Patents by Inventor Xikun WEI

Xikun WEI has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Meteorological big data fusion method based on deep learning
    Patent number: 11836605
    Abstract: The present disclosure provides a meteorological big data fusion method based on deep learning, including the following steps: constructing multi-source meteorological data samples; according to an original resolution of different climate variables, selecting a corresponding super-resolution multiple to obtain an optimized super-resolution module under the constraint of maximizing information retention efficiency; constructing a spatial-temporal attention module using a focused attention mechanism, and selecting a corresponding time stride according to periodic characteristics of different climate variables; constructing a meteorological data fusion model in combination with the optimized super-resolution model and the spatial-temporal attention module; taking a minimum resolution of climate variables as a loss function, and training the meteorological data fusion model with the multi-source meteorological data samples; and importing the acquired real-time meteorological data from multiple data sources into t
    Type: Grant
    Filed: September 16, 2022
    Date of Patent: December 5, 2023
    Assignees: Nanjing University of Information Science and Technology, National Climate Center
    Inventors: Guojie Wang, Xikun Wei, Guofu Wang, Tong Jiang, Yanjun Wang, Mingyue Lu
  • METEOROLOGICAL BIG DATA FUSION METHOD BASED ON DEEP LEARNING
    Publication number: 20230351164
    Abstract: The present disclosure provides a meteorological big data fusion method based on deep learning, including the following steps: constructing multi-source meteorological data samples; according to an original resolution of different climate variables, selecting a corresponding super-resolution multiple to obtain an optimized super-resolution module under the constraint of maximizing information retention efficiency; constructing a spatial-temporal attention module using a focused attention mechanism, and selecting a corresponding time stride according to periodic characteristics of different climate variables; constructing a meteorological data fusion model in combination with the optimized super-resolution model and the spatial-temporal attention module; taking a minimum resolution of climate variables as a loss function, and training the meteorological data fusion model with the multi-source meteorological data samples; and importing the acquired real-time meteorological data from multiple data sources into t
    Type: Application
    Filed: September 16, 2022
    Publication date: November 2, 2023
    Inventors: Guojie WANG, Xikun WEI, Guofu WANG, Tong JIANG, Yanjun WANG, Mingyue LU