Track Anomaly Detection Using Grid-Based Historical Location Analysis
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Solution Overview
Problem
Existing methods for detecting track anomalies in vehicle movement data are resource-intensive due to the high calculation and storage requirements, especially in scenarios with a large number of vehicles generating location data.
Innovation Solution
A track anomaly detection method that uses gridding processing to determine the target grid and historical grids for a moving object, analyzing the location relationship between these grids to identify anomalies, thereby reducing the computational load and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location data from all historical locations is used for anomaly detection, then detection accuracy is improved, but calculation amount and resource consumption increase dramatically
Solution Approach 1:
The patent divides the continuous geographic area into discrete grid cells. Instead of processing all historical location data points, the system only processes location data that falls within the same grid cell as the current location being detected. This segmentation dramatically reduces the amount of data that needs to be processed while maintaining detection accuracy for locations within the same spatial context.
Solution Approach 2:
The patent applies different processing rules based on spatial location. By using grid cells, the system focuses computational resources locally on relevant historical locations within the same grid, rather than uniformly processing all historical data. This local quality approach ensures that anomaly detection is performed only on geographically relevant historical data, reducing overall calculation load.
2Reliability
If location data is collected over a long travel time span, then more location data is generated improving detection coverage, but resource overheads increase
Solution Approach 1:
The patent extracts and stores only the essential grid cell information from historical location data, rather than storing complete location coordinates and timestamps for all historical points. This extraction reduces the storage requirements and computational overhead while preserving the spatial context needed for anomaly detection over long time spans.
Solution Approach 2:
By segmenting the geographic space into grids and only retaining grid-level historical information, the system can accumulate data over long periods without proportionally increasing resource overhead. The grid segmentation acts as a compression mechanism that maintains detection coverage while reducing storage and processing requirements.
Data Source
AI summary
A track anomaly detection method, performed by a computer device includes, acquiring a location to be detected in a moving track generated by a moving object; determining a target grid to which the location to be detected belongs from grids obtained by performing, based on an actual geographic area, gridding processing; determining at least one historical grid related to the moving object, the at least one historical grid being obtained based on a historical location, located within a preset time range, in the moving track generated by the moving object; and determining, according to a location relationship between the target grid and the at least one historical grid, an anomaly detection result of the moving track, transmitting, based on the anomaly detection result indicating the location to be detected is an abnormal location point, information to a terminal, to cause the terminal to display a warning prompt to a user.