Drift Analysis for Sensor-Based Premises Safety
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Solution Overview
Problem
Existing security and access control systems in commercial and residential premises lack effective data analysis and prediction capabilities, limiting their ability to timely detect potential security threats and equipment issues.
Innovation Solution
A computer program product that collects sensor data from multiple sensors, applies unsupervised learning models to analyze and predict operational states, detects drift sequences, and generates alerts for potential security risks, using a state transition matrix to classify states as safe or unsafe, enabling timely corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor systems are used to monitor premises, then the system structure is simple and easy to implement, but the system lacks predictive capabilities and cannot timely detect potential security threats and equipment issues
Solution Approach 1:
The patent introduces an intermediary processing layer between traditional sensors and the monitoring system. This layer includes drift detection modules, state transition matrix computations, and unsupervised learning models that analyze sensor data to predict equipment failures and security threats before they occur, thereby adding predictive capability without completely redesigning the sensor system itself
Solution Approach 2:
The monitoring system is segmented into distinct functional modules: sensor data collection, drift sequence detection, state transition analysis, and alert generation. This segmentation allows the system to incrementally add predictive capabilities through modular components rather than requiring a complete system overhaul, managing complexity through structured division of functions
2Measurement precision
If sensor data is collected and analyzed continuously to detect drift sequences, then the detection precision and timeliness improve, but the computational resources and processing time increase
Solution Approach 1:
The system applies partial action by focusing computational resources only on detecting drift sequences and state transitions that indicate potential problems, rather than analyzing all sensor data uniformly. The unsupervised learning models selectively process data points that show deviations from normal operational patterns, reducing overall computational energy consumption while maintaining high detection precision for critical events
Solution Approach 2:
The system changes the parameter of data representation by transforming raw sensor readings into state transitions and drift sequences. This transformation compresses the data into meaningful patterns that are easier to analyze, reducing the computational energy required for continuous monitoring while improving drift detection precision through structured data representation
3Loss of information
If unsupervised learning models are applied to analyze sensor information, then the system can produce operational states and detect drift sequences, but the device complexity and computational requirements increase
Solution Approach 1:
The unsupervised learning models operate autonomously to analyze sensor information, produce operational states, and detect drift sequences without requiring constant human intervention or complex external processing systems. The models self-adjust and learn from the data patterns, reducing the need for manually configured complex analysis pipelines while maintaining high data analysis capability
Solution Approach 2:
The unsupervised learning models serve multiple functions simultaneously: they cluster sensor data into operational states, detect drift sequences indicating potential failures, and generate predictions about future system behavior. This multi-functionality reduces the need for separate specialized systems, managing processing complexity through versatile algorithms that handle multiple analytical tasks
Data Source
AI summary
Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.