【摘要】 针对企业在制造装备设计时，选型困难，已有信息利用不足等问题。本文提出了一种挖掘历史订单信息并联合二分K-Means聚类的方法，结合三维CAD软件二次开发出了制造装备智能优选系统。通过数据挖掘技术结合历史订单信息、实时物料信息等，对历史选型数据预处理方法开展了研究，实现了历史方案数据信息的K-Means聚类，采用多种相似度距离进行模型优选，最终实现制造装备的快速设计，以某制麦产线用输送设备为例，验证了方法的有效性。更多还原

【Abstract】 To address issues faced by enterprises during the design of manufacturing equipment, such as difficulties in model selection and insufficient utilization of existing information, this paper proposes a method that mines historical order information and combines it with binary K-Means clustering. Furthermore, an intelligent optimal selection system for manufacturing equipment is developed through the secondary development of 3D CAD software.By integrating data mining technology with historical order information, real-time material information, and other relevant data, research is conducted on preprocessing methods for historical model selection data. This enables the implementation of K-Means clustering on historical scheme data information. Multiple similarity distance metrics are employed for model optimization, ultimately achieving rapid design of manufacturing equipment.Taking the conveying equipment used in a certain wheat processing production line as an example, the effectiveness of the proposed method is verified.更多还原