CAD-Based Control Program Generation With Smart Tag Context
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Solution Overview
Problem
Industrial automation systems face challenges in deriving actionable insights from large volumes of unstructured and uncorrelated industrial data, leading to inefficiencies in machine learning and AI analytics, as well as the identification of spurious correlations, which increases processing time and storage requirements.
Innovation Solution
A smart gateway platform leverages domain expertise to selectively model and contextualize industrial data, reducing the data space for AI analytics by pre-defining relevant data subsets and correlations, and using smart tags to label data items based on business objectives, thereby streamlining data analysis and reducing processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all industrial data is collected and stored for AI analytics, then comprehensive analysis coverage is improved, but data storage requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-defining data subsets and correlations based on domain expertise before AI analytics are executed. Smart tags are pre-applied to label data items according to business objectives, so that when analytics are needed, the relevant data is already identified and organized, eliminating the need to process all raw industrial data from scratch
Solution Approach 2:
The system extracts only the relevant portions of industrial data needed for specific AI analytics tasks. By using smart tags and pre-defined data subsets, the system pulls out only the necessary data items related to particular business objectives, rather than analyzing the entire dataset, thus reducing processing time while maintaining analysis quality
2Reliability
If all industrial data is stored for future analytics, then data availability is improved, but storage requirements and costs increase
Solution Approach 1:
The system segments industrial data into meaningful subsets based on business objectives and domain expertise. Each segment is labeled with smart tags that indicate its relevance to specific analytics tasks. This segmentation allows the system to store and retrieve only the necessary data portions for each analytics operation, reducing overall storage requirements while maintaining data availability when needed
Solution Approach 2:
The system introduces smart tags as an intermediary layer between raw industrial data and AI analytics. These tags metadata structures serve as indexes that enable rapid identification and retrieval of relevant data without requiring the entire dataset to be loaded or processed, thus reducing storage needs while preserving data accessibility
3Measurement precision
If domain expertise is used to pre-model data, then analytics accuracy is improved, but system complexity increases
Solution Approach 1:
The system changes the parameter organization of industrial data by applying smart tags and metadata structures that reflect domain expertise. Instead of storing raw data in its original format, the system transforms it into a structured format with predefined subsets and correlations, making the data more suitable for specific AI analytics while improving accuracy
Data Source
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AI summary
An industrial control programming development platform simplifies generation of an industrial control program and associated tag definitions by generating at least a portion of the control program and tag definitions based on analysis of digital engineering drawings of an automation system to be monitored and controlled. This drawing-based program generation includes creation and configuration of smart data tags that model and contextualize controller data at the device level for processing by higher level analytic systems. This device-level contextualization can be based in part on inferences drawn from the digital engineering drawings.