Automated Drawing Information Mapping for Semantic Digital Twins
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of aligning drawing data from process plants to industrial standards, such as DEXPI, is manual and time-consuming, requiring domain expert involvement and lacking automated semantic enrichment.
Innovation Solution
A method and device for automatically extracting drawing information from process plant diagrams, using named entity recognition models to map this information to industrial standards, and generating instances conforming to these standards, thereby reducing manual effort and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping of drawing information to industrial standards is performed, then mapping accuracy and semantic enrichment are improved, but time consumption and manual effort increase significantly
Solution Approach 1:
The system enables automatic self-mapping of drawing information to industrial standards through a named entity recognition model that processes extracted entities and autonomously assigns them to appropriate standard classes without requiring manual intervention from domain experts
Solution Approach 2:
The manual mechanical process of expert review and classification is replaced with an automated computational system using named entity recognition models that perform semantic enrichment and mapping through algorithmic processing
2Productivity
If automated information extraction is implemented, then productivity is improved, but the complexity of the extraction and mapping system increases
Solution Approach 1:
The complex mapping system is segmented into distinct functional modules: an extraction module for obtaining drawing information, a named entity recognition module for identifying and classifying entities, and a mapping module for assigning standard classes, making each component more manageable and maintainable
Solution Approach 2:
A named entity recognition model serves as an intermediary component between the extraction module and the mapping module, processing extracted entities through standardized classification layers and facilitating smooth data flow between system components
3Reliability
If domain expert involvement is required for semantic enrichment, then mapping reliability is improved, but the process becomes less scalable and more time-consuming
Solution Approach 1:
The system performs autonomous semantic enrichment by using a pre-trained named entity recognition model that automatically identifies and classifies entities according to industrial standards without requiring domain experts to manually review and annotate each entity
Solution Approach 2:
The named entity recognition model is pre-trained with knowledge of industrial standards and entity classifications, enabling it to perform reliable mapping operations independently before human review is needed, thus maintaining quality while improving efficiency
Data Source
Figure 1~2
Figure 3~4
Figure 5
AI summary
Semantic digital twins of process plants shall be created faster on the basis of piping and instrumentation diagrams. Therefore, there is provided a method of mapping drawing information representing a design of a process plant to an industrial standard including the steps of automatically extracting (13) the drawing information from a drawing (12) representing the process plant, automatically mapping (17) the drawing information to classes (25, 28) of the industrial standard by using a named entity recognition model and generating an instance of the class of the industrial standard the drawing information is mapped to.