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Mask Payment App System Based on Fine Dust Measurement

  • Journal of Knowledge Information Technology and Systems
  • Abbr : JKITS
  • 2020, 15(5), pp.739-747
  • DOI : 10.34163/jkits.2020.15.5.017
  • Publisher : Korea Knowledge Information Technology Society
  • Research Area : Interdisciplinary Studies > Interdisciplinary Research
  • Received : August 21, 2020
  • Accepted : October 13, 2020
  • Published : October 31, 2020

Jung-Keun Kim 1 Ji-Hye Koo 1 Yong Do Her 1

1건양대학교

Accredited

ABSTRACT

Recently, various solutions have been proposed according to the rapid increase in the level of fine dust. However, technologies such as artificial rainfall and large-sized air purifiers that have been developed or studied to date have the disadvantage of lack of feasibility. Rather, the use of a mask, which has become more important and convenient due to the corona crisis, is emerging as one of the most feasible measures among domestic fine dust countermeasures. Therefore, this paper aims to develop an app system that enables people to efficiently and conveniently pay or purchase masks. In addition, through the developed app system, a response system that allows users to know in real time when a disaster alert such as fine dust occurs can be built. When a fine dust disaster alarm occurs, information on fine dust in the area can be immediately checked in real time using a mobile phone app. When a fine dust alarm occurs, QR code issuance is activated, and through a map in the application, the location of the nearest mask dispensing station, phone number, and mask inventory can be checked in real time. When a mask is provided to a user, the mask dispensing station can modify the mask-related data in the corresponding dispensing station database in real time, and various statistics related to the mask can be checked in real time on the web page and in the dedicated application for administrators. In the future, additional functions will continue to be supplemented by securing disease information and data on various epidemic diseases such as cold and flu, and the algorithm will be continuously improved to more accurately predict the level of fine dust.

Citation status

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