Ontology-Driven Digital Twin Knowledge Graph Updates

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current digital twin technologies face challenges in maintaining and updating digital twins to reflect evolving systems, lack compatibility across different tools and industries, and are limited in handling complex data types such as time-series and hierarchical data, which restricts their ability to provide meaningful insights and scalability.

Innovation Solution

The implementation of ontology-driven modeling processes and tools that adaptively generate and update knowledge graph models, allowing for the creation of self-adaptive digital twins by leveraging ontologies to construct knowledge hierarchies, perform similarity determinations, and generate recommendations for updating digital twins, enabling seamless scaling and customization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual digital twin creation tools are used, then digital twins can be created for specific systems, but the process is data intensive and requires expertise

Engineering Contradiction:

Improveaccuracy of digital twin representationVSAvoidcomplexity of creation process

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system enables digital twins to automatically update themselves by ingesting data from their real-world counterparts and autonomously generating knowledge graphs, eliminating the need for manual expert intervention in the creation and maintenance process

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual expert-driven processes are replaced with automated AI/ML-based systems that perform data processing, knowledge graph generation, and digital twin updates without human intervention

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If entity-specific digital twin platforms are used, then digital twins can be created for specific systems, but they cannot be utilized for other systems or customized by users

Engineering Contradiction:

Improveaccuracy of digital twin representationVSAvoidcustomization capability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system provides a universal digital twin platform that can create and maintain digital twins across different industries and systems through standardized ontologies and knowledge graph structures, while allowing customization through domain-specific ontology extensions

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The digital twin structure is designed to be dynamic and adaptable, allowing the ontology and knowledge graph to evolve and customize themselves based on the specific requirements of different domains and use cases

Inventive Principle:

Principle #15Dynamics

3Reliability

If static digital twin design tools are used, then digital twins can be created for specific use cases, but they cannot reflect changes to the use case or real world counterpart

Engineering Contradiction:

Improveaccuracy of digital twin representationVSAvoidvalidity period of digital twin

Core Design Contradiction:

ReliabilityVSDuration of action of stationary object

Solution Approach 1:

The system implements continuous feedback loops where digital twins automatically ingest data from their real-world counterparts, detect changes, and update their knowledge graphs to reflect current state, ensuring ongoing accuracy and validity

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The digital twin structure transitions from static to dynamic, with automated mechanisms that continuously adapt the knowledge graph to reflect changes in the real-world system being modeled

Inventive Principle:

Principle #15Dynamics

4Reliability

If existing digital twin tools are used, then digital twins can be created, but they do not support customization of information types or handling of complex data structures like time-series and hierarchical data

Engineering Contradiction:

Improveaccuracy of digital twin representationVSAvoiddata type compatibility

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system segments data handling into domain-specific ontology modules that can independently process different data types (time-series, hierarchical, sensor data) while maintaining a unified knowledge graph structure through standardized relationships

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20240281671A1System and methods for updating digital twins

Publication Date: 2024.08.22 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support ontology driven processes to generate and/or update digital twins using a partially automated process. To generate the digital twin, an ontology may be obtained and used to generate a hierarchy model, from which a knowledge graph is generated and represents a digital twin. Similarity measurements may be performed on the knowledge graph, such as components thereof, to determine a similarity between the components. Components are ranked and clustered to identify clusters of similar components and new relationships between components. This new information may be used to update the knowledge model and/or knowledge graph. Updating the knowledge graph or model may enable generation of an updated digital twin and enable continued updating of additional different data structures for other similar elements.