基于PCA-SVR的集装箱码头设备配置OA

This paper proposes an optimized model(PCA-SVR)that combines support vector regression(SVR)and principal component analysis(PCA)to optimize the equipment configuration of container termi-nals,aiming to improve the efficiency of terminal handling operations.By collecting equipment configuration da-ta from multiple container terminals,the SVR model is constructed and trained,optimizing model parameters through cross-validation and grid search techniques.The data indicate that the model using the radial basis function(RBF)kernel exhibited the best performance after optimization.To address the issue of feature corre-lation,PCA was introduced for dimensionality reduction,which effectively accelerated model training and en-hanced its robustness.Model results show that the PCA-SVR model has high accuracy in identifying redundan-cies or deficiencies in equipment configuration,demonstrating superior fitting performance.