基于OPC UA的烟叶成品下线管理系统分析与设计

从烟叶的收购到生产出成品,要经过五大工序。由于生产链条长、生产分散、信息多源异构等问题,造成生产线上下游信息脱节和不透明。在烟叶成品下线环节,指标数据存储于实时监控系统、业务数据存储于生产管理系统,无法做到实时、有效的数据交互。因此,提出在监控系统和生产系统之间搭接桥梁———对象链接与嵌入的过程控制统一架构(OPC UA)客户端,以获取、过滤实时有效数据。未建立OPC UA时,成品下线业务由人工干预,需手动打印成品下线标签且无法全面填补业务数据,劳时费力、工作繁重。建立OPC UA后,可实时从监控系统中获取下线时间、成品烟箱重量等业务数据传递到生产系统,从而实现烟叶数据特征分析、数据过滤、标签打印等业务,为烟叶成品下线环节提供实时、精准、全面的数据支持。通过对某烟草企业复烤业务的深入研究,构建成品下线管理系统。该成果可为中间件技术的技术落地、分布式方向上的工控系统提供借鉴和参考。

Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering

In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.