Meteorological big data fusion method based on deep learning
US11836605B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Sep 16, 2022 |
| Grant date | Dec 5, 2023 |
| Priority date | — |
| Expiry date | Oct 25, 2042 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02A90/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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 the trained meteorological data fusion model to obtain high-resolution fused meteorological data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.