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:
Aug. 26, 2025

Filed:

Apr. 08, 2025
Applicant:

Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai Univ. of T.c.m., Shanghai, CN;

Inventors:

Chunyan Zhang, Shanghai, CN;

Chuting Wu, Shanghai, CN;

Yuan Gao, Shanghai, CN;

Yunyi Li, Shanghai, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01N 21/35 (2014.01); A61B 34/30 (2016.01); A61B 90/00 (2016.01);
U.S. Cl.
CPC ...
G01N 21/35 (2013.01); A61B 34/30 (2016.02); A61B 90/06 (2016.02); A61B 90/37 (2016.02); A61B 2090/376 (2016.02); G01N 2021/3595 (2013.01);
Abstract

A method for monitoring temperature-induced deformation of components in an intelligent moxibustion robot based on infrared spectroscopy includes the following steps. Infrared spectral images and frequency spectra of a target component of the intelligent moxibustion robot at different detection points are acquired. Motion influence confidence factors for each detection position are constructed based on frequency differences between peaks and troughs in the frequency spectrum. The box-counting method is used to obtain scale-relationship graphs of infrared spectra for all detection positions. The overall light absorption difference index of the target component is determined according to the scale-relationship graphs. By combining the overall light absorption difference index with motion influence confidence factors, local outlier factors (LOF) for each detection position in the thermal data sequence are calculated using a LOF anomaly detection algorithm. Finally, a temperature deformation risk of the target component is evaluated based on a thermal alarm threshold.


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