基于数据驱动的产品质量安全风险分类监管系统研究

摘要：

【目的】产品质量安全一直是世界各国关注的重点，研究基于数据驱动的产品质量安全风险分类监管系统，通过引入人工智能和大数据等先进技术，可以实现对企业和产品的实时动态监测，并依据风险等级自动调整检查频率和强度，合理配置监管资源。【方法】本文首先介绍了基于数据驱动的产品质量安全风险分类监管系统构成，然后阐述了监管系统主要功能模块的业务流程。【结果】基于数据驱动的产品质量安全风险分类监管系统能够自动化收集和分析大量数据，快速识别潜在风险。【结论】使得监管部门能够更精准地定位问题产品和企业，提高了监管科学性和精准性。

Abstract:

[Objective] Product quality and safety have always been a focus of countries around the world. Researching a data-driven product quality and safety risk classification and supervision system, by introducing advanced technologies such as artificial intelligence and big data, can achieve real-time dynamic monitoring of enterprises and products, and automatically adjust inspection frequency and intensity based on risk levels, and reasonably allocate regulatory resources. [Methods]This paper first introduces the composition of a data-driven product quality and safety risk classification and supervision system, and then elaborates on the business processes of the main functional modules of the supervision system. [Results]The data-driven product quality and safety risk classification and supervision system can automatically collect and analyze large amounts of data, quickly identify potential risks. [Conclusion] enable regulatory authorities to more accurately locate problematic products and enterprises, and improve the scientific and accurate nature of supervision. 

引用格式：许应成,王岩峰,李亚,等.基于数据驱动的产品质量安全风险分类监管系统研究[J].标准科学,2025(2):23-27

基金项目：本文受中央基本科研业务经费项目“基于数据驱动的产品质量安全风险分类技术研究与应用”（项目编号：552023Y-10371）和国家重点研发计划项目“典型智能产品质量安全风险控制重要技术标准研究”（项目编号：2022YFF0607100）资助。 

作者简介：许应成，研究员，研究方向为产品质量安全、企业标准化等。 王岩峰，通信作者，高级工程师，研究方向为质量管理、企业标准化。