本文選題：公共安全 + 風(fēng)險分析　； 參考：《中國安全科學(xué)學(xué)報》2017年07期

【摘要】：為動態(tài)探究影響人群聚集風(fēng)險的主要因素及定量評估人群聚集風(fēng)險,依據(jù)貝葉斯估計理論改進靜態(tài)貝葉斯網(wǎng)絡(luò)模型,建立動態(tài)貝葉斯網(wǎng)絡(luò)模型。用該模型可根據(jù)實時采集數(shù)據(jù)計算后驗參數(shù),獲取動態(tài)定量風(fēng)險評估結(jié)果。利用所建的動態(tài)貝葉斯網(wǎng)絡(luò)模型動態(tài)定量評估北京市某大型商業(yè)街區(qū)人群聚集風(fēng)險。結(jié)果表明:該街區(qū)初始人群聚集擁堵概率為0.8×10~(-3),踩踏概率為7.6×10~(-6)。隨著實時觀測數(shù)據(jù)的引入,最終擁堵概率為2.4×10~(-3),踩踏概率為1.63×10~(-5),其中疏散不及時、疏散通道不暢、疏散標(biāo)志不清等3個因素的相對重要度影響因子較大,是主要影響因素。實例中各基本事件的發(fā)生概率和相對影響因子動態(tài)變化,證明該模型有效。
[Abstract]:In order to explore the main factors that affect the crowd aggregation risk and evaluate the crowd aggregation risk quantitatively, the static Bayesian network model is improved and the dynamic Bayesian network model is established according to the Bayesian estimation theory. The dynamic quantitative risk assessment results can be obtained by using the model to calculate the posterior parameters according to the real-time data acquisition. The dynamic Bayesian network model is used to evaluate the crowd aggregation risk in a large commercial district in Beijing. The results show that the initial crowd congestion probability is 0.8 脳 10 ~ (-3) and the stampede probability is 7.6 脳 10 ~ (-6). With the introduction of real-time observation data, the final congestion probability is 2.4 脳 10 ~ (-3) and the stampede probability is 1.63 脳 10 ~ (-5). Among them, the relative importance factors of three factors, such as untimely evacuation, poor evacuation passage and unclear evacuation sign, are the main influencing factors. It is proved that the model is effective by the dynamic change of the occurrence probability and relative influence factors of each basic event in an example.
【作者單位】： 首都經(jīng)濟貿(mào)易大學(xué)安全與環(huán)境工程學(xué)院;
【基金】：國家自然科學(xué)基金資助(71471121)
【分類號】：C912.4
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本文編號：2099359