A number recognition method, device, equipment, medium and computer program product

By analyzing voice signaling and internet signaling data, and combining multi-dimensional features and causal relationships, fraudulent numbers can be identified, solving the problems of single detection dimensions and high false alarm rates in existing technologies, and achieving more efficient fraudulent call identification.

CN121771325BActive Publication Date: 2026-05-19CHINA MOBILE GROUP JILIN BRANCH +1
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Patent Information

Application Number
CN202610250911.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-03
Publication Date
2026-05-19
Estimated Expiration
2046-03-03

AI Technical Summary

Technical Problem

Existing methods for identifying fraudulent calls have limited detection dimensions and incomplete evidence chains, resulting in a high false alarm rate.

Method used

By analyzing voice signaling data to obtain call scores for caller ID numbers, and combining this with internet signaling data to analyze abnormal behavior characteristics of called users, a multi-dimensional fraud identification mechanism is established. This mechanism uses causal relationships to identify fraudulent numbers, including obtaining call scores, causal strength values, and abnormal behavior similarity, and determining the probability and confidence level of risk assessment.

Benefits of technology

It significantly improves the integrity of the evidence chain for identifying fraudulent calls, reduces the false alarm rate, and achieves more accurate identification of fraudulent calls.

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Abstract

The application provides a number identification method, device, equipment, medium and computer program product, and relates to the technical field of fraudulent call identification. The method comprises the following steps: acquiring target numbers with call scores exceeding a first threshold value from a plurality of calling numbers according to voice signaling data; analyzing the online behaviors of a plurality of called users corresponding to each target number within a first preset time period according to online signaling data, and acquiring abnormal behavior characteristics of the called users; acquiring a causal strength value of the target number according to the abnormal behavior characteristics; and identifying a first type number in the target number according to the call score and the causal strength value. By fully utilizing the voice signaling data and the online signaling data, and by analyzing the causal relationship between the abnormal communication characteristics of the calling number and the abnormal network behaviors of the called user after the call, a multi-dimensional and three-dimensional fraud identification mechanism is established, the one-sidedness problem caused by the single detection dimension is solved, and the completeness of the evidence chain is significantly improved.
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