The Construction of a Fire Monitoring System Based on Multi-Sensor and Neural Network

Abstract

An automated fire alarm system is a vital safety facility for modern fire fighting. It is an essential guarantee for people to find fires early and take effective measures to control and extinguish them in time. This article proposes a multi-sensor data fusion algorithm based on artificial neural network (ANN) technology, which intelligently processes various environmental characteristic parameters detected by multi-sensors, effectively detects real fire signals, and realizes early fire monitoring and alarm. The simulation results show that compared with the fuzzy clustering algorithm (FCM), the MAE of the proposed data fusion algorithm is improved by about 15%, and the recall is improved by about 10%. It can not only overcome the instability and limitation of a single sensor, but also grasp the system information more comprehensively and accurately. The data fusion technology is applied to the fire monitoring system, and multiple sensorsmultiple sensors collect the data collect the data, and then processed by data fusion technology. By making full use of multidimensional information, the fire monitoring and identification can be better completed, the false alarm rate and the false alarm rate can be reduced, the system is more sensitive and reliable, and the performance of the fire alarm system can be improved.