Meteorological grid point data storage and query method and device and storage medium

Abstract

The invention provides a meteorological grid point data storage and query method. The method comprises the steps of migrating meteorological grid point data to a column storage database in advance, wherein each data record of the column storage database stores the value of one grid point in the meteorological grid point data and the index corresponding to the grid point; the indexes correspondingto the grid points are determined according to the row numbers and the column numbers of the grid points; acquiring position information of to-be-queried data; determining an index of the to-be-queried data according to the position information; and reading the value of the grid point corresponding to the index of the to-be-queried data from the column storage database. Corresponding to the meteorological grid point data storage and query method, the invention further provides a meteorological grid point data storage and query device, electronic equipment and a computer readable storage medium.

Description

technical field [0001] One or more embodiments of this specification relate to data processing technology, in particular to a method for storing and querying meteorological grid point data, a query storage and device for meteorological grid point data, electronic equipment, and a computer-readable storage medium. Background technique [0002] With the increase of the types of meteorological observation data and the increase of observation frequency, the amount of meteorological data is increasing. Meteorological data is a typical type of big data, which has the characteristics of large data volume, high timeliness, and rich data types. At present, many meteorological data are stored in the form of grid point data, also known as meteorological grid point data, for example, precipitation data or temperature data in a certain area, and so on. Meteorological grid point data usually divides the space into multiple grids according to latitude and longitude, each grid is called a ...

AI Technical Summary

Problems solved by technology

However, this method of adding a relational database to a file system is often difficult to withstand the increase in the number of files with a large amount of data, and the tree structure of the file directory cannot well meet the characteristics of sequential access to data, and the access time is relatively long

In addition, although the traditional relational database has the ability of sorting and indexing, its fixed relational table schema makes the system design complicated, and its disk storage structure also limits the optimization of ordered access.

Therefore, this mode cannot well meet the high-performance query of meteorological data

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