基于K-means聚类算法的白酒分段阈值分析OA

The traditional sensory-based liquor fractionation process faces a core issue of unstable base liq-uor quality,which severely restricts the large-scale and standardized development of the liquor industry.This study,by taking the CJ fermentation type from a certain distillery as the research object,collects 392 batches of manual liquor fractionation production data,and combines brewing process characteristics.In-stantaneous concentration,cumulative flow,and comprehensive concentration are selected as core indica-tors,and the K-means clustering algorithm is used to perform clustering analysis on the data at various sta-ges of liquor distillation and fractionation.To address the subjectivity in selecting the k value,the study in-tegrates the Elbow Method,silhouette coefficient,and adjusted Rand index to construct a multidimensional evaluation system,scientifically determining the optimal number of clusters.The study shows that the K-means algorithm can classify the liquor collection data into six clusters,and using the cluster centroids as fractionation segmentation threshold forms a targeted liquor collection strategy.The innovation of this re-search lies in selecting differentiated clustering dimensions according to the characteristics of different liq-uor collection stages,and validating clustering effectiveness through multiple indicators.This compensates for the lack of specificity in traditional clustering methods for liquor fractionation and has significant prac-tical value for technical upgrades in the brewing industry.