周超,唐海华,罗斌,等.水利行业大数据汇集管理体系建设的思考[J].水利信息化,2021(4):.

水利行业大数据汇集管理体系建设的思考

Think of Building Big Data Aggregation and Management Structure in Water Conservancy

投稿时间：2020-12-18 修订日期：2021-05-26

DOI：

英文关键词:Water conservancy big data, Data aggregation, Data management, Intelligent water conservancy, Water conservancy informatization

基金项目:

作者	单位	邮编
周超^*	长江勘测规划设计研究有限责任公司	430010
唐海华
罗斌
李琪

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中文摘要:

面向当前国内水利行业大数据建设，以提高数据管控效果、提升大数据价值为目标，思考提出了一套水利大数据汇聚管控的顶层体系。首先，研究提出了不同管理权限水利数据节点之间的数据汇集管理结构，分析了国家、流域、省、市和库站等不同管理类别和层级的数据汇集管理过程，梳理其中数据管理的主体与内容，并针对不同情境实现数据的直属管理、统筹管理及协商管理，在管理过程中实现权限分配及运行监督；进一步，针对单个节点的内部数据管控，研究设计了基于水利业务分类的管理体系，实现按基础对象数据关联专业业务数据的管理过程；最后结合实际场景案例说明并分析了具体的运行方式。通过该数据汇集管理体系的研究，为水利大数据进一步建设的实际工作开展提供理论参考。

英文摘要:

In order to improve the effect of data control and enhance the value of big data, this paper proposes a top-level system of water conservancy big data aggregation and control for the current thinking of big data construction in the domestic water conservancy industry. Firstly, the paper puts forward the data collection management structure among water conservancy data nodes with different management authority, analyzes the data collection management process of different management categories and levels, such as national, river basin, provincial, municipal and database station, and sorts out the subject and content of data management.?Meanwhile, direct management, overall management and negotiation management of data are realized in different situations, and authority distribution and operation supervision are realized in the management process. Further, a management system based on water conservancy business classification is designed to realize the management process of associating professional business data according to the basic object data for the internal data control of a single node. Finally, the specific operation mode combined with the actual scene case is explained and analyzed. Through the study of the data collection management system, this work provides a theoretical reference for the further construction of water conservancy big data.

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