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

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

Abstract: A method for flood disaster monitoring and disaster analysis based on vision transformer is provided. It includes: step (1), constructing a bi-temporal image change detection model based on vision transformer; step (2), selecting bi-temporal remote sensing images to make flood disaster labels; and step (3), performing flood monitoring and disaster analysis according to the bi-temporal image change detection model constructed in the step (1). In combination with the bi-temporal image change detection model based on an advanced vision transformer in deep learning and radar data which is not affected by time and weather and has strong penetration ability, data when floods occur can be obtained and recognition accuracy is improved.

Abstract: A deep-sea submarine gas hydrate collecting method and a production house for the first time, the collecting method comprises the steps of: determining an active methane leakage zone near a landward limit of a submarine gas hydrate stability zone, acquiring submarine methane leakage in-situ observation data, determining a methane leakage rate and evaluating its economy; mounting a production house on the seabed, opening a monitoring system after the mounting, monitoring the submarine methane leakage condition and hydrate generation progress in real time, evaluating a hydrate generation amount, and performing hydrate acquisition work; and rapidly processing the gas hydrate in the house by a gas hydrate collecting system of an offshore platform, and continuously monitoring the methane leakage condition. A large amount of methane leaked can be collected, thereof, the method has dual meanings of resources and environment.

Abstract: A deep-sea submarine gas hydrate collecting method and a production house for the first time, the collecting method comprises the steps of: determining an active methane leakage zone near a landward limit of a submarine gas hydrate stability zone, acquiring submarine methane leakage in-situ observation data, determining a methane leakage rate and evaluating its economy; mounting a production house on the seabed, opening a monitoring system after the mounting, monitoring the submarine methane leakage condition and hydrate generation progress in real time, evaluating a hydrate generation amount, and performing hydrate acquisition work; and rapidly processing the gas hydrate in the house by a gas hydrate collecting system of an offshore platform, and continuously monitoring the methane leakage condition. A large amount of methane leaked can be collected, thereof, the method has dual meanings of resources and environment.