The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 29, 2024

Filed:

Sep. 25, 2023
Applicants:

Changshu Institute of Technology, Suzhou, CN;

Dongnan Elevator Co., Ltd., Suzhou, CN;

Inventors:

Fusheng Zhang, Suzhou, CN;

Yang Ge, Suzhou, CN;

Anbo Jiang, Suzhou, CN;

Lingyun Ma, Suzhou, CN;

Zhen Zhao, Suzhou, CN;

Jianxin Ding, Suzhou, CN;

Jiancong Qin, Suzhou, CN;

Yong Ren, Suzhou, CN;

Guodong Sun, Suzhou, CN;

Yong Feng, Suzhou, CN;

Linzhong Tang, Suzhou, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B66B 1/24 (2006.01); B66B 1/28 (2006.01); B66B 1/34 (2006.01);
U.S. Cl.
CPC ...
B66B 1/28 (2013.01); B66B 1/2408 (2013.01); B66B 1/3476 (2013.01); B66B 2201/222 (2013.01); B66B 2201/403 (2013.01);
Abstract

A collaborative scheduling method for high-rise elevators based on Internet of Things is provided. The method includes: obtaining the number of people carried at the current moment of each elevator in the elevator group, the target distance corresponding to the current moment of each elevator, and the number of people waiting at the current moment of each floor; predicting the number of people waiting for the going up and the number of people waiting for the going down at the current moment of each floor based on the monitoring video data of the elevator door every day in the preset historical days, and constructing the corresponding feature vectors of each elevator at the current moment and the corresponding feature vectors of the skyscraper at the current moment, and then obtaining the corresponding state vectors at the current moment, controlling each elevator based on state vector and a reinforcement learning network.


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