# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 4 Simulation Analysis
### Performance Analysis of Pest and Disease Spread Evolution Process Based on the GSPN Model
#### 4.1.1. Busy Probability of a Place  
In the GSPN system, when a place contains resources, it indicates that the corresponding place is busy during the pest and disease spread evolution process. First, the token distribution in each place under each marking state is presented to identify which parts of the system are active or utilized at each point. The analysis results are shown in Table 7, which details the number of tokens present in each place under each reachable marking.  
Next, in conjunction with the place busy probability from Formula (17), the busy probability for each place is calculated as shown in Table 8.  
In the GSPN model, the relatively busy places are P\({}_{8}\) and P\({}_{10}\), with busy probabilities reaching 29% and 47%, respectively. This indicates that invasion severity assessment and implementation of control measures are the two most active stages in the entire pest and disease spread model. These stages are prone to information accumulation and thus represent critical nodes for model optimization. The invasion severity assessment stage (P\({}_{8}\)) occupies a pivotal position in the decision-making chain. Any delay or error at this stage may have significant cascading effects on the overall system. To enhance the accuracy of this assessment, it is often necessary to conduct multi-level analyses and verifications, including preliminary diagnosis, refined analysis, and final confirmation. The high busy rate at the implementation of control measure stage (P\({}_{10}\)) suggests that this process involves the coordination of multiple resources, such as pesticides, labor, equipment, and funding. The scheduling and allocation of these resources are characterized by high levels of dynamism and uncertainty. Furthermore, the pest and disease control process  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** & **P9** & **P10** & **P11** & **P12** \\ \hline M1 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ M2 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ M3 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ M4 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ M5 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ M6 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ M7 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ M8 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ M9 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ M10 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ M11 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ M12 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ M13 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ M14 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ M15 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ M16 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ M17 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ M18 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ M19 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ M20 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ M21 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 0 \\ M22 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Distribution of tokens in the library under different identification states.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Place** & **Busy Probability** & **Place** & **Busy Probability** \\ \hline M1 & 0.013443 & M7 & 0.092184 \\ M2 & 0.013443 & M8 & 0.289034 \\ M3 & 0.013443 & M9 & 0.199731 \\ M4 & 0.013443 & M10 & 0.466486 \\ M5 & 0.013443 & M11 & 0.222777 \\ M6 & 0.008066 & M12 & 0.084694 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Busy probability for each place.  
requires real-time monitoring of treatment efficacy and timely adjustment of strategies in response to continuously evolving pest and disease symptoms.