Research on fire warning based on improved artificial fish swarm optimization BP neural network

The traditional fire warning system with a single threshold value is widely used. However, due to the development process of fire, the variation of parameters in each stage is quite different, so it is difficult to achieve rapid and accurate fire warning with a single parameter. Aiming at the problems existing in the traditional fire alarm system, a BP neural network prediction algorithm based on improved artificial fish swarm optimization is proposed. Combined with multi-data fusion technology, the characteristic temperature, CO concentration and smoke concentration in the early stage of fire are used as inputs, and the three stages of fire development are used as output of BP neural network for fire prediction. On the basis of the standard artificial fish swarm optimization BP neural network, chaos algorithm is used to improve the artificial fish swarm, optimize the search ability of artificial fish swarm algorithm, accelerate the convergence speed of neural network, and output the expected results. The experimental results of MATLAB simulation software show that compared with the standard artificial fish swarm optimization BP neural network, the convergence accuracy is improved, which proves the superiority of this method.