30 results about "Trajectory compression" patented technology

The bus GPS deviation correction method based on abnormal point elimination and trajectory compression algorithm designed by the present invention comprises the following steps: Step 1: obtaining the historical GPS data of the designated bus route; Step 2: applying local The abnormal factor algorithm is used for filtering; Step 3: Simulate the GPS trajectory data of the specified bus route; Step 4: Based on the GPS coordinate point information of the specified bus line, use the specified bus route to real-time GPS data obtained from the message middleware The GPS track data of the bus is used for track correction, and the coordinate value of the midpoint of the two coordinate points closest to the real-time GPS point on the GPS track of the specified bus route is taken as the correction point of the bus GPS positioning drift point. The invention can improve the accuracy of the GPS route point on the map position of a specific vehicle shift.

The invention provides a time trajectory representation and compression framework that can fully utilize data features, and relates to the field of trajectory compression, especially the compression of time trajectory. The large and redundant vehicle trajectory data leads to many overheads in data storage, communication and processing. Currently, raw trajectories are usually decomposed into spatial paths and time series. This paper proposes a new framework for time series compression, including a representation constructor and four compressors. Specifically, we first construct a time trajectory representation with four elements, which has low entropy and storage; then construct four compressors to compress the corresponding four types of elements respectively, and improve the compression efficiency.

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.

The invention discloses a trajectory compression method based on a sliding window. Firstly, the current trajectory point to be compressed in the sliding window and the vertical distance of a straight line from a reference trajectory point with a maximum offset distance to its initial trajectory point and an end trajectory point are respectively calculated; Then make a judgment: if any of these two distances is greater than the distance threshold for trajectory compression, add the current trajectory point to be compressed to the compressed trajectory set, and use the current trajectory point to be compressed as the start of the sliding window track point to set a new sliding window; otherwise, according to the size of the two distances, update the maximum offset distance reference track point, and move the current track point to be compressed and the end track point of the sliding window back by one position at the same time. The above processing is repeated until the compression of all track points is completed. The method can effectively speed up the track compression processing speed, improve the operation efficiency, and reduce the complexity of the algorithm.

The present invention provides a method and device for spatio-temporal trajectory compression and segmented state expression. The method includes: batch extraction of offline trajectories; performing primary filtering processing; constructing trajectory point representation; performing secondary filtering and compression; performing trajectory state expression based on speed . The present invention provides a method and device for spatio-temporal trajectory compression and segmented state expression, which can not only fully retain the motion characteristics of the trajectory, but also compress the redundant part of the trajectory, reduce the storage capacity, improve the efficiency of display expression, and solve the problem of The problem of missing some trajectory features in the existing technical solutions.

The invention discloses a vehicle trajectory compression method based on sequence similarity. The method is as follows: 1) Synthesize the trajectory of vehicles with the same ID in unit time into a long trajectory; the long trajectory is Trajectory ID ={(l 1 ,t 1 ),(l 2 ,t 2 ),…,(l n ,t n )}, where the information collected for the nth time includes position l n , time information t n , denoted as the collection point (l n , t n ); 2) Cut the long trajectory according to the set speed threshold to obtain multiple segmented trajectories; 3) Based on the sequence similarity between the segmented trajectories, perform global trajectory compression on the long trajectory. The invention greatly saves storage overhead.

The invention relates to a spatio-temporal trajectory aggregation pattern mining algorithm based on R* tree index, comprising three steps of trajectory compression, trajectory clustering and aggregation detection. The algorithm comprehensively utilizes the motion direction, motion speed and offset information of the trajectory data to compress the trajectory and improve the compression effect of the algorithm; uses the line segment DBSCAN based on the R* tree index to cluster the trajectory segments to improve the efficiency of the algorithm clustering. Relevant experiments show that the algorithm has improved mining effect compared with similar algorithms. The next work is to apply this algorithm to the actual trajectory data aggregation pattern mining task to improve the effect of data mining.

The invention discloses a spatiotemporal DP method based on extraction of ship trajectory feature points, which belongs to the technical field of ship trajectory compression. , and then remove the noise points; Step 2: Identify and retain the characteristic trajectory points such as the change of the ship's course, the speed of the ship, and the ship's entry and exit from a certain area; Step 3: Use the starting point and end point of the ship's trajectory and the characteristic trajectory points retained in Step 2. As the initial point, the AIS data is compressed while considering the spatiotemporal characteristics of the AIS data. The invention can effectively compress the redundant AIS data, the difference between the compressed ship trajectory and the original trajectory is very small, and at the same time, the information of the change point of the ship's motion state and the boundary point of the ship's entry and exit areas can be retained, and the reuse value space is large, and it can be used for the history of the ship. Data analysis and ship behavior identification lay the foundation for data processing.

The present invention relates to an online track compression method and device, computer equipment and computer-readable storage medium. The method includes: intercepting a set of track points from an original track; initializing a current track to be compressed, and adding the starting point of the current track to be compressed In the compressed track; according to the starting point and end point of the current track to be compressed, traverse backward from the end point to the starting point, and check whether the track points in the current track to be compressed are compressible points one by one; if they are all compressible points, execute compression , move the starting point of the current track to be compressed to the track point where the end point of the current track to be compressed is located, and then move the end point of the current track to be compressed to the last track point in the group of track points; when it is checked that a track point is not a compressible point , then move the end point of the current track to be compressed to the track point that is not a compressible point; determine whether to end the compression. The present invention can process with constant time and space, and achieve higher compression ratio.

The invention relates to a processing method for compressing space-time trajectory data, comprising: splitting the accessed data to calculate a complete base station information table; grouping the data by device numbers and sorting multiple pieces of data in each group; generating a first One track compressed data block and establish parameter information; mark the latitude and longitude of the track compressed data block; sequentially perform iterative processing on each piece of data in each group; establish multiple track compressed data blocks and determine the type of each track compressed data block in turn; calculate The time difference between two adjacent static data blocks is assigned to it; the steps are repeated to complete the compression of each group of track compressed data blocks in sequence. The invention determines the type of each track compressed data block, and assigns value to each dynamic data block according to the speed of each dynamic data block after the determination, so that the data can be efficiently compressed while ensuring that the features of the compressed data are extracted. and the data compression efficiency of the method of the present invention is effectively improved.