Federated Digital Twin Framework for Sustainable Data Management
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Solution Overview
Problem
Current digital twin frameworks face challenges in making data-intensive digital twins sustainable without compromising performance, leading to resource consumption issues and data management failures due to handling large volumes of data.
Innovation Solution
A sustainable and self-adaptive federated digital twin framework is provided, where a global digital twin assigns communication modes and task assignments to local digital twins, updates models based on task assignments, and receives model updates, enabling efficient data management and quality maintenance through a Q-learning framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized digital twin framework is used to handle large volumes of data, then data management capability is improved, but resource consumption increases and system sustainability deteriorates
Solution Approach 1:
The patent divides the centralized digital twin system into multiple local digital twins distributed across different devices. Each local digital twin handles data locally, segmenting the data processing workload and reducing the resource burden on any single system while maintaining overall data management capability.
Solution Approach 2:
The patent introduces a federated architecture dimension that combines distributed local processing with centralized coordination. This dimensional change allows the system to achieve both local resource efficiency and global data management effectiveness simultaneously.
2Measurement precision
If data collection frequency is increased to maintain data quality, then data quality is improved, but resource consumption and system sustainability worsen
Solution Approach 1:
The patent implements dynamic communication mode assignment where the data collection frequency and communication intensity are adjusted based on real-time system state, task requirements, and data quality needs. This dynamic adaptation maintains data quality while optimizing resource consumption by avoiding unnecessary frequent data collection when not required.
Solution Approach 2:
The system changes operational parameters such as communication mode and data collection frequency based on task assignments and system conditions. This parameter adjustment allows the system to maintain data quality thresholds while reducing resource consumption during periods of lower data quality requirements.
3Adaptability or versatility
If communication between local and global digital twins is intensified to improve coordination, then system adaptability is improved, but resource consumption increases
Solution Approach 1:
The patent implements periodic communication between local and global digital twins based on task cycles and system events rather than continuous communication. This periodic action maintains system adaptability and coordination while significantly reducing communication overhead and resource consumption compared to continuous intensive communication.
Data Source
AI summary
A first device may provide, via a global digital twin of the first device, a communication mode assignment, of a communication mode, to a local digital twin of a second device. The communication mode assignment is to cause the local digital twin to communicate with the global digital twin via the communication mode. The first device may generate, via the global digital twin, a task assignment, and may provide, via the global digital, the task assignment to the local digital twin. The first device may update, via the global digital twin, a model based on the task assignment, and may receive, via the global digital twin and from the local digital twin, a model update associated with the local digital twin. The first device may update, via the global digital twin, the model based on the model update.