论文部分内容阅读
城市典型生命线以供热、电力、燃气系统为代表,具有公共性强、风险性高、关联性显著的特点。研究城市典型生命线运行风险因素识别问题对于相关部门防范风险具有重要意义,但现实中由多系统、多风险因素以及各类关联形成的复杂关联情境增加了解决该问题的难度。为此,本文首先构建了具有层级网络结构的风险因素识别框架,然后提出了一种考虑复杂关联情境的风险因素识别方法,将各专家针对系统关联和风险因素关联给出的语言评价信息转化为二元语义,并将决策试验与评价实验室(DEMATEL)法和Two-Additive Choquet(TAC)积分算子扩展到二元语义环境,进而实现各类关联的综合集成,从而确定风险因素的排序和归类,便于决策者研判风险根源、明晰风险因素本质。最后,以北京某样区为例验证了所提方法的潜在应用价值,并根据识别结果制定了针对性的风险防范策略,能够为相关部门联调联动开展风险防范提供决策支持。
The typical lifeline of city is represented by heating, electricity and gas system, which has the characteristics of publicity, high risk and obvious relevance. Studying the identification of operational risk factors for typical lifeline in urban areas is of great importance to the relevant departments in preventing risks. However, in reality, the complicated correlation situations formed by multiple systems, multiple risk factors and various kinds of associations increase the difficulty of solving this problem. To this end, this paper first builds a risk factor identification framework with hierarchical network structure, and then proposes a risk factor recognition method that takes into account the complex context, and translates the linguistic assessment information given by each expert in relation to system correlation and risk factors into Binary semantics, and extending the DEMATEL method and the Two-Additive Choquet (TAC) integral operator to the binary semantic environment so as to realize the integrated integration of various kinds of associations so as to determine the ranking of risk factors and Classified, easy to judge the root causes of risk, clear the nature of risk factors. Finally, a case study of Beijing is taken as an example to verify the potential application value of the proposed method. According to the identification result, a targeted risk prevention strategy is formulated, which can provide decision-making support for the joint departmental risk prevention of relevant departments.