A smart home user control behavior habit mining method with a learning forgetting capability

Abstract

The invention provides a smart home user control behavior habit mining method with a learning forgetting capability. The method comprises the steps of collecting behavior control data of a certain device of a user in combination with a wireless or wired network; Carrying out data preprocessing on the user data, and mapping the spatial data of each dimension to the same data space; Obtaining prediction feature vectors of a plurality of potential control behavior habits of the user through a self-organizing clustering algorithm with a learning forgetting capability; And carrying out reflection reduction on the prediction feature vector according to the mapping principle in the second step to obtain a final intelligent recommendation scheme conforming to the user control behavior habit. According to the method, the recent control behavior habit of the user on the intelligent household equipment can be predicted according to the historical control record data of a large number of users, sothat the intelligent level of the household equipment is improved.

Description

technical field [0001] The invention relates to the field of smart home, in particular to a method for mining user manipulation behavior habits of smart home users with the ability to forget and learn. Background technique [0002] In the field of prediction and recommendation of smart home user manipulation behavior habits, the traditional single-device mining algorithm mainly has the following shortcomings: [0003] ①Traditional algorithms cannot identify the weight of user manipulation records based on the date of generation of user manipulation records, resulting in too old user manipulation records that have a greater impact on the prediction of current user manipulation behavior habits. In this case, if the ratio of too old historical records to recent historical records is close to 1:1 or even greater, the user's manipulation behavior habits generated by the algorithm will deviate from the user's current real manipulation behavior habits and will be more consistent wi...

AI Technical Summary

Problems solved by technology

[0003] ①Traditional algorithms cannot identify the weight of user manipulation records based on the date of generation of user manipulation records, resulting in too old user manipulation records that have a greater impact on the prediction of current user manipulation behavior habits

[0005] Traditional mining algorithms lack the self-organizing initialization learning ability and cannot forget the user's too old manipulation records, resulting in the algorithm requiring too much manual intervention and the mined user's manipulation behavior habits cannot be used to change the user's manipulation behavior habits and seriously deviate from the user's latest Defects in actual manipulative behavior habits

Images