介绍了国内外不同粉尘浓度连续监测技术的测量原理，包括过滤称重法、β射线法、光散射法、电荷感应法和微量振荡天平法，并从准确性、灵敏度和实时性等方面分析比较了不同监测技术的优点和局限性。深入探讨了呼吸性粉尘颗粒物的国内外连续分离技术和标准，并系统分析了目前粉尘浓度连续监测技术在仪器测量精度、可靠性、稳定性、环境适应性、智能化自动校准及功耗优化等方面面临的挑战。讨论了粉尘浓度监测技术的发展趋势：从传统的单一总粉尘浓度监测向总粉尘和呼吸性粉尘共同监测方向发展，从点监测向面监测和区域监测方向快速推进。提出未来应致力于将粉尘浓度监测技术与机器学习、深度学习、计算机视觉及大数据分析和预测等新兴技术相结合，以促进智能检测技术与粉尘职业危害监测预警的深度融合与应用，为实现未来工业场景下的智能化、自动化粉尘治理提供参考。

This paper introduces the measurement principles of various domestic and international dust concentration continuous monitoring technologies, including the filter weighing method, β-ray method, light scattering method, charge induction method, and micro-oscillating balance method. It compares and analyzes the advantages and limitations of these monitoring technologies in terms of accuracy, sensitivity, and real-time performance. The paper also delves into the continuous separation technologies and standards for respirable dust particles on a global scale and systematically examines the challenges that current dust concentration continuous monitoring technologies face in terms of instrumental measurement precision, reliability, stability, environmental adaptability, intelligent automatic calibration, and power consumption optimization. The discussion covers the development trends in dust concentration monitoring technology: the shift from traditional single total dust concentration monitoring to a combined monitoring of total and respirable dust, and rapid transition from point monitoring to area monitoring and regional monitoring. It is proposed that future efforts should be dedicated to integrating dust concentration monitoring technologies with emerging technologies such as machine learning, deep learning, computer vision, and big data analysis and prediction. This integration will facilitate the integration and application of intelligent detection technologies with dust-related occupational hazard monitoring and early warning systems and provide reference for intelligent and automated dust control in future industrial scenarios.