## 4 Experimental Evaluation and Discussion  
### Experimental Environment Design  
The multilevel planning system includes an enterprise-level plan, a workshop-level plan, and an equipment-level plan. The enterprise-level plan mainly revolves around project management. The PERT method is used in project management to decompose plans. The project management environment is mainly based on the enterprise resource planning (ERP) system. The ERP system distributes the plan to the production workshop. The workshop receives tasks through the MES and dispatches them to equipment to form a resource plan. The management of resource plans is mainly based on MES. ERP systems and MES collect, transmit, and control equipment data through industrial control networks. This paper officially runs the PERT-RP-DDPGAO algorithm through the industrial information implementation framework shown in Figure 4.  
Mechanical processing is the most common processing method used in manufacturing enterprises. The main types of mechanical processing equipment used are small mechanical processing equipment, medium mechanical processing equipment, and large mechanical processing equipment. Among them, large-scale mechanical processing equipment is expensive, with a small number of pieces of equipment, and most of the time, it undertakes the processing of key and difficult products. In the actual production process, resource conflicts often occur.  
Therefore, this paper focuses on the mechanical processing tasks of large-scale products in manufacturing enterprises, collects resource plans on large-scale mechanical processing equipment through production information systems, and conducts experimental analysis. The collection frequency of the resource plans is 7 days.  
Figure 4: Industrial informatization implementation framework.  
Through the experimental environment, the paper extracted the resource plan of a certain device and formed Table 1 using a visual approach. The outcomes of the resource planning matrix are presented in Table 1, which indicates that the paper represents three different items on the equipment in different colors, and the number of tasks for the day is calculated according to the previous formula. Then, the difference between the number of tasks and the equipment capacity is calculated to obtain the carrying capacity. If the total number of tasks is greater than the device's faculty, the image is red. If the number of tasks is less than the device's capacity, the image is green. Large-scale mechanical processing equipment is generally single-piece processing, and the equipment's capacity is based on a 12 h working system with a maximum of two product tasks to be undertaken on the same day.  
This paper adopts visualization processing to contrast the actual performance of the model after reinforcement learning training. The preliminary extracted resource plan visualization graph is illustrated in Figure 5, which indicates the task distribution of three large-scale machining tasks over a period of 7 consecutive days. The green and red columns show that there is no conflict issue with respect to the tasks on Days 1, 2, and 5, while there is a conflict issue with respect to the tasks on Days 3, 4, 6, and 7; these issues need to be automatically adjusted through the model.