Current status and prospects of research on landslide disasters in mine slopes based on multi-source information fusion
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摘要: 为克服单一信息源无法精确表征矿山滑坡灾害演化特征的问题，基于多源信息融合技术，从矿山边坡多源信息获取、矿山边坡多源信息融合、矿山边坡位移预测及滑坡风险评价3个方面概述了矿山边坡滑坡灾害研究进展。总结了典型的“天”“空”“地”边坡监测手段及“天−空−地”一体化协同监测方法；梳理了包含数据级、特征级和决策级融合的边坡多源信息融合流程；整理了位移与应力、位移与水文气象及其他不同类型的监测数据融合形式；阐述了基于多源信息融合的边坡位移预测及滑坡风险评价相关研究现状。基于当前矿山边坡滑坡灾害研究存在的灾害分析的准确性严重依赖监测数据质量、对岩石力学机理知识利用不足等问题，指出了矿山边坡滑坡灾害研究发展趋势：统一多源数据采集接入标准；开发监测数据与岩石力学机理融合的矿山边坡滑坡灾害分析方法；优化“天−空−地”多源信息的时空关联挖掘算法；加强基于多源信息融合的矿山边坡滑坡灾害预警平台建设。Abstract: In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of mine slope landslide disasters from three aspects: multi-source information acquisition of mine slopes, multi-source information fusion of mine slopes, and mine slope displacement prediction and landslide risk assessment. The study summarizes typical slope monitoring methods of "sky", "air", and "ground" , as well as integrated collaborative monitoring method of "sky-air-ground". The study sorts out the slope multi-source information fusion process that includes data level, feature level, and decision level fusion. The paper organizes the fusion forms of displacement and stress, displacement and hydrological and meteorological monitoring information, as well as other different types. This paper elaborates on the current research status of slope displacement prediction and landslide risk assessment based on multi-source information fusion. The accuracy of disaster analysis in current research on mine slope landslide disasters heavily depends on the quality of monitoring data and insufficient utilization of knowledge of rock mechanics mechanisms. Based on the above problems, the development trends of research on landslide disasters in mine slopes are pointed out. The multi-source data collection and access standards are unified. The method for analyzing landslide disasters in mine slopes is developed by integrating monitoring data with rock mechanics mechanisms. The spatiotemporal association mining algorithm for multi-source information from the "sky-air-ground" is optimized. The construction of a mine slope landslide disaster warning platform based on multi-source information fusion is strengthened.
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0. 引言
粉尘是煤矿作业中的主要危害因素之一，对矿工身体健康危害极大[1-2]。长期暴露在高浓度的煤矿粉尘环境中，粉尘颗粒逐渐沉积于肺部，可能引发不可逆的纤维化病变[3-4]，最终导致严重的肺部疾病。开展粉尘浓度连续监测不仅是保障工人健康的重要手段，还能为制定有效的粉尘防控策略提供科学依据，从而降低尘肺病的发生概率。
光散射法是粉尘浓度连续监测领域的重要技术，研究者围绕该方法在理论与应用方面进行了广泛探索。J. R. Patts等[5]开发了一种结合光散射监测仪与移动摄像设备的系统，用于评估工人的粉尘暴露水平。A. Konoshonkin等[6]通过物理光学近似法研究了复杂形状大颗粒的光散射行为。Lin Chengjun等[7]针对粒径反演不稳定问题，提出了一种基于多参数优化的解决方案。Cui Xiaojun等[8]优化了鞘流结构设计，提升了粉尘散射信号的检测精度。Zheng Xiuting等[9]分析了粒径、折射率和光波长对粉尘浓度监测的影响规律。戴珺等[10]通过改进反演算法显著提高了测量系统的稳定性和抗干扰能力。吴娟等[11]利用非线性特征向量提取技术与神经网络模型，实现了粉尘颗粒的粒径解析和特性识别。
然而，现有方法在煤矿井下高湿度环境中的应用仍存在一定局限性。当环境相对湿度达到或超过60%时，粉尘颗粒会发生显著的吸湿生长，导致粉尘浓度监测结果产生偏差[12]。针对该问题，C. Sioutas等[13]采用扩散干燥剂降低空气湿度，但需要定期维护干燥剂，以确保其正常工作。另外，一些研究尝试结合湿度传感器与经验算法，通过引入修正因子来补偿湿度对粉尘浓度的影响[14-16]，但这种方法无法实时反映吸湿后粉尘颗粒粒径变化对浓度监测结果的影响，因此在一定程度上限制了监测的准确性。
为了提高煤矿井下高湿度环境下的粉尘浓度监测精度，通过观察不同湿度条件下煤矿粉尘吸湿生长的形貌特征，分析了湿度对煤矿粉尘颗粒粒径变化和粉尘浓度监测的影响规律；基于湿度环境下多角度光散射粉尘浓度监测实验，提出一种基于光散射原理的在线湿度补偿技术。
1. 煤矿粉尘颗粒吸湿生长形貌特征及粒径变化规律
为了提升煤矿粉尘浓度监测的实效性与精准度，需要实时捕捉粉尘颗粒吸湿状况的动态变化。因此，从微观层面出发，运用扫描电子显微镜，研究煤矿粉尘颗粒吸湿生长时所展现出的形貌特性及颗粒粒径变化规律。
1.1 实验准备
实验所用粉尘需接近煤矿实际环境中的特性。将采集自煤矿的原始粉尘进行研磨处理后，采用自动分样筛进行筛分，获得中位粒径不超过75 μm的颗粒样本，以确保其粒度满足实验要求。
实验仪器包括以下4种：
1） 扫描电子显微镜。用于观察粉尘颗粒在吸湿条件下的微观形貌变化，其技术参数：分辨率高于3.0 nm@30 kV，放大倍数为6～300 000，加速电压为0～30 kV；样品台支持X轴移动≤80 mm，Y轴移动≤60 mm，Z轴移动≤50 mm，旋转角度为360°连续，倾斜角度为−5～+90°，可容纳直径为80 mm的样品，最大样品直径为110 mm。
2） 粉尘采样器与分析天平。用于定量测定粉尘浓度。其中，粉尘采样器负载能力≥200 Pa，测量误差为±10%；分析天平的量程为120/42 g，分辨率分别为0.1 mg和0.01 mg。
3） 粒径分析仪。用于评估粉尘颗粒的粒径分布，测试范围为0～150 μm，精度为±10%。
4） 温湿度计。用于监测实验环境的温湿度条件，温度测量范围为−10～80 ℃，误差为±0.5 ℃；相对湿度测量范围为10%～99%，误差为±1%RH。
实验系统由粉尘喷射装置、温湿度控制设备、实验舱、加湿器和取样装置等组成。粉尘喷射装置负责将粉尘样本均匀分布于实验舱内，取样装置用于在不同温湿度条件下收集粉尘颗粒，为实验分析提供样本。通过温控和湿度调节设备，实现实验舱内环境的稳定控制，覆盖温度范围10～30 ℃及相对湿度范围30%～95%。实验舱如图1所示。
实验步骤如下：
1） 粒径分析。将实验粉尘置于温度为25 °C、相对湿度为30%的恒温恒湿箱中静置处理24 h，以确保其达到稳定状态。随后取出一部分粉尘样品，通过粒度分析仪对其颗粒分布进行检测，并详细记录分析结果。
2） 温湿度控制。启动环境实验舱的温湿度调节设备，将舱内温度稳定控制在（25±1）°C。根据实验要求，设置相对湿度范围为35%～98%，以模拟不同湿度条件下的实验环境。
3） 粉尘喷射。待舱内温湿度达到并稳定在预设值后，启动粉尘发生装置，将煤矿粉尘均匀释放到舱内。经过一段时间喷射操作后关闭装置，静待实验舱内的粉尘浓度达到平衡状态。
4） 电镜采样分析。在粉尘自然沉降至取样台的导电胶表面后，小心移除导电胶，并对样品进行喷金处理。将处理后的样品放入扫描电子显微镜中，观察煤矿粉尘颗粒吸湿生长后的微观形貌特征。
1.2 不同湿度下煤矿粉尘颗粒形貌特征变化
在高湿度环境下，粉尘颗粒会因吸湿作用发生尺寸增长的现象[17]。通过扫描电子显微镜对沉积在导电胶上的粉尘颗粒进行观察，记录不同湿度条件下颗粒的形貌变化。选取粒径为10 μm的粉尘颗粒作为研究对象，对其在吸湿生长过程中的微观形貌进行分析。将扫描电镜放大1 500倍，粒径10 μm煤矿粉尘颗粒的吸湿生长形貌特征如图2所示。
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图 2 粒径10 μm煤矿粉尘颗粒的吸湿生长形貌特征Figure 2. Hygroscopic growth and morphological characteristics of coal mine dust particles with a particle size of 10 μm从图2可看出：当环境相对湿度低于40%时，粉尘颗粒表面相对粗糙，与原始状态对比，并未出现明显的吸湿现象；当湿度达到50%时，颗粒表面开始附着不同尺寸的小颗粒，粉尘颗粒开始发生吸湿生长；当湿度增至65%时，吸湿现象更加明显，颗粒表面附着了更多片状物质，且粉尘颗粒的粒径显著增大；当湿度达到80%时，吸湿生长现象更加显著，颗粒的粒径变化更为明显。可见，当环境相对湿度达到或超过50%时，粉尘颗粒开始显现吸湿生长现象。随着湿度的进一步增加，颗粒的粒径持续增大，湿度越高，煤矿粉尘颗粒的凝并生长速度越快，生成的颗粒结构更加紧密。
1.3 煤矿粉尘颗粒粒径与湿度的关系
为了进一步研究粉尘颗粒的吸湿生长规律，在湿度由35%升至98%的过程中，使用导电胶进行粉尘采样并确保发尘浓度保持恒定，通过扫描电子显微镜观察湿度升高过程中煤矿粉尘颗粒的粒径。绘制煤矿粉尘颗粒粒径与环境湿度的关系曲线，如图3所示。可看出煤矿粉尘颗粒粒径随着湿度的增加呈现指数型增长趋势，其拟合关系为
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图 3 煤矿粉尘颗粒粒径与环境湿度的关系曲线Figure 3. Relationship curve between coal mine dust particle size and environmental humidity$$ D=D_0(1+kh^a) $$ (1) 式中：D为在相对湿度$ h $（通常取0～100%）下的粒径；D0为干燥条件下的粒径；k为吸湿性常数，其值与颗粒化学性质相关；a为指数参数，通常为1～2。
2. 煤矿粉尘浓度与湿度的关系
为了探究湿度对煤矿粉尘浓度的影响，首先在环境湿度低于50%的条件下，通过手工采样称重法测得粉尘浓度为11.3 mg/m³。在保持发尘浓度恒定的前提下，采用相同的手工采样方法，在不同湿度环境下测量并记录粉尘浓度变化，得到不同环境湿度下的粉尘浓度变化曲线，如图4所示。可看出当环境湿度由50%逐步增加至90%时，煤矿粉尘浓度呈现指数型增长趋势。经拟合，得到煤矿粉尘浓度与环境湿度之间的关系：
$$ C_{\mathrm{m}}(h)=C_{\mathrm{m}}(0)(1+kf^b(h)^{ }) $$ (2) 式中：$ C_{\mathrm{m}}(h) $为不同湿度下的煤矿粉尘浓度；$ {C_{\mathrm{m}}}(0) $为干燥条件下的煤矿粉尘浓度；$ f(h) $为湿度增量函数，通常为非线性函数；b为指数参数。
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图 4 不同环境湿度下的煤矿粉尘浓度变化曲线Figure 4. Variation curve of coal mine dust concentration under different environmental humidity levels3. 用于煤矿粉尘浓度监测的湿度补偿技术
为减少湿度变化引起的粉尘浓度监测误差，需在煤矿粉尘浓度监测中进行湿度在线补偿。当前煤矿粉尘浓度监测中常用光散射法，因此提出一种基于不同角度散射光特性的湿度在线补偿技术。
3.1 多角度光散射粉尘浓度监测单元
基于多角度光散射理论，当光散射角度为π/2和3π/2时，能够实现最理想的粉尘浓度监测效果[18-19]。故在光散射角度为π/2和3π/2处分别安置光散射接收器，并将激光光源设置在与接收角垂直的方向。粉尘在2个接收器及激光光源所构成的空间中通过。
多角度光散射粉尘浓度监测单元主要由激光发射、散射光接收及光陷阱3个部分构成，如图5 所示。激光发射部分包含激光光源和光学透镜组，散射光接收部分由光电传感器和检测电路组成，光陷阱的作用是吸收入射光及干扰反射光，以确保监测过程的准确性。
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图 5 多角度光散射粉尘浓度监测单元Figure 5. Light-scattering dust concentration monitoring unit with multiple angles进行粉尘浓度监测时，开启激光光源，激光经过光学透镜组产生1组光强稳定的平行入射光。当被测粉尘颗粒在光敏感区通过时，依据Mie散射原理，光电传感器1和2会接收到散射光。通过特定的换算方法可得出被测粉尘的浓度值。
3.2 湿度补偿实验模型
为研究不同湿度、不同粉尘颗粒粒径与散射光通量之间存在的关系，构建用于粉尘浓度监测的湿度补偿实验模型。实验所使用的粉尘、仪器及系统均与1.1节中保持一致，仅在实验步骤的最后一个环节增加采集光通量值的操作：将多角度光散射粉尘浓度监测单元放置于环境实验舱内，进行发尘操作；待粉尘浓度稳定后，采集多角度光散射粉尘浓度监测单元在不同湿度、不同粒径粉尘情况下光散射角度为π/2和3π/2时的散射光通量值。发尘操作使粉尘浓度达到11.3 mg/m³，经过反复多次实验，采集同一种中位粒径粉尘在不同湿度条件下光散射角度为π/2和3π/2时的散射光通量并计算比值，绘制中位粒径为11.3 μm的粉尘在不同湿度下的散射光通量比值曲线，如图6所示。经回归分析[20-21]，得到同种粒径粉尘的散射光通量比值与湿度的关系。
$$ {k_{\mathrm{h}}} = {m_{{\mathrm{d}}2}}{\exp(h)} $$ (3) 式中：$ {k_{\mathrm{h}}} $为同种粒径粉尘的散射光通量比值与湿度的关系因子；$ {m_{{\mathrm{d}}2}} $为修正系数。
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图 6 中位粒径为11.3 μm的粉尘在不同湿度下的散射光通量比值曲线Figure 6. Scattered luminous flux ratio of dust with median particle size of 11.3 μm under different humidity采集不同中位粒径粉尘在相同湿度环境下光散射角度为π/2和3π/2时的散射光通量并计算比值，得到不同中位粒径粉尘在90%RH湿度下2个角度的散射光通量比值曲线，如图7所示。经回归分析，得到相同湿度情况下粉尘中位粒径与散射光通量比值的关系。
$$ k_{\mathrm{p}}=m_{\mathrm{d}1}\ln\frac{d}{\mathrm{mm}}+m_1 $$ (4) 式中：$ {k_{\mathrm{p}}} $为不同中位粒径粉尘在相同湿度下的散射光通量比值的补偿因子；$ {m_{{\mathrm{d}}1}} $为修正系数；d为被测粉尘颗粒粒径；$ {m_1} $为补偿量。
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图 7 不同中位粒径粉尘在90%RH湿度下2个角度的散射光通量比值曲线Figure 7. Scattered light flux ratio curve at two angles for dust particles with different median particle sizes under 90% RH humidity经过实验研究和数学分析，建立实时反馈粉尘颗粒吸湿生长引起粒径变化的湿度补偿实验模型：
$$ C_{\Delta\mathrm{m}}=k_{\mathrm{p}}k_{\mathrm{h}}(I_1+I_2) $$ (5) 式中：CΔm为粉尘浓度；I1，I2分别为散射角度为π/2和3π/2对应位置接收到的散射光强度。
4. 煤矿粉尘浓度监测实验
设置实验环境温度为（25±2）°C，相对湿度为（80±2）%，以滤膜采样法为标准方法，采用带湿度补偿与不带湿度补偿的粉尘浓度监测单元进行对比，得到不同浓度下的监测误差，如图8所示。可看出采用湿度补偿的多角度光散射粉尘浓度监测单元监测误差≤11.2%，相比未进行湿度补偿的监测单元，误差降低了2.9%。
5. 结论
1） 观察煤矿粉尘颗粒在吸湿生长过程中的形貌特征，通过实验得到不同湿度条件下煤矿粉尘颗粒粒径和粉尘浓度的变化规律。
2） 研究了环境湿度、粉尘颗粒粒径与散射光通量之间的关联性，建立了湿度补偿实验模型。
3） 开展了基于湿度补偿的粉尘浓度监测实验，结果表明，采用湿度补偿的多角度光散射粉尘浓度监测单元的粉尘浓度误差≤11.2%，较不带湿度补偿的粉尘浓度监测单元降低了2.9%，降低了湿度对粉尘浓度监测的影响。
【编者按】煤矿灾害一旦发生，其影响范围和严重程度非常大；不同灾害之间可能存在相互影响和关联，使得灾害预防和应对变得复杂；煤矿灾害影响一般不会随着灾害的结束而立即消除；预防和应对煤矿灾害需要专业知识和技能等。针对煤矿灾害的多数工作都是建立在有效感知灾害的前提之上。但通过灾害数据和现象来感知灾害十分困难，特别是在数据不全、及时性和实时性差等条件下更为困难。随着智能感知技术的成熟、应用和实践，为实现煤矿灾害感知提供了智能方法。为介绍和推动智能感知技术在煤矿灾害预测中的应用，交流相关理论方法和研究成果，《工矿自动化》编辑部特邀沈阳理工大学崔铁军教授担任客座主编，于2024年第6期组织出版“煤矿灾害智能感知新技术与实践”专题。在专题出刊之际，衷心感谢各位专家学者的大力支持！ -
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