CAJ | 학술논문

为了在冷链物流全程监测中，科学而高效地采集信息，该文应用压缩感知（compressed sensing，CS）技术，构建了面向鲜食葡萄冷链物流过程的监测方法，该方法根据感知数据特征构建了双正交小波变换稀疏矩阵，以更好的实现对传感数据的稀疏表示，根据冷链物流的特点构建了数据传输模型，以更好的完成数据的压缩采样传输。同时在模拟冷链条件下对方法系统进行了重构误差性能及节点能耗情况测试验证。结果表明，所提出的方法具有良好的压缩效果，数据的压缩比约为91%，能够通过对传感数据的压缩采样传输，实现数据的高精度重构，恒温下温湿度重构绝对误差分别为0.07℃和0.05%，变温下的则为0.15℃和0.006%。采样压缩采用传输后的汇聚节点电压衰减速率要低于直接传输方式，能有效的延长网络的生命周期。
위료재랭련물류전정감측중，과학이고효지채집신식，해문응용압축감지（compressed sensing，CS）기술，구건료면향선식포도랭련물류과정적감측방법，해방법근거감지수거특정구건료쌍정교소파변환희소구진，이경호적실현대전감수거적희소표시，근거랭련물류적특점구건료수거전수모형，이경호적완성수거적압축채양전수。동시재모의랭련조건하대방법계통진행료중구오차성능급절점능모정황측시험증。결과표명，소제출적방법구유량호적압축효과，수거적압축비약위91%，능구통과대전감수거적압축채양전수，실현수거적고정도중구，항온하온습도중구절대오차분별위0.07℃화0.05%，변온하적칙위0.15℃화0.006%。채양압축채용전수후적회취절점전압쇠감속솔요저우직접전수방식，능유효적연장망락적생명주기。
A compressed data gathering method based on compressed sensing (CS) for table grape cold-chain logistics was proposed, which aimed to not only monitor the quality and safety of the table grapes and improve the transparency, but also to gather the sensing data scientifically and efficiently during cold-chain logistics. CS is a new theory by which a signal can be recovered efficiently with just a few samples. The proposed method exploits the compressibility of the signal to reduce the number of samples required to recover the sampled signal at the remote receiver. All the sensor nodes will send their sensing data to the aggregation node when they receive the control instruction that was sent by the aggregation node at the initial time of the network. The sensor nodes will discard the abnormal data and acquire again, then they will go into sleep and wait for the next control instruction when the data is sent successfully. The aggregation node will send the compressed data that was measured by using the measurement matrix to the remote data receiver by the GPRS wireless technology. The remote data receiver will finish the reconstruction of the compressed data by using the reconstruction algorithm when the compressed data is received. We adopted the Gaussian random distribution matrix as the measurement matrix and the orthogonal matching pursuit algorithm as the data reconstruction algorithm, for they are the classical and efficient algorithm for the solution of the compressed sensing. In addition, we built the biorthogonal wavelet transform sparse matrix according to the characteristics of the sensing data to realize the sparse representation of the compressed data. Finally, we undertook the performance test of the method and the system under the simulation of cold-chain conditions located in the simulation lab of agricultural products traceability of College of Information and Electrical Engineering, China Agricultural University. The test included the reconstruction error of compressed sensing and the energy consumption of the nodes. The result showed that the method had a good compression result, whose data compression ratio can be 91%, and it could reconstruct the sensing data with high accuracy by transmitting the compressed data to the remote receiver. The absolute error of the reconstructed data of temperature and humidity was 0.07℃ and 0.05% respectively under the constant temperature, 0.15℃and 0.006% respectively under the variable temperature. The voltage decay rate of the aggregation node under the compression mode was less than that under the direct mode, which showed that the method we proposed could prolong the lifetime of the network efficiently.