不完全信息下的重大事故顺序决策研究

Fire Pub Date : 2024-02-06 DOI:10.3390/fire7020049
Dengyou Xia, Changlin Chen, Ce Zheng, Jing Xin, Yi Zhu
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引用次数: 0

摘要

为了解决信息不完全的应急决策问题,处理重大事故现场不同时间序列的事故信息,本文利用 D-S 证据理论和博弈论的相关知识,提出了一种顺序决策的方法。首先,我们以一起油罐着火事故为例,梳理历史案例和专家经验,建立了事故中关键事故现场与事故现场症状之间的逻辑关系。同时,运用逻辑回归分析方法,得到了油罐火灾中各关键事故现场的基本概率分布,并在此基础上构建了火灾证据集。其次,基于 D-S 证据理论,我们对知识不确定性和证据不确定性进行了有效量化,将不完整、不充分的信息作为重点事故现场发展的证据体系,构建了这些事故现场的态势预测模型。第三,基于博弈论,将应急决策者与重大事故视为博弈的双方,对不同时间点的事故状态进行比较分析,解决了决策损失成本与信息收集成本之间的矛盾。因此,本文为不同时间阶段的事故方案优化提供了解决方案,从而实现了重大事故现场的顺序决策。此外,我们还将态势预测模型与顺序决策相结合,基本步骤如下:(1)在信息极度缺乏的情况下,我们制定了初步的行动计划;然后,(2)开始处理事故,构建事故识别框架;(3)随着事故的演变,收集并处理不断增加的证据信息;(4)在评估不同证据后,计算关键事故情景的置信度,进而预测下一阶段的事故状态;(5)计算当前决策方案与下一阶段决策方案的盈亏比。最后,我们(6)重复步骤(3)至(5),直到事故完全消失。我们以 4 月 6 日福建漳州 P.X. 项目爆炸事故为例,验证了所提方法的可行性。该方法以 D-S 证据理论为基础,对重大事故现场获取的不完整、不充分信息进行近似推理,实现了对事故关键现场的情况预测。此外,该方法利用博弈论解决了决策损失成本与信息收集成本之间的矛盾,从而优化了重大事故不同时间阶段的决策方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Sequential Decision-Making of Major Accidents with Incomplete Information
In order to solve the problem of emergency decision-making with incomplete information and deal with the accident information in different time series at the scenes of major accidents, this paper proposes a method of sequential decision-making by utilizing the relevant knowledge of D-S evidence theory and game theory. Firstly, we took an oil tank fire accident as an example and sorted out historical cases and expert experiences to establish a logical relationship between key accident scenes and accident scene symptoms in the accident. Meanwhile, we applied the logistic regression analysis method to obtain the basic probability distribution of each key accident scene in the oil tank fire, and on this basis, we constructed an evidence set of the fire. Secondly, based on the D-S evidence theory, we effectively quantified the knowledge uncertainty and evidence uncertainty, with the incomplete and insufficient information taken as an evidence system of the development of key accident scenes to construct a situation prediction model of these accident scenes. Thirdly, based on the game theory, we viewed emergency decision-makers and major accidents as two sides of the game to compare and analyze accident states at different time points and solve the contradiction between loss costs of decision-making and information collection costs. Therefore, this paper has provided a solution for the optimization of accident schemes at different time stages, thus realizing the sequential decision-making at the scenes of major accidents. Furthermore, we combined the situation prediction model with sequential decision-making, with the basic steps described below: (1) We drew up an initial action plan in the case of an extreme lack of information; then, we (2) started to address the accident and constructed a framework of accident identification, (3) collected and dealt with the continuously added evidence information with the evolution of the accident, (4) calculated the confidence levels of key accident scenarios after evaluating different evidence and then predicted the accident state in the next stage, and (5) calculated the profit–loss ratio between the current decision-making scheme and the decision-making scheme of the next stage. Finally, we (6) repeated steps (3) to (5) until the accident completely vanished. We verified the feasibility of the proposed method with the explosion accident of the Zhangzhou P.X. project in Fujian on 6 April used as an example. Based on the D-S evidence theory, this method employs approximate reasoning on the incomplete and insufficient information obtained at the scenes of major accidents, thus realizing the situation prediction of key scenes of these accidents. Additionally, this method uses the game theory to solve the contradiction between decision-making loss costs and information collection costs, thus optimizing the decision-making schemes at different time stages of major accidents.
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