Application Research on Internet of Things, Big Data Analysis and Machine Learning Technology in Backup for Disaster Recovery

LIU Wei-jia; LI Bo-quan

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Application Research on Internet of Things, Big Data Analysis and Machine Learning Technology in Backup for Disaster Recovery

更新时间：2026-09-04

- Application Research on Internet of Things, Big Data Analysis and Machine Learning Technology in Backup for Disaster Recovery
- Vol. 35, Issue 12, Pages: 55-58(2018)
- 作者机构：
  
  1. 农信银资金清算中心有限责任公司,北京,100029
  2. 中国科学院机构国家空间科学中心,北京,100190
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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刘伟佳, 李博权. 物联网、大数据分析和机器学习技术在灾备中的应用研究[J]. 微电子学与计算机, 2018, 35(12): 55-58.

LIU Wei-jia, LI Bo-quan. Application Research on Internet of Things, Big Data Analysis and Machine Learning Technology in Backup for Disaster Recovery[J]. 2018, 35(12): 55-58.
刘伟佳, 李博权. 物联网、大数据分析和机器学习技术在灾备中的应用研究[J]. 微电子学与计算机, 2018, 35(12): 55-58. DOI：

LIU Wei-jia, LI Bo-quan. Application Research on Internet of Things, Big Data Analysis and Machine Learning Technology in Backup for Disaster Recovery[J]. 2018, 35(12): 55-58. DOI：

摘要

物联网、大数据分析、机器学习是智能时代的三驾马车

对于科技的智能化发展具有重大意义.本文先对灾备的发展趋势进行了分析

提出"智慧+"灾备的思路

在此基础上

简要分析了物联网、大数据分析、机器学习等关键技术

最后将这些技术应用到灾备系统设计中

提出一种物联网环境下基于大数据分析和机器学习的"智慧+"灾备系统架构

并对该架构的实现原理和功能进行了分析.

Abstract

Internet of things

big data analysis and machine learning are the troika of the intelligent age

which is of great significance to the intelligent development of science and technology. This paper first analyses the development trend of the backup for disaster recovery

then provides the thinking of "wisdom +" backup system for disaster recovery

on this basis

analys briefly the key technologies of the Internet of things

big data analysis and machine learning

finally uses these techniques to the design of backup system for disaster recovery

and provides a kind of "wisdom+" architecture of backup system for disaster recovery based on the big data analysis and machine learning in the environment of Internet of things

also analys the implementation principle and function of this system.

关键词

Keywords

references

Yan B, Huang G. Application of RFID and internet of things in monitoring and anti-counterfeiting for products[J]. International Seminar on Business % Information Management, 2009, 1 (1):392-395. DOI: 392

Lemley J, Bazrafkan S, Corcoran P. Deep learning for consumer devices and services: pushing the limits for machine learning, artificial intelligence, and computer vision[J]. IEEE Consumer Electronics Magazine, 2017, 6 (2) :48-56. DOI: 48

Rosten E, Drummond T. Machine learning for high-speed corner detection[C]// European Conference on Computer Vision, Austria, Graz, 2006, 3951: 430-443.

Alam S, MMR Chowdhury, Noll J. SenaaS: an event-driven sensor virtualization approach for Internet of Things cloud[C]//IEEE International Conference on Networked Embedded Systems for Enterprise Applications. Suzhou, China, 2010: 1-6, DOI: 10.1109/NESEA.2010.5678060.

Yan T, Limin L. Application of internet of things technology in enterprise production safety[C]//Computer Technology % Development, CDAIT Board meeting. Atlanta, Georgia. 2012.

iang H, Zhang Y. Short-term distribution system state forecast based on optimal synchrophasor sensor placement and extreme learning machine[C]// Power % Energy Society General Meeting.IEEE, Boston, USA, 2016.

Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities[J]. Bmc Medical Genomics, 2015, 8(1):1-12. DOI: 1

Liu X, CR Center. The strategy of big data for alibaba group and its practices of credit reference[J]. Credit Reference, 2014, 32(10):10-14. DOI: 10

Ahuja S P, Moore B. State of big data analysis in the cloud[J]. Network % Communication Technologies, 2013, 2 (1):62-68. DOI: 62

Chen S, BaiducomInc. Big data applications and practices of baidu[J]. Big Data Research, 2015(11):104-114. DOI: 104

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