2026数据科学与大数据技术一套基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过(论文+程序代码源码+Q1T2的图像与模型保存+数据表)定稿.zip

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2026-06-25
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数据科学与大数据技术基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过.docx(1.36 MB)
毕业代码&附件
- 毕业代码&附件
  - (原附件11)库存数据清洗后的结果.csv(76.09 KB)
  - (原附件3)各个仓库属性.csv(5.23 KB)
  - (原附件4)各品类关联度.csv(85.96 KB)
  - (原附件5)高级分类.csv(10.34 KB)
  - affinity_matrix.csv(495.95 KB)
  - affinity_matrix_gbk.csv(495.96 KB)
  - Q1T2的图像与模型保存
    - category102_prediction.png(43.43 KB)
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  - result11(ownchange).csv(14.98 KB)
  - 代码
    - 代码
      - 代码
        Q1总代码
        (数据清洗)Q1_data_附件1.py(2.54 KB)
        (数据清洗)Q1_data_附件2.py(1.53 KB)
        (无关紧要的代码)分离销量预测.py(1.9 KB)
        Q1_日销量.py(6.85 KB)
        Q1_月均库存量_Xboost.py(4.98 KB)
        众数填充.py(765 B)
        去除首行时间.py(577 B)
        可视化1.py(773 B)
        缺失数据处理.py(1.93 KB)
        Q2总代码
        (非主要)关联度矩阵转化.py(1.31 KB)
        Q2模拟退火.py(5.08 KB)
        Q2的第二种解题方式PSO.py(4.87 KB)
        Q3总代码
        Q3.py(8.22 KB)
        编码方式检查.py(354 B)
        编码方式转换.py(488 B)
  - 分离空缺销量.xlsx(11.94 KB)
  - 副本库存重排数据.xlsx(33.9 KB)
  - 副本销量重排数据.xlsx(289.91 KB)
  - 日销量2.csv(576.71 KB)
  - 月均库存量1.csv(9.67 KB)
  - 未来三个月预测值.xlsx(9.78 KB)
  - 销量重排数据(去时间)(填充)(众数填充).xlsx(309.2 KB)
  - 附件1.csv(86.85 KB)
  - 附件2.csv(1.48 MB)
  - 附件22.csv(1.23 MB)

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碎片内容

2026数据科学与大数据技术一套基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过(论文+程序代码源码+Q1T2的图像与模型保存+数据表)定稿.zip

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2026数据科学与大数据技术一套基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过(论文+程序代码源码+Q1T2的图像与模型保存+数据表)定稿.zip

2026数据科学与大数据技术一套基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过(论文+程序代码源码+Q1T2的图像与模型保存+数据表)定稿.zip

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2026数据科学与大数据技术一套 基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过(论文+程序代码源码+Q1T2的图像与模型保存+数据表)定稿.zip

2026数据科学与大数据技术一套 基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过(论文+程序代码源码+Q1T2的图像与模型保存+数据表)定稿.zip

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2026数据科学与大数据技术一套基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过(论文+程序代码源码+Q1T2的图像与模型保存+数据表)定稿.zip

2026数据科学与大数据技术一套基于XGBoost-Prophet预测与智能优化的仓储网络库存分配模型研究4.26-AI通过(论文+程序代码源码+Q1T2的图像与模型保存+数据表)定稿.zip