Tag-Driven Data Pipelines for PLC-to-ML Association Tracking
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Solution Overview
Problem
Industrial automation environments face challenges in effectively tracking associations between large numbers of program tags, process data, and data processing systems, making data analysis difficult and inefficient.
Innovation Solution
A system and method for integrating machine learning models into industrial automation environments by establishing data pipelines between design applications and machine learning models, allowing for the instantiation and surfacing of data pipelines corresponding to program tags, which facilitate timely communication and data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of program tags and data sources is increased to capture more industrial process information, then the quantity and quality of data for analysis is improved, but the complexity of tracking associations between tags, data, and processing systems increases
Solution Approach 1:
The patent introduces a data pipeline management system that acts as an intermediary between data sources (program tags) and data processing systems. This intermediary automatically establishes and tracks data pipelines, maintaining association metadata that links tags to their corresponding data flows and processing destinations, thereby resolving the complexity of tracking associations in large-scale industrial environments.
Solution Approach 2:
The system performs preliminary action by automatically generating data pipeline associations when program tags are created or modified. The data pipeline management system proactively establishes tracking relationships before data processing occurs, ensuring associations are already in place and documented, which eliminates the need for complex post-hoc tracking of tag-data-processing relationships.
2Productivity
If manual methods are used to track and manage data associations between program tags and processing systems, then device complexity is reduced, but productivity and data analysis efficiency deteriorate
Solution Approach 1:
The data pipeline management system operates autonomously, automatically discovering program tags, generating data pipeline associations, and maintaining tracking metadata without manual intervention. The system self-configures data flows and maintains association records, eliminating the need for manual tracking while significantly improving data analysis efficiency through automated pipeline management.
3Productivity
If automated data pipeline management is implemented to improve productivity and tracking efficiency, then data analysis efficiency is improved, but the complexity of the system architecture increases
Solution Approach 1:
The data pipeline management system is designed as a universal platform that handles multiple functions: automatic tag discovery, pipeline generation, association tracking, and data flow management. By consolidating these functions into a single multi-functional system, the patent reduces overall architectural complexity compared to having separate systems for each function, while maintaining high data analysis efficiency.
Data Source
AI summary
Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to surface data pipelines in an industrial automation environment. In some examples, a design component generates a control program configured for implementation by a programmable logic controller to control an industrial process. The design component adds program tags to the control program and implements the control program through the programmable logic controller. The design component establishes data pipelines that correspond to the program tags in the control program between data sources associated with the program tags and a machine learning system that consumes process data generated by the data sources. A pipeline management component generates a pipeline suggestion that indicates individual ones of the data pipelines and their corresponding program tags. The pipeline management component surfaces the pipeline suggestion via a software user interface.