Maximum information entropy-based chemical data stream real-time abnormity detection method

A technology of maximum information entropy and anomaly detection, applied in the field of anomaly detection, can solve problems such as poor robustness, achieve the effect of improving efficiency, effective algorithm, and reducing the number of invalid grids

Active Publication Date: 2015-04-15
BEIJING UNIV OF CHEM TECH
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Problems solved by technology

In addition, the algorithm effectively solves the problem of poor robustness of the traditional density algorithm due to its sensitivity to scanning radius and density threshold by setting the similarity of dimensional clusters, and can better adapt to streaming data

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  • Maximum information entropy-based chemical data stream real-time abnormity detection method
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  • Maximum information entropy-based chemical data stream real-time abnormity detection method

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Embodiment Construction

[0036] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0037] The process fault monitoring method based on the improved dynamic visible graph of the present invention comprises the following steps:

[0038] S101, initialize the window, and read n data from the data stream, where n is the width of the sliding window;

[0039] S102, using the DSC-Stream (The Dimension Space Cluster-Stream) algorithm to calculate the micro-cluster information entropy;

[0040] The algorithm flow is as follows:

[0041] S201, calculate the group distance r of each dimension from the different dimensional spaces of the data j , group the data according to the group distance of each dimension;

[0042] S202, select the group with the highest den...

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Abstract

The invention discloses a maximum information entropy-based chemical data stream real-time abnormity detection method. According to the method, a real-time clustering algorithm DSC-Stream (The Dimension Space Cluster-Stream) is used; a data dimension space is used as a starting point to divide the data dimension space into a plurality of dimension clusters according to the maximum entropy principle; the data in the same dimension cluster forms a micro cluster so as to realize the real-time clustering of the data streams; the algorithm can be used for effectively decreasing the quantity of the invalid grids generated in the traditional density grid algorithm so as to greatly improve the operational efficiency; meanwhile, through a method for setting dimension cluster similarity, the algorithm can be used for effectively solving the problem that the traditional density algorithm is sensitive to the scanning radius and the density threshold to cause poor robustness and can be better adapted to the stream data.

Description

technical field [0001] The invention relates to the field of anomaly detection, in particular to a method for real-time anomaly detection of chemical data streams based on maximum information entropy. Background technique [0002] Anomaly detection is an important field of chemical production monitoring. The working mechanism of anomaly detection is that anomalies deviate from most of the data in the data set, which makes people suspect that the deviation of these data is not generated by random factors, but by a completely different mechanism. [0003] The clustering of chemical process data flow is one of the new hotspots in the field of data mining research recently, and it widely exists in the form of real-time, continuous and orderly data sequences. Chemical production monitoring data is streaming data, which has the characteristics of large data volume, continuous and rapid, unpredictable and short-lived. It is unrealistic to store all the elements in the monitoring pr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG05B23/0221
Inventor 耿志强姬威韩永明朱群雄徐圆
Owner BEIJING UNIV OF CHEM TECH
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