Multi-AI module collaborative intelligent task processing system and iterative optimization method

Abstract

The invention discloses a multi-AI module collaborative intelligent task processing system and an iterative optimization method. The multi-AI module collaborative intelligent task processing system comprises a demand understanding AI module, an implementation AI module, a verification AI module and a correction AI module. The demand understanding AI module is used for receiving a natural language demand of a user and outputting a result to the implementation AI module, the implementation AI module is used for outputting a preliminary implementation result to the verification AI module, and the verification AI module outputs an implementation result which does not conform to a standard to the correction AI module in combination with the quality evaluation database; and the correction AI module combines a correction strategy knowledge base and a historical learning experience base to generate a correction instruction optimization strategy for the implementation result which does not meet the standard, and outputs the correction instruction optimization strategy to the implementation AI module again. The algorithm part comprises a constructed intelligent difference quantization formula and a correction loop updating formula, and accurate and automatic iterative optimization can be realized through the established vectorization model based on the multi-dimensional feature space.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence task processing technology, specifically to an intelligent task processing system and iterative optimization method that enables collaboration among multiple AI modules. Background Technology

[0002] In the field of artificial intelligence task processing, current systems generally employ a single AI model or a sequential processing mode, resulting in problems such as low efficiency, uncontrollable quality, and the need for significant human intervention. Especially in complex task scenarios such as code generation, document creation, and design generation, existing technologies suffer from the following major shortcomings:

[0003] 1. Serial without feedback mechanism: Existing AI systems typically adopt a unidirectional linear process of "input-processing-output", which lacks effective feedback loops and iterative optimization capabilities, resulting in unpredictable output quality and difficulty in continuous improvement.

[0004] 2. Inaccurate understanding of requirements: The system has misunderstandings when converting natural language requirements into executable targets, making it difficult to fully capture the implicit conditions and constraints in user requirements, and lacking a unified format for representing requirements.

[0005] 3. Incomplete quality assessment: Existing verification mainly focuses on functional correctness, neglecting other important quality dimensions such as code style, performance indicators, and compatibility, and lacks objective quantitative evaluation standards.

[0006] 4. Lack of correction mechanism: The system has difficulty accurately identifying the root cause of output problems, lacks a systematic correction method and strategy library, and the correction process mainly relies on manual intervention, with a low degree of automation.

[0007] 5. Dependence on manual verification: The generated output must be verified and corrected manually, which seriously affects the overall efficiency advantage of the AI ​​system, and the verification standards of different personnel are different. Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent task processing system and iterative optimization method that enables multi-AI module collaboration, thereby improving the reliability, efficiency and quality of AI systems and addressing the shortcomings of existing technologies.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] An iterative optimization method for a multi-AI module collaborative intelligent task processing system includes the following steps:

[0011] S1: Requirements understanding and structured transformation, including:

[0012] 1) Receive natural language user input and perform deep semantic analysis and intent recognition;

[0013] 2) Extract key requirement elements, including main objectives, functional requirements, performance indicators, and constraints;

[0014] 3) Verify the completeness of the requirements to ensure that all necessary information has been obtained;

[0015] 4) Output structured requirements and objectives in a standardized Markdown format, including functional requirements, quality requirements, and acceptance criteria;

[0016] S2: Task implementation and initial output generation, including:

[0017] 1) Decompose and plan tasks based on structured requirements and objectives;

[0018] 2) Select the appropriate AI model based on the task type to generate code / documentation / design;

[0019] 3) Allocate necessary computing and storage resources and track the implementation process status;

[0020] 4) Output preliminary implementation results, supporting multiple output types such as code, documents, and design drawings;

[0021] S3: Multi-dimensional quality verification and difference analysis, including:

[0022] 1) Receive the implementation results and original requirements, and verify the correctness of the functions;

[0023] 2) Check the code for compliance with standards, including coding style, commenting style, and naming conventions;

[0024] 3) Evaluate performance metrics, including execution efficiency, memory usage, and response time;

[0025] 4) Conduct compatibility testing to ensure environment dependencies, interface compatibility, and version compatibility;

[0026] 5) Quantify the gap between the achieved results and the required objectives, and generate a comprehensive verification report;

[0027] S4: Intelligent correction and iterative optimization, including:

[0028] 1) Receive the verification report and difference analysis results to diagnose the root cause of the problem;

[0029] 2) Select a suitable calibration method from the calibration strategy library and generate a calibration instruction sequence;

[0030] 3) Feedback the correction command to S2 and re-execute the implementation steps;

[0031] 4) Repeat the verification and correction process until the output quality meets the preset standard;

[0032] S5: Final Output and Lessons Learned, including:

[0033] 1) Output high-quality final results when validation passes;

[0034] 2) Record the processing steps and correction experiences of this task;

[0035] 3) Update the correction strategy library and the historical learning experience library;

[0036] 4) Provide traceability of the processing procedure and interpretability of the results.

[0037] Furthermore, it also includes the development of an intelligent gap quantification algorithm to transform abstract quality requirements into calculable comprehensive gap scores, as follows:

[0038] (1)

[0039] In the formula, The overall gap score is a scalar value that comprehensively measures the normalized gap between the current output and the target requirement. The smaller the value, the higher the output quality.

[0040] For the target vector components: the first The target requirement values ​​for each dimension are derived from the output of requirement understanding and structured transformation in S1;

[0041] For the current vector components: the first The current measured output values ​​for each dimension are derived from the multi-dimensional quality verification analysis in S3;

[0042] For adaptive weights: the weights of the i-th dimension satisfy ∑wᵢ = 1;

[0043] Smoothing factor: a very small positive value to prevent the denominator from being zero and to ensure the formula is smooth. Numerical stability when the target extremum is reached.

[0044] Furthermore, adaptive weights It can be dynamically adjusted based on task type and user preferences to focus on key issues.

[0045] Furthermore, it also includes a correction loop update algorithm, which can drive the entire algorithm output to continuously approach the target based on the gap analysis results, as detailed below:

[0046] (2)

[0047] In the formula, The next-generation output vector: the new output generated after this round of correction; The current output vector is the output before this round of correction. For the correction policy vector: the first one retrieved from the correction policy library. The operation vector corresponding to each strategy; Policy utility weights: policy The activation weight is calculated by the "policy matching algorithm" based on the similarity between the current problem and the policy and the historical success rate of the policy. Difference gradient: the combined difference score Γ on the output vector The gradient direction indicates the most effective local optimization direction for reducing the gap; The learning rate is a dynamically adjusted step size factor that controls the magnitude of correction based on the gradient direction. It can be adaptively adjusted according to the improvement efficiency of historical iterations to balance convergence speed and stability.

[0048] Furthermore, the workflow of the intelligent gap quantization algorithm and the correction loop update algorithm is as follows:

[0049] S1: Gap Calculation: Calculate the current output using formula (1). With the goal The overall gap Γ;

[0050] S2: Convergence judgment: If Γ is lower than the predetermined threshold, the process terminates and outputs the result; otherwise, it enters the correction loop.

[0051] S3: Intelligent Correction: Applying formula (2), combined with the policy library and gradient information, to generate a new generation of output. ;

[0052] S4: Iterative loop: using replace Repeat S1-S3 until convergence.

[0053] This invention provides another technical solution: an intelligent task processing system with multiple AI modules working together, including a requirement understanding AI module, an implementation AI module, a verification AI module, and a correction AI module; the requirement understanding AI module receives user natural language requirements and converts them into structured requirement target JSON format, which is then output to the implementation AI module; the implementation AI module outputs preliminary implementation results to the verification AI module; the verification AI module evaluates the compliance of the verification results using a quality assessment database and outputs non-compliant implementation results to the correction AI module; the correction AI module uses a correction strategy knowledge base and a historical learning experience base to generate correction instruction optimization strategies for non-compliant implementation results and re-outputs the correction instruction optimization strategies to the implementation AI module.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] 1. The intelligent task processing system and iterative optimization method of the present invention with multi-AI module collaboration improves output quality: through multiple rounds of iterative verification and intelligent correction, the code function accuracy is greatly improved, the entire process is automated to eliminate manual waiting time, and the workload of manual intervention is reduced by more than 85%.

[0056] 2. The intelligent task processing system and iterative optimization method of the present invention with multi-AI module collaboration enhances system reliability: modular design enables fault isolation, output quality is stable and predictable, and it has automatic fault tolerance and recovery capabilities.

[0057] 3. The intelligent task processing system and iterative optimization method of the present invention with multi-AI module collaboration significantly reduces costs: it reduces the need for manual verification and correction personnel, reduces labor costs by 60-80%, reduces training costs by 50%, and significantly reduces rework costs caused by errors.

[0058] 4. The intelligent task processing system and iterative optimization method of the present invention with multi-AI module collaboration has a wide range of applications: it supports multiple fields such as software development, document creation, design generation, education and training, and data analysis, and adapts to task requirements of different scales and complexities. Attached Figure Description

[0059] Figure 1 This is the overall architecture diagram of the multi-AI module collaborative working system of the present invention;

[0060] Figure 2 This is a flowchart of the algorithm workflow of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] This invention provides an iterative optimization method for an intelligent task processing system with multi-AI module collaboration, comprising the following steps:

[0063] S1: Requirements understanding and structured transformation, including:

[0064] 1) Receive natural language user input and perform deep semantic analysis and intent recognition;

[0065] 2) Extract key requirement elements, including main objectives, functional requirements, performance indicators, and constraints;

[0066] 3) Verify the completeness of the requirements to ensure that all necessary information has been obtained;

[0067] 4) Output structured requirements and objectives in a standardized Markdown format, including functional requirements, quality requirements, and acceptance criteria;

[0068] S2: Task implementation and initial output generation, including:

[0069] 1) Decompose and plan tasks based on structured requirements and objectives;

[0070] 2) Select the appropriate AI model based on the task type to generate code / documentation / design;

[0071] 3) Allocate necessary computing and storage resources and track the implementation process status;

[0072] 4) Output preliminary implementation results, supporting multiple output types such as code, documents, and design drawings;

[0073] S3: Multi-dimensional quality verification and difference analysis, including:

[0074] 1) Receive the implementation results and original requirements, and verify the correctness of the functions;

[0075] 2) Check the code for compliance with standards, including coding style, commenting style, and naming conventions;

[0076] 3) Evaluate performance metrics, including execution efficiency, memory usage, and response time;

[0077] 4) Conduct compatibility testing to ensure environment dependencies, interface compatibility, and version compatibility;

[0078] 5) Quantify the gap between the achieved results and the required objectives, and generate a comprehensive verification report;

[0079] S4: Intelligent correction and iterative optimization, including:

[0080] 1) Receive the verification report and difference analysis results to diagnose the root cause of the problem;

[0081] 2) Select a suitable calibration method from the calibration strategy library and generate a calibration instruction sequence;

[0082] 3) Feedback the correction command to S2 and re-execute the implementation steps;

[0083] 4) Repeat the verification and correction process until the output quality meets the preset standard;

[0084] S5: Final Output and Lessons Learned, including:

[0085] 1) Output high-quality final results when validation passes;

[0086] 2) Record the processing steps and correction experiences of this task;

[0087] 3) Update the correction strategy library and the historical learning experience library;

[0088] 4) Provide traceability of the processing procedure and interpretability of the results.

[0089] The above method aims to address the problem of quantifying and automatically correcting the gap between the target and the current situation in AI task processing. This invention establishes a vectorized model based on a multi-dimensional feature space and achieves accurate and automated iterative optimization through the following two algorithms, as detailed below:

[0090] An intelligent gap quantification algorithm is established to transform abstract quality requirements into calculable comprehensive gap scores, providing a clear optimization objective for the system, as shown in the following formula (1):

[0091] (1)

[0092] In the formula, (Comprehensive Gap Score): A scalar value that comprehensively measures the normalized gap between the current output and the target requirement. The smaller the value, the higher the output quality.

[0093] (Target vector component): The first The target requirement values ​​for each dimension originate from the output of the "Requirement Understanding and Structured Transformation" module. For example, Represents the goal of functional correctness. Represents performance target metrics.

[0094] (Current state vector components): The first The current measured output values ​​for each dimension are derived from the analysis of the "Multi-Dimensional Quality Verification" module. For example, Represents the test pass rate. This indicates the degree of conformity with coding standards.

[0095] (Adaptive Weights): The weight of the i-th dimension, satisfying ∑wᵢ = 1. This weight can be dynamically adjusted according to task type (e.g., code generation focuses on functionality, document generation focuses on format) and user preferences to focus on key issues.

[0096] (Smoothing factor): A very small positive value to prevent the denominator from being zero and to ensure the formula is smooth. Numerical stability when the target extremum is reached.

[0097] Formula (1) innovatively integrates the differences in functionality, performance, code style, compatibility and other dimensions into a single, precise quantitative indicator that can guide optimization.

[0098] Based on the above formula (1), the algorithm of the present invention also includes a correction loop update algorithm, which defines the system's self-optimization mechanism and how to drive the system output to continuously approach the target based on the gap analysis results, as follows:

[0099] (2)

[0100] In the formula, (Next-generation output vector): The new output generated by the system after this round of correction.

[0101] (Current output vector): The system output before this round of correction.

[0102] (Correction Strategy Vector): The first vector retrieved from the correction strategy library. The operation vector corresponding to each strategy. For example, a strategy for "loop performance optimization" will have a positive correction in the performance dimension.

[0103] (Strategy Utility Weight): Strategy The activation weight is calculated by the "policy matching algorithm" based on the similarity between the current problem and the policy and the historical success rate of the policy.

[0104] (Difference Gradient): The sum of the difference scores Γ on the output vector The gradient direction indicates the most effective local optimization direction for reducing the gap.

[0105] (Learning Rate): A dynamically adjusted step size factor that controls the magnitude of correction based on the gradient direction. It is adaptively adjusted based on the improvement efficiency of previous iterations to balance convergence speed and stability.

[0106] Formula (2) innovatively integrates strategy retrieval based on historical experience (∑U_j · _j) together with gradient optimization based on the current state (η · ∇Γ) constitute a strong adaptive correction loop that combines "memory" and "reasoning" capabilities.

[0107] like Figure 2As shown, the workflow of this invention based on the above-mentioned intelligent gap quantization algorithm and correction loop update algorithm is as follows:

[0108] S1: Gap Calculation: Calculate the current output using formula (1). With the goal The overall gap Γ;

[0109] S2: Convergence judgment: If Γ is lower than the predetermined threshold, the process terminates and outputs the result; otherwise, it enters the correction loop.

[0110] S3: Intelligent Correction: Applying formula (2), combined with the policy library and gradient information, to generate a new generation of output. ;

[0111] S4: Iterative loop: using replace Repeat S1-S3 until convergence.

[0112] To further illustrate the above method, embodiments of the present invention also provide an intelligent task processing system that integrates multiple AI modules, such as... Figure 2 As shown, it includes a requirement understanding AI module, an AI implementation module, an AI verification module, and an AI calibration module.

[0113] The AI ​​module for understanding user needs in natural language receives user needs and converts them into structured JSON format, which is then output to the implementation AI module. The implementation AI module outputs preliminary implementation results to the verification AI module. The verification AI module evaluates the compliance of the verification results using a quality assessment database and outputs non-compliant implementation results to the correction AI module. The correction AI module uses a correction strategy knowledge base and a historical learning experience base to generate correction instructions and optimization strategies for non-compliant implementation results and outputs the correction instructions and optimization strategies back to the implementation AI module.

[0114] In summary, this invention provides a multi-AI module collaborative intelligent task processing system and iterative optimization method. It proposes and implements for the first time a closed-loop collaborative architecture of four dedicated AI modules (requirement understanding, implementation, verification, and correction), achieving seamless integration through standardized interfaces and data formats. Compared to existing single AI models, it significantly improves the ability to handle complex tasks and output quality. Secondly, this invention develops a unified structured requirement representation format and deep understanding algorithm, achieving accurate conversion of natural language requirements into machine-understandable targets. Through multi-level requirement parsing and integrity verification, it fundamentally solves the problem of inaccurate requirement understanding. Thirdly, this invention establishes a comprehensive multi-dimensional verification system, automatically evaluating implementation quality from multiple perspectives such as functionality, specifications, performance, and compatibility. Through quantitative difference analysis and comprehensive scoring, it provides objective quality assessment criteria. Furthermore, this invention constructs a correction strategy engine based on machine learning and rule reasoning, capable of intelligently generating effective correction schemes. Through root cause diagnosis and experience learning, it achieves continuous self-optimization and improvement of the system. This invention designs a complete "understanding-implementation-verification-correction" iterative cycle, introducing an intelligent iterative control mechanism based on quality metrics to optimize processing efficiency while ensuring output quality.

[0115] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An iterative optimization method for an intelligent task processing system with multi-AI module collaboration, characterized in that, Includes the following steps: S1: Requirements understanding and structured transformation, including: 1) Receive natural language user input and perform deep semantic analysis and intent recognition; 2) Extract key requirement elements, including main objectives, functional requirements, performance indicators, and constraints; 3) Verify the completeness of the requirements to ensure that all necessary information has been obtained; 4) Output structured requirements and objectives in a standardized Markdown format, including functional requirements, quality requirements, and acceptance criteria; S2: Task implementation and initial output generation, including: 1) Decompose and plan tasks based on structured requirements and objectives; 2) Select the appropriate AI model based on the task type to generate code / documentation / design; 3) Allocate necessary computing and storage resources and track the implementation process status; 4) Output preliminary implementation results, supporting multiple output types such as code, documents, and design drawings; S3: Multi-dimensional quality verification and difference analysis, including: 1) Receive the implementation results and original requirements, and verify the correctness of the functions; 2) Check the code for compliance with standards, including coding style, commenting style, and naming conventions; 3) Evaluate performance metrics, including execution efficiency, memory usage, and response time; 4) Conduct compatibility testing to ensure environment dependencies, interface compatibility, and version compatibility; 5) Quantify the gap between the achieved results and the required objectives, and generate a comprehensive verification report; S4: Intelligent correction and iterative optimization, including: 1) Receive the verification report and difference analysis results to diagnose the root cause of the problem; 2) Select a suitable calibration method from the calibration strategy library and generate a calibration instruction sequence; 3) Feedback the correction command to S2 and re-execute the implementation steps; 4) Repeat the verification and correction process until the output quality meets the preset standard; S5: Final Output and Lessons Learned, including: 1) Output high-quality final results when validation passes; 2) Record the processing steps and correction experiences of this task; 3) Update the correction strategy library and the historical learning experience library; 4) Provide traceability of the processing procedure and interpretability of the results.

2. The iterative optimization method for a multi-AI module collaborative intelligent task processing system as described in claim 1, characterized in that: It also includes an established intelligent gap quantification algorithm, used to transform abstract quality requirements into a calculable comprehensive gap score, as follows: （1） In the formula, The overall gap score is a scalar value that comprehensively measures the normalized gap between the current output and the target requirement. The smaller the value, the higher the output quality. For the target vector components: the first The target requirement values ​​for each dimension are derived from the output of requirement understanding and structured transformation in S1; For the current vector components: the first The current measured output values ​​for each dimension are derived from the multi-dimensional quality verification analysis in S3; For adaptive weights: the weights of the i-th dimension satisfy ∑wᵢ = 1; Smoothing factor: a very small positive value to prevent the denominator from being zero and to ensure the formula is smooth. Numerical stability when the target extremum is reached.

3. The iterative optimization method for a multi-AI module collaborative intelligent task processing system as described in claim 2, characterized in that: Adaptive weights It can be dynamically adjusted based on task type and user preferences to focus on key issues.

4. The iterative optimization method for a multi-AI module collaborative intelligent task processing system as described in claim 2, characterized in that: It also includes a correction loop update algorithm, which can drive the entire algorithm output to continuously approach the target based on the gap analysis results, as detailed below: （2） In the formula, The next-generation output vector: the new output generated after this round of correction; The current output vector is the output before this round of correction. For the correction policy vector: the first one retrieved from the correction policy library. The operation vector corresponding to each strategy; Policy utility weights: policy The activation weight is calculated by the "policy matching algorithm" based on the similarity between the current problem and the policy and the historical success rate of the policy. Difference gradient: the combined difference score Γ on the output vector The gradient direction indicates the most effective local optimization direction for reducing the gap; The learning rate is a dynamically adjusted step size factor that controls the magnitude of correction based on the gradient direction. It can be adaptively adjusted according to the improvement efficiency of historical iterations to balance convergence speed and stability.

5. The iterative optimization method for a multi-AI module collaborative intelligent task processing system as described in claim 4, characterized in that: The workflow of the intelligent gap quantization algorithm and the correction loop update algorithm is as follows: S1: Gap Calculation: Calculate the current output using formula (1). With the goal The overall gap Γ; S2: Convergence judgment: If Γ is lower than the predetermined threshold, the process terminates and outputs the result; otherwise, it enters the correction loop. S3: Intelligent Correction: Applying formula (2), combined with the policy library and gradient information, to generate a new generation of output. ; S4: Iterative loop: using replace Repeat S1-S3 until convergence.

6. A multi-AI module collaborative intelligent task processing system, used to implement the iterative optimization method of the multi-AI module collaborative intelligent task processing system as described in claim 1, characterized in that: It includes a requirement understanding AI module, an implementation AI module, a verification AI module, and a correction AI module. The requirement understanding AI module receives user natural language requirements and converts them into structured requirement target JSON format, which is then output to the implementation AI module. The implementation AI module outputs preliminary implementation results to the verification AI module. The verification AI module evaluates the compliance of the verification results using a quality assessment database and outputs non-compliant implementation results to the correction AI module. The correction AI module uses a correction strategy knowledge base and a historical learning experience base to generate correction instruction optimization strategies for non-compliant implementation results and re-outputs the correction instruction optimization strategies to the implementation AI module.

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