摘要
针对飞机货舱烟雾探测器探测参数单一、误报漏报率高、无可视化交互等问题,设计了一款使用树莓派开发的复合火灾探测系统。以树莓派为嵌入式控制中心,连接CO传感器、TVOC传感器、PM10传感器进行数据采集;采用KNN算法进行多传感器数据融合,输出结果为有火与无火,准确率达到98%,并将处理后的数据存入SQLite数据库;树莓派搭建Web服务器并接入机载服务器局域网,设备通过访问HTML网页,实现可视化界面的交互。经试验测试:在高为2 m的货舱该系统可在46 s内发出告警指示,功能指标满足货舱火警探测的设计要求,并且漏报率为0,误报率低于1%,可为机载火警探测系统设计提供良好方案。
Aiming at the problems of single detection parameter of smoke detectors in aircraft cargo compartment,high false alarm rate and no visual interaction,a composite fire detection system based on machine learning using Raspberry Pi is designed and implemented.Raspberry Pi is used as control center and to connect CO sensor,TVOC sensor,PM10 sensor for data collection.The KNN algorithm is used for multi-sensor data fusion,the output result is fire and no fire,the accuracy rate reaches 98%,and the processed data is stored in the SQLite database.The Raspberry Pi builds a Web server and connects to the onboard server local area network,the device realizes the interaction of the visual interface by accessing the HTML web page.Through the experimental test,the system can generate an alarm indication within 46 s in a cargo hold with a height of 2 m.The functional indicators meet the design requirements of cargo hold fire detection,and the underreport rate is 0 and the false alarm rate is less than 1%,and provides a reliable solution for the design of the airborne fire detection system.
作者
邓力
吴丹丹
朱博
刘全义
Deng Li;Wu Dandan;Zhu Bo;Liu Quanyi(China College of Civil Aviation Safety Engineering,Civil Aviation Flight University of China,Sichuan Guanghan 618307,China)
出处
《消防科学与技术》
CAS
北大核心
2022年第11期1520-1524,共5页
Fire Science and Technology
基金
国家自然科学基金项目(U1933105)
德阳市科技局重点研发项目(2021SZ001)
中国民用航空飞行学院面上项目(J2020-120)
四川省院省校合作项目(2022YFSY0048)。
