Cross-border electronic customs declaration inspection early warning method and system based on dynamic risk learning

By constructing a dual-channel deep forest model and comprehensively extracting static and dynamic features, the problems of insufficient recognition of complex abnormal patterns and poor adaptability to dynamic changes in cross-border electronic customs declaration inspections are solved, and efficient and accurate cross-border electronic customs declaration inspection early warnings are achieved.

CN120746425AActive Publication Date: 2025-10-03LOTA TECHNOLOGY (NINGBO) CO LTD
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Patent Information

Application Number
CN202511165748.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-20
Publication Date
2025-10-03
Estimated Expiration
2045-08-20

AI Technical Summary

Technical Problem

Existing cross-border electronic customs declaration inspection methods have limited capabilities in handling complex abnormal patterns, ignore the information in semi-structured data, and fail to respond to dynamic changes in customs declaration behavior in real time.

Method used

By building a dual-channel deep forest model, we can comprehensively extract static and dynamic features, including information from structured and semi-structured data, and use temporal behavior characteristics and state migration characteristics to adjust risk thresholds in real time, thereby achieving comprehensive and dynamic analysis of customs declaration data.

Benefits of technology

It improves the accuracy and flexibility of early warning for cross-border electronic customs declaration inspection, can detect abnormal declaration behaviors in a timely manner, adapt to the dynamic changes of different enterprises and time periods, and enhance the generalization ability of the model.

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Abstract

The invention provides a cross-border electronic customs declaration checking and early warning method and system based on dynamic risk learning, and the method comprises the steps: obtaining historical declaration data and real-time declaration data, the historical declaration data comprises a structured field and a semi-structured field, and the real-time declaration data comprises a state mark, a declaration timestamp and an enterprise number; static features are extracted from the structured fields and the semi-structured fields, and dynamic features are extracted from the state marks, the declaration timestamps and the enterprise numbers; constructing a dual-channel deep forest model, and respectively inputting the static features and the dynamic features into the dual-channel deep forest model to obtain a static risk probability and a dynamic risk probability; and obtaining a risk probability according to the static risk probability and the dynamic risk probability, and executing a corresponding strategy according to the risk probability. According to the method, the static features and the dynamic features are comprehensively extracted, and the two-channel deep forest model is constructed for risk assessment, so that comprehensive and dynamic analysis of customs declaration data can be realized.
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