装配车间在依据计划调度方案进行产品装配时，会出现设备故障、物料缺失、人员流动等不确定性因素，这些不确定性因素会影响计划调度方案的准确性，使计划调度方案无法准确指导实际装配，影响车间效益。传统车间的计划调度系统在处理这些不确定因素时，由于管控机制不全面、监控数据实时性差、调度智能化水平低，导致装配过程中不确定性因素不能被及时处理。数字孪生技术的出现为解决该问题提供了重要方向。本文将数字孪生技术与计划调度系统进行融合，形成全方位的动态计划调度系统管控机制，并基于Petri网形成装配车间动态计划调度系统，系统的解决管控机制不全面、监控数据实时性差、调度智能化水平低等问题，使装配过程中的不确定性因素能够被实时监控和及时处理。针对管控机制不全面的问题，形成基于数字孪生的装配车间动态计划调度系统管控机制。从装配前、中、后，综合考虑预测技术、仿真技术、全要素的实时数据获取技术，在全装配要素预测的基础上，运用仿真验证和实时数据一致性检验，实现装配计划能够准确指导实际装配的目的。针对监控数据实时性差的问题，研究基于Petri网模型的装配车间计划调度资源主动感知方法。运用Petri网模型进行计划调度资源状态和活动描述，并根据资源类型配置相应RFID设备。将Petri网模型存储至RFID设备，进行计划调度资源之间的交互，同时通过MES获取计划调度资源的实时数据。针对调度智能化水平低的问题，搭建基于Petri网的装配车间动态计划调度模型。对基于Petri网的动态计划调度系统进行分析，找到不确定性因素被发现的条件，以及不确定性因素与计划调度方案的关联关系，以装配车间惩罚费用最小为目标，建立基于Petri网的动态计划调度模型。以A企业为实例对象，首先运用计划调度系统管控机制中的思想，进行计划调度系统的优化，接着运用CPN TOOLS仿真工具获得计划调度资源的实时数据，最后在此基础上搭建含有调度模型的车架厂动态计划调度系统，进行车架厂不确定因素的实时监控和及时处理。这为车架厂带来了良好的经济效益。

When the assembly workshop assembles the products according to the planning and scheduling scheme, there will be some uncertain factors such as failure of equipment, lack of material and flow of personnel. These uncertain factors will affect the accuracy of the planning and scheduling scheme, making the planning and scheduling scheme can not accurately guide the actual assembly, thus affecting the efficiency of the assembly workshop. In the traditional workshops, the planning and scheduling system of traditional workshops dealing with these uncertain factor, due to the incompleteness of management and control mechanisms, poor real-time capability of monitoring data, and low level of intelligence in scheduling, the uncertain factors in the assembly process cannot be immediate treatment. The emergence of digital twin technology provides an important direction for solving this problem. This paper integrate the digital twin technology with the planning and scheduling systems to form a comprehensive management and control mechanisms of the dynamic planning and scheduling system, and a dynamic planning and scheduling system for assembly workshop based on the Petri net is formed, which solves the problems of incompleteness of management and control mechanisms, poor real-time capability of monitoring data, and low level of intelligence in scheduling, so that the uncertainty factors in assembly can be monitored and processed in real time. In response to the incompleteness of management and control mechanisms, the management and control mechanisms of dynamic planning and scheduling system based on digital twin was formed. From before, during and after the assembly, the prediction technology, simulation technology and real-time data acquisition technology of all elements are considered  comprehensively. Based on the prediction of all assembly elements, the simulation verification and consistency test of real-time data are used to achieve the purpose that the assembly plan can accurately guide the actual assembly. In response to the poor real-time capability of monitoring data, the active sensing method of planning and scheduling resources in assembly shop based on the Petri net model is studied. The Petri net model is used to describe the state and activity of the planning and scheduling resources, and the corresponding RFID equipment is configured according to the resource type. The Petri net model is stored in the RFID device for the interaction of the planning and scheduling resources, and the real-time data of the planning and scheduling resources are obtained through MES. In response to the low level of intelligence in scheduling, the dynamic planning and scheduling model for assembly workshop based on Petri net is built. The dynamic planning and scheduling system based on Petri net is analyzed to find the conditions for the discovery of uncertain factors and the correlation between uncertain factors and planning and scheduling schemes. The planning and scheduling model based on Petri net is established with the minimum penalty cost of assembly workshop as the goal. The enterprise of A is taken as an example. Firstly, the planning and scheduling system is optimized by using the idea of the management and control mechanisms of dynamic planning and scheduling system. Then, the real-time data of the planning and scheduling resources is obtained by using the CPN TOOLS simulation tool. Finally, on this basis, the dynamic planning and scheduling system of the vehicle frame plant with the scheduling model is built to conduct real-time monitoring and timely processing of uncertain factors in the vehicle frame plant. This has brought good economic benefits to the vehicle frame plant.

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