Exception detection method based on data flow concept drift

A concept drift and anomaly detection technology, applied in digital data information retrieval, electrical digital data processing, special data processing applications, etc.

Pending Publication Date: 2020-05-12
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

Once the concept drifts in the data stream, the diagnostic performance of the original anomaly diagnosis model generated based on the old concept will decline under the new data, which is prone to misjudg...

Method used

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  • Exception detection method based on data flow concept drift
  • Exception detection method based on data flow concept drift
  • Exception detection method based on data flow concept drift

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Embodiment Construction

[0033] The present invention will be further described in detail with reference to the accompanying drawings and embodiments.

[0034] Such as figure 1 As shown, the anomaly detection method based on data flow concept drift in this embodiment includes the following steps:

[0035] S1. Obtain the real data currently collected by the system to be tested at different times to form a real data stream X={x 1 , x 2 ,...,x t , x t+1 ,...,x N}, and establish the current prediction model M of the system to be tested according to the real data flow, where xt represents the real data of the system to be tested at time t.

[0036] S2, predict the data of the system to be detected in the next period through the prediction model M, and obtain the predicted data flow Y={y 1 ,y 2 ,...,y t ,y t+1 ,...,y N}, where y t Indicates the predicted data of the system to be tested at time t.

[0037] S3, calculate the real data flow X={x 1 , x 2 ,...,x t , x t+1 ,...,x N} and predicted...

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Abstract

The invention provides an exception detection method based on data flow concept drift, belongs to the field of data mining and exception detection, and aims to provide an exception detection method based on data flow concept drift which can detect concept drift in time. The method comprises the steps of S1, obtaining the real data collected by a to-be-detected system at different moments to form areal data stream, and establishing a current prediction model of the to-be-detected system according to the real data stream; S2, predicting the data of the next time period through a prediction model to obtain a prediction data stream; S3, calculating a similarity data set between the real data flow and the prediction data flow; S4, judging whether concept drift occurs or not according to the similarity data set and a current concept drift threshold value of the to-be-detected system; S5, if not, repeating the steps S2 to S4; S6, if yes, updating the prediction model, the concept drift threshold value and the exception detection threshold value, and repeating the steps S2 to S6 according to the updated prediction model and the concept drift threshold value.

Description

technical field [0001] The invention relates to the technical field of data mining and anomaly detection, in particular to an anomaly detection method based on data flow concept drift. Background technique [0002] In systems that collect data by time, such as actual production, there are often data that do not conform to normal data or existing data change patterns, and these data are so-called abnormal data. There are many methods for anomaly data detection, but they rarely consider the phenomenon of concept drift in data flow. Concept drift means that the concept contained in the data flow has changed, for example, with the improvement of the process flow, the aging of the machine, the update of the equipment and the emergence of unknown working conditions, the target concept of the data flow has changed, making the old data and The concepts contained in the new data are no longer consistent. Once the concept drifts in the data stream, the diagnostic performance of the ...

Claims

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Application Information

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IPC IPC(8): G06F16/2455
CPCG06F16/24568
Inventor 郭宏任必聪闫献国陈峙田青任党阳白旭
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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