海洋湍流数据实时压缩方法研究
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摘要
海洋湍流因具有随机性特点,目前多采用统计学理论进行研究,因此需要获取大量的湍流观测数据,这给湍流观测设备的数据存储和传输带来挑战。针对上述问题,本文在分析海洋湍流数据特征的基础上,提出了一种高效实时的无损数据压缩方法。以大量的湍流数据增量信息作为数据源构建霍夫曼编码表,并以此作为湍流压缩和解压的字典,从而提高了压缩效率。通过对历史海洋湍流数据进行压缩实验,证明该方法的湍流数据压缩比低至25%,并且具有压缩速度快、处理器占用率低等特点。
Because of the randomness of ocean turbulence, most related studies of it are based on statistical theory.It is necessary to obtain a large amount of turbulence observation data, which brings us enormous challenge to data storage and transmission for turbulence observation equipment. In this study, an efficient real-time lossless data compression method, based on the analysis of the characteristics of ocean turbulence data, is proposed properly. The Huffman coding table constructed with a large amount of turbulence data increment information is used as a dictionary for turbulence compression and decompression. The above characteristic makes this method possess a high compression efficiency. Our experimental results indicate that the compression ratio of turbulent data can be as low as 25% through compression experiment of historical ocean turbulence data using this efficient method, and this method also has the characteristics of fast compression speed and low processor occupancy rate.
Because of the randomness of ocean turbulence, most related studies of it are based on statistical theory.It is necessary to obtain a large amount of turbulence observation data, which brings us enormous challenge to data storage and transmission for turbulence observation equipment. In this study, an efficient real-time lossless data compression method, based on the analysis of the characteristics of ocean turbulence data, is proposed properly. The Huffman coding table constructed with a large amount of turbulence data increment information is used as a dictionary for turbulence compression and decompression. The above characteristic makes this method possess a high compression efficiency. Our experimental results indicate that the compression ratio of turbulent data can be as low as 25% through compression experiment of historical ocean turbulence data using this efficient method, and this method also has the characteristics of fast compression speed and low processor occupancy rate.
引文
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[2]田川.深海湍流混合长期连续观测技术与应用研究[D].天津:天津大学,2012.Tian Chuan.Study on long-term turbulence monitoring technique and its application in deep-sea mixing research[D].Tianjin:Tianjin University,2012.
[3]Sea&Sun Marine Tech.Microstructure Probes:the MSS-family[EB/OL].http://www.sea-sun-tech.com/marine-tech/oceanography/mss-microstructure-probe.html,2018-7-30.
[4]Rockland Scientific.VMP 6000 Deep-Sea Vertical Microstructure Profiler[EB/OL].https://rocklandscientific.com/products/profilers/vmp-6000/,2018-7-30.
[5]贾鹏飞.智能浮标物理接驳盒的设计与开发[D].青岛:中国海洋大学,2018.Jia Pengfei.Design and Development of Physical Junction Box Based On Smart Float[D].Qingdao:Ocean university of China,2018.
[6]胡斌,李忠强,刘婷婷,等.Huffman与LZW算法在海洋观测浮标通信数据压缩中的应用研究[J].海洋科学,2018,42(1):6-10.Hu Bin,Li Zhongqiang,Liu Tingting,et al.Application of Huffman and LZW algorithms in data compression for ocean-observation-buoy communication[J].Marine Sciences,2018,42(1):6-10.
[7]王文彦,李文庆,王晓燕,等.数据压缩技术在海洋资料浮标通信系统中的应用[J].山东科学,2015,28(2):1-5.Wang Wenyan,Li Wenqing,Wang Xiaoyan,et al.Application of data compression in the communications system of an oceandata buoy[J].Shandong Science,2015,28(2):1-5.
[8]付东洋,李宇隆,侯骏雄,等.卫星海洋遥感数据的无损压缩及解压算法[J].海洋技术学报,2012,31(2):45-48.Fu Dongyang,Li Yulong,Hou Junxiong,et al.Lossless compression and decompression algorithms for satellite ocean remote sensing data[J].Ocean Technology,2012,31(2):45-48.
[9]李浩琦,王海斌,汪俊.基于CUDA的海洋监测数据并行压缩技术研究[J].海洋技术学报,2014,33(1):39-44.Li Haoqi,Wang Haibin,Wang Jun.Study on the parallel compression technique for marine monitoring data based on CUDA[J].Ocean Technology,2014,33(1):39-44.
[10]单海艳,熊学军,郭延良,等.CTD海洋观测数据多重压缩加密算法设计[J].海岸工程,2018,37(2):23-29.Shan Haiyan,Xiong Xuejun,Guo Yanliang,et al.Design of multiple compression encryption algorithm for CTDocean observation data[J].Coastal Engineering,2018,37(2):23-29.
[11]赵忠生,袁志伟,黄磊,等.深海潜标ADCP的实时数据传输[J].海洋科学,2012,36(8):94-97.Zhao Zhongsheng,Yuan Zhiwei,Huang Lei,et al.Realtime data transmission of the ADCP installed on subsurface mooring system[J].Marine Sciences,2012,36(8):94-97.
[12]Lee S,Kim J,Lee M.A real-time ECG data compression and transmission algorithm for an e-health device[J].IEEE Transactions on Bio-medical Engineering,2011,58(9):2448.
[13]朱凌云,李世星,林红胜.一种基于S3C2440的嵌入式实时心电数据压缩算法[J].北京生物医学工程,2012,31(3):258-261.Zhu Lingyun,Li Shixing,Lin Hongsheng.A real-time ECG data compression algorithm based on S3C2440[J].Beijing Biomedical Engineering,2012,31(3):258-261.
[14]Medeiros H P,Maciel M C,Demo Souza R,et al.Lightweight Data Compression in Wireless Sensor Networks Using Huffman Coding[J].International Journal of Distributed Sensor Networks,2014,2014(1):1-11.
[15]乜云利.自容式湍流剖面观测仪设计与试验研究[D].青岛:中国海洋大学,2015.Nie Yunli.The design of self-contained profiler turbulence observation instrument and test research[D].Qingdao:Ocean University of China,2015.
[16]夏萍.数据压缩技术的研究[D].太原:中北大学,2010.Xia Ping.Research on data compression technology[D].Taiyuan:North University of China,2010.
[17]Huffman D A.A method for the construction of minimum-redundancy codes[J].Proceedings of the IRE,1952,40(9):1098-1101.
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