| 人工智能赋能废气在线监控的探索与展望 |
| Exploration and Prospects of Artificial Intelligence in Waste Gas Online Monitoring |
| 投稿时间：2025-10-07 修订日期：2025-11-06 |
| DOI：10.19316/j.issn.1002-6002.2025.S1.09 |
| 中文关键词: 废气在线监控 人工智能 智能监管 异常检测 反造假 数据融合 |
| 英文关键词:waste gas online monitoring artificial intelligence intelligent supervision anomaly detection tamper resistance data fusion |
| 基金项目: |
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| 摘要点击次数: 384 |
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| 中文摘要: |
| 为推进人工智能赋能污染源在线监控,助力生态环境监测数智化转型,探讨了人工智能赋能废气在线监控的路径。针对传统系统预警滞后、误报率高及"反造假"能力弱等痛点,提出三大路径:利用无监督与有监督学习构建动态感知与预测预警能力;融合物理规律与前沿算法建立智能"反造假"防火墙;借助多模态融合与图神经网络打破"信息孤岛",实现从企业到区域的协同智能监管。旨在为构建智能、精准、高效的污染源监管新模式提供理论参考与技术展望。 |
| 英文摘要: |
| To accelerate the integration of artificial intelligence (AI) into pollution-source online monitoring and facilitate the digital-intelligent transformation of eco-environmental governance,this paper investigates AI-enabled pathways for real-time waste-gas surveillance.Addressing chronic limitations of conventional systems-delayed alerts,high false-positive rates,and feeble tamper resistance-we propose a three-pronged strategy.First,synergistic unsupervised and supervised learning algorithms are deployed to create dynamic sensing and predictive early-warning capabilities.Second,physical constraints are fused with state-of-the-art AI models to erect an intelligent "anti-fraud firewall" that proactively identifies data forgery or sensor manipulation.Third,multi-modal data fusion and graph neural networks dismantle information silos,enabling cooperative intelligence that scales from individual plants to regional clusters.The envisioned framework offers both theoretical guidance and technical foresight for establishing a smarter,more accurate,and more efficient paradigm of pollution-source supervision. |
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