Abstract: This article proposes a multi- parameter data fusion method based on genetic algorithm to optimize BP neural network to realize fire detection, which can significantly improve the accuracy of fire detection. In view of the low stability of the random weight and threshold of BP neural network, it is proposed to use genetic algorithm to optimize the BP neural network to optimize the initial weight and threshold of the neural network to improve the generalization performance of the model, and use the model to compare the standard data fusion of temperature, smoke concentration and CO concentration in open flame and smoldering fire realize fire detection. The simulation results show that, compared with the pure BP neural network, the BP neural network fire detection algorithm optimized by the genetic algorithm can realize fire detection more quickly and accurately, the accuracy of fire detection is significantly improved, and the accuracy of fire recognition is increased to 98.84%.

Key words: fire detection, genetic algorithm, BP neural network, multi-parameter