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Cutoff identification method and device based on deep learning

A technology of deep learning and identification method, applied in the field of outage identification based on deep learning, can solve problems such as inaccurate detection, achieve the effect of improving accuracy, improving resource allocation efficiency, and improving user service experience

Active Publication Date: 2020-06-26
上海飞旗网络技术股份有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the traditional outage detection scheme usually only judges whether there is an outage from the dimension of time characteristics, for example, by detecting the traffic forwarding situation in a certain preset time period, when there is almost no traffic forwarding, it is judged that there is a outage situation
However, in actual scenarios, the disconnection may be affected by the delay of many network environments, resulting in inaccurate detection.

Method used

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  • Cutoff identification method and device based on deep learning
  • Cutoff identification method and device based on deep learning
  • Cutoff identification method and device based on deep learning

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Embodiment Construction

[0043]In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the appended The figures are only for the purpose of illustration and description, and are not used to limit the protection scope of the present application. Additionally, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented in accordance with some of the embodiments of this application. It should be understood that the operations of the flowcharts may be performed out of order, and steps that have no logical context may be performed in reverse order or concurrently. In addition, those skilled in the art may add one or more other opera...

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Abstract

The embodiment of the invention provides a cutoff identification method and device based on deep learning. The method comprises the steps of obtaining a cutoff node corresponding to a data cutoff record in a cutoff state and a cutoff service type from a data stream statistical record; extracting first feature information of each cutoff node in the data cutoff record and second feature informationof a data forwarding node associated with each cutoff node according to a feature extraction strategy corresponding to the cutoff service type, and then performing deep learning; therefore, the data flow in each service forwarding process is identified, the cutoff identification accuracy is improved, the resource configuration efficiency is effectively improved, and the user service experience isimproved.

Description

technical field [0001] The present application relates to the technical field of deep learning of big data artificial intelligence, and specifically relates to a method and device for identifying a flow interruption based on deep learning. Background technique [0002] During the forwarding process of the data flow, there is usually a flow interruption phenomenon, that is, the data flow is not forwarded at a certain node. The result of the flow interruption detection can be used to optimize the configuration of subsequent network resources, so as to realize the network Optimal allocation of resources. [0003] However, the traditional outage detection scheme usually only judges whether there is an outage from the dimension of time characteristics, for example, by detecting the traffic forwarding situation in a certain preset time period, when there is almost no traffic forwarding, it is judged that there is a outage situation . However, in actual scenarios, the disconnecti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/851H04L12/26G06N3/04G06N3/08G06K9/62
CPCH04L47/2483H04L43/08G06N3/08G06N3/045G06F18/24G06F18/214
Inventor 杨贻宏
Owner 上海飞旗网络技术股份有限公司
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