Internet of things-based data aggregation method for applied psychology

Abstract

The present invention discloses an Internet of Things (IoT)-based data aggregation method for applied psychology, including: performing grouping processing on data formats and update frequencies using k-means clustering based on an aligned original dataset, classifying signals with similar features into the same cluster, and determining a set of grouped feature clusters; if the classified difference labels indicate high heterogeneity, projecting the highly heterogeneous clusters into a unified vector space using dimensional mapping technology to obtain mapped unified vector representations; extracting cross-cluster association patterns from the mapped unified vector representations, analyzing dependency relationships between vectors through a correlation coefficient calculation method, and determining an association pattern set; hierarchically merging elements in the pattern set using a recursive fusion algorithm; if the integrity of the merged result is lower than a preset integrity threshold during the fusion process, backtracking to the upper-level pattern and adjusting the fusion path to obtain an integrated dataset. The present invention improves the accuracy and reliability of psychological assessments.