Hybrid cloud scene-oriented time series data anomaly prediction method based on ensemble learning technology

A time-series data, integrated learning technology, applied in machine learning, electrical digital data processing, special data processing applications, etc., can solve problems such as low detection accuracy, easy to form data islands, etc., to improve accuracy, reduce business failure risks, The effect of reducing false positives and false negatives

Pending Publication Date: 2020-12-25
HEFEI CITY COULD DATA CENT +1
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AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to solve the defects of low detection accuracy and easy formation of data islands in the detection of time-series data anomalies in the prior art, and to provide a time-series data anomaly detection method based on integrated learning technology for hybrid cloud scenarios to solve the above problems

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  • Hybrid cloud scene-oriented time series data anomaly prediction method based on ensemble learning technology
  • Hybrid cloud scene-oriented time series data anomaly prediction method based on ensemble learning technology
  • Hybrid cloud scene-oriented time series data anomaly prediction method based on ensemble learning technology

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[0053] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, a detailed description is provided in conjunction with preferred embodiments and accompanying drawings, as follows:

[0054] like figure 1 and figure 2 As shown, a kind of time series data anomaly prediction method based on integrated learning technology for hybrid cloud scene described in the present invention comprises the following steps:

[0055] The first step is the acquisition and preprocessing of historical data. Collect historical operating data of the system in the hybrid cloud scenario and perform data preprocessing. Data preprocessing includes missing value processing, data normalization, and sliding window. Among them, the mean value interpolation method can be used for missing value processing, and min-max standardization can be used Methods The normalization processing and the sliding window processing adopted a fixe...

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Abstract

The invention discloses a hybrid cloud time series data-oriented anomaly detection method based on an ensemble learning technology, and relates to the technical field of data processing and anomaly detection. The method comprises the following steps: obtaining and preprocessing historical data; establishing a time series data anomaly detection model; jointly training the three basic prediction models; prediction the timing data anomalies. According to the method, through analysis and processing of log time series data and an integrated learning framework based on STL, ARIMA and LSTM, a unifiedand integrated anomaly detection model for coping with a hybrid cloud complex scene is established, anomaly detection and prediction of service indexes are carried out in time, possible abnormal changes are found in advance, fault emergency processing is carried out, and the probability of service fault risks is reduced.

Description

technical field [0001] The present invention relates to a time-series data processing method, in particular to a time-series data anomaly detection method for hybrid cloud scenarios based on integrated learning technology. Background technique [0002] In a hybrid cloud environment, large-scale distributed software systems are large in scale, complex in composition and operating logic, so various system abnormalities will inevitably occur during system operation, and these system abnormalities may lead to subsequent system failures. thereby causing losses. In order to ensure the high availability and reliability of large-scale distributed software systems and hardware devices, it is necessary to monitor the status of the hybrid cloud system, analyze the log time series data of the hybrid cloud system, and timely discover the abnormal behavior status of the system, and then Avoid possible system failures. [0003] In recent years, machine learning and deep learning have ach...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/215G06F16/2458G06N3/04G06N20/00
CPCG06F16/215G06F16/2474G06N20/00G06N3/048G06N3/044G06N3/045
Inventor 刘浩刘胜军倪志伟周芳陈千朱旭辉倪丽萍
Owner HEFEI CITY COULD DATA CENT
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