## 3 Method
### Module Framework and Data Acquisition  
The research object of this article is the most common machining processes in manufacturing enterprises. Mechanical processing is generally divided into small product mechanical processing and large product mechanical processing. The ordinary mechanical processing of small products has a short time cycle and can be performed using multiple pieces of equipment, generally without causing resource conflicts. Large-scale product machining generally involves medium-to-large-scale machining centers, which have high difficulty in product processing, high equipment value, and long production cycles and are prone to resource conflicts during mixed-line production. To solve practical production problems, this paper focuses on the mechanical processing of medium- and large-sized products and explains the content of the PERT-RP-DDPGAO algorithm model.  
Figure 1 shows the PERT-RP-DDPGAO algorithm framework, which is divided into an enterprise planning layer, a production unit planning layer, and an equipment planning layer based on the application scenarios. At the enterprise planning level, the model obtains product structure tree information and standard operating time information from the manufacturing execution system. The PERT model takes a structural tree model and standard operating time information as inputs to form a product plan and the total time difference for each product plan. Through the PERT model, project plan decomposition is achieved, resulting in a planned dispatch from the enterprise to the production unit. After receiving the plan, the production unit forms a resource plan for equipment or work-stations based on process information and work hour quota information. Through this mechanism, a planned dispatch from the production unit to the equipment is formed. The last layer is the equipment planning layer. At the device planning level, the DDPGAO model takes resource planning on mechanical processing equipment as an intelligent agent and optimizes resource planning autonomously through reinforcement learning. During the optimization of the DDPGAO model, the operation processing plan is adjusted. After changes in the process processing plan, the process processing plan is fed back to the product plan, which has an impact on the total time difference. This paper adds a feedback algorithm to the PERT model so that the results of the equipment planning layer can be fed back to the enterprise planning layer, enhancing the robustness of the entire planning system.  
According to the theory of project management, manufacturing enterprises determine the composition structure of products based on the product structure tree when designing the product project organization. The composition structure of a product represents the logical sequence of processing from parts to components and from components to the final finished product. In addition, most manufacturing enterprises have established information systems that can conveniently determine the processing cycle of each product. Therefore, with knowledge of the product processing logic and processing cycle, the project can be decomposed into work. Usually, companies add standard operating times to the product structure tree. The product structure tree information is represented by \(B\), as shown in Equation (1). In Equation (1), \(M\) represents the product name, \(L\) represents the product-level code, and the product processing logic relationship can be obtained through the product-level number. \(t_{1}\) represents the standard operating time, \(t_{2}\) represents the loose operating time, and \(t_{3}\) represents the emergency operating time. \(\sigma_{1}\) is the loose coefficient, usually taken as 1.3. \(\sigma_{2}\) is emergency coefficient, usually taken as 0.8.  
\[B=(M,L,t_{1},t_{2},t_{3}) \tag{1}\]  
\[t_{2}=\sigma_{1}*t_{1} \tag{2}\]  
\[t_{3}=\sigma_{2}*t_{1} \tag{3}\]  
The calculation equations for the loose operation time and emergency operation time are shown in Equation (2) and Equation (3), respectively.