| 基于全光谱与人工智能的水质监测新感知技术研发和应用 |
| Based on Full-Spectrum Analysis and Artificial Intelligence:Research and Application of Novel Sensing Technologies for Water Quality Monitoring |
| 投稿时间：2025-10-07 修订日期：2025-10-28 |
| DOI：10.19316/j.issn.1002-6002.2025.S1.08 |
| 中文关键词: 全光谱分析 人工智能 深度学习 水质监测 多参数反演 |
| 英文关键词:full-spectrum analysis artificial intelligence deep learning water quality monitoring multi-parameter inversion |
| 基金项目: |
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| 通讯作者:伍恒赟* 江西省生态环境监测中心, 江西 南昌 330000 |
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| 中文摘要: |
| 水环境污染是制约可持续发展的重要因素,传统水质监测方法存在滞后性高、成本高、难以捕捉瞬时污染等问题。为实现水环境的实时、多参数、智能化监测,探讨了全光谱分析与人工智能技术相结合的水环境监测新方法。通过采集水体在紫外、可见光至近红外波段(200~900 nm)的连续吸收光谱,并结合深度学习算法建立光谱特征与水质参数之间的非线性映射关系,实现了对化学需氧量、总氮、氨氮及总磷等重点水质参数的快速反演与分析。实际应用表明:该技术在污水处理厂原水、入河排污口及地表水监测场景中表现出较高的监测精度与稳定性,参数反演通过率多数超过90%,平均相对误差显著低于行业规范要求。该技术为水环境精细化管理与智能决策提供了有效支持。 |
| 英文摘要: |
| Water pollution poses a significant constraint to sustainable development,while traditional monitoring methods suffer from high latency,substantial costs,and difficulties in capturing transient pollution events.To achieve real-time,multi-parameter,and intelligent water quality monitoring,this study investigates a novel approach integrating full-spectrum analysis with artificial intelligence technology.By capturing continuous absorption spectra of water across ultraviolet,visible,and near-infrared range (200-900 nm) and establishing nonlinear mapping relationships between spectral features and key water quality parameters using deep learning algorithms,rapid inversion and analysis of parameters including chemical oxygen demand (COD),total nitrogen (TN),ammonia nitrogen (NH3-N),and total phosphorus (TP) were achieved.Practical applications show that this technology exhibits high monitoring accuracy and stability in scenarios such as raw water in sewage treatment plants,river discharge outlets,and surface water monitoring.The inversion success rate for most parameters exceeds 90%,with the average relative error being significantly lower than industry standard requirements.This technology provides effective support for refined management and intelligent decision-making in water environmental protection. |
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