Control method of mechanical smoke emission in high-rise building corridor

Abstract

The traditional method has a large control error in the corridor mechanical smoke control method. Therefore, a multi-task convolutional neural network-based high-rise building corridor mechanical smoke control method is proposed. Through the mechanical smoke exhaust principle of high-rise building corridors, the threshold of mechanical smoke exhaust is set to predict the mechanical smoke exhaust volume of high-rise building corridors. The movement of mechanical smoke in high-rise building corridors is simulated according to fire dynamics simulator to determine the turbulence state of mechanical smoke in high-rise building corridors. Input the mechanical smoke exhaust data of high-rise building corridors into the multi-task convolutional neural network to complete the mechanical smoke exhaust control of high-rise building corridors. Experimental results show that the maximum accuracy of this method is about 98%, and the control time is always less than 1 second.