The assessment of cold chain logistics for fresh products can be more precise with high-dimensional information data, providing valuable insights for the optimization of associated costs. Nonetheless, traditional data processing techniques fail to meet the processing efficiency required for such high-dimensional cold chain logistics data. Therefore, this paper proposes a spectral clustering algorithm based on the local standard deviation and optimized initial center, which comprehensively analyzes the fixed, transportation, refrigeration, and cargo damage costs of cold chain logistics. Additionally, this algorithm includes a variation operator based on clustering and introduces a large neighborhood search mechanism for optimizing the individual connectivity gene layer after selecting the gene layer site for variation. Simulation results demonstrate that the proposed algorithm exhibits better convergence in 15 iterations, reduces error rates, and significantly cuts down on the clustering process time. This ultimately leads to a reduction in the total cost of cold chain calculation.

通过高维度的信息数据，可以更加精准地对生鲜冷链物流进行评估，为相关成本的优化提供有价值的见解。然而，传统的数据处理技术无法满足如此高维的冷链物流数据所需的处理效率。因此，本文提出一种基于局部标准差和优化初始中心的谱聚类算法，综合分析冷链物流的固定成本、运输成本、冷藏成本和货损成本。此外，该算法包括基于聚类的变异算子，并引入大邻域搜索机制，用于在选择变异的基因层位点后优化个体连接性基因层。仿真结果表明，该算法在 15 次迭代中表现出更好的收敛性，降低了错误率，并显着减少了聚类过程时间。这最终导致冷链计算总成本的降低。