Research progress and prospects of intelligent mine ventilation
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摘要:
从通风参数监测技术、通风网络实时解算方法、通风灾变应急调控技术和矿井智能通风系统架构4个方面，分析了通风参数检测设备、测定方案优化、网络实时监测与解算、异常诊断方法及应急调控技术等方面的研究进展。指出当前智能通风系统面临三大关键挑战：现有通风参数检测装置在稳定性和准确率方面存在不足，导致系统难以实现秒级响应与精准调控；矿井环境的高度不确定性与人机协同决策自动化水平低，制约着通风网络的自适应优化能力；火与瓦斯复合灾害的实时辨识技术尚未突破，限制了灾害工况下的应急通风调控效能。针对上述问题，提出未来重点攻关方向：① 通过研发抗干扰性能强的新型传感设备，构建“固定监测+移动巡检”融合的时空动态监测网络，实现通风多参数的精准感知。② 基于数字孪生技术建立通风网络超实时仿真模型，结合强化学习与博弈论方法优化局部决策与全局策略的协同机制，推动智能决策体系向云边端协同模式发展。③ 构建数字孪生驱动的灾情演化推演平台，集成动态逃生路径规划与机器人集群快速布防技术，形成“灾情预警−区域隔离−智能救援”三级应急响应体系。
Abstract:The research progress has been analyzed from four aspects: ventilation parameter monitoring technology, real-time calculation methods for ventilation networks, emergency regulation technology for ventilation disasters, and the architecture of intelligent mine ventilation systems, focusing on ventilation parameter detection devices, optimization of measurement schemes, real-time monitoring and calculation of networks, anomaly diagnosis methods, and emergency regulation technologies. Currently, intelligent ventilation systems face three major challenges. First, existing ventilation parameter detection devices are insufficient in stability and accuracy, making it difficult for the system to achieve response within seconds and precise regulation. Second, the high uncertainty of the mine environment and the low level of automation in human-machine collaborative decision-making limit the adaptive optimization capabilities of the ventilation network. Third, real-time identification technologies of fire and gas compound disasters have not yet been broken through, restricting the effectiveness of emergency ventilation regulation in disaster conditions. To address these challenges, the future research directions should focus on: ① Developing new sensor devices with strong anti-interference capabilities to construct a spatiotemporal dynamic monitoring network that integrates "fixed-point monitoring+mobile inspection", enabling precise multi-parameter ventilation perception. ② Establishing an ultra-real-time simulation model for the ventilation network based on digital twin technology, and combining reinforcement learning and game theory methods to optimize the coordination between local decision-making and global strategy, thereby driving intelligent decision-making systems toward a cloud-edge-device collaborative mode. ③ Building a digital twin-driven disaster evolution simulation platform, integrating dynamic evacuation path planning and rapid deployment of robot clusters, thereby forming a three-tier emergency response system of "disaster warning-regional isolation-intelligent rescue".
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图 2 矿井通风系统异常预警指标体系
Figure 2. Anomaly regulation framework for intelligent mine ventilation
表 1 光学类瓦斯传感器技术
Table 1 Optical gas sensor technologies
传感器类型 检测限度/% 响应时间/s 适用场景 TDLAS 0.001～0.01 1～2 高精度定点/分布式监测 FTIR 0.1～1 5～10 便携设备/常规区域监测 光声光谱 0.000 1 <10 痕量气体实验室分析 光纤光栅 0.1～1 30～60 长期稳定分布式监测 
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