粒理论及其应用于水利大数据分析的展望

Expectation on granular computing and its applications in water resources big data analysis

为实现大数据技术在水利领域的应用,需要对水利大数据的时空特性展开研究。研究表明,时空大数据挖掘的计算框架与粒计算所蕴含的计算范式高度契合,因此,以粒理论为基础,构造由水利数据场景下的信息粒化分析、多粒度水利场景数据融合和时空数据挖掘等部分组成的方法与技术体系,从而降低基于大数据的水利复杂多时空尺度问题求解的时间和计算复杂度,是可行的方案之一。

In order to realize the applications of big data technologies in the field of water conservancy, it is necessary to make research on the temporal and spatial characteristics of big data of water conservancy. The research shows that the computing framework of space-time large data mining is highly compatible with the computational paradigm contained in the granular computing, so based on the theory of granular computing, it is one of appropriate solutions to construct the method and technology system consisted of information granulation analysis, multi-granularity water resources scene data fusion and multi-granularity spatio-temporal data mining to reduce space-time scales and computational complexity of water conservancy in water resources big data.