A method and system for predicting ozone concentration distribution based on spatiotemporal deep learning

Abstract

The invention provides an ozone concentration distribution prediction method and system based on space-time deep learning. The method includes: obtaining an ozone concentration distribution map of a current moment, and obtaining meteorological data of a to-be-predicted moment; and processing the ozone concentration distribution map of the current moment and the meteorological data of the to-be-predicted moment through a trained ozone concentration prediction model based on the meteorological data, and obtaining an ozone concentration distribution map of the to-be-predicted moment. According tothe method and system, ozone concentration distribution map sequences and meteorology-time sequences are obtained through processing by methods including interpolation; historical data of a period isprocessed by employing a recurrent neural network, and trend characteristics of ozone concentration changes are extracted; historical data of a day ago or a week ago is processed by employing a convolutional neural network, and periodic characteristics of ozone are employed to the maximum; and the meteorological data and time data of the predicted moment are regarded as extra input, and the prediction accuracy is further improved by employing the influences of meteorology and time on the ozone.

Description

Technical field [0001] The present invention relates to the technical field of ozone concentration analysis, and more specifically, to a method and system for predicting ozone concentration distribution based on space-time deep learning. Background technique [0002] Ozone is a trace gas in the earth’s atmosphere, ozone (O 3 ), also known as super oxygen, is oxygen (O 2 The allotrope of ), at room temperature, it is a light blue gas with a special smell. It is formed because oxygen molecules in the atmosphere are decomposed into oxygen atoms by solar radiation, and the oxygen atoms combine with surrounding oxygen molecules, and it contains 3 oxygen atoms. More than 90% of the ozone in the atmosphere exists in the upper part of the atmosphere or in the stratosphere, 10 to 50 kilometers away from the ground. This is the atmospheric ozone layer that needs human protection. There are also a small number of ozone molecules hovering near the ground, which can still have a certain effe...

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Problems solved by technology

[0006] The present invention provides an ozone concentration distribution prediction method and system based on spatio-temporal deep learning that overcomes the above problems or at least partially solves the above problems. The problem of inaccurate prediction results caused by accuracy

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