Education, Science, Technology, Innovation and Life
Open Access
Sign In

Construction and Key Technology of a Rapid Response Platform for Emergency Decision-Making under the Background of Big Data

Download as PDF

DOI: 10.23977/jsoce.2021.030212 | Downloads: 14 | Views: 869

Author(s)

Xinyi Zhang 1, Tong Shang 1

Affiliation(s)

1 School of Public Administration, Xiangtan University, Xiangtan 411105, Hunan

Corresponding Author

Xinyi Zhang

ABSTRACT

The development and implementation of big data has provided people with many conveniences. Due to the endless emergence of various emergencies in our lives, the establishment of a rapid response platform for emergency decision-making will help prevent and reduce the impact of natural disasters, public health incidents and social security accidents. Therefore, this article focuses on the construction of a rapid response platform and key technologies for emergency decision-making in the context of big data. This article analyzes and gives examples of the key technologies of the rapid response platform for emergency decision-making, and reveals the application methods of this technology. This paper starts from the needs of emergency decision-making, and designs the key functional modules of the platform. In order to verify the feasibility and reliability of the system, this paper has completed the functional test and non-functional test of the system. The test results show that the response time of the system function is less than 3s, and the CPU utilization rate and memory occupancy rate are between 20% and 52%. It can be seen that the system basically meets the design requirements.

KEYWORDS

Big Data, Emergencies, Emergency Management Platform, Emergency Decision-Making

CITE THIS PAPER

Xinyi Zhang, Tong Shang. Construction and Key Technology of a Rapid Response Platform for Emergency Decision-Making under the Background of Big Data. Journal of Sociology and Ethnology (2021) 3: 91-99. DOI: http://dx.doi.org/10.23977/jsoce.2021.030212.

REFERENCES

[1] Liu C , Zeng Q , Duan H , et al. E-Net Modeling and Analysis of Emergency Response Processes Constrained by Resources and Uncertain Durations[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 45(1):84-96.
[2] Farhad L , Abbas R , Mohsen K . A Multi-Element Approach to Location Inference of Twitter: A Case for Emergency Response[J]. International Journal of Geo-Information, 2016, 5(5):56.
[3] Avvenuti M , Cresci S , Marchetti A , et al. Predictability or Early Warning: Using Social Media in Modern Emergency Response[J]. IEEE Internet Computing, 2016, 20(6):4-6.
[4] REdD S C , FriEdEn T R . CDC's Evolving Approach to Emergency Response[J]. Health Secur, 2017, 15(1):41-52.
[5] Jian K , Zhang J , Bai Y . Modeling and evaluation of the oil-spill emergency response capability based on linguistic variables[J]. Marine Pollution Bulletin, 2016, 113(1-2).
[6] Cao H , Li T , Li S , et al. An integrated emergency response model for toxic gas release accidents based on cellular automata[J]. Annals of Operations Research, 2017, 255(1-2):617-638.
[7] Shanlin, Mao, Wenpeng, et al. [Design and implementation of an integrated information platform for emergency interconnection].[J]. Zhonghua wei zhong bing ji jiu yi xue, 2019, 31(7):884-889.
[8] Yefeng, Zhang, Hui. Enhancing Knowledge Management and Decision-Making Capability of China's Emergency Operations Center Using Big Data[J]. Intelligent Automation and Soft Computing, 2018, 24(1):107-114.
[9] Wang J , Wu Y , Yen N , et al. Big Data Analytics for Emergency Communication Networks: A Survey[J]. IEEE Communications Surveys & Tutorials, 2016, 18(3):1758-1778.
[10] Li F , Reiss M . Building emergency operations and big data[J]. Asia Pacific Fire Magazine, 2017(61):66-68.
[11] Elsayad A S , Eldesouky A I , Salem M M , et al. A Deep Learning H2O Framework for Emergency Prediction in Biomedical Big Data[J]. IEEE Access, 2020, PP(99):1-1.
[12] Dagaeva M , Garaeva A , Anikin I , et al. Big spatio-temporal data mining for emergency management information systems[J]. IET Intelligent Transport Systems, 2019, 13(11):1649-1657.

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.