Cold chain logistics distribution cost analysis based on data mining

In order to improve the cost control efficiency in cold chain logistics and distribution, a multi-dimensional heterogeneous data-driven cost analysis and optimization framework is constructed, and the study accurately identifies the key factors affecting the unit cost through cost decomposition modeling, design of multi-source data collection system, and systematic data pre-processing by integrating clustering analysis, association rule mining, and the composite GRU-MLP prediction model. The scheduling optimization strategy is designed based on the mixed integer nonlinear programming method, and the dynamic adjustment of paths and resources is realized by combining the predicted outputs. The results show that the constructed model reduces the unit distribution cost by 13.7% on average in the empirical simulation, and the optimization effect is especially significant on the high fuel consumption and high delay samples, which has good promotion value.