Elevator system self-learning optimal control method and system based on deep reinforcement learning

Abstract

The invention relates to an elevator system self-learning optimal control method and system based on deep reinforcement learning. The control method comprises the following steps: establishing an operation model and a probability distribution model; preprocessing the data information of the elevator system to obtain current data information; carrying out global iteration is according to the current data information, and carrying out local processing through iteration of a plurality of asynchronous threads: for each asynchronous thread, training a local action evaluation network through deep reinforcement learning according to the current data information, and correcting the weight of the action evaluation network; determining a global action evaluation network according to the weight of the action evaluation network until multi-thread iteration and global iteration are finished; and obtaining an optimal elevator control strategy according to the global action evaluation network so as to determine the average waiting time. In the global iteration process, local processing is carried out through iteration of the plurality of asynchronous threads, the weight of the action evaluation network is determined, and the optimal elevator control strategy is obtained through self-learning.

Description

technical field [0001] The invention relates to the technical field of intelligent optimization control, in particular to a self-learning optimal control method and system for an elevator system based on deep reinforcement learning. Background technique [0002] With the development and progress of society, a large number of working people flow to the city to work, and the population density of buildings in large and medium cities has reached an unprecedented height. Ensuring the efficient flow of people in a building is a prerequisite for maintaining the normal operation of the building, and the elevator system plays an extremely important role in the efficient flow of people. The number, capacity, running speed and scheduling algorithm of the elevator cars determine the efficiency of the elevator system. Since the number, capacity and running speed of the cars are more or less limited by the hardware conditions of the building, the elevator scheduling algorithm has become ...

AI Technical Summary

Problems solved by technology

[0003] Researchers have tried to explore optimal solutions in different ways, including expert systems, fuzzy mathematics, genetic algorithms, and reinforcement learning, but none of them can achieve optimal control of elevator efficiency.

Images