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Cloud platform anomaly detection method and system based on multi-data channel analysis

An anomaly detection and data channel technology, applied in digital data information retrieval, electrical digital data processing, special data processing applications, etc., can solve the problems of time-consuming and labor-intensive, false detection of empirical values, etc., and achieve the effect of improving sensitivity

Pending Publication Date: 2021-01-15
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] As far as the inventor knows, the traditional detection method mainly relies on manual inspection and regular maintenance and inspection, and whether the equipment is abnormal needs to be judged by the staff based on past experience. This abnormal detection is time-consuming and labor-intensive; With the development of technology, there are already related technologies for actively detecting data anomalies with the help of computers, but at least the following problems exist:
[0005] Whether the data is abnormal is usually based on the preset empirical value. When the data exceeds this value, it is considered abnormal. However, if the equipment is in different environments, such as outdoor and indoor in winter, or the same equipment is put into operation at different The operating parameters of the equipment, such as the transmission rate, will also change, so relying on preset empirical values ​​may cause false detection

Method used

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  • Cloud platform anomaly detection method and system based on multi-data channel analysis
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  • Cloud platform anomaly detection method and system based on multi-data channel analysis

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Embodiment 1

[0048] The present embodiment discloses a cloud platform anomaly detection method based on multi-data channel analysis, comprising the following steps:

[0049] Step 1: Obtain monitoring data flow;

[0050] Specifically, suppose a given monitoring data stream starts from time 1 and contains N data points, namely:

[0051] F={f 1 ,f 2 ,...,f N} (1)

[0052] Among them, f i Denotes multivariate observations at time i.

[0053] Step 2: For the monitoring data stream, obtain a set of candidate segmentation points according to the data distribution of each data channel, and pre-segment the monitoring data stream to obtain multiple subsequences;

[0054] Definition: Detect a set of segmentation points from F, namely:

[0055] Ε:={f r1 ,f r2 ,...,f rk} (2)

[0056] in, is of size r k The set of split points, r k i ∈{1,2,...,N}.

[0057]Among them, the cutting of each data channel is based on the following basic idea: the data flow F is regarded as a combination of a s...

Embodiment 2

[0101] The purpose of this embodiment is to provide a cloud platform anomaly detection system based on multi-data channel analysis.

[0102] A cloud platform anomaly detection system based on multi-data channel analysis, including:

[0103] A data stream acquisition module configured to acquire the monitoring data stream;

[0104] The data stream pre-segmentation module is configured to pre-segment the monitoring data stream according to the data distribution of each data channel of the monitoring data stream to obtain multiple subsequences;

[0105] The data distribution statistical module is configured to perform statistics on the data distribution difference between every two adjacent subsequences;

[0106] The change detection module is configured to perform change detection of the monitored data flow according to the statistical result of the data distribution difference.

Embodiment 3

[0108] The purpose of this embodiment is to provide an electronic device.

[0109] An electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program, it realizes the multi-data channel-based analysis as described in Embodiment 1 Cloud platform anomaly detection method.

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Abstract

The invention discloses a cloud platform anomaly detection method and system based on multi-data-channel analysis. The method comprises the following steps: acquiring a monitoring data stream; pre-segmenting the monitoring data stream according to the data distribution of each data channel of the monitoring data stream to obtain a plurality of sub-sequences; counting the data distribution difference of every two adjacent sub-sequences; and according to the statistical result of the data distribution difference, detecting monitoring data flow changes. According to the invention, based on the time continuity characteristic of the monitoring data flow, data anomaly detection is carried out based on data distribution of a plurality of data channels, and the detection precision is higher.

Description

technical field [0001] The invention belongs to the technical field of anomaly detection of a centralized electric power monitoring system, and in particular relates to a cloud platform anomaly detection method and system based on multi-data channel analysis. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Most of the systems in use today are unlikely to operate fully automatically and reliably, and are subject to unforeseen failures which often cause the system to malfunction and reduce its level of performance. Anomaly detection such as intentional attacks, technical faults, and disturbances are crucial in various applications in power systems. In order to keep the system in the best state and avoid casualties and economic losses, various anomaly detection methods have been developed and effectively used for system anomaly detection. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2455G06F16/2458G06K9/62
CPCG06F16/24568G06F16/2474G06F16/2462G06F18/253
Inventor 吕晨王潇卢国梁吕蕾刘弘
Owner SHANDONG NORMAL UNIV
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