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Travel anomaly detection method based on hidden Markov model

An anomaly detection and model technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as low precision and poor reliability

Inactive Publication Date: 2014-05-14
浙江远图技术股份有限公司
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Problems solved by technology

[0004] In order to overcome the shortcomings of low precision and poor reliability of the existing anomaly detection technology, the present invention provides a travel anomaly detection method based on hidden Markov model with high precision and good reliability

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  • Travel anomaly detection method based on hidden Markov model
  • Travel anomaly detection method based on hidden Markov model
  • Travel anomaly detection method based on hidden Markov model

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Embodiment Construction

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] refer to figure 1 , a travel anomaly detection method based on hidden Markov model, including the following steps:

[0053] (1) Use Beidou or GPS positioning equipment to collect monitoring data, including longitude, latitude and date, and use the GPRS module to send these data to the travel behavior monitoring platform. When the signal is lost, use the linear interpolation method to fill in the missing coordinates point;

[0054] (2) Divide coordinate data into weekday data and holiday data The k-medoids algorithm is used for clustering, and two sets of clustering marker points are obtained respectively. { q 1 w , q 2 w , . . . , q k w }...

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Abstract

A travel anomaly detection method based on a hidden Markov model includes the following steps that firstly, a Beidou or GPS positioning device is utilized to acquire monitoring data such as longitude, latitude and dates, and the data are sent to a travel behavior monitoring platform through a GPS module; secondly, coordinate data are divided into workday data and holiday data, and the workday data and the holiday data are clustered through a k-medoids algorithm to acquire two clustering mark point sets respectively; thirdly, the workday coordinate data and the holiday coordinate data are used as two sets of observed quantities, an HMM model is trained through a Baum-Welch algorithm, and then a workday travel rule model and a holiday travel rule model are respectively acquired; fourthly, travel behavior anomalies are detected through the travel rule models. The travel anomaly detection method is high in accuracy and good in reliability.

Description

technical field [0001] The invention relates to the field of travel abnormality detection, in particular to a travel abnormality detection method for special groups such as the elderly or children. Background technique [0002] The Global Navigation Satellite System (GNSS) includes GPS in the United States, Glonass in Russia, Galileo in Europe and Beidou satellite navigation system in China, etc., which have been widely used in military and civilian fields. In terms of the application of satellite navigation systems, China has broad market prospects, such as mobile intelligent vehicle terminals using GPS and GPRS, and vehicle monitoring and traffic guidance systems based on GPS or Beidou navigation secondary development, which are widely used in public security, medical , fire protection, transportation, logistics and other fields, but most of them are only for basic services such as vehicle navigation and positioning, and there is no solution for human behavior positioning ...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 刘翔吴俊宏李仁旺张标标杨彦斌
Owner 浙江远图技术股份有限公司
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