Meteorological ground disaster monitoring and early warning system based on big data AI analysis

By collecting and fusing multi-source heterogeneous data, and combining deep learning and machine learning, the meteorological and geological disaster monitoring system solves the problem of insufficient assessment caused by the single data source in the existing system. It realizes high spatiotemporal resolution meteorological and geological disaster early warning and dynamic trend prediction, and improves the scientific nature of the early warning and the efficiency of the response.

CN121768147APending Publication Date: 2026-03-31GUIZHOU UNIV OF ENG SCI
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Patent Information

Application Number
CN202610062090.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-17
Publication Date
2026-03-31

AI Technical Summary

Technical Problem

Existing meteorological and geological disaster monitoring systems rely on a single data source, resulting in a lack of comprehensiveness and foresight in disaster risk assessment, insufficient early warning decision-making, and difficulty in meeting the needs of multi-source heterogeneous data fusion, dynamic trend prediction, and cross-regional collaborative early warning.

Method used

Employing a multi-source heterogeneous data acquisition and fusion module, combined with deep learning time series models and disaster chain analysis, data is collected and fused through devices such as drones, satellite remote sensing, and IoT sensors. Deep learning and machine learning algorithms are used to perform high spatiotemporal resolution prediction and disaster risk assessment, generate urgency levels, and provide intelligent early warnings.

Benefits of technology

It has enabled real-time monitoring and accurate early warning of meteorological and geological disasters, improved the scientific nature of early warning and response efficiency, reduced casualties and property losses, and promoted the application of big data and artificial intelligence in the field of disaster prevention and mitigation.

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Abstract

The invention discloses a meteorological ground disaster monitoring and early warning system based on big data AI analysis, and the system comprises a multi-source heterogeneous data collection and fusion module which collects the multi-source heterogeneous data of an affected area, and generates a unified fusion data flow; the dynamic weather prediction and disaster chain analysis module is used for generating predicted weather data, driving a coupled disaster chain model based on the predicted weather data, and simulating and outputting a disaster risk space-time distribution diagram; the refined terrain and hydrological analysis module is used for performing three-dimensional terrain feature quantitative extraction and distributed hydrological dynamic simulation to generate terrain complexity parameters and hydrological risk indexes; the disaster influence evaluation and emergency degree grading module is used for performing nonlinear comprehensive calculation through a trained machine learning evaluation model and outputting an emergency degree quantitative score and a corresponding rescue emergency degree grade; and the early warning terminal and decision support module is used for issuing early warning information through a multi-mode channel based on the rescue emergency degree grade, and calling an emergency plan knowledge base to provide a rescue plan.
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