Optimization Of Urban Road Dangerous Goods Transportation Safety Management System Based On Internet Of Things Technology

Urban transport of dangerous goods for the city of transporting large amounts of energy at the same time,also to the urban ecological environment,urban residents personal and property safety brought safe hidden trouble,because of the particularity and complexity of the urban road transport of dangerous goods safety management,the traditional manual safety tube model has been unable to meet the modern transport of dangerous goods safety management system.In view of the characteristic of the dangerous goods transportation,it is urgent to establish a set of scientific city road dangerous goods transportation safety management system,and carries on the optimization research.Combining domestic and foreign experts and scholars to establish multiple sets of safe transportation management system,so as to determine the basic function structure of system and integration of multiple modern means of information technology(Networking,GPS,GIS)build urban road dangerous goods transportation safety management system,and the safety management system in the scheduling module of the algorithm to optimize.1.Using the improved BP neural network solution for screening compliance with the conditions of transport of dangerous goods vehicle traffic safety,improved BP neural network classifies the road between two points distribution and screened passable alternative roads and not pass road,finds out the safe passage of alternative roads to ensure that dangerous goods transport vehicles to safe and efficient to reach the corresponding distribution.2.Using improved ant colony algorithm to determine the distribution point for the shortest path optimization,in order to reduce the fuel consumption of the vehicle by the improved algorithm model,reduce the time of stay in the city.Compared with the traditional optimization algorithm,the improved algorithm model is less than 9.18% of the fuel consumption of transportation vehicles,and the decrease of the time of staying in the city by 5.83%.The results show that the algorithm is reasonable and effective.