智慧水利大数据内涵特征、基础架构和标准体系研究

Research on connotation and features, basic architecture and standard system hydrological big data for Smart Water Conservancy

为解决水利大数据概念内涵不清晰,体系架构不统一,标准规范不完善,应用目标不明确等问题,将大数据理论技术与水利行业特点相结合开展水利大数据理论框架的研究。在充分研究调研大数据应用现状基础上,解析水利大数据的体量巨大、复杂多样、新老结合、价值很高、模糊很大、交互性、效能性和共情性等内涵特征;从数据源、管理、计算和应用等4个层面提出适用于智慧水利建设的水利大数据基础体系架构,并从功能、技术、部署角度进行阐述;设计水利大数据实时与离线分析场景的应用框架,用于支撑智慧水利环境下的水利大数据分析和决策;提出水利大数据相关管理规范和应用标准体系框架;总结水资源、水环境水生态、水灾害、水工程、水监督、水行政、水公共服务等水利大数据的应用场景。研究成果符合水利行业实际情况,可以被借鉴用来指导水利大数据的研究和应用工作。

In order to solve the problems of unclear connotation, inconsistent system structure, imperfect standards and specifications, and unclear application objectives of hydrological big data, this paper researches on its theoretical framework combining the big data’s theory and technologies with the characteristics of the water conservancy. Firstly, through an investigation on the application situation of big data, the connotation and features of hydrological big data, such as huge volume, complexity and diversity, combination of old data and new data, high value, great ambiguity, interaction, efficiency and empathy, are analyzed. Secondly, a hydrological big data basic architecture is proposed for Smart Water Conservancy. The basic architecture includes data source, data management, data computing and data application, and is described from the aspects of function, technology and deployment. And then, an application framework of real time and off-line hydrological big data analysis is proposed to support hydrological big data analysis and decision for the smart water. Furthermore, the related management and application standard system of hydrological big data is also presented. Finally, application scenarios of hydrological big data are summarized, which can be used in the fields of water resources, water environment, water ecology, water disasters, water projects, water supervision, water administration, water public services,et al. The research results of this paper conform to the actual situation of the water industry, and can be used for reference to guide the study on and application of hydrological big data.