基于深度学习的高铁冷链站点识别方法OA

To effectively address the lack of scientific and systematic guidance in the planning and functional positioning of high speed rail(HSR)cold chain logistics networks,a deep learning-based identification method for HSR cold chain station siting was proposed to enhance the scientific rationality and accuracy of logistics hub configuration.Eleven key factors influencing the siting of HSR cold chain stations were systematically selected from macro-,meso-,and micro-level perspectives.An attention mechanism was incorporated to strengthen the model's ability to distinguish among different feature dimensions.During the modeling process,the impact of data characteristics and model architecture on predictive performance was thoroughly analyzed.Multiple evaluation metrics were established,and the performances of various loss functions under F1-Score and AUC indicators were compared.Weighted cross entropy was identified as the optimal loss function.The SHAP method was employed to rank feature importance and reveal the key variables dominating the station recognition decisions.Furthermore,a combination of grid search and Bayesian optimization was used to jointly tune the hyperparameters,thereby improving the model's robustness and generalization ability.Upon completion of model training,Liaoning Province,representative within the national cold chain logistics network,was selected as the case study area,and the model's adaptability and prediction capacity were validated using node centrality metrics derived from hypernetwork theory.The results demonstrate that the proposed model exhibits outstanding performance in the cold chain station prediction task,achieving a prediction accuracy close to 100%.Nevertheless,its practical application may require integration with local station operational conditions and future development plans.The study concludes that the deep learning identification framework not only improves the scientific validity of HSR cold chain station layout but also provides reliable technical support and strategic guidance for the optimization of China's HSR cold chain logistics system.