Remote sensing monitoring system for automotive exhaust emission of urban road network

Abstract

The invention discloses a remote sensing monitoring system for automotive exhaust emission of an urban road network. The system is mainly composed of a remote measurement device layer, a site selection and position arrangement layer, and a data processing layer. Through mobile, horizontal and vertical exhaust remote measurement devices, real-time data of the automotive exhaust emission in running is obtained; by adopting an advanced site selection and position arrangement method, the remote measurement devices are scientifically networked; and in combination with external data of weather, traffic, geographic information and the like, the real-time remote measurement data of the automotive exhaust emission is subjected to intelligent analysis and data mining by adopting big data processing and analysis technologies such as deep learning and the like, and key indexes and statistical data with optimal identification performance are obtained, so that effective support is provided for government departments to make related decisions.

Description

technical field [0001] The invention specifically relates to a remote-sensing monitoring system for exhaust emission of motor vehicles in an urban road network, and belongs to the technical field of environmental monitoring. Background technique [0002] Due to the rapid growth of the number of motor vehicles in the country in recent years, traffic congestion in urban areas and various places has become increasingly serious, and the quality of the atmospheric environment has also shown a trend of deterioration. The monitoring of motor vehicle exhaust pollution is facing severe challenges. Motor vehicle exhaust is an important pollutant of urban air pollution and the main source of urban air pollution. In terms of urban environmental pollution monitoring, motor vehicle exhaust monitoring accounts for an increasing proportion and has become an important part of environmental protection and management. . Therefore, it is necessary to establish a complete system to monitor and ...

AI Technical Summary

Problems solved by technology

Invention patent "multi-lane motor vehicle exhaust PM2.5 telemetry device" (application number: 201310655684.4) and "multi-lane motor vehicle exhaust remote measurement device" (application number: 200910241681. The integration of modules such as wind direction detection unit, road condition recognition unit, license plate recording unit, and control unit realizes the exhaust gas telemetry equipment for different pollutants. It is essentially a horizontal exhaust gas telemetry equipment that requires only one vehicle to pass in a short period of time. condition of the monitoring point, that is, it is less suitable for multi-lane traffic with large traffic flow

This method combines the numerical forecasting method with the statistical forecasting method, and to some extent overcomes the shortcomings of the two forecasting methods when they are used alone, that is, the numerical forecasting method is better for non-heavy pollution periods, but for complex weather conditions. The forecast error of pollutant transport, diffusion, and transformation during heavy pollution periods is as high as 400%; while the statistical forecast method has higher accuracy and calculation efficiency, but is highly dependent on historical data and lacks certain physical meaning

Invention patents "A PM25 Concentration Prediction Method Based on Eigenvector and Least Squares Support Vector Machine" (Application No.: CN201410201739.9), "A Method for Predicting Urban Air Quality Level Based on Multi-field Features" (Application No.: CN201410452557 .9) and "A Method for Predicting the Concentration of Air Pollutants" (Application No.: CN201510767342.0) both realize the forecasting of the concentration of air pollutants at present or at a certain moment in the future based on historical air pollutant concentration monitoring data, but they have a common The problem is: the forecasting method is relatively complicated, the utilization and integration of historical data need to be strengthened, and the generalization ability and forecasting accuracy rate need to be improved

In the calculation process, this method not only needs the speed and acceleration data of the vehicle, but also needs the input of data such as the basic emission factor and the emission rate in the MOVES database, so the calculation process is more complicated; Does not take into account the impact of weather conditions on motor vehicle emissions

[0009] Restricted by economic level and scientific research ability, my country's air quality monitoring work started relatively late. After more than 40 years of development since the 1970s, many provinces and cities in my country have established air quality monitoring systems. There is still a lot of room for improvement in the detection of roadside air pollutant concentrations

The main reasons are as follows: 1. At present, the equipment used to detect the concentration of air pollutants on the roadside is mainly an air monitoring station, which is expensive and can only be equipped with a limited number of stations in the city. and the surrounding environment are complex, the feasibility of real-time prediction of roadside air pollutant concentrations in various areas of the city through detection equipment is very low

2. Based on the low feasibility of comprehensive detection of equipment, scholars from various countries try to solve this problem through prediction methods. At present, in the research on the concentration of roadside air pollutants at home and abroad, the methods adopted are mainly divided into two categories: 1. Gaussian model and Subsequent series of line source models based on Gaussian model, as described in "Urban Traffic Planning Theory and Its Application" (Southeast University Press, 1998) by Wang Wei et al. Different models, and the accuracy of the model for complex roads is not high; 2. Roadside pollutant concentration detection based on neural network, such as Yang Zhongzhen et al. in "Neural Network Based Road Traffic Pollutant Concentration Prediction" (Jilin University Journal (Work ), 2007, No. 37), this type of method can identify the simple nonlinear relationship between the input and output data, but it has great limitations in learning the more essential feature mapping between the input and output data. Each neural network can only represent the relationship between one pollutant and the input, which has great defects in real-time and mobility

[0010] Although the domestic remote sensing monitoring method has slowly begun to develop and popularize, its follow-up work is still relatively blank

Although relevant data platforms have been established in many places, the data storage is scattered, cannot be effectively combined, and has not been managed uniformly

At the same time, the obtained data lacks diversity and is not closely integrated with data such as car owners, real-time weather, and current road conditions.

All of these have caused great difficulties for subsequent data analysis and environmental protection policy proposals.

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