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.
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