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

A deep neural network, web page quality technology, applied in the field of real-time web page quality assessment, can solve problems such as insufficient generality

Active Publication Date: 2022-04-01
BEIJING UNIV OF POSTS & TELECOMM
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  • Summary
  • 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 assessment method and system based on deep neural network
  • Real-time webpage quality assessment method and system based on deep neural network
  • Real-time webpage quality assessment 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 web page quality evaluation method and system based on a deep neural network, wherein the method includes the following steps: obtaining web page information of a target page; obtaining network-level original data of web page information from an edge router or a gateway, and Convert the format of the original data to obtain the data in the target format; use the data in the target format to train the preset classification model based on the deep neural network in the WebQMon.ai framework, so that the trained preset classification model can predict the preferred Screen delay; through the preset classification model based on deep neural network, the first screen delay of the target webpage is obtained, and the webpage quality evaluation result is generated. This method does not depend on any formula or threshold, and uses a small amount of application layer data and a large amount of network layer data to evaluate user experience. The trained model requires a small storage space, can quickly predict user experience, and has a high accuracy rate .

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 Patents(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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