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Method and system for estimating PM2.5 based on empirical Bayesian Kriging model, and medium

A PM2.5 and model technology, applied in computing, complex mathematical operations, instruments, etc., can solve the problem of low estimation accuracy of ground PM2.5, achieve accurate estimation, improve estimation accuracy, and save costs.

Inactive Publication Date: 2019-05-21
深圳航天智慧城市系统技术研究院有限公司
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[0003] The present invention provides a method, system and storage medium for estimating PM2.5 based on the empirical Bayesian kriging model, so as to solve the problem that the estimation accuracy of ground PM2.5 is not high by the existing spatial interpolation method

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  • Method and system for estimating PM2.5 based on empirical Bayesian Kriging model, and medium

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[0022] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0023] Because traditional Kriging interpolation methods have too strong assumptions about reality, the accuracy of PM2.5 estimates obtained by using traditional Kriging interpolation is not high. First, traditional kriging interpolation assumes that the spatial attributes are uniform. That is, for any point in space, there is the same expectation and variance. Second, traditional kriging assumes that the estimated semivariogram is the true semivariogram. This requires that the data come from a Gaussian distribution. If and only when the distribution of the data obeys the Gaussian distribution, the best estimate will be obtained by using the traditional kriging interpolation. However, the data distribution in the real world is difficult to obey the Gaussian distribution. Therefore, these assumptions are difficult ...

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Abstract

The invention discloses a method and a system for estimating PM2.5 based on an empirical Bayesian skill model, and a storage medium. The method comprises the following steps: resampling ground PM2.5 observation data of a to-be-estimated region into a pre-created grid, and matching the resampled ground PM2.5 observation data; Dividing the matched grid data into a plurality of overlapped subsets with specific sizes; Estimating a semi-variation function through the PM2.5 observation data in the subset; Taking the estimated semi-variation function as a model, and performing unconditional simulation at each input position through the model to generate a new PM2.5 simulation value; Estimating a new semi-variation function through the PM2.5 analog value, and calculating the weight of the semi-variation function according to an empirical Bayesian rule; And repeating the fourth step and the fifth step for several times, weighting the semi-variation function by the weight obtained at the last time, and carrying out PM2.5 prediction on a weighting result at an unknown position. According to the method, the cost can be saved, and the estimation precision of PM2.5 is greatly improved.

Description

technical field [0001] The invention relates to the technical field of air quality detection, in particular to a method, system and storage medium for estimating PM2.5 based on an empirical Bayesian kriging model. Background technique [0002] Air pollution is one of the most concerned topics in today's society, because it endangers the health of humans and other living things and causes huge losses. PM2.5 (also known as fine particles, fine particles) refers to particulate matter in the ambient air with an aerodynamic equivalent diameter less than or equal to 2.5 microns, also known as particulate matter that can enter the lungs. Its diameter is less than 1 / 20 of the thickness of a human hair. Although PM2.5 is only a small component in the earth's atmosphere, it has an important impact on air quality and visibility. It can be suspended in the air for a long time, and the higher its concentration in the air, the more serious the air pollution. Long-term exposure to PM2.5...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06F17/18
Inventor 刘湘湘
Owner 深圳航天智慧城市系统技术研究院有限公司
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