## 5 Experimental Simulation
### Simulation Results and the Analysis  
According to the above experimental steps, we simulate and analyze the route network model, based on which we introduce the SEIR infectious disease transmission dynamics formula for delay process analysis, and at the same time, we introduce GA, through which the algorithm randomly searches for the appropriate infected node, and then we limit the final result of the infected node with the fitness function and constantly optimize the iteration in order to find the optimal infected node. As shown in Figure 7, the optimal output result is found at about the 20 th generation, and the best fitness function extreme value is obtained, so the input node corresponding to this result is exactly the key node to be found in this paper. Since degree centrality can better reflect the connectivity of nodes in complex networks, the five nodes with the largest degree centrality are considered to be selected as the comparison experiments in this paper. As shown in Figure 8a, the five nodes with degree centrality are LADIX, P07, UDETI, VOR (YCH), and VOR (PUD). Meanwhile, Figure 5 GA adaptation screening simulation results in an infection scenario where the output five nodes are ALDAP, MAGLI, RAXEV, ANRAT, and NDB (AR), as shown in Figure 8b. In order to find the nodes in the route network that have the greatest impact on delay propagation, this paper introduces random point finding (any five nodes are selected as a control group to jointly analyze the node infection), and according to the flow chart shown in Figure 6, it inputs five nodes and assigns them to state I, simulates the delay propagation, and outputs four states after 30 iterations (S, E, I, R) in the simu  
Figure 7: GA seeking optimization cross-mutation process. of the centrality of the degree and the identification of the nodes of Gas. The simulation results are shown in Figure 9.  
According to the simulation results, we integrate the output nodes of the three methods and calculate the weighted values of the simulation results based on the fitness function f (x) = 0.0553 \(\times\) S + 0.2622 \(\times\) E + 0.5650 \(\times\) I + 0.1175 \(\times\) R, as shown in Table 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **S** & **E** & **I** & **R** & **Weighted Sum** \\ \hline Random point & 73 & 12 & 14 & 5 & 15.6808 \\ Degree centrality & 67 & 8 & 27 & 2 & 21.2927 \\ GA & 35 & 35 & 22 & 12 & 24.9525 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Optimize weighted values. Figure 8: (**a**) Degree Centrality Node Identification; (**b**) GA Node Recognition. Figure 9: Comparison of simulation results. According to the data in Figure 10 and Table 8, and after comparison, it is found that the delay nodes (state I) of degree centrality at T = 30 are the most (27). However, there are still two-thirds of normal nodes (67), so although there are a lot of delayed segments, they can still maintain the normal operation of the network, while the GA's normal nodes are only one-third (35), and at this time, the route network has appeared to be delayed in a number of segments and cannot be operated normally. The weighted sum values can also visualize that the GA is better than degree centrality and random point finding, so the key nodes found by the GA have a higher degree of influence on the route network and need to be protected. In order to experience the completeness of the experimental process, this paper sets the number of iterations to 200 and carries out delay propagation simulation analysis for the degree centrality key node and the GA key node, as shown in Figures 10 and 11. From the line graph, it can be clearly seen that there are only more than 10 normal nodes left in the route network under the key nodes of GA at 40 generations, and the  
Figure 11: GA critical node infection simulation. Figure 10: Degree-centered critical node infection simulation.