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Atmospheric environmental pollutant prediction model based on dynamic space-time attention mechanism

An atmospheric environment and forecasting model technology, applied in weather condition forecasting, biological neural network model, forecasting, etc., can solve problems such as poor forecasting accuracy, and achieve the effect of increasing receptive field and improving learning ability.

Pending Publication Date: 2021-08-31
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0004] The present invention proposes an atmospheric environment pollutant prediction model based on a dynamic space-time attention mechanism, which solves the problem of poor prediction accuracy of the air pollutant prediction model in the prior art

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  • Atmospheric environmental pollutant prediction model based on dynamic space-time attention mechanism

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Embodiment Construction

[0074] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts all involve the protection scope of the present invention.

[0075] Such as figure 1 As shown, it is a flow chart of the prediction model of this embodiment, including:

[0076] Step S100: Obtain the concentration data, temperature data and humidity data of pollutants at S monitoring stations, and construct an input matrix [X 1 , X 2 ,...,X s ,...,X S ];

[0077] Step S200: performing a one-dimensional convolution operation on the input matrix to obtain the original sequence;

[0078] Step S300: Construct a space-time encoder, a...

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Abstract

The invention relates to the technical field of environmental monitoring, and provides an atmospheric environmental pollutant prediction model based on a dynamic space-time attention mechanism. The model comprises the following steps: acquiring concentration data, temperature data and humidity data of pollutants at S monitoring stations, and constructing an input matrix; performing one-dimensional convolution operation on the input matrix to obtain an original sequence; constructing a space-time encoder, and inputting the original sequence into the space-time encoder; adding a convolution gating unit to control the output of the space-time encoder; constructing a static attention mechanism and a dynamic attention mechanism, and fusing the static attention mechanism and the dynamic attention mechanism with output information of the space-time encoder to obtain encoding information of a target site; and constructing a decoder, decoding the coding information of the target site, and outputting a prediction result. By means of the technical scheme, the problem that in the prior art, an air pollutant prediction model is poor in prediction accuracy is solved.

Description

technical field [0001] The invention relates to the technical field of environmental monitoring, in particular to an atmospheric environmental pollutant prediction model based on a dynamic spatio-temporal attention mechanism. Background technique [0002] Air pollution is an important public health issue that affects human health, ecological environment and climate change. The air pollutants announced by the World Health Organization (WHO) mainly include particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), sulfur dioxide (SO2), and nitrogen (NO2) and ozone (O3). In recent years, ozone has become the primary pollutant that plagues urban air quality improvement and compliance after SO2 and PM2.5. The ozone pollution in many areas at home and abroad is severe, and the concentration of ozone near the ground remains high. The prevention and control of ozone pollution Governance an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/08G06N3/04G01N33/00G01W1/10
CPCG06Q10/04G06N3/08G01N33/0004G01W1/10G06N3/045Y02A90/10
Inventor 周围张航涛张英俊
Owner BEIJING JIAOTONG UNIV
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