火灾环境中应急疏散算法研究

发布时间：2018-04-22 11:31

本文选题：社会力模型 + 蚁群算法　；参考：《河南师范大学》2017年硕士论文

【摘要】：火灾环境中的应急疏散问题一直以来都是国内外公共安全领域研究的热点,该问题自从被提出后,国内外学者取得了一定的研究进展和成果。在初期阶段,研究者们相继提出的例如社会力模型、势能场模型以及排队论模型等来描述个体的行为特征,但是这些模型侧重描述的是个体之间的微观作用,从而忽视了疏散人员作为一个整体的动态移动过程和机理作用。为了能从整体的角度对群体疏散行为进行分析,提出运用蚁群算法来解决火灾环境中的群体路径规划问题。本文基于现有的社会力模型和蚁群算法理论,对社会力模型中的所有个体变化规律相同的特点以及蚁群算法中容易陷入局部最优的弊端进行改进,为群体中的所有个体建立情绪模型,以改进的社会力模型来描述个体之间的微观行为,蚁群算法来描述疏散群体的整体机理作用,采用微观模型和宏观模型相结合的策略进行了定性和定量的研究。本文的主要研究工作如下:(1)针对高密度群体疏散问题,构造了一种基于改进的社会力模型的团体情绪疏散算法。首先,对传统的社会力模型中所有个体变化规律相同的问题进行改进,在团体形式的基础上设置最优速度对团体中成员的速度加以约束;其次,依据人格特征理论为所有个体建立情绪模型,将个体的情绪状态分为积极情绪和消极情绪,通过个体不同的影响力建立情绪影响机制;最后,根据个体的情绪分布给出指挥策略。实验仿真结果表明,改进的社会力模型消除了个体在狭窄通道里的震荡现象,避免了个体速度无限加速或者保持临界速度的缺陷,并为何时加入引导策路提供了依据。(2)在火灾环境中,针对蚁群算法容易陷入局部最优的问题,本文使用了一种改进的蚁群算法来解决火灾环境中人群疏散的路径规划问题。针对火灾环境的特点,对蚁群算法的以下两个方面进行改进:第一,在蚁群算法的启发式函数中考虑火灾产物的基础上同时考虑人员密度因素;第二,动态自适应调整信息素强度,在更新路径上的信息素时采取局部和全局信息素更新相结合的策略,并引入交叉操作,加快算法逃逸能力。由于在火灾环境中个体情绪差异对路径选择的影响较大,在本文的规划方法中,为个体建立情绪数学模型,不同情绪的个体对路径的选择是不同的。仿真实验表明,本文提出的规划方法能够为不同情绪类型的个体规划出最优逃生路径,避免了局部最优且收敛速度较快。
[Abstract]:The problem of emergency evacuation in fire environment has always been a hot topic in the field of public safety at home and abroad. Since the issue was raised, scholars at home and abroad have made some progress and achievements. In the initial stage, researchers have proposed such as social force model, potential energy field model and queuing theory model to describe the behavior characteristics of individuals, but these models focus on the microcosmic interaction between individuals. Therefore, the dynamic movement process and mechanism of evacuees as a whole are ignored. In order to analyze the behavior of group evacuation from the whole point of view, an ant colony algorithm is proposed to solve the problem of group path planning in fire environment. Based on the existing social force model and ant colony algorithm theory, this paper improves the same characteristics of all individuals in the social force model and the disadvantages of ant colony algorithm which is prone to fall into local optimum. The emotion model is established for all individuals in the group, and the improved social force model is used to describe the microcosmic behavior between individuals, and the ant colony algorithm is used to describe the overall mechanism of the evacuation population. Qualitative and quantitative studies are carried out by combining micro model with macro model. The main work of this paper is as follows: (1) A group emotional evacuation algorithm based on an improved social force model is proposed to solve the problem of high-density group evacuation. First of all, we improve the traditional social force model in which all individuals change in the same law, and set the optimal speed on the basis of the group form to restrict the speed of the members of the group. According to the personality characteristic theory, the emotion model is established for all individuals, the emotional state of the individual is divided into positive emotion and negative emotion, and the emotional influence mechanism is established through different influence of individual. Finally, The command strategy is given according to the individual's emotional distribution. The experimental results show that the improved social force model eliminates the phenomenon of individual oscillation in the narrow channel and avoids the defect of unlimited acceleration of individual velocity or keeping critical velocity. In view of the problem that ant colony algorithm is easy to fall into local optimum, an improved ant colony algorithm is used to solve the problem of evacuation path planning in fire environment. According to the characteristics of fire environment, the following two aspects of ant colony algorithm are improved: first, considering the fire product in the heuristic function of ant colony algorithm; second, The pheromone intensity is adjusted dynamically and the local and global pheromone updating strategy is adopted in updating the pheromone on the path and the crossover operation is introduced to accelerate the escape ability of the algorithm. Because the individual emotion difference has a great influence on the path choice in the fire environment, in the planning method of this paper, the mathematical model of emotion is established for the individual, and the individual with different emotion has different path choice. The simulation results show that the proposed planning method can plan the optimal escape path for individuals with different emotional types and avoid the local optimum and the convergence speed is faster.
【学位授予单位】：河南师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TU998.1

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