智能制造系统中的数据采集与处理方法 -机械设计制造及其自动化专业

摘要

随着工业4.0的推进，智能制造系统成为制造业转型升级的关键，其中数据采集与处理是实现智能制造的核心环节。本文提出一种融合多源异构数据的采集架构，通过传感器网络实现生产现场全面感知，并利用边缘智能算法进行预处理，减轻云端负担。创新性地引入深度学习模型优化数据清洗流程，有效去除噪声干扰，提高数据质量。同时，设计了基于区块链的数据溯源机制，确保数据安全可信。实验结果表明，该方法能够显著提升数据采集效率30%以上，降低传输延迟25%，并将数据准确性提高至98%以上。通过对某汽车制造企业的实际应用验证，证明该方案可有效支持智能决策，为生产过程优化提供可靠依据，具有重要的理论意义和实用价值，为推动智能制造发展提供了新的思路和技术支撑。

关键词：智能制造数据采集与处理多源异构数据

Abstract

With the advancement of Industry 4.0, intelligent manufacturing system has become the key to the transformation and upgrading of manufacturing industry, in which data collection and processing is the core link to realize intelligent manufacturing. This paper proposes a collection architecture that integrates multi-source heterogeneous data, realizes the overall perception of production site through sensor network, and uses edge intelligence algorithm for pre-processing to reduce the burden of cloud. Innovative introduction of deep learning model to optimize data cleaning process, effectively remove noise interference, improve data quality. At the same time, a data traceability mechanism based on blockchain is designed to ensure data security and trust. The experimental results show that this method can significantly improve the data acquisition efficiency by more than 30%, reduce the transmission delay by 25%, and improve the data accuracy to more than 98%. Through the practical application verification of an automobile manufacturing enterprise, it is proved that the scheme can effectively support intelligent decision-making, provide a reliable basis for production process optimization, has important theoretical significance and practical value, and provides a new idea and technical support for promoting the development of intelligent manufacturing.

Keyword:intelligent manufacturing Data acquisition and processing Heterogeneous data from multiple sources

1绪论 1

1.1研究背景及意义 1

1.2数据采集与处理的意义 1

1.3国内外研究现状综述 1

1.4本文研究方法概述 2

2数据采集技术体系 2

2.1传感器网络构建方式 2

2.2工业物联网平台应用 3

2.3多源异构数据融合 3

2.4数据质量控制策略 4

3数据处理关键技术 4