Industrial Plant Data Model Generation via 3D Imaging
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Solution Overview
Problem
Industrial Internet of Things (IIoT) platforms face challenges in interpreting and analyzing data from complex, distributed process devices in industrial plants due to the lack of contextual information, requiring in-depth knowledge and understanding of data point labels and environmental conditions.
Innovation Solution
A method and device for generating a data model using two- or three-dimensional images of industrial plant components, which includes identifying process devices, capturing contextual information, and transmitting this information to an evaluation unit for enhanced data analysis and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from complex distributed process devices are collected and stored in IIoT platforms, then data quantity and monitoring coverage are improved, but data interpretability and analysis quality deteriorate due to lack of contextual information
Solution Approach 1:
The patent applies preliminary action by capturing images of process devices and generating data models before data analysis occurs. The system pre-processes visual information to extract contextual details about device appearance, installation environment, and spatial relationships, storing this contextual data in advance so that when data analysis is performed later, the contextual information is already available to enhance interpretability without requiring additional on-demand processing
Solution Approach 2:
The patent creates a digital copy (data model) of the physical process device based on captured images. This copy includes contextual information about the device's appearance, installation context, and environmental conditions. The data model serves as a virtual representation that preserves spatial and contextual relationships, allowing analysis to be performed on the copy rather than requiring direct interpretation of raw data from the complex physical system
2Measurement precision
If detailed analysis of data point labels and environmental conditions is performed, then analysis accuracy is improved, but the requirement for technical expertise and time consumption increases
Solution Approach 1:
The system applies self-service by automatically extracting contextual information from images of process devices and generating data models without requiring manual intervention. The image processing and data model generation occur autonomously, capturing device appearance, installation environment, and spatial relationships automatically. This eliminates the need for experts to manually document contextual details, maintaining high analysis accuracy while significantly reducing time consumption and expertise requirements
Solution Approach 2:
The patent replaces manual expert analysis with automated image processing and data model generation systems. Instead of relying on technical experts to interpret data point labels and environmental conditions, the system uses computational methods to automatically extract contextual information from visual data. This substitution of mechanical expert knowledge with automated processing maintains analytical precision while eliminating time consumption associated with manual expertise application
3Reliability
If process devices are identified and contextual information is captured using images, then data model quality and monitoring capability are improved, but device complexity and system requirements increase
Solution Approach 1:
The patent applies universality by using a single imaging system to perform multiple functions: identifying process devices, capturing their appearance, documenting installation environment, and extracting spatial relationships. The same image capture device and processing pipeline serve all these purposes simultaneously, generating a comprehensive data model from one unified process. This multi-functionality improves monitoring capability without proportionally increasing system complexity, as one system accomplishes what would otherwise require multiple specialized subsystems
Data Source
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AI summary
The invention relates to a method for generating a data model (AM) for monitoring an industrial plant (A), wherein a, for example, two- or three-dimensional image (AB1, AB2) of at least a part of the plant (A) is captured on site in the plant (A) (V1), wherein the image (AB1, AB2) serves to generate and/or extend a data model (AM) belonging to the plant (A) (V5).