Intelligent on-duty early warning method and system fused with meteorological service data

By combining historical prior database, mutual information method, deep learning and Kalman filtering, the problem of difficulty in integrating multi-source data in traditional meteorological warning systems has been solved, efficient and accurate warning of complex meteorological events has been achieved, and the intelligence level of the meteorological warning system has been improved.

CN120689997AActive Publication Date: 2025-09-23河南省气象台 +1
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Patent Information

Application Number
CN202510638713.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-19
Publication Date
2025-09-23
Estimated Expiration
2045-05-19

AI Technical Summary

Technical Problem

Traditional meteorological warning systems rely on manual judgment and traditional mathematical models, and it is difficult to effectively integrate multi-source heterogeneous meteorological data, resulting in low forecast accuracy. In particular, it is difficult to achieve efficient and accurate warnings in complex meteorological events.

Method used

A method combining historical prior database, mutual information method, deep learning and Kalman filtering is adopted. The mutual information method is used to screen the characteristics of multi-source data, deep learning is used to process severe convection sudden warnings, and Kalman filtering is used to process slow risk warnings, thus realizing dynamic association and efficient fusion of data.

Benefits of technology

It improves the accuracy and real-time performance of the meteorological warning system, can effectively handle complex and changeable meteorological phenomena, and enhances the intelligence and practicality of meteorological warnings.

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Abstract

The invention relates to the technical field of meteorological early warning, and discloses an intelligent on-duty early warning method and system fused with meteorological service data, and the system comprises a historical prior database which comprises a plurality of historical forecast types and a plurality of pieces of historical feature data, and each historical forecast type corresponds to one piece of historical feature data; the acquisition unit acquires meteorological data, radar data and satellite data of a preset area to form feature data; the judgment unit collects feature data and compares the feature data with a historical prior database to determine a forecast weather type; when severe convection emergency early warning is carried out, the processing unit carries out data fusion on the feature data based on deep learning; and during slow risk early warning, performing data fusion on the feature data based on Kalman filtering, and determining an early warning level according to a fusion result. According to the invention, through the method of automatically selecting deep learning and Kalman filtering, the accuracy and real-time performance of the meteorological early warning system are improved.
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