Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A large-scale mobile customer traffic consumption intelligent prediction method

An intelligent prediction, large-scale technology, applied in prediction, network traffic/resource management, character and pattern recognition, etc., can solve problems such as insufficient accuracy and robustness, achieve high accuracy and robustness, improve traffic Revenue, effects of improving model performance and stability

Active Publication Date: 2020-06-19
SOUTH CHINA UNIV OF TECH +1
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The traditional flow prediction method only predicts the user's flow consumption value through the regression method, which is easily disturbed by noise in the data, and the accuracy and robustness are insufficient.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A large-scale mobile customer traffic consumption intelligent prediction method
  • A large-scale mobile customer traffic consumption intelligent prediction method
  • A large-scale mobile customer traffic consumption intelligent prediction method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048] The present invention will be further described below in conjunction with specific examples.

[0049] Such as figure 1 As shown, the large-scale mobile customer traffic consumption intelligent prediction method provided in this embodiment includes the following steps:

[0050] 1) Collect mobile user attribute characteristics and consumption behavior data, visualize them, and perform preprocessing operations;

[0051] 1.1) The visualization process is as follows:

[0052] 1.1.1) Carry out hash bucket intervalization operation on all characteristic fields of mobile users;

[0053] 1.1.2) Take any two feature fields, one is the X axis and the other is the Y axis, and draw data points on the Cartesian coordinate system;

[0054]1.1.3) Data points with the same eigenvalues ​​will not overlap each other, but will be plotted in a closely arranged point by point.

[0055] 1.2) Combined with the visualization results, perform preprocessing operations, the process is as follo...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a large-scale mobile client flow consumption intelligent prediction method, which comprises the following steps: 1) collecting mobile user attribute characteristics and consumption behavior data, visualizing, and pre-processing; 2) constructing a classification predictor and a regression predictor, and performing training to obtain two prediction models with different scales; 3) the joint classification predictor and the regression predictor are a trainable linear combination, and second-stage training is carried out to obtain a joint prediction model; And 4) predictingthe flow consumption value of the user in the next month by using the joint prediction model according to the attribute characteristics and the consumption behavior of the mobile user. According to the invention, the classification predictor and the regression predictor are combined, and two-stage training is carried out on large-scale mobile user data, so that the obtained joint flow predictionmodel has higher accuracy and robustness, and a more accurate and effective marketing idea is provided for mobile service popularization.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a method for intelligently predicting traffic consumption of large-scale mobile customers. Background technique [0002] With the popularization of 4G mobile communication technology and the vigorous development of mobile Internet, the lifestyle of users has gradually changed. The marketing focus of telecom operators has gradually shifted from traditional voice services to traffic services. Accurately predicting users' future traffic consumption can enable operators to more effectively promote traffic services, stimulate user consumption, and increase traffic revenue. [0003] The traditional flow prediction method only predicts the user's flow consumption value through the regression method, which is easily disturbed by noise in the data, and the accuracy and robustness are insufficient. The present invention utilizes the numerical category duality of the discretized traff...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/24H04W24/06H04W28/10G06Q50/30G06Q10/04G06N3/04G06K9/62
Inventor 胡金龙陈浪杨疆黄敏杰雷蕾王睿苏良良刘南海冯静芳董守斌
Owner SOUTH CHINA UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com