摘要
提出一种基于知识模式挖掘的共同配送系统,将共同配送数据日志和知识学习日志有机集成,建立实例案例库,基于案例推理方法(CBR)分析新旧案例的相似性,将参与者面对新问题时所需要的知识主题挖掘出来。使用Markov模型和序列模式算法读取用户在通过知识库对知识检索时的面向主题的知识学习模式,为参与者提供符合学习习惯的知识序列。
Since the knowledge involved in joint distribution is extensive and complex,participants should be supplied with sufficient and effective knowledge when faced with new problems in joint distribution. This paper proposes a joint distribution system based on knowledge pattern mining,which organically combines the data log and the knowledge learning log of the joint distribution process,establishes a case library by uncovering the similarity between old and new cases based on case-based reasoning (CBR),and excavates the knowledge topics needed by the participants in face of new problems. Moreover,the Markov model and sequence pattern algorithm are used to read the topic- oriented knowledge learning pattern of the users when searching knowledge in the knowledge library,providing the participants with knowledge sequences fitting their learning habits.
作者
杨珊珊
吕秋子
徐庭君
王婉婷
吴金瑞
Yang Shanshan;Lv Qiuzi;Xu Tingjun;Wang Wanting;Wu Jinrui(School of Management,Harbin University of Commerce,Harbin 150000,China)
出处
《物流技术》
2019年第7期65-70,共6页
Logistics Technology
关键词
共同配送
案例推理
MARKOV模型
知识模式挖掘
知识推荐系统
joint distribution
case-based reasoning
Markov model
knowledge pattern mining
knowledge recommendation system
