Research on Fire Detection Based on Multi-source Sensor Data Fusion

Abstract

This paper verifies the method of multi-sensor data fusion in the field of smart home. A multi-source sensor data fusion model based on fire detection is proposed, and the methods of fusion calculation and decision analysis are mainly studied. Nowadays, the traditional single sensor is replaced by a multi-source sensor especially in fire detection (Wang in Comp Intell Syst 1(1–4):1–22 [1]), and three main detector (CO, smoke, and temperature) are significantly changed when the fire occurs, and the data fusion algorithm is used to calculate the result, thereby improving the system accuracy and reducing the false alarm rate by using the self-learning and self-adaptive ability of BP neural network on the feature layer. Then, the result is transmitted into the decision-making layer, and the fuzzy logic inference algorithm is used to make the final decision and judgment. Simulation experiments show that the fusion system can meet the data collection, processing, fusion, and analysis of fire detection scenarios in smart home security, can effectively identify the accurate fire situation, and can respond quickly.