Learning Alarm System Reducing False Alarms via Environmental Data
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Solution Overview
Problem
Conventional smoke detectors and alarm systems experience high frequencies of false alarms due to nuisance events, leading to user frustration and potential disabling of the alarms, as they struggle to differentiate between real hazards and controlled or non-fire sources.
Innovation Solution
A learning alarm system equipped with sensors, processors, and user interfaces that store and compare environmental property data to suppress alerts during known nuisance conditions, allowing for discrimination between hazardous and non-hazardous events, thereby reducing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the alarm sensitivity is increased to detect real hazards, then the reliability of fire detection is improved, but the frequency of false alarms during nuisance events increases
Solution Approach 1:
The system performs preliminary learning during a training period where it collects and stores environmental property data from actual fire events and nuisance events. This preliminary action enables the alarm to later distinguish between real hazards and nuisance conditions without requiring continuous manual intervention, thereby maintaining high detection reliability while reducing false alarms.
Solution Approach 2:
The system incorporates feedback mechanisms where users can indicate when an alarm is a false alarm, and this feedback is used to refine the alarm's detection algorithms. The processor compares detected environmental properties against stored patterns from actual fires and nuisance events, adjusting sensitivity dynamically to maintain reliability while reducing false alarms.
2Object-generated harmful factors
If the alarm sensitivity is decreased to reduce false alarms, then the nuisance alarm frequency is reduced, but the ability to detect real fire hazards deteriorates
Solution Approach 1:
During the training period, the system preliminarily learns the environmental characteristics of both actual fires and nuisance events by collecting data and storing it in memory. This preliminary learning enables the alarm to maintain appropriate sensitivity for detecting real hazards while being able to recognize and ignore nuisance patterns, thus avoiding the need to permanently lower sensitivity.
Solution Approach 2:
The alarm transitions from a static sensitivity setting to a dynamic detection system that adapts its response based on learned patterns. The processor dynamically compares detected environmental properties against stored nuisance and fire patterns, allowing the system to maintain high sensitivity for real hazards while selectively suppressing nuisance alarms based on contextual understanding.
3Object-generated harmful factors
If manual suppression of nuisance alarms is used, then false alarms can be silenced, but user frustration increases and may lead to alarm disabling
Solution Approach 1:
The system performs self-service by automatically learning and suppressing nuisance alarms without requiring continuous manual intervention. During the training period, the system autonomously collects environmental data, identifies nuisance patterns, and configures its suppression logic, thereby reducing the operational burden on users while maintaining effective false alarm suppression.
Solution Approach 2:
The system performs preliminary learning and configuration during a training period before full operation begins. This preliminary action includes storing environmental property data from nuisance events and configuring the suppression algorithm, which then operates autonomously during normal use, eliminating the need for repeated manual suppression actions.
4Device complexity
If the alarm system is simplified to reduce cost, then the device complexity is reduced, but the ability to differentiate between hazards and nuisance events deteriorates
Solution Approach 1:
The system creates a digital copy or model of environmental patterns from actual fires and nuisance events during the training period. These stored patterns serve as reference templates that the processor compares against real-time sensor data, enabling the system to differentiate between hazards and nuisance events using software-based pattern recognition rather than complex hardware.
Solution Approach 2:
The system replaces complex mechanical or hardware-based discrimination mechanisms with software-based pattern recognition. Instead of using multiple physical sensors or complex circuitry to distinguish fire from nuisance conditions, the system uses a processor to compare detected environmental properties against stored digital patterns, achieving discrimination capability through software rather than hardware complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system significantly lowers the frequency of false alarms, reducing the likelihood of users disabling the alarms and enhancing the reliability of fire safety systems by accurately distinguishing between real hazards and nuisance events.
Implementation Method 1
a sensor operatively connected to a processor to detect environmental properties
Data Source
AI summary
A learning alarm includes a sensor operatively connected to a processor to detect environmental properties and an alarm operatively connected to the processor to provide an alert if the environmental properties are outside an acceptable range. A user interface is operatively connected to the processor to accept user input indicating an alert corresponds to a nuisance condition. A memory is also operatively connected to the processor for storing detected environmental properties corresponding to the nuisance condition. The processor is configured to suppress alerts from the alarm based on detected environmental properties corresponding to the environmental properties of the nuisance condition stored in the memory.