Research On Crowd Evacuation Simulation Method And System Based On Raspberry Pi Data Drive

In the past few decades,the social and economic development has been increased rapidly,and people's living standards are constantly improving.At the same time,a variety of large public places and comprehensive entertainment shopping square are entering into everyone's daily life,and the number of crowded public places is also increasing.Although these shopping squares can greatly facilitate people's lives,they also bring some potential safety hazards to people.Due to the concentrated distribution of these public plazas,in the event of an emergency such as a fire or earthquake,it is easy to cause a safety accident such as group trampling.Therefore,how to evacuate people safely and efficiently in public places has become a hot issue.In recent years,computer simulation technology has been applied in this field to simulate crowd evacuation behaviors in real situations.Such research can provide strong decision support for safety supervision and management departments and architects in large public places to develop accident evacuation plans and reasonable interior design plans.Therefore,crowd evacuation simulation is also a key research topic in the field of computer animation and virtual reality,which has important social and practical significance.In order to reproduce the specific situation of crowd evacuation more realistically in real scenes and overcome the disadvantages of poor authenticity of evacuation effect of traditional methods,we propose a crowd evacuation simulation method based on the real data collected by Raspberry Pi.This method consists of three parts: In the first part,a data collection system based on Raspberry Pi is designed and constructed to solve the problem of large workload and time consuming for traditional methods.The system can capture the data of crowd movement in real time and is less affected by environmental factors.In the second part,the data processing model based on the least squares data fitting is designed.The model can quickly process the crowd data collected by the Raspberry Pi in the real scene,so as to obtain the functional relationship between crowd density and movement speed.In the third part,a crowd evacuation simulation method driven by real data collection based on Raspberry Pi is used.This method combines the functional relationship between crowd density and movement speed obtained in the second part with the RVO(Reciprocal Velocity Obstacles)to compute the evacuation path,so as to improve the efficiency of crowd evacuation.Finally,in order to improve the authenticity of the crowd evacuation simulation,the process and results of the crowd movement are visually displayed through the realistic rendering platform.The innovations and main work of this paper are as follows:1.Aiming at a series of existing problems such as complex motion data collected from real scenes,large workload and serious environmental conditions,the Raspberry Pi is used to collect the evacuation data of the crowd.Firstly,we use the Linux command internally for the Raspberry Pi to configure the internal software environment and system.After the configuration is completed,the pre-compiled Python program can be run inside the Raspberry Pi.Secondly,we connect the wireless WiFi network card with monitoring function and the infrared sensor module of human body on the Raspberry Pi.Using the Linux command and the internally compiled Python program,the external wireless WiFi network card and the infrared sensor module can be controlled to collect crowd data.Finally,the Raspberry Pi connected with the wireless WiFi network card and the human body infrared sensing module is deployed as a sensor node to the real scene.Then,the data of crowd in the scene is collected through the Linux command of the remote PC control terminal control deployment in the Raspberry Pi.These real data can be applied to the crowd evacuation simulation,so as to optimize the individual export choice and path planning,and make it more in line with the real situation to improve the authenticity of the crowd evacuation simulation.2.In order to quantify the influence of crowd density on crowd evacuation speed in real scenarios,a data processing model based on least squares data fitting will be proposed.The model can process the data packets captured by the wireless network card with monitoring function and the crowd time data collected by the infrared sensing module by using a data processing model based on the least squares method,thereby obtaining a function of population density and speed.Then,the resulting functional relationship will be quickly applied to the crowd evacuation simulation platform.3.Facing the real scene,we propose a crowd evacuation simulation method based on the real data collected by Raspberry Pi.This method consists of two modules: data-driven crowd evacuation simulation and crowd movement results display.Among them,the data-driven crowd evacuation simulation module mainly extracts the scene semantics and constructs the scene.Then,the crowd movement can be calculated by using RVO algorithm with the relationship between crowd density and evacuation speed.The crowd movement display module is mainly used to render and output the crowd movement simulation result,which can directly display the results of crowd evacuation simulation method through the realistic rendering platform.The experiments show that the method proposed in this paper has the advantages of lower complexity,convenience and rapidity compared with the traditional method while simulating the crowd evacuation movement more realistically and efficiently.