Track multi-dimensional distributed compression method based on space-time database

Abstract

The invention relates to a track multi-dimensional distributed compression method based on a space-time database. The track multi-dimensional distributed compression method comprises the following steps that 1, massive space-time track data are stored in a database; step 2, for the same moving object, performing real-time compression on the same moving object when subsequent track point data is inserted, namely compressing multiple rows of data into one row of data; and step 3, when the track point of the single object reaches a certain limited size, performing table splitting processing on the track point, and performing parallel compression and storage by adopting a distributed compression algorithm for undetermined. According to the method, multi-dimensional compression is carried out on mass trajectory data based on the distributed spatio-temporal database, on the premise that main trajectory features of an original trajectory are reserved, the storage pressure of the trajectory data is greatly reduced, storage resources are saved, and meanwhile computing resources for trajectory data mining are greatly reduced.

Description

technical field [0001] The invention relates to the technical field of data compression, in particular to a trajectory multi-dimensional distributed compression method based on a spatio-temporal database. Background technique [0002] With the popularization of Beidou global satellite navigation system and 5G communication technology in all walks of life, countless terminals have generated massive Beidou space-time trajectory data. [0003] When processing trajectory data, the traditional Douglas-Peucker algorithm connects a straight line to the first and last points of each trajectory, finds the distance between all other trajectory points and the straight line, and finds the maximum distance value d max , with d max Compared with the tolerance D: if d max <D, all the intermediate points on this trajectory are discarded; if d max >D, keep d max The corresponding coordinate point, and with this point as the boundary, the trajectory is divided into two parts, and th...

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Problems solved by technology

[0004] There are two problems with the Douglas-Peucker algorithm used to process spatio-temporal trajectory data: First, because it only uses the vertical Euclidean distance from each trajectory point to the starting point as the only criterion for judging whether to discard trajectory points, it may remove some Trajectory semantic points with obvious trajectory characteristics, which in turn affect the relevant effects of subsequent trajectory data mining; second, the time complexity is high, and it is not ideal to use it to directly process massive spatiotemporal trajectory data, or a data with a huge number of trajectory points.

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