Abstract
The management and protection of catchment water resource are effective measures to promote the harmonious coexistence of human and nature, and accelerate the construction of ecological civilization. Effective storage, management, and retrieval of large spatial temporal data in catchment water resource are facing enormous challenges. At the same time, higher requirements are put forward for data concurrent access support capability and security reliability. Therefore, it is urgent to carry out research on intelligent management and control of large spatial temporal data in catchment water resource. This paper develops a hybrid architecture storage and retrieval system for large spatial temporal data of catchment water resource, which solves the problems of efficient storage and retrieval of massive multi-source heterogeneous data and concurrent access support. Combined with the technical specifications of water resources and geographic information related countries and industries, the existing water-related management system is migrated and integrated by using the “one-source-one-repository” model, avoiding repeated collection and storage of observation data, improving data consistency, and facilitating data sharing among various subsystems. HBase-based tile pyramid storage is used to implement fast visual display and query of data. Metadata model based on MongoDB document model is used to simplify metadata description. At the same time, the Elasticsearch search engine is used to build metadata full-text index, which provides multiple matching methods such as exact matching, fuzzy search, and range query. Spatial vector feature data storage model is established based on GeoJSON and MongoDB, build spatial index, design auxiliary index to accelerate data query and filtering, design sharing strategy in shared replication cluster, balance the contradiction between data distribution and query efficiency.
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