Real-time webpage quality evaluation method and system based on deep neural network

A technology of deep neural network and webpage quality, which is applied in the field of real-time webpage quality assessment, and can solve problems such as not being universal enough

Active Publication Date: 2019-10-22
BEIJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this model is only for video services and is still not general enough

Method used

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  • Real-time webpage quality evaluation method and system based on deep neural network
  • Real-time webpage quality evaluation method and system based on deep neural network
  • Real-time webpage quality evaluation method and system based on deep neural network

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Embodiment Construction

[0039] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0040] First of all, in order to be able to construct a general webpage quality assessment model, the embodiment of the present invention introduces the WebQMon.ai framework, which is a webpage QoE assessment method using machine learning, does not depend on any formula or threshold, and uses a small number of application layers Data and a large amount of network layer data can be used to evaluate user experience. After the internal model is trained, it does not require a small storage space, so that the WebQMon.ai framework can b...

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Abstract

The invention discloses a real-time webpage quality evaluation method and system based on a deep neural network, and the method comprises the following steps: obtaining the webpage information of a target page; obtaining network-level original data of the webpage information from an edge router or a gateway, and converting the format of the original data to obtain target format data; training a preset classification model based on a deep neural network in the WebQMon.ai framework by utilizing the target format data, so that the trained preset classification model predicts first screen time delay when a user accesses different webpages; and obtaining the first screen time delay of the target webpage through a preset classification model based on the deep neural network, and generating a webpage quality evaluation result. The method does not depend on any formula or threshold, uses a small amount of application layer data and a large amount of network layer data to evaluate the user experience, the trained model needs a very small storage space, the user experience can be quickly predicted, and the accuracy is very high.

Description

technical field [0001] The invention relates to the technical field of deep neural network learning, in particular to a real-time webpage quality evaluation method and system based on deep neural network. Background technique [0002] A recent study found that interactive HTTP traffic once again dominates residential broadband Internet traffic, accounting for more than 50% of traffic, and it is gradually becoming the narrow waist of the Internet. People often visit various websites during work or leisure time, including search engines, video sites and social networking sites. Whether or not a website can load successfully in a short amount of time affects the likelihood that a user will continue browsing the page. Even tiny network delays in web page loading can cause huge damage to user experience. However, since the content of different websites is very different, it is difficult to construct a general evaluation model of user accessing webpage experience through traditi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/958G06N20/00
CPCG06F16/958G06N20/00
Inventor 潘恬黄韬宋恩格贾晨昊刘韵洁
Owner BEIJING UNIV OF POSTS & TELECOMM
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