Industrial Control Logic Workspace With ML-Guided Data Selection
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Solution Overview
Problem
Industrial automation environments face challenges in leveraging operational data for control code programming due to the vast amount of data and lack of accessibility, leading to overlooked important variables and inefficient data integration, which hampers effective control logic and data science capabilities.
Innovation Solution
Integration of machine learning engines within industrial programming and data science environments to provide accessible operational data, suggest auto-completions, and surface relevant data, along with machine learning-based tools for assisted programming and analytics, enhancing engineering and data science collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If control programmers manually edit control programs using operational data, then control logic accuracy improves, but time consumption and complexity increase significantly
Solution Approach 1:
The system enables self-service by allowing control programmers to directly access and utilize operational data, statistics, and insights within the programming environment without requiring external data science expertise. The integrated environment automatically provides relevant data and machine learning insights, enabling programmers to independently improve control logic accuracy while reducing time consumption.
Solution Approach 2:
The patent merges the control programming environment with data science and machine learning capabilities into a single integrated workspace. This combination allows programmers to access operational data, statistical analysis, and ML-generated insights directly within their programming environment, eliminating the need for separate data access processes and significantly reducing time consumption while maintaining high control logic accuracy.
2Manufacturing precision
If control programmers access runtime data and operational information, then control logic quality improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system introduces an intermediary layer consisting of machine learning models and data processing components that automatically analyze operational data and present simplified, relevant insights to control programmers. This intermediary handles the complexity of data processing, statistical analysis, and pattern recognition, while providing programmers with easily consumable information that improves control logic quality without requiring them to manage system complexity.
Solution Approach 2:
The integrated environment provides self-service capabilities where the system automatically retrieves, processes, and presents relevant operational data and insights to programmers as needed. The machine learning models autonomously analyze data patterns and generate recommendations, reducing the burden on programmers while improving control logic quality through data-driven decisions.
3Manufacturing precision
If machine learning models process operational data to identify relevant variables, then data relevance improves, but computing power and processing time increase
Solution Approach 1:
The system applies local quality by using machine learning models to identify and prioritize only the most relevant variables, data points, and patterns specific to each control programming context. Rather than processing all operational data uniformly, the ML models analyze and surface locally relevant information tailored to the specific control logic being developed, improving data relevance while reducing overall computing power requirements by focusing processing efforts where they are most needed.
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.