Product Risk Interaction Analysis of Multiple Products Based on Accident Big Data

Abstract

In order to prevent product accidents in the home, it is essential to prevent not only accidents involving individual products but also those involving product pairs that consumers actually use in combination. The purpose of this study is to identify the product pairs that increase the risk of accidents due to product interaction. For this purpose, an interaction analysis of product pairs was conducted by text mining from the big data of household accidents. Using a logistic regression method, 19 pairs of products that have significant synergistic effects (p<0.05) were identified. In addition, we developed an accident visualization system for providing risk information to consumers by integrating an accident case database, a combination risk database, and a visualization engine.