Urban area tail gas pollution prediction method

A pollution prediction and urban area technology, applied in the field of environmental detection, can solve the problem of not taking into account the influence of road network structure information, etc., and achieve the effect of high regional exhaust gas prediction and accurate regional exhaust gas prediction

Pending Publication Date: 2019-11-26
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

The current related work estimates the spatio-temporal distribution of exhaust gas by standardizing the area into a grid, without considering the influence of

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  • Urban area tail gas pollution prediction method
  • Urban area tail gas pollution prediction method
  • Urban area tail gas pollution prediction method

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[0039] In order to further illustrate the features of the present invention, please refer to the following detailed description and accompanying drawings of the present invention. The accompanying drawings are for reference and description only, and are not intended to limit the protection scope of the present invention.

[0040] Such as figure 1 As shown, this embodiment discloses a method for predicting exhaust gas pollution in urban areas, including the following steps S1 to S3:

[0041] S1. Create a training sample set by using the sequence data of the historical exhaust time-space distribution map and the external environment characteristic data;

[0042] S2. Using the training sample set to train the deep spatio-temporal graph convolutional network model to obtain a prediction model of the deep spatio-temporal graph convolutional network;

[0043] S3. Based on the deep spatio-temporal graph convolutional network prediction model, use the external environmental characte...

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Abstract

The invention discloses an urban area tail gas pollution prediction method, which belongs to the technical field of environment detection, and comprises the steps of creating a training sample set byusing historical tail gas space-time distribution diagram sequence data and external environment feature data; training the deep space-time diagram convolutional network model by using the training sample set to obtain a deep space-time diagram convolutional network prediction model; and based on the deep space-time diagram convolutional network prediction model, predicting the regional exhaust emission at the moment t by using the external environment feature data at the current moment t and the historical exhaust space-time distribution diagram sequence data before the moment t-1. Accordingto the method, the exhaust space-time distribution is constructed into the graph structure data by utilizing the road network connectivity data, so that the regional exhaust emission prediction problem is converted into the space-time graph sequence prediction problem, and the regional exhaust prediction with higher precision can be realized on the real telemetry data by adopting the deep space-time graph convolution model.

Description

technical field [0001] The invention relates to the technical field of environmental detection, in particular to a method for predicting tail gas pollution in urban areas. Background technique [0002] In the past few years, with the rapid growth of the number of motor vehicles, the ecological and environmental problems caused by motor vehicle exhaust have attracted widespread attention from the society. Greenhouse gases in exhaust emissions, carbon monoxide (CO), carbon dioxide (CO 2 ), hydrocarbons (HC), nitrogen oxides (NO x ), and solid particulate matter (PM2.5) have also become the main source of urban air pollution. Real-time acquisition of spatial and temporal distribution information of exhaust gas in urban areas is of great benefit to the prevention and control of motor vehicle pollution and environmental protection. If the temporal and spatial distribution of exhaust gas in urban areas can be obtained at any time, it can provide early warning of exhaust polluti...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q10/04G06Q50/26
CPCG06N3/08G06Q10/04G06Q50/26G06N3/045G06F18/214
Inventor 康宇许镇义曹洋李泽瑞吕文君
Owner UNIV OF SCI & TECH OF CHINA
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