基于大数据分析技术智能化维护气象设备的应用研究

Research on the Application of Intelligent Maintenance of Meteorological Equipment Based on Big Data Analytics Technology

摘要: 气象设备的稳定运行对航空安全与效率至关重要，而传统维护模式面临设备老化、人工经验依赖及多源数据整合不足等挑战。本研究提出基于Hadoop与Spark技术的智能化维护解决方案：通过模块化传感器组网、边缘计算节点及Kafka消息队列实现数据高效采集与实时同步；运用3σ准则、滑动窗口算法、插值法及NTP协议完成数据清洗与标准化；从原始数据中提取设备健康指标及时序特征，结合环境变量通过Spark MLlib训练LSTM故障预测模型；最终通过动态预警阈值、工单智能生成及三维态势可视化实现维护决策优化。研究成果显著提升了维护效率与准确性，未来可结合边缘计算与跨域数据融合技术进一步优化。

Abstract: The stable operation of meteorological equipment is crucial for aviation safety and efficiency, but traditional maintenance models face challenges such as equipment aging, reliance on manual experience, and insufficient integration of multi-source data. This study proposes an intelligent maintenance solution based on Hadoop and Spark technologies: efficient data collection and real-time synchronization are achieved through modular sensor networking, edge computing nodes, and Kafka message queues; data cleaning and standardization are completed using the 3σ criterion, sliding window algorithm, interpolation method, and NTP protocol; equipment health indicators and time-series features are extracted from raw data, and an LSTM fault prediction model is trained via Spark MLlib in combination with environmental variables; finally, maintenance decision optimization is realized through dynamic early warning thresholds, intelligent work order generation, and 3D situation visualization. The research results significantly improve maintenance efficiency and accuracy, and future optimization can be further enhanced by integrating edge computing and cross-domain data fusion technologies.