Paper
20 April 2023 Research and optimization of meteorological big data storage technology
Shiyang Zhang, Hansheng Xie, Fei Luo, Xi Zhang
Author Affiliations +
Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126022A (2023) https://doi.org/10.1117/12.2668029
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China
Abstract
With the rapid development of the Internet, data analysis and other information fields, the amount of meteorological data is increasing exponentially every year, and the types of meteorological data are increasing. The storage of meteorological data should not only reach the response speed of millisecond level, but also provide data products and services in a short time. By analyzing the characteristics of meteorological data structure, this paper puts forward different storage methods for different data types. According to the data requirements of the meteorological business operations, this paper proposes the use of hierarchical storage, to store frequently used or soon-to-be-used data stored in high-performance memory. At the same time, a record table based on file access characteristics is established to record data attributes, and quickly locate the data that needs to be migrated. By simulating the data response efficiency of querying historical similar weather, and comparing the performance of the original single storage mode with the optimized storage mode, it is verified that hierarchical storage can improve the demand for data response of meteorological services.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shiyang Zhang, Hansheng Xie, Fei Luo, and Xi Zhang "Research and optimization of meteorological big data storage technology", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126022A (20 April 2023); https://doi.org/10.1117/12.2668029
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KEYWORDS
Data storage

Meteorology

Binary data

Data modeling

Technology

Databases

Environmental sensing

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