An end-to-end intelligent system development method and system based on an AI large model

Abstract

The application provides an AI large model-based end-to-end intelligent system development method and system, which comprises the following steps: S1, receiving a system requirement described by a user through a natural language; S2, analyzing the requirement through an AI large model and converting it into a formal specification; S3, automatically generating a system architecture and component division; S4, recursively generating component codes; S5, dynamically detecting and repairing the generated code problems; S6, automatically integrating all components to form a complete system; and S7, continuously optimizing the system based on system feedback or user feedback. The application automatically completes the work of requirement analysis, architecture design, module division, code writing and testing and the like, and realizes distributed integration and intelligent contract: distributed integration verification is realized through a blockchain intelligent contract and a graph database, data consistency, interface matching and safety among components are ensured, the integration process is optimized, cross-field cooperation is realized, and the development efficiency is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent system development, and particularly relates to an end-to-end intelligent system development method and system based on an AI large model. BACKGROUND

[0002] With the rapid development of artificial intelligence (AI) technology, the development method of intelligent systems is also evolving. In the traditional software development process, developers usually need to manually write a large amount of code, perform system architecture design, module division, code verification, and other tedious tasks. As the complexity of the system continues to increase, the traditional development mode often shows low efficiency, unstable quality, and resource waste when facing the growing demand.

[0003] In the traditional software development process, the development team usually includes project managers, architecture designers, development engineers, testers, and other roles. Each role needs to perform different tasks and communicate and coordinate repeatedly during the development process. However, as the project scale continues to expand, the traditional development mode faces many challenges:

[0004] Low development efficiency: developers need to manually complete demand analysis, architecture design, module division, code writing, and testing, and other stages of work, which often involves a large number of repetitive tasks, resulting in a prolonged development cycle and requiring a large number of human resources.

[0005] Difficult cross-domain collaboration: team members come from different technical fields, and the barriers to information transmission and collaboration result in low knowledge sharing efficiency, affecting the overall performance and design optimization of the system.

[0006] To solve the above problems, we propose an end-to-end intelligent system development method and system based on an AI large model. SUMMARY

[0007] The purpose of the present application is to overcome the shortcomings of the prior art, and to provide an end-to-end intelligent system development method and system based on an AI large model, which automatically completes demand analysis, architecture design, module division, code writing, and testing, and other stages of work, distributed integration and smart contract: distributed integration verification through blockchain smart contract and graph database, ensuring data consistency, interface matching and security between components, and optimizing the integration process, cross-domain collaboration, and high development efficiency.

[0008] To achieve the above purpose, the present application provides the following technical solutions:

[0009] An end-to-end intelligent system development method based on an AI large model, comprising the following steps:

[0010] S1, receive system requirements described by natural language by users;

[0011] S2, analyze requirements and convert them into formal specifications through AI large models;

[0012] S3, automatically generate system architecture and component division;

[0013] S4, recursively generate component code;

[0014] S5, automatically detect and repair code problems generated;

[0015] S6, automatically integrate all components to form a complete system;

[0016] S7, continuously optimize the system based on system feedback or user feedback.

[0017] Step S2 specifically includes requirement deduction and mapping:

[0018] Deep graph neural network and transformation network are used to embed the semantics of requirements and deduce implicit requirements, which are modeled by the following formula:

[0019]

[0020] Where, α i ,β i The weighting coefficients obtained by AI learning automatically adjust the influence weight of different requirements on the final technical specifications.

[0021] Step S3 specifically includes architecture optimization algorithm:

[0022] Introduce intelligent optimization method combining genetic algorithm and reinforcement learning to realize adaptive search and generation in system architecture design. In the optimization process, the following optimization formula is used to ensure the optimality of the architecture:

[0023]

[0024] Where, R i (θ) is the real-time feedback reward of each architecture design scheme, λ is the penalty factor of control complexity, and A i ) represents the calculation and resource overhead of the architecture;

[0025] Combine graph clustering algorithm to consider the dependency and coupling degree between modules when dividing components, so that the divided components have higher cohesion and lower coupling degree while meeting the requirements, thereby achieving higher performance.

[0026] Step S4 specifically includes generative adversarial network code generation:

[0027] Component code generation through a generative adversarial network, where the generator generates code and the discriminator evaluates code quality, recursively generates module code and optimizes it;

[0028] Code optimization and adaptive generation: Introduce adaptive gradient optimization algorithms and formalize time and space complexity evaluations to optimize the efficiency of generated code:

[0029]

[0030] Where γ is the optimization factor that balances time and space complexity, dynamically adjusts the generated code according to system requirements, and ensures maximum execution efficiency.

[0031] Step S5 specifically includes: deep reinforcement learning detection and repair:

[0032] Combining deep reinforcement learning and self-supervised learning models for automated code defect detection and repair, AI adjusts detection strategies and repair behavior automatically after generating code based on historical repair experience and system performance feedback, and optimizes repair behavior through the following formula:

[0033]

[0034] Where performance loss (P j ) is the performance loss after repair, and repair consumption (C j ) is the computing resources required for repair.

[0035] Step S6 specifically includes intelligent integration optimization:

[0036] Through distributed integration verification based on graph databases and blockchain smart contracts, automatically ensure interface matching, data consistency and security between components, and automatically adjust integration strategies according to system performance feedback, and evaluate integration results using the following formula:

[0037]

[0038] Where θ k is a tunable weight factor representing the importance of interfaces and data consistency in system integration.

[0039] Step S7 optimization process includes adaptive incremental learning optimization:

[0040] Combining incremental learning and multi-objective optimization algorithms, the system continuously optimizes the architecture and components based on real-time feedback, and automatically optimizes the system through the following multi-objective optimization formula:

[0041]

[0042] Wherein, η is a penalty factor of resource consumption, balancing performance improvement and resource overhead, realizing optimal strategy.

[0043] The demand mapping formula in step S2 uses an inference mechanism based on a graph neural network to automatically deduce implicit demands and dependency relationships during the analysis process and dynamically adjust demand weights.

[0044] The automatic detection and repair in step S5 introduces a combination of deep reinforcement learning and self-supervised learning,

[0045] The accuracy of the repair strategy is continuously improved in the continuous learning process.

[0046] An end-to-end intelligent system development system based on an AI large model, comprising:

[0047] An AI development engine for generating and optimizing code based on an AI large model;

[0048] A demand understanding module for parsing user requirements described in natural language and converting them into formal technical specifications;

[0049] A system generator for automatically designing system architecture and dividing components;

[0050] A self-verification mechanism module for quality detection of generated code and ensuring code stability and compliance with technical requirements;

[0051] A multi-agent collaboration system for simulating multiple roles in a traditional development team to ensure collaborative work at each link;

[0052] An integration and optimization module for automatically integrating all generated components and ensuring smooth collaboration between system modules;

[0053] A continuous feedback and optimization module for real-time adjustment and optimization of the system according to system running state, user feedback and performance data, so that the system continuously evolves during operation;

[0054] A cross-domain knowledge base module for providing rich domain knowledge support for system development to ensure that the system adapts to various complex tasks in a multi-domain, multi-demand environment.

[0055] The beneficial effects of the present application are:

[0056] (1) Automatic completion of requirement analysis, architecture design, module division, code writing and testing, distributed integration and smart contract: through the distributed integration verification of the blockchain smart contract and the graph database, the data consistency, interface matching and security between components are ensured, and the integration process is optimized, the cross-field cooperation and the development efficiency are high.

[0057] (2) Through the deduction mechanism combined with the deep graph neural network and the transformation network, not only the demand can be analyzed, but also the hidden demand and the dependency relationship can be mined. The formulaic demand mapping makes the conversion of different types of demand more efficient and accurate: through the intelligent architecture optimization combined with the genetic algorithm and the reinforcement learning, the system can adaptively adjust the architecture design, and the multi-objective optimization formula is used for weighting, so that the optimal balance of the performance, the resource consumption and the scalability of the architecture is ensured.

[0058] (3) Recursive code generation and adaptive optimization: the generative adversarial network (GAN) is introduced for recursive code generation, and the formulaic time and space complexity optimization strategy is combined to ensure that the generated code is efficient and saves resources. Deep reinforcement learning and self-supervised repair: based on the combination of deep reinforcement learning (DRL) and self-supervised learning, the system can automatically optimize the repair strategy after detecting the code problem, improve the accuracy of repair and reduce manual intervention. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 It is a schematic diagram of the method steps of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0061] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0062] In addition, the terms "first", "second", "third", etc. are used only for descriptive purposes and are not to be construed as indicating or implying relative importance or an ordered ranking of the indicated technical features. Thus, features defined with "first", "second" or "third" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality" is two or more, unless otherwise explicitly and specifically limited.

[0063] Embodiment one: as shown, the embodiment provides an AI large model-based end-to-end intelligent system development method, including the following steps: Figure 1

[0064] S1, receiving the system requirements described by the user in natural language, the requirements including but not limited to functional requirements, performance requirements, non-functional requirements, security requirements, compliance requirements, environmental adaptation requirements and potential constraint conditions;

[0065] S2, analyzing the requirements by AI large model and converting them into formal specifications;

[0066] S3, automatically generating system architecture and component division;

[0067] S4, recursively generating component codes;

[0068] S5, automatically detecting and repairing code problems generated;

[0069] S6, automatically integrating all components to form a complete system;

[0070] S7, continuously optimizing the system based on system feedback or user feedback.

[0071] Step S2 specifically includes requirement deduction and mapping:

[0072] Deep graph neural network (GNN) and transformation network (Transformer) are used to embed and deduce the semantics of the requirements. In this process, AI not only analyzes the literal requirements, but also automatically discovers the potential dependency relationship between the requirements through the reasoning mechanism, and accurately maps them into formal technical specifications:

[0073] The present application proposes to analyze, embed and reason the user's natural language requirements through deep graph neural network (GNN) and Transformer network, and further mine the potential dependency relationship between the requirements, to complete the formal expression of the requirements. In order to express the contribution of different requirements to the final technical specifications, the following mapping function is introduced:

[0074]

[0075] Wherein:

[0076] ​N: represents the technical specification model obtained by mapping the entire set of requirements, that is, the formalized input for architecture generation and code generation;

[0077] F i : the i-th functional requirement (Functional Requirements), such as "user registration", "data upload", "AI recognition", etc.

[0078] NF i : the i-th non-functional requirement (Non-Functional Requirements), such as "response time <1 second", "system availability >99%", "secure encrypted transmission", etc.

[0079] α i : the functional requirement weight learned by the AI model according to the context semantics, historical data, industry knowledge, etc., indicating the importance of the requirement to the final design of the system;

[0080] β i : the non-functional requirement weight learned by the AI model, also used to represent the influence on the technical specification design;

[0081] m: the total number of requirements.

[0082] Step S3 specifically includes:

[0083] Architecture optimization algorithm: an intelligent optimization method combining genetic algorithm (GA) and reinforcement learning (RL) is introduced to realize adaptive search and generation in system architecture design. Through self-learning according to user demand feedback, system performance constraints and historical development experience, the optimal architecture is automatically generated.

[0084] To realize the automatic optimization and module division of system architecture, the present application introduces an adaptive structure search mechanism based on genetic algorithm (GA) and reinforcement learning (RL) combination. In this mechanism, the system takes user requirements, historical development feedback and resource consumption as input, constantly generates candidate architectures, and evaluates and selects them through the following optimization model:

[0085]

[0086] Where, θ: represents the parameter set of the current candidate architecture design scheme;

[0087] (R i (θ): represents the actual reward value (such as performance, scalability, energy efficiency, security, etc.) obtained by architecture θ under the i-th evaluation index, which can come from user historical preferences or system feedback.

[0088] Lambda: a complexity penalty factor to ensure that the generated architecture not only has superior performance but also avoids unnecessary structural redundancy;

[0089] Complexity (A i ): represents the comprehensive complexity indicators of the candidate architecture scheme A i , such as computational resource consumption, module quantity, coupling degree, etc.

[0090] n: the number of evaluation indicators;

[0091] This scoring mechanism ensures that the system architecture design takes into account both performance maximization and complexity minimization, forming the optimal structure scheme. At the same time, combined with graph partitioning algorithm and clustering method, the component partitioning is automatically completed on the basis of architecture, ensuring low coupling and high cohesion between the divided modules, thereby realizing better structured design and system performance.

[0092] Step S4 specifically includes:

[0093] GAN code generation: component code generation is performed through a generative adversarial network (GAN), where the generator generates code and the discriminator evaluates code quality, recursively generates module code and optimizes it. The generated code not only considers the current module function, but also automatically adjusts in combination with the overall system architecture.

[0094] To achieve high-quality code generation and running efficiency optimization of each component, the present application introduces an intelligent code generation mechanism based on a generative adversarial network (GAN). The GAN model consists of a generator and a discriminator. The generator automatically generates candidate code based on component functionality, the discriminator evaluates the normativity and functional integrity of the generated code, and the evaluation results are used as feedback to continuously optimize the generation strategy.

[0095] At the same time, in order to ensure the running efficiency of the code, a target optimization function combining execution time and space complexity is proposed:

[0096]

[0097] Where C represents a candidate code snippet;

[0098] Execution time (C): represents the actual running time of the code in the target environment;

[0099] Space complexity (C): represents the memory or resource size occupied by the code during execution;

[0100] Gamma is an optimization factor that controls the balance between time and space, which can be dynamically adjusted according to the system scenario (such as embedded, edge computing or cloud platform), supporting performance optimization solution selection in different running environments.

[0101] By introducing the formula, the system can adaptively generate optimal code that meets specific operating constraints. Combined with the recursive optimization mechanism, it ensures that each module code generated by the system meets the functional requirements while achieving optimal overall performance.

[0102] Step S5 specifically includes:

[0103] Deep Reinforcement Learning (DRL) detection and repair: combined with deep reinforcement learning (DRL) and self-supervised learning model for automatic code defect detection and repair, AI can automatically adjust detection strategy and repair behavior according to historical repair experience and system performance feedback after generating code.

[0104] The present application proposes a code defect automatic detection and repair method based on deep reinforcement learning (DRL) and self-supervised learning fusion model. This method can automatically identify potential defects through a strategy agent model after code generation, and select the optimal solution among multiple repair strategies for automatic repair. The repair behavior is optimized by the following objective function:

[0105]

[0106] Where S represents the repair behavior or repair strategy set.

[0107] m: the number of problems to be repaired or the number of alternative repair methods.

[0108] P j Performance loss indicator, indicating the impact on system performance after the jth repair action is executed (the smaller the better), such as introducing new logical judgment which may increase the running time; increasing log or debugging information may increase IO overhead;

[0109] C j is the repair cost indicator, which represents the consumption of the repair strategy in terms of computing resources, time or power consumption.

[0110] Overall optimization goal: select the repair strategy that minimizes the comprehensive cost (performance loss x repair cost) and resource consumption during execution. This mechanism automatically selects low-cost, high-value repair actions while building a reinforcement learning closed loop to continuously optimize repair strategies through historical feedback, ensuring that system code quality is guaranteed during the generation phase.

[0111] Step S6 specifically includes:

[0112] Intelligent integrated optimization: through distributed integrated verification based on graph database and blockchain smart contract, it automatically ensures the interface matching, data consistency and security between components, and automatically adjusts the integration strategy according to system performance feedback.

[0113] To automatically complete the system-level integration of multiple AI generation components, the present application proposes an integration optimization method based on the joint of a graph database and a blockchain smart contract. After the component is generated, its interface, protocol, data model, etc. Information is automatically entered into the graph database, and interface connection verification is completed through a structure matching algorithm; at the same time, the interface compatibility and data consistency are verified by the blockchain smart contract in a distributed manner, ensuring the safety, correctness and traceability of the system integration process.

[0114] To measure the integration effect, the following evaluation function is further defined:

[0115]

[0116] Among them, I: represents the system integration effect index;

[0117] p: represents the total number of component pairs in the system that are attempted to be integrated;

[0118] I k : represents the integration relationship of the kth pair of components;

[0119] Interface compatibility (I k ): measures whether the interface parameters of two components are consistent, such as data type matching, consistent calling protocol, version compatibility, etc.

[0120] Data consistency (I k ): measures whether the structure specification, data integrity, and data verification rules of two components in the interaction data are consistent;

[0121] θ k : is an adjustable weight factor, used to represent the importance of "data consistency" in a pair of components. For example: if a module handles financial transaction data, θ k can be set to a higher value to improve the priority of data consistency in integration. This mechanism combines integration feedback to continuously optimize integration behavior and improve system stability and automation level.

[0122] Step S7 optimization process includes:

[0123] Adaptive incremental learning optimization: combining incremental learning and multi-objective optimization algorithm, the system can continuously dynamically optimize the architecture and components according to real-time feedback.

[0124] The present application proposes a continuous optimization mechanism based on system operation feedback and user behavior feedback, combining incremental learning and multi-objective optimization algorithm to realize dynamic optimization of architecture and components. The optimal optimization path is automatically evaluated and selected through the following objective function:

[0125]

[0126] θ: represents the current optimization strategy or parameter configuration (may involve model hyperparameters, component connection strategy, cache allocation scheme, etc.);

[0127] i = 1 ~ n: represents a plurality of components or functional modules within the system;

[0128] F i : Feedback promotion index, measures the positive enhancement degree of user behavior or system index feedback to the i module. For example: page response time is shortened, user stay time is increased, error rate is decreased;

[0129] P i : Performance improvement, such as CPU usage reduction, processing speed improvement, model prediction accuracy improvement, etc.;

[0130] R i : Resource consumption index, such as memory usage, bandwidth occupation, power consumption, power consumption, etc.;

[0131] η: Resource sensitivity factor, used to balance performance improvement and resource consumption. It can be dynamically set according to device type (such as server vs mobile terminal).

[0132] Through automatic data collection, real-time feedback modeling and incremental optimization decision, the continuous evolution and performance optimization of AI generated system are realized.

[0133] In step S2, the user natural language input requirement content is converted into structured graph data, and a requirement graph is established through a graph neural network (GNN) model. The graph structure includes explicit requirement nodes and their dependence edges. In the analysis process, the GNN model automatically deduces the implicit requirements and potential dependence relationships through graph convolution inference mechanism, forming a more complete requirement set.

[0134] In addition, the model also dynamically adjusts the weight of each requirement in the subsequent mapping according to the position, connection degree and context relationship of the node in the dependence graph, ensures that the system generation process is guided by key requirements, and improves the rationality and realizability of the overall system design.

[0135] The automatic detection and repair in step S5 introduces a combination of deep reinforcement learning (DRL) and self-supervised learning, which continuously improves the accuracy of the repair strategy in the continuous learning process. Specifically: the code defect detection and repair module in step S5 introduces a combination of deep reinforcement learning (DRL) and self-supervised learning mechanism, realizes efficient repair behavior optimization. Through self-supervised learning, the system can automatically construct pseudo-labeled training data and preliminarily establish a repair model; then introduce DRL mechanism, continuously adjust the repair strategy in code running and feedback, and finally realize the refinement, self-adaptation and optimal strategy convergence of the repair scheme. This mechanism significantly improves the adaptability and repair accuracy of the system to unknown defects.

[0136] Cross-domain collaboration mechanism, the mechanism includes: multi-agent system (MAS): based on multi-agent system (MAS) for cross-domain knowledge sharing and collaboration, through the simulation of the behavior of multiple development roles, ensure the high degree of coordination of the role in the system development. In the process, through the interaction of the agent, constantly optimize the system design scheme.

[0137] Embodiment two: the embodiment provides an end-to-end intelligent system development system based on AI large model, comprising: AI development engine, for generating and optimizing code based on AI large model, the AI development engine includes code generation module, optimization module, automatic repair module and code verification module; code generation module: generate component code that meets the demand specification; code optimization module: optimize the execution efficiency and resource consumption of the generated code; automatic repair module: automatically detect and repair potential defects in the generated code; self-learning optimization module: dynamically optimize the generated code based on historical repair data and performance feedback.

[0138] Requirement understanding module, for parsing the requirements described by natural language by the user, and converting it into formal technical specifications, the requirement understanding module module includes: natural language analysis module: use natural language processing (NLP) and deep learning algorithm to convert natural language requirements into structured technical specifications; implicit requirement deduction module: based on graph neural network (GNN) and deep reasoning mechanism, automatically deduce the implicit function and constraint in the requirement which is not shown; requirement quantification and mapping module: through formulaic transformation of requirements, generate technical specifications that can be quantified and optimized.

[0139] System generator, for automatically designing system architecture and dividing components, the system generator includes: architecture design module: automatically generate system architecture based on requirements; component division module: modularize the system according to the system architecture, ensure high cohesion and low coupling; architecture optimization module: dynamically optimize the architecture through reinforcement learning to meet performance requirements and resource constraints.

[0140] Self verification mechanism module, for quality detection of the generated code and ensuring the stability and compliance of the code with technical requirements, the self verification mechanism module includes: static analysis module: perform static syntax analysis on the generated code to find potential errors and performance bottlenecks; dynamic analysis module: through actual running, dynamically monitor the code to find runtime errors, memory leaks and other problems; automatic test module: automatically generate and execute unit test and integration test to ensure that the generated code is correct and efficient; automatic repair module: based on the detected errors, automatically repair through algorithm.

[0141] A multi-agent collaboration system (MCP) is used to simulate multiple roles in a traditional development team, ensuring the coordination of each link. The MCP includes: a task allocation module that intelligently allocates tasks for different roles such as development, testing, and design, ensuring the timeliness of task completion; a role simulation module that simulates project managers, developers, testers, and other roles through intelligent agents to achieve cross-role collaboration; and a knowledge sharing module that provides real-time knowledge support and decision guidance for intelligent agents of different roles based on cross-domain knowledge graphs.

[0142] An integration and optimization module is used to automatically integrate all generated components and ensure smooth collaboration between modules of the system. The integration and optimization module includes: an automatic integration module that automatically integrates components and performs interface matching to ensure smooth operation of the system after integration; an intelligent contract module that uses blockchain technology to ensure data consistency and security during system integration; and a performance optimization module that performs performance optimization based on running feedback data and user data to automatically adjust system architecture and code implementation.

[0143] A continuous feedback and optimization module is used to adjust and optimize the system in real time based on system running status, user feedback, and performance data, allowing the system to evolve continuously during operation. The continuous feedback and optimization module includes: a user behavior analysis module that collects user behavior data, analyzes user needs, and automatically derives new functional requirements; a performance feedback module that analyzes and provides performance bottlenecks and optimization suggestions based on system performance data; and an intelligent optimization module that adjusts system architecture and algorithms based on feedback data to continuously improve system stability and performance.

[0144] A cross-domain knowledge base module is used to provide rich domain knowledge support for system development, ensuring that the system can adapt to various complex tasks in a multi-domain, multi-demand environment. The cross-domain knowledge base module includes: a knowledge graph construction module that automatically constructs and maintains a cross-domain knowledge graph to support system invocation of domain knowledge during development; an intelligent reasoning module that performs automated reasoning and decision support based on the domain knowledge graph to help developers optimize design; and a knowledge fusion module that fuses knowledge from different domains to ensure that the system can flexibly handle multiple technical and business requirements.

[0145] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for developing an end-to-end intelligent system based on a large AI model, characterized in that, Includes the following steps: S1. Receive system requests described by users in natural language; S2. Analyze requirements using a large AI model and transform them into formal specifications; S3. Automatically generate system architecture and component partitioning; S4. Recursively generate code for each component; S5. Automatically detects and fixes problems in generated code; S6. Automatically integrates all components to form a complete system; S7. Continuously optimize the system based on system feedback or user feedback; Step S3 specifically includes the architecture optimization algorithm: An intelligent optimization method combining genetic algorithms and reinforcement learning is introduced to achieve adaptive search and generation in system architecture design. During the optimization process, the optimality of the architecture is ensured through the following optimization formula: ; in, This represents the parameter set of the current candidate architecture design scheme; n is the number of evaluation metrics; This indicates that under the i-th evaluation metric, the architecture The actual reward value obtained; A penalty factor to control complexity; Indicate candidate architecture schemes The overall complexity index; By combining graph clustering algorithms, the dependencies and coupling between modules are considered when partitioning components, so that the partitioned components can meet the requirements while having higher cohesion and lower coupling, thereby achieving higher performance. Step S4 specifically includes generating adversarial network code: Generative adversarial networks are used to generate component code, where a generator generates code, a discriminator evaluates code quality, and module code is recursively generated and optimized. Code Optimization and Adaptive Generation: Adaptive gradient optimization algorithms and formulaic time and space complexity evaluations are introduced to optimize the efficiency of generated code. ; Where C represents the candidate code segment, To balance the optimization factors of time and space complexity, the generated code is dynamically adjusted according to system requirements to ensure maximum execution efficiency; Step S5 specifically includes: deep reinforcement learning detection and repair. By combining deep reinforcement learning and self-supervised learning models for automated code defect detection and repair, the AI ​​automatically adjusts the detection strategy and repair behavior after generating code, based on historical repair experience and system performance feedback. The repair behavior is optimized using the following formula: ; Where S represents the set of repair behaviors or repair strategies; m is the number of problems to be repaired or the number of alternative repair methods. For the performance loss after repair, To repair the necessary computing resources; Step S6 specifically includes intelligent integration optimization: Through distributed integration verification based on graph databases and blockchain smart contracts, the system automatically ensures interface compatibility, data consistency, and security between components, and automatically adjusts the integration strategy based on system performance feedback. The integration effect is evaluated using the following formula; ； Where I represents the system integration effectiveness index; p represents the total number of component pairs that were attempted to be integrated in the system; This represents the integration relationship of the k-th pair of components. It is an adjustable weighting factor that represents the importance of interface and data consistency in system integration.

2. The end-to-end intelligent system development method based on a large AI model according to claim 1, characterized in that, Step S2 specifically includes requirement deduction and mapping: By combining deep graph neural networks with transform networks, semantic embedding of requirements is performed, and implicit requirements are deduced, modeled using the following formula: ; Where N represents the technical specification model mapped from the entire set of requirements; h is the total number of requirement items; This refers to the i-th functional requirement; For the i-th non-functional requirement, , The weighting coefficients obtained through AI learning automatically adjust the weight of the impact of different requirements on the final technical specifications.

3. The end-to-end intelligent system development method based on a large AI model according to claim 2, characterized in that, Step S7 optimization process includes adaptive incremental learning optimization: Combining incremental learning and multi-objective optimization algorithms, the system continuously and dynamically optimizes its architecture and components based on real-time feedback. Automatic system optimization is achieved through the following multi-objective optimization formula: ; in, This indicates the current optimization strategy or parameter configuration; b represents the number of components or functional modules within the system. Let i be the feedback improvement metric for the i-th module; For performance improvement; As a resource consumption indicator; It acts as a penalty factor for resource consumption, balancing performance improvement and resource overhead to achieve an optimal strategy.

4. The end-to-end intelligent system development method based on a large AI model according to claim 3, characterized in that, The demand mapping formula in step S2 uses a graph neural network-based reasoning mechanism to automatically deduce implicit demands and dependencies during the parsing process and dynamically adjust demand weights.

5. The end-to-end intelligent system development method based on a large AI model according to claim 4, characterized in that, The automatic detection and repair in step S5 introduces a combination of deep reinforcement learning and self-supervised learning to continuously improve the accuracy of the repair strategy during the learning process.

6. An end-to-end intelligent system development system based on an AI large-scale model, applied to the end-to-end intelligent system development method based on an AI large-scale model according to any one of claims 1-5, characterized in that, include: An AI development engine, used to generate and optimize code based on large AI models; The requirement understanding module is used to parse the requirements described by users in natural language and transform them into formal technical specifications. A system generator, used to automatically design system architecture and divide components; The self-verification mechanism module is used to perform quality checks on the generated code and ensure the stability and compliance of the code with technical requirements. A multi-agent collaboration system is used to simulate multiple roles in a traditional development team to ensure collaborative work at each stage. An integration and optimization module is used to automatically integrate all generated components and ensure smooth collaboration between various modules of the system. The continuous feedback and optimization module is used to adjust and optimize the system in real time based on the system's operating status, user feedback, and performance data, so that the system can continuously evolve during operation. A cross-domain knowledge base module is used to provide rich domain knowledge support for system development, ensuring that the system can adapt to various complex tasks in multi-domain and multi-demand environments.

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