A method and a system for predicting the tail gas pollution distribution in a city road network

Abstract

The invention provides a method for predicting the tail gas pollution distribution in a city road network. The method comprises the following steps: acquiring multi-source heterogeneous data; carryingout stack-type self-encode features dimension reduction, and constructing a multi-layer sparse self-encoder network structure to extract the features of the multi-source heterogeneous data; generating sequential data based on spatio-temporal semi-supervised learning; pre-training a deep spatio-temporal network model replacing the corrected model data with the telemetry data of the real monitoringpoints, and re-training the corrected regional tail gas emission prediction model; determining the weighted parameters of the model to obtain a deep spatio-temporal network model, and inputting the multi-source heterogeneous data t to obtain a predicted regional tail gas pollution emission result. The invention is based on a stack-type self-encoder dimension reduction feature extraction method, which can learn essential feature mapping between road network information, meteorological information, traffic flow information, POIs information and regional tail gas emission, and can realize higherprecision regional tail gas prediction on real telemetry data.

Description

technical field [0001] The invention relates to the technical field of tail gas pollution prediction in urban areas, in particular to a method and system for predicting distribution of tail gas pollution in an urban road network. Background technique [0002] The urban road network refers to the network structure composed of roads with different functions, grades and locations within a city, with a certain density and an appropriate form. With the increase of the number of motor vehicles in the urban road network and the removal of industrial pollution sources, motor vehicle exhaust emissions have gradually become the main source of urban air pollution. The main pollutants of automobile exhaust are carbon monoxide CO and carbon dioxide CO. 2 , Nitrogen oxide NO 2 Wait. A large number of studies have shown that residents living in living areas close to main roads, due to long-term exposure to exhaust pollution, have a greatly increased probability of suffering from asthma, ...

AI Technical Summary

Problems solved by technology

[0003] Due to the high construction and maintenance costs of vehicle remote sensing monitoring equipment in the city, the monitoring equipment points are sparse, resulting in a high cost of obtaining monitoring data

Moreover, due to equipment failures, the data incompleteness caused by the lack of monitoring data for a period of time also brings challenges to the prediction of exhaust emissions in urban areas.

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