边坡多场光纤神经感知与安全保障体系

人类工程活动往往易引发边坡灾害，进而对施工人员安全与工程进度产生重要影响。为获取边坡实时状态信息，基于光电联合监测技术，开展边坡变形、温度、渗流及地电等多场光纤神经感知研究，精准掌握边坡时空连续状态。通过引用物联网、大数据、人工智能等技术，对多场信息进行数据清洗、关联挖掘、聚类分析，挖掘出影响边坡稳定性的控制因素，分析各控制因素的叠加效应，在此基础上建立集异常感知、信息传输、数据处理、决策预警为一体的边坡多场安全保障体系，对边坡潜在风险进行识别、评估及预警，进而作出科学决策并实施应急预案，为保障边坡工程安全与防灾减灾提供参考依据。

Multi-field fiber optic neural sensing and safety guarantee system for slope

Human engineering activities often lead to slope disasters, significantly impacting the safety of construction personnel and project progress. To obtain real-time status information of slopes based on optoelectronic joint monitoring technology, research on multi-field information perception of slope deformation, temperature, seepage, and geoelectricity is carried out to grasp the spatiotemporal continuous state of slopes accurately. By citing technologies such as the Internet of things, big data, data mining, and artificial intelligence, data cleaning, association mining, and clustering analysis are carried out on multiple fields of data to identify control factors that affect slope stability and analyze the superposition effect of each control factor, On this basis, establish a slope multi-field safety guarantee system that integrates anomaly perception, information transmission, data processing, and decision warning. Identify, evaluate, and warn of potential slope risks, then make scientific decisions and emergency plans, providing a reference basis for ensuring the safety of slope engineering, disaster prevention and reduction.