Distributed time series geographic information service system and method

Abstract

The invention relates to a distributed time series geographic information service system and method. The system comprises a data acquisition module, a data storage module and a data query module, wherein the data acquisition module is used for acquiring data in distributed automatic meteorological stations in a message queue manner; the data storage module is used for carrying out columnar storage on the data acquired by the data acquisition module in a time series form through a distributed non-relationship type database; and the data query module is used for independently interacting with the data storage module by adoption of a plurality of distributed processes so as to realize a flexible query service. According to the system and method provided by the invention, through a process of realizing the data acquisition, storage and query, simple and high-availability data services more suitable for meteorological data can be provided for the clients.

Description

Technical field [0001] The invention relates to the application field of meteorological science and big data technology, and in particular to a distributed time series geographic information service system and method. Background technique [0002] Due to the huge base of automatic weather stations, each automatic station generates data every minute, and the data is becoming increasingly large. The use of dedicated relational databases to maintain this level of data costs is quite high; there are also plans to use distributed solutions to solve the problem of data storage, but it still requires a lot of research and development efforts for data samples. At present, the technical solution in this technical field that is relatively close to this proposal is to address the problem of traditional centralized data storage and single-point query. It provides a storage and retrieval method based on massive meteorological data, using the Hadoop platform, and distributing non-relational da...

AI Technical Summary

Problems solved by technology

[0004] 1. The existing technology only describes the theory of HBase to establish a secondary index, and does not provide a specific table building scheme and secondary index scheme for meteorological data

[0005] 2. The secondary index theory of the prior art is the general theory of HBase secondary index, which is not optimized for meteorological data, wasting storage space

[0006] 3. The existing technology only proposes a storage and retrieval scheme, and a systematic overall solution is needed to finally improve the usage of meteorological data

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