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基于数字孪生的海底悬浮物时空变化可视化技术实现与应用

乔玥 单红仙 王宏威 朱超祁 孙志文 贾永刚

乔玥,单红仙,王宏威,等. 基于数字孪生的海底悬浮物时空变化可视化技术实现与应用[J]. 海洋学报,2023,45(8):166–177 doi: 10.12284/hyxb2023082
引用本文: 乔玥,单红仙,王宏威,等. 基于数字孪生的海底悬浮物时空变化可视化技术实现与应用[J]. 海洋学报,2023,45(8):166–177 doi: 10.12284/hyxb2023082
Qiao Yue,Shan Hongxian,Wang Hongwei, et al. Implementation and application of digital twin-based visualization technology for spatial and temporal variation of seafloor suspensions[J]. Haiyang Xuebao,2023, 45(8):166–177 doi: 10.12284/hyxb2023082
Citation: Qiao Yue,Shan Hongxian,Wang Hongwei, et al. Implementation and application of digital twin-based visualization technology for spatial and temporal variation of seafloor suspensions[J]. Haiyang Xuebao,2023, 45(8):166–177 doi: 10.12284/hyxb2023082

基于数字孪生的海底悬浮物时空变化可视化技术实现与应用

doi: 10.12284/hyxb2023082
基金项目: 国家自然科学基金重点项目(41831280);国家自然科学基金面上项目(41877223);国家自然科学基金青年项目(42207173)。
详细信息
    作者简介:

    乔玥(1998-),女,江苏省徐州市人,研究方向为基于数字孪生技术的海底地质灾害监测预警。E-mail: jay0127@qq.com

    通讯作者:

    贾永刚(1965-),男,吉林省伊通满族自治县人,教授,博士,研究方向为海洋工程地质与环境。E-mail: yonggang@ouc.edu.cn

  • 中图分类号: P751;P734.2+3

Implementation and application of digital twin-based visualization technology for spatial and temporal variation of seafloor suspensions

  • 摘要: 数字孪生技术构建的虚拟海洋平台,能够进一步实现海底环境监测数据的三维可视化分析。本文基于深海底长期原位监测数据,使用Unity3D技术构建出多模型融合的虚拟海洋环境,初步建立海洋工程地质环境数字孪生系统;结合MATLAB、ArcGIS数据分析技术实现智能监测、数据分析、人机交互、辅助决策等功能;研究进一步构建虚拟环境粒子系统,对南海北部陆坡神狐海域近底层悬浮物浓度升高事件进行了三维可视化分析,结果表明:在虚拟环境粒子系统中悬浮物浓度、粒子数量与聚集程度具有较大的时空差异。特别是悬浮物浓度维持在较高水平时,发现粒子间相互碰撞、重叠,并在空间中衍生了密集程度更高的微团,当悬浮物浓度进一步升至峰值时,微团数量增加并占据空间大部分体积,形成悬浮颗粒高度聚集,覆盖范围更广的悬浮物聚合体。本文基于图像分析技术,将可视化分析结果与真实海底摄像进行对比,相对误差在0.16%~2.80%范围内,具有较高的可行性。
  • 图  1  海洋工程地质环境数字孪生系统框架

    Fig.  1  Framework for a digital twin system for the marine engineering geological environment

    图  2  SEEGeo工作模式

    Fig.  2  SEEGeo working mode

    图  3  虚拟层场景实现过程

    Fig.  3  Virtual layer scenario realisation process

    图  4  设备虚拟仿真模型

    Fig.  4  Virtual simulation models of equipment

    图  5  海底地形模型

    Fig.  5  Submarine terrain model

    图  6  海洋环境仿真模型

    Fig.  6  Marine environment simulation model

    图  7  虚拟现实模型实现框架

    Fig.  7  Virtual reality model implementation framework

    图  8  智能监测功能

    Fig.  8  Intelligent monitoring functions

    图  9  数据分析功能

    Fig.  9  Data analysis functions

    图  10  研究区位置

    Fig.  10  Location of the study area

    图  11  浊度随时间变化关系

    Fig.  11  Relationship between turbidity and time

    图  12  浊度与悬浮物颗粒浓度之间的线性关系

    Fig.  12  Linear relationship between turbidity and suspended particle concentration

    图  13  悬浮颗粒物粒径的垂直分布及中值曲线

    Fig.  13  Vertical distribution and median curves of suspended particulate matter particle sizes

    图  14  悬浮物浓度时间变化曲线及对应数字孪生虚拟粒子系统可视化分析图

    Fig.  14  Temporal variation curve of suspended matter concentration and corresponding digital twin virtual particle system visualisation

    图  15  ROV海底摄像影片和数字孪生虚拟粒子系统可视化分析对比图

    Fig.  15  Comparison of ROV subsea camera film and digital twin virtual particle system visualisation analysis

    表  1  近底层悬浮物颗粒虚拟仿真粒子信息表

    Tab.  1  Virtual simulation particle information sheet for near-bottom suspended particles

    虚拟仿真参数名称参数信息
    悬浮物平均粒径30 μm
    悬浮物体积14 130 μm3
    悬浮物平均密度1.48 g/cm3
    悬浮物体积(1 mg/L)3 943 mm3
    悬浮物数量(1 m3279 021 059
    传感器有效探测范围125 cm3
    空间体积放大比例1∶4 096 000
    空间体积512 m3
    单个粒子体积放大比例1∶621 378 370
    粒子体积8 780 mm3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-08
  • 修回日期:  2023-03-08
  • 网络出版日期:  2023-08-16
  • 刊出日期:  2023-08-31

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