综合GAN与CNN的应急疏散快速风险评估方法  

Rapid risk assessment method of emergency evacuation based on GAN and CNN

作　　者：李佳旭 胡玉玲 李嘉锋[1,2] LI Jia-xu;HU Yu-ling;LI Jia-feng(School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Key Laboratory of Intelligent Processing for Building Big Data,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)

机构地区：[1]北京建筑大学电气与信息工程学院,北京100044 [2]北京建筑大学建筑大数据智能处理方法研究北京市重点实验室,北京100044

出　　处：《消防科学与技术》2022年第2期210-215,共6页Fire Science and Technology

基　　金：国家重点研发项目(2018YFC0807806);北京建筑大学基本科研业务基金项目(X20109)。

摘　　要：针对大型公共场馆疏散风险评估问题,提出一种综合生成对抗网络(GAN)与卷积神经网络(CNN)的应急疏散深度学习评估模型,通过WGAN(Wasserstein GAN)进行数据增强,解决疏散数据不足的问题,并基于CNN,分别采用LeNet以及ResNet两种网络结构进行数据训练。以某大型体育馆为例,应用该方法进行疏散风险评估。研究结果表明,该方法能够建立有效的风险评估模型,实现应急疏散的快速风险评估。Against the problem of large public venues evacuation risk assessment, a deep learning evaluation model for emergency evacuation that integrates generative adversarial networks (GAN) and convolutional neural networks (CNN) was proposed. The problem of insufficient evacuation data is solved by data enhancement through WGAN (Wasserstein GAN).Based on CNN, two network structures of LeNet and ResNet were used for data training. A large gymnasium was taken as an example, to perform the evacuation risk evaluation by the method. The research results showed that an effective risk assessment model can be established to achieve rapid risk assessment for emergency evacuation.

关 键 词：应急疏散 生成对抗网络 卷积神经网络 风险评估 

分 类 号：X928.7[环境科学与工程—安全科学] TP391.9[自动化与计算机技术—计算机应用技术]