Research on personalized recommendation algorithm for micromechanical sensors based on cloud model

Abstract In order to improve the ability of micromechanical sensors update data-matching nodes in real-time and speed up establishment a neighbor relationship between nodes, this paper proposes personalized recommendation algorithm for based on cloud model stability performance sensors. The uses service attribute values computing clustering method set feature term labels establish similarity matrix so as meet user-matched manufacturing requirements. intra-class technique is applied measure effect, evaluation function each number calculated basis considering time sequence determine best result complete path To verify application effect model, experiments are conducted. results show that can always control node energy consumption below 2.5×103, average discovery delay stable 41-43 seconds. And sensor response 12.1 seconds, absolute deviation value 0.23, which nearly 1.3 times smaller than 0.53 0.52 collaborative hybrid algorithm. It be seen solves problem excessive communication process effectively improves