Research on location selection of preset points for emergency supplies based on K-means clustering

In order to effectively deal with emergencies and mitigate the possible consequences of various emergencies, this study uses the K-means clustering method to tackle the problems of the indeterminate number, location, and coverage of the existing emergency supplies preset points. Combined with the distribution of demand points, the size of demand, the distribution distance, the location of the reserve and other factors, the 165 demand points in Xuzhou are regionally divided, and the contour coefficient is used as the evaluation index to determine the optimal number of clusters of demand points. Then through python programming, a relocation model is constructed, and the demand weight and distance are used as influencing factors to traverse all demand points in different clustering areas, thereby determining the reserve of each clustering area. The research results can optimize the location of the reserve, reduce the rescue cost, and provide a reference for the location of the reserve for emergency supplies in China.