
FOLLOWUS
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
School of Software Engineering, Tongji University, Shanghai 201804, China
School of Civil Engineering, Tongji University, Shanghai 200092, China
Research Center of Digital Media and Human-Computer Interaction, Hangzhou Normal University, Hangzhou 310013, China
[ "Feng-ting YAN, E-mail: yanfengting2008@163.com" ]
Jin-yuan JIA, E-mail: jiajy@tongji.edu.cn
收稿:2017-10-07,
修回:2019-;7-;3,
纸质出版:2019-08
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闫丰亭, 胡永豪, 贾金原, 等. RFES:一种面向移动Web3D的实时火灾逃生系统[J]. 信息与电子工程前沿(英文), 2019,20(8):1061-1074.
Feng-ting YAN, Yong-hao HU, Jin-yuan JIA, et al. RFES: a real-time fire evacuation system for Mobile Web3D[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1061-1074.
闫丰亭, 胡永豪, 贾金原, 等. RFES:一种面向移动Web3D的实时火灾逃生系统[J]. 信息与电子工程前沿(英文), 2019,20(8):1061-1074. DOI: 10.1631/FITEE.1700548.
Feng-ting YAN, Yong-hao HU, Jin-yuan JIA, et al. RFES: a real-time fire evacuation system for Mobile Web3D[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1061-1074. DOI: 10.1631/FITEE.1700548.
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。
There are many bottlenecks that limit the computing power of the Mobile Web3D 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 Web3D 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.
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