Dynamic Threshold Hazard Alarm for Fire Detection
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Solution Overview
Problem
Conventional hazard alarm systems often trigger false alarms due to nuisance events such as burnt food or hairspray, leading to unnecessary alerts and user dissatisfaction, while failing to promptly detect real fire hazards.
Innovation Solution
A hazard safety device equipped with smoke, temperature, and carbon monoxide sensors, coupled with a processor that uses computer learning techniques to differentiate between dangerous fires and nuisance events by adjusting sensor thresholds based on signal rates and ambient conditions, implementing state diagrams to accurately classify sensor data and issue alarms only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the smoke sensor threshold is set low to detect real fire hazards quickly, then detection speed is improved, but false alarms increase due to nuisance events
Solution Approach 1:
The patent applies dynamics by making the alarm threshold variable rather than fixed. The processor dynamically adjusts the smoke sensor threshold based on real-time temperature readings and rate-of-change calculations. When temperature rises rapidly (indicating real fire), the threshold lowers to enable quick detection. When temperature is stable (indicating nuisance events like cooking), the threshold raises to prevent false alarms. This dynamic threshold adjustment resolves the contradiction between fast detection and false alarm reduction.
Solution Approach 2:
The patent changes the parameter of the alarm threshold from a static value to a dynamic value that varies based on temperature conditions. By monitoring temperature and its rate of change, the system adjusts the smoke detection threshold parameter in real-time. This parameter change allows the system to maintain high sensitivity during actual fires while reducing sensitivity during nuisance events, thereby resolving the contradiction between detection speed and reliability.
2Reliability
If the alarm threshold is set high to reduce false alarms, then false alarm rate decreases, but detection speed slows down for real fire hazards
Solution Approach 1:
The system uses dynamic threshold adjustment based on temperature conditions. When the temperature sensor detects rapid temperature increase (a key indicator of real fire), the processor automatically lowers the smoke alarm threshold, enabling fast detection. When temperature is stable or rising slowly (typical of nuisance events), the threshold remains high to prevent false alarms. This dynamic behavior resolves the contradiction by adapting the threshold to the actual fire risk level.
Solution Approach 2:
The system implements feedback by continuously monitoring temperature and using it to adjust the smoke alarm threshold. The temperature sensor provides feedback about environmental conditions, and the processor uses this feedback to modulate the alarm sensitivity. This closed-loop feedback mechanism ensures the threshold is appropriately adjusted based on real-time conditions, resolving the contradiction between high threshold (fewer false alarms) and low threshold (faster detection).
3Measurement precision
If multiple sensors and processing algorithms are added to differentiate fire from nuisance events, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies multi-functionality by using a single microprocessor to perform multiple tasks: reading smoke sensor data, reading temperature sensor data, calculating rate of change, comparing against thresholds, and controlling the alarm output. Rather than adding separate dedicated circuits for each function, the processor handles all detection and decision-making logic, improving accuracy without proportionally increasing hardware complexity.
Solution Approach 2:
The system merges the smoke detection function and temperature monitoring function into a unified detection system. The processor combines data from both sensors and uses their relationship (temperature rate of change combined with smoke levels) to make alarm decisions. This merging allows the system to achieve higher detection accuracy through multi-parameter analysis while avoiding the complexity of completely separate detection systems.
Data Source
AI summary
Embodiments relate to systems for, and methods of, providing low nuisance, fast response hazard notification. Advantageously, the disclosed techniques avoid sounding an alarm in response to typical nuisance events, such as burnt food.