Vector Trajectory Compression Algorithm And Its Application In Qinghai-Tibet Scientific Expedition

With the development of positioning technology and the popularity of positioning equipment,vector data under the background of big data is around us,and presents an explosive growth.Vector trajectory compression technology has become a hot issue in the fields of optimized storage,network transmission and visualization.Vector trajectory can not be separated from the acquisition and processing of vector data,such as real-time tracking of aerospace,aviation and satellite trajectory,unmanned driving,bus platform display and arrival reminder,real-time positioning of network platform,and the position change of pedestrians all the time.However,vector data is continuously produced and output,but the processing capacity of the devices and computers used to store data is always limited,so it is very meaningful and imperative to study the vector data compression algorithm.The main research work of this paper is as follows(1)The collection and extraction of the original trajectory data sets,first of all,the vector data sets in many industries are sorted and extracted,and the original vector trajectory data sets are obtained by group classification.(2)This paper proposes a new lossy compression algorithm.Based on the research and implementation of the traditional lossy compression algorithm,it summarizes the advantages of predecessors and improves its shortcomings.Finally,it proposes an efficient and fast vector trajectory compression algorithm,cosine vertical distance discrimination(CVDD).(3)Build a map visualization analysis model.For each group of original vector trajectory data with both size and direction obtained in(1),through coordinate calculation in different lossy compression algorithms,different analysis and comparison experimental results are obtained.(4)Finally,the proposed CVDD compression algorithm is applied to the Qinghai Tibet scientific research service system to improve the response speed of the system's Web services,reduce the delay of network data requests,and reduce the storage and cost of the database.