Fire Detection Sensor Network for Real-Time False Alarm Reduction
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Solution Overview
Problem
Conventional fire early detection systems rely heavily on human operators, are prone to false alarms, and lack automated, real-time analysis, which can divert resources and delay response to actual threats.
Innovation Solution
A fire early detection system (FEDS) utilizing a network of sensory nodes with sensors for various environmental parameters, a gateway device, and a warning system that employs deep learning to analyze data and transmit automated alerts via network communication protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators are used to analyze detection data, then the system can identify fires, but the response time increases and subjectivity is introduced
Solution Approach 1:
The patent replaces the mechanical human analysis process with automated electronic detection systems including thermographic cameras, acoustic sensors, and algorithms that automatically analyze data and identify fire signatures, eliminating human response time delays and subjectivity while maintaining detection accuracy
Solution Approach 2:
The system implements self-service through automated alert generation and notification mechanisms that automatically respond to detected fire conditions without requiring human intervention for analysis, enabling the system to detect, analyze, and alert independently in real-time
2Measurement precision
If multiple sensors are deployed to improve detection capability, then fire detection accuracy improves, but the risk of false alarms increases
Solution Approach 1:
The patent combines multiple different sensor types (acoustic sensors, thermographic cameras, temperature sensors) into an integrated detection system that cross-validates signals across different modalities, allowing the system to distinguish true fire events from false alarm sources by requiring corroboration across sensor types
Solution Approach 2:
The system implements feedback mechanisms where detected anomalies are analyzed against historical data and patterns, with the ability to learn from false alarms and adjust detection thresholds, creating a self-correcting system that reduces false positives while maintaining sensitivity to actual fire events
3Productivity
If automated real-time analysis is implemented, then response speed improves, but system complexity increases
Solution Approach 1:
The patent segments the automated analysis system into modular functional components including data acquisition modules, processing algorithms, and communication interfaces, allowing complex real-time analysis to be achieved through coordinated simple modules that can be independently optimized and maintained
Solution Approach 2:
The system employs intermediary processing layers that translate complex sensor data into standardized formats and use intermediate algorithms to pre-process and filter data before final analysis, reducing the computational complexity required at each stage while maintaining real-time detection capability
Data Source
AI summary
A fire early detection system (FEDS) is disclosed for use with a gateway device. The FDS includes: a sensory node configured to be disposed at a location; and a warning system configured to receive a detection signal from a gateway device and to output a fire warning signal based on the detection signal. The sensory node includes: a sensor configured to detect a parameter of an environment surrounding the sensory node at the location and to output a parameter signal based on the detected parameter; a communicator configured to wirelessly transmit the detection signal; a memory; a processor configured to execute instructions stored in the memory to: generate, based on the parameter signal, the detection signal; and cause the communicator to wirelessly transmit the detection signal; and a power source configured to supply power to the sensor, the communicator, the memory and the processor.