中文摘要：

针对低空救援过程中物资需求的模糊性，提出基于K-means范例推理法的物资需求预测。首先，对范例属性用粗糙集进行简约，并计算各属性的权重值；其次，将简约后的范例运用基于DB Index准则的K-means算法进行聚类分析；然后，计算当前范例与距离最小类中所有范例的相关系数，检索出相似度最大的目标范例，并根据目标范例的消耗量线性求解当前范例的需求量；最后，比较该方法与遗传优化BP算法的准确性．结果表明基于K-means范例推理的预测算法具有更高精度。

英文摘要：

Aiming at the fuzziness of supplies demand in the process of low altitude rescue, a CBR （case based reasoning） prediction algorithm based on K-means is proposed. Firstly, the attributes of case are reduced through rough set, the weight values of attributes are calculated. Next, a cluster analysis is made on simplified case through DB Index K-means algorithm; then, correlation coefficient is calculated between the current case and each case in the nearest group, retrievaling the target case with maximum similarity. Finally, according to the supplies of target case, demand of the current case is obtained. A real seismic data is conducted to compare the accuracy of the current approach and genetic optimization BP algorithm. Result shows that the K-means CBR algorithm has higher precision.