Industrial IDE Code Conversion From Plain Language Inputs
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Solution Overview
Problem
Conventional industrial automation systems require specialized knowledge and extended time for programming and configuration due to the need for expert understanding of programming languages, device configuration settings, and industrial control processes, limiting development to skilled engineers.
Innovation Solution
An integrated development environment (IDE) utilizing generative artificial intelligence (AI) to interpret plain language inputs and generate industrial control code in desired formats, supported by a generative AI model trained on industrial control code samples and standards, enabling non-experts to develop automation projects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional programming methods are used, then code functionality and reliability are ensured, but development time and expertise requirements increase
Solution Approach 1:
The patent introduces an intermediary system comprising a chat interface and code generation model that mediates between the user's natural language requirements and the target code. This intermediary automatically translates plain language into functional code, eliminating the need for users to manually write complex programming while maintaining code reliability through structured generation processes and validation mechanisms.
Solution Approach 2:
The patent replaces the mechanical process of manual code writing and debugging with an automated AI-based code generation system. Instead of requiring users to mechanically write, test, and debug code line-by-line, the system automatically generates functional code from natural language descriptions, significantly reducing development time while maintaining reliability through the AI model's training on established coding patterns and best practices.
2Reliability
If specialized programming knowledge is required, then code quality and industrial standards compliance are maintained, but accessibility and ease of operation decrease
Solution Approach 1:
The chat interface acts as an intermediary that translates between natural language (accessible to all users) and specialized code (requiring expert knowledge). Users can express requirements in plain language without needing to understand programming syntax, while the system ensures code quality and industrial standards compliance through the generation model's training on standardized coding practices and validation processes.
Solution Approach 2:
The system substitutes the need for specialized programming knowledge with an automated code generation mechanism. Instead of requiring users to manually write and understand complex code, the AI model automatically generates high-quality, standards-compliant code from natural language descriptions, making the system accessible to non-programmers while maintaining professional code quality through the model's training on industrial standards.
3Reliability
If expert understanding of industrial control processes is required, then process accuracy and reliability are improved, but device complexity and learning curve increase
Solution Approach 1:
The code generation model is designed with multi-functionality to handle various industrial control processes and domains. By training the model on diverse industrial control data and standards, it becomes a universal system that can generate accurate code for different process types without requiring users to have specialized knowledge of each specific domain. The model internally manages the complexity of different industrial processes while presenting a simple natural language interface to users.
Data Source
AI summary
An integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system uses a generative artificial intelligence (AI) model and associated neural networks to generate portions of an industrial automation project in accordance with functional requirements provided to the industrial IDE system in intuitive formats, such as spoken or written plain language text. The system uses generative AI to translate plain language requests or functional specifications into industrial control code, human-machine interface (HMI) applications, device configuration settings, or other aspects of an industrial control project.