedback and Optimization

[0095] Users can put forward adjustment suggestions based on the generated content, and the system will dynamically optimize the solution.

[0096] Final Round Confirmation

[0097] When the user has completed all feedback and there are no outstanding tasks, the system will automatically pop up a final round confirmation prompt:

[0098] The discussion process has been completed. Please select the next operation:

[0099] [Export Final Report Immediately]

[0100] [Continue to Optimize the Current Solution]

[0101] [Integrate the Optimized Content to Form and Output the Optimized Final Solution]

[0102] [End the Discussion Process]

[0103] If the user does not operate, the system will automatically export the analysis report after 10 seconds and prompt: "The system has automatically exported the final solution for you. Please check it." to ensure a closed-loop process.

[0104] Process Progress Guidance:

[0105] Current Step: Step 4 / 5 - Discussion Process.

[0106] Next Step: Complete the final round confirmation and generate the final solution. The system outputs the optimized complete solution, and the user confirms whether further adjustment is needed.

[0107] Technical Term Association:

[0108] Feedback Loop: User feedback triggers the optimization of the prompt, and adjusts the system inference path.

[0109] Python Logic Trigger Point Annotation:

[0110] Analysis of Differences → Adjustment of the Solution Generation Strategy.

[0111] Dynamic Optimization of Discussion Content → Improvement of Adaptability.

[0112] Step 5: Output and Deposition

[0113] Output of the Final Solution

[0114] System Generation:

[0115] Concise Execution Steps: Suitable for direct operation.

[0116] In-depth Analysis and Suggestions: Including logical paths and priority assignments.

[0117] Knowledge precipitation

[0118] Automatically save the user's discussion records and final solutions to form a standardized knowledge list for subsequent reuse.

[0119] Technical term association:

[0120] Contextual Memory: Enhance the dialogue context memory ability through Python logic to ensure the efficient invocation of long-term user data.

[0121] Python logic trigger point annotation:

[0122] Save user interaction records → Dynamically update the knowledge base.

[0123] Provide long-term optimization suggestions → Strengthen personalized support.

[0124] The results of the embodiments of the present invention based on the above process are as follows. The following is the content output by the large model:

[0125] According to the content you provided, the following is the complete execution process and preliminary suggestions:

[0126] Step 1: Input basic information

[0127] Today's topic

[0128] How to choose a subject direction in the first year of high school?

[0129] Brief description

[0130] The child is in the first year of high school, with excellent and balanced academic performance, but the interests and career directions are not yet clear, and currently feels confused about subject selection.

[0131] Purpose requirements

[0132] Clarify the subject selection direction in the first year of high school to provide the best support for future development.

[0133] Step 2: Set discussion parameters

[0134] Question type

[0135] Analytical (question assessment and strategy optimization)

[0136] Question complexity

[0137] Multi-domain questions (comprehensive education planning and career development)

[0138] Desired output type

[0139] Detailed solution (suitable for in-depth execution and reference)

[0140] Step 3: Role Configuration

[0141] Basic Roles

[0142] 1) Task Planner: Generate subject selection strategies based on the characteristics of the education system and the situation of students.

[0143] 2) Optimization Advisor: Provide adjustment suggestions that match the goals.

[0144] Extended Roles

[0145] 1) Data Analyst: Analyze career trends and the connection between subjects.

[0146] 2) User Representative: Simulate the future needs of students and optimize subject selection strategies.

[0147] Step 4: Discussion Process

[0148] First Round of Discussion: Generate a Preliminary Plan

[0149] Subject Evaluation:

[0150] 1) Arts and Sciences Division Selection: Prioritize selecting subjects with a balanced combination of arts and sciences, such as mathematics, English, etc., to retain more possibilities for future choices.

[0151] 2) Foundation of Core Subjects: Pay attention to basic subjects such as mathematics, Chinese, English, etc., as these subjects play an important role in any field.

[0152] 3) Interest Stimulation and Expansion: Increase diverse elective courses, such as physics, biology, history, art, etc., to help discover points of interest.

[0153] Selection Strategies When the Career Direction is Unclear:

[0154] 1) Maintain Balance: Choose a course combination without being overly focused on one subject to avoid premature orientation.

[0155] 2) Participate in Career Experience Activities: Help students gradually clarify their interest directions.

[0156] Second Round of Discussion: Feedback and Optimization

[0157] Analysis of future career trends can be introduced to view the demand for subjects in current popular fields (such as AI, healthcare, engineering, etc.).

[0158] If it is found that students are more interested in certain subjects, the course selection plan can be adjusted in the second semester.

[0159] Step 5: Output and Accumulation

[0160] Final Plan

[0161] 1) Subject Selection Strategy

[0162] Core Subjects: Mathematics, English, Chinese

[0163] Exploratory Subjects: Physics, Biology, History (adjusted according to specific interests)

[0164] 2) Implementation Plan

[0165] Step 1: Discuss preliminary interest points with the child and prioritize the subjects.

[0166] Step 2: Consult the school teachers or education experts to ensure that the choices are in line with the overall college entrance examination and career development plans.

[0167] Step 3: Regularly review the subject performance and interest changes and make timely adjustments.

[0168] 3) Long-term Plan

[0169] Participate in career experience programs during winter and summer vacations or on weekends to gradually clarify the future direction.

[0170] Summary and Suggestions

[0171] 1) Maintain a balanced selection to have more possibilities in the future.

[0172] 2) Explore diverse interests and gradually discover passions through elective courses and extracurricular activities.

[0173] 3) Dynamically adjust the strategy and optimize the choices based on subject performance and interest changes.

[0174] The ultimate goal is to provide a more comprehensive development path for the child through a reasonable subject combination and flexible adjustment, while avoiding the pressure caused by premature orientation.

[0175] For a more detailed personalized plan, please continue to provide relevant background information or feedback!

[0176] Second Round

[0177] You said:

[0178] Please output the final complete plan.

[0179] AI Large Model said:

[0180] How to Choose the Subject Direction in the First Year of High School: Final Plan

[0181] I. Plan Summary

[0182] For high school freshmen with balanced grades and unclear interests and career directions, a set of scientific and reasonable subject selection and planning strategies are provided, aiming to maintain subject balance, stimulate potential interests, and retain multiple possibilities for future development.

[0183] 2. Discipline Selection Strategy

[0184] Core disciplines first

[0185] Mathematics: As a basic subject, mathematical and logical thinking skills are essential regardless of your future career direction.

[0186] English: An international language tool and an important guarantee for further study and career development.

[0187] Language: Language and expression skills are basic skills in all fields.

[0188] Explore subject combinations

[0189] Balance of arts and sciences: Choose subjects such as physics, biology, and history to broaden your knowledge.

[0190] Interest-based: If students have a specific interest in a subject, such as art or programming, they can choose it as a supplementary elective.

[0191] Retain flexibility

[0192] Avoid premature orientation and give priority to a diverse combination of disciplines to provide room for future changes in direction.

[0193] III. Action Plan

[0194] Short-term plan: subject selection and adaptation

[0195] Communicate deeply with children: understand their interest in various subjects and their learning experience.

[0196] Consult teachers and experts: Listen to the school’s suggestions on students’ subject choices and make a comprehensive assessment based on their subject strengths.

[0197] First round of subject selection: Determine core and exploratory subjects based on the principles of balance and breadth.

[0198] Mid-term plan: interest discovery and optimization

[0199] Interest cultivation: Discover children’s potential interests through extracurricular activities, reading and practice.

[0200] 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.

[0201] Long-term plan: career experience and positioning

[0202] Career experience activities: Participate in career experience programs during summer and winter vacations or on weekends, such as company visits, practical activities, etc.

[0203] Future trend analysis: Understand the subject requirements of current hot fields (such as artificial intelligence, healthcare, engineering, etc.).

[0204] IV. Specific Implementation Plan

[0205]

[0206] V. Suggestions and Precautions

[0207] 1) Focus on balanced selection and gradually clarify the direction. Premature orientation may limit future selection space, and a balanced curriculum helps provide more possibilities for children.

[0208] 2) Actively participate in extracurricular activities to stimulate interest. Through elective courses, clubs, career experiences, etc., help children discover their interested fields faster.

[0209] 3) Make dynamic adjustments and flexibly respond to changes. Regularly communicate with children, pay attention to academic performance, interest changes, and mental state, and adjust the direction in a timely manner when necessary.

[0210] VI. Conclusion

[0211] The first year of high school is a crucial period for subject selection and interest cultivation. A steady and flexible strategy should be adopted, based on maintaining balanced development, gradually stimulating interest, exploring directions, and gradually positioning future directions in combination with career experience activities. Through dynamic adjustment and multi-party support, help children lay a solid foundation for the future.

[0212] If you need more in-depth personalized tutoring or career planning advice, please feel free to provide further background information, and we will optimize the plan!

[0213] The technical features of the general user version are as follows:

[0214] Core Features

[0215] 1. Prompt + Guiding Word Technology

[0216] The system parses the user's natural language input through Python logic, extracts key information (such as goals, backgrounds, and limiting conditions), and generates standardized prompts to ensure efficient interaction with the AI model.

[0217] 1) Function Highlights:

[0218] Dynamically generate prompts to adapt to task types and question complexities.

[0219] User feedback drives the guiding word module to optimize the inference path in real time.

[0220] 2) Differences:

[0221] The general user version provides a fixed input template, which is suitable for quickly starting tasks, differentiating from the domain adaptation of the technical user version and the dynamic completion function of the enterprise user version.

[0222] 2. Fixed Role Configuration

[0223] The system assigns roles by default, such as task planners and optimization advisors, to automatically complete the task division of labor.

[0224] 1) Feature Highlights:

[0225] The role configuration is simple and efficient, suitable for non-professional users to quickly get started.

[0226] 2) Differences:

[0227] Different from the technical user version that supports extended roles and the dynamic collaboration of the enterprise user version, the general user version emphasizes simplicity and the fixed division of basic roles.

[0228] 3. Linear Discussion Process

[0229] Users complete the discussion process step by step, and each link has clear task guidance.

[0230] 1) Feature Highlights:

[0231] The system gradually generates solutions according to user input and supports multi-round feedback and adjustment.

[0232] 2) Differences:

[0233] The discussion process of the general user version is linearly designed, while the technical user version emphasizes difference analysis, and the enterprise user version provides non-linear jump support.

[0234] 4. Final Round Confirmation and Automatic Export

[0235] After the user completes the discussion, the system automatically enters the final round confirmation, prompting the user to select the next operation.

[0236] 1) Feature Highlights:

[0237] Support the user to choose to export the final report or continue to optimize.

[0238] When the user has no operation, the system automatically generates a standardized output document.

[0239] 2) Differences:

[0240] The general user version provides fixed-format final round confirmation prompts, which are significantly different from the domain recommendation prompts of the technical user version and the dynamic recommendation function of the enterprise user version.

[0241] 5. Fixed Output Template and Knowledge Deposition

[0242] The output content includes problem description, solution, resource constraints, and follow-up suggestions, adopting a fixed structure to ensure consistency.

[0243] 1) Functional highlights:

[0244] Automatically save discussion records to form a standardized knowledge list for easy reuse.

[0245] 2) Differences:

[0246] The output structure of the general user version is concise and standardized, complementing the domain expansion content of the technical user version and the visualization template of the enterprise user version.

[0247] Technical value of the general user version

[0248] 1) Universality and ease of use: Through fixed templates and linear process design, the general user version is suitable for ordinary users to get started quickly.

[0249] 2) Efficiency and stability: Provide clear task paths and dynamic feedback support to ensure efficient task completion.

[0250] 3) Long-term support: Through the knowledge precipitation function, help users record discussion results and reuse solutions.

[0251] 2. Technical user scenario: Open the custom function of the prompt module, support technical users to edit prompt words and guiding word logic based on domain requirements, and dynamically call resources. This version is optimized based on the technical module of "Domain Expert Mode", and is based on the dynamic adaptation module of natural language and software language. Combining natural language interaction and Python logic processing, it provides accurate domain support and an efficient dynamic feedback mechanism, providing intelligent and efficient dialogue strategy support, especially suitable for technical users and industry users, meeting the multi-level needs of technical users and industry users. Through keyword triggering of the domain expert mode module, authoritative resource call, dynamic feedback mechanism of the dynamic feedback module, and role collaboration of the simulation role module, generate professional solutions. The specific steps are as follows:

[0252] Step 1: Enter basic information (required content for users)

[0253] 1. Today's topic: (Please briefly describe the problem to be solved.) Example:

[0254] 1) How to improve the product recommendation algorithm?

[0255] 2) How to optimize the market growth strategy?

[0256] 2. Briefly describe: (Please describe the problem background and requirements in detail.) Example:

[0257] 1) The current algorithm has bottlenecks in performance and cannot meet the needs of real-time recommendation.

[0258] 2) It is necessary to deeply analyze the market growth points and formulate strategies.

[0259] 3. Objectives and requirements: (Please clarify the goals and expected results.) Example:

[0260] 1) Improve the performance of the recommendation algorithm and increase the user click-through rate.

[0261] 2) Improve the accuracy of market analysis and support decision-making.

[0262] Background information: (Please supplement the necessary background) (such as time range, resource constraints, etc.). Example:

[0263] 1) The project budget is limited, and it is necessary to select an efficient solution.

[0264] 2) Involving multi-department collaboration, different goals need to be taken into account.

[0265] The embodiments of the present invention are as follows:

[0266] Today's theme ({Design a future home drone});

[0267] Brief description ({Design requirements, the future home drone product should be suitable for use by urban families or families in remote rural areas. Fly within 100 kilometers. Solve the congestion problem in the city and the inconvenient transportation in remote rural areas. The drone can hover on the roof or balcony of the home for convenient getting on and off, etc.});

[0268] Objectives and requirements ({It is required to be able to carry three or five people. The mobile phone can determine the route. The price is within the threshold x. Provide the appearance design of the drone and related design parameters for submission to the factory for production.});

[0269] Association of technical terms:

[0270] Prompt: The user's natural language input triggers the Python logic parsing, generates structured data and stimulates the inference path of the AI large model.

[0271] Python logic trigger point annotation:

[0272] User input parsing → Generate domain-specific prompt.

[0273] Adjust the module call path → Improve adaptability.

[0274] Step 2: Set discussion parameters (system guidance and partial automatic generation)

[0275] Domain keyword trigger:

[0276] The user inputs domain keywords (such as "academic", "industry", "technical").

[0277] The system automatically switches to the corresponding professional mode through the domain expert mode, and loads the guiding word module and the resource calling logic.

[0278] Subject type (user selection or system-generated guiding parameters):

[0279] Academic type (e.g., exploration of cutting-edge technologies, analysis of theoretical support).

[0280] Industry type (e.g., insight into market trends, analysis of industry reports).

[0281] Technical type (e.g., patent evaluation, optimization of technology application).

[0282] Problem complexity (user filling or system allocation):

[0283] Single-domain problem (e.g., optimization of data cleaning).

[0284] Cross-domain problem (e.g., combination of industry analysis and technology implementation).

[0285] Goals and expected results:

[0286] The user selects one of the following goals and specifies the specific content:

[0287] Innovation goal: Generate original technical solutions.

[0288] Market applicability goal: Improve user conversion rate.

[0289] Authority goal: Generate highly credible solutions based on academic resources.

[0290] Technical term association:

[0291] Domain expert mode: Dynamically call professional domain modules through Python logic and load key resource support.

[0292] Python logic trigger point annotation:

[0293] Match the module according to the domain keyword → Call authoritative resources.

[0294] Dynamically optimize the discussion parameters → Improve the accuracy of the generated solutions.

[0295] Step 3: Role configuration (system automatically assigns and allows users to adjust)

[0296] Basic roles (system default generation):

[0297] Technical idea generator: Propose the core idea or the preliminary concept of the theme.

[0298] Technical evaluator: Analyze the feasibility of the solution and the technical implementation path.

[0299] Optimization feedback provider: Provide improvement suggestions from the application perspective.

[0300] Application predictor: Predict the applicability of the solution in future scenarios.

[0301] Extended roles (users can add):

[0302] Academic advisor: Provide theoretical support and critical literature analysis.

[0303] Data analyst: Support multi-dimensional data analysis and structured output.

[0304] Industry advisor: Provide market trend insights and strategic optimization suggestions.

[0305] Technical term association:

[0306] Guide words: User feedback triggers Python logic to adjust the role task division and module call path.

[0307] Python logic trigger point annotation:

[0308] Dynamic role configuration → Assign responsibilities based on user needs.

[0309] Module task optimization → Improve discussion efficiency.

[0310] Step 4: Discussion process (system automatically guides and dynamically adjusts)

[0311] First round of discussion: Generate a preliminary solution

[0312] The system generates a preliminary solution and analysis results based on role tasks. Example:

[0313] Idea generator: Put forward preliminary ideas.

[0314] Technical evaluator: Analyze the feasibility of technical implementation.

[0315] Optimization feedback provider: Put forward specific improvement suggestions.

[0316] Application predictor: Predict the future applicability of the solution.

[0317] Multiple rounds of iteration and feedback optimization:

[0318] After the user provides feedback, the system adjusts the solution content through Python logic, including resource call logic and role division.

[0319] Final round confirmation:

[0320] When the user has completed all feedback and there are no pending tasks, the system will automatically pop up a final confirmation prompt: The discussion process is complete. Please select the next step:

[0321] [Export the final report immediately]

[0322] [Continue to optimize the current plan]

[0323] [Integrate the optimized content to form and output the optimized final plan]

[0324] [Exit and save the current progress]

[0325] Dynamic prompt: Based on the field or task type selected by the user, the system can recommend the next step:

[0326] Academic tasks: It is recommended that the user export the analysis report and add references.

[0327] Technical tasks: It is recommended that the user add technical parameters and enter the next round of discussion.

[0328] Technical term association:

[0329] Feedback loop: User feedback triggers the optimization of the prompt text and adjusts the system's reasoning path.

[0330] Python logic trigger point annotation:

[0331] Analysis of differences → Adjust the strategy for generating the plan.

[0332] Dynamically optimize the discussion content → Improve the adaptability.

[0333] Step 5: Output and precipitation

[0334] Output of the final plan:

[0335] The system generates a complete document, including:

[0336] Support from authoritative arguments: A list of theoretical supports with academic or industry backgrounds.

[0337] Solution path diagram: Detailed implementation steps and priorities.

[0338] Domain dynamic recommendation: Recommendations on the latest domain dynamics and cutting-edge achievements.

[0339] Knowledge precipitation:

[0340] Automatically save the discussion records, dynamically adjusted content, and output documents to form a user-specific knowledge base.

[0341] Technical term association:

[0342] Background Memory: Enhance the dialogue context memory ability through Python logic to ensure the efficient invocation of long-term user data.

[0343] Python Logic Trigger Point Annotation:

[0344] Save user interaction records → Dynamically update the knowledge base.

[0345] Provide long-term optimization suggestions → Strengthen personalized support.

[0346] The results of the embodiments of the present invention based on the above process are as follows. The following is the content output by the large model:

[0347] The following is the discussion plan framework design based on the content you provided:

[0348] Step 1: Basic Information Input

[0349] Today's Topic: Design a future home drone

[0350] Brief Description: The future home drone needs to be suitable for urban families and remote rural areas and have the following characteristics:

[0351] 100-kilometer flight distance.

[0352] Alleviate urban traffic congestion.

[0353] Meet the transportation needs of remote areas.

[0354] Can hover on the roof or balcony of the home for easy access.

[0355] Purpose Requirements:

[0356] Carry 3 - 5 people and support route setting via smartphone.

[0357] Control the cost within the threshold x.

[0358] Provide the appearance design and design parameters to provide a basis for factory production.

[0359] Step 2: Set Discussion Parameters

[0360] Domain Keyword Trigger

[0361] User enters keywords: Technical (drone design, parameter optimization).

[0362] System switches to mode: Domain expert mode is activated, and drone-related resources are loaded.

[0363] Topic Type:

[0364] Technical: Focus on patent evaluation and technology application optimization.

[0365] Problem Complexity:

[0366] Cross - domain problem (combination of transportation and technology).

[0367] Goals and Expected Results:

[0368] Innovative Goal: Generate original drone design solutions to meet urban and rural needs.

[0369] Step 3: Role Configuration

[0370] Basic Roles:

[0371] Technology Idea Generator: Design the appearance and functions of the drone.

[0372] Technology Evaluator: Evaluate the feasibility of flight distance, load - carrying capacity, and cost control.

[0373] Optimization Feedback Provider: Provide improvement suggestions, such as energy efficiency and ease of use.

[0374] Application Predictor: Predict future market applicability and potential application scenarios.

[0375] Extended Roles:

[0376] Academic Advisor: Analyze flight principles and cutting - edge technology trends.

[0377] Industry Advisor: Provide suggestions on market demand and pricing strategies.

[0378] Step 4: Discussion Process

[0379] First Round of Discussion: Generate Preliminary Solutions

[0380] Tasks of the System - Guiding Role:

[0381] Idea Generator: Propose the appearance and docking design of the drone.

[0382] Technology Evaluator: Conduct a preliminary analysis of the drone's performance parameters (flight speed, energy type).

[0383] Optimization Feedback Provider: Evaluate the user experience (ease of use and safety).

[0384] Application Predictor: Predict market demand.

[0385] Multiple Rounds of Iteration and Feedback Optimization:

[0386] 1) After the user provides feedback, the system optimizes the design:

[0387] Improve flight stability.

[0388] Optimize cost and energy allocation.

[0389] 2) Dynamic adjustment plan:

[0390] Add solar charging or hybrid energy mode.

[0391] Improve the hovering mechanism and safety measures.

[0392] Final round confirmation: Generate the final optimization plan.

[0393] Step 5: Output and precipitation

[0394] Output of the final plan:

[0395] 1) Support from authoritative arguments:

[0396] Including technical parameters and market trend analysis.

[0397] 2) Solution path map:

[0398] Drone design process and production priorities.

[0399] 3) Recommendations on field dynamics:

[0400] Latest drone technology dynamics and research results.

[0401] Knowledge precipitation:

[0402] Automatically save the complete discussion record, establish a dedicated knowledge base for drone design, and support subsequent optimization and R & D.

[0403] Association of technical terms and Python logic trigger points

[0404] 1) Prompt parsing: Parse user requirements and generate technical design prompts.

[0405] 2) Feedback loop: Optimize the technical path and design plan according to feedback.

[0406] 3) Background memory: Save interaction records, update the knowledge base, and improve future support capabilities.

[0407] Hope this design framework can help you quickly generate high-quality drone design plans! If you need a detailed discussion of each step, please let us know!

[0408] You said: Please provide a detailed specific plan.

[0409] The AI large model said:

[0410] AI large model

[0411] The following is a detailed future home drone design plan, including appearance, functional design, and parameter configuration:

[0412] Future home drone design plan

[0413] I. Project Overview

[0414] Subject: Design of Home-use Drone

[0415] Objective: To meet the needs of urban and rural families and provide a convenient and safe short-distance transportation vehicle.

[0416] II. Core Content of Drone Design

[0417] 1. Appearance Design

[0418] Shape: Streamlined design, similar to a flying saucer or a small helicopter, to reduce air resistance and enhance aesthetics.

[0419] Material: Lightweight and high-strength composite materials (such as carbon fiber or aluminum alloy).

[0420] Color: Customizable, providing multiple color schemes.

[0421] Signature Design: Equipped with foldable rotors for easy docking and storage.

[0422] 2. Function Design

[0423] 1) Load-bearing Capacity:

[0424] Payload: 3 - 5 people, with a total weight of about 300 - 500 kg.

[0425] Seating Layout: Compact cockpit, ergonomically designed and equipped with seat belts.

[0426] 2) Flight Ability:

[0427] Flight Distance: Endurance of 100 km per single charge.

[0428] Flight Altitude: 200 - 500 meters to avoid conflicts with buildings.

[0429] Flight Speed: Up to 100 km / h, with a cruising speed of 60 - 80 km / h.

[0430] 3) Power System:

[0431] Active electric drive, using lithium batteries or solid-state batteries.

[0432] Equipped with solar-assisted charging panels (automatic charging when parked on the roof).

[0433] 4) Intelligent Control:

[0434] Set the route through a mobile application, supporting both autonomous driving and manual driving modes.

[0435] GPS real-time navigation system, combined with geofencing technology, to avoid no-fly zones.

[0436] Equipped with LIDAR (Light Detection and Ranging) and ultrasonic sensors for obstacle avoidance and precise landing.

[0437] 5) Takeoff and landing system:

[0438] Vertical Takeoff and Landing (VTOL) technology, allowing direct takeoff and landing on the rooftops, balconies or parking lots of homes.

[0439] Equipped with a safety locking mechanism to ensure stable docking.

[0440] 6) Safety measures:

[0441] Multiple redundant flight control systems to prevent single-point failures.

[0442] Emergency parachute system.

[0443] Wind and rain protection design to adapt to complex climate conditions.

[0444] 7) Intelligent assistance functions:

[0445] Voice recognition and intelligent assistant for convenient operation.

[0446] Real-time flight status monitoring (such as battery level, location, wind speed).

[0447] III. Technical parameters

[0448]

[0449] IV. Market applicability analysis

[0450] 1) Urban households

[0451] Main uses: Daily commuting, avoiding congested traffic.

[0452] Characteristic advantages: Convenient docking (on rooftops or balconies), intelligent navigation.

[0453] 2) Rural households

[0454] Main uses: Solving transportation inconvenience in remote areas and shortening travel time.

[0455] Characteristic advantages: Long battery life, adapting to various complex terrains.

[0456] V. Production costs and market prices

[0457] 1) Production costs

[0458] Key components (such as batteries, radars, composite materials) account for approximately 70% of the total cost.

[0459] The estimated manufacturing cost per unit is not expected to exceed the threshold x price.

[0460] 2) Market selling price

[0461] Pricing strategy: Below the threshold x price to meet the needs of household consumption.

[0462] Value-added services: Provide customized color matching and maintenance services, charged additionally.

[0463] VI. Project implementation path

[0464] 1) Technology R & D

[0465] Phase 1: Verify core technologies (such as power system, vertical takeoff and landing technology).

[0466] Phase 2: Develop a prototype and conduct small-scale tests.

[0467] 2) Testing and optimization

[0468] Safety testing: Verify the emergency system and obstacle avoidance ability.

[0469] Performance optimization: Improve endurance and intelligent navigation.

[0470] 3) Mass production and sales

[0471] Produce in small batches and test the market response.

[0472] Promote through e-commerce platforms and offline channels.

[0473] VII. Additional functions and future upgrades

[0474] Add a detachable cargo hold to support logistics transportation.

[0475] Develop a shared drone mode for urban short-term rental services.

[0476] Equip with an unmanned driving AI assistant to further improve the automation level.

[0477] 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!

[0478] You said: Provide the final complete solution. Including drawings.

[0479] The AI large model said:

[0480] The AI large model generated drawings, such as Figure 4 as shown.

[0481] The final drone design solution has been generated and is accompanied by detailed design drawings. The drawings show a household drone suitable for urban and rural areas, with the following characteristics:

[0482] 1) Appearance: Streamlined design, folding rotors, convenient for storage and docking.

[0483] 2) Function: Can carry 3 - 5 people, supports electric and solar hybrid power.

[0484] 3) Docking: Vertical takeoff and landing, suitable for docking on rooftops and balconies.

[0485] 4) Intelligent control: Combines mobile APP route planning and autonomous driving.

[0486] You can click on the image to view and download for further reference or submit for factory production. If you need to add more details, feel free to let us know!

[0487] The technical features of the technical user version are as follows:

[0488] Core features

[0489] 1. Domain keyword triggering and professional module loading

[0490] The system activates the domain expert mode through keyword input (such as "technical type", "industry type") and loads professional domain modules and resources.

[0491] 1) Functional highlights:

[0492] Automatically matches keywords with task templates to improve adaptation efficiency.

[0493] Supports multi - domain switching to meet the needs of cross - domain collaboration.

[0494] 2) Differences:

[0495] Compared with the ordinary user version, the technical user version supports more fine - grained domain module loading.

[0496] Different from the enterprise user version, the technical user version pays more attention to the user's explicit domain input rather than automatic scenario prediction.

[0497] 2. Customized role configuration

[0498] Users can dynamically adjust role tasks on the basis roles automatically generated by the system (such as technical idea generators, technical evaluators).

[0499] 1) Functional highlights:

[0500] Provides extended roles (such as industry consultants, academic consultants) to provide professional support for specific tasks.

[0501] The role division is clearer, supporting refined operations for complex tasks.

[0502] 2) Differences:

[0503] The roles in the general user version are fixed, those in the enterprise user version have dynamic collaboration, while the technology user version focuses on role expansion in the professional field.

[0504] 3. Multi-round discussion and feedback optimization

[0505] Support multi-round feedback adjustment by users, and the system optimizes the solution logic according to the feedback.

[0506] 1) Feature highlights:

[0507] Provide difference analysis and resource call adjustment to gradually improve the solution.

[0508] User feedback triggers role reallocation and parameter adjustment.

[0509] 2) Differences:

[0510] Compared with the linear discussion process of the general user version, the technology user version adds the difference analysis logic in the feedback.

[0511] The enterprise user version pays more attention to real-time optimization, while the optimization of the technology user version is based on the call and adjustment of domain resources.

[0512] 4. Professional final confirmation and dynamic prompt

[0513] After the discussion ends, the system provides a clear final confirmation prompt and recommends dynamic operations according to the task type.

[0514] 1) Feature highlights:

[0515] Dynamically prompt users to export the analysis report or enter the next round of optimization.

[0516] Provide customized output suggestions according to academic or technical tasks.

[0517] 2) Differences:

[0518] Compared with the fixed confirmation logic of the general user version, the technology user version dynamically generates prompts in combination with domain characteristics.

[0519] The enterprise user version provides a more flexible jump mechanism, while the technology user version emphasizes domain-specific final suggestions.

[0520] 5. Output structure and knowledge precipitation

[0521] The system supports generating structured documents and saving user discussion records.

[0522] 1) Feature highlights:

[0523] The output includes the solution path diagram, the list of theoretical supports, and domain dynamic recommendations.

[0524] The knowledge precipitation function automatically saves the adjusted content and updates it to the user's knowledge base.

[0525] 2) Difference:

[0526] It provides structured output consistent with the general user version, but the output of the technical user version focuses on domain extension content.

[0527] The output of the enterprise user version adds visualization support, and the technical user version leans towards professional templates.

[0528] Technical value of the technical user version

[0529] 1) Professional domain support: Quickly locate task requirements through domain keyword triggering and module loading in the domain expert mode.

[0530] 2) Fine-grained role division: Support the dynamic configuration of extended roles to meet the professional needs of complex tasks.

[0531] 3) Domain optimization logic: Gradually generate efficient and accurate solutions by combining user feedback and resource call adjustment.

[0532] 3. Enterprise user scenario: Through the domain expert mode module, provide an integrated solution for complex processes for enterprises, and support the complex logical requirements of multi-department collaboration. This version is optimized based on the "prompt + guiding word" and "domain expert mode" technical modules, combined with natural language interaction, dynamic feedback optimization and long-term memory function, providing high-intelligence and high-efficiency solution generation support. Users can obtain accurate and efficient solutions in multi-domain and multi-level tasks, and at the same time support multi-round interaction and long-term knowledge management to meet the complex needs of technical users and industry users. The specific steps are as follows:

[0533] Step 1: Input basic information (required content for users)

[0534] 1. Today's topic: Please briefly describe the problem to be solved. Example:

[0535] 1) How to improve the recommendation system algorithm?

[0536] 2) How to formulate a marketing strategy?

[0537] 2. Simple description: Please describe the problem background and related requirements in detail. Example:

[0538] 1) The current algorithm has bottlenecks and is difficult to meet the real-time recommendation requirements.

[0539] 2) Need to formulate an efficient marketing plan to increase quarterly sales.

[0540] 3. Objectives and Requirements: Please describe your expected results. Example:

[0541] 1) Improve the click-through rate and conversion rate of the recommendation system.

[0542] 2) Optimize market analysis and increase the coverage of target users.

[0543] 4. Background Information: The system will automatically identify and complete the missing parts of the information. Example:

[0544] 1) Project budget constraints.

[0545] 2) Involve collaboration among multiple departments.

[0546] The embodiments of the present invention are as follows:

[0547] Today's Topic: "[Feasibility Analysis of the Cultural and Tourism AI Model Project]"

[0548] Brief Description: "[The company is preparing to invest in the cultural and tourism AI model project]"

[0549] Objectives and Requirements: "[Judge whether the project has prospects. How large is the market space? How big is the investment risk? And give a specific marketing plan for the project.]"

[0550] Background Information: "[Provide the white paper of the local cultural and tourism AI model project in the attachment.]" New Features: Input Intelligent Completion and Scenario Prediction

[0551] The system automatically identifies the missing information in the input and prompts the user to supplement it.

[0552] Based on keyword analysis, recommend task scenarios (such as technical, industry).

[0553] Technical Term Association:

[0554] Dynamic Generation of Hints: The user input is parsed through Python logic to generate domain-adapted hints.

[0555] Scenario Prediction: The system intelligently activates domain templates and related resources based on the input content.

[0556] Step 2: Set Discussion Parameters (System Guidance and Dynamic Adjustment)

[0557] Domain Keyword Trigger:

[0558] The user enters keywords (such as "technical", "industry"), and the system automatically activates the domain expert mode.

[0559] Problem Complexity (User Selection or System Recommendation):

[0560] Single-domain problem: The task is single, focusing on specific execution.

[0561] Cross-domain problem: Require collaboration among multiple departments or technology integration.

[0562] Goals and expected results (system recommendation):

[0563] Innovative goal: Propose an original technical solution.

[0564] Market applicability goal: Optimize market coverage and user conversion.

[0565] Authority goal: Generate highly credible analyses and reports.

[0566] Step 3: Role configuration (dynamic adjustment and collaboration support)

[0567] Basic roles:

[0568] Idea generator: Propose the core idea or theme concept.

[0569] Technical evaluator: Analyze the feasibility of the solution and provide improvement suggestions.

[0570] Optimization feedback provider: Optimize the solution from the application perspective.

[0571] Application predictor: Predict the market applicability of the solution.

[0572] Dynamic role adjustment:

[0573] After the user provides feedback, the system dynamically adjusts the role tasks according to the new requirements. Example: When the user adds "industry adaptation analysis", the system automatically enables the "industry consultant".

[0574] Cross-role collaboration:

[0575] Support synchronous collaboration between role modules to improve the discussion efficiency. Example: The technical evaluator and the data analyst collaborate to generate a feasibility report.

[0576] Step 4: Discussion process (non-linear jump and real-time optimization)

[0577] First round of discussion: Generate a preliminary solution

[0578] The system generates a preliminary solution based on the user input and role tasks.

[0579] Multiple rounds of feedback and optimization:

[0580] The user provides feedback, and the system adjusts the solution and division of labor in real time through Python logic. Example: Add "market segmentation analysis" or adjust technical parameters.

[0581] Final round of confirmation:

[0582] After the discussion process is completed, the system pops up a final round confirmation prompt:

[0583] [Export the final report immediately]

[0584] [Continue to optimize the current plan]

[0585] [Integrate the optimized content to form and output the optimized final plan]

[0586] [Exit and save the current progress]

[0587] Dynamic recommendation:

[0588] Technical tasks: It is recommended to export an execution plan containing technical parameters.

[0589] Marketing tasks: It is recommended to generate a user segmentation report.

[0590] Non-linear jump support:

[0591] Users can jump to other discussion steps at any time (such as directly generating a plan).

[0592] Technical term association:

[0593] Feedback loop: User feedback triggers the adjustment of prompt words to optimize the plan.

[0594] Step 5: Output and knowledge precipitation

[0595] Diversified output templates:

[0596] Solution path diagram: Clearly define the steps and priorities.

[0597] Dynamic analysis charts: Such as market trend charts, technical parameter comparison charts.

[0598] Domain recommendation report: Generate extended reading based on the latest domain dynamics.

[0599] Knowledge precipitation and long-term memory management:

[0600] The system saves the discussion records and dynamically updates the user knowledge base.

[0601] Personalized optimization: Generate user-preferred plans by combining long-term memory.

[0602] The results of the embodiments of the present invention based on the above process are as follows. The following is the content output by the large model:

[0603] According to the input content you provided and the background information in the white paper of the local cultural and tourism AI large model, I suggest carrying out project analysis and plan design according to the following steps:

[0604] Today's Topic: "Feasibility Analysis of the Cultural and Tourism AI Model Project" Brief Description: "The company is preparing to invest in the cultural and tourism AI model project"

[0605] Purpose and Requirements: "Judge whether the project has prospects, how large the market space is, the investment risks, and propose a specific marketing plan" Background Information: "Based on the industry analysis and technology trends in the white paper of the local cultural and tourism AI model project"

[0606] Step 2: Recommended Intelligent Completion and Scenario Prediction

[0607] 1) Input Completion Suggestions: If the user does not provide complete market demands or goal settings, supplements can be made based on the market analysis in the white paper (such as the innovative applications of AI technology in the cultural and tourism industry).

[0608] 2) Scenario Prediction Recommendations: According to the keywords "technology-based" and "industry-based", recommend technology scenarios (such as intelligent guided tour systems, data analysis support) and industry scenarios (such as the creation and marketing promotion of local characteristic IPs).

[0609] Step 3: Discussion on Parameter Settings

[0610] 1. Domain Keyword Trigger: Activate the domain expert mode and focus on "digitalization of the cultural and tourism industry", "local IP innovation", and "data-driven precise insights".

[0611] 2. Problem Complexity Selection: Select "cross-domain problems" and comprehensively evaluate various factors such as technology and market.

[0612] 3. Goals and Expected Results:

[0613] 1) Innovation Goals: Propose a new cultural and tourism AI model architecture and functional design.

[0614] 2) Market Applicability Goals: Define the market positioning and user coverage of the project.

[0615] 3) Authority Goals: Provide highly credible market forecasts and industry reports.

[0616] Step 4: Role Configuration and Discussion Process

[0617] 1. Role Configuration:

[0618] 1) Creative Generators: Design innovative products such as cultural and tourism IP digital humans and metaverse virtual scenic spots.

[0619] 2) Technical Evaluators: Analyze the technical architecture and security of the local cultural and tourism AI model.

[0620] 3) Optimization Feedbackers: Optimize the matching degree between the product and user needs.

[0621] 4) Application predictors: Evaluate the market potential and operational risks of the model.

[0622] 2. Discussion process:

[0623] 1) First round: Generate a preliminary plan based on the content of the white paper, with a focus on market analysis.

[0624] 2) Multiple rounds of optimization: Refine the technical architecture and marketing strategy based on user feedback.

[0625] 3) Final confirmation: Output a complete report, including the technical solution, market analysis, risk assessment, and marketing plan.

[0626] Step 5: Output and knowledge precipitation

[0627] 1. Output templates:

[0628] 1) Solution roadmap: Define the key steps of technology development and market promotion.

[0629] 2) Dynamic analysis charts: Such as market demand trend charts and technical application scenario comparison tables.

[0630] 3) Domain recommendation report: Provide extended reading on the industry applications of the cultural and tourism AI model by combining cases and data in the white paper.

[0631] 2. Knowledge precipitation: Systematically save the analysis records, update the user knowledge base, and provide data support for subsequent decision-making.

[0632] If you need to further refine the specific technical solution or marketing strategy, more information or requirements can be provided at any time!

[0633] You say: Continue to optimize

[0634] The AI large model says: No.

[0635] Based on the current analysis and your need for further optimization, the following is a further deepening and optimization plan for the feasibility analysis and solution design of the cultural and tourism AI model project:

[0636] Optimize the content structure and process

[0637] 1. Input and scenario intelligent optimization

[0638] Optimization of the information completion mechanism: The system automatically detects whether the input contains the following key factors:

[0639] 1) Market size and demand: Target customer groups, consumption preferences, market capacity.

[0640] 2) Technical feasibility: The technical foundation and implementation path of the project.

[0641] 3) Risk factors: technical risks, market fluctuations, and capital planning.

[0642] Scene intelligent recommendation: Introduce the scene simulation function:

[0643] 1) For "technical" tasks such as AI tour guides and digital content generation, recommend technical paths.

[0644] 2) For "industry" tasks such as cultural and tourism IP operation, generate industry trend analysis and profit models.

[0645] 2. Deepen dynamic role configuration

[0646] Strengthen basic role tasks:

[0647] Creative generator: Further refine the image design and content production process of the cultural and tourism IP digital human, such as personalized recommendation and applications combined with the metaverse.

[0648] Technical assessor: Use the model architecture analysis based on white papers to evaluate the computing requirements, data security, and scalability of AI technologies.

[0649] Optimization feedbacker: Optimize the recommendation algorithm and interactive experience through user data simulation.

[0650] Application predictor: Based on market trend modeling, predict the project's revenue and market share within three years.

[0651] Dynamic role adjustment and new functions:

[0652] Risk management expert: Specialize in analyzing the data security and market fluctuation risks that the project may face.

[0653] Cultural consultant: Provide suggestions for in-depth exploration of local cultural content and IP creation.

[0654] 3. Optimize technical solutions and architectures

[0655] Model architecture suggestions:

[0656] Use RouteLLM as the core architecture and combine the following components:

[0657] Natural language processing module: Support multilingual interaction and personalized recommendation.

[0658] Large-scale knowledge graph: Integrate local culture, scenic spot information, and tourist behavior data.

[0659] Low-computing-power lightweight model: Optimize costs while ensuring the model's operation efficiency.

[0660] Multi-model collaboration solution: Combine general models (such as large AI models) with local customized models to enhance the ability to explore cultural connotations.

[0661] Platform function expansion:

[0662] Digital content generation tool: Support the efficient production of cultural and tourism promotion content.

[0663] Interactive experience module: Develop AR / VR-based scenario experiences to attract tourists to participate deeply.

[0664] Data analysis and management platform: Real-time monitor market data and provide support for strategy optimization.

[0665] 4. Market applicability and marketing strategy optimization

[0666] Market segmentation and positioning:

[0667] According to the market data analyzed in the white paper, segment customer groups, such as parent-child families, silver-haired people, and cultural enthusiasts.

[0668] Identify the core needs of the segmented market and design targeted products and services.

[0669] Marketing strategy:

[0670] Launch a pilot project for the application of the cultural and tourism AI model (such as the digital human guide service in well-known local scenic spots).

[0671] Promote through social media and stimulate user participation based on user-generated content (UGC).

[0672] Introduce cross-platform cooperation, such as cooperating with online travel (OTA) platforms to link online reservations with offline experiences.

[0673] 5. Risk assessment and dynamic feedback mechanism

[0674] Risk assessment:

[0675] Market volatility risk: Analyze the impact of peak and off-peak tourist seasons and external factors through scenario simulation.

[0676] Technical risk: Evaluate the operation efficiency, failure rate, and data privacy protection capabilities of the AI model.

[0677] Dynamic feedback mechanism:

[0678] Integrate user feedback into the optimization process in real time to generate a closed loop of "suggestion → adjustment → test → feedback".

[0679] Provide an interactive feedback interface on the user side to optimize the personalized service capabilities of the model.

[0680] 6. Output optimization

[0681] Report structure optimization:

[0682] Cover: Brief description of the project background and objectives.

[0683] Technical solution: Architecture design and functional highlights.

[0684] Market analysis: Target market size, customer profile, trend prediction.

[0685] Risks and countermeasures: Including detailed analysis at the technical and market levels.

[0686] Implementation plan: Phased objectives and resource allocation.

[0687] Appendix: Abstract of literature citation.

[0688] Diversified output forms:

[0689] Visual path map: Display the key nodes of the project from R & D to marketization through a flow chart.

[0690] Market dynamic charts: Including market trends, user growth prediction, investment return analysis.

[0691] Product demonstration video: Simulate the application scenarios of the AI model, such as a digital human explaining scenic spot videos.

[0692] Through these optimizations, you can more comprehensively judge the feasibility of the cultural and tourism AI model project, and at the same time generate a more systematic and highly credible report. If you need specific implementation steps or tool support, please continue to provide your requirements!

[0693] You say: Generate and output a more systematic and highly credible complete report

[0694] The large AI model says:

[0695] Feasibility analysis report of the cultural and tourism AI model project

[0696] I. Project background

[0697] The purpose of this project is to combine the innovative technologies and industrial demands of the local cultural and tourism AI model, empower the cultural and 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 and tourism industry, the company plans to invest in the development of the cultural and tourism AI model project, using artificial intelligence technology and big data analysis to achieve functions such as cultural and tourism IP digital humans, intelligent guides, and metaverse experiences. This report aims to comprehensively analyze the feasibility of the project from aspects such as industry status, technical analysis, market potential, investment planning, and risk assessment.

[0698] II. Industry and market analysis

[0699] 1. Current situation of the cultural and tourism industry

[0700] In recent years, the cultural and tourism industry has been booming. At the same time, technologies such as AI and the metaverse have been widely applied in the cultural and tourism industry, providing personalized and immersive experiences for tourists and helping the industry to transform and upgrade.

[0701] 2. Market Demand and Characteristics

[0702] Diverse demands: Tourists pay more attention to personalized and high-quality tourism services. Parent-child families and the elderly are important segmented markets.

[0703] Digital trends: The demand for intelligent guides, digital content generation, and interactive experiences has increased significantly.

[0704] Competition pattern: The local cultural and tourism market shows differentiated competition, and the creation of digital IP has become the core competitiveness.

[0705] 3. Potential Market of the Project

[0706] Cultural and tourism IP digital humans, intelligent guides, and metaverse experience projects will become important driving forces for promoting the upgrading of the cultural and tourism industry. By deeply exploring local cultural characteristics and combining AI technology, the project has strong market competitiveness and sustainable development potential.

[0707] III. Technical and Architecture Analysis

[0708] 1. Technical Architecture

[0709] The technical architecture of the project is based on multi-model collaboration, adopts the RouteLLM framework, combines general large language models and local customized models, and realizes the deep integration of technology and culture. The main features include:

[0710] Modular design: With the LangChain knowledge base as the core, it supports flexible expansion and low computing power operation.

[0711] Natural language processing module: Multi-language interaction and personalized recommendation to enhance the tourist experience.

[0712] Cultural and tourism data analysis platform: Integrate local cultural and tourism data to provide accurate decision-making support.

[0713] Enhanced interactive experience: Develop immersive scenarios based on AR / VR technology to innovate the user interactive experience.

[0714] 2. Technical Functions and Innovations

[0715] Cultural and tourism IP digital humans: Develop digital human images with local cultural characteristics for intelligent guides and cultural dissemination.

[0716] Metaverse virtual scenes: Create digital scenic spots through virtual reality technology to achieve immersive experiences.

[0717] Data-driven Decision-making: Analyze tourists' behavior data in real time to optimize operation strategies.

[0718] IV. Marketing Strategies and Implementation Plans

[0719] 1. Market Positioning and Objectives

[0720] Target Customers: Domestic and international tourists, especially parent-child families and culture enthusiasts, as well as cultural and tourism enterprises.

[0721] Market Objectives: Cover major domestic tourist cities within three years and achieve a market share of 20%.

[0722] 2. Marketing Strategies

[0723] Brand Building: Create local IP digital humans, deeply integrate with local culture, and enhance brand influence.

[0724] Channel Promotion: Promote cultural and tourism AI applications through social media, online travel platforms, and short videos.

[0725] User Engagement: Encourage tourists to generate content to enhance interactivity and dissemination effects.

[0726] 3. Implementation Plan

[0727] First Phase (0 - 6 months): Build the technical architecture, complete the development of digital human guides and content generation tools, and pilot apply them to local iconic scenic spots.

[0728] Second Phase (6 - 18 months): Expand to regional cooperation scenic spots, improve marketing strategies, and optimize technology and services through feedback.

[0729] Third Phase (18 - 36 months): Promote comprehensively to major cities across the country, deepen functional modules such as metaverse virtual scenes, and form a standardized operation model.

[0730] V. Investment and Risk Assessment

[0731] 1. Investment Budget

[0732] Technology R & D: Determined based on model development and data management platform construction.

[0733] Marketing Promotion: Determined based on brand building and channel promotion.

[0734] Operation and Maintenance: Determined based on server and labor costs.

[0735] 2. Risk Analysis

[0736] Market Risk: The acceptance of digital products by tourists may fluctuate.

[0737] Technical risk: The operational efficiency of the AI model and its matching degree with user needs need to be continuously optimized.

[0738] 3. Risk countermeasures

[0739] Strengthen market research and optimize product functions and user experience.

[0740] Adopt a modular architecture to flexibly meet the needs of technology upgrades.

[0741] VI. Project expected benefits and social value

[0742] 1. Economic benefits

[0743] Benefit prediction: It is expected to achieve an investment return rate of about 50% within three years.

[0744] Cost optimization: Significantly reduce operating costs through modular design and low-computing-power models.

[0745] 2. Social benefits

[0746] Promote the digital transformation of local culture and tourism and enhance the tourist experience.

[0747] Facilitate the dissemination and inheritance of local culture and enhance cultural confidence.

[0748] Create employment opportunities and drive regional economic development.

[0749] VII. Conclusions and suggestions

[0750] The cultural and tourism AI model project combines local cultural characteristics and the advantages of AI technology, with significant market potential and technical feasibility. Through reasonable planning and implementation, high economic and social benefits can be achieved.

[0751] Suggestions:

[0752] 1. Prioritize piloting local cultural and tourism IP digital human projects to verify technology and market potential.

[0753] 2. Deepen the technical architecture to ensure the deep integration of culture and technology.

[0754] 3. Establish a dynamic feedback mechanism to continuously optimize product functions and user experience.

[0755] This project conforms to the current trend of digital transformation in the cultural and tourism industry and is an important innovative measure to promote the development of local cultural and tourism industries.

[0756] The technical features of the enterprise user version are as follows:

[0757] Core features

[0758] 1. Input intelligent completion and scenario prediction

[0759] The system can intelligently identify and complete missing information based on the user's natural language input, and automatically recommend task scenarios according to keyword analysis.

[0760] 1) Functional highlights:

[0761] Automatically complete the input content, reducing the operation threshold for users.

[0762] The scenario prediction function activates templates in the corresponding field, providing highly customized task support.

[0763] 2) Differences:

[0764] Compared with the fixed input template of the ordinary user version, the enterprise user version has the ability of dynamic completion and scenario recommendation.

[0765] Different from the domain adaptation of the technical user version, the input completion of the enterprise user version is more automated and multi-level.

[0766] 2. Dynamic role configuration and cross-role collaboration

[0767] After the user provides feedback, the system can dynamically adjust the role tasks and support synchronous collaboration between modules.

[0768] 1) Functional highlights:

[0769] Adjust the role task division according to the user's needs, such as adding industry consultants or academic analysis roles.

[0770] Collaboration between roles supports the generation of more accurate and comprehensive solutions.

[0771] 2) Differences:

[0772] The enterprise user version realizes cross-role collaboration and real-time dynamic adjustment, which is different from the relatively fixed role configuration in the technical user version.

[0773] 3. Non-linear discussion process and dynamic optimization

[0774] Support users to jump or adjust in the discussion process without strictly following linear steps.

[0775] 1) Functional highlights:

[0776] Users can jump to any step according to their needs, such as directly entering the solution output from the feedback stage.

[0777] The system optimizes the current solution or generates a new solution path through real-time feedback.

[0778] 2) Differences:

[0779] 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.

[0780] The enterprise user version supports more complex logic adjustments in multiple rounds of optimization to meet advanced requirements.

[0781] 4. Dynamic Recommendation and Final Round Confirmation

[0782] The system recommends suitable output forms or next steps based on the task type and domain characteristics.

[0783] 1) Feature Highlights:

[0784] Dynamically prompt users whether to export the technical parameter plan, generate a market segmentation report, or continue to optimize.

[0785] Final round confirmation ensures the integrity of the discussion results and automatically exports the report when there is no operation.

[0786] 2) Differences:

[0787] The final round confirmation of the enterprise user version combines the domain dynamic recommendation function and is more intelligent than the general user version and the technical user version.

[0788] 5. Diverse Output Templates and Long-Term Memory Support

[0789] The output content not only includes path diagrams and dynamic charts but also combines domain dynamics to generate recommendation reports.

[0790] The system saves the user's discussion records to support knowledge precipitation and long-term optimization.

[0791] 1) Feature Highlights:

[0792] The output results have a high degree of visualization ability, such as generating market trend charts, parameter comparison charts, etc.

[0793] Combined with long-term memory, it intelligently generates optimization solutions preferred by users.

[0794] 2) Differences:

[0795] The diverse output and long-term memory functions of the enterprise user version are extended capabilities that cannot be fully covered by the general user version and the technical user version.

[0796] Technical Value of the Enterprise User Version

[0797] 1) Highly intelligent: Automatic input completion, dynamic role adjustment, and non-linear processes provide accurate and efficient solutions for complex problems.

[0798] 2) Scalability and visualization: Support the graphical presentation of complex tasks and diverse template outputs to meet the needs of users at multiple levels.

[0799] 3) Domain Adaptation and Memory Support: Provide unique personalized optimization capabilities through long-term memory and domain dynamic recommendations.

[0800] The main differences between the three application versions in the present invention are as follows:

[0801]

[0802] The above embodiments are used to explain the present invention rather than limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims of the present invention 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: It is used to parse the topic, content description, target requirements and background information input by the user, generate relevant prompts respectively, and obtain a structured problem description; extract key entities, logical relationships and contextual semantics through semantic analysis, and provide high-quality input for the guide word module; Guide word module: used for prompts generated based on the prompt word module. It generates multi-level guide words in real time and dynamically according to the natural language or task description input by the user. It stimulates the AI ​​big model to respond through prompt words, adjusts the prompt word logic and guide word weights in real time through user feedback, and dynamically adjusts the reasoning path through guide words. 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. Simulation role module: used to dynamically generate simulation roles in the corresponding technical field according to user needs, supporting the generation of technical analysts, optimization consultants and market analysts, and sharing tasks through collaborative logic to support multi-dimensional analysis and optimization of cross-domain problems; through role collaboration, multiple rounds of interaction are achieved to 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 integrate keyword triggering, resource call logic, and role collaboration mechanism, generate specialized dynamic solutions in related technical fields, and continuously optimize resource paths and logic; 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 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.

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

5. 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.

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 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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