Spatio-temporal data storage and retrieval method based on Beidou grid codes

Abstract

The embodiment of the invention provides a spatio-temporal data storage and retrieval method and device based on Beidou grid codes, equipment and a computer readable storage medium. The method comprises the following steps: acquiring spatio-temporal data; according to a fusion strategy, fusing low-level Beidou grid codes in the spatio-temporal data upwards to form high-level Beidou grid codes, andobtaining and storing the fused spatio-temporal data; establishing a Beidou grid code index of the fused spatio-temporal data according to an FST rule of the Beidou grid code index; and receiving a Beidou grid code index input by a user, and retrieving the stored fused spatio-temporal data according to the FST rule. In this way, the user retrieval speed can be increased, and meanwhile the computing pressure and the communication broadband pressure of a service provider are greatly relieved.

Description

technical field [0001] Embodiments of the present disclosure generally relate to the field of data retrieval, and more specifically, relate to a method, device, device, and computer-readable storage medium for storing and retrieving spatio-temporal data based on Beidou grid codes. Background technique [0002] With the advent of the era of big data, the value of big data has been reflected in all walks of life. How to manage organizations, analyze and apply big data uniformly and effectively has become a top priority. [0003] The traditional space-time data query adopts the traditional latitude and longitude method, and obtains satisfactory retrieval data by intersecting and calculating the selected latitude and longitude retrieval range and the latitude and longitude range of the data in the database. [0004] However, traditional methods have higher requirements for databases and information systems. When the large amount of data is frequently retrieved, the database ove...

AI Technical Summary

Problems solved by technology

When the large amount of data is frequently retrieved, the database overhead will be too large and the retrieval efficiency will be reduced; especially for spatiotemporal data such as vector, image, terrain, oblique photography, street view, etc., the traditional method of using data longitude and latitude intersection and matching is more dependent on the database. Large; during retrieval, all data will be traversed to find the retrieval target, resulting in high retrieval overhead and slow retrieval

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