Scheduling Strategy Optimization Of Elevator Group Control System

Elevator,as the only vertical means of transportation,is becoming more and more indispensable in the modern urban process of our country.In order to alleviate the vertical traffic problem,a large number of elevators have emerged.However,the existing elevator group control method has relatively single performance,low transportation efficiency,and low ride quality,so it is necessary to improve it.In this paper,the group control scheduling optimization is taken as the research purpose.the fuzzy control algorithm and neural network fuzzy control group control algorithm based on the target floor reservation are established by introducing a new elevator call method.The effectiveness is verified by simulation.Firstly,based on the comprehensive analysis of the research status of elevator group control at home and abroad,the characteristics of elevator group control and the traditional group control scheduling algorithm are introduced.Secondly,by assigning different weight coefficients to each evaluation index in different traffic modes,the fuzzy reasoning method is used to effectively identify the elevator traffic modes,which can weaken the impact of passenger flow changes on the group control system and make the group control system adapt to the changes of the environment.Thirdly,in view of the shortcomings of the "two-step" call mode of the traditional group control system,such as slow information collection,inconvenient operation and low data integrity,a "one-step" call method for destination floor reservation is introduced.Based on the existing four evaluation indexes of average waiting time,average running time,long waiting percent and runing power consume,the evaluation index of car crowding is added,and the characteristics of the energy-responsive group control system are constructed.Based on the comprehensive evaluation function,the fuzzy dispatching algorithm is studied,and the group control system scheduling model composed of multiple fuzzy variables is established.The performance of the group control scheduling is optimized by selecting the elevator with the most credibility.In order to make up for the shortcomings of fuzzy control which is greatly affected by fuzzy rules,the neural network fuzzy dispatching algorithm is studied by using the advantages of neural network self-learning.Through self-learning,the weights are continuously adjusted,and the correspondence between output and output is continuously optimized.The scheduling performance of the group control algorithm is further improved.Finally,the MATLAB software is used to verify the designed group control simulation system,and it is operated and displayed in the GUI interface to realize the visual dynamic simulation operation of the elevator group control.By constructing a passenger flow simulation module based on Poisson distribution,the approximate real call information in different traffic modes is generated.Under the same simulation environment,through comparison of operation,data analysis and algorithm comparison,it is verified that the scheduling algorithm studied in this paper has been improved in the aspects of operation efficiency,service quality,energy saving and emission reduction,etc.,which has good practical application value.