Crowd grouping evacuation simulation method and system based on half DBSCAN clustering algorithm

Abstract

The present invention relates to a crowd grouping evacuation simulation method and system based on a half DBSCAN clustering algorithm. The method comprises: creating an evacuation scene model and a crowd individual model, and initializing evacuation scene information and crowd individual information, so as to obtain initialization parameters; according to the initialization parameters, dividing a crowd into multiple groups by using a half DBSCAN algorithm, so as to obtain a crowd grouping result; and using the crowd grouping result as an initial state of crowd motion, and making a local path plan by using a social force model, so as to obtain a motion path of each individual in an evacuation scene and perform crowd evacuation simulation. According to the method, the half DBSCAN clustering algorithm is combined with the social force model to complete crowd evacuation simulation in a complex scene, so that crowd evacuation simulation in the complex scene is really and effectively realized, the accuracy of crowd evacuation simulation is improved, and the grouping phenomenon in the evacuation process is demonstrated vividly.

Description

technical field [0001] The invention belongs to the technical field of crowd evacuation simulation, and in particular relates to a crowd group evacuation simulation method and system based on a half DBSCAN clustering algorithm. Background technique [0002] With the growth of the global population and the acceleration of the world's urbanization process, in mainland China, urban development has entered a period of rapid growth, the scale of the city is expanding rapidly, and high-rise and large buildings are increasing day by day. People are highly dense, and crowding and stampede accidents are most likely to occur in open places and in densely built areas when sudden fires, earthquakes, and terrorist attacks occur, which poses severe challenges to social public safety and the safety of people's lives and property. Deeply excavate the action rules of dense crowds under emergencies, conduct hidden danger investigations that may cause crowded riots or even trampled incidents, ...

AI Technical Summary

Problems solved by technology

However, the k-Medoids algorithm based on distance and relational multi-influencing factors to group people is not satisfactory in terms of accuracy.

[0005] To sum up, in the crowd evacuation simulation of the prior art, there is still a lack of an effective solution to the problem of how to improve the simulation accuracy of the group movement among relatives, friends and colleagues in crowd evacuation in an emergency

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