高温干旱复合灾害监测预警方法及系统
By combining multi-source data fusion and elliptic analysis techniques with machine learning models, the problem of spatial distribution heterogeneity in the monitoring of combined high-temperature and drought disasters was solved, enabling efficient disaster early warning and adaptive updates, and improving the accuracy and real-time performance of early warnings.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOWAY ENG TECH CO LTD
- Filing Date
- 2026-04-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies are insufficient to effectively capture the spatial heterogeneity of high temperature and drought distribution within a region, as well as its spatial coupling patterns and dynamic changes, in the monitoring of combined high temperature and drought disasters. This results in insufficient timeliness for early signal identification and warning of combined disasters.
By collecting multi-source monitoring data in real time, a fusion dataset is constructed. Elliptic analysis combined with eccentricity is used to quantify the dynamic changes in spatial morphology. The data is calibrated using environmental parameter correction coefficients to construct a time-series dynamic analysis structure. The time-series feature vectors of key indicators of high temperature and drought are integrated and input into a machine learning classification model for disaster probability calculation and early warning.
It has improved the accuracy and reliability of monitoring combined high temperature and drought disasters, realized the intelligent and adaptive updating of disaster early warning, and improved the real-time performance and reliability of early warning.
Smart Images
Figure CN121999596B_ABST