AI Code Block Selection for Industrial Automation Code Updates
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Solution Overview
Problem
The challenge of efficiently and consistently managing and selecting appropriate code blocks for control software in industrial automation environments is hindered by the complexity and diversity of code versions, leading to inefficiencies and resource waste due to the use of flawed or inferior code blocks.
Innovation Solution
A generative artificial intelligence (AI) code block selector and codebase updating system that includes a code block repository, AI model library, and a code block selector to evaluate and select code blocks based on configuration criteria, ensuring adherence to quality parameters through isolated testing and AI model evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If engineers manually track and evaluate code block versions, then code blocks can be maintained and updated, but the process becomes inefficient and inconsistent leading to wasted time and resources
Solution Approach 1:
The system enables automated self-evaluation of code blocks through AI models that automatically assess code quality, performance metrics, and compatibility without requiring manual engineer intervention for each code block evaluation
Solution Approach 2:
Manual engineering processes for code block tracking and evaluation are replaced with an automated AI-based system that uses machine learning models to assess and select optimal code blocks, substituting human mechanical evaluation with intelligent automation
2Adaptability or versatility
If multiple versions of code blocks are maintained for flexibility, then adaptability to different tasks is improved, but system complexity increases making management difficult
Solution Approach 1:
The system implements automated feedback loops where AI models continuously evaluate code block performance and provide quality assessments, enabling automatic selection of the best code blocks for specific tasks without manual intervention
Solution Approach 2:
The AI-based code block selector serves multiple functions including quality evaluation, performance assessment, compatibility checking, and automatic selection, consolidating what would otherwise require multiple separate manual processes into a single universal system
3Speed
If code blocks are deployed without thorough evaluation, then deployment speed increases, but the risk of deploying flawed or inferior code blocks increases
Solution Approach 1:
The system performs preliminary AI-based evaluation and quality assessment of code blocks before deployment, automatically identifying potential issues and selecting only high-quality code blocks for deployment, thus ensuring reliability before the deployment action occurs
Solution Approach 2:
An AI-based evaluation system acts as an intermediary between code block creation and deployment, automatically assessing quality and performance metrics to filter out flawed code blocks before they reach the deployment stage
Data Source
AI summary
Intelligent code block selection and codebase updating using generative AI is disclosed herein. A user may request a code block for performing a task based on a given quality parameter (e.g., most energy efficient, fastest, or the like). The system may select an AI model for evaluating code blocks to meet the quality parameter. The system may identify code blocks for evaluation and execute each code block in an isolated testing environment. The selected AI model evaluates each code block execution and selects a code block based on completing the task in a way that most adheres to the quality parameter. The selected code block is returned via a user interface. The selected code block may be stored in a configuration code building block library associated with the quality parameter and the task and used when developing and revising software for the industrial automation environment.