Fire Smoke Detector Using Multi-Wavelength Scattering Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional fire smoke detectors often produce false alarms due to interference from non-fire aerosols like cooking fumes, dust, and water vapor, as they rely on optical scattering principles that cannot accurately distinguish between fire smoke and interfering aerosols.
Innovation Solution
A fire smoke detection method and detector based on particle shape characteristics, utilizing a light source combination with blue and infrared light sources, and a trained neural network classifier to differentiate between fire smoke and interfering aerosols by analyzing the scattering characteristics at different angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional photoelectric smoke detectors use optical scattering principle to detect smoke particles, then the detection structure remains simple and cost-effective, but the detector cannot accurately distinguish between fire smoke and interfering aerosols like cooking fumes, dust, and water vapor, leading to false alarms
Solution Approach 1:
The detection process is segmented into multiple independent measurement channels, each using a different light source (blue LED at 450nm, infrared LED at 950nm, and green LED at 530nm). Each channel measures scattered light intensity at specific angles, creating separate data streams that are later integrated through classification algorithms to achieve accurate particle identification
Solution Approach 2:
The detection system transitions from single-dimensional measurement (one light source, one angle) to multi-dimensional measurement by incorporating multiple light sources with different wavelengths and multiple detection angles. This creates a multidimensional feature space where particle characteristics can be more precisely differentiated, allowing the system to distinguish fire smoke from interfering aerosols based on their unique scattering signatures across multiple dimensions
2Reliability
If the detector uses multiple light sources and classification algorithms to distinguish particle types, then the false alarm rate decreases, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary measurements by collecting scattered light intensity data from multiple light sources and angles before进行分类. The classification algorithm processes this pre-collected multidimensional data to identify particle types, reducing the need for complex real-time decision-making and simplifying the overall system architecture
Solution Approach 2:
The system changes detection parameters by using light sources with different wavelengths (450nm blue, 950nm infrared, 530nm green) and measuring scattered light at different angles. These parameter variations create distinct measurement signatures for different particle types, enabling the classification algorithm to reliably distinguish between fire smoke and interfering aerosols while maintaining a relatively simple hardware structure
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
This approach reduces the false alarm rate of fire smoke detectors by accurately distinguishing between fire smoke and interfering aerosols based on particle shape characteristics, improving the accuracy and reliability of fire alarms.
Implementation Method 1
This type of detector mainly utilizes the optical scattering principle of particles. When smoke produced by combustion enters the detection chamber, due to the scattering effect of smoke particles on light, the scattered light signal collected by the photoelectric conversion device will increase
Implementation Method 2
Inside the optical dark chamber, there is a light source and a photoelectric conversion device
Data Source
AI summary
A fire smoke-detection method and detector based on particle shape characteristics for detecting particles in an optical dark chamber. An optical path angle between one blue light and one infrared light and a photoelectric conversion module is an acute angle, while an optical path angle between another blue light and the photoelectric conversion module is an obtuse angle. The detection method includes: starting a light source combination, calculating a change value between a current scattered light optical power and a background value of each light source, when the change value exceeds a set threshold, continuously starting the light source combination and constructing a space vector from the recorded change values, and using a classifier to classify a plurality of space vectors to obtain a plurality of classification results with categories including fire smoke and interfering aerosols; and counting the classification results to obtain the correct classification result.