Digital Twin Sensor Reconfiguration for Detection Accuracy
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Solution Overview
Problem
Digital twins often face inaccuracies due to suboptimal sensor configurations, which can lead to delayed or missed detection of issues in real-world environments, affecting their ability to provide timely and precise feedback.
Innovation Solution
A method that instantiates a real-world environment with sensor devices, creates a digital twin simulation, applies predictive analytics to forecast potential issues, and automatically reconfigures sensors to improve detection accuracy and speed by determining an optimal sensor configuration using machine logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor configuration is optimized for accurate detection, then detection precision improves, but system complexity increases
Solution Approach 1:
The patent implements dynamic sensor configuration where the system automatically adjusts sensor parameters (sampling rate, activation state, measurement frequency) based on real-time digital twin simulation requirements and detected issue types. This transforms static sensor configurations into adaptive, dynamic systems that optimize detection accuracy for specific conditions without permanent complexity increases.
Solution Approach 2:
The system changes sensor operating parameters (sampling rate, precision level, activation/deactivation state) based on digital twin analysis of potential issues. By dynamically adjusting these parameters rather than maintaining fixed high-precision configurations, the system achieves accurate detection when needed while reducing overall system complexity and resource consumption.
2Reliability
If more sensors are activated to improve detection coverage, then detection completeness improves, but energy consumption increases
Solution Approach 1:
The patent applies partial action by activating only the subset of sensors necessary for detecting specific potential issues identified through digital twin predictive analytics. Rather than continuously activating all sensors, the system selectively engages sensors based on simulated risk assessments, achieving sufficient detection coverage while minimizing energy consumption through targeted, partial sensor activation.
Solution Approach 2:
The system performs self-service by using its own digital twin simulation capabilities to determine which sensors should be activated. The digital twin analyzes potential issues and automatically configures sensor activation states without external intervention, enabling the system to self-optimize its detection coverage and energy usage based on simulated operational conditions.
3Speed
If sensor sampling rate is increased to improve detection speed, then response time improves, but data processing load increases
Solution Approach 1:
The patent applies preliminary action by using digital twin simulations to predict potential issues before they occur in the physical system. This advance prediction allows the system to pre-configure sensor sampling rates and activation states specifically for detected potential issues, rather than maintaining high sampling rates continuously. The processing load is concentrated on analyzing simulated predictions rather than continuously processing high-volume sensor data.
Data Source
AI summary
Computer technology where predictive analytics are used in connection with a digital twin simulation to predict an issue with a target (that is, environment, physical object and/or process). Based on the predicted issue, a machine learning algorithm is used to reconfigure the sensor set that monitors the target to more accurately, precisely and/or quickly detects a real world occurrence of the predicted issue.