PM2.5 comprehensive domain space-time calculation inference method based on multi-source city big data

A big data and urban technology, applied in the direction of reasoning methods, complex mathematical operations, etc., can solve problems such as sampling deviation, lack of matching of time and space in training data, and achieve the effect of reducing pollution emissions

Active Publication Date: 2021-08-24
SICHUAN UNIV
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Problems solved by technology

[0007] In order to achieve the purpose of effectively solving the problems of time-space under-matching and sampling deviation in the fusion of air quality multi-source information, the present invention provides a PM based on multi-source urban big data. 2.5 The comprehensive domain spatio-temporal calculation and inference method, its key iterative filling technology gives full pl

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  • PM2.5 comprehensive domain space-time calculation inference method based on multi-source city big data
  • PM2.5 comprehensive domain space-time calculation inference method based on multi-source city big data
  • PM2.5 comprehensive domain space-time calculation inference method based on multi-source city big data

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Embodiment

[0113] A certain city is an important industrial city in the Central Plains. Due to heavy industrial structure, energy structure partial to coal, unreasonable industrial layout, and slow environmental infrastructure construction, etc., it is facing unprecedented pressure to improve environmental quality in the early stage of the battle against environmental pollution. The primary pollutants are mainly particulate matter. PM for urban scale 2.5 The reconstruction of high-resolution spatio-temporal distribution is an important basis for fine-grained air quality control. Real-time discovery and location of high-potential pollution sources will help the city's air pollution prevention and control work.

[0114] This embodiment utilizes iterative filling-gradient booster algorithm (II-GBM), based on the XGBoost machine learning calculation module, for the ground PM monitored by fixed stations and sensors 2.5 Concentration, as well as the multi-angle atmospheric correction algorith...

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Abstract

The invention relates to the field of atmospheric pollutant space-time distribution calculation, and discloses a fine particulate matter (PM2.5) comprehensive domain space-time calculation inference method based on multi-source city big data. According to the method, PM2.5 concentration data of a fixed station and a sensor, satellite remote sensing aerosol optical thickness and other environment covariants are collected, an iteration vacancy filling-machine learning model is established, and the problems of data heterogeneity, space-time insufficient matching, sampling deviation and the like existing in multi-source data fusion are effectively solved. According to the method, multi-source data of a fixed station, a sensor, satellite remote sensing and the like are flexibly and efficiently fused, high-resolution spatial-temporal distribution of PM2.5 can be more accurately reconstructed, a high-resolution spatial-temporal distribution result of PM2.5 hourly concentration of a 1km grid is formed, the method is an important technical basis for realizing fine management and control of air quality, high potential pollution sources can be explored and positioned in real time, and finally, pollution discharge can be monitored and controlled in a targeted manner.

Description

technical field [0001] The invention relates to the field of calculating the temporal and spatial distribution of atmospheric pollutants, specifically PM based on multi-source urban big data 2.5 A comprehensive domain spatiotemporal computational inference method. Background technique [0002] In recent years, the air quality in most parts of my country has been significantly improved, but the overall pollution level is still high, among which fine particulate matter (PM 2.5 ) is still the primary air pollutant in most areas, mastering PM 2.5 The high-resolution spatio-temporal distribution of atmospheric pollutant concentrations is of great value for fine-grained air quality management. While adding ground monitoring stations, based on multi-dimensional environmental data and machine learning, it is economical and efficient Accurately obtain the high-precision spatio-temporal distribution of atmospheric pollutant concentrations (such as 1km grid PM 2.5 hourly concentrati...

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Application Information

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IPC IPC(8): G06F17/10G06N5/04
CPCG06F17/10G06N5/041
Inventor 詹宇唐蝶付建博王春迎李涛李春圆刘莘义朱瑢昕马红楠马景金
Owner SICHUAN UNIV
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