期刊文献+

船舶营运大数据挖掘与应用思考 被引量:17

Thoughts on Ship Operation Big Data Mining and Application
下载PDF
导出
摘要 船舶营运大数据来源于航运信息平台和营运船舶监测,通过对这些海量数据的分析与挖掘,能发现很多有价值的信息与规律,可应用于多载况下船舶功率与航速评估、船舶能效设计指数(EEDI)实船验证方法的完善、MRV机制实施的技术支持;风浪对航速的影响研究;节能技术的节能效果评价;船舶进坞清污最佳时机分析;船舶设备运行的精细化管理等,对于促进造船和航运业的技术进步具有重要的意义。 The ship operation big data comes from the shipping information platform and ship operation monitoring. Through the analysis and mining of these massive data, many valuable information and regularity can be discovered, which can be used in many aspects, such as the evaluation of engine power and ship speed under various loading conditions, the improvement of ship energy efficiency design index(EEDI) full scale sea trial method, the technical support of MRV mechanism application, the study of the influence of wind and wave on ship speed, the evaluation of energy saving technology effect, the optimal ship dock cleaning timing, the refined management of ship equipment operation, and so on. So it has an important significance for promoting the technical progress in shipbuilding and shipping industry.
出处 《船舶与海洋工程》 2015年第1期5-8,共4页 Naval Architecture and Ocean Engineering
关键词 大数据 数据挖掘 船舶能效设计指数 营运船舶 功率航速 big data data mining ship energy efficiency index ship in operation power and speed
  • 相关文献

参考文献12

  • 1Barwick H. The four Vs of Big Data [N/OL]. COMPUTERWORLD. (2011-08-05) [2014-08-20]. http://www. computerworld.com.au/article/396198/iiis_four_vs_data/.
  • 2Linco.一文认识并读懂大数据[N/OL].36大数据.(2013-10-31)[2014-08-20].http://www.36dsj.com/archives/4203.
  • 3MUNIN. Munin Brochure. http://www.unmanned-ship.org/munin/wp-eontent/uploads/2013/01/MUNIN-Brochure.pdf.
  • 4钢联资讯.日本船舶着手“大数据路标”工作[N/OL].物联网世界.(2014-07-21)[2014-08-20].http://www.iotworld.com.cn/html/News/201407/936a2efl58665dd7.shtml.
  • 5何山,马云涌.我国航运企业信息化发展趋势及战略选择[J].武汉理工大学学报(信息与管理工程版),2010,32(5):782-786. 被引量:6
  • 6苏敏.大数据时代航运业的转型发展[N/OL].中国国际海运网.2013-06-24(2014-08-20)[2014-08-20].http://gss2012.shippingchina.com/opening/detail/id/11.html.
  • 7马云涌.航运企业的船岸信息一体化IT架构[N/OL].百度文库.http://wenku.baidu.com/link?url=MNttm5Xfl0IFkGPyoBW8T9FC9noZ6DfchTzlCx9I-Kp70lbamb_-LdQ-0NoN3ykja8ul6U-954YVctT6YqSOHJ60M7rsA0qKmARlwMMgrIq.
  • 8Rahra,E., Do,H.H. Data Cleaning'. Problems and Current Approaches[J]. IEEE Data Engineering Bulletin, 2000,23(4): 3-13.
  • 9王曰芬,章成志,张蓓蓓,吴婷婷.数据清洗研究综述[J].现代图书情报技术,2007(12):50-56. 被引量:75
  • 10郭志懋,周傲英.数据质量和数据清洗研究综述[J].软件学报,2002,13(11):2076-2082. 被引量:264

二级参考文献67

共引文献350

同被引文献121

  • 1夏利清,范佘明.实船测试航速修正方法评述[J].船舶工程,2005,27(6):49-51. 被引量:4
  • 2李珩,李育学,马茂.主成分分析在柴油机运转状况描述中的应用[J].船海工程,2005,34(6):34-37. 被引量:1
  • 3杨善林,李永森,胡笑旋,潘若愚.K-MEANS算法中的K值优化问题研究[J].系统工程理论与实践,2006,26(2):97-101. 被引量:188
  • 4李妍,赖伟行."海事达沃斯"羊城再办"-带-路"成全场爆点[N].广州日报,2015-11-07(2).
  • 5KWON O, LEE N, SHIN B. Data quality manage- ment, data usage experience and acquisition inten- tion of big data analytics [J]. International Journal of Information Management, 2014, 34 (3) 387- 394.
  • 6JIN Xiaolong, WAH BE W, CHENG Xueqi, et al. Significance and challenges of big data research [J]. Big Data Research, 2015(2) : 59-64.
  • 7KAMBATLA K, KOLLIAS G, Vipin Kumar, et al. Trends in big data analyties [J]. Journal of Par- allel and Distributed Computing, 2014, 74 (7) 2561-2573.
  • 8HUANG D, ZHAO D, WEI L, et al. Modeling and analysis in marine big data. Advances and challen- ges [J]. Mathematical Problems in Engineering, 2015(2015) . 1-13.
  • 9张西陆,黄少宏.南沙将成“-带-路”最重要枢纽[N].南方日报,2015-11-06(aa3).
  • 10CHEN H, MOAN T, VERHOEVEN H. Effect o[ DGPS failures on dynamic positioning of mobile drilling units in the North Sea[J]. Accident Anal- ysis b- Prevention, 2009, 41(6): 1164-1171.

引证文献17

二级引证文献53

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部