Urban safety regional risk analysis method and system

Abstract

The invention relates to the field of urban safety risk management and control, and discloses an urban safety regional risk analysis method and system. The method comprises the following steps: acquiring video data of a crowd activity region by video monitoring equipment; acquiring block port data of each main entrance of a region; counting and calculating the traffic flow of a block port according to the block port data of the region, analyzing the proportion of vehicles to personnel entering the area, acquiring the data of vehicles remaining in the region, and carrying out early warning about regional road congestion; analyzing video data of the crowd activity region based on a video detection technology, and identifying crowd real-time statistics, density estimation and regional dynamichuman flow abnormal characteristic analysis under multiple scenes in the region by adopting a depth learning algorithm; and carrying out probability analysis and verification by utilizing regional pedestrian flow historical data, block port flow historical data, crowd real-time statistical data and the block port data of main entrances, calculating regional entrance pedestrian flow and traffic flow density, and carrying out early warning about regional safety risk in advance. By the method, real-time sensing of human flow and traffic flow in a key region is realized, and safety risk early warning and prevention in the key region are also realized.

Description

technical field

[0001] The invention relates to the field of urban security risk management and control, in particular to a regional risk analysis method and system for urban security. Background technique

[0002] The number of self-driving travelers in key tourist areas and urban complexes increases sharply during the peak period, but the existing key tourist areas, urban complex roadside parking spaces and open-air parking spaces are mostly managed manually. At the same time, the main road traffic information detection is single, The detection data is not sticky enough, and there is a lack of information release channels, which leads to the lack of understanding of congestion and berth channels for personnel, and they continue to enter the congestion area, resulting in a vicious circle. Moreover, traffic management lacks data basis and can only be commanded by experience. At the same time, it is impossible to predict the flow of people , there is no way to limit the curre...

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