Instance-based learning multi-Agent cooperation crowd evacuation simulation method and device

Abstract

The invention discloses an instance-based learning multi-Agent cooperation crowd evacuation simulation method and device. Sufficient path information samples are acquired through real videos and simulation, and original path information is extracted. Scene information is extracted according to the scene, and region segmentation is performed on the scene information so as to construct a scene information base. The knowledge path is segmented in a way of being corresponding to the scene region segmentation situation, and an original local path information base is constructed. Pedestrians are matched with the scene information database, the groups are divided according to the similarity and each group is guided by the guide Agent. The guide Agent is matched with the local path information base, and an approximation function is established and the local path is ensured to be optimal. Scene local path information exchange is realized by using the information interaction cooperation function in the multi-Agent system. The isomorphic instance is constructed by using interaction information through analogical learning and path selection is optimized. The path information drives the guide Agent, and the low-level motion of the Agent of the same group is driven by the social force model until all the pedestrians in the scene are evacuated to the safe location and the process is ended.

Description

Technical field [0001] The invention belongs to the field of crowd evacuation simulation, and in particular relates to a multi-agent collaborative crowd evacuation simulation method and device based on case learning. Background technique [0002] With the rapid development of my country’s economy, people’s living standards have been rapidly improved, and people travel more frequently, especially in crowded public places, such as railway stations, squares, shopping centers, etc., where there is a huge flow of people in a short period of time. Small disturbances will have a great impact on the rapid evacuation of the crowd, and the potential safety hazard is greater. If the crowd cannot be effectively controlled, it will easily lead to crowded stampede incidents. Moreover, studies have shown that in the event of emergency events such as fires and earthquakes, if there is no reasonable guidance, the crowd will fall into extreme panic, and due to the influence of the nearby psychology...

AI Technical Summary

Problems solved by technology

In the more common computer simulation models at present, the macroscopic model regards the crowd as a whole. Although the simulated evacuation speed is fast, it ignores the differences among pedestrians, so the evacuation effect is not ideal. Although the microscopic model is independent, the individual The movement only follows the rules defined by the model, lacks macroscopic target selection and path navigation, and requires global movement planning for each evacuated individual. The computational overhead is too large, and it can only be used to deal with small-scale groups. In real-time Difficult to apply in computing environment

Therefore, the existing evacuation models do not make good use of real evacuation to guide the simulation

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