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计算机系统应用英文版:2020,29(10):120-126
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基于大数据的火灾智能预警系统
(1.湖州市 吴兴区 织里镇公共事业服务中心, 湖州 313000;2.巢湖学院 信息工程学院, 合肥 238024;3.合肥工业大学 计算机与信息学院, 合肥 230009)
Fire Intelligent Early Warning System Based on Big Data
(1.Public Safety Supervision and Administration Center of Zhili Town, Wuxing District, Huzhou, Huzhou 230018, China;2.School of Information Engineering, Chaohu University, Hefei 238024, China;3.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China)
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Received:December 01, 2019    Revised:December 23, 2019
中文摘要: 传统基于GSM蜂窝网的无线火灾预警系统由于成本高、感知终端电池耗电快等原因,一直很难得到大规模的应用.而LPWAN技术的快速发展,极大地降低了系统成本和感知终端的能耗,推动了火灾智能预警系统的快速发展.本文设计了一种基于大数据的无线火灾智能预警系统,通过该系统不但能够及时发现火灾,实时掌握火灾发生位置,还可以实现多个部门及个人的联动.实际使用结果表明,该系统不但通信成本低、电池寿命长,而且有效地降低了城市火灾发生率,提高了城市火灾救援效率和消防服务水平,并得到了广泛的推广和应用.
中文关键词: 物联网  LoRa协议  大数据平台  消防预警
Abstract:The traditional wireless fire warning system based on GSM costs too much and the battery on the sensing terminal drains so quickly that it has been difficult to obtain large-scale application. However, with the rapid development of LPWAN, the system cost and the energy consumption of the sensing terminal have reduced greatly, and the development of the intelligent fire warning system has been promoted quickly. In this study, a wireless fire intelligent early warning system is designed based on big data. Through this system, not only can the fire be discovered in time, but also the location of the fire can be grasped in real time. At the same time, the linkage between multiple departments and the individual can be realized. The actual results show that the system has low communication cost and long life of battery. Moreover, the system effectively reduces the incidence of the urban fire, as well as it has improved the efficiency of fire rescue and the level of fire service, which has been widely promoted and applied.
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基金项目:浙江省智慧城市示范试点工程之“智慧织里”项目
引用文本:
车辉,邢慧芬,樊玉琦,郑淑丽.基于大数据的火灾智能预警系统.计算机系统应用,2020,29(10):120-126
CHE Hui,XING Hui-Fen,FAN Yu-Qi,ZHENG Shu-Li.Fire Intelligent Early Warning System Based on Big Data.COMPUTER SYSTEMS APPLICATIONS,2020,29(10):120-126