Air quality PM2.5 predicating method and air quality PM2.5 predicating system

Abstract

The invention is suitable for the technical field of environmental monitoring, and provides an air quality PM2.5 predicating method and an air quality PM2.5 predicating system. The air quality PM2.5 predicating method comprises the following steps: performing meshing on a researched region; obtaining influence factor data in each mesh and air quality data monitored by a monitoring station; integrating the influence factor data to form a fourth-order tensor, and performing normalization processing on the four-order tensor; based on the influence factor tensor data and the PM 2.5 concentration data subjected to the normalization processing, establishing a tensor sample database; constructing an initial support tensor regression model, and training the initial support tensor regression model by taking the PM2.5 influence data tensor data and the PM2.5 concentration data as input data; determining parameters of the initial support tensor regression model through an alternating projection algorithm, and obtaining the final support tensor regression model; predicating a target sample through the final support tensor regression model to obtain the PM2.5 concentration data in the target sample.

Description

technical field [0001] The invention belongs to the technical field of environmental monitoring, and in particular relates to an air quality PM2.5 prediction method and system. Background technique [0002] Particulate matter (PM), is the term for suspended particles in the air, including dust, dirt, soot, and liquid droplets. "Coarse" particles (PM10) with a diameter between 2.5 mm and 10 microns can be inhaled by the human body and accumulate in the respiratory system (respiratory tract), and "fine" particles with a diameter of less than 2.5 microns (PM2.5) are extremely small (about 1 / 30 of the diameter of a human hair) can be inhaled by the human body and penetrate deep into the lung tissue, affecting lung function. It is currently the most harmful particle. Crushing and grinding processes and freshly paved or unpaved roads produce "coarse" particles such as dust; "fine" particles originate from all types of combustion activities (motor vehicles, power plants, incomplet...

AI Technical Summary

Problems solved by technology

[0003] Due to the obvious spatio-temporal and non-linear changes of PM2.5-related impact factor data, the following problems are likely to occur in the existing vector-based PM2.5 prediction method: 1) it is easy to destroy the spatio-temporal structure information of the original data; 2) It is easy to destroy the inherent correlation of the original data and cover up the original high-order dependence of the data; 3) The computational complexity and storage cost are high

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