Investigation on the Construction of Urban Intelligent Emergency Management System Based on Data Mining Technology

Urban management is one of the most prominent problems in modern and contemporary social governance. With the scale of the city, the flow of industries and population becomes larger and larger, and the city takes on more and more risks and emergencies. In order to minimize losses and prevent in adva...

Full description

Saved in:
Bibliographic Details
Published in:Mobile information systems 2022-09, Vol.2022, p.1-14
Main Author: Jiang, Faming
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Crowdsourcing
Data mining
Data processing
Datasets
Emergency management
Emergency preparedness
Emergency response
Emergency warning programs
Internet of Things
Optimization
Search strategies
Smart cities
Supervision
Urban planning
User needs
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Urban management is one of the most prominent problems in modern and contemporary social governance. With the scale of the city, the flow of industries and population becomes larger and larger, and the city takes on more and more risks and emergencies. In order to minimize losses and prevent in advance, early drills are often the best way to prevent emergencies. This paper aims to use data mining technology to design a smart city emergency management system to achieve the role of early warning of emergency time and emergency response to emergency events. In response to this, this paper proposes the EIM-DS algorithm, an optimized data mining algorithm based on high-utility itemset mining. This paper improves the threshold setting and data classification method, which greatly develops the execution efficiency of the algorithm. In addition, an intelligent emergency management system is also designed based on the optimization algorithm. The system focuses on analyzing enterprise data, emergency data and emergency command data. The combination of the three data can evaluate the order of the entire city. The experimental results of this paper prove that the calculation time of the algorithm in this paper is about 50% lower than that of other algorithms under different datasets, which has better efficiency, and the designed system can run more smoothly through black-box and white-box experiments.
ISSN:1574-017X
1875-905X
DOI:10.1155/2022/2703096