RFES: a real-time fire evacuation system for Mobile Web3D

RFES：一种面向移动Web3D的实时火灾逃生系统（英文）

There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key problems:(1)The scene data for large-scale building information modeling(BIM)are huge,so it is difficult to transmit the data via the Internet and visualize them on the Web;(2)The raw fire dynamic simulator(FDS)smoke diffusion data are also very large,so it is extremely difficult to transmit the data via the Internet and visualize them on the Web;(3)A smart artificial intelligence fire evacuation app for the public should be accurate and real-time.To address these problems,the following solutions are proposed:(1)The large-scale scene model is made lightweight;(2)The amount of dynamic smoke is also made lightweight;(3)The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method.We propose a real-time fire evacuation system based on the ant colony optimization(RFES-ACO)algorithm with reused dynamic pheromones.Simulation results show that the public could use Mobile Web3 D devices to experience fire evacuation drills in real time smoothly.The real-time fire evacuation system(RFES)is efficient and the evacuation rate is better than those of the other two algorithms,i.e.,the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.

基于Web3D构建公共消防疏散系统存在许多限制其计算能力的瓶颈。本文集中解决3个关键问题:(1)大型建筑场景数据量大,难以通过互联网传输并在网络终端设备实现可视化;(2)原始火灾动态模拟器烟气数据量大,且实时动态变化,难以通过互联网传输并在网络终端设备实现可视化;(3)为公众提供的智能消防疏散系统往往难以兼顾准确性和实时性。针对以上问题,本文提出3个解决方案:(1)将大型场景模型简化为轻量型场景模型;(2)将动态烟雾简化为轻量级烟雾模型;(3)利用场景模型和烟雾数据建立动态障碍物图,规划最优疏散路径。本文提出一种基于蚁群优化算法(RFES-ACO)的实时消防疏散系统,该算法基于动态信息素重用。仿真结果表明,公众可在移动Web3D设备上实时、顺畅地进行消防疏散演练的交互和体验。最后,与leader-follower算法和随机算法的对比实验,证明了实时消防疏散系统(RFES)的高效性,其疏散率优于其它两种算法。