Aiming at the problem of high false alarm rate of fire detectors, this paper uses the principle of different scattering signals of different wavelengths of light to particles to detect fire smoke, and proposes to use blue light scattering signal (characterized as surface area concentration), infrared light scattering signal (characterized as volume concentration), and temperature joint detection, combined with genetic algorithm to optimize BP neural network - GA-BP neural network, establish a joint detection model of multiple fire characteristic parameters, and train the fire recognition accuracy of the model through experimental data to realize the fire state of different types of combustibles , water vapor and dust and other non-fire interference particulate matter identification.
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