摘要
随着工业4.0的深入推进，智能制造系统已成为制造业转型升级的核心驱动力，而大数据处理与决策支持技术在其中扮演着关键角色。本研究旨在探索智能制造环境中大数据处理的关键技术及其对智能决策的支持机制，以提升制造系统的效率和智能化水平。研究通过构建基于边缘计算与云计算协同架构的大数据处理框架，结合深度学习算法优化数据挖掘能力，并引入多源异构数据融合方法以增强信息完整性。同时，设计了一种动态决策支持模型，能够根据实时数据流调整生产策略并预测潜在风险。实验结果表明，所提出的方法显著提高了数据处理速度和决策精度，特别是在复杂制造场景下的适应性和鲁棒性方面表现出色。本研究的主要创新点在于将边缘-云协同架构与智能决策支持深度融合，为智能制造系统提供了高效、灵活的技术解决方案，其成果可广泛应用于智能工厂的生产优化和运营管理，为未来智能制造的发展奠定了坚实基础
关键词：智能制造；大数据处理；边缘-云协同架构；动态决策支持；多源异构数据融合
Abstract
With the deepening of Industry 4.0, smart manufacturing systems have become the core driving force for the transformation and upgrading of the manufacturing industry, where big data processing and decision support technologies play a critical role. This study aims to explore the key technologies of big data processing in smart manufacturing environments and their mechanisms for supporting intelligent decision-making, thereby enhancing the efficiency and intelligence level of manufacturing systems. By constructing a big data processing fr amework based on the collaborative architecture of edge computing and cloud computing, this research optimizes data mining capabilities through deep learning algorithms and introduces multi-source heterogeneous data fusion methods to improve information integrity. Additionally, a dynamic decision support model is designed, which can adjust production strategies and predict potential risks according to real-time data streams. Experimental results demonstrate that the proposed method significantly improves data processing speed and decision accuracy, particularly showing excellent adaptability and robustness in complex manufacturing scenarios. The main innovation of this study lies in the deep integration of edge-cloud collaborative architecture with intelligent decision support, providing an efficient and flexible technical solution for smart manufacturing systems. Its outcomes can be widely applied to production optimization and operational management in smart factories, laying a solid foundation for the future development of smart manufacturing.
Keywords：Intelligent Manufacturing; Big Data Processing; Edge-Cloud Collaborative Architecture; Dynamic Decision Support; Multi-Source Heterogeneous Data Fusion
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能制造与大数据的背景意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法与技术路线 2
二、大数据处理关键技术在智能制造中的应用 2
(一) 数据采集与预处理方法 2
(二) 数据存储与管理策略 3
(三) 数据分析与挖掘算法 3
三、决策支持系统的设计与实现 4
(一) 决策支持系统的框架构建 4
(二) 基于大数据的预测模型开发 4
(三) 实时决策支持机制研究 5
四、智能制造中大数据处理与决策支持的案例分析 6
(一) 典型行业应用场景概述 6
(二) 数据驱动的优化方案设计 6
(三) 实施效果与改进建议 7
结 论 8
参考文献 9
