Wi-Fi-based Passive Location Of Public Building Personnel And Crowd Flow Detection Method And Application

As the urbanization process continues to advance,multi-functional public building sites are becoming the first choice for people to carry out activities,which also brings great challenges to daily fire safety management and evacuation of people in emergency situations such as fire emergencies.Therefore,the management staff obtains the distribution pattern and behavior characteristics of the crowd when the user is "insensitive",takes timely intervention and evacuation measures for the high-risk aggregation behavior,and implements accurate positioning and rescue of the crowd during emergency rescue.Provide a strong guarantee for the safe operation of indoor places and the safety of people's lives and property.Existing indoor positioning technology can meet the high-precision positioning of individuals,and the realization of group positioning puts forward higher requirements for positioning technology.Passive indoor positioning technology based on Wi-Fi has a wider application basis,which is The analysis of crowd flow provides the possibility of application.This paper aims at improving indoor two-dimensional and floor positioning accuracy,realizing mobile data visualization and dynamic monitoring and analysis of crowd distribution,and using improved passive positioning method of Wi-Fi indoor fingerprint to realize mobile data visualization.The main work and contributions of this paper are:(1)For the problem of low accuracy of offline fingerprint database in the realization of Wi-Fi signal indoor positioning,the analysis and selection of the filtering result is better.The Gaussian filtering method obtains high accuracy and high stability.Fingerprint database;(2)For the attenuation of radio signals during the propagation process,use the K-means clustering method to optimize the fingerprint database in combination with the indoor structure to achieve floor discrimination;(3)For the lower accuracy of existing matching algorithms,use Improved WKNN algorithm,combined with K-means clustering method to improve positioning accuracy;(4)Visualization of mobile data,analysis of crowd flow from three aspects of crowd distribution,crowd migration,and individual trajectory tracking,visually displaying The movement trend of the crowd has realized the flow direction monitoring based on Wi-Fi passive positioning.