3results about How to "High scientific value" patented technology

The application provides a land use and cover grid data downscaling method and system based on AlphaEarth Foundations and machine learning in future years, which identifies coarse scale pixels in which land classes change in future years by comparing coarse scale data in future years and a base year; maps the coarse scale pixels to fine scale data in the base year to determine a target area; counts the number of fine scale pixels of each land class in the target area to obtain the net increase of fine scale pixels of each land class in future years; in the target area, generates a conversion probability corresponding to each land class for each fine scale pixel by using a land class conversion probability prediction model; constructs an optimization model with the sum of the conversion probabilities corresponding to the land classes to which all fine scale pixels in the target area are assigned as an objective function, and solves the optimization model to obtain an optimal land class assignment result of the fine scale pixels in the target area; and fuses the optimal land class assignment result with the fine scale data in the base year to generate fine scale land use and cover grid data in future years after downscaling.

This invention relates to the field of medical and health information processing technology, specifically disclosing an early risk stratification and warning system and method for diabetic nephropathy. The system includes a data fusion preprocessing module, a multi-scale causal network construction module, a dynamic risk trajectory calculation module, and a stratified warning decision-making module, connected sequentially. By fusing multimodal data to construct an interpretable causal network and utilizing a network-constrained prediction model, it achieves dynamic trajectory prediction of future renal function indicators and comprehensive risk indices. Based on the prediction results, it triggers different levels of risk warnings and outputs decision support information, thereby achieving early, dynamic, and causally interpretable risk stratification and warning.

This application relates to the field of underwater robot navigation and environmental perception technology, and discloses a positioning method and system for underwater environments. The positioning method includes: acquiring flow velocity profile data, temperature data, salinity data, and depth data at sampling points; obtaining normalized flow field feature vectors and physical feature vectors corresponding to each sampling point; calculating the cosine similarity of the normalized flow field feature vectors and the physical feature similarity of the normalized three-dimensional physical feature vectors; weightedly fusing the cosine similarity and physical feature similarity to obtain a fusion similarity; combining the fusion similarity with preset constraint rules to obtain position revisit constraint factors; adding the position revisit constraint factors to the factor graph to construct a complete factor graph model; and using the iSAM2 algorithm to solve the factor graph to obtain the globally optimized position sequence of the underwater vehicle. This method can correct cumulative drift errors and effectively ensure the reliability of the AUV's trajectory in visually and acoustically limited scenarios.