Smart home user manipulation behavior recommendation method based on temporal causality analysis
A causal relationship and smart home technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as unreliable causality, limited performance of Bayesian models, failure to mine frequent pattern dependencies, etc. , to achieve the effect of reducing the computational complexity and enhancing the strong correlation of time
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[0060] The present invention will be further described below in conjunction with specific embodiment:
[0061] Such as figure 1As shown, a method for recommending manipulation behaviors of smart home users based on temporal causality analysis described in this embodiment includes the following steps:
[0062] S1: Combine wireless or wired network to collect user behavior habit data;
[0063] S2: Perform preprocessing of user behavior habit data;
[0064] S3: Mining frequent manipulation sequences of smart home users;
[0065] S4: Construct Bayesian learning model causality;
[0066] S5: Form a smart home control behavior recommendation scheme.
[0067] It mainly includes four parts: data preprocessing, pattern mining, building Bayesian network model causality, and the formation of user manipulation behavior recommendation schemes. The following instructions will explain each process in detail:
[0068] Such as figure 2 As shown, the preprocessing of user behavior habit...
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