Research on nearly 14-day trajectory tracking warning system

Zichen Yao; Zhiyong Zhang; Mingyi Liu; Hongmei Hu

doi:10.1117/12.2636399

6 May 2022 Research on nearly 14-day trajectory tracking warning system

Zichen Yao, Zhiyong Zhang, Mingyi Liu, Hongmei Hu

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217627 (2022) https://doi.org/10.1117/12.2636399
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Studies on prevention and control practices show that the mobility of personnel is the main reason for the spread of the epidemic. The epidemic prevention and control work is gradually becoming normalized. By giving full play to scientific and technological advantages, mining the intrinsic value of information and analyzing and utilizing trajectory data, transmission can be effectively avoided from the source, which is of great significance for epidemic prevention and control. This paper realizes the analysis of personal trajectory data through the research of point aggregation algorithm and visual field following algorithm, processes the track big data by using big data technologies such as Kafka and Spark, displays the track data according to WeChat applet and visualization large screen, and designs the individual trajectory and track big data visualization end system based on the trajectory data. The experimental results show that the system can display the whereabouts in real time, which has good practicability and versatility, and has positive reference significance for helping residents to travel and supporting the government's decision to fight the epidemic.

Citation Download Citation

Zichen Yao, Zhiyong Zhang, Mingyi Liu, and Hongmei Hu "Research on nearly 14-day trajectory tracking warning system", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217627 (6 May 2022); https://doi.org/10.1117/12.2636399

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available