A track compression and decompression method

Abstract

The invention discloses a trajectory compression method, comprising: S1. Scanning the trajectory to be compressed to obtain a fixed point in the trajectory to be compressed; the trajectory is a path generated by a moving object moving in space, and the fixed point is the starting point of the trajectory, Road section intersection or end point; S2, query the pre-configured frequent successor table, and determine whether the successor of the fixed point in the trajectory to be compressed is the most frequent successor fixed point of the fixed point; the subsequent fixed point is the next fixed point adjacent to the fixed point; if so, then Execute step S3; S3, represent the most frequent subsequent fixed points with negative numbers to obtain a set of fixed points and negative numbers, which is the compressed trajectory. Correspondingly, the present invention also provides a method for decompressing the compressed track obtained by applying the above track compression method. By adopting the embodiment of the present invention, it is possible to perform lossless compression on a track with a high compression rate and high efficiency by using the track characteristics.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a track compression and decompression method. Background technique [0002] In recent years, big data and its related research have become the hottest issues in the computer field, and gradually become the mainstream research direction. People began to realize the value of the information hidden in the data, and began to pay more and more attention to the research of data. With the development of technology, various technologies and related devices that can collect valuable data are also emerging. Among them, a very typical type of equipment is a device capable of detecting and collecting trajectory data, such as GPS (Global Positioning System, global positioning system), smart phones, RFID (Radio Frequency Identification, radio frequency identification) tags and so on. Such trajectory data generally has three dimensions, that is, the longitude and latitude representing th...

AI Technical Summary

Problems solved by technology

So the storage problem is obvious

Second, it is expensive and time-consuming to transfer, query, etc. such a huge amount of data

Transmitting large amounts of data over cellular or satellite networks can cost a lot of money

Third, when the scale of trajectory data is too large, it will become very difficult to extract useful information from it, such as analyzing motion patterns

However, although traditional compression methods based on information theory are lossless compression, they treat spatio-temporal data in the same way as other data, that is, use the same compression method

This compression method does not take into account the special properties of space-time trajectory data, so it is bound to fail to achieve the best results

Moreover, the compressed data cannot be used. If the compressed data needs to be further analyzed, the entire file must be decompressed. For huge spatiotemporal data, decompressing the entire file will consume a lot of time and computing resources

Moreover, many trajectory compression algorithms are based on the deletion of trajectory points, which are actually lossy compression, and cannot be restored to the original data after compression, resulting in algorithm errors or accidental deletion during the compression process, which will have a negative impact on subsequent data analysis. great influence

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