摘要
文章围绕纺织生产线数字孪生系统的动态数据驱动优化展开研究,通过实时数据采集与智能分析提升生产效率、降低能耗并提高产品质量。研究指出,纺织生产线数字化转型面临数据准确性、实时性及安全性等挑战。通过部署高精度传感器网络,结合物联网技术,实现生产环境与设备状态的实时监控;利用多源数据融合算法与机器学习模型,构建数字孪生系统,动态预测设备故障并优化生产流程。案例分析表明,某纺织企业采用数字孪生技术,废品率降低15%,生产效率提升15%,能耗减少10%。研究强调数据安全策略(如区块链加密)与迭代优化机制的重要性,为纺织行业智能化升级提供了理论支持与实践方案。
This article focuses on the dynamic data-driven optimization of the digital twin system in textile production lines,aiming to improve production efficiency,reduce energy consumption,and improve product quality through real-time data collection and intelligent analysis.Research has pointed out that the digital transformation of textile production lines faces challenges such as data accuracy,real-time performance,and security.By deploying high-precision sensor networks and combining IoT technology,real-time monitoring of production environment and equipment status can be achieved;using multi-source data fusion algorithms and machine learning models,a digital twin system is constructed to dynamically predict equipment failures and optimize production processes.Case analysis shows that a certain textile enterprise has reduced waste rate by 15%,increased production efficiency by 15%,and reduced energy consumption by 10%through digital twin technology.The study emphasizes the importance of data security strategies(such as blockchain encryption)and iterative optimization mechanisms,providing theoretical support and practical solutions for the intelligent upgrading of the textile industry.
作者
陈秀芳
宋仙丽
Chen Xiufang;Song Xianli(Shangqiu Institute of Technology,Shangqiu 476000,China)
关键词
数字孪生技术
动态数据驱动优化
纺织生产线
实时数据采集
digital twin technology
dynamic data-driven optimization
textile production line
real-time data collection
