Environmental data lattice point processing method based on graph convolutional network

Abstract

The invention relates to the field of data processing, in particular to an environmental data lattice point processing method based on a graph convolutional network. The method comprises the following steps: acquiring air quality monitoring data and meteorological monitoring data in a target area; missing processing is carried out on all the monitoring data, and the monitoring data of the sites are mapped into a lattice point matrix divided by the target area; generating a dynamic wind field graph by using the wind direction data and the wind speed data, and calculating a wind field adjacent matrix by using a Dijkstra algorithm; constructing a mask matrix at each moment according to the air mass concentration data, and constructing a feature vector set Z at each moment according to the wind field adjacent matrix, the mask matrix and the meteorological monitoring data; generating a target matrix Y at each moment according to the mask matrix and the air mass concentration data; and inputting the eigenvector set Z matrix into the trained graph convolutional neural network model to obtain an estimation matrix P of the target matrix. According to the invention, the accuracy of environmental data gridding can be improved.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a grid point processing method of environmental data based on a graph convolutional network. Background technique [0002] In recent years, the problem of environmental pollution has attracted people's attention, and the density of automatic environmental monitoring stations has also been greatly increased. Under the background of vigorously developing intelligent grid forecasting technology and the requirements of human production activities for location-based air quality services, the monitoring station space will Monitoring data with irregular resolution and discrete distribution to generate regular grid data has important social service and business application value. [0003] Commonly used methods for grid point processing of site data include objective analysis, remote sensing inversion, data assimilation, statistical interpolation, Kerry interpolation, and tensor completion,...

AI Technical Summary

Problems solved by technology

[0003] Commonly used methods for grid point processing of site data include objective analysis, remote sensing inversion, data assimilation, statistical interpolation, Kerry interpolation, and tensor completion, etc., but the existing technologies only process single environmental data , did not consider the impact of other factors on the target data, resulting in unsatisfactory gridding effect

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