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

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。

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

The virtual ocean platform constructed by digital twin technology can further realize the 3D visualization and analysis of seabed environmental monitoring data. Based on the long-term in-situ monitoring data of the deep seabed, this paper uses Unity3D technology to build a virtual marine environment with multi-model fusion, and initially establishes a digital twin system for marine engineering geological environment; combines MATLAB and ArcGIS data analysis technology to realize intelligent monitoring, data analysis, human-computer interaction and auxiliary decision-making; the study further constructs a virtual environment particle system to conduct a 3D visualization and analysis of the near-bottom suspended sediment concentration elevation event in the northern part of the South China Sea. The results show that there are large spatial and temporal differences in suspended matter concentration, particle number and aggregation degree in the virtual environmental particle system. In particular, when the suspended matter concentration is maintained at a high level, particles are found to collide and overlap with each other, and the denser microclusters are derived in space. When the suspended matter concentration rises further to the peak, the number of microclusters increases and occupies most of the volume of space, forming a highly aggregated suspended matter aggregate with a wider coverage of suspended particles. This paper is based on image analysis techniques and compares the visualization results with real seafloor cameras with relative errors in the range of 0.16%–2.80%, which is highly feasible.