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Method and equipment for predicting rainfall

A rainfall and equipment technology, applied in measurement devices, neural learning methods, radio wave measurement systems, etc., can solve the problem of low rainfall forecast accuracy, and achieve the effect of improving forecast accuracy

Inactive Publication Date: 2020-08-04
上海眼控科技股份有限公司
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Problems solved by technology

[0004] The purpose of this application is to provide a method and equipment for predicting rainfall to solve the technical problem of low rainfall prediction accuracy in the prior art

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  • Method and equipment for predicting rainfall
  • Method and equipment for predicting rainfall

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Embodiment Construction

[0035] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0036] In a typical configuration of the present application, each module and trusted party of the system includes one or more processors (CPU), input / output interface, network interface and memory.

[0037] Memory may include non-permanent storage in computer readable media, in the form of random access memory (RAM) and / or nonvolatile memory such as read only memory (ROM) or flash RAM. Memory is an example of computer readable media.

[0038] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented by any method or technology for storage of information. Information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynam...

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PUM

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Abstract

Compared with the prior art, the invention discloses a method for predicting rainfall. The method comprises the following steps of acquiring a first radar echo image sequence, wherein the first radarecho image sequence includes continuous frame radar echo images based on a preset frame number; and then determining a second radar echo image sequence based on the first radar echo image sequence, and finally inputting the first radar echo image sequence and the second radar echo image sequence into a trained improved SlowFast neural network model to predict the rainfall of a preset station within a preset time period. By means of the method, the trained improved SlowFast neural network model is used for predicting the future short-time rainfall, prediction accuracy is greatly improved, and the method can be applied to various related application scenes and meet actual requirements.

Description

technical field [0001] The present application relates to the technical field of computer image processing, in particular to a technique for predicting rainfall. Background technique [0002] Precipitation forecast plays an important role in many agricultural production, urban life and work, such as rainfall forecast is an important guarantee for airport operation. The radar echo image can show the shape of the cloud over a certain area, and the continuous frame radar echo image sequence can reflect the movement and change track of the cloud over the area, and the shape and movement trend of the cloud can be used to judge the future weather. [0003] After the rise of deep learning, there has been the use of neural network models to predict rainfall, such as the TrajGRU (TrajectoryGated Recurrent Unit, Trajectory Gated Recurrent Unit) neural network model, which uses radar echo image data to capture the characteristics of radar echo images. Predict future radar echo images ...

Claims

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Application Information

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IPC IPC(8): G01S13/95G01S7/41G06N3/04G06N3/08
CPCG01S13/95G01S7/417G06N3/08G06N3/045Y02A90/10
Inventor 周康明杨光
Owner 上海眼控科技股份有限公司
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