AI Control Code Generation With Virtual Verification Feedback
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Solution Overview
Problem
Current automated control code generation methods face challenges in flexibility, accuracy, and efficiency, particularly in complex scenarios, due to reliance on manual programming and templated approaches that are prone to errors and require extensive debugging, and struggle with the complexity of motion control APIs.
Innovation Solution
A method utilizing a large language model to generate control codes from natural language commands, combined with vector and knowledge graph databases, followed by multi-level virtual operation verification to ensure accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual programming is used, then code can be customized to meet specific needs, but development time and error rate increase significantly
Solution Approach 1:
The patent replaces manual mechanical programming with an AI-based automated code generation system. The large language model automatically generates control code from natural language commands, substituting the manual coding process with intelligent automation that maintains customization capability while dramatically reducing development time and error rates.
Solution Approach 2:
The system enables self-service code generation where the AI model autonomously creates control code without requiring manual programming expertise. The model understands natural language requirements and automatically produces functional control code, allowing non-programmers to customize control behavior without undergoing extensive training or debugging cycles.
2Productivity
If templated programming is used, then development efficiency improves, but flexibility to handle complex scenarios decreases
Solution Approach 1:
The patent transitions from static code templates to dynamic AI-generated code. The large language model adapts its code generation based on the specific requirements of each scenario, allowing the system to maintain high efficiency while handling complex and varied control scenarios without being constrained by predefined template structures.
Solution Approach 2:
The system changes the parameters of code generation from fixed template parameters to flexible natural language descriptors. The AI model interprets natural language commands and generates appropriate control code with suitable parameters automatically determined, enabling both efficiency and adaptability to complex scenarios.
3Productivity
If automated code generation is implemented, then development speed increases, but code accuracy and reliability may decrease
Solution Approach 1:
The patent implements a feedback mechanism where the generated control code is automatically verified against the original natural language requirements. The system checks whether the generated code correctly implements the intended control logic, providing feedback to ensure accuracy and reliability while maintaining high generation speed through automated verification rather than manual testing.
Data Source
AI summary
A method for automated control code generation and verification includes: receiving a natural language command, the natural language command being configured to instruct the large language model to output a code text that meets control requirements corresponding to the natural language command; performing matching retrieval on a vector database according to the natural language command to obtain a sample code snippet; obtaining API structured information corresponding to the sample code snippet from a knowledge graph database; generating an initial control code according to the sample code snippet and the API structured information; and performing a multi-level virtual operation verification on the initial control code in a software motion control system, and generating a target control code according to multi-level verification results confirmed multiple times by the user and the initial control code.