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Data abnormal mutation point detection algorithm based on limited accompanying group mechanism

A technology for data anomalies and detection algorithms, applied in computing, computer components, instruments, etc., can solve problems such as different computational complexity, order dependence, and large time overhead, so as to reduce computing space, improve detection efficiency, and improve accuracy Effect

Inactive Publication Date: 2020-09-22
JIANGSU FRONTIER ELECTRIC TECH +2
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Problems solved by technology

However, traditional detection methods are difficult to meet the needs of real-time industrial data flow anomaly detection with large amounts of data.
For example, statistical methods are suitable for detecting discrete and sudden value anomalies in the introduction, but it is difficult to effectively identify continuous abnormal sequence intervals
Clustering-based methods quantify the distance between abnormal points and normal point clusters to judge outliers. The computational complexity of different clustering models is different, and the detection results are more dependent on the quality of clustering. At the same time, it is not suitable for large-scale Anomaly detection on datasets and real-time data streams
The method based on similarity measurement calculates the similarity between standardized sequences to judge whether there is abnormal data, but this method has a large time cost and is not time-sensitive
In the method based on rule constraints, the researchers proposed that order dependence and speed constraints can effectively use the timing characteristics in time series to repair highly abnormal data, but this method is usually difficult to meet the needs of sequence anomaly detection with variable patterns

Method used

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  • Data abnormal mutation point detection algorithm based on limited accompanying group mechanism
  • Data abnormal mutation point detection algorithm based on limited accompanying group mechanism
  • Data abnormal mutation point detection algorithm based on limited accompanying group mechanism

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Embodiment Construction

[0022] Such as figure 1 As shown, the method of the present invention mainly includes four parts of point group construction to be detected, limited jury selection, review and result output, the main task of the construction of the point group to be detected is to obtain the points to be detected in the data stream, And constitute the point group to be detected with the detected data points, which can be used as the evaluation target. The limited jury selection includes two steps: (1) setting a limited jury selection mechanism; (2) generating a limited jury based on the set limited jury selection mechanism. The limited jury is the main basis for evaluating whether the point to be detected is an abnormal mutation. The review includes two steps: (1) setting the review mechanism; the review mechanism is a limited jury review method for the point group to be tested; (2) giving an evaluation. The limited jury members evaluate whether the point group to be detected is an abnormal ...

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Abstract

The invention discloses a novel abnormal mutation point detection method, and particularly relates to a data abnormal mutation point detection algorithm based on a limited accompanying group mechanism. A finite accompanying group mechanism is utilized to help to efficiently and accurately identify and detect the mutation points in the industrial data stream. According to the method, a limited accompanying group thought is provided, and effective identification and detection of abnormal mutation points of the data stream are realized by selecting a limited high-quality accompanying group and constructing a to-be-detected point group. The algorithm has low delay, and abnormal mutation points can be timely identified in real-time data streams.

Description

technical field [0001] The invention relates to a novel data abnormal mutation point detection algorithm, in particular to a data abnormal mutation point detection algorithm based on a limited jury mechanism. Background technique [0002] Since the concept of the Industrial Internet was proposed, the industry has stored a large amount of data related to equipment runtime through data collection and big data transmission and storage technologies. In terms of ensuring the safe operation of industrial equipment, in addition to manual maintenance and repair of equipment, the industry has been constantly trying to use existing historical data and real-time monitoring data streams of equipment to conduct related research to achieve more efficient equipment monitoring and data anomalies diagnostic function. At the same time, more and more scholars have begun to study fault diagnosis algorithms based on industrial data in order to provide a more efficient and accurate diagnosis alg...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q10/00
CPCG06Q10/20G06F18/2433
Inventor 孙栓柱余长州高阳周春蕾李逗孙彬王林王其祥高进李春岩沈洋黄治军张磊傅高健
Owner JIANGSU FRONTIER ELECTRIC TECH
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