Smart Hazard Sensor Profiling for Multi-Threat Detection
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Solution Overview
Problem
Existing hazard detection systems are inefficient, costly, and require multiple isolated sensors, leading to labor-intensive implementations, false positives, and an inability to detect a variety of hazards effectively.
Innovation Solution
An all-in-one smart sensor system utilizing artificial intelligence and machine learning to generate and analyze data, creating a location-specific profile to identify discrepancies and generate alerts for potential hazards, including fire, gas leaks, and other threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple isolated sensors are used to detect different hazards, then hazard detection capability is improved, but system complexity and cost increase
Solution Approach 1:
The patent combines multiple hazard detection sensors (fire, gas, CO, water, etc.) into a single integrated smart sensor device that can detect multiple types of hazards simultaneously. This merging approach maintains comprehensive hazard detection capability while reducing system complexity by eliminating the need for multiple separate sensor installations.
Solution Approach 2:
The smart sensor device is designed with multi-functionality to detect various hazards including fire, gas leaks, carbon monoxide, and water issues through a single device. This universal approach allows one sensor to perform the functions of multiple specialized sensors, reducing overall system complexity while maintaining detection reliability.
2Reliability
If multiple isolated sensors are deployed, then hazard detection coverage is improved, but installation and upkeep costs increase
Solution Approach 1:
By merging multiple hazard detection capabilities into one device, the patent reduces the number of units that need to be manufactured, installed, and maintained. This single-device approach covers multiple hazard types, thereby reducing installation costs while maintaining comprehensive detection coverage.
Solution Approach 2:
The universal smart sensor performs multiple detection functions (fire, gas, CO, water) in one device, reducing the total quantity of sensors needed for deployment. This multi-functionality directly reduces installation and upkeep costs compared to deploying multiple separate specialized sensors.
3Measurement precision
If conventional sensors are used, then specific hazard detection is achieved, but detection speed is slow
Solution Approach 1:
The smart sensor continuously monitors multiple hazard parameters simultaneously using AI and machine learning algorithms, enabling real-time detection of developing hazards. This continuous multi-parameter monitoring accelerates detection speed compared to conventional sensors that monitor single parameters intermittently, while maintaining detection accuracy through advanced algorithms.
Solution Approach 2:
The system uses AI and machine learning to analyze sensor data in real-time, providing rapid feedback on hazard conditions. This intelligent feedback mechanism enables faster detection and response to hazards compared to conventional sensors, while maintaining measurement precision through algorithmic analysis.
4Measurement precision
If location-specific profiles are built using AI, then false positives are reduced, but data processing requirements increase
Solution Approach 1:
The smart sensor performs self-learning by automatically building location-specific profiles through AI and machine learning algorithms. This self-service capability reduces false positives by adapting to local environmental conditions without requiring manual configuration, while the energy cost is offset by the system's autonomous operation and intelligent data processing.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on location-specific profiles generated by AI analysis. This parameter adaptation reduces false positives by accounting for local variations in environmental conditions, while the energy expenditure for processing is justified by the significant reduction in erroneous alerts.
Data Source
AI summary
A sensor for detecting hazards is described that includes a memory and a processor. The processor may be configured to generate sensor profile data associated with a location and apply the sensor profile data to a sensor model profile associated with the location wherein the sensor model profile includes a plurality of parameter levels for the location generated by a machine learning model. The processor may also be configured to identify a discrepancy between the sensor profile data and the sensor model profile and determine a potential hazard at the location based upon the discrepancy between the sensor profile data and the sensor model profile. The processor may further be configured to generate an alert based upon the potential hazard at the location.