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

Abstract

The present invention relates to a self-learning optimal control method and system of an elevator system based on deep reinforcement learning. The control method includes: establishing an operation model and a probability distribution model; preprocessing the data information of the elevator system to obtain current data information; Perform global iteration according to the current data information, and perform local processing through multiple asynchronous thread iterations: for each asynchronous thread, according to the current data information, use deep reinforcement learning to train the local action evaluation network, and correct the weight of the action evaluation network; until multiple At the end of the thread iteration and the end of the global iteration, the global action evaluation network is determined according to the weight of the action evaluation network; the optimal elevator control strategy is obtained according to the global action evaluation network to determine the average waiting time. In the global iterative process, the present invention performs local processing through a plurality of asynchronous thread iterations, determines the weight value of the action evaluation network, and obtains the optimal elevator control strategy 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.

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