应急决策的理论与方法探讨

围绕基于模板的规划、组织决策协调与基于Agent系统的协调机制以及马尔可夫决策规划等应急决策的关键理论与方法,对国内外相关研究进行分析与总结;提出以决策理论规划为应急决策研究的主要建模和分析框架;采用逻辑程序与规划相结合的思想,研究基于应急预案模板的应急决策规划方法;基于应急处置任务的时间、资源约束关系,研究应急处置任务的多Agent马尔可夫决策建模及求解方法。基于决策理论规划的应急决策理论可以很好地用于应急决策的多阶段动态过程建模,而且能够利用预案模板降低模型求解的难度。上述研究完善和丰富了应急决策的理论和方法,为科学的应急决策实践以及应急决策支持系统提供了一种理论和方法。     

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.     

基于集对分析-吸收马尔可夫链的铁路移动通信系统安全动态评估*

为了更有效地评估和防范铁路数字移动通信系统（GSM-R）的安全风险，针对未考虑系统不同时期系统风险状况不同、研究多局限于静态评估的不足，提出基于马尔可夫链的风险指数量化计算方法，构建基于集对分析-吸收马尔可夫链的铁路数字移动通信系统（GSM-R）风险评估模型。首先使用吸收马尔可夫链对通信系统风险进行定量计算，运用集对分析关联度的同一性、差异性和矛盾性动态分析通信系统安全风险的趋势，通过结合马尔可夫链计算下一阶段的关联度以获得通信系统最终安全风险评估结果，选取通信系统6个时间段数据进行分析，并进行验证。结果表明:本文评估方法能实现铁路数字移动通信系统（GSM-R）安全风险的全面动态评估，验证通信系统安全风险动态评估模型的科学性、有效性和可行性。