【摘要】 本文旨在系统阐述数据驱动的预测性质量管理（PQM）的核心方法、关键技术及实施路径。通过系统综述方法，分析了人工智能、工业物联网、大数据和数字孪生等多项技术在质量预测、异常检测与工艺优化中的应用机制，并提出了从数据基础建设到模型迭代的五阶段实施路径。研究结果表明，PQM有助于实现从“控制变异”到“预见未来”的范式转变，显著提升质量管理的准确性与主动性。更多还原

【Abstract】 This paper aims to systematically elaborate on the core methods, key technologies, and implementation pathways of data-driven predictive quality management(PQM). Through a systematic review method, it analyzes the application mechanisms of multiple technologies, including artificial intelligence, industrial Internet of Things, big data, and digital twins, in quality prediction, anomaly detection, and process optimization, and proposes a five-stage implementation pathway from data infrastructure construction to model iteration. The research results indicate that PQM can achieve a paradigm shift from “controlling variation” to “predicting the future,” significantly improving the accuracy and proactivity of quality management.更多还原