Smart home user manipulation behavior recommendation method based on temporal causality analysis

Abstract

The invention relates to a smart home user manipulation behavior recommendation methodbased on temporal causality analysis. A large number of user behavior habit data are segmented, and then the sequence of frequent user operations is extracted from the data by a sequential pattern mining algorithm. Based on the sequence of frequent user operations, the causal relationship between the mining sequences of Bayesian network is constructed, so as to construct the process of intelligent home manipulation behavior recommendation scheme. The invention combines the advantages of a pattern mining algorithm and a Bayesian model, reduces the entire data set to a selected set of frequently manipulated sequences through pattern mining, greatly reduces the computational complexity and the noise in the causal relationship computation, which is conducive to more efficient formation of a more user-friendly smart home manipulation behavior recommendation scheme, and at the same time enhances the strongtime correlation between each device of the smart home manipulation behavior recommendation scheme, and fills in the shortcomings of the traditional algorithm.

Description

technical field [0001] The invention relates to the technical field of smart home user manipulation behavior recommendation, in particular to a smart home user manipulation behavior recommendation method based on temporal causality analysis. Background technique [0002] Smart home is the embodiment of the connection of things under the influence of the Internet. It connects various devices in the home (such as audio and video equipment, lighting systems, curtain control, air conditioning control, security systems, digital theater systems, audio-visual servers, etc.) through the Internet of Things technology. shadow cabinet system, network home appliances, etc.) to provide home appliance control, lighting control, telephone remote control, indoor and outdoor remote control, anti-theft alarm, environmental monitoring, HVAC control, infrared forwarding and programmable timing control and other functions and means . The smart home control behavior recommendation is more conven...

AI Technical Summary

Problems solved by technology

[0004] ① The method of pattern-based mining of home manipulation behavior aims to extract frequently occurring patterns from historical data. These patterns can capture the inherent laws of user manipulation, but to a large extent limit the availability of pattern sets and fail to mine Identify the dependencies between frequent patterns;

[0005] ② Based on the association rule algorithm, related transactions can be mined from a large amount of data, but only the correlation between transactions can be obtained, and the causal relationship between transactions cannot be analyzed;

[0006] ③Traditional causality algorithm based on Bayesian learning model, if there is a lot of noise in the data and the data is sparse, the performance of the Bayesian model is very limited, and it is very likely to capture unreliable causality

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