Motor-vehicle tail-gas concentration prediction method of LSTM-based deep space-time residual-network

Abstract

The invention provides a motor-vehicle tail-gas concentration prediction method of an LSTM-based deep space-time residual-network. Powerful data nonlinear-approximation capability and self-learning capability of a deep learning method are utilized for establishing an LSTM-based depth-space residual-network structure, space-time prediction of urban motor-vehicle tail-gas concentration is realized,urban motor-vehicle tail-gas space-time data and external influence factor data are collected, the urban motor-vehicle tail-gas space-time data are used for storage, training and prediction, two-dimensional prediction analysis of time and space is directly carried out, external conditions of weather, holidays and the like are considered at the same time, and a fully-connected two-layer network isused for simulating external influence factor characteristics to enable a prediction result to be more comprehensive and accurate.

Description

technical field [0001] The invention belongs to the technical field of environmental monitoring, and relates to a method for predicting the concentration of motor vehicle exhaust, in particular to a method for predicting the concentration of motor vehicle exhaust based on LSTM deep spatio-temporal residual network. Background technique [0002] With social development and urban progress, in recent years, the number of motor vehicles in urban areas has continued to increase, and many social problems have emerged, such as serious urban traffic congestion, increased traffic accidents, motor vehicle exhaust pollution, drunk driving, etc. In big cities such as Beijing, Shanghai, and Guangzhou, motor vehicles have become the largest source of pollutants such as carbon monoxide, nitrogen oxides, and hydrocarbons. Since the emission of automobile exhaust gas is mainly between 0.3m and 2m, which happens to be within the breathing range of the human body, the health damage to the huma...

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Problems solved by technology

[0004] Exhaust emissions from motor vehicles are highly nonlinear, and the concentration of pollutants is affected by multiple factors in the surrounding environment, including meteorological conditions, air environment, geographical environment, road conditions, traffic flow factors, etc. Prediction and prediction of pollutant concentration in a single dimension, the prediction effect is not ideal

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