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A city-wide air quality index estimation method based on deep multi-source data fusion

An air quality index and multi-source data technology, applied in data processing applications, calculations, resources, etc., can solve problems such as ignoring air quality correlations, and achieve fast and more accurate predictions

Active Publication Date: 2021-06-25
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

However, the existing air quality estimation methods independently estimate the air quality of each region when modeling urban air quality, ignoring the correlation between the air quality of each region and the correlation between the influencing factors of each region

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  • A city-wide air quality index estimation method based on deep multi-source data fusion
  • A city-wide air quality index estimation method based on deep multi-source data fusion
  • A city-wide air quality index estimation method based on deep multi-source data fusion

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Embodiment Construction

[0024] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and do not limit the protection scope of the present invention.

[0025] see Figure 1 to Figure 10 The method for estimating the city-wide air quality index based on deep multi-source data fusion provided in this embodiment includes the following steps:

[0026] Step 1. Divide a city into several grid units with the same size and side length l, denoted as G, g ij ∈G represents the grid cell at row i, column j.

[0027] Divide a city into several grid units with the same size and side length l, denoted as G, such as image 3 shown. where g ij ∈G represents the grid cell in row i, column j. The present invention assumes that the a...

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Abstract

The invention discloses a method for estimating the city's air quality index based on deep multi-source data fusion. The specific implementation steps are as follows: 1) Construct a feature image based on urban multi-source data at each moment as a sample to obtain a training data set; 2) Use the deep neural network to fuse each feature image to estimate the complete air quality index feature image, and then get the estimated value of the air quality index in each region; The air quality index estimation model is jointly trained with the feature image reconstruction three losses, and the air quality index estimation model after parameter tuning is obtained. The present invention combines deep learning and image fusion to estimate the air quality in various regions of the city, and has broad application prospects in the fields of hygiene and health, environmental governance, urban planning, and the like.

Description

technical field [0001] The invention relates to the field of urban air quality estimation, in particular to a method for estimating the city's air quality index based on deep multi-source data fusion. Background technique [0002] With the continuous advancement of urbanization and industrialization, many cities have problems such as reduced atmospheric visibility and substandard air quality. More and more people are beginning to pay attention to the surrounding air quality. However, the number of air quality monitoring stations in cities is limited, and it is impossible to provide air quality information in any area. Urban air quality estimation can estimate the air quality of any region, which is a very valuable research direction in urban computing, and has broad application prospects in the fields of health, environmental governance, and urban planning. [0003] Traditional urban air quality estimation research generally uses semi-supervised machine learning methods com...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04G06Q10/06
CPCG06Q10/06395G06N3/045G06F18/253G06F18/214
Inventor 陈岭龙晗宇
Owner ZHEJIANG UNIV