Coupled Digital Twin Optimization for Resource Allocation Control
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Solution Overview
Problem
Conventional digital twin (DT) systems focus on object-level behaviors and lack efficient methods to capture interaction flows between multiple DTs and business flows, leading to suboptimal resource allocation and operational inefficiencies.
Innovation Solution
A system comprising multiple digital twins interconnected through an asset optimizer module and a system optimizer module, which selects and optimizes DTs based on accuracy, time coverage, computation time, or contribution to variance, generating operation protocols for real-world asset systems to achieve business objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a monolithic representation approach is used to aggregate all facets and data from different lifecycle stages, then a comprehensive digital representation is achieved, but the resulting object quickly becomes bloated and inefficient
Solution Approach 1:
The patent divides the digital representation into separate digital twins for different lifecycle stages (design, engineering, production, operation, service, maintenance, end of life) rather than aggregating all data into a single monolithic object. Each digital twin captures specific facets relevant to its lifecycle stage, maintaining comprehensiveness while avoiding bloat through modular organization.
2Reliability
If conventional DT implementations focus on object-level behaviors, then object efficiency and reliability are improved, but interaction flows between multiple DTs and business flows are not captured
Solution Approach 1:
The patent merges object-level digital twins with business-level digital twins into an integrated system. The business digital twin captures business flows and interactions between multiple assets, while object digital twins maintain their operational details. This combination preserves object efficiency while adding the capability to model and optimize interaction flows and business processes.
3Productivity
If multiple digital twins are interconnected for comprehensive optimization, then resource allocation and operational efficiency are improved, but computational complexity increases
Solution Approach 1:
The patent segments the optimization problem into hierarchical levels: object-level optimization for individual digital twins and business-level optimization for the integrated system. This segmentation allows computational tasks to be distributed and managed at appropriate levels, improving resource allocation efficiency while controlling computational complexity through structured problem decomposition.
Solution Approach 2:
The patent adds a temporal dimension to the digital twin architecture by incorporating lifecycle stage transitions and time-based optimization. This dimensional expansion allows the system to optimize across different time horizons and lifecycle stages, improving overall productivity while managing computational complexity through time-based problem structuring.
Data Source
AI summary
Optimization systems and methods for optimizing business operations and asset systems are disclosed. A system includes digital twins corresponding to asset systems; business models corresponding to business operations; and an electronic control unit (ECU). The ECU is programmed to: implement an asset optimizer module, where implementing the asset optimizer module interconnects the digital twins for optimization; execute the asset optimizer module, where the asset optimizer module optimizes the digital twins to obtain one or more optimization parameters for the asset systems; implement a system optimizer module, where the system optimizer module receives the one or more optimization parameters and the business models; execute the system optimizer module, where the system optimizer module generates operation protocols for the business models; and output, to a user, the operation protocols for implementation in a real-world asset system.