酱香型出仓大曲质量标准监测分析数据集合

数据采集遵循分层随机抽样规则，按产区、作坊规模、制曲工艺类型分层，确保样本代表性；数据预处理阶段，采用Z-score标准化消除量纲差异，运用箱型图法剔除异常值；分析环节，通过关联规则算法挖掘理化指标与微生物指标的内在联系，利用聚类算法对大曲质量等级进行划分；数据存储采用关系型数据库（MySQL），按“样本编号-检测指标-结果数值”的逻辑结构构建数据模型，保障数据检索与调用的便捷性。

Data collection adopts stratified random sampling, with stratification conducted across producing areas, workshop scales, and starter-making process types to guarantee sample representativeness; During the data preprocessing phase, Z-score standardization is applied to eliminate dimensional differences, and the box plot method is utilized to exclude outliers; In the analysis phase, association rule algorithms are employed to explore the internal correlations between physicochemical and microbial indicators, while clustering algorithms are used to categorize the quality grades of Daqu; Data storage is implemented via a relational database (MySQL), and a data model is constructed based on the logical structure of "sample ID - detection indicator - result value" to ensure convenient data retrieval and access.

搜集汇总

背景与挑战

背景概述

该数据集由贵州酱酒集团有限公司自行产生，专注于酱香型白酒出仓大曲的质量标准监测分析，数据规模为10G且无定期更新。其特点在于应用场景广泛，涵盖企业工艺优化、科研关联研究、行业标准制定和教学支持，并采用分层抽样、标准化处理和机器学习算法（如关联规则和聚类）来确保数据质量和分析深度，但具体数据结构字段未公开。