Adaptive Weighted Case-Based Reasoning for Intelligent Coal Mine Decision Support Systems

Abstract

Under the background of intelligent transformation of coal mines, an intelligent decision support system based on case-based reasoning (CBR) has become crucial for improving production control. This paper constructs such a system and innovatively proposes an adaptive weight dynamic case retrieval algorithm (AWDCR). The algorithm leverages real-time monitoring of multi-source production data, dynamically adjusting case attribute weights based on data change characteristics and decision influence through a hybrid AHP-entropy weight mechanism. Using MATLAB simulation with 100,000+ actual production records across 100 scenarios (normal, equipment failure, environmental anomaly), results show AWDCR reduces average retrieval time by 20% and improves decision accuracy from 80% to 90% compared to traditional CBR. enhancing retrieval accuracy by 20%. The system effectively enhances production efficiency and safety, laying a foundation for intelligent coal mining.

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Authors

  • Zhiqiang Zhang National Energy Group Ningxia Coal Industry Co., Ltd. Hongliu Coal Mine
  • Kai Tan Chongqing Institute of China Coal Technology & Engineering Group
  • Changjun Yang National Energy Group Ningxia Coal Industry Co., Ltd. Hongliu Coal Min
  • Yong Li Chongqing Institute of China Coal Technology & Engineering Group
  • Chunhua Huang Chongqing Institute of China Coal Technology & Engineering Group
  • Wei Li National Energy Group Ningxia Coal Industry Co., Ltd. Hongliu Coal Mine

DOI:

https://doi.org/10.31449/inf.v49i34.9666

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Published

08/26/2025

How to Cite

Zhang, Z., Tan, K., Yang, C., Li, Y., Huang, C., & Li, W. (2025). Adaptive Weighted Case-Based Reasoning for Intelligent Coal Mine Decision Support Systems. Informatica, 49(34). https://doi.org/10.31449/inf.v49i34.9666

Issue

Vol. 49 No. 34 (2025): Online-only issue

Section

Online-only

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