Atmospheric pollution monitoring and management method as well as system based on high-density deployment of sensors

Abstract

The invention provides atmospheric pollution monitoring and a management method as well as a system based on high-density deployment of sensors. The method comprises the steps of acquiring status information in a deployment area, and deploying a plurality of sensors with high density in the deployment area; acquiring sensor data of the plurality of sensors, and performing joint correction on the sensor data; inferring atmospheric pollutant data of location points where the sensors are not deployed in space by utilizing a gauss inference model; performing forecast and analysis on atmospheric pollutant data of the location points with the deployed sensors and the location points where the sensors are not deployed by adopting a prediction model in time series; sending the atmospheric pollutant data to a monitoring center, and monitoring the atmospheric pollution state in the whole area in real time by monitoring personnel. According to the atmospheric pollution monitoring and management method as well as the system based on the high-density deployment of the sensors provided by the invention, purposes of real-time monitoring and evidence obtaining, quantized rating and fine management are achieved, perfect visual display is provided, and integrated management that pollution sources are monitored and discovered, evidences are obtained, and cooperation of enforcement is performed is realized.

Description

technical field [0001] The invention relates to the technical field of Internet of Things and pollutant monitoring, in particular to an air pollution monitoring and management method and system based on large-density deployed sensors. Background technique [0002] In recent years, the public has paid more and more attention to air pollution, from pollution data to family and self-safety protection. The government and environmental protection departments are faster and earlier to include air pollution control in the scope of work. As early as 2012, the Ministry of Environmental Protection and 31 provinces (autonomous regions, municipalities) jointly signed the "Air Pollution Prevention and Control Target Responsibility Letter" clearly stated that Beijing, Tianjin, and Hebei will conduct comprehensive analysis and treatment in an integrated manner, and it is expected to achieve PM2 in 2017. .5 target of 25% reduction in average annual concentrations. It can be seen that the ...

AI Technical Summary

Problems solved by technology

[0004] Specifically, although public monitoring stations can provide corresponding statistical analysis and display results, such as common pollution days statistics, pollution trend statistics and prediction judgments, etc., the sparseness of their deployment density makes them useful in the diversity of monitoring scenarios, There are inherent deficiencies in the fine-grained monitoring of the microenvironment

Satellite remote sensing uses meteorological satellites to take pictures of atmospheric clouds twice a day, and then uses image science knowledge to analyze data, but only large-scale pollution information can be obtained, and it cannot truly reflect the real pollution situation at low altitudes. At the same time, scanning and data processing twice a day Make the real-time performance of the data poor

UAV monitoring is suitable for air pollution emergency events, but it is difficult to meet the needs of all-weather monitoring

[0005] In addition, the density of sensors in the existing air pollution monitoring is low and lacks scientific

Specifically, the existing method is to randomly deploy several sensors in a large area, lack of planning for sensor deployment points, resulting in the lack of representativeness of the collected data, which cannot accurately reflect the air pollution data of the current area

[0006] To sum up, the various existing smog monitoring methods have certain problems, which cannot meet the needs of refined management, and the monitoring accuracy and monitoring efficiency are limited.

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