2results about How to "Disaster loss reduction" patented technology

The invention relates to the crossing field of intelligent agricultural insurance and intelligent agriculture, in particular to a wheat planting complete cost insurance double-precision double-reduction technology integrated intelligent system based on space-air-ground multi-source data, which comprises a data acquisition module for acquiring satellite remote sensing, unmanned aerial vehicle aerial survey and internet-of-things perception covering a wheat planting complete period, space-air-ground multi-source data such as cross-department government affairs and market sharing can be realized; and a data preprocessing, outputting and active pushing module. According to the method, wheat exclusive monitoring equipment is adopted, air-space-ground multi-source data and standardized technical parameters are integrated, through planting area grading verification and marking, a growing period dynamic compensation model is fused, data accuracy and consistency are ensured, single data dimension limitation is broken through, full-period all-dimensional accurate monitoring from sowing to harvesting is achieved, and the method is suitable for large-scale popularization and application. Causal association and forecasting of risks and yield, quality and price are achieved, the planting area underwriting error is smaller than or equal to 3%, and the loss assessment error is smaller than or equal to 5%; the claim settlement period is shortened to be within 3 days from traditional 15-30 days, the farmer experience and insurance claim settlement efficiency and precision are greatly improved, and high-quality data support is provided for double-precision business and double-reduction intervention.

This invention discloses a method for predicting urban flood disasters based on satellite infrared brightness temperature and precipitation data. The method includes: combining satellite infrared brightness temperature and precipitation data to identify relevant data of complete lifecycle MCSs (Multi-Category System Components); extracting and filtering CCS area evolution data; determining the optimal value of the sensitivity coefficient; fitting the relationship function between CCS area and time, determining the most vigorous development time as the center point, determining the slope critical threshold based on the sensitivity coefficient, and finding boundary points to divide the development, maturity, and dissipation stages; finally, integrating urban underlying surface characteristics and a waterlogging model to quantitatively predict the level of urban flood disasters and push emergency response measures, optimizing the prediction results and response plans through a dynamic feedback mechanism. This invention can accurately capture the essential characteristics of each stage of MCSs, and based on the capture results, achieve full-process automation from meteorological monitoring to flood prediction and emergency response.