Multi-Modal Digital Twin for Physical Asset Security
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Solution Overview
Problem
Conventional security systems for physical infrastructures are limited in providing effective surveillance, prone to errors due to cluttered backgrounds and illumination changes, and lack real-time dynamic threat prediction and prevention without human intervention, requiring extensive data processing that is time-consuming and difficult to implement in real-time environments.
Innovation Solution
A method and system that utilize multi-modal inputs from sensors, including acoustic signals, to generate a digital asset of physical assets, detect events of interest, and simulate potential faults or threats, enabling real-time security threat prediction and prevention through a digital twin generation and machine learning-based analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional surveillance cameras are used for security monitoring, then surveillance coverage is provided, but the system produces erroneous results due to cluttered backgrounds and illumination changes
Solution Approach 1:
The patent combines multiple sensing modalities (acoustic sensors, thermal sensors, motion sensors, and visual cameras) into an integrated surveillance system. By fusing data from these different sensor types, the system overcomes the limitations of individual sensors and achieves more reliable security monitoring that is not easily fooled by background clutter or illumination changes.
Solution Approach 2:
The patent introduces acoustic signals as an intermediary modality that provides additional information about physical assets. Acoustic sensors detect sounds generated by or reflected from assets, creating an independent verification channel that mediates between visual input and asset identification, thereby improving detection accuracy in challenging visual conditions.
2Reliability
If conventional surveillance techniques are used, then basic monitoring is achieved, but the system lacks real-time dynamic threat prediction and prevention capabilities
Solution Approach 1:
The patent implements a digital twin that continuously simulates the physical asset and its environment. By performing preliminary simulations of various scenarios including potential security threats, the system predicts possible failures and threats before they occur in the real world, enabling proactive rather than reactive security measures.
Solution Approach 2:
The patent establishes a closed-loop feedback system where sensor data from the physical asset continuously updates the digital twin simulation. The simulation results feed back into the system to refine threat predictions and trigger automated prevention responses, creating a self-improving automated security system that learns from ongoing operations.
3Measurement precision
If extensive data processing is performed for accurate classification, then detection accuracy improves, but the process becomes time-consuming and difficult to implement in real-time environments
Solution Approach 1:
The patent segments the data processing task by creating a digital twin that pre-processes and structures data during simulation. By dividing the complex classification problem into manageable simulation scenarios and using the digital twin to organize data beforehand, the system achieves accurate classification without requiring excessive real-time computational resources.
Solution Approach 2:
The patent creates a digital copy (digital twin) of the physical asset that can be processed independently. This copy contains all necessary information about the asset's behavior, environment, and parameters, allowing extensive data analysis to be performed on the copy without affecting real-time operations of the physical asset, thus achieving both accuracy and speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances security by providing real-time dynamic threat prediction and prevention, improving the reliability and reducing the risk of security failures within physical infrastructures without the need for human intervention, through the use of multi-modal sensor data and machine learning models.
Implementation Method 1
The multi-modal input may comprise acoustic signal generated by or reflected off the physical asset and captured by a set of acoustic sensors
Data Source
AI summary
The disclosure relates to a system and method for providing enhanced security of physical assets within a physical infrastructure. The method includes receiving an overall layout of the physical infrastructure and multi-modal input with respect to the physical asset from a plurality of sensors installed within the physical infrastructure. The multi-modal input includes acoustic signal generated by or reflected off the physical asset and captured by a set of acoustic sensors. The method further includes generating a digital asset corresponding to the physical asset by determining an identification, a location, a shape, a size, and a behavior of the physical asset based on the multi-modal input and the overall layout, detecting one or more events of interest involving the digital asset based on the behavior of the physical asset, and simulating the one or more events of interest to evaluate a possible fault or a possible security threat.