Study on large-scale crowd evacuation method in cultural museum using mutation prediction RFID

Abstract

The cultural museum is one of the places with a large flow of people. Once an emergency occurs, if the people are not evacuated in time, the consequences will be unimaginable. In the event of an emergency, the population speed and evacuation effect are linear in the case of a small population density. However, when the population density is large, the population speed and evacuation effect are nonlinear. The traditional method is aimed at evacuation control with a small density and a smooth change in a large flow of people. When the density is large, the evacuation speed cannot be too fast, and once the pedalling event occurs, the stability of the model is destroyed. In order to solve the above problems, this paper takes a large-scale crowd of cultural museum as the studied object and proposes a large-scale crowd evacuation method based on the mutation theory of RFID (radio frequency identification). The algorithm uses the mutation prediction RFID to judge the arrival rate change trend by the arrival rate reverse mutation rule, so that the modelling length can dynamically adapt to the RFID tag arrival rate change and overcome the contradiction between the prediction accuracy and the tracking speed. The accuracy of the algorithm to predict the flow of large-scale people is improved, making the evacuation model more relevant to the actual situation. Through the experiment tests on typical scenes, the evacuation control problems of four groups of people are analysed and discussed. The results prove that the evacuation method proposed in this paper can provide guidance for crowd evacuation in the cultural museum.