资源环境综合科学考察是掌握和获取国家和区域资源环境本底数据的重要手段。综合科学考察数据来之不易、非常宝贵,但是由于其所具有的多来源、多类型、多学科等综合性特点,这不仅影响到大量已有考察活动积累数据的集成,而且给当前正在开展的许多科学考察研究活动的数据集成管理带来困难。针对这一需求,论文分析认为缺少"一致性的数据模型"是综合科学考察数据集成的核心问题,提出了综合科学考察多维数据管理的理念,对综合科学考察数据的多维特性进行分析,并在此基础上提出多维数据管理的共性维度概念模型。结合当前正在开展的跨中国、俄罗斯、蒙古三国的中国北方及其毗邻地区综合科学考察研究实践,建立了综合科学考察数据集成与管理的三个共性维度基准。具体包括与考察任务内容对应的数据要素维基准、按照考察数据采集和更新周期的数据时间维基准、按照地理空间格网体系建立的数据空间维基准。这一模式初步应用于中国北方及其毗邻地区综合科学考察的数据集成与管理,取得较好效果。

Integrated scientific expedition in the field of resources and environment is one of the important approaches for basic and strategic national and regional background resource and environment data acquiring and accumulation. It is difficult to integrate and manage these scientific expedition data, because they have natural multi-sources, multi-types, and multi-disciplinary features. This not only affects historical expedition data integration and sharing, but also affects ongoing and future scientific expedition data management. Face to this problem, the paper puts forward the idea of multi-dimensional data management. Consistency data model is the core for multi-dimensional scientific expedition data integration and management in the field of resources and environment. Traditionally, these scientific expedition data have various dimensions, including disciplinary dimension, thematic dimension, spatial scale dimension, temporal dimension, application dimension, etc. According to analysis, there are three common dimensions that can be generalized, i.e., data element dimension, data temporal dimension and data spatial scale dimension. A simple common data management concept model is designed based on these common dimensions. One logical data model was designed based on the research of integrated scientific expedition project in northern China and its neighboring area. This project’s expedition scope covers three countries of northern China, Russia and Mongolia. The concept model has three common dimensions’ benchmark. Data element dimension benchmark was designed based on the scientific expedition work duty closely. Data temporal dimension benchmark was designed based on its data collection and update period. Data spatial scale dimension benchmark was designed based on GRID system. This model was used in the expedition data integration and management implementation and was proved feasible for scientific data integration and sharing initially.