## 3 Experiments
### Result and Discussion
Specifically, the mean difference between the two methods is 46, indicating that the greedy screening method is 46 units higher than the improved EM algorithm screening method in terms of screening effectiveness. In addition, the 95% confidence interval ranges from 9.07 to 82.93, which means that we can assume with 95% certainty that the actual mean difference falls within this interval. These experimental results show that using the improved EM algorithm proposed in this paper to solve the problem can effectively eliminate most of the duplicate coverage data existing in the remote sensing datasets, and obtain the smallest possible dataset that satisfies the conditions, and the optimal solution of the improved EM algorithm is improved by about 9.78% on average compared with the greedy algorithm. Regarding the computational cost, the greedy screening method takes much less time than the improved EM algorithm. Although the improved EM algorithm performs well in screening results, its higher computational cost may limit its application on large-scale datasets. In the future, we will explore optimization strategies to reduce the computational complexity through algorithm optimization and hardware acceleration (e.g., GPU parallel computing). The experimental data in this study are mainly from four regions, and there may be a dependence on region-specific data, which may be affected by the uniformity of regional data distribution. In order to improve the generalization ability of the algorithm, we plan to introduce more diverse datasets in future studies. The solution of the SCP is not only of theoretical significance, but also has a wide range of applications in real life. For example, by solving the SCP, we can apply its methodology to the fire station location problem [6]. In fire station layout, we need to determine the location \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Region**} & \multirow{3}{*}{**Raw**} & **Greedy** & **ACO** & \multirow{3}{*}{**GA Screening**} & **PSO** & **Improved EM** \\  & & **Algorithm** & & **Screening** & & **Results/Scene** & **Screening** \\  & & & **Results/Scene** & & **Results/Scene** & **Results/Scene** & **Results/Scene** \\ \hline United States & 1786 & 340 & 359 & 346 & 315 & 300 \\ Hebei 1 & 2202 & 835 & 841 & 824 & 790 & 775 \\ Hebei 2 & 511 & 156 & 152 & 151 & 144 & 140 \\ Inner & 2150 & 685 & 645 & 648 & 628 & 617 \\ Mongolia & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results presented by different algorithms when screening different regions. of the minimum number of fire stations that can respond quickly to fire and emergency rescue needs throughout the city. This corresponds directly to the goal in the set coverage problem: to select the fewest sets (or regions) such that the concatenation of these sets covers the entire fire risk area of the city. Therefore, the solution of the SCP is not only a theoretical algorithmic optimization, but also an effective tool for solving important problems in real life. As future work, we plan to explore more intelligent optimization algorithms. Additionally, we will actively expand cross-disciplinary applications by integrating deep learning, and apply the results to fields such as urban planning and disaster warning.