Facility Surveillance Using Digital Twins for Critical State Classification
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Solution Overview
Problem
Current facility surveillance systems face challenges in accurately classifying anomalous states and reducing false alarms, particularly in complex environments with varying conditions, and require extensive on-site training and data collection for effective operation.
Innovation Solution
A surveillance system that combines multiple sensors and modalities, such as color, depth, and thermal imagery, using machine learning to adapt to specific environments and contextual dependencies, allowing for automatic detection and classification of critical states with reduced false positives and negatives, and requiring lower on-site training efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional surveillance systems are used, then system simplicity is maintained, but measurement precision and anomaly detection accuracy deteriorate
Solution Approach 1:
The patent combines multiple sensor modalities (RGB cameras, depth cameras, infrared cameras, microphones, and other surveillance sensors) into an integrated surveillance system. This merging of different sensing capabilities enables multi-modal data fusion, which significantly improves anomaly detection accuracy by cross-validating signals across different sensor types and detecting patterns that would be invisible to single sensor types.
Solution Approach 2:
The surveillance system is designed with multi-functional capabilities, where a single system performs diverse functions including visual surveillance, thermal detection, audio monitoring, depth mapping, and anomaly classification. The system can adaptively select and combine different sensor modalities based on environmental conditions and detection requirements, making it universally applicable across various facility types and surveillance scenarios.
2Measurement precision
If extensive on-site training data collection is performed, then model accuracy improves, but loss of time and training effort increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and pre-processing surveillance data from multiple modalities before deployment. Synthetic training data is generated through simulations and digital twins of facility environments, allowing the machine learning models to be pre-trained offline. This preliminary data preparation significantly reduces the need for extensive on-site data collection and model fine-tuning after deployment.
Solution Approach 2:
The patent employs digital twins and synthetic data generation to create virtual copies of facility environments and surveillance scenarios. These synthetic copies serve as training data, eliminating the need to collect equivalent real-world data during on-site deployment. The system uses copied environmental characteristics, sensor responses, and anomaly patterns from virtual models to train classification algorithms, dramatically reducing on-site training requirements.
3Reliability
If simple classification models are used, then ease of operation is maintained, but reliability of anomaly classification deteriorates
Solution Approach 1:
The classification system is segmented into multiple specialized components, each handling specific aspects of anomaly detection. Separate machine learning models are trained for different sensor modalities (RGB classification, depth classification, infrared classification, audio classification), and their outputs are independently processed before being fused. This segmentation allows each component to be optimized for its specific function while maintaining overall system reliability through ensemble decision-making.
Solution Approach 2:
The system implements feedback mechanisms where classification results from multiple sensor modalities and models are continuously evaluated and cross-validated. The system provides feedback loops that adjust classification thresholds, re-weight sensor contributions, and refine anomaly detection based on accumulated operational data. This feedback-driven adaptation improves classification reliability while maintaining operational simplicity through automated adjustments.
Data Source
AI summary
Systems and methods for surveillance of a facility including facility elements. The system includes a central computing unit providing a digital model of the facility providing topological or logical or functional relationships of the facility elements, surveillance sensors adapted for surveillance of a plurality of the facility elements and for generation of surveillance data, communication means for transmitting data from the surveillance sensors to the central computing unit, and state derivation means configured to analyse the surveillance data and derive a state of a respective facility element. The central computing unit is configured to record a state pattern by combining states of at least one facility element based on at least one relationship of the facility element provided by the facility model, provide a state pattern critical-noncritical classification model which considers relationships provided by the facility model, and perform a criticality-classification based on the relationship.