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

Abstract

The invention provides an end-to-end intelligent system development method and system based on an AI large model. The method comprises the following steps: S1, receiving a system demand described by a user through a natural language; s2, analyzing the requirements through an AI large model and converting the requirements into formalized specifications; s3, automatically generating a system architecture and dividing components; s4, generating each component code in a recursive manner; s5, dynamically detecting and repairing the generated code problem; s6, automatically integrating all components to form a complete system; and S7, continuously optimizing the system based on system feedback or user feedback. According to the method, work of multiple stages such as demand analysis, architecture design, module division, code writing and testing is automatically completed, distributed integration and smart contract are performed, distributed integration verification is performed through a block chain smart contract and a graph database, data consistency, interface matching and security among components are ensured, the integration process is optimized, and the integration efficiency is improved. Cross-domain cooperation is achieved, and the development efficiency is high.

Description

Technical Field

[0001] This invention relates to the field of intelligent system development technology, and in particular to an end-to-end intelligent system development method and system based on a large AI model. Background Technology

[0002] With the rapid development of artificial intelligence (AI) technology, the development methods for intelligent systems are also constantly evolving. In traditional software development, developers typically need to manually write large amounts of code and perform tedious tasks such as system architecture design, module division, and code verification. However, as system complexity continues to increase, traditional development models often exhibit problems such as inefficiency, unstable quality, and resource waste when facing ever-growing demands.

[0003] In traditional software development, a development team typically includes multiple roles such as project manager, architect, development engineer, and tester. Each role needs to perform different tasks and repeatedly communicate and coordinate throughout the development process. However, as projects grow in scale, traditional development models face many challenges:

[0004] Low development efficiency: Developers need to manually complete multiple stages of work, such as requirements analysis, architecture design, module division, code writing and testing. This process often involves a large number of repetitive tasks, which leads to a longer development cycle and requires a lot of human resources.

[0005] Cross-domain collaboration difficulties: Development team members come from different technical fields, and barriers to information transfer and collaboration lead to low efficiency in knowledge sharing, affecting the overall performance and design optimization of the system.

[0006] To address the aforementioned shortcomings, we propose an end-to-end intelligent system development method and system based on a large AI model. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by providing an end-to-end intelligent system development method and system based on a large AI model. This method automatically completes multiple stages of work, including requirements analysis, architecture design, module division, code writing, and testing. It also features distributed integration and smart contracts: distributed integration verification is performed through blockchain smart contracts and graph databases to ensure data consistency, interface matching, and security among components. Furthermore, it optimizes the integration process, enabling cross-domain collaboration and high development efficiency.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

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

[0010] S1. Receive system requests described by users in natural language;

[0011] S2. Analyze requirements using a large AI model and transform them into formal specifications;

[0012] S3, automatically generates system architecture and component partitioning;

[0013] S4. Recursively generate code for each component;

[0014] S5. Automatically detects and fixes problems in generated code;

[0015] S6. Automatically integrates 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] 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:

[0019]

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

[0021] Step S3 specifically includes the architecture optimization algorithm:

[0022] 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:

[0023]

[0024] Among them, R i (θ) represents the real-time feedback reward for each architectural design scheme, and λ is the penalty factor for controlling complexity. i This represents the computational and resource overhead of the architecture;

[0025] 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.

[0026] Step S4 specifically includes generating adversarial network code:

[0027] 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.

[0028] 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.

[0029]

[0030] Here, γ is an optimization factor that balances time and space complexity, dynamically adjusting the generated code according to system requirements to ensure maximum execution efficiency.

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

[0032] 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:

[0033]

[0034] Among them, performance loss (P) j The repair cost (C) represents the performance loss after the repair. j (This refers to 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, the interface matching, data consistency and security between components are automatically ensured, and the integration strategy is automatically adjusted according to system performance feedback. The integration effect is evaluated using the following formula.

[0037]

[0038] Where, θ k It is an adjustable weighting factor that represents the importance of interface 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 and dynamically optimizes its architecture and components based on real-time feedback. Automatic system optimization is achieved through the following multi-objective optimization formula:

[0041]

[0042] Here, η is the penalty factor for resource consumption, which balances performance improvement and resource overhead to achieve the optimal strategy.

[0043] 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.

[0044] Step S5's automatic detection and repair incorporates a combination of deep reinforcement learning and self-supervised learning.

[0045] We continuously improve the accuracy of our repair strategies through ongoing learning.

[0046] An end-to-end intelligent system development system based on a large AI model includes:

[0047] An AI development engine, used to generate and optimize code based on large AI models;

[0048] The requirement understanding module is used to parse the requirements described by users in natural language and transform them into formal technical specifications.

[0049] A system generator, used to automatically design system architecture and divide components;

[0050] 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.

[0051] A multi-agent collaboration system is used to simulate multiple roles in a traditional development team to ensure collaborative work at each stage.

[0052] An integration and optimization module is used to automatically integrate all generated components and ensure smooth collaboration between various modules of the system.

[0053] 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.

[0054] 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.

[0055] The beneficial effects of this invention are as follows:

[0056] (1) Automatically complete multiple stages of work such as requirements analysis, architecture design, module division, code writing and testing. Distributed integration and smart contracts: Distributed integration verification is carried out through blockchain smart contracts and graph databases to ensure data consistency, interface matching and security between components, and optimize the integration process, cross-domain collaboration and high development efficiency.

[0057] (2) Through the deductive mechanism combining deep graph neural networks and transformation networks, not only can requirements be analyzed, but also hidden requirements and dependencies can be discovered. The formulaic requirement mapping makes the conversion of different types of requirements more efficient and accurate: through intelligent architecture optimization combining genetic algorithms and reinforcement learning, the system can adaptively adjust the architecture design and make trade-offs according to the multi-objective optimization formula to ensure the optimal balance of architecture in terms of performance, resource consumption and scalability.

[0058] (3) Recursive Code Generation and Adaptive Optimization: Generative Adversarial Networks (GANs) are introduced for recursive code generation, combined with a formulaic time and space complexity optimization strategy to ensure that the generated code is efficient and resource-saving. 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 code problems, improving the accuracy of repair and reducing manual intervention. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0060] 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.

[0061] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0062] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0063] Example 1: As Figure 1 As shown, this embodiment provides a method for developing an end-to-end intelligent system based on a large AI model, including the following steps:

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

[0065] S2. Analyze requirements using a large AI model and transform them into formal specifications;

[0066] S3, automatically generates system architecture and component partitioning;

[0067] S4. Recursively generate code for each component;

[0068] S5. Automatically detects and fixes problems in generated code;

[0069] S6. Automatically integrates all components to form a complete system;

[0070] S7. Continuously optimize the system based on system feedback or user feedback.

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

[0072] By combining deep graphical neural networks (GNNs) with transform networks, semantic embedding of requirements is performed, and implicit requirements are inferred. In this process, AI not only parses the literal requirements, but also automatically discovers the potential dependencies between requirements through reasoning mechanisms, accurately mapping them into formalized technical specifications.

[0073] This invention proposes using deep graphical neural networks (GNNs) and Transformer networks to parse, embed, and infer user natural language requirements, and further explores the potential dependencies between requirements to achieve a formal expression of those requirements. To express the contribution of different requirements to the final technical specifications, the following mapping function is introduced:

[0074]

[0075] in:

[0076] N: Represents the technical specification model obtained by mapping the entire set of requirements, which is a formal input that can be used for architecture generation and code generation;

[0077] F i The i-th functional requirement, such as "user registration", "data upload", "AI recognition", etc.

[0078] NF i The i-th non-functional requirement, such as "response time < 1 second", "system availability > 99%", "secure encrypted transmission", etc.

[0079] α i The AI ​​model learns functional requirement weights based on contextual semantics, historical data, and industry knowledge, which represent the importance of the requirement to the final system design.

[0080] β i The non-functional requirement weights learned by the AI ​​model are also used to represent the influence on the design of technical specifications;

[0081] m: The total number of demand items.

[0082] Step S3 specifically includes:

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

[0084] To achieve automatic optimization and modularization of the system architecture, this invention introduces an adaptive architecture search mechanism based on a combination of genetic algorithm (GA) and reinforcement learning (RL). In this mechanism, the system continuously generates candidate architectures based on user needs, historical development feedback, and resource consumption, and evaluates and selects the appropriate architecture using 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 the architecture θ under the i-th evaluation metric, which can come from user historical preferences or system feedback.

[0088] λ: 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 ): Indicates candidate architecture scheme A i The comprehensive complexity indicators include computational resource consumption, number of modules, and degree of coupling;

[0090] n: the number of evaluation indicators;

[0091] This scoring mechanism ensures that the system architecture design balances performance maximization and complexity minimization, resulting in an optimal structural solution. Simultaneously, by combining graph partitioning algorithms and clustering methods, it automatically partitions components based on the architecture, ensuring low coupling and high cohesion between the partitioned modules, thereby achieving better structured design and system performance.

[0092] Step S4 specifically includes:

[0093] Generative Adversarial Network (GAN) code generation: Component code is generated using a Generative Adversarial Network (GAN). A generator produces code, a discriminator evaluates code quality, and the module code is recursively generated and optimized. The generated code not only considers the current module's functionality but also automatically adjusts to the overall system architecture.

[0094] To achieve high-quality code generation and optimized runtime efficiency for each component, this invention introduces an intelligent code generation mechanism based on Generative Adversarial Networks (GANs). The GAN model consists of a generator and a discriminator. The generator automatically generates candidate code based on the component's function, while the discriminator evaluates the standardization and functional completeness of the generated code and uses the evaluation results as feedback to continuously optimize the generation strategy.

[0095] Meanwhile, to ensure code execution efficiency, a target optimization function that combines execution time and space complexity is proposed:

[0096]

[0097] Where C represents a candidate code segment;

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

[0099] Space complexity (C): Represents the amount of memory or resources used by the code during execution;

[0100] γ is an optimization factor that controls the balance between time and space. It can be dynamically adjusted according to the system scenario (such as embedded, edge computing or cloud platform) and supports the selection of the optimal performance solution under different operating environments.

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

[0102] Step S5 specifically includes:

[0103] Deep Reinforcement Learning (DRL) Detection and Repair: Combining deep reinforcement learning (DRL) with self-supervised learning models for automated code defect detection and repair, AI can automatically adjust detection strategies and repair behaviors based on historical repair experience and system performance feedback after code generation.

[0104] This invention proposes an automatic code defect detection and repair method based on a fusion model of deep reinforcement learning (DRL) and self-supervised learning. This method can automatically identify potential defects after code generation through a policy proxy model and select the optimal solution from multiple repair strategies for automatic repair. The repair behavior is optimized using the following objective function:

[0105]

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

[0107] m: The number of issues to be fixed or the number of alternative fixes.

[0108] P j : Performance loss metric, representing the impact of the j-th repair action on system performance (the smaller the better). For example, introducing new logical judgments may increase runtime; adding logs or debugging information may increase I / O overhead.

[0109] C j The repair consumption metric indicates the computational resources, time, or power consumption consumed by the repair strategy.

[0110] Overall optimization goal: To select the repair strategy that minimizes the overall cost (performance loss × repair cost) and resource overhead during execution. This mechanism automatically selects low-cost, high-value repair actions while constructing a reinforcement learning loop to continuously optimize the repair strategy through historical feedback, ensuring system code quality is guaranteed from the generation stage.

[0111] Step S6 specifically includes:

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

[0113] To automate the system-level integration of multiple AI-generated components, this invention proposes an integration optimization method based on graph databases and blockchain smart contracts. After a component is generated, its interface, protocol, data model, and other information are automatically entered into the graph database, and interface connectivity verification is completed through a structure matching algorithm. Simultaneously, blockchain smart contracts are used to perform distributed verification of interface compatibility and data consistency, ensuring the security, correctness, and traceability of the system integration process.

[0114] To measure the ensemble effect, the following evaluation function is further defined;

[0115]

[0116] Where I represents the system integration effectiveness index;

[0117] p: represents the total number of component pairs that the system attempts to integrate;

[0118] I k : Indicates the integration relationship of the k-th pair of components;

[0119] Interface compatibility (I) k ): This measures whether the interface parameters of two components are consistent, such as matching data types, consistent calling protocols, and version compatibility;

[0120] Data consistency (I k ): This measures whether the structural specifications, data integrity, and data validation rules of the two components are consistent in the interactive data;

[0121] θ k θ is an adjustable weighting factor used to represent the relative importance of "data consistency" between a pair of components. For example, if a module processes financial transaction data, then θ... k This value can be set to a higher level to prioritize data consistency during integration. This mechanism, combined with integration feedback, enables continuous optimization of integration behavior, improving system stability and automation.

[0122] Step S7 optimization process includes:

[0123] Adaptive incremental learning optimization: Combining incremental learning and multi-objective optimization algorithms, the system can continuously and dynamically optimize the architecture and components based on real-time feedback.

[0124] This invention proposes a continuous optimization mechanism based on system operation feedback and user behavior feedback, combining incremental learning and multi-objective optimization algorithms to achieve dynamic tuning of the architecture and components. The optimal path is automatically evaluated and selected through the following objective function;

[0125]

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

[0127] i = 1 to n: represents multiple components or functional modules within the system;

[0128] F i Feedback improvement metrics measure the positive enhancement effect of user behavior or system metrics on the feedback of module i. Examples include: shorter page response time, increased user dwell time, and lower error rate.

[0129] P i Performance improvements, such as reduced CPU utilization, increased processing speed, and improved model prediction accuracy;

[0130] R i Resource consumption metrics, such as memory usage, bandwidth usage, power consumption, and electricity consumption;

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

[0132] Through automatic data collection, real-time feedback modeling, and incremental optimization decision-making, the AI ​​generation system achieves continuous evolution and performance optimization.

[0133] In step S2, the user's natural language input requirements are transformed into structured graph data, and a requirement graph is built using a graph neural network (GNN) model. This graph structure contains explicit requirement nodes and their dependencies. During the parsing process, the GNN model automatically infers implicit requirements and potential dependencies through graph convolutional inference mechanisms, forming a more complete set of requirements.

[0134] In addition, the model dynamically adjusts the weight of each requirement in subsequent mappings based on the node's position, connectivity, and contextual relationships in the dependency graph, ensuring that the system generation process is guided by key requirements, thereby improving the rationality and feasibility of the overall system design.

[0135] Step S5's automatic detection and repair incorporates a combination of Deep Reinforcement Learning (DRL) and self-supervised learning to continuously improve the accuracy of the repair strategy through ongoing learning. Specifically, the code defect detection and repair module in step S5 utilizes a mechanism combining DRL and self-supervised learning to achieve efficient optimization of repair behavior. Through self-supervised learning, the system automatically constructs pseudo-labeled training data to initially establish a repair model. Subsequently, the DRL mechanism is introduced to continuously adjust the repair strategy during code execution and feedback, ultimately achieving refined, adaptive, and optimal strategy convergence in the repair scheme. This mechanism significantly improves the system's adaptability to unknown defects and the accuracy of its repair.

[0136] Cross-domain collaboration mechanisms include: Multi-Agent Systems (MAS): MAS facilitates cross-domain knowledge sharing and collaboration by simulating the behavior of multiple development roles, ensuring high synergy among these roles during system development. Throughout this process, the system design is continuously optimized through the interaction and collaboration of the agents.

[0137] Example 2: This example provides an end-to-end intelligent system development system based on a large AI model, including: an AI development engine for generating and optimizing code based on the large AI model; the AI ​​development engine includes a code generation module, an optimization module, an automatic repair module, and a code verification module; the code generation module generates component code that meets the requirements specifications; the code optimization module optimizes the execution efficiency and resource consumption of the generated code; the automatic repair module automatically detects and repairs potential defects in the generated code; and the self-learning optimization module dynamically optimizes the generated code based on historical repair data and performance feedback.

[0138] The Requirements Understanding module is used to parse user requirements described in natural language and transform them into formalized technical specifications. The Requirements Understanding module includes: a Natural Language Parsing module, which uses Natural Language Processing (NLP) and deep learning algorithms to transform natural language requirements into structured technical specifications; an Implicit Requirements Inference module, which automatically infers implicit functions and constraints not explicitly stated in the requirements based on Graph Neural Networks (GNN) and deep reasoning mechanisms; and a Requirements Quantification and Mapping module, which transforms requirements into formulas to generate technical specifications that can be quantified and optimized.

[0139] The system generator is used to automatically design system architecture and divide it into components. The system generator includes: an architecture design module: automatically generating system architecture based on requirements; a component division module: modularizing the system according to the system architecture to ensure high cohesion and low coupling; and an architecture optimization module: dynamically optimizing the architecture through reinforcement learning to meet performance requirements and resource constraints.

[0140] The self-verification mechanism module is used to perform quality checks on the generated code and ensure its stability and compliance with technical requirements. This module includes: a static analysis module (performing static syntax analysis to identify potential errors and performance bottlenecks), a dynamic analysis module (monitoring the code dynamically during runtime to detect runtime errors, memory leaks, etc.), an automated testing module (automatically generating and executing unit tests and integration tests to ensure the generated code is correct and efficient), and an automatic repair module (automatically repairing detected errors using algorithms).

[0141] The Multi-Agent Collaboration System (MCP) is used to simulate multiple roles in a traditional development team, ensuring collaborative work across all stages. The MCP includes: a task allocation module: intelligently allocating tasks to different roles such as development, testing, and design, ensuring timely task completion; a role simulation module: using intelligent agents to simulate roles such as project managers, developers, and testers, enabling cross-role collaboration; and a knowledge sharing module: based on a cross-domain knowledge graph, providing real-time knowledge support and decision-making guidance for intelligent agents in different roles.

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

[0143] The Continuous Feedback and Optimization module is used to adjust and optimize the system in real time based on system operating status, user feedback, and performance data, enabling the system to continuously evolve during operation. This module includes: a User Behavior Analysis module: collecting user behavior data, analyzing user needs, and automatically deriving new functional requirements; a Performance Feedback module: analyzing and providing performance bottlenecks and optimization suggestions based on system performance data; and an Intelligent Optimization module: adjusting the system architecture and algorithms based on feedback data to continuously improve system stability and performance.

[0144] The cross-domain knowledge base module provides rich domain knowledge support for system development, ensuring that the system can adapt to various complex tasks in multi-domain and multi-demand environments. This module includes: a knowledge graph construction module, which automatically builds and maintains cross-domain knowledge graphs, supporting the system in calling domain knowledge during development; an intelligent reasoning module, which provides automated reasoning and decision support based on the domain knowledge graph, helping developers optimize designs; and a knowledge fusion module, which integrates knowledge from different domains to ensure the system can flexibly respond to various technical and business needs.

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

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 generates 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.

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, α i ,β i 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 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: Among them, R i (θ) represents the real-time feedback reward for each architectural design scheme, and λ is the penalty factor for controlling complexity. i This represents the computational and resource overhead of the architecture; 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.

4. The end-to-end intelligent system development method based on a large AI model according to claim 3, characterized in that, 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. Here, γ is an optimization factor that balances time and space complexity, dynamically adjusting the generated code according to system requirements to ensure maximum execution efficiency.

5. The end-to-end intelligent system development method based on a large AI model according to claim 4, characterized in that, 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: Among them, performance loss (P) j The repair cost (C) represents the performance loss after the repair. j (This refers to the computing resources required for repair.) 6. The end-to-end intelligent system development method based on a large AI model according to claim 5, characterized in that, Step S6 specifically includes intelligent integration optimization: Through distributed integration verification based on graph databases and blockchain smart contracts, the system automatically ensures interface matching, 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, θ k It is an adjustable weighting factor that represents the importance of interface and data consistency in system integration.

7. The end-to-end intelligent system development method based on a large AI model according to claim 6, 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: Here, η is the penalty factor for resource consumption, which balances performance improvement and resource overhead to achieve the optimal strategy.

8. The end-to-end intelligent system development method based on a large AI model according to claim 7, 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.

9. The end-to-end intelligent system development method based on a large AI model according to claim 8, 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.

10. An end-to-end intelligent system development system based on an AI large model, applied to the end-to-end intelligent system development method based on an AI large model according to any one of claims 1-9, 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.

Images