Meteorological big data fusion method based on deep learning

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United States of America Patent

PATENT NO 11836605
APP PUB NO 20230351164A1
SERIAL NO

17932860

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Abstract

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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 the trained meteorological data fusion model to obtain high-resolution fused meteorological data.

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Patent Owner(s)

Patent OwnerAddress
NATIONAL CLIMATE CENTERNO 46 ZHONGGUANCUN SOUTH STREET HAIDIAN DISTRICT BEIJING 100081

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Jiang, Tong Beijing, CN 57 313
Lu, Mingyue Nanjing, CN 3 0
Wang, Guofu Beijing, CN 13 15
Wang, Guojie Nanjing, CN 16 4
Wang, Yanjun Nanjing, CN 19 26
Wei, Xikun Xinghua, CN 1 0

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