摘要
针对非常规突发灾害事故情景建模中信息资源丰富但来源众多、异质特性明显及微观分析不足等问题,研究采用知识元表示法,从突发灾害事故的情景状态、应急救援活动、孕灾环境及承灾体4个角度进行描述,结合灾害事故演变不确定性、自组织性的特点,构建了基于动态贝叶斯网络的灾害事故动态情景模型。利用青岛"11·22"输油管道泄漏爆炸事故,验证了基于知识元和贝叶斯网络的非常规突发灾害事故动态情景模型的科学性和有效性。结果表明:动态情景模型的情景演化路径与实际管道泄漏事故演化的顺序基本相同。研究结果可为制定非常规突发灾害事故的预防方案提供一定的参考依据。
The scenario modeling of unconventional sudden disasters and accidents has abundant information resources, but it is faced with several problems, e.g. numerous sources, obvious heterogeneity and insufficient microanalysis. To make up for these deficiencies, the knowledge element representation method was used in this paper to describe sudden disasters and accidents from the aspects of scenario state, emergency rescue activities, disaster inducing environment and disaster bearing body. Then, based on the uncertainty and self-organization characteristics of disaster and accident evolution, a dynamic scenario model based on dynamic Bayesian network for disasters and accidents was constructed. Finally, the scientificalness and effectiveness of the dynamic scenario model based on knowledge element and Bayesian network for unconventional sudden disasters and accidents were verified by conducting a case study on the "11·22" Qingdao leakage and explosion accident of oil pipeline. It is shown that the scenario evolution path presented in the dynamic scenario model is basically the same as the evolution sequence of the actual pipeline leakage accident. The research results can provide a reference for the prevention of unconventional sudden disasters and accidents.
作者
张志霞
郝纹慧
ZHANG Zhixia;HAO Wenhui(School of Management, Xi'an University of Architecture and Technology)
出处
《油气储运》
CAS
北大核心
2019年第9期980-987,995,共9页
Oil & Gas Storage and Transportation
基金
国家自然科学基金资助项目“面向大数据的混合存储布局优化和安全迁移机制研究”,61672416
陕西省自然科学基金资助项目“多源信息环境下突发事件情景推演及应急决策研究”,2019JM521,陕西省自然科学基金资助项目“基于岩体破坏的多源异质流数据智能融合与预警研究”,2017JM7005,陕西省自然科学基金资助项目“多源信息环境下突发事件情景推演及应急决策研究”,2019JM-521
关键词
非常规突发灾害事故
知识元
动态贝叶斯网络
情景模型
unconventional sudden disasters and accidents
knowledge element
dynamic Bayesian network
scenario model