## 4 Experimental Evaluation and Discussion
### Experiment and Discussion on the Automatic Coordination of Resource Planning  
This paper substitutes the data in Table 1 into the new model for calculation and obtains the optimized results of the model. Then, the optimized results are visualized to form Figure 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time** & **Project 1** & **Project 2** & **Project 3** & **Total** & **Equipment** & **Load** \\
**(Day)** & **Blue** & **Orange** & **Gray** & **Tasks** & **Total Tasks** & **Index** \\ \hline
2 & 0 & 1 & 0 & 1 & 2 & Green \\
4 & 1 & 1 & 1 & 3 & 2 & Red \\
5 & 1 & 0 & 0 & 1 & 2 & Green \\
6 & 1 & 1 & 1 & 3 & 2 & Red \\
7 & 1 & 1 & 1 & 3 & 2 & Red \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Resource plan matrix data. Figure 5: Visual graphics of actual resource planning. Green represents the total number of tasks that have not exceeded the maximum capacity of the device. Figure 6 shows that the model has successfully coordinated the resource plan through continuous attempts. All tasks do not exceed the maximum capacity of the device. Comparing Figures 5 and 6, this paper finds that the model moved Project 3 from Day 3 to Day 1 and moved Project 3 from Day 4 to Day 2. The main reason for this result is that our reward function stipulates that the fewer parts there are beyond our ability, the greater the total time difference of the task, and the greater the reward. Similarly, Project 3 on Day 6 was moved to Day 5. In subsequent calculations, the paper finds that the model ultimately exhibited low convergence. Therefore, we add a control function for the total time difference in the reward function. The control function of the total time difference calculates the total time difference impact of each project task. This total time difference affects the search for the corresponding project's total time difference. If the impact on the total time difference can be tolerated, there will be rewards; if it cannot be tolerated, there will be punishments. We see this punishment as \"hitting a wall\". Therefore, the results in Figure 6 are closely related to the setting of the reward function. The feasibility of the model is verified through the experiments in this paper. In the process of continuous model calculation, this paper discovers another advantage of intelligent algorithms. Managers often have great confidence in their own judgments, with the main goal of solving practical problems. Moreover, intelligent algorithms may achieve leaner results. Therefore, this paper analyzes and processes resource plans with single-point conflicts for experienced managers and intelligent models, forming Figure 7; Figure 7A shows the target resource plan; Figure 7B shows the results of the manager's analysis and processing, and Figure 7C shows the results of the intelligent algorithm's analysis and processing. This paper finds that managers achieve a balance in resource planning by eliminating peaks and filling in low values in management. Managers believe that their experience plays an important role when they do not see the results of intelligent algorithms. The intelligent algorithm yields even better results. This paper requires the optimal sum of the total time difference of project tasks in the reward function. Therefore, the intelligent algorithm obtains the results in Figure 7C. These results not only achieve a balance in resource planning but also improve the total time difference between project tasks compared to Figure 7B. The greater the total time difference, the stronger and more stable the anti-interference ability of the project plan. Figure 6: Resource plan after coordination. From the above, it can be seen that managers may be influenced by various factors, such as personal abilities, work environment, and work status, when balancing resources, resulting in inadequate consideration. In other words, most of the decisions made by managers are correct solutions rather than optimal solutions.