## 3 Method
### PERT Optimization Model  
Current product projects are very complex. If the final product is unfolded in the form of a product tree, a very complex tree-like structure is obtained, and the number of branches can reach thousands or tens of thousands. To cope with complex project management, manufacturing enterprises have developed the PERT model based on operations research theory. This model calculates key time parameters based on project task decomposition and the operation time of the task. By analyzing time arguments, elements such as the planned time, critical work, critical path, and total duration are obtained to support managers in better project management. The setting of the model needs to consider the field requirements of the information system. \(C\) must include the logical relationship expression of the task and related time parameters, as detailed in Equation (4). The nodes before and after the task are represented by \(d_{i}\), and \(d_{j}\) represents the logical relationship between related tasks. The duration of the task is represented by m. The pre-task and post-task work nodes are assigned by \(P_{i}\) and \(p_{j}\) represents. Total float \(T_{ij}\) and task completion status \(w\) are key control parameters. \(w\) can be determined through the task handover procedure.  
\[C=(d_{i},d_{j},T_{ij},m,P_{i},p_{j},w) \tag{4}\]  
The difference between the earliest start time and the latest start time is \(T_{ij}\). For a one-to-one link, the earliest start time of point \(P\) on the left side of node \(i\) is denoted by  
Figure 1: PERT-RP-DDPGAO algorithm framework.  
\(t_{ES}(P,i)\) represents. The duration between node \(i\) and node \(j\) is represented by \(t(P,i)\). For many-to-one links, it is \(ES^{\prime}_{ij}\). The calculation methods are shown in Equations (5) and (6).  
\[ES_{ij}=t_{ES}(P,i)+t(P,i) \tag{5}\]  
\[ES^{\prime}_{ij}=max(t_{ES}(P,i)+t(P,i)) \tag{6}\]  
Compared with Equation (6), the main logic for the latest start time is to change from the maximum value to the minimum value. Duration between node \(j\) and node \(k\) is represented by \(t(j,p)\). Latest start time of the node on the right side of node \(j\) is represented by \(t_{LS}(j,p)\).  
\[LS^{\prime}_{ij}=min(t_{LS}(j,p)-t(j,p)) \tag{7}\]  
Using the principle of PERT technology, Equations (8) and (9) can be derived through Equations (5)-(7).  
\[T_{ij}=ES^{\prime}_{ij}-LS^{\prime}_{ij} \tag{8}\]  
\[T_{ij}=max(t_{ES}(P,i)+t(P,i))-min(t_{LS}(p,k)-t(p,k)) \tag{9}\]  
The backend of the algorithm program uses Python 3.5 language and the NetworkX 1.11 module. In the front-end design, the paper adopts SpringCloud. SpringCloud is able to utilize the development of Spring Boot to simplify the development of distributed system infrastructure.  
To increase the robustness of the PERT model, \(t_{3}\) and \(t_{1}\) are subtracted from Equation (3), as shown in Equation (10).  
\[t^{\prime}=t_{1}-t_{3} \tag{10}\]  
This paper incorporates a feedback mechanism into the PERT model, as shown in Figure 2.  
The focus of the feedback mechanism is to record and monitor the total time difference. In addition, this paper introduces the emergency time in Equation (3). By using the emergency time, the compressible time of the task can be calcul  
Figure 2: PERT model with added feedback mechanism.  
time of key tasks, the total project duration can be shortened, ultimately achieving the goal of controlling the total project duration.