基于多源数据融合的大气环境智慧监管平台建设研究

Research on Constructing a Smart Supervision Platform for Atmospheric Environment Using Multi-Source Data Fusion

摘要: 本研究构建了基于“真、准、全、快、新”五维数据质量保障体系的大气环境智慧监管平台，通过物联网技术整合环境质量监测网、污染源监控网等12类异构数据(日均处理量10 TB)，建立多源数据中心与AI算法中心双核驱动架构。平台创新性实现“感知预测–精准溯源–动态管控–效果评估”闭环管理：采用LSTM模型提升污染态势预判能力，结合PMF源解析技术识别重点污染源；依托三级垂直管理机制与“智慧调度一张图”，实现指令10分钟级穿透与跨部门协同，为破解“精准治污、科学治污、系统治污”的实践难题提供技术支撑。

Abstract: This study developed an intelligent atmospheric environment supervision platform based on a five-dimensional data quality assurance framework (“Truthfulness, Accuracy, Completeness, Timeliness, and Novelty”). The platform integrates heterogeneous data from 12 sources, including environmental quality monitoring networks and pollution source monitoring networks, via Internet of Things (IoT) technology, processing an average of 10 terabytes (TB) of data daily. It establishes a dual-core-driven architecture comprising a Multi-source Data Center and an AI Algorithm Center. The platform innovatively achieves closed-loop management encompassing “perception & prediction—precise source identification—dynamic control—effectiveness evaluation”. Specifically, it employs Long Short-Term Memory (LSTM) models to enhance pollution trend forecasting capabilities and combines Positive Matrix Factorization (PMF) source apportionment techniques to identify key pollution sources. Leveraging a three-tier vertical management structure and a “Smart Dispatch Map”, the platform enables directive penetration within 10 minutes and facilitates cross-departmental coordination. This system provides robust technical support for addressing the practical challenges of implementing “targeted, scientific, and systematic pollution control”.