基于微服务架构和GRU算法的卷烟质量监控预警系统设计
2024,32(12):153-158
摘要：针对传统的卷烟质量监控预警系统质量监控自动化程度低,提出一种基于微服务架构的卷烟质量监控预警系统；该系统采用Spring Cloud微服务架构,搭建卷烟生产过程监控数据库,收集海量卷烟加工过程质量数据,结合深度学习算法门控循环神经网络Gated Recurrent Unit(GRU)建立卷烟质量监控模型,更加有效的实现质量预警,提高卷烟加工过程自动化程度；经过测试证明,基于微服务架构和GRU算法的卷烟质量监控预警系统具备高灵活性、扩展性,解决了卷烟生产系统在实际应用中效率低、质量难以把控的问题。
关键词：微服务架构;深度学习;质量监控;系统开发;Spring Cloud;门控神经网络
Design and Application of a Cigarette Quality Monitoring and Early Warning System Based on Microservices Architecture
Abstract：Traditional cigarette quality monitoring and early warning systems often suffer from low automation levels. In this study, we propose a cigarette quality monitoring and early warning system based on microservices architecture to address this issue. The system utilizes the Spring Cloud microservices framework to construct a database for monitoring the cigarette production process. Massive amounts of data regarding the quality of cigarette processing are collected. Deep learning algorithms, specifically the Gated Recurrent Unit (GRU) neural networks, are employed to establish a cigarette quality monitoring model. This approach significantly improves the effectiveness of quality early warnings and enhances the automation level of the cigarette processing process. Through rigorous testing, it has been demonstrated that the cigarette quality monitoring and early warning system, based on microservices architecture and the GRU algorithm, exhibits high flexibility and scalability. This system successfully resolves the problems of low efficiency and difficulty in quality control faced by cigarette production systems in practical applications.
Key words：microservice architectures; deep learning; quality monitoring; system development; spring cloud; gated recurrent unit
收稿日期：2023-10-23
基金项目：江西中烟工业有限责任公司科技项目“构建卷烟工艺加工过程质量评价模型”(赣烟工科计2020-09)
