Machine learning algorithm based intelligent energy-saving control method and equipment for APs (Access Points) in WiFi system

Abstract

The invention relates to a machine learning algorithm based intelligent energy-saving control method and equipment for APs (Access Points) in a WiFi system. The method comprises the steps of: S1: collecting load historical data of each AP in a wireless network system; S2: selecting features in time and space dimensions and carrying out modeling on a load of each AP by utilizing a random forest algorithm to obtain a load model for predicting whether the AP is in an idle state or a non-idle state; S3: based on the load model of each AP, predicting a future state of the AP; and S4: for each AP, extracting time in which the state is continuously idle, and if the time exceeds a preset length, controlling the AP to be closed in the continuously idle time interval. Compared to the prior art, according to the invention, by dynamic control, the idle APs are closed, so that for the large-scale wireless network system, power consumption can be obviously reduced, and energy saving can be implemented.

Description

technical field [0001] The present invention relates to an energy-saving control method, in particular to an AP intelligent energy-saving control method and equipment in a WiFi system based on a machine learning algorithm. Background technique [0002] In a wireless network system, using geographical distribution and signal strength to divide clusters or using methods such as Queueing models to dynamically switch APs to achieve energy saving can achieve better results in scenarios where APs are densely distributed. However, in a large-scale wireless network system, thousands or even tens of thousands of APs are scattered in a large geographical area, and the coverage area of ​​different APs overlaps very little, so this method is no longer applicable. [0003] Machine learning algorithms are usually used to simulate or implement human learning behaviors on computers, that is, to continuously summarize and synthesize to acquire new knowledge or skills, and to reorganize exist...

AI Technical Summary

Problems solved by technology

However, through case analysis, it is found that in these large-scale wireless network systems, a considerable proportion of APs are idle for a long time

Obviously, APs that have not been associated with users for a long time have been kept working, which has brought about a large waste of energy

However, if you simply turn off these APs that have not been associated with users for a long time, although the effect of energy saving can be achieved, due to the inherent mobility of users in the wireless network system, the coverage of wireless network signals will inevitably be reduced, thus affecting the terminal Negative impact on user experience

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