To solve the problems of high false alarm rate and poor adaptability of artificial feature extraction in aircraft cargo compartment fire alarms,this paper uses a one-dimensional convolutional neural network to establish a multi-modal fusion fire alarm prediction model for feature extraction,evaluation and verification of the model,and integration of feature extraction and classification.Because the particle size of fire smoke particles is often smaller than that of interfering particles,when the two have the same volume concentration,the fire smoke particles have a larger surface area concentration.Therefore,this paper uses a dual-wavelength smoke detection module to obtain the scattering signals of blue and red light of smoke particles and the Sauter mean particle size,to distinguish smoke particles from interfering particles.Besides,this paper uses K-type armored thermocouple to obtain the temperature of the fire scene and characterize the fire alarm at different stages.After that,this paper establishes a multi-modal fusion fire detection model through a one-dimensional convolutional neural network combined with different modal fire information.Through the strategy of weight sharing,the convolutional neural network adopts fewer parameters than the artificial neural network,constructs deeper network layers in an end-to-end manner,solves the problem of poor adaptability of artificial feature extraction,and has natural advantages in deep feature extraction.Through the stacking of the convolution layer and pooling layer,the convolution neural network realizes the deep feature extraction of different modal fire alarm information.The classifier is constructed by Softmax and the full connection layer,and the multi-modal fusion is carried out.According to the three labels of no fire,smolder and open fire,the classification warning is carried out.The experimental results show that the end-to-end multi-modal fusion fire prediction model based on the one-dimensional convolutional neural network can reach more than 0.95 in detection accuracy,which is better than all single-modal fire detection models.The results show that the multi-modal fusion fire detection technology can effectively improve detection accuracy.
维普
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