基于机器感知与学习的空气颗粒物智能检测、识别与预警方法研究综述

Review of Intelligent Detection,Identification and Warning Methods for Airborne Particulate Matter Based on Machine Perception and Learning

随着空气污染问题的不断加剧,准确检测和及时预警空气颗粒物(particulate matter,PM)的重要性日益突出。传统方法依赖专业设备,不适用于实时检测。与传统方法相比,基于机器感知与学习的方法体现出技术优势,具有可实时检测、准确性高等优点。因此,对近几年的基于机器感知与学习的PM智能检测、识别与预警方法进行详细综述。首先,对PM的标准和来源进行介绍;然后,从检测、识别和预警这3个方面详细总结了各类方法,并对比各方法的特点和性能,其中,基于机器学习和深度学习的方法在各研究中取得了较大进展;最后,总结全文主要内容,并提出当前领域面临的挑战以及未来的重点研究方向。未来的研究应该继续关注技术创新和数据质量,以实现更好的空气质量监测和管理。

As air pollution problems continue to grow,accurate detection and timely warning of particulate matter(PM)are becoming increasingly important.Traditional methods rely on specialized equipment and are not suitable for real-time monitoring.In comparison,machine perception and learning-based methods have technological advantages such as real-time monitoring and high accuracy.Therefore,a detailed review of intelligent PM detection,identification and early warning methods based on machine perception and learning in recent years was presented.First,an introduction to the standards and sources of PM was presented.Then,various methods from the detection,identification,and warning fields were comprehensively summarized,highlighting the characteristics and performance differences among them.Methods based on machine learning and deep learning have made significant progress in each of these research areas.Finally,the paper concluded by summarizing the main content and suggesting the current challenge in the field and future research direction.Future research effort should continue to prioritize technological innovation and data quality to achieve improved air quality monitoring and management.