ML-Assisted Control Logic Programming With Contextual Data Surfacing
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Solution Overview
Problem
Industrial automation environments face challenges in efficiently leveraging operational data for control code programming due to the vast amount of data and lack of accessibility, leading to overlooked important variables and connections, which complicates data science and data analytics.
Innovation Solution
Integration of machine learning engines within industrial programming and data science environments to provide accessible operational data, auto-completion suggestions, and contextual information, enhancing engineering and data science capabilities by surfacing relevant data and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data analysis and control code programming are performed without machine learning assistance, then programmers have full control over the programming process, but the time and computational resources required increase significantly
Solution Approach 1:
A machine learning model serves as an intermediary between operational data and control code programming. The model automatically analyzes operational data, identifies patterns and relationships, and generates programming suggestions, thereby reducing the time and effort required for manual data analysis and code development.
Solution Approach 2:
The system enables self-service programming by allowing the machine learning model to autonomously analyze operational data and generate control code suggestions without requiring extensive manual intervention. The model continuously learns from operational data and automatically improves its programming suggestions over time.
2Reliability
If all operational data is made accessible to programmers for comprehensive analysis, then important variables and connections are less likely to be overlooked, but the complexity of the programming environment increases
Solution Approach 1:
The machine learning model extracts only the most relevant variables, patterns, and relationships from the vast operational data and presents them to programmers in a simplified format. This extraction process maintains completeness of control logic by identifying all important elements while reducing the apparent complexity by filtering out unnecessary information.
Solution Approach 2:
The system applies local quality by providing different levels of data detail to different users based on their needs. The machine learning model analyzes the entire operational data comprehensively but presents customized, context-relevant information to programmers, ensuring reliability without overwhelming them with unnecessary complexity.
Data Source
AI summary
Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a user interface component configured to display a programming environment for editing control logic, wherein operational data from the industrial automation environment is accessible from within the programming environment through a data pipeline. A machine learning-based data science engine is configured to process the operational data from the industrial automation environment to generate processed data and identify a portion of the processed data relevant to a component of the control logic. The user interface component is further configured to surface the portion of the processed data in the programming environment.