Data flow abnormality detection method based on parallel Kalman algorithm

A technology of anomaly detection and data flow, applied in the field of big data management, which can solve problems such as inaccuracy and efficiency of anomaly detection

Inactive Publication Date: 2017-05-24
HOHAI UNIV
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[0004] Purpose of the invention: The present invention provides a data stream anomaly detection method based on the para

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  • Data flow abnormality detection method based on parallel Kalman algorithm
  • Data flow abnormality detection method based on parallel Kalman algorithm
  • Data flow abnormality detection method based on parallel Kalman algorithm

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[0061] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0062] The parallel Kalman algorithm based on multi-dimensional impact factors, first of all, in order to improve the accuracy of abnormal detection results, and to determine the type and cause of abnormality, the anomaly detection of data streams adds three dimensions of time, space and other origin information. Influencing factors, a Kalman method based on multi-dimensional influencing factors is proposed; then, in order to improve the efficiency of the algorithm, the algorithm is decomposed into...

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Abstract

The invention discloses a data flow abnormality detection method based on a parallel Kalman algorithm. The data flow abnormality detection method comprises the following steps that 1, measurement data of a sensor in a period of time is acquired; 2, the measurement data is compared with a measurement value in a previous period of time, once a change is generated, an estimation value is calculated through the Kalman algorithm according to the measurement value, an absolute value of a difference between the estimation value and the measurement value is compared with a specified threshold value, and if the absolute value is not smaller than the threshold value, the absolute value is judged to be an abnormal value, and the next step is conducted; 3, the generation reasons of the abnormal value are judged by considering a time influence factor, a space influence factor and other factors such as the flood period, the weather and the human factors which influence abnormality detection and recorded, and information is stored in a database. According to the data flow abnormality detection method, the time influence factor, the space influence factor and the other provenance information influence factor are taken into account; an algorithm task is decomposed and processed in parallel in order to improve the algorithm efficiency, and the detection precision is improved.

Description

technical field [0001] The invention belongs to the technical field of big data management, and in particular relates to a data flow anomaly detection method based on a parallel Kalman algorithm. Background technique [0002] Anomaly detection methods for data streams usually require complex calculations, as well as modification and fusion of data. This is a complex process, so how to ensure the accuracy and efficiency of detection is crucial. [0003] Although there is a Kalman algorithm in the prior art, it does not take time, space and other origin information into consideration, and only performs calculation and detection of a single item. As a result, the accuracy of overall data flow anomaly detection is not high, which may cause errors in the detection process. Contents of the invention [0004] Purpose of the invention: The present invention provides a data stream anomaly detection method based on the parallel Kalman algorithm, which can solve the problems of inac...

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 许国艳花青石水倩
Owner HOHAI UNIV
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