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<p>This paper combines the actual situation of security management in colleges and universities, and analyzes the demand for security innovative management platform in colleges and universities to facilitate teachers and students to live happily and research in a comfortable and modernized campus. According to the different functionality, the innovative campus system can be divided into the 3D campus roaming navigation module based on VR technology, the intelligent attendance module based on the combination of convolutional neural network and face recognition, and the induced parking module based on ant colony algorithm. The structure of each module and the operation process are all described in detail. Simulation experiment analysis is used empirically to analyze the intelligent campus management platform. The results show that the method of this paper has an average pass rate of about 89.55% for a given number of vehicles, and compared with the pre-optimization period, the waiting time for parking has been reduced by 19.5%, with an optimization rate range of 72.37%. Even after the optimization of the method in this paper can not wholly avoid the parking wait, but it can reduce the parking wait time, which for the parking lot, can ease the parking congestion in the peak time. The theoretical guidance provided by this study is used to construct a more brilliant campus, promoting the development of security and intelligent management in colleges and universities.</p>
</abstract>

This paper combines the actual situation of security management in colleges and universities, and analyzes the demand for security innovative management platform in colleges and universities to facilitate teachers and students to live happily and research in a comfortable and modernized campus. According to the different functionality, the innovative campus system can be divided into the 3D campus roaming navigation module based on VR technology, the intelligent attendance module based on the combination of convolutional neural network and face recognition, and the induced parking module based on ant colony algorithm. The structure of each module and the operation process are all described in detail. Simulation experiment analysis is used empirically to analyze the intelligent campus management platform. The results show that the method of this paper has an average pass rate of about 89.55% for a given number of vehicles, and compared with the pre-optimization period, the waiting time for parking has been reduced by 19.5%, with an optimization rate range of 72.37%. Even after the optimization of the method in this paper can not wholly avoid the parking wait, but it can reduce the parking wait time, which for the parking lot, can ease the parking congestion in the peak time. The theoretical guidance provided by this study is used to construct a more brilliant campus, promoting the development of security and intelligent management in colleges and universities.

The design of university security intelligent management platform based on VR technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper combines the actual situation of security management in colleges and universities, and analyzes the demand for security innovative management...

The design of university security intelligent management platform based on VR technology

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 18. Apr. 2024

Akzeptiert: 25. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-0893

Schlüsselwörter
VR technology, Convolutional neural network, Face recognition, Ant colony algorithm, Innovative management platform

© 2024 Zhenhua Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The design of university security intelligent management platform based on VR technology

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 18. Apr. 2024

Akzeptiert: 25. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-0893

SchlüsselwörterVR technology, Convolutional neural network, Face recognition, Ant colony algorithm, Innovative management platform

© 2024 Zhenhua Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
VR technology, Convolutional neural network, Face recognition, Ant colony algorithm, Innovative management platform