Smart home energy consumption optimization method and device based on cloud computing

Abstract

The invention relates to a smart home energy consumption optimization method and device based on cloud computing. A data segmentation proportion is calculated through a data collection module, collected sensing data aresegmented according to the data segmentation proportion, the segmented sensing data are uploaded to a cloud computing center, the cloud computing center evaluates the uploaded data by using an incentive mechanism and feeds back the evaluated data to the data acquisition module, the data acquisition module adjusts the data segmentation proportion according to the evaluation feedback of the cloud computing center, and sends the adjusted data segmentation proportion to each home node, and the home nodes segment sensing data to be uploaded according to the adjusted data segmentation proportion and upload the sensing data to the cloud computing center through the data acquisition module. Due to the adoption of the data segmentation method, the quantity of data uploaded by each home node is reduced, and the segmentation proportion is dynamically adjusted through cloud excitation, so that the energy consumption generated during data uploading is reduced, and the energy consumption optimization of the smart home is realized.

Description

technical field [0001] The invention belongs to the technical field of smart home, and in particular relates to a method and equipment for optimizing energy consumption of smart home based on cloud computing. Background technique [0002] In recent years, with the rapid development of Internet of Things technology, various smart devices have penetrated into every aspect of our lives. At the same time, a large number of IoT terminal devices are applied to smart homes. While people enjoy the convenience brought by smart homes, smart homes also generate a large amount of sensory data, including APP usage, fault self-diagnosis information, service operation information, user portraits, device usage status, user usage behavior, and APP interaction behavior. , user information data, device function information, user information, device function information, device logs, APP logs, sub-device parameters and operating status, and other data, including data from hardware sensors, dat...

AI Technical Summary

Problems solved by technology

However, if all the sensing data is transmitted from the terminal to the cloud computing center, it will increase the power consumption of the terminal device, which will lead to higher electricity costs for users and higher network delay.

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