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大数据时代地质灾害数据管理及应用模式探讨
引用本文:刘军旗,刘强,刘千慧,张夏林,林晨,周鑫,李国策.大数据时代地质灾害数据管理及应用模式探讨[J].地质科技通报,2021,40(6):276.
作者姓名:刘军旗  刘强  刘千慧  张夏林  林晨  周鑫  李国策
基金项目:国家自然科学基金项目41572336水利部重点项目"基于大数据的三峡工程湖北库区移民安置区地质安全智能管控关键技术研究
摘    要:地质灾害数据是一种多源异构数据,是典型的大数据。关系型数据库是目前地质灾害数据的主流管理方法。在地质灾害数据中,非结构化数据占有很大的比例。由于关系模型难以有效地管理非结构化数据,因而关系型数据库对地质灾害数据的管理效果并不理想。这种弱点,在大数据时代将会被进一步放大,并对地质数据挖掘和大数据分析造成一定的影响。针对大数据时代地质灾害数据的管理模式,从泛结构化地质数据管理、应用模型和分布式异构系统的集成等方面进行了探讨。认为地质灾害数据的有效管理应该把文件系统、关系型数据库和NoSQL结合起来,并提出了一种基于双C模型和中间件结合的泛结构化地质数据管理与应用模式。这种模式已应用在多个工程中,取得了良好的效果。 

关 键 词:大数据    泛结构化地质数据    关系型数据库    NoSQL    双C模型    中间件
收稿时间:2020-09-17

Discussion of geological hazard data management and application model in big data era
Abstract:Geological disaster data is a kind of multi-source heterogeneous data with multi-source, large quantity, multi-type, multi-format, multi-scale and multi-precision.It is a typical "big data".Relational database is the mainstream management method of geological disaster data at present.There are a large proportion of unstructured data in geological disaster data.Because it is difficult to manage unstructured data effectively by using relational model, the effect of this management method is not ideal.This weakness will be further amplified in the era of big data, and will have a certain impact on geological data mining and big data analysis.This paper focuses on the management mode of geological disaster data in the era of big data.Discusses the management of pan-structured geological data, application model and integration of distributed heterogeneous system.It is considered that the effective management of geological disaster data should combine file system, relational database and NoSQL, and a pan-structured geological data management and application mode based on the combination of double C model and middleware is proposed.The double C model has an implicit internal data access and management center and an explicit outer analysis and application center.Through the double C model, a variety of work can be integrated into a simple and orderly whole, such as geological data collection, management, statistics, calculation, retrieval, geological map compilation, three dimensional modeling, attribute analysis, spatial analysis and so on. 
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