Big data-based scenic spot visitor flow statistic analysis system

Abstract

The invention discloses a big data-based scenic spot visitor flow statistic analysis system. The system comprises a data server, a visitor flow image information subsystem, a WiFi probe information subsystem, a data storage unit and a data display unit; the visitor flow image information subsystem, the WiFi probe information subsystem, the data storage unit, and the data display unit are all connected with the data server; the data server is used for performing comparative analysis on data acquired by the visitor flow image information subsystem and the WiFi probe information subsystem; and comparative analysis results are displayed by the data display unit. With the system of the invention adopted, the improvement of the accuracy of visitor flow statistics in scenic spots can be benefitted, and limitations brought by a single statistical method can be avoided.

Description

technical field [0001] The invention relates to the field of the Internet of Things, in particular to a big data-based statistical analysis system for passenger flow in scenic spots. Background technique [0002] With the increase in the number of tourists, the smart facilities in scenic spots also need to be constantly updated. At present, many scenic spots generally cover a large area, and the number of tourists is unevenly distributed. There are often too many tourists in certain areas, which leads to congestion of tourists in this area, causing safety hazards, and even reaching the upper limit of passenger flow in the scenic area as a whole. However, the administrators of the scenic spot cannot grasp the passenger flow information and distribution information of the scenic spot in time, and cannot make timely emergency measures, which may easily cause safety accidents and affect the tourist experience of tourists. Most of the current passenger flow recognition systems a...

AI Technical Summary

Problems solved by technology

At present, many scenic spots generally cover a large area, and the number of tourists is unevenly distributed. There are often too many tourists in certain areas, which leads to congestion of tourists in this area, causing safety hazards, and even reaching the upper limit of passenger flow in the scenic area as a whole.

However, the administrators of the scenic spot cannot grasp the passenger flow information and distribution information of the scenic spot in time, and cannot make timely emergency measures, which may easily cause safety accidents and affect the tourist experience of tourists.

Most of the current passenger flow recognition systems are based on image recognition technology, but they have the defect of a single statistical method, which cannot fully reflect the passenger flow situation of scenic spots

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