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An anomaly detection scheme for data stream in cold chain logistics.

Zhibo Xie1, Heng Long1, Chengyi Ling1

  • 1School of information and intelligent engineering, Zhejiang wanli University, Ningbo, China.

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|March 10, 2025
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This study introduces an improved anomaly detection algorithm for cold chain logistics (CCL), enhancing detection accuracy and timeliness. The new method significantly outperforms existing techniques, ensuring better product quality in temperature-sensitive supply chains.

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Area of Science:

  • Logistics and Supply Chain Management
  • Data Science and Analytics
  • Quality Control and Assurance

Background:

  • Anomaly detection is crucial in cold chain logistics (CCL) but often suffers from poor performance due to high costs and technical challenges.
  • Delayed or missed anomaly detection in CCL negatively impacts the quality and safety of sensitive goods.
  • Existing methods struggle to effectively identify anomalies in complex CCL data streams.

Purpose of the Study:

  • To develop a novel and effective anomaly detection scheme specifically for cold chain logistics.
  • To address the limitations of current anomaly detection techniques in terms of cost, timeliness, and performance.
  • To improve the overall quality assurance of goods transported under controlled temperatures.

Main Methods:

  • Analysis of CCL data characteristics and establishment of a mathematical data flow model.
  • Definition of sliding window and correlation coefficient for anomaly identification.
  • Development of an improved isolated forest algorithm incorporating subsampling and cross-factor adjustments.
  • Deduction of three abnormal judgment conditions based on the correlation coefficient (ρjk).

Main Results:

  • The proposed anomaly detection scheme demonstrates superior performance metrics (Precision, Recall, F1 score, AUC) compared to Support Vector Machines (SVM), Local Outlier Factor (LOF), and standard isolated forests (iForest).
  • Achieved average performance indicators: Precision (P) of 0.8784, Recall (R) of 0.8731, F1 score of 0.8639, and Area Under the Curve (AUC) of 0.9064.
  • The enhanced algorithm shows increasing superiority as data dimensionality increases, indicating robustness.

Conclusions:

  • The novel anomaly detection scheme offers a significant advancement for cold chain logistics, improving the accuracy and speed of identifying critical deviations.
  • The improved isolated forest algorithm effectively overcomes the limitations of traditional iForest, providing better anomaly detection capabilities.
  • While execution time is slightly increased compared to iForest, the substantial gains in detection performance justify its use for maintaining cold chain integrity and product quality.