Cosine vertical distance discrimination method, device and equipment for vector trajectory compression

Abstract

The invention relates to a cosine vertical distance discrimination method, device and equipment for vector trajectory compression. The method comprises the following steps: firstly, acquiring an original vector trajectory data set, constructing a loop body, then sequentially taking three points to form a triple during circulation; and when the triple is processed, dividing each triple into two layers, wherein on the point level, the distance between the front and back adjacent edges of the middle element of the triad is taken as a condition, and the triad is identified to form a dense point set or a sparse point set, and on the online level, a local track formed by connecting triads in sequence is recognized as a curve line condition and a curve line condition through taking a cosine value as a judgment condition; and finally, judging whether the middle vertex elements in each condition are compressed or not further through different vertical distance threshold values d_max and d_min. In the whole processing process, various conditions are considered, a plurality of judgment values are used and work cooperatively, and finally each intermediate point element which does not meet the condition is compressed. Compared with an existing processing method, the invention has a better compression effect and a higher 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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