论文标题:基于数据仓库与案例推理的机组产能模型研究与应用
Research and Application of Throughput Model Based on Data Warehouse and Case-based Reasoning
论文作者 张勇
论文导师 张焕水；王伟,论文学位 硕士,论文专业 控制理论与控制工程
论文单位 大连理工大学,点击次数 98,论文页数 62页File Size4709k
2005-02-27论文网 http://www.lw23.com/lunwen_363788902/ 产能模型;数据仓库;数据驱动;基于案例推理
throughput model; data warehouse; data driven; case-based reasoning
数据仓库技术是近几年的一个研究热点。作为一种优化管理、提供决策支持的企业数据解决方案,已经被许多企业采纳。 基于案例推理是对人们求解现实问题过程的一种合理描述。与传统的基于规则的系统相比,基于案例推理的系统具有简化知识获取、便于知识积累等优点。 本文以上海宝钢股份有限公司冷轧薄板厂各机组生产过程为背景,结合生产计划与实时优化调度系统的开发,研究了用于冷轧薄板生产过程的机组产能模型建立技术,并建立了基于数据仓库与案例推理的机组产能模型。 本文中数据仓库系统设计采用数据驱动的方法:利用企业原有数据库系统的数据,按照分析领域(主题)对数据及数据之间的联系重新考虑,组织数据仓库中的主题,利用数据模型有效地识别数据与数据仓库中主体数据的“共同性”以及建立主题间相互联系的属性。本机组产能模型利用数据挖掘对冷轧薄板生产过程各机组的生产情况进行了分析,建立面向生产计划部门与生产管理部门的局部数据仓库(数据集市),利用企业的历史生产数据,配合现场生产人员的专家经验,建立用于案例推理方法的案例库,从而实现以历史生产数据和专家经验为依据预测机组产能。 利用该模型可以准确地预测各机组的产能。利用该技术生成的产能模型具有自学习的功能,可自动反映由于技术改进或加工品种变化等因素造成的产能变化,具有较强的适应性。
Data warehouse technology has become one of the research hotspots today. As a kind of optimum management, enterprise"s data solution that offers decision supports, data warehouse technology has already been adopted and functioned by a lot of enterprises.Case-based reasoning is a kind of logical descriptions that used to solve realistic problems. Comparing to traditional rule-based system, it has many advantages, which can predigest knowledge gathering and accumulate knowledge easily.Based on each machine production processes of Shanghai BaoSteel CO, LTD. cold rolling thin strip mill, combined with the development of optimizing production planning and production scheduling system in the mill, this paper studied model building technology that used to predict machine throughput, and has build machine throughput model based on data warehouse and case-based reasoning.In this paper, data driven method has been adopted to design data warehouse: By using the existed data that belong to the legacy database of the enterprise, the data and the relationships of the data have been reconsidered according to the analyzing fields, the subjects in the data warehouse have been organized, the "commonalities" of the data and subject data in the data warehouse has been discerned effectively by using the data model, and the attributes which connect each other among the subjects have been set up. This model uses data mining to analyze the production conditions of the whole cold rolling thin strip production processes, and builds local data warehouse (independent data mart) that used by production planning department and production administration department, using historical production data of the enterprise, cooperating with the on-site producer"s experience, builds case base for the case-based reasoning method, thus realizes taking historical production data and expert experience as basis to predict the machine throughput.This model can predict each machine throughput accurately. This model has the capacity of self-learning and self-adjustability; it can reflect the throughput changes due to such factors as technology improving and the varieties of processing changes automatically. It has strong adaptability.
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