User behavior analysis-based energy-saving method for mobile network

Abstract

The invention provides a user behavior analysis-based energy-saving method for a mobile network. The method comprises the following steps of (a) predicting a time interval for user activities, i.e. a user active time interval; (b) predicting a time interval for network activities caused by applications, i.e. an application active time interval, when the mobile terminal is not operated; (c) formulating an optimization strategy for the network activities, namely allocating a user time interval for the network activities caused by each application when the mobile terminal is not operated, moving the network activities to the corresponding user active time interval, and disconnecting the mobile network within the original application active time interval, so that energy is maximally saved, and punishment influencing the normal use of a user is minimized. According to the method, the utilization rate of the mobile network can be increased, and meanwhile, unnecessary connection time for the network is reduced, so that the aim of energy saving is fulfilled.

Description

technical field [0001] The invention relates to the field of mobile networks, in particular to an energy-saving optimization method based on user behavior analysis. Background technique [0002] In recent years, the mobile application market has developed rapidly. According to statistics, in the Android application market, there are millions of applications available for users to download. Among the dazzling array of applications, a large number of applications (such as Renren, Phoenix News) rely on the mobile network for information interaction and software upgrades. Although relying on the mobile network, these applications can bring great convenience to users, but the mobile network also has a prominent defect, that is, huge energy consumption. Now that there is no major breakthrough in battery technology, how to solve the contradiction between users' ever-increasing network usage needs and extremely limited battery resources has become a top priority. [0003] Faced wi...

AI Technical Summary

Problems solved by technology

However, these two methods have very obvious defects: (1) the energy utilization rate improvement is extremely limited; (2) the optimization method is likely to affect the normal use of users

The main reason for this dilemma is that the current research work focuses on analyzing and optimizing the network communication behavior of mobile applications, while ignoring the important influence of users in these network activities.

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