A multi-terminal pushing method for emergency information in a smart city based on deep learning

Zheng Xie and Xin Su

International Journal of Information Technology and Management, 2022, vol. 21, issue 1, 80-96

Abstract: In order to overcome the problems of large information identification error and high error rate in traditional emergency information push methods, a multi-terminal push method of emergency information in a smart city based on deep learning is proposed. The convolution neural network based on deep learning is used to identify the real-time collection results of intelligent city operation monitoring information, and the combination of softmax loss function and centre loss function is used to reduce the identification error. Through the process of retrieval, adaptation and detection of crisis cases, crisis types are determined and emergency decision-making is selected. Based on XMPP protocol, a multi-terminal push network of emergency information is constructed to push monitoring information and emergency decision to each push terminal. The experimental results show that the information recognition rate of the proposed method is higher than 99%, and the emergency information push result has high precision.

Keywords: deep learning; smart city; emergency information; multi-terminal; push; case reasoning. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=121339 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:21:y:2022:i:1:p:80-96

Access Statistics for this article

More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().