Research on infrared optical CO detection based on BP neural network algorithm

The article takes the prevention of accidents caused by CO in daily life as the background and introduces a method of CO concentration detection parameter modulation and system denoising. The system adopts a hybrid detection technology of tunable semiconductor laser absorption spectroscopy (TDLAS) and wavelength modulation (WMS). It is achieving effective suppression of background gases in the environment. The system is combined with a BP neural network to invert the concentration of CO in the gas. Finally, the system noise is processed through wavelet transform to achieve the completion and optimization of the smoke detection algorithm. This has high practical value and guiding significance for the combination of TDLAS CO detection simulation system and hardware system of WOA-BP neural network.