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期刊信息
  • 主管单位:
  • 上海市教育委员会
  • 主办单位:
  • 上海理工大学、上海市能源研究会、上海电气(集团)总公司
  • 主  编:
  • 陈康民
  • 地  址:
  • 上海市军工路516号
  • 邮政编码:
  • 200093
  • 联系电话:
  • 021-55272843
  • 电子邮件:
  • eribjb@usst.edu.cn
  • 国际标准刊号:
  • 1008-8857
  • 国内统一刊号:
  • 31-1410/TK
  • 邮发代号:
  • 单    价:
  • 5.00
  • 定    价:
  • 20.00
基于边缘云计算的FPSO现场生产运营数据平台
Data platform design for FPSO on-site production and operation based on edge cloud computing
投稿时间:2022-07-20  
DOI:10.13259/j.cnki.eri.2023.01.009
中文关键词:  边缘  云计算  浮式生产储存卸货装置  现场  生产运营  数据平台
英文关键词:edge  cloud computing  floating production storage and offloading  on-site  production and operation  data platform
基金项目:
作者单位
杨波 中海油能源发展股份有限公司采油服务分公司, 天津 300451 
王鑫章 中海油能源发展股份有限公司采油服务分公司, 天津 300451 
萧阳 中海油能源发展股份有限公司采油服务分公司, 天津 300451 
彭程 中海油能源发展股份有限公司采油服务分公司, 天津 300451 
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中文摘要:
      传统浮式生产储存卸货装置(FPSO)现场生产运营数据平台在动态生产运营过程中存在数据采集性能较低的问题,导致数据容量储存能力较差。引入边缘云计算技术,设计了一种新的FPSO现场生产运营数据平台。该平台采用B/S硬件架构,可为硬件与软件的高效运行提供稳定的环境。设计了生产运营数据采集模块,用于分析与挖掘数据潜在信息;依据边缘计算原理,设计了平台边缘云计算层,以获取数据的优先级值;设计了FPSO数据库,从而提高了平台数据存储与管理的安全性与便捷性。测试结果表明,该数据平台的数据存储容量与数据采集规模均高于传统平台,可行性较高。
英文摘要:
      The on-site production and operation data platform of traditional floating production storage and offloading (FPSO) had the problem of low data acquisition performance in the dynamic production and operation, resulting in the poor data storage capacity. Edge cloud computing technology was introduced in this paper to design a new FPSO on-site production and operation data platform. B/S hardware architecture was adopted to provide a stable environment for the efficient operation of hardware and software. The acquisition module for the production and operation data was designed for the analysis and mining of the potential information from the data. According to the principles of edge computing, the edge cloud computing layers of the platform was designed to obtain the priority value of the data. FPSO database was designed to improve the storage and management of platform data with security and convenience. The tests show that the data storage capacity and data collection scale of the data platform were higher than those of the traditional platform, and the new data platform was feasible.
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