基于多源信息融合的半开放环境智能火灾探测系统设计

Design of an Intelligent Fire Detection System for Semi-Open Environments Based on Multi-Source Information Fusion

摘要: 由于城市发展中半开放环境日益增多，因此火灾防控中必然要面对风速干扰、烟雾扩散快、物体遮挡、阴燃火情难识别诸种难题。为此，文章提出了基于红外、图像与烟雾多源信息融合的智能火灾探测器。该系统以STM32单片机为核心，将多模态传感器数据与神经网络算法有机结合，实现火情智能识别与风险等级评估，并集成本地声光报警、远程信息推送。通过在半开放场景的实验验证，探测器表现出误报率显著降低、响应速度加快、识别精度提高的优势。结果表明，基于多源信息融合及智能算法设计的智能火灾探测器能切实提高半开放环境火灾早期预警能力，也是公共安全防控极好的技术补充。

Abstract: With the increasing number of semi-open environments in urban development, fire prevention and control inevitably face challenges such as wind speed interference, rapid smoke diffusion, object occlusion, and difficulty in identifying smoldering fires. To address these issues, this paper proposes an intelligent fire detector based on multi-source information fusion of infrared, image, and smoke data. Taking the STM32 microcontroller as the core, the system organically combines multi-modal sensor data with neural network algorithms to realize intelligent fire identification and risk level assessment, and integrates local acousto-optic alarm and remote information push functions. Experimental verification in semi-open scenarios shows that the detector has significant advantages including a remarkably reduced false alarm rate, accelerated response speed and improved recognition accuracy. The results indicate that the intelligent fire detector designed based on multi-source information fusion and intelligent algorithms can effectively improve the early fire warning capability in semi-open environments, and serves as an excellent technical supplement for public safety prevention and control.