Method and system for detecting change of target state from position data stream

Abstract

The invention provides a method and a system for detecting change of a target state from a position data stream. The method comprises the following steps of: recording the precedent state BL of the target; receiving the position data one by one from the position data stream; when the position data is received every time, determining the accumulation state changing trend of the target from the precedent state BL to each state Bi (i ranges from 1, 2 to I); detecting whether the position data stream has the state changed point according to the determined accumulation state changing trend. When the state changed point is detected, the method of the invention can further describe the position of the state changed point and the target changed state. Compared with the prior art, the method for detecting the change of the target state is more quick and accurate, and the detection result is descriptive.

Description

Technical field [0001] The present invention generally relates to the processing of a location data stream, and more specifically, to a method and system for detecting a target state change from a location data stream. Background technique [0002] As the basis of context-aware computing, the status of a moving target (user or object) can generally be mined and inferred based on the real-time location data of the target. Regarding state mining, the detection of target state changes is a key step. Based on the detected target state change, various services can be provided to the user according to the context. Applications usually require state change detection to have the following characteristics: 1) as fast as possible; 2) accurate (with few false alarms); 3) descriptive, that is, it can not only explain when the state change occurs, but also show that it has occurred Which state changes. However, the existing technology has not yet fully met the above requirements. [0003] T...

AI Technical Summary

Problems solved by technology

[0010] It can be seen that, in the prior art proposed by Shai Ben-David et al., difference tests with different window lengths will result in different state change detection points, making it difficult to judge

In addition, this method is difficult to balance the requirements of "fast" and "accurate"

Too large a window size may lead to detection delays, while too small a window size may lead to false alarms, making it difficult to compromise between different change detections

[0011] In addition, the computational complexity of the difference test with K parallel branches is too high, and the window size, distance function and threshold are difficult for users to choose

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