Research On Elevator Intelligent Group Control Scheduling Algorithm

With the development of urban high-rise buildings,the single-lift operation in the building has been unable to meet the transportation needs of passengers.The multi-elevator intelligent group control scheduling technology can be well adapted to the needs of a variety of passenger elevators,and based on different traffic modes and passengers' needs to make a flexible dispatching strategy.Therefore,the application of smart group control technology for controlling elevators has become increasingly common.The elevator intelligent group control dispatch system is mainly divided into three aspects: elevator passenger traffic flow prediction,traffic pattern recognition,and group control algorithm scheduling.Among them,elevator passenger traffic flow forecasting is the premise of realizing the elevator intelligent group control system.Traffic pattern recognition is the basis of realizing the elevator intelligent group control system.The group control scheduling algorithm is the core of implementing the elevator intelligent group control system.This article aims to reduce passengers waiting time,reduce unnecessary stop of elevators,save the energy consumption of elevator operation,and give full play to the superiority of modern intelligent control theory as a means to predict traffic flow of elevator intelligent group control system.Three aspects of traffic pattern recognition and elevator intelligent group control algorithm are studied and analyzed.At the same time,a critical full load discrimination method is proposed for the operation of the critical full load condition of the elevator at this stage.The prediction of elevator passenger traffic flow is the premise to achieve intelligent group control scheduling of elevators.According to the small-sample characteristics of elevator passenger traffic flow,according to the principle of equal dimension and innovation,an improved gray GM(1,1)model is proposed to analyze the periodicity and development trend of elevator traffic flow respectively prediction.Considering the cyclical characteristics of elevator traffic flow and its development trend,the two sets of forecasting data are adaptively weighted combined forecasting.Comparing the traffic flow data combination prediction with the wavelet neural network prediction model shows that the elevator traffic flow combined prediction error is smaller when the actual traffic data of the passenger traffic flow is less.Elevator traffic pattern recognition is the key to group control optimization.Aiming at the problems of influencing the traffic pattern recognition with many and complex factors,according to the knowledge of rough set theory,an elevator traffic mode fuzzy identification method based on attribute reduction is proposed..The attribute reduction and the fuzzy inference rules affecting elevator traffic pattern recognition are attributed to reduce the fault-tolerance when the model recognizes the elevator traffic mode and reduces the number of fuzzy inference rules.The simulation results show that the recognition accuracy of elevator traffic pattern based on attribute reduction method is higher than the accuracy rate of multi-value classification recognition using least squares support vector machine(LSSVM).The elevator group control scheduling algorithm is the core of implementing the elevator intelligent group control system to send the ladder properly.It can obtain the optimal dispatching ladder scheme according to the passengers' actual riding demand.This paper takes the passenger waiting time and the elevator energy consumption as the optimization goal,establishes an intelligent multi-objective optimization model of elevator group control,optimizes the dispatching plan by means of an improved genetic algorithm,and finally uses the elevator intelligent group control dispatching system simulation platform to compare this design proposal.The simulation test is conducted.The experimental results show that the improved genetic algorithm not only reduces the waiting time of passengers,but also reduces the elevator power consumption.Aiming at the problem of repeated dispatching in elevator group control and invalid parking due to incomplete elevator information,especially when the elevator traffic flow is in the downward peak traffic mode,the car load is not saturated due to the degree of congestion of the elevator The paper proposes an elevator full load status identification method to reduce the unnecessary stopping of the car,effectively reducing the passenger waiting time and reducing the energy consumption of the elevator system.