Fault prediction method, device and storage medium based on hybrid gated neural network
A network fault and neural network technology, applied in the field of artificial intelligence, can solve problems such as failure to predict failures, failure to predict the impact of other failures, etc., to achieve the effect of improving accuracy, improving prediction accuracy, and saving time and cost
Active Publication Date: 2022-05-17
HUBEI UNIV OF TECH
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[0004] The purpose of the present invention is to provide a fault prediction method, device and storage medium based on a hybrid gated neural network. The problem of the impact of other faults, so that the fault cannot be predicted, so as to provide a technical solution that can accurately predict network faults
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[0077] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
[0078] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude one or more other Presence or addition of characteristics, wholes, steps, operations, elements, components and / or collections.
[0079] In order ...
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The present invention provides a network fault prediction method based on a hybrid gated neural network, which preprocesses network fault data into time series data, wherein the network fault data includes time node information and network node device information when the fault occurs , each time series data represents all fault types that occur in the current time period; the feedback data of the faulty network node equipment is converted into text label data; the hybrid gated neural network model is constructed, and the hybrid gated neural network model includes An embedding layer, a mixed gating layer, a neighborhood attention layer, and an automatic codec layer, input the time series data and the text label data into the mixed gating-based neural network model, and the mixed gating-based neural network model The network model makes predictions on the data in the network failure data. Through the above method, the correlation between fault data can be mined to improve the accuracy of fault prediction.
Description
technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a fault prediction method, device and storage medium based on a hybrid gated neural network. Background technique [0002] With the popularization of electronic devices, while facilitating our daily life, it brings frequent network failures. The continuous occurrence of network failures is currently a widespread concern of major operators. These network failures include service outages, low network speeds, and network noise. However, the complexity and randomness of network faults make it difficult to use traditional methods to effectively predict network faults. [0003] How to effectively and quickly predict the occurrence of network faults and carry out a certain degree of early warning is of great significance, but based on traditional statistical methods, it is only possible to judge whether the network is faulty under a certain threshold, which will happen...
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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/048G06N3/045Y04S10/50
Inventor 高榕张意灵邵雄凯
Owner HUBEI UNIV OF TECH