为解决运营隧道健康监测系统中多节点传感器网络下高频采样带来的大数据储存和传输问题，在运营隧道结构健康监测系统数据采集和传输层引入传感器网络框架表示模型，对传统的压缩采样数据重构算法进行改进，用迭代方法求解最小化范数问题，提出基于传感器网络的压缩感知重构算法，并通过工程实例对提出的方法进行了验证。结果表明： 1）在一定采样率条件下，基于传感器网络的压缩感知重构算法可以有效地增加数据恢复的效率，加速度信号采样率越大其重构精度越高； 2）相同采样率条件下，基于传感器网络的压缩感知重构算法得到的重构数据精度要高于传统单节点压缩感知重构算法； 3）压缩采样率为60%以上时，信号重构误差不超过20%，符合工程应用标准； 4）基于传感器网络的压缩感知重构算法具有局限性，传感器节点个数存在耦合上限，当传感器节点超过6个时，重构信号的精度无明显变化。
The big data storage and transmission brought by highfrequency
sampling under multinode sensor networks in operating
tunnel health monitoring systems are of great significance. Hence, the
framework representation model of a sensor network is introduced to the data
acquisition and transmission layer of operating tunnel structural health
monitoring system, the traditional compressed sampling data reconstruction
algorithm is improved, the minimum norm problem is solved by an iterative
method, and the compressed sensing algorithm based on sensor network is
proposed. The proposed method is verified by an engineering example. The
results show the following. (1) Under the condition of a certain sampling rate,
the compression reconstruction algorithm based on the sensor network can
effectively increase the efficiency of data recovery, and the higher the
sampling rate of the acceleration signal, the higher the reconstruction
accuracy. (2) Under the condition of the same sampling rate, the accuracy of
the reconstructed data obtained from the compression reconstruction algorithm
based on the sensor network is higher than that of the traditional single nodecompression perception reconstruction algorithm. (3) When the
compressionsampling rate is above 60%, the signal
reconstruction error is less than 20%, which conforms to the engineering
application standard. (4) The reconstruction algorithm based on sensor networks
has limitations: there is an upper coupling limit for the number of sensor
nodes, and when the number of sensor nodes exceeds 6, the precision of
reconstructed signals does not significantly change.
