Research on Fire Detection Method of Complex Space Based on Multi-Sensor Data Fusion

Abstract Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. This paper proposes a fire detection method based on a multi-sensor gradient data fusion model to overcome the limitations of traditional fire detection systems that lack comprehensive analysis of multidimensional data, which lead to false alarms and missed detections. By considering spatial characteristics of older residential communities, establish fire models for various working conditions. The proposed method utilizes multiple sensors, including smoke sensors, temperature sensors, CO sensors, and flame sensors, to collect data from different aspects of fire events. A fire detection model is constructed by the collected multi-dimensional data which are fused by improved adaptive filtering and fuzzy Bayesian logic inference algorithm. to Additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.