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基于Hadoop大数据平台的煤矿智能化工作面开采规划系统

Intelligent mining face planning system for coal mines based on hadoop big data platform

  • 摘要: 为解决采煤工作面智能化开采过程中智能分析与精准决策的难题,利用Hadoop大数据平台,融合分析决策技术、机器学习方法以及分布式存储架构,设计并实现了一套集数据采集、存储、处理和分析于一体的煤矿智能化工作面开采规划系统。该系统通过多层数据架构,实现了对矿压、瓦斯、设备状态等数据的全生命周期管理,其中,数据采集层利用井下传感器网络、Sqoop、Flume和Kafka等组件实现对多源异构数据的实时采集与传输;数据存储与处理层利用HDFS、HBase等分布式技术实现对结构化数据、半结构化数据和非结构化数据的分类存储与高效处理,通过MapReduce、Spark等框架完成数据的清洗与聚合;决策规划层结合主成分分析法对高维数据进行降维,筛选出矿压、瓦斯、设备状态等关键指标,并基于K-Means聚类算法将数据划分为具有不同特征的簇,进一步通过CART决策树模型对聚类结果进行精细化处理,快速定位出影响工作面安全的关键因素,处理连续型和离散型监测数据,实现对智能化采煤工作面安全状况的实时监测与预警,试验过程中,模型测试集准确率达92.3%、F1分数为0.91,表现出较高的可靠性和实用性。最终,系统通过多源信息融合与动态迭代优化机制,构建覆盖感知、分析、决策的闭环智能规划体系,为煤矿安全高效的生产提供了可靠的技术支撑。

     

    Abstract: To address the challenges of intelligent analysis and precise decision-making in the process of intelligent mining in coal mining faces, By leveraging the Hadoop big data platform, integrates analysis and decision-making technologies, machine learning methods, and distributed storage architectures, and designs and implements a set of intelligent mining face planning systems for coal mines that integrate data collection, storage, processing, and analysis.This system achieves full lifecycle management of data such as mine pressure, gas, and equipment status through a multi-layer data architecture.Specifically, the data collection layer uses underground sensor networks, Sqoop, Flume, and Kafka components to achieve real-time collection and transmission of multi-source heterogeneous data; the data storage and processing layer uses distributed technologies such as HDFS and HBase to achieve classified storage and efficient processing of structured, semi-structured, and unstructured data, and uses MapReduce and Spark frameworks to complete data cleaning and aggregation; the decision planning layer combines principal component analysis to reduce the dimensionality of high-dimensional data, selects key indicators such as mine pressure, gas, and equipment status, and based on the K-Means clustering algorithm divides the data into clusters with different characteristics, further processes the clustering results through the CART decision tree model to precisely identify the key factors affecting the safety of the mining face, processes continuous and discrete monitoring data, and realizes real-time monitoring and early warning of the safety status of the intelligent mining face.During the experiment, the accuracy of the model test set reached 92.3% and the F1 score was 0.91, demonstrating high reliability and practicality.Finally, the system builds a closed-loop intelligent planning system covering perception, analysis, and decision-making through multi-source information fusion and dynamic iterative optimization mechanisms, providing reliable technical support for the safe and efficient production of coal mines.

     

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