Ontology-Driven Digital Twin Generation for Complex Ecosystems
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Solution Overview
Problem
Current digital twin creation tools are limited in their ability to handle diverse use cases, are often entity-specific, and struggle with customization and scalability, particularly when dealing with complex data types like time series or hierarchical data, leading to limitations in analysis and insights.
Innovation Solution
The system employs ontology-driven modeling processes to generate digital twins by constructing knowledge graphs with semantic relationships, enabling extensions such as data embedding, probabilistic querying, and optimization under uncertainty, allowing for the incorporation of various data types and complex analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing digital twin creation tools are used, then digital twins can be created for specific use cases, but the tools are limited in handling diverse use cases and lack versatility
Solution Approach 1:
The patent implements a universal digital twin platform that can handle multiple use cases including physical spaces, processes, and systems through a common ontology-driven architecture. The platform provides standardized interfaces and data models that enable creation of digital twins across different domains without requiring separate specialized tools for each use case.
Solution Approach 2:
The patent employs dynamic configuration capabilities that allow the digital twin platform to adapt its structure and behavior based on the specific use case requirements. The ontology model can be dynamically extended and customized to accommodate different data types, relationships, and analytical needs while maintaining a consistent core framework.
2Adaptability or versatility
If entity-specific digital twin platforms are used, then the platform is optimized for specific systems or products, but the platform cannot be utilized for other systems, products, or services
Solution Approach 1:
The patent creates a universal platform that maintains optimized performance for specific use cases while enabling cross-system compatibility through standardized ontology models and interfaces. The platform can be configured to provide use-case-specific optimizations while remaining applicable to multiple domains.
Solution Approach 2:
The patent segments the digital twin platform into modular components including ontology definitions, data ingestion modules, and analysis engines that can be independently configured for different use cases. This modular architecture allows specific optimizations for particular systems while maintaining overall platform versatility through standardized connection interfaces.
3Adaptability or versatility
If static digital twin design tools are used, then the tools are designed for specific use cases with predefined data, but the tools cannot support customization or incorporation of new data types like time series or hierarchical data
Solution Approach 1:
The patent implements dynamic ontology models that can be customized to accommodate new data types and structures. The platform allows users to define custom ontologies that extend beyond predefined templates, enabling incorporation of time series data, hierarchical data, and other complex data structures while maintaining systematic organization through the ontology framework.
Solution Approach 2:
The patent enables parameter changes in the ontology model to adapt to different data types and structures. The system allows modification of ontology parameters, relationships, and constraints to accommodate evolving data requirements while maintaining the structured nature of the digital twin through controlled ontology evolution.
4Reliability
If digital twins are rebuilt each time changes occur in the real world counterpart, then the digital twin remains accurate, but the process requires significant time and resources
Solution Approach 1:
The patent implements feedback mechanisms that automatically detect changes in the real world counterpart and trigger selective updates to the digital twin. The system monitors changes and uses the ontology model to determine which aspects of the digital twin need updating, enabling automatic synchronization without complete rebuilding while maintaining accuracy.
Solution Approach 2:
The patent establishes preliminary ontology models and data structures that anticipate potential changes in the real world counterpart. By pre-defining the framework and relationships, the system can rapidly accommodate changes through configuration updates rather than complete redesign, reducing the time and resources required for updates.
5Adaptability or versatility
If multiple digital twin creation tools are used to cover different portions of a use case, then comprehensive coverage is achieved, but the digital twins created using different tools are incompatible with each other
Solution Approach 1:
The patent merges multiple digital twin creation capabilities into a single unified platform that provides comprehensive use case coverage. The ontology-driven architecture integrates functions that were previously distributed across multiple specialized tools, enabling creation of complete digital twins for complex systems within a single cohesive environment.
Solution Approach 2:
The patent creates a universal platform that consolidates multiple digital twin creation tools into one system capable of handling diverse use cases. The standardized ontology model and interfaces provide compatibility across all functionality, eliminating integration issues between separate tools while maintaining comprehensive coverage through multi-functional capabilities.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support ontology driven processes to generate digital twins having extended capabilities. To generate the digital twin, an ontology may be obtained and modified to define additional types of data, such as events and metrics, for incorporation into the digital twin. The ontology, once modified, may be instantiated as a knowledge graph having the additional types of data embedded therein. The embedded data may be used to convert the knowledge graph to a probabilistic graph model that may be queried to extract information from the digital twin in a probabilistic manner. Additionally, multiple ontologies may be utilized to create a digital twin-of-digital twins, which enables more complex digital twins to be generated (e.g., digital twins of entire ecosystems), and enables new insights and understanding of the various components and interactions between the components of the ecosystem.