以降低矿井火灾的发生率为目标，梳理了矿井煤自燃灾害现有监测预警技术的发展现状，总结了现有技术的局限性和不足，指出未来发展应注重先进监测技术的应用、预测技术准确性的提升、预警系统和智能化管理的发展等。针对矿井煤自燃灾害隐蔽性、持续性、扩散性和难以控制的特点，提出矿井煤自燃灾害监测预警技术未来发展方向：提升煤自然发火临界值确定方法的综合性，利用新兴设备和先进技术完善煤自然发火临界值的确定方法；针对矿井煤自燃复杂环境，可通过研究声波探测等新型先进技术手段，提高矿井煤自燃监测参数的全面性和精细化；推动矿井煤自燃监测预警技术的创新性和决策的智能化，利用数字孪生技术提供精准的模拟与预测能力、优化防控方案、提供培训和应急管理以及数据分析和决策支持等方面的帮助；聚焦矿井自燃灾害全面感知、精准预测与防控智能一体化建设，加强对矿井自燃灾害数据分析、灾前主动预警、灾后事故治理、火区自动封闭等技术的研究。

For reducing the incidence of mine fires, the limitations and shortcomings of the existing monitoring and early warning technologies for coal spontaneous combustion disaster are sorted out, and it is pointed out that the future development should focus on the application of advanced monitoring technology, the improvement of the accuracy of prediction technology, the development of early warning system and intelligent management. Aiming at the hidden, persistent, proliferation and difficult to control characteristics of coal spontaneous combustion disaster, the future development direction of coal spontaneous combustion disaster monitoring and early warning technology is proposed: improve the comprehensiveness of the method for determining the critical value of natural ignition of coal, and use emerging equipment and advanced technology to improve the method of determining the critical value of natural ignition of coal; for the complex coal spontaneous combustion environment of the mine, and the comprehensive and refined monitoring parameters of the spontaneous combustion of coal can be improved by studying new advanced technologies such as acoustic wave detection; promote the innovation of coal spontaneous combustion monitoring and early warning technology and the intelligence of decision-making, and use digital twin technology to provide accurate simulation and prediction capabilities, optimize prevention and control plans, provide training and emergency management, as well as the help in data analysis and decision support; focus on the intelligent integration of comprehensive perception, accurate prediction and prevention and control of coal spontaneous combustion disasters, and strengthen the research on technologies such as data analysis of coal spontaneous combustion disasters, active early warning before disasters, post-disaster accident management, and automatic closure of fire areas.