PLC Copilot Code Generation Using Manuals and Sample Logic
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The conventional approach to configuring and programming industrial devices for manufacturing processes requires specialized knowledge, limiting development to experienced engineers and extending the time required for solution development.
Innovation Solution
An industrial integrated development environment (IDE) system using generative artificial intelligence (AI) to generate industrial control code based on natural language inputs, sample control code, and text-based documents, while embedding functional documentation into the code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional programming approaches are used, then code reliability is maintained through expert knowledge, but development time increases and accessibility decreases
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the PLC programming system. This AI assistant analyzes natural language requirements, generates appropriate PLC code, and provides guidance to users, thereby bridging the gap between non-expert users and complex programming tasks while maintaining code reliability through expert-level analysis and generation
2Ease of operation
If specialized knowledge requirements are enforced, then code quality is ensured, but user accessibility decreases
Solution Approach 1:
The system enables self-service by allowing users to interact with the PLC programming system through natural language without requiring specialized programming knowledge. The AI assistant autonomously handles code generation, validation, and optimization, making the system accessible to non-experts while maintaining professional code quality standards
3Productivity
If expert understanding is required, then programming accuracy is maintained, but development efficiency decreases
Solution Approach 1:
The AI assistant performs preliminary actions by pre-analyzing requirements, pre-generating code structures, and pre-validating logic before final implementation. This preliminary processing by the AI assistant captures expert knowledge in advance, allowing users to benefit from expert-level accuracy without needing to possess the specialized knowledge themselves, thereby improving development efficiency while maintaining programming accuracy
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An integrated development environment (IDE) for uses a generative artificial intelligence (AI) model to generate industrial control code in accordance with functional requirements provided to the industrial IDE system as natural language prompts. The system's generative AI model leverages both a code repository storing sample control code and a document repository that stores device or software manuals, program instruction manuals, functional specification documents, or other technical documents. These repositories are synchronized by digitizing selected portions of document text from the document repository into control code for storage in the code repository, as well as contextualizing control code from the code repository into text-based documentation for storage in the document repository.