由于智能变电站内二次回路需要模拟的对象较多,并且具有时序性特征与非时序性特征,导致智能变电站二次回路三维可视化效果较差。基于此设计了一个基于电网信息模型(grid information model,GIM)模型的智能变电站二次回路三维可视化系统...由于智能变电站内二次回路需要模拟的对象较多,并且具有时序性特征与非时序性特征,导致智能变电站二次回路三维可视化效果较差。基于此设计了一个基于电网信息模型(grid information model,GIM)模型的智能变电站二次回路三维可视化系统。系统硬件部分,重点设计处理器、互感器采样模块与互感器采样模块;系统软件部分,对空间数据组织与管理、几何元素间求交计算、模型切割、模型贴合。在此基础上采用GIM模型建立时序模型,完成智能变电站二次回路三维可视化系统的设计。实验结果表明,所提基于GIM模型的智能变电站二次回路三维可视化系统不仅提高了建模帧速、可视化效率与可视化效果,所设计系统的可视化结果基本不存在缺陷。所设计系统还减少了施工变更次数、施工成本与施工周期,施工成本最大降幅接近0.5倍。因此,说明所设计系统提高了变电站二次回路三维可视化效果,可以满足系统设计需求。展开更多
Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean...Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.展开更多
The deformation responses of surface cap rocks of Underground Gas Storage( UGS) in Hutubi,Xinjiang during gas injection and production were investigated with the GPS data recorded by the deformation monitoring network...The deformation responses of surface cap rocks of Underground Gas Storage( UGS) in Hutubi,Xinjiang during gas injection and production were investigated with the GPS data recorded by the deformation monitoring network,which includes 13 observation sites. The time series of three-dimensional deformation of the surface cap rocks was obtained in the UGS operation process,and the deformation signals in different phases were identified by combining the GPS data with wellhead pressure data. The results show that the respiration response of surface cap rock deformation is obvious during gas injection and production of UGS,and the surface deformation due to a 1MPa change of wellhead pressure is 1. 02 mm in gas injection and 1. 24 mm in gas production horizontally, and- 1. 11 mm in gas injection and 0. 86 mm in gas production vertically.展开更多
文摘由于智能变电站内二次回路需要模拟的对象较多,并且具有时序性特征与非时序性特征,导致智能变电站二次回路三维可视化效果较差。基于此设计了一个基于电网信息模型(grid information model,GIM)模型的智能变电站二次回路三维可视化系统。系统硬件部分,重点设计处理器、互感器采样模块与互感器采样模块;系统软件部分,对空间数据组织与管理、几何元素间求交计算、模型切割、模型贴合。在此基础上采用GIM模型建立时序模型,完成智能变电站二次回路三维可视化系统的设计。实验结果表明,所提基于GIM模型的智能变电站二次回路三维可视化系统不仅提高了建模帧速、可视化效率与可视化效果,所设计系统的可视化结果基本不存在缺陷。所设计系统还减少了施工变更次数、施工成本与施工周期,施工成本最大降幅接近0.5倍。因此,说明所设计系统提高了变电站二次回路三维可视化效果,可以满足系统设计需求。
基金The National Key R&D Program of China under contract No.2021YFC3101603.
文摘Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.
基金sponsored by the National Natural Science Foundation of China(41474097,41304067,47474016,41474051,41404015)
文摘The deformation responses of surface cap rocks of Underground Gas Storage( UGS) in Hutubi,Xinjiang during gas injection and production were investigated with the GPS data recorded by the deformation monitoring network,which includes 13 observation sites. The time series of three-dimensional deformation of the surface cap rocks was obtained in the UGS operation process,and the deformation signals in different phases were identified by combining the GPS data with wellhead pressure data. The results show that the respiration response of surface cap rock deformation is obvious during gas injection and production of UGS,and the surface deformation due to a 1MPa change of wellhead pressure is 1. 02 mm in gas injection and 1. 24 mm in gas production horizontally, and- 1. 11 mm in gas injection and 0. 86 mm in gas production vertically.
