Research On Scheduling Method Of Elevator Group Control System For New Building Intelligent Platform

With the emergence of a large number of high-rise buildings,the passenger elevator traffic flow within the building has become increasingly complex,the demand for passenger elevator services in the building continues to increase,and the energy consumption of elevators has also increased.How to use a reasonable scheduling algorithm to solve the problems of slow elevator scheduling response and high energy consumption in the traditional group control algorithm is one of the current research hotspots of elevator group control technology.The current elevator group control system mainly adopts a distributed control architecture,which may face insufficient computing power of the central group controller when processing complex passenger call signals,resulting in poor timeliness of system dispatch and low passenger experience.The new building intelligent platform based on a centerless and flat architecture performs collaborative computing through data interaction between CPNs(Computing Processing Node,CPN),which can effectively overcome the shortcomings of the traditional architecture.This paper designs an elevator group control method based on an improved artificial bee colony(ABC)algorithm for a new intelligent building platform,using basic data generated by a human movement model and a traffic pattern recognition model to solve different optimal dispatching scheme under different traffic through collaborative computing between CPNs,and realizes the efficient operation of the elevator group control system under complex passenger call signals.The main research work carried out in this paper is as follows:Firstly,a human movement model is constructed based on the group intelligence simulation platform,and the traffic pattern recognition is carried out based on the traffic flow data generated by human movement model.In order to generate passenger traffic flow data to support for the elevator group control dispatching method,and to ensure that the simulation model can be connected to the new building intelligent platform,a Markov chain is used to construct a human mobility model for simulating the passenger traffic flow data in the building.The traffic pattern recognition model uses a support vector machine to distinguish traffic patterns from traffic flow data at different moments in the simulation of the human movement model.The model uses the characteristic values of the number of people entering the building,the number of leaving the building,the number of up-calls,and the number of down-calls per unit time to construct a data set,and a classifier with a higher recognition rate is trained.The experimental results show that the recognition accuracy of the traffic pattern recognition model reaches 99%,which meets the traffic pattern recognition requirements of the group control system.Secondly,combined with the new building intelligent platform,an elevator group control dispatching method based on the improved ABC algorithm is designed,which solves the problem of quickly and efficiently solving the dispatching scheme under passenger complex call signals.On the basis of the traditional ABC algorithm,the improved ABC algorithm increases the data interaction between CPNs,that is,after each algorithm iteration,the optimal fitness value and optimal dispatching solution in each CPN are exchanged,and the objective function characterizes the passenger waiting time and elevator energy consumption is used to compare different dispatching schemes to ensure that the algorithm in each CPN next time iterates based on the optimal fitness value and the optimal dispatching scheme.The experimental results show that the average waiting time of the improved ABC algorithm is significantly reduced,and the operating efficiency of the elevator group control system is improved.Finally,combined with the group intelligent hardware platform and elevator group control dispatching software,an elevator group control simulation experiment system was built,which realized the data generation of passenger traffic flow,the recognition of elevator group control system traffic patterns,the generation of elevator dispatching schemes,storage elevator operation data,and management elevator system historical data.At the same time,the performance test of the elevator group control dispatching method verifies the feasibility and effectiveness of the method,and provides theoretical guidance for practical engineering applications.Figure[42] table[12] reference[53]...