Industrial HMI Generation From Engineering Drawings
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Solution Overview
Problem
Existing industrial automation systems face challenges in efficiently extracting business value from industrial data due to the complexity of handling large volumes of unstructured and uncorrelated data, leading to high processing times and storage requirements, as well as the identification of spurious correlations.
Innovation Solution
The implementation of a smart gateway platform that leverages industrial expertise to identify relevant industrial data subsets for specific business objectives, applying pre-defined correlations and causalities to assist AI analytics in quickly deriving valuable insights and reducing data processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If AI analytics is applied to large volumes of unstructured industrial data, then valuable insights can be derived, but processing time and storage requirements increase significantly
Solution Approach 1:
The patent extracts and isolates only the relevant subset of industrial data needed for specific business objectives, separating it from the larger unstructured data volume. This extraction approach reduces processing time while maintaining information quality by focusing analytics on pre-identified relevant data portions rather than processing all available data.
Solution Approach 2:
The patent segments industrial data into distinct subsets based on business objectives and pre-defined correlations. By dividing the large unstructured data volume into organized segments with known relationships, the system enables faster targeted analytics while preserving the contextual information needed for accurate insights.
2Measurement precision
If comprehensive industrial data is processed to ensure complete analysis, then insight accuracy improves, but storage requirements and processing overhead increase
Solution Approach 1:
The patent performs preliminary organization of industrial data by establishing pre-defined correlations and causalities before analytics execution. This advance structuring identifies and retains only the data subsets relevant to specific business objectives, reducing storage requirements while ensuring that the organized data maintains the contextual relationships needed for accurate insights.
3Productivity
If all available industrial data is analyzed, then comprehensive insights are obtained, but spurious correlations may be identified
Solution Approach 1:
The patent applies different data organization and correlation rules to different subsets of industrial data based on specific business objectives. By tailoring the correlation structures to local data contexts rather than applying uniform analysis across all data, the system identifies accurate correlations within each domain while avoiding spurious correlations that arise from indiscriminate cross-data analysis.
Data Source
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AI summary
A human-machine interface (HMI) development platform simplifies generation of an industrial visualization application by generating at least a portion of the visualization application based on analysis of digital engineering drawings of an automation system to be monitored and controlled. This drawing-based approach includes creation of HMI interface displays comprising animated graphics representing industrial assets represented in the drawings, and setting animation data sources for the graphics based on analysis of the drawings.