Research On Down-peak Traffic Pattern Optimization Of Elevator Group Control System

With the increasing number of modern cities and the rapid development of high-rise buildings,high-rise buildings traffic becomes more complex,originally one or two elevators may complete the task,and now it needs several or even a dozen elevators that is composed of an elevator group to complete.So a lot of experts and scholars are interested in elevator group control algorithm.According to the different requirements of passengers,the elevator needs a control system that adapts to a variety of requirements.In this paper,the fuzzy neural network is used to identify the traffic pattern.For a traffic pattern,that is,the down-peak traffic pattern is optimized.And we get the optimization method,which greatly increases the efficiency,and we verify the performance of the algorithm.First,we explain the concept of elevator group control,review the development history and current situation,put forward all kinds of the intelligent scheduling method,introduce the field research results in recent years,and show the content arrangement.Then,the fuzzy control and neural network are introduced.Through the analysis of the passenger flow model,several different traffic patterns are obtained,and the characteristic values are extracted.The fuzzy neural network is created and it is used to identify the traffic pattern,and the recognition process is described and the results are analyzed.Finally,we determine the objective function of the down-peak traffic pattern,use the method of partitioning multiple objectives to make mathematical modeling,and use a computer to simulate and compare the resultsThe main work of this paper is to optimize the elevator group control system deeply by using the popular fuzzy neural network technology and makes the performance of the average waiting time,the average taking lift time and energy consumption have more advantages than ever before.