Method and device for processing spatial point data

Abstract

The invention discloses a method and device for processing spatial point data, which are used to store data of spatial points by statically dividing grid numbers, thereby improving data storage efficiency and being suitable for massive data storage. A method for processing spatial point data provided by the present invention includes: determining the grid range according to the range where the coordinates of the known spatial points are located, and determining the grid level according to the number of known spatial points, according to the grid grid range and grid level to generate a grid system, the grid system includes a plurality of grids; for the coordinates of each known spatial point, according to the coordinates of the known spatial point, determine the grid system in the grid system The grid number corresponding to the known spatial point is known; the data of each known spatial point is stored in the grid corresponding to the grid number in the grid system.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method and device for processing spatial point data. Background technique [0002] As the amount of data that can be generated and captured by human activities expands year by year, mankind has entered the era of big data. This kind of coordinate point data characterized by including longitude and latitude fields, such as GPS data, static geographic location element coordinate point data, etc., has also achieved unprecedented growth in the type and amount of data. Taking the coordinate point data of buildings, places, etc. across the country as an example, there may be hundreds of millions of pieces. Faced with such a massive amount of data, traditional relational databases, even if a joint index of longitude and latitude coordinates are established, the response time of the spatial range query sometimes reaches tens of seconds; while many new NoSQL databases, although...

AI Technical Summary

Problems solved by technology

[0005] For range queries that retrieve all data at one time, it is difficult to convert multiple random reads into a small number of continuous reads. When the query range is large, the performance will be quite low;

[0006] When writing spatial point data, due to the existence of dynamic grid division, the existing data will be modified at any time, resulting in low performance. In the distributed case, additional overhead is required to ensure its consistency;

[0007] It is difficult to ensure the stability of page turning by adopting a random grid method when turning pages

For the situation where only a small amount of data needs to be extracted each time, if the extracted data is directly paginated according to the original method, the following two adverse consequences may occur: 1. Assuming that the amount of data is too large, all the data should be extracted at one time , and then paging on the client side may lead to memory overflow; 2. If the database limit is used to retrieve the first n pieces of data, since the data is stored according to the grid number, the result is that when the query range is large , the data entries retrieved from each page are concentrated in a small range of points, which is unacceptable for map points

[0008] To sum up, in the prior art, the grid is dynamically divided according to the number of spatial points, which leads to low data storage efficiency and is not suitable for mass data storage

Images