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:
Nov. 29, 2022

Filed:

Jun. 16, 2022
Applicant:

Jiangnan University, Wuxi, CN;

Inventors:

Wei Fang, Wuxi, CN;

Runsu Zhu, Wuxi, CN;

Hengyang Lu, Wuxi, CN;

Xin Zhang, Wuxi, CN;

Jun Sun, Wuxi, CN;

Xiaojun Wu, Wuxi, CN;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/06 (2012.01); G06Q 10/04 (2012.01); G06N 20/00 (2019.01); G06F 17/00 (2019.01); F24F 11/49 (2018.01); F24F 11/39 (2018.01); F24F 110/65 (2018.01);
U.S. Cl.
CPC ...
F24F 11/49 (2018.01); F24F 11/39 (2018.01); G06F 17/00 (2013.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G06Q 10/06 (2013.01); F24F 2110/65 (2018.01);
Abstract

The present disclosure discloses a deep spatial-temporal similarity method for air quality prediction, and belongs to the technical field of environmental protection. When the method predicts air quality-related indexes of a target site, a temporal change of air pollution and a spatial diffusion relationship are effectively combined, and then spatial-temporal similarity sites of the target site are selected; air quality monitoring data collected by the target site, the spatial-temporal similarity site of the target site and geographical neighbour sites of the target site and meteorological data are respectively taken as inputs of a long short term memory network (LSTM) model to obtain uncorrelated output results, and then predicted values of air quality-related index data of the target site are obtained in a mode of support vector regression (SVR) integration. The present disclosure effectively combines the temporal change of air pollution with the spatial diffusion relationship, and namely proposes a more efficient way to select more highly relevant data to predict air quality so that a prediction result is more accurate.


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