A Deep Learning-Based Short-Term Targeted Early Warning Method for Rainstorm Disasters at New Energy Power Stations

By using deep learning-based methods, we can identify strong convective cloud systems at new energy power stations and correct rainfall patterns, thus solving the problems of accuracy, real-time updates, and differentiation in rainstorm disaster early warning for new energy power station clusters and achieving efficient targeted early warning.

CN122313641APending Publication Date: 2026-06-30METEOROLOGICAL SERVICE CENT OF GUANGXI ZHUANG AUTONOMOUS REGION +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
METEOROLOGICAL SERVICE CENT OF GUANGXI ZHUANG AUTONOMOUS REGION
Filing Date
2026-04-21
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing technologies are insufficient to achieve accurate, real-time, and differentiated rainstorm disaster early warnings in new energy power plant clusters. In particular, when facing a large number of dispersed power plant clusters, they suffer from low efficiency, inconsistent standards, and slow response. Furthermore, they lack a dynamic correction mechanism for the rapid evolution of local severe convection, resulting in insufficient timeliness of early warnings.

Method used

Using a deep learning-based approach, early warning units are divided and a baseline analysis grid is constructed by acquiring spatial boundary data of new energy power stations and radar monitoring grid parameters. Combined with historical observations and expert rules, strong convective cloud systems are identified, and precipitation field correction is performed through a multi-branch fusion network to generate targeted early warning information.

Benefits of technology

It enables differentiated, real-time, and precise early warning for new energy power stations, and can directly deliver early warning results to specific early warning units, meeting the minute-level response requirements of new energy power stations for short-term heavy rainfall, and improving the hit rate and timeliness of early warnings.

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Abstract

This invention discloses a deep learning-based short-term targeted early warning method for rainstorm disasters at new energy power stations, aiming to solve the problem of differentiated, accurate, and advanced early warning for numerous, scattered new energy power stations during rainstorms. This method divides the power station into multiple early warning units, establishing spatial mapping relationships and a digital decision-making knowledge base. It utilizes adjacent time-series radar three-dimensional reflectivity combined with DBSCAN to identify strong convective cloud systems, extracting boundary, centroid, movement direction, and state features. Potentially threatening strong convective clouds are screened and bound to the affected early warning units. When the early warning triggering conditions are met, cloud system features, initial quantitative rainfall forecasts, and background features are fused to output the corrected rainfall field for the early warning unit for the next 0 to 3 hours. Combined with spatial mapping relationships, a risk discrimination matrix, and early warning information templates, targeted early warning information is generated and released. This method can be used for refined short-term early warning of rainstorm disasters at new energy power stations, improving the targeting and timeliness of early warnings.
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