基于微服务与机器学习的煤矿安全监测系统

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

煤矿安全监测监控系统种类繁多、架构不一、信息不共享、功能无互助、监测数据难以得到有效利用,煤矿安全生产缺少可靠的数据支撑。为此,设计了一种基于微服务架构的安全监测系统,将信息管理与自动监测各业务系统统一,构建成一个灵活、稳健、高效的系统平台,以适应大数据分析与挖掘应用。通过基于Hadoop构建的煤矿安全监测大数据平台,实现对海量环境监测数据的分布式存储、选择性抽取和高效计算。通过对生产环境监测数据的集成和深入挖掘,建立机器学习模型,自动识别安全隐患并推荐相应的处理措施,起到对煤矿环境安全综合研判和科学决策的辅助作用,推动实现煤矿安全管理的智能化。

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.