Violation short message identification method and system based on deep semantic understanding

By adopting a two-layer cascaded detection architecture and a dynamic knowledge base update mechanism, the problems of accuracy and real-time performance in identifying illegal SMS messages in existing technologies are solved. This enables efficient identification of new variants and complex interference of illegal SMS messages, while reducing the consumption of computing resources.

CN121665247APending Publication Date: 2026-03-13SHANGHAI YUNXIN LIUKE INFORMATION TECH CO LTD
1 Cites 5 Cited by

Patent Information

Application Number
CN202511848170.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-09
Publication Date
2026-03-13

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately identify new variants and adversarial SMS messages, and cannot balance the accuracy of deep semantic detection with real-time processing efficiency in large-scale concurrent scenarios.

Method used

A two-layer cascaded detection architecture is adopted. The first layer performs initial screening through rule matching and a lightweight pre-trained model. The second layer uses a dual-tower fusion neural network for deep semantic detection and combines a dynamic knowledge base for incremental updates of model parameters.

Benefits of technology

It achieves a balance between recognition accuracy and efficiency in high-concurrency scenarios, reduces computing resource consumption, improves the ability to recognize new variants and complex interference, and reduces false alarm rate and false negative rate.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of network security and data processing, and discloses a violation short message recognition method and system based on deep semantic understanding, and the method comprises the steps: firstly cleaning an original short message, generating a mixed embedding vector through characters, sub-words and pinyin, and carrying out the recognition of the violation short message; then processing through a double-layer detection engine, wherein the first layer utilizes rules and a lightweight model for rapid preliminary screening; in the second layer, for suspected samples, a double-tower fusion neural network architecture is adopted, local and global features are combined, fusion is carried out through a gating unit, and a large language model is input to carry out deep semantic reasoning. The system executes strategies such as interception or flow limiting according to the risk score, and realizes model iteration through a dynamic knowledge base and incremental learning. According to the method, the resource consumption and the detection precision are balanced through the layered architecture, the antagonistic variants are effectively identified by utilizing multi-dimensional feature fusion, and the method has the adaptive evolution capability for a novel violation mode.
Need to check novelty before this filing date? Find Prior Art