Digital Twin–Physical Artifact Coupling with Adaptive Granularity
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Solution Overview
Problem
Existing digital twin systems require manual intervention for repetitive tasks, leading to inefficiencies and delays in detecting anomalies, and often produce false positives or negatives, lacking real-time adaptability and compliance with policy requirements.
Innovation Solution
A self-adaptive solution that autonomously adjusts the granularity and degree of coupling between digital twins and physical artifacts by dynamically changing coupling depth and frequency based on real-time conditions, user input, and policy preferences, using elasticity triggers to optimize interaction and synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for monitoring and managing digital twins, then operational control is maintained, but efficiency is reduced and anomaly detection is delayed
Solution Approach 1:
The system implements self-service through automated anomaly detection algorithms that continuously monitor digital twin data without human intervention. The elasticity mechanism automatically adjusts coupling depth and triggers remediation actions based on detected anomalies, eliminating the need for manual monitoring while improving both efficiency and detection speed.
Solution Approach 2:
The system establishes feedback loops where sensor data from physical artifacts continuously updates the digital twin, which then feeds into anomaly detection algorithms. The results trigger automated responses that adjust the coupling between digital and physical systems, creating a closed-loop feedback mechanism that improves productivity and reduces detection delays.
2Adaptability or versatility
If fixed coupling depth is used between digital twin and physical artifact, then system simplicity is maintained, but adaptability to changing conditions is reduced
Solution Approach 1:
The system implements dynamic coupling depth adjustment where the connection between digital twin and physical artifact adapts in real-time based on operational conditions. The elasticity mechanism modifies coupling parameters automatically, allowing the system to respond to changing conditions while managing complexity through automated control algorithms.
Solution Approach 2:
The system changes coupling parameters (depth, frequency, granularity) dynamically based on detected conditions and policy requirements. This parameter adaptation enables real-time flexibility without manual intervention, as the elasticity mechanism automatically adjusts these parameters to optimize system performance under varying conditions.
3Measurement precision
If high-frequency data collection is implemented, then real-time monitoring accuracy is improved, but energy consumption increases
Solution Approach 1:
The system applies partial action by collecting data at varying frequencies rather than continuously at maximum rate. The elasticity mechanism adjusts the coupling frequency dynamically, collecting high-frequency data only when anomalies are detected or suspected, while using lower-frequency monitoring during normal operations, thus maintaining detection accuracy while reducing overall energy consumption.
Solution Approach 2:
The system implements periodic data collection with variable intervals rather than continuous sampling. The coupling frequency is adjusted periodically based on system state and detected conditions, allowing high-precision monitoring when needed while reducing energy consumption during stable operational periods through less frequent data collection cycles.
Data Source
AI summary
A method includes collecting data of states from a physical artifact at a first frequency using one or more data collection devices, wherein the states describe performance of the physical artifact in real-time, instantiating a first digital representation comprising a digital twin of the physical artifact, wherein the first digital representation mimics the physical artifact and a first state of the states, conducting an analysis to determine whether a first granularity of the collected data is sufficient based upon whether the first state falls within boundaries of operation that are expected for the digital twin, and adjusting a resolution at which the digital twin represents the physical artifact based upon the analysis to determine whether the first granularity of the collected data is sufficient.