Android-based system resource management method and device

Abstract

The invention provides an Android-based system resource management method and device. The method includes: S1, acquiring using rate of any application program by an order context awareness frame inside an application system; S2, sequencing all application programs according to corresponding using rates from high to low to acquire an initial application set; S3, applying a self-adaptive mechanism to adjust the initial application set to acquire a final application set. The using rates of the application programs are acquired through context awareness inside the system, resource management of the system is realized, dependence on hardness is lowered, interaction is enriched, and user experience of multi-task management is improved.

Description

technical field [0001] The invention relates to the field of predictive control of mobile systems, in particular to an Android-based system resource management method and device. Background technique [0002] With the rapid development of the mobile Internet, Android, as an operating system, has occupied 85% of the global mobile terminal systems, and is widely used in devices such as smart phones and tablet computers. At the same time, with the continuous improvement of the mobile terminal and the multitasking operating system loaded therein, the fluency and efficiency of the interaction between the mobile terminal and the user are also improved. In particular, the proposed numerical optimization method not only optimizes system resource management, but also improves the user experience of mobile phone multitasking management and expands the scope of system services. [0003] In order to enrich the interaction and give full play to the multi-task management function of the ...

AI Technical Summary

Problems solved by technology

[0004] At present, in context-based system resource management, some studies treat the applications in the system as strings and use string prediction algorithms to predict the sequence and future time points of the possibility that the applications will be used in the future, and have obtained high Prediction accuracy, but requires a long time of data training

In addition, some studies directly modify the system structure and working mode to expand the available space of the system for storage, which obviously improves the state that the system can store and retrieve, and accelerates the startup speed of the application program, but this method is directly affected by the hardware design structure. influences

There is also research on setting the working mode in the application program, and regularly collecting sensor data information according to the working mode to dynamically adjust the application program status, which has achieved very good multi-task management efficiency, but is affected by the quality and limitations of sensor information.

[0005] These methods are limited due to the difficulty of data training and hardware dependence, thus limiting the user-centric real-time and dynamic nature of system resource management mechanisms

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