Industrial IDE Code Assistant for Natural-Language Control Logic
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Solution Overview
Problem
The conventional approach to configuring and programming industrial devices for manufacturing processes requires specialized knowledge of programming languages, device configuration settings, and industrial control processes, limiting the development of industrial control projects to expert engineers and extending the time required for solution development.
Innovation Solution
An integrated development environment (IDE) system that uses generative artificial intelligence (AI) to analyze natural language inputs and generate control code for industrial system projects, simplifying the process by inferring the type of industrial application or vertical and providing code recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional programming approaches are used with specialized programming languages and device configuration settings, then control code accuracy and compliance with industrial standards are improved, but the complexity of operation and time required for development increase
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing modules and code generation algorithms that translate human-readable natural language requirements into standardized control code. This intermediary layer eliminates the need for users to directly learn complex programming languages while ensuring compliance with industrial standards through built-in validation rules and templates.
Solution Approach 2:
The patent replaces the mechanical process of manual code writing and configuration with an automated system that generates control code through AI-driven natural language interpretation. This substitution transforms the development process from a skill-intensive manual operation to an automated process that maintains reliability while reducing operational complexity.
2Reliability
If conventional programming approaches are used with specialized programming languages and device configuration settings, then control code accuracy and compliance with industrial standards are improved, but the time required for solution development increases
Solution Approach 1:
The patent implements preliminary action by pre-configuring code templates, validation rules, and industrial standard compliance checks within the generation system. These preliminary preparations allow the system to rapidly generate accurate control code without requiring users to perform time-consuming manual configuration or learning processes, thereby maintaining reliability while significantly reducing development time.
Solution Approach 2:
The patent replaces the time-intensive mechanical process of manual programming with an automated code generation system that instantly translates natural language requirements into compliant control code, eliminating the time loss associated with learning curves and manual coding while preserving code accuracy through systematic validation.
3Reliability
If expert understanding of industrial control processes is required, then the quality and compliance of control code are improved, but the accessibility and ease of operation deteriorate
Solution Approach 1:
The patent introduces an intermediary knowledge base that encapsulates expert understanding of industrial control processes and standards. This knowledge base serves as a mediator between user requirements and compliant code generation, allowing non-experts to access embedded expertise through natural language interfaces without needing to directly understand complex industrial control concepts.
Solution Approach 2:
The patent replaces the requirement for human expert knowledge with an automated system that embeds industrial control expertise within its algorithms and validation rules. This substitution maintains code compliance and quality while dramatically improving accessibility by removing the barrier of specialized knowledge requirements.
Data Source
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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.