基于机器学习的地震台站巡检路径规划

Seismic station inspection routes planning based on machine learning

利用机器学习中的模拟退火和K均值聚类算法优化了地震台站的巡检路径规划。以重庆市为例,通过模拟退火算法和百度地图API计算实际路径距离和时间,找到近似最优路径。在分组巡检时,引入K均值聚类算法先对台站进行分组再进行路径规划,提高了巡检效率。结果表明,在台站数量较多的情况下,机器学习算法相较于人工规划更能有效地找到最优路径,提高工作效率。

This study optimizes the inspection path planning of seismic stations using the simulated annealing and K-means clustering algorithms in machine learning.Taking Chongqing as an example,the simulated annealing algorithm and Baidu Maps API are employed to calculate actual path distances and times,successfully identifying an approximate optimal path.The introduction of the K-means clustering algorithm in group inspections categorizes the stations,which enhanced inspection efficiency through optimized path planning.The results indicate that,especially with a large number of stations,machine learning algorithms are more effective than manual planning in finding the optimal path,thereby improve overall efficiency.