The complexity and uncertainty of a fire in a large public building with complex structure leads to risks during emergency evacuation. Therefore, it is crucial to conduct intelligent emergency evacuation according to the situational development of the accident environment, the crowding degree of evacuation routes, as well as the reliability and flexibility of data transmission in large complex building structures. In this study, we develop an Intelligent Emergency Evacuation System (IEES) based on the Long Range (LoRa) communication technology and the Evacuation Decision Optimization Model (EDOM). The IEES comprises three parts, i.e., multi-sensor Fire Monitoring System (FMS), Central Control System (CCS), and Wireless Evacuation Indication System (WEIS). The LoRa communication technology is used for data transmission among the three parts of the IEES to assist large scale evacuation. The EDOM is developed to support the CCS based on the improved adaptive ant colony algorithm. To facilitate the evacuation decision optimization, the initial node database in the evacuation spatial network is established intelligently on the Building Information Modeling (BIM) platform. An on-site application in a subway station with three underground floors is conducted. The results demonstrate that the IEES can guide people in evacuating from hazardous locations according to the state of the fire. Moreover, the phenomena of crowd retention and congestion at a few certain evacuation exits are considerably reduced to achieve global optimization of the evacuation path and accomplish the objective of intelligent emergency evacuation. Furthermore, WEIS can maintain normal operation and have good visibility in the fire environment of heat radiation and thick smoke. The IEES can realize dual wireless indication of vision and sound perception, and its response time is slightly affected by the building structure and transmission distance. The electromagnetic signal of electrical equipment and the operational state of vehicles in and out of the subway station causes the response time to a relatively larger lag. The response time of the IEES is delayed up to 14 ms. Nonetheless, it fulfills the requirements of response time of evacuation indication signs stated in the .

中文翻译：

---

基于远程通信技术的紧急疏散

大型公共建筑结构复杂，火灾的复杂性和不确定性导致紧急疏散存在风险。因此，根据事故环境的形势发展、疏散路线的拥挤程度以及大型复杂建筑结构数据传输的可靠性和灵活性，进行智能应急疏散至关重要。在本研究中，我们开发了一种基于远程（LoRa）通信技术和疏散决策优化模型（EDOM）的智能紧急疏散系统（IEES）。 IEES由多传感器火灾监控系统（FMS）、中央控制系统（CCS）和无线疏散指示系统（WEIS）三部分组成。 IEES的三个部分之间采用LoRa通信技术进行数据传输，协助大规模疏散。 EDOM是基于改进的自适应蚁群算法而开发的，用于支持CCS。为了便于疏散决策优化，疏散空间网络中的初始节点数据库在建筑信息模型（BIM）平台上智能建立。在某地下三层地铁站进行了现场应用。结果表明，IEES可以根据火灾状况引导人们从危险场所撤离。而且，大幅减少少数特定疏散出口的人群滞留和拥堵现象，实现疏散路径的全局优化，达到智能应急疏散的目的。此外，WEIS在热辐射和浓烟的火灾环境中仍能保持正常运行并具有良好的能见度。 IEES可实现视觉和声音感知的双重无线指示，其响应时间受建筑结构和传输距离影响较小。电气设备的电磁信号以及进出地铁站的车辆运行状态导致响应时间存在较大的滞后。 IEES 的响应时间最多延迟 14 毫秒。尽管如此，它还是满足了《疏散指示标志》中规定的疏散指示标志响应时间的要求。