Ship Trajectory Compression Using Spatio-Temporal Characteristic Points
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional ship trajectory compression algorithms fail to effectively retain characteristic points, particularly dynamic information such as speed and course changes, and entry/exit points from areas, leading to reduced data utility and inefficient compression.
Innovation Solution
A spatio-temporal Douglas-Peucker method that performs clustering analysis, converts coordinates to Mercator projection, calculates and retains speed and course change rates, identifies significant trajectory points, and compresses data by considering spatio-temporal characteristics, using thresholds and distance calculations to retain key points while discarding less significant data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If conventional trajectory compression algorithms are used to reduce data volume, then data compression ratio is improved, but characteristic points (speed/course changes, area entry/exit points) are lost
Solution Approach 1:
The patent introduces spatio-temporal parameters (speed change rate, course change rate, area entry/exit detection) to transform the compression approach from simple geometric distance-based to a multi-parameter characteristic-based method, thereby preserving important dynamic information while compressing data
Solution Approach 2:
The patent applies different treatment to different parts of the trajectory by identifying and preserving local characteristic points (where speed/course changes occur or area boundaries are crossed) while compressing non-characteristic segments, ensuring that important local information is retained while reducing overall data volume
2Device complexity
If only geometric distance is considered for compression, then computational simplicity is improved, but trajectory shape and dynamic information are poorly retained
Solution Approach 1:
The patent extends the compression criterion from two-dimensional geometric distance to three-dimensional spatio-temporal distance by incorporating time dimension and dynamic parameters (speed, course), enabling better trajectory shape retention through enhanced distance calculation that accounts for temporal and dynamic characteristics
3Measurement precision
If sensor fluctuation points are retained to maintain accuracy, then measurement precision is improved, but data volume increases significantly
Solution Approach 1:
The patent applies a threshold-based filtering approach that retains only those fluctuation points exceeding significant change thresholds (speed change rate, course change rate), rather than retaining all fluctuation points, thus achieving a balance between preserving meaningful dynamic information and controlling data volume
Data Source
AI summary
A spatio-temporal DP method based on ship trajectory characteristic point extraction, which belongs to the technical field of ship trajectory compression and includes: Step 1: performing clustering analysis on AIS raw data using a clustering algorithm to identify outliers in the AIS data and then eliminate noise points; Step 2: identifying and retaining the characteristic trajectory points of the ship course change, ship speed change, and the ship entering and exiting from a certain area and the like; Step 3: compressing the AIS data by taking the start and end points of the ship trajectory and the characteristic trajectory points retained in step 2 as the initial points, and considering the spatio-temporal characteristics of the AIS data at the same time. The compressed ship can effectively compress redundant AIS data. The compressed ship trajectory has very little difference from the original trajectory, can retain the information of points of the ship motion state change and the points of the ship entering and exiting from the boundary of an area at the same time, has a large reuse value space, and is used for laving the foundation of data processing for ship historical data analysis and ship behavior recognition.