Unified Digital Twin Platform Integrating External Feedback
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Solution Overview
Problem
Current digital twin generation processes are inefficient and require massive teams of specialized engineers due to the use of disparate engineering tools, leading to a 'spaghetti monster' of code and data, which complicates the integration of external feedback and exacerbates security issues and delays in design, manufacturing, and certification.
Innovation Solution
A unified, scalable, and secure digital model collaboration platform that integrates external feedback from physical, virtual, and human sources, using model splicing to combine disparate data sources and designate an authoritative source of truth through agent-configurable mechanisms, enabling iterative updates and improvements of digital twins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If disparate engineering tools are used for digital twin generation, then specialized engineering capabilities are utilized, but system complexity and integration difficulty increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (integration platform or adapter system) that mediates between disparate engineering tools and the digital twin generation process. This intermediary standardizes data exchange formats, manages tool-specific protocols, and coordinates workflows across multiple tools without requiring direct integration between each tool pair, thereby maintaining engineering versatility while controlling system complexity.
Solution Approach 2:
The patent implements a universal interface or common data model that enables multiple specialized engineering tools to interact through standardized mechanisms. This universal layer allows different tools (CAD, FEA, CFD, etc.) to contribute to digital twin generation using consistent data structures and communication protocols, reducing integration complexity while preserving access to specialized capabilities.
2Reliability
If massive teams of specialized engineers are deployed, then comprehensive engineering coverage is achieved, but project time and cost increase
Solution Approach 1:
The patent implements automated workflows where the system performs engineering tasks autonomously using embedded algorithms, AI/ML models, and rule-based systems. Common tasks such as data validation, model generation, simulation execution, and result analysis are automated, reducing dependency on large teams of specialized engineers while maintaining comprehensive engineering coverage through systematic automated processes.
Solution Approach 2:
The patent employs parameter-driven configuration where engineering behaviors, model complexities, and analysis depths are controlled through adjustable parameters rather than requiring manual intervention from specialized engineers for each case. This allows the system to adapt engineering coverage to project needs by changing parameters, reducing time and resource requirements while maintaining reliability.
3Adaptability or versatility
If extensive code and data integration is performed, then functional completeness is achieved, but security vulnerabilities and integration delays increase
Solution Approach 1:
The patent segments the code and data integration architecture into isolated, modular components with defined boundaries and interfaces. Each module handles specific integration tasks independently, allowing security to be implemented at each segment level rather than requiring comprehensive security across the entire integrated system. This modular approach maintains functional completeness while reducing security vulnerabilities through localized security controls.
4Ease of manufacture
If traditional digital twin generation processes are used, then digital models are created, but iterative updates with external feedback become cumbersome and slow
Solution Approach 1:
The patent implements structured feedback mechanisms where external feedback (from sensors, users, simulations, or other systems) is systematically captured, processed, and integrated into digital twin updates. Automated feedback loops enable continuous synchronization between digital twins and their physical counterparts or source systems, making iterative updates efficient and streamlined rather than cumbersome.
Solution Approach 2:
The patent establishes preliminary frameworks during initial digital twin creation, including pre-configured data exchange interfaces, standardized update protocols, and predefined feedback processing workflows. This preliminary setup enables rapid iterative updates later without requiring extensive reconfiguration, maintaining ease of model creation while significantly improving update efficiency.
Data Source
AI summary
Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and/or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.