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Construction of Tourism Area Capacity Early Warning System Based on Internet of Things Technology.
- Source :
- Journal of Sensors; 8/16/2022, p1-8, 8p
- Publication Year :
- 2022
-
Abstract
- In order to make up for the shortage of smart tourism construction highlighted by public tourism safety accidents due to the bearing capacity of scenic spots, this paper proposes a tourism area capacity early warning system based on the Internet of Things technology. By combining the main characteristics of a park and a place, this paper determines the composition of its tourism capacity and the corresponding calculation methods. At the same time, in order to control the number of tourists in peak hours within a reasonable range, the peak constraint method is proposed to improve the traditional algorithm of daily space capacity; then this paper calculates the tourism capacity of a park and a place by combining the relevant data obtained by investigation and observation methods. The experimental results show that according to the given number, the instantaneous tourism capacity of a park can be accurately calculated as 11841 person times/day and the daily tourism capacity as 21313 person times/day. The instantaneous tourism capacity of a certain place is 1066 person times/day, and the daily tourism capacity is 9594 person times/day. The construction of the early warning system of the tourism area based on the Internet of Things can effectively solve the problems such as public tourism safety accidents caused by the problem of carrying capacity. It will play an important role in the customized services of tourists, the innovation of scenic spot business processes, and the integration of tourism enterprise resources and provide data support for the early warning system. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1687725X
- Database :
- Complementary Index
- Journal :
- Journal of Sensors
- Publication Type :
- Academic Journal
- Accession number :
- 158544122
- Full Text :
- https://doi.org/10.1155/2022/8249032