Research On Emergency Decision Making Based On Situation Deduction Model

The suddenness and particularity of public safety emergencies determine the complexity of the emergency decision-making process.Real-time analysis of situation data,precise allocation of rescue forces,and optimal feedback of decision-making information all rely on a dynamic and intelligent decision-making system.The organic combination of situation deduction and emergency decision-making helps decision makers to grasp the development direction of the event in time,optimize rescue plans,allocate emergency teams and evacuate people at the scene,which can effectively improve the emergency response capabilities of decision makers.This research focuses on the above topics,and the main work includes:(1)In view of evolution characteristics of the public safety event situation in space and time dimensions,situation deduction models are constructed based on the cellular automata method.Cells are used to represent the spatial location of the event-bearing body,and the relationship between cells represents the spatial interaction of the disaster-bearing bodies;combined with Markov characteristics,the transfer rate among states is used to determine the transfer rule of the cell states in the time dimension.Through the deduction simulation analysis of fire events and derivative events,it is shown that the deduction model can respond to the real-time environment,infer the risk situation of each spatial location of the event in the future,and provide real-time situation data of key moments and key areas for emergency decision making.(2)In view of the real-time situation environment,combined with the emergency decisionmaking organization structure,establish a multi-layer rescue decision-making model.At the mission level,deduction models carry out a limited number of "intervention-feedbackadjustment" to optimize rescue missions in real-time situations;at the action level,use reinforcement learning to establish the action rules of multiple rescue teams in a dynamic environment.In view of the large state space of public safety events,the LSTD algorithm is used to linearly approximate the state-action value function to obtain the optimal action plan corresponding to the real-time state.The simulation results show that the established rescue decision-making model is effective for avoiding potential risks of events and improving rescue effects.(3)In view of the danger and dynamic uncertainty of the evacuation environment in public safety events,a decision model for evacuation route is established based on the output data of the situation deduction model and the rescue decision model.In the personnel behavior strategy,the field strength theory is used to stipulate the way personnel face the target,obstacles and dangerous areas,and the "warning range" parameter is used to balance the relationship between avoiding danger and improving evacuation efficiency.In the evacuation route decision model,the heuristic function of the ant colony algorithm is constructed based on the behavior strategy;the pheromone update rule is constructed for the multi-source and multi-sink characteristics of the crowd evacuation problem and the demand for sharing of evacuation experience.The simulation results show that the established decision model not only helps the trapped people avoid real-time danger,but also improves the evacuation efficiency and emergency response.