In-Line AI Code Editor for Industrial Control Programming Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional industrial control programming requires specialized knowledge and extends development time due to the need for expert understanding of programming languages, device configuration, and industrial control processes, limiting it to skilled engineers.
Innovation Solution
An integrated development environment (IDE) system utilizing generative AI techniques generates industrial control code and configurations based on intuitive natural language inputs, leveraging custom models and large language models to assist in programming and configuration tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional industrial control programming methods are used, then programming accuracy and control reliability are maintained, but the complexity of operation and development time increase significantly
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the industrial control programming system. The AI assistant translates natural language requirements into structured control code, eliminating the need for users to directly learn complex programming languages while maintaining system reliability through validated code generation.
Solution Approach 2:
The patent replaces the mechanical process of manual code writing and configuration with an automated AI-based system. The AI assistant performs the intellectual work of translating high-level requirements into executable control code, substituting the manual mechanical process of programming with an intelligent automated process.
2Manufacturing precision
If conventional industrial control programming methods are used, then control precision is maintained, but development time extends significantly
Solution Approach 1:
The AI assistant performs preliminary analysis and structuring of control logic before actual code generation. It pre-processes natural language requirements, identifies control patterns, and prepares structured representations that can be quickly translated into precise control code, reducing overall development time while maintaining precision.
Solution Approach 2:
The patent changes the fundamental parameter of code creation from manual character-by-character writing to AI-generated structured output. By transforming the input method from low-level syntax to high-level natural language, the system maintains control precision through validated generation while dramatically reducing the time parameter.
3Reliability
If specialized programming knowledge is required, then code quality and system reliability are ensured, but the adaptability of the system to different user skill levels decreases
Solution Approach 1:
The AI assistant serves multiple functions: it acts as a translator for beginners, a code generator for intermediate users, and a validation tool for experienced programmers. This multi-functional approach allows the system to adapt to different skill levels while maintaining code quality through consistent AI-generated and validated output.
Solution Approach 2:
The patent segments the programming task into distinct phases: natural language interpretation, control logic structuring, code generation, and validation. The AI assistant handles the complex segments while allowing users to interact at their comfort level, thereby maintaining code quality through systematic processing while adapting to various user expertise levels.
Data Source
AI summary
An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.