引水隧洞的结构稳定是保障水电站安全运行的关键，开展定期检查是保障工程安全的重要手段。受制于洞内光线弱、空间封闭、斜井不可达的影响，导致现有监测手段效率低、缺陷识别不全、量化表征精度低等问题。借助传感、机器人、深度学习、多元统计等技术和方法，搭建隧洞安全“原位监测-移动巡检-缺陷量化-风险识别”一体化技术，并在大渡河猴子岩电站引水隧洞技术应用。结果表明：2013—2021年，原位监测下1^#和3^#隧洞位移、锚杆应力、接缝开合度、渗透水压等先呈现先急剧变化后逐步减缓最后趋于稳定；基于最小二乘拟合法的缺陷预测模型拟合效果较好（R²=0.60~0.91），可预测各缺陷随时间变化趋势；机器人巡检结果显示3^#隧洞渗漏、裂缝等缺陷主要分布于左、右洞腰及洞顶部位，以裂缝渗水最为突出，有明显渗水现象的裂缝和渗漏点共107处。借助多元约束性排序发现，猴子岩1^#和3^#引水隧洞各项缺陷尚处于“较稳定”状态范围内，渗流、渗压、裂缝是影响隧洞安全的关键风险因子。机器人巡检技术安全且高效，不受环境和主观意识影响，可填补原位传感监测手段无法获取到的数据空白。该研究通过建立“监测-巡检-识别-评价”技术链，推动了隧洞安全管控从“被动抢险”到“主动防控”的跨越，研究成果可为国内外隧洞安全检测和风险评价提供有效的技术手段和科学理论参考，具有重要科研和工程实用价值。

The structural stability of water diversion tunnels is paramount for ensuring the safe operation of hydropower stations, with regular inspections being a critical measure to maintain engineering safety. However, existing monitoring methods are often plagued by inefficiency, incomplete defect identification, and low accuracy in quantitative characterization, primarily due to challenges such as poor lighting, enclosed spaces, and inaccessible inclined shafts. By leveraging technologies and methods such as sensing, robotics, deep learning, and multivariate statistics, an integrated technology for tunnel safety, namely "in-situ monitoring-mobile inspection-defect quantification-risk identification", has been established and applied in the water diversion tunnel technology of the Houziyan Hydropower Station on the Dadu River. The results show that from 2013 to 2021, under in-situ monitoring, the displacement of tunnels 1^# and 3^#, the stress of anchor rods, the opening degree of joints, and the osmotic water pressure first presents a sharp change, then gradually slows down, and finally tends to stabilize. The defect prediction model based on the least squares fitting method has a better fitting effect (R² ranges from 0.60 to 0.91), and can predict the changing trend of each defect over time. The inspection results of the robot show that the leakage, cracks and other defects of Tunnel 3^# are mainly distributed on the left and right waisides and the top of the tunnel. Among them, the leakage through cracks is the most prominent. There are a total of 107 cracks and leakage points with obvious leakage phenomena. Multivariate constrained ranking analysis indicated that the defects in Tunnels 1^# and 3^# of the Houziyan Hydropower Station remain within the "relatively stable" range, with seepage, seepage pressure, and cracks identified as key risk factors affecting tunnel safety. Robotic inspection technology proved to be both safe and efficient, unaffected by environmental conditions or subjective biases, and capable of capturing data that in-situ sensing methods could not. The study achieves a transformative shift in tunnel safety management from "passive emergency response" to "active prevention and control" through its integrated "Monitoring-Inspection-Identification-Evaluation" technological chain. The research outcomes provide effective technical solutions and scientific theoretical references for safety inspection and risk assessment of tunnels both domestically and internationally, demonstrating significant academic research and practical engineering value.