Modular Ontology Synthesis for Digital Twin Data Integration
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Solution Overview
Problem
Existing digital twin technologies face challenges in efficiently managing complex systems with dynamic and heterogeneous data sources, as they often require cumbersome, static ontologies that are difficult to implement and update, limiting their applicability and performance.
Innovation Solution
A computer-implemented method synthesizes a dynamic operational ontology for digital twins using an ETL system, contextualizing data with specific ontologies that include geo-spatial and temporal identifiers, allowing for quick generation and updating of digital twins even in systems with diverse data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic ontology schema is built to allow evolution in digital twin environment, then the ontology can accommodate changes to application types and entity types, but the ontology becomes cumbersome to implement, takes longer to process, and negatively impacts performance
Solution Approach 1:
The patent divides the monolithic ontology schema into multiple smaller, modular ontology schemas. Each module represents a specific domain or aspect (e.g., asset management, maintenance, operations) and can be independently processed, loaded, and updated. This segmentation maintains adaptability while improving processing speed and reducing implementation complexity.
Solution Approach 2:
The patent implements a dynamic ontology loading mechanism where only the required ontology modules are loaded into memory based on the specific digital twin instance being created. This allows the system to maintain a comprehensive ontology structure while loading only necessary portions, thus improving processing speed and memory efficiency without sacrificing adaptability.
2Adaptability or versatility
If a monolithic ontology schema is built to allow evolution in digital twin environment, then the ontology can accommodate changes to application types and entity types, but it becomes more difficult to implement and update
Solution Approach 1:
The patent segments the ontology into modular schemas that can be independently developed, validated, and deployed. Each module can be implemented separately and updated without affecting the entire ontology system, making implementation and maintenance significantly easier while preserving evolution capability.
Solution Approach 2:
The patent creates a universal framework with standardized interfaces and data models that work across all ontology modules. This universal structure allows different modules to be combined in various configurations to support diverse application types and entity types, simplifying implementation while maintaining versatility.
3Ease of operation
If static ontologies are used for digital twins, then the ontology structure is simple and stable, but the system cannot adapt to changes in data structures or new entity types
Solution Approach 1:
The patent implements dynamic ontology loading where the system can load different ontology modules based on the specific data structures and entity types encountered. This allows the ontology to adapt dynamically to new data formats and entity types while maintaining a simple, stable core structure for common operations.
Solution Approach 2:
The patent introduces an intermediary layer between the static ontology modules and the heterogeneous data sources. This intermediary handles the adaptation and transformation of various data structures into the standardized ontology format, maintaining simplicity in the core ontology while enabling adaptability to diverse data sources.
Data Source
AI summary
A computer-implemented method for synthesizing an operational ontology for a digital twin of a real or virtual entity which includes at least one data source. The method includes obtaining, using an extract transform load, ETL, system, data from the entity, as part of the ETL process, contextualising the obtained data using a plurality of specific ontologies. Each specific ontology includes one or more ontology fragments forming a set of characteristics for a data classification of the data from the entity. For each specific ontology, a representation of that specific ontology is used to contextualise a data classification of the data in a data store. An operational ontology for the entity is synthesized using the plurality of specific ontologies.