Cosine Offset Discrimination Method, Device and Equipment for Vector Trajectory Compression

Abstract

The present application relates to a cosine vertical distance discrimination method, device and equipment for vector trajectory compression. Among them, the original vector trajectory data set is obtained first, and a loop body is constructed, and then three points are sequentially taken to form a triplet during the loop, and each triplet is divided into two levels when the triplet is processed. At the level, the distance between the front and rear adjacent sides of the middle element of the triplet is used as a condition to first identify the triplet as a dense point set or a sparse point set; at the online level, the cosine value is used as the judgment condition to recognize the local trajectory of the triplet in order There are two cases of curved line and curved line, and finally judge whether to compress the intermediate vertex elements in each case through different vertical distance thresholds d_max and d_min. The whole processing process considers multiple situations, uses multiple judgment values, and works together with each other, and finally compresses each intermediate point element that does not meet the conditions. Compared with the existing processing method, it has better compression effect and faster processing speed.

Description

technical field [0001] The present application relates to the technical field of vector data compression, in particular to a cosine-offset discrimination method, device and equipment for vector trajectory compression. Background technique [0002] With the development of positioning technology and the popularization of positioning equipment, vector data is growing explosively. The research on vector data compression technology has become a hot issue in the fields of optimized storage, network transmission and visualization. Vector data refers to a type of data set with both size and direction, and is usually used to represent the position and shape data of map graphics or geographic entities. [0003] At present, massive data applications in the context of big data are all around us, ranging from real-time tracking of aerospace, aviation, and satellite trajectories, to small areas such as unmanned driving, bus platform display and arrival reminders, and online booking platfo...

AI Technical Summary

Problems solved by technology

However, these algorithms have their own problems. For example, the vertical distance limit method and the angle limit method are judged based on a set threshold, which cannot consider the special changes that the route may encounter, nor can it consider the overall shape elements of the route; The area deviation control method requires a large number of calculations such as solving the equation of the intersection point of the straight line space, resulting in a long processing time; the time complexity of the D-P algorithm is high, when the amount of data is large, the processing time is very long, and only offline data can be processed , which is difficult to scale to real-time processing

[0005] That is to say, the various current vector trajectory compression algorithms have their own problems, which lead to the limitation of practical application scenarios and effects.

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