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:
Sep. 09, 2025

Filed:

Nov. 27, 2024
Applicant:

Jinan University, Guangzhou, CN;

Inventors:

Feiran Huang, Guangzhou, CN;

Jinming Zhong, Guangzhou, CN;

Zhibo Zhou, Guangzhou, CN;

Jian Weng, Guangzhou, CN;

Assignee:

JINAN UNIVERSITY, Guangzhou, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/045 (2023.01); G06N 3/084 (2023.01); G06N 3/088 (2023.01);
U.S. Cl.
CPC ...
G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06N 3/088 (2013.01);
Abstract

A traffic anomaly detection method and system based on improved BERT integrating contrastive learning is provided, the method includes: obtaining traffic data and preprocessing the data; building an improved BERT model including an embedding layer and 12 Transformer encoder networks; performing weight sharing operation among a first 6 Transformer encoder networks and a last 6 Transformer encoder networks, respectively; building a classification network; building a total loss function based on a cross-entropy loss and a contrastive loss; performing unsupervised pre-training on the improved Bidirectional Encoder Representations from Transformers, BERT model; fine-tune training the improved BERT model; updating model parameters through backpropagation to obtain a trained improved BERT model; feed test traffic data into the trained improved BERT model; and obtain a traffic detection outcome. The present disclosure enhances the generalization ability of the model while maintaining stability and accuracy.


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