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海洋水文数据建模与可视化过程中充分挖掘数据时空依赖关系是当前研究热点之一。借鉴位置社会感知思想,提出一种时空数据感知模型(spatio-temporal data awareness model,SDAM):用数据语义标签描述采样点时空信息上下文、温盐密等非视觉物理量值;用三线性插值法感知空间邻近域数据语义,通过挖掘时空频繁模式感知(推演)时间邻近域数据语义,构建表征时空耦合特征的水文感知数据集;对高分辨率的底层感知数据进行相似性度量,通过时空聚类构建时空特征类簇,获取宏观的、低分辨率时空主题应用数据集。通过对中国沿海2014年第一季度海洋温盐深数据Web环境下三维可视化描述,验证了水文时空数据感知模型的可行性和有效性。
It is one of the hot spots to fully exploit the spatio-temporal dependence of data in the modeling and visualization of ocean hydrological data. In this paper, a spatio-temporal data awareness model (SDAM) is proposed based on the idea of social perception of location. Non-visual physical values of spatio-temporal information context, Spatial semantics of spatial domain data, perceive (deduce) the semantics of time adjacent domain data by mining temporal and spatial frequent patterns, construct a hydrological sensing dataset that characterizes spatio-temporal coupling characteristics, measure the similarity of high-resolution underlying perception data, construct spatio-temporal clustering Spatio-temporal feature clusters, access to macro, low-resolution spatio-temporal theme application data set. Through the three-dimensional visualization of China’s coastal ocean temperature-salinity deep-sea data Web environment in the first quarter of 2014, the feasibility and validity of the hydrological spatio-temporal data perception model are verified.