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:
Dec. 12, 2023

Filed:

Jan. 29, 2022
Applicants:

China Electric Power Research Institute, Beijing, CN;

State Grid Corporation of China, Beijing, CN;

Inventors:

Xingqi Liu, Beijing, CN;

Enguo Zhu, Beijing, CN;

Heping Zou, Beijing, CN;

Fantao Lin, Beijing, CN;

Min Lei, Beijing, CN;

Yinghui Xu, Beijing, CN;

Hao Chen, Beijing, CN;

Zhongxing Wu, Beijing, CN;

Yupeng Zhang, Beijing, CN;

Zixu Zhu, Beijing, CN;

Yue Han, Beijing, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G01R 19/25 (2006.01); G16Y 10/35 (2020.01);
U.S. Cl.
CPC ...
G01R 19/2513 (2013.01); G16Y 10/35 (2020.01);
Abstract

A system for load identification in collaboration with a cloud end includes: a smart Internet of Things electricity meter module, used for matching extracted feature quantity data with a first load feature library of the smart Internet of Things electricity meter module, and determining load feature data corresponding to unmatched feature quantity data in the feature quantity data as target load feature data; a use information front-end/acquisition module, used for calling the target load feature data and transmitting the target load feature data to a main station load identification module; and a main station load identification module, used for receiving the target load feature data, performing data direction processing on the target load feature data, determining an optimal matching strategy matching the feature data to be identified, and identifying an optimal matching solution corresponding to the feature quantity data to be identified in a second load feature library.


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