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A Method for Urban Regional Air Quality Estimation Based on Collaborative Training

A technology for air quality and urban areas, applied in computing, computer parts, instruments, etc., can solve problems such as inability to achieve good results, and achieve the effect of avoiding model performance degradation

Active Publication Date: 2018-03-27
HANGZHOU SUNKING TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, when the number of air quality monitoring stations is very limited, such supervised methods cannot achieve good results due to the lack of sufficiently diverse labeled training samples.

Method used

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  • A Method for Urban Regional Air Quality Estimation Based on Collaborative Training
  • A Method for Urban Regional Air Quality Estimation Based on Collaborative Training
  • A Method for Urban Regional Air Quality Estimation Based on Collaborative Training

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Embodiment

[0033] Example: such as figure 1 As shown, a method for air quality estimation in urban areas based on collaborative training, including a preprocessing stage, a training stage, and an estimation stage;

[0034] The preprocessing stages include:

[0035] 1) Divide the city into disjoint grids, each grid g has the same length and width, g.A represents the influence area of ​​g, which consists of g and its surrounding eight grids. Such as figure 2 As shown, each small square is a grid, and the area of ​​influence of the shaded small square grid is the large black framed square area. The air quality of each grid area g is regarded as the same, so the size of g should not be too large, for example, it can be set to 1km×1km;

[0036] 2) Find grids containing air monitoring stations and extract hourly air quality and spatial features corresponding to these grids, including traffic-related features F t , POI-related features F p , road network structure-related features F r , ...

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Abstract

The present invention relates to a method for estimating air quality in urban areas based on collaborative training. The present invention makes full use of regional spatial features, such as traffic conditions and road network structures in the area, and is a semi-supervised learning method based on multi-classifier collaborative training. Model the feature vector of the region to learn multiple classifiers; then pruning these classifiers to form the final combined classifier; use the pruned combined classifier model to perform air monitoring for areas without air monitoring stations. quality rating estimates. This method can estimate the air quality level according to the spatial differences between the areas with air monitoring stations and the areas without air monitoring stations under the condition of limited air monitoring stations, and the estimation results are accurate.

Description

technical field [0001] The invention relates to the field of air quality monitoring, in particular to a method for estimating air quality in urban areas based on collaborative training. Background technique [0002] In recent years, air pollution has become more and more serious, and many cities often have smog weather, which has given birth to the strengthening of people's awareness of ecological and environmental protection, and air pollution has attracted more and more attention. In order to monitor air pollutants, the government has established a number of air quality monitoring stations in cities, which are the basic platform for air quality control and reasonable assessment of air quality, and an infrastructure for urban air environmental protection. However, the establishment of an air quality monitoring station requires a certain amount of construction funds, floor space, manpower, etc., so each city has a limited number of air monitoring stations. [0003] For a ce...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/241
Inventor 陈岭王敬昌赵江奇赵丽娜蔡雅雅
Owner HANGZHOU SUNKING TECH CO LTD
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