多视图协同的海洋热浪可视分析

doi:10.11996/JG.j.2095-302X.2025051018

图学学报 ›› 2025, Vol. 46 ›› Issue (5): 1018-1027.DOI: 10.11996/JG.j.2095-302X.2025051018

• 计算机图形学与虚拟现实 • 上一篇下一篇

多视图协同的海洋热浪可视分析

贺琪¹(), 解秋寒¹, 黄冬梅², 陈括³(), 王建¹

¹ 上海海洋大学信息学院，上海 201306
² 上海电力大学电气工程学院，上海 200090
³ 自然资源部东海海域海岛中心，上海 200136

收稿日期:2025-02-28 接受日期:2025-05-13 出版日期:2025-10-30 发布日期:2025-09-10
通讯作者:陈括(1988-)，男，工程师，硕士。主要研究方向为海洋数据质量控制与信息化应用。E-mail：chenkuo@ecs.mnr.gov.cn
第一作者:贺琪(1979-)，女，教授，博士。主要研究方向为海洋大数据存储、云计算。E-mail：qihe@shou.edu.cn
基金资助:
国家重点研发计划项目(2024YFD2400404);国家自然科学基金面上项目(42376194)

Multi-view synergistic visual analysis of ocean heat waves

HE Qi¹(), XIE Qiuhan¹, HUANG Dongmei², CHEN Kuo³(), WANG Jian¹

¹ School of Information, Shanghai Ocean University, Shanghai 201306, China
² School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
³ East China Sea Island Center, Ministry of Natural Resources, Shanghai 200136, China

Received:2025-02-28 Accepted:2025-05-13 Published:2025-10-30 Online:2025-09-10
First author：HE Qi (1979-), professor, PhD. Her main research interests cover marine big data storage, cloud computing. E-mail：qihe@shou.edu.cn
Supported by:
National Key Research and Development Program Project(2024YFD2400404);National Natural Science Foundation of China General Project(42376194)

摘要/Abstract

摘要：

在全球气候变暖不断加剧的背景下，海洋热浪的发生频率和强度持续攀升，对海洋生态系统和沿海经济活动产生了严重影响。针对现有研究方法难以全面捕捉海洋热浪多因子耦合、多尺度交互的复杂特性，特别是在时空动态演变过程的量化表征方面存在明显不足的问题，提出了融合高维时空特征的多视图协同分析方法。首先基于时空图卷积网络(ST-GCN)的特征提取技术，通过构建包含热浪强度、频次和持续时间等指标的多维特征矩阵，结合改进的Delaunay三角剖分算法建立动态空间邻接关系，实现了对海洋热浪时空演变规律的精准刻画。其次，创新性地设计了支持多要素关联分析的可视化系统，采用多维标度法和HDBSCAN聚类算法，能够深入解析海洋热浪事件与海表温度异常、风速场等关键环境驱动因子之间的非线性耦合关系。该系统通过多视图协同交互能够直观探索海洋热浪的时空分布模式及驱动机制。

关键词: 海洋热浪, 多视图, 时空图卷积, 可视分析系统, 协同交互

Abstract:

Against the background of increasing global warming, the frequency and intensity of ocean heat waves continue to rise, imposing serious impacts on marine ecosystems and coastal economic activities. Existing research methods were found to inadequately capture the complex characteristics of multi-factor coupling and multi-scale interaction of ocean heat waves, especially in the quantitative characterization of the spatio-temporal dynamic evolution. To address this scientific problem, a multi-view synergistic analysis methodology incorporating high-dimensional spatio-temporal features was proposed. Firstly, a feature extraction technique based on spatio-temporal graph convolutional network (ST-GCN) was developed. It realized the accurate portrayal of the spatio-temporal evolution law of ocean heat waves by constructing a multi-dimensional feature matrix containing heat wave intensity, frequency, duration and other indicators, and establishing dynamic spatial adjacencies by combining with the improved Delaunay triangular dissection algorithm. Secondly, a visualization system supporting multi-factor correlation analysis was innovatively designed. Multi-dimensional scaling method and the HDBSCAN clustering algorithm were adopted to deeply analyze the nonlinear coupling relationship between the ocean-heat-wave events and the key environmental drivers, such as sea-surface-temperature anomalies and wind-speed field. The system enabled researchers to intuitively explore the spatial and temporal distribution patterns of ocean heat waves and their driving mechanisms through the synergistic interaction of multiple views.

Key words: marine heat wave, multi-view, spatio-temporal map convolution, visual analysis system, collaborative interaction

中图分类号:

TP183
P71

贺琪, 解秋寒, 黄冬梅, 陈括, 王建. 多视图协同的海洋热浪可视分析[J]. 图学学报, 2025, 46(5): 1018-1027.

HE Qi, XIE Qiuhan, HUANG Dongmei, CHEN Kuo, WANG Jian. Multi-view synergistic visual analysis of ocean heat waves[J]. Journal of Graphics, 2025, 46(5): 1018-1027.

图/表 14

图1 多视图协同的海洋热浪可视分析流程图

Fig. 1 Multi-view collaborative visual analysis flow chart of ocean heat waves

图2 MWHVIS系统界面((a) 参数选择器；(b) 热浪特征视图；(c) 热浪分布视图；(d) 时空模态分析视图；(e) 多维联动视图；(f) 成因关联视图)

Fig. 2 MWHVIS system interface ((a) Parameter selector; (b) Heat wave characterization view; (c) Heat wave distribution view; (d) Spatio-temporal modal analysis view; (e) Multi-dimensional linkage view; (f) Causal linkage view)

图3 多视图协同分析框架流程图

Fig. 3 Multi-view collaborative analysis framework flowchart

图4 渤海-北黄海热浪3类空间分布模式

Fig. 4 Three types of spatial distribution patterns of heat waves in the Bohai-North Yellow Sea

图5 累计频次分布图

Fig. 5 Cumulative frequency distribution plot

图6 平均强度分布图

Fig. 6 Average intensity distribution map

图7 最大强度分布图

Fig. 7 Maximum intensity distribution map

图8 平均持续时间分布图

Fig. 8 Average duration distribution plot

图9 热浪季节趋势图

Fig. 9 Heat wave season trend chart

图10 多维联动视图

Fig. 10 Multidimensional linkage view

图11 成因关联视图

Fig. 11 Correlation analysis view

图12 热浪成因要素分析图

Fig. 12 Heatwave causal elements analysis chart

图13 系统任务处理效率对比图

Fig. 13 Comparison chart of system task processing efficiency

图14 分析精度提升幅度图

Fig. 14 Analysis accuracy improvement graph

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多视图协同的海洋热浪可视分析

Multi-view synergistic visual analysis of ocean heat waves

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