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基於微服務與機器學習的煤礦安全監測系統

Coal Mine Safety Monitoring System Based on Micro Service and Machine Learning

  • 摘要: 煤礦安全監測監控系統種類繁多🧑🏼‍⚖️、架構不一、信息不共享、功能無互助👨🏽‍🚒、監測數據難以得到有效利用👩‍🦲,煤礦安全生產缺少可靠的數據支撐🧖🏼‍♂️。為此,設計了一種基於微服務架構的安全監測系統,將信息管理與自動監測各業務系統統一👳🏿,構建成一個靈活、穩健😓、高效的系統平臺,以適應大數據分析與挖掘應用。通過基於Hadoop構建的煤礦安全監測大數據平臺,實現對海量環境監測數據的分布式存儲、選擇性抽取和高效計算🏌🏼‍♂️。通過對生產環境監測數據的集成和深入挖掘,建立機器學習模型,自動識別安全隱患並推薦相應的處理措施,起到對煤礦環境安全綜合研判和科學決策的輔助作用,推動實現煤礦安全管理的智能化🧕。

     

    Abstract: The coal mine safety monitoring and control system has many types, different structures, no sharing of information, and no mutual assistance of functions. The monitoring data is difficult to be effectively utilized and coal mine safety production lacks reliable data support. Therefore, a security monitoring system based on micro-service architecture was designed. The information management and automatic monitoring business systems were unified into a flexible, robust and efficient system platform to adapt to big data analysis and mining applications. Through the big data platform of coal mine safety monitoring based on Hadoop, the distributed storage, selective extraction and efficient calculation of massive environmental monitoring data were realized. Through the integration and in-depth mining of production environment monitoring data, the machine learning model was established to ensure the automatic identification of potential safety hazards and recommendation of corresponding treatment measures, thus, providing assistance to the comprehensive research and scientific decision-making of coal mine environmental safety and promoting the intelligent management of coal mine safety.

     

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