基于压缩感知的贮运信息动态获取方法
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摘要
文物、石油等高价值物质的贮运安全问题越来越突出,然而由于贮运过程复杂、监测时间长、不同阶段监测策略复杂,导致目前没有可行的贮运全程监测方法。针对这一问题,提出一种基于压缩感知的贮运全程动态获取方法。该方法充分利用贮运信号稀疏性,提出随机节点布设和随机时间采样方法,解决了贮运监测过程大数据、低功耗的难题。依据该方法研制了集贮运环境监测与物流管理于一体的"智能条形码","智能条形码"的功耗水平降至500μA以下,体积小于60×80×10 mm3。试验表明,基于压缩感知的贮运信息动态获取方法在不降低监测分辨率的情况下显著减少了布设数量,降低系统功耗,并且数据重构误差在9.2%以内。
The storage and transportation safety problems of cultural relics,oil and other high-value substances are more and more prominent,but there are not reliable monitoring methods in the whole process of storage and transportation due to complexities of storage and transportation process,long monitoring time and complex monitoring strategies in different stages. Here,to resolve this problem,a dynamic acquisition method for the whole process of storage and transportation based on compressed sensing was proposed. This method fully adopted the sparsity of storage and transportation signals to put forward the random node layout and the random time sampling,and solve difficult problems of monitoring process' s large data and low power consumption. According to this method,an intelligent bar code was developed. It had functions of storage and transportation environment monitoring and logistics management,and its power consumption level was less than 500 μA,its volume was less than 60 × 80 × 10 mm3. The test results showed that the dynamic acquisition method for storage and transportation information based on compressed sensing significantly decreases the layout number,and reduces the system power consumption under the condition of not reducing the monitoring resolution,the data reconstruction errors are within 9. 2%.
The storage and transportation safety problems of cultural relics,oil and other high-value substances are more and more prominent,but there are not reliable monitoring methods in the whole process of storage and transportation due to complexities of storage and transportation process,long monitoring time and complex monitoring strategies in different stages. Here,to resolve this problem,a dynamic acquisition method for the whole process of storage and transportation based on compressed sensing was proposed. This method fully adopted the sparsity of storage and transportation signals to put forward the random node layout and the random time sampling,and solve difficult problems of monitoring process' s large data and low power consumption. According to this method,an intelligent bar code was developed. It had functions of storage and transportation environment monitoring and logistics management,and its power consumption level was less than 500 μA,its volume was less than 60 × 80 × 10 mm3. The test results showed that the dynamic acquisition method for storage and transportation information based on compressed sensing significantly decreases the layout number,and reduces the system power consumption under the condition of not reducing the monitoring resolution,the data reconstruction errors are within 9. 2%.
引文
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[2]WANG,JAMES C F.An overview of hydrogen storage for transportation applications[J].JOM,2004,56(11):52-53.
[3]齐林,韩玉冰,张小栓,等.基于WSN的水产品冷链物流实时监测系统[J].农业机械学报,2012,43(8):134-140.QI Lin,HAN Yubing,ZHANG Xiaoshuan,et al.Rea time monitoring system for aquatic cold-chain logistics based on WSN[J].Transactions of the Chinese Society for Agricultural Machiner,2012,43(8):134-140.
[4]申悦,刘军,王程安.仓储环境监测的研究和进展[J].物流技术,2015,34(8):256-268.SHEN Yue,LIU Jun,WANG Chengan.Process of researches on warehouse environment monitoring[J].Logistics Technology,2015,34(8):256-268.
[5]王湘云,董大波,吴南健,等.基于短距离无线/有线通信的燃气监测报警系统[J].电子技术应用,2013,39(7):83-86.WANG Xiangyun,DONG Dabo,WU Nanjian,et al.Gas monitoring and alarm system based on short-range wireless/wired communication technology[J].Application of Electronic Technique,2013,39(7):83-86.
[6]吴芳,刘亚利,马昌喜.基于GPRS的危险货物仓储环境实时监测系统[J].北京理工大学学报,2013,33(8):806-810.WU Fang,LIU Yali,MA Changxi.Real time monitoring system of dangerous goods in storage environment based on GPRS[J].Transactions of Beijing Institute of Technology,2013,33(8):806-810.
[7]TOMIOKA K,KONDO K.Business innovation products and technologies:ubiquitous sensor network system[J].NEC Technical Journal,2006,1(1):78-82.
[8]CHOW H K H,CHOY K L,LEE W B,et al.Design of a RFID case-based resource management system for warehouse operations[J].Expert Systems with Application,2006,30(4):561-576.
[9]任秀丽,于海斌.Zig Bee技术的无线传感器网络的安全性研究[J].仪器仪表学报,2007,28(12):2132-2138.REN Xiuli,YU Haibin.Study on security of Zig Bee wireless sensor network[J].Chinese Journal of Scientific Instrument,2007,28(12):2132-2138.
[10]CHEN W,WASSELL I J.Energy efficient signal acquisition in wireless sensor networks:a compressive sensing framework[J].IET Wireless Sensor Systems,2012,2(1):1-8.
[11]周俊,伍星,迟毅林,等.基于改进频域压缩感知的轴承符合故障欠定盲提取[J].振动与冲击,2015,34(14):123-134.ZHOU Jun,WU Xing,CHI Yilin,et al.Underdetermined blind extraction for bearings complex failures based on improved frequency domain compressive sensing[J].Journal of Vibration and Shock,2015,34(14):123-134.
[12]WANG K,LIU Y L,WAN Q,et al.Compressed sensing of wireless sensor networks data with missed measurements[J].Chinese Journal of Electronics,2015,24(2):388-392.
[13]刘功亮,康文静.基于压缩感知的水下稀疏传感网信息获取技术[J].仪器仪表学报,2014,35(2):253-260.LIU Gongliang,KANG Wenjing.Information acquisition technology for sparse underwater sensor networks based on compressed sensing[J].Chinese Journal of Scientific Instrument,2014,35(2):253-260.
[14]焦李成,杨淑媛.压缩感知回顾与展望[J].电子学报,2011,39(7):1651-1662.JIAO Licheng,YANG Shuyuan.Development and prospect of compressive sensing[J].Journal of Electronic,2011,39(7):1651-1662.
[15]尹继豪,孙建颖.基于压缩感知理论的波段重构方法[J].红外与激光工程,2014,43(4):1260-1264.YIN Jihao,SUN Jianying.Hyperspectral band reconstruction based on compressed sensing theory[J].Infrared Laser Engineering,2014,43(4):1260-1264.
[16]STUBBS C.Compilation strategies:alternate approaches to achieve low power consumption[J].Electronic Component News,2008,52(4):11-13.
[17]AKIRA S,MASAAKI K,TSUTOMU H,et al.Low power consumption technology for ultra-high resolution mobile display by using RGBW system[J].Ieice Transactions on Electronics,2013(11):1367-1372.
[18]曾超,汤宝平,肖鑫,等.低功耗机械振动无线传感器网络节点结构设计[J].振动与冲击,2017,36(14):33-37.ZENG Chao,TANG Baoping,XIAO Xin,et al.Design of airborne pod control system based on STM32[J].Journal of Vibration and Shock,2017,36(14):33-37.