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Hydrological data abnormal mode detection method based on similarity measurement

A similarity measurement and data anomaly technology, applied in digital data processing, character and pattern recognition, special data processing applications, etc., can solve problems such as low accuracy and slow detection speed, and achieve the effect of accurate abnormal patterns

Inactive Publication Date: 2019-12-13
HOHAI UNIV
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Problems solved by technology

[0004] Purpose of the invention: Aiming at the problems of low accuracy and slow detection speed of abnormal situations in the above-mentioned prior art, the present invention provides a method for detecting abnormal patterns of hydrological data based on similarity measurement, which provides a theoretical basis for detecting abnormal patterns existing in hydrological data

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  • Hydrological data abnormal mode detection method based on similarity measurement
  • Hydrological data abnormal mode detection method based on similarity measurement
  • Hydrological data abnormal mode detection method based on similarity measurement

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[0049] In order to describe the technical solution disclosed in the present invention in detail, further elaboration will be made below in conjunction with the accompanying drawings and specific embodiments.

[0050] An anomaly pattern detection method for hydrological data based on a similarity measure, such as figure 1 As shown, the invented hydrological data abnormal pattern detection method based on similarity measurement includes three modules: KPRA-PLR algorithm based on key point linear segment representation, time series pattern similarity measurement method, and K-nearest neighbor principle. In this embodiment, the hydrological data is selected to select the hourly flow rate of the Longmen hydrometric station during the flood season (unit is m 3 / s) data, using the measured hourly flow data of the station during the flood season from 2000 / 6 / 1 6:00:00 to 2015 / 9 / 30 17:39:00 for the experiment.

[0051] First, based on the KPRA-PLR algorithm based on the linear segment ...

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Abstract

The invention discloses a hydrological data abnormal mode detection method based on similarity measurement. The method is based on a linear piecewise representation KPRA-PLR algorithm of a key point,hydrological data is cut according to the definition of the key point, straight line fitting is carried out on each sub-sequence through the PLR algorithm, and the slope ai and the time interval deltat of a straight line are used for representing the sub-sequence; wherein each segmented sub-sequence is called as a meta-mode, adjacent meta-modes are combined to obtain a sequence mode, a weighted distance and an SDTW algorithm are respectively used for similarity measurement of the meta-mode and the sequence mode, and then an abnormal score of each sequence mode, namely a reciprocal of an average distance between the mode and other modes, is calculated; wherein the abnormal score is the k-nearest neighbor distance of the sequence mode Sx, and calculating a local abnormal factor LOF according to a k-nearest neighbor local detection principle. The abnormal mode detected by using the similarity measurement method is more accurate, and a new technology is provided for hydrological data abnormal mode detection from the perspective of data analysis.

Description

technical field [0001] The invention belongs to hydrological detection technology, in particular to a method for detecting abnormal patterns of hydrological data based on similarity measurement. Background technique [0002] The hydrological time series is the observation value of a series of physical quantities (water level, flow, rainfall, etc.) obtained by the observation system in chronological order. Hydrological time series is a common complex data type, which objectively records the observation information obtained by the observation system in chronological order. In my country, with the development of water conservancy informatization, the hydrological time series will be transmitted to the water conservancy information sharing platform, processed by the staff and stored in the national hydrological library. There are some abnormal data in the hydrological time series due to the measurement error of the acquisition equipment, the error of manual operation, and the c...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F17/50
CPCG06F18/22
Inventor 万定生张祥
Owner HOHAI UNIV
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