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Smart city air quality high-precision measurement method

An air quality and air quality technology, applied in neural learning methods, predictions, biological neural network models, etc., can solve problems such as inability to warn, system calculation speed cannot meet requirements, and monitoring values ​​cannot accurately reflect air quality conditions, etc. The effect of accuracy

Active Publication Date: 2019-12-03
CENT SOUTH UNIV
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AI Technical Summary

Problems solved by technology

[0003] At this stage, in general, only one set of air quality detection sensors in the area is set up, and the scope of work is relatively large, and the monitoring values ​​often cannot accurately reflect the air quality conditions of all locations within the monitoring range
However, when collecting redundant designs in the area and arranging a large number of detection sensors, the calculation speed of the system cannot meet the requirements, and it is impossible to make effective early warnings for the future

Method used

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Embodiment Construction

[0029] Step 1: Data collection.

[0030] The sensor groups at each monitoring point in the area start to work to collect initial air quality data. The collection of air quality data is based on the existing particle concentration detection sensors and gas component concentration analysis sensors. The sampling interval is 5 minutes.

[0031] The air quality data includes: PM2.5 concentration, PM10 concentration, SO2 concentration, NO2 concentration, O3 concentration and CO concentration. In particular, the initial startup needs to collect the geographical location information of the monitoring point, that is, the precise latitude and longitude coordinates of the site.

[0032] In particular, the sensors in this area adopt a redundant design, and a large number of detection sensor group monitoring sites are evenly spaced within the spatial range, and the distance between adjacent monitoring points is 1KM. Adjacent monitoring stations, using the air quality data of these 3 stati...

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Abstract

The invention discloses a smart city air quality high-precision measurement method, which improves the accuracy of regional air quality measurement from the perspectives of sensor spatial arrangementoptimization, air quality prediction and correction and the like, and conforms to the perception measurement of human bodies at different positions in a region on the air quality. The diffusion factors of particulate matters and part of gas components are considered, the air quality sensor groups in the region should be redundantly arranged at different places, meanwhile, the influence of different gas component changes on future air quality data is considered, the future air quality data is accurately predicted, and the most accurate measurement result of the regional air quality is obtained.The abnormal condition of the sensor can be checked, and enough early warning time is provided for air pollution.

Description

technical field [0001] The invention relates to the field of air quality detection and early warning, in particular to a high-precision measurement method for air quality in a smart city. Background technique [0002] The problem of air quality has become the focus of attention of various countries in recent years. Harmful fine particles produced by waste and exhaust gas cause atmospheric pollution, which are well-known PM2.5 and PM10. The problem of air quality will not only cause permanent harm to human health, but also adversely affect the ecological system and social production. Therefore, the monitoring and management of air quality can solve the impact of air pollution to a certain extent. [0003] At present, there is usually only one set of air quality detection sensors in the area, and the scope of work is relatively large, and the monitoring values ​​often cannot accurately reflect the air quality conditions of all locations within the monitoring range. However,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06N3/08G06N3/00G06K9/62
CPCG06Q10/04G06Q50/26G06N3/084G06N3/006G06N3/044G06N3/045G06F18/25
Inventor 刘辉徐一楠李燕飞
Owner CENT SOUTH UNIV
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