Industrial Automation IDE Using Generative AI for Control Code
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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 infer functional requirements and generate industrial control code from plain language input, leveraging a generative AI model trained on industrial control code samples, standards, and protocols, supporting features across the automation lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional programming methods are used with specialized knowledge requirements, then control code quality and reliability are maintained, but development time increases and accessibility decreases
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing modules and code generation algorithms that translate plain language inputs into industrial control code. This intermediary layer eliminates the need for users to directly learn programming languages while maintaining code quality through structured translation rules and validation mechanisms.
Solution Approach 2:
The patent replaces the mechanical process of manual code writing and configuration with an automated system that generates control code through natural language processing. This substitution eliminates the need for users to manually navigate complex programming interfaces and syntax rules, dramatically reducing development time and expertise requirements.
2Reliability
If expert understanding of industrial control processes is required, then control system reliability is ensured, but device complexity increases
Solution Approach 1:
The system performs self-validation and self-correction by automatically checking generated code against industrial standards and best practices. The code generation engine incorporates built-in knowledge of control logic patterns and validates outputs to ensure reliability without requiring external expert intervention during the programming process.
Solution Approach 2:
The patent transforms the parameter of user expertise from a requirement into a non-factor by changing the input interface from technical programming languages to natural language. This parameter change maintains reliability through the translation layer that ensures generated code meets industrial standards while eliminating the complexity barrier for users.
3Ease of manufacture
If multiple separate configuration applications are used for different devices, then device-specific functionality is optimized, but system integration complexity increases
Solution Approach 1:
The patent merges multiple device-specific configuration applications into a single unified natural language interface. The system processes inputs for controllers, HMIs, and other industrial devices through a common processing framework that generates appropriate code for each device type, eliminating the need for users to switch between multiple specialized tools and reducing integration complexity.
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.