【摘要】 为了实现对城市地下综合管廊全域温度精准、高效、低成本的在线监测，解决分布式光纤温度传感（RDTS,raman distributed temperature sensor）面临的信噪比低、空间分辨率有限、小尺度异常不敏感、数据存储成本高及无法二维监测等技术瓶颈。本文提出一套RDTS性能提升方法：包括采用GraphSAGE图神经网络的信号降噪方法；结合全变差反卷积与全连接神经网络的空间分辨率提升方法；利用注意力机制与K-Means聚类检测的小尺度异常检测方法；通过隐式神经表示的数据压缩方法；最终基于处理后的一维温度信号，通过特殊布线策略的二维温度场构建方法。该套方法能有效提升RDTS在测量精度、异常检测灵敏度和覆盖维度上的性能，为管廊全域温度监测提供了高准确度、小尺度、低成本的解决方案。更多还原

【Abstract】 In order to achieve accurate, efficient, and low-cost online monitoring of the temperature throughout the urban underground utility tunnels, and to address the technical bottlenecks faced by distributed optical fiber temperature sensing(RDTS), such as low signal-to-noise ratio, limited spatial resolution, insensitivity to small-scale anomalies, high data storage costs and inability to conduct two-dimensional monitoring, a set of methods for improving RDTS performance is proposed, including a signal denoising method using GraphSAGE graph neural network, a spatial resolution improvement method combining total variation deconvolution and fully connected neural networks, smallscale anomaly detection method utilizing attention mechanism and K-Means clustering detection, data compression method through implicit neural representation, and finally, based on the processed one-dimensional temperature signal, a two-dimensional temperature field construction method through the special wiring strategy. This set of methods can effectively improve the performance of RDTS in measurement accuracy, anomaly detection sensitivity and coverage dimension, providing a high-precision, small-scale and low-cost solution for temperature monitoring throughout utility tunnel.更多还原