渔业科学数据中心建设研究

doi:10.19788/j.issn.2096-6369.190306

摘要/Abstract

摘要：

渔业科学数据是在渔业科技活动过程中产生的原始性、基础性数据，在农业、海洋和经济等相关领域具有重要的科学意义和实用价值。渔业科学数据中心作为渔业科学数据管理和应用的重要载体，不仅是渔业科技创新和产业发展的重要战略资源池，也是国家制定渔业发展战略、进行科学决策的重要技术助推器，对提升渔业现代化水平具有十分重要的意义。为提升渔业科学数据中心的综合服务能力和智能决策能力，实现对渔业科学数据资源的保存、管理、共享及深入挖掘，本文以渔业科学数据的应用需求为背景，分析了渔业科学数据的特点、来源以及应用场景。结合当下渔业领域科技创新对科学数据的需求，明确了渔业科学数据中心的功能和定位。围绕数据融合、大数据分析、云计算服务等内容，设计了渔业科学数据中心建设的总体架构，给出了多源异构渔业数据存储与融合平台，渔业科学大数据分析与应用平台、渔业科学数据中心云服务平台的总体技术路线。从数据汇交、体系建设、标准规范、共享服务模式、人才培养、绿色节能等角度出发，探讨了渔业科学数据中心的可持续发展思路，从而保障数据中心的持续运行，充分发挥渔业科学数据资源的应用价值，为进一步具体开展渔业科学数据中心建设指明方向。

关键词: 渔业科学数据, 数据中心, 数据融合, 数据挖掘, 云计算, 科学数据, 大数据, 数据共享

Abstract:

Basic, original fisheries science data is generated in the process of fishery technological activities, and has important scientific significance and practical value for agricultural, marine and economic fields. The Fishery Science Data Center, which manages fishery science data and applications of that data, is a crucial strategic resource for technological innovation and industrial development. It also provides important technical support to development strategy and scientific decisions, and improves the modernization of fishery. This study analyzes the characteristics, sources, and possible applications of fishery science data in the context of needing for fishery science data application, with the goal of improving the comprehensive service and smart decision-making ability of the data center and effectively preserving, managing, sharing and mining fishery science data.In the context of needing for fishery science data application We analyze and describe the function and position of the data center in terms of the demands for scientific data in the fishery technological innovation process. The overall architecture of the data center supports data fusion, big data analysis and cloud computing services. Technical roadmaps identify a storage and fusion platform for multi-source heterogeneous fishery data, a big data analysis and application platform in fishery science, and a cloud service platform. The study also considers factors in the sustainable development of the data center, including data collection, systems construction, standards setting, shared services mode, skills and training, and energy saving. The end goal is to ensure the continuous operation of the data center, maximize the value of fishery science data, point the direction for further building fishery science data .

Key words: fisheries science data, data center, data fusion, data mining, cloud computing, scientific data, big data, data sharing

中图分类号:

G203

鲁峰,王立华,徐硕. 渔业科学数据中心建设研究[J]. 农业大数据学报, 2019, 1(3): 57-70.

Feng Lu,Lihua Wang,Shuo Xu. Research on Construction of Fisheries Science Data Center[J]. Journal of Agricultural Big Data, 2019, 1(3): 57-70.

图/表 7

表1

渔业科学大数据来源与特点"

	数据类别	数据属性	数据来源
渔业资源与环境信息	物种资源与生物特征数据	物种、形态特征、分类、分布特征、产卵场、栖息地、索饵场、洄游通道、生活习性	资源调查、文献、捕捞日志
	渔业水域资源与生态特征数据	水域名称、位置、环境状况、常见物种	资源调查、卫星遥感
	生物资源调查数据	物种、分布状况、资源量	资源调查、捕捞日志、声学探测
	生态环境调查数据	气候、水文、地形地貌、种群、生态结构	监测站点、浮标、潜标、卫星遥感
	声学数据	探鱼仪、声呐	声学数据分析平台
渔船渔港动态监测信息	渔船基本数据	船名、呼号、船型、长度、宽度、船东、国籍、装备传感器	相关业务信息系统
	渔船运行和生产状态数据	油耗、发电机、舵机、推进系统、变频器、曳纲张力等设备运行数据	燃油箱出口流量计或油箱液位计
	渔港基本数据	名称、级别、经纬度、容纳量	相关业务信息系统
	渔船位置数据	经纬度、航速、航向	GPS、AIS、北斗等
	进出港数据	渔船编码、渔港名称、渔港位置、进出港时间、船员	RFID、视频监控设备、相关业务信息系统
	多媒体数据	语音、视频、图片	视频监控设备、通信设备等
渔业生产信息	捕捞作业数据	下网时刻地点、起网时刻地点，渔获物重量	绞车操作手柄处获取放网和收网信号、电子捕捞日志
	养殖数据	温度、pH值、溶解氧、氨氮、亚硝酸盐	物联网监测、传感器
	水产病害与防治数据	疾病/灾害名称、防治方法、检测手段	科学实验、相关业务系统
	水产品加工数据	加工岂可、产品名称、生产要素	车间、相关业务系统
	渔药数据	药品名称、成分、药理、功能疗效、用法、适用对象	制药实验、相关业务系统
水产遗传育种与生物技术	水产育种数据	名称、品种特性、亲体培育、苗种培育、孵化、中间培育、养成	水产育种实验、文献资料
水产遗传育种与生物技术	渔业生物技术数据	基因组数据、蛋白质组数据、基因特征与结构数据、蛋白质特征与结构数据、遗传学图谱、染色体	基因组测序、蛋白质组测序、科学实验、相关业务系统
海洋立体观测信息	气象数据	温度、湿度、光照、风速、风向、雨量、视频	气象站、卫星遥感图像、相关气象数据中心等
	水文数据	温度、盐度、叶绿素、溶解氧、海流、海面高度、涡流	船载感知设备，浮标、潜标、相关海洋数据中心等
	遥感图像数据	图片、数据反演	海洋卫星、气象卫星、资源卫星等
	渔船作业海域地形地貌信息	坐标数据、深度数据	多波束测深仪、声纳设备
渔业装备与设施信息	渔业装备数据	名称、用途、规格型号	实验、生产厂家
渔业装备与设施信息	渔业设施数据	名称、用途、地点、规模	工程实施
渔业科技、经济与战略信息	水产品价格监测	来源、市场、产地价格	现场采集、相关业务系统
	进出口贸易	国家、品种、价格	相关业务系统
	科技成果	图书、文献、专利	相关业务系统
	渔业统计	总产值、渔业人口、生产力	相关业务系统

表1

图1

图2

图3

图4

图5

图6

参考文献 48

1	王立华, 孙璐, 孙英泽, 等. 渔业科学数据共享平台建设研究 [J]. 中国海洋大学学报, 2010, 40(S1): 201–206.
	Wang L H, Sun L, Sun Y Z, et al. Construction of Fishery Scientific Data Sharing Platform [J]. Periodical of Ocean University of China, 2010, 40(S1): 201–206.
2	董诚, 黄鼎成.科学数据资源的管理[J]. 中国基础科学, 2006, 8(6): 20–24.
	Dong C, Huang D C. The Management of Scientific Data Resources. China Basic Science, 2006, 8(6): 20–24.
3	李励年, 谢营梁. FAO网站水产信息资源利用与检索途径 [J]. 现代渔业信息, 2004, 19(7): 17–19.
	Li L N, Xie Y L. Utilization of Fishery Information Resource and Search Approach at FAO Website [J]. Modern Fisheries Information, 2004, 19(7): 17–19.
4	Tacon A G J, Metian M, Turchini G M, et al. Responsible Aquaculture and Trophic Level Implications to Global Fish Supply [J]. Reviews in Fisheries Science, 2009, 18(1): 94–105.
5	Rittenschober D, Stadlmayr B, Nowak V, et al. Report on The Development of The FAO/INFOODS User Database for Fish and Shellfish (uFiSh) – Challenges and Possible Solutions [J]. Food Chemistry,2016, 193: 112–120.
6	Niwa, H S. Exploitation Dynamics of Fish Stocks [J]. Ecological Informatics, 2006, 1(1): 87–99.
7	Desiere S, Hung Y, Verbeke W, et al. Assessing Current and Future Meat and Fish Consumption in Sub-Sahara Africa: Learnings From FAO Food Balance Sheets and LSMS Household Survey Data [J]. Global Food Security,2018, 16: 116–126.
8	Yugui Z H U, Hongbing L V, Jiansong C H U. Prediction of Global Sea Cucumber Capture Production Based on The Exponential Smoothing and ARIMA Models [J]. Iranian Journal of Fisheries Sciences, 2016, 15(3): 1089–1107.
9	Russo T, Morello, E B, Parisi A, et al. A Model Combining Landings and VMS Data to Estimate Landings by Fishing Ground and Harbor [J]. Fisheries Research,2017, 199: 218–230.
10	Costa-Pierce B A. Sustainable Ecological Aquaculture Systems: The Need for a New Social Contract for Aquaculture Development [J]. Marine Technology Society Journal, 2010, 44(3): 88-112.
11	Grüss A, Palomares M L D, Poelen J H, et al. Building Bridges Between Global Information Systems on marine Organisms and Ecosystem Models [J]. Ecological Modelling,2019, 398: 1–19.
12	Halpin P N, Read A J, Fujioka E, et al. OBIS-SEAMAP: The World Data Center for Marine Mammal, Sea Bird, and Sea Turtle Distributions [J]. Oceanography, 2009, 22(2): 104–115.
13	Fujioka E, Soldevilla M S, Read A J, et al. Integration of Passive Acoustic Monitoring Data into OBIS-SEAMAP, A Global Biogeographic Database, to Advance Spatially-Explicit Ecological Assessments [J]. Ecological Informatics,2014, 21: 59–73.
14	Vandepitte L, Bosch S, Tyberghein L, et al. Fishing for Data and Sorting The Catch: Assessing The Data Quality, Completeness and Fitness for Use of Data in Marine Biogeographic Databases [J]. Database, 2015: 1–14.
15	De Pooter D, Appeltans W, Bailly N, et al. Toward A New Data Standard for Combined Marine Biological and Environmental Datasets – Expanding OBIS Beyond Species Occurrences [J]. Biodiversity Data Journal, 2017, 5: e10989.
16	Bunsick S M. Update On The NOAA Aquaculture Program [J]. Journal of Shellfish Research, 2009, 28(3): 645–646.
17	Apeti D A, Lauenstein G G, Evans D W, et al. Recent Status of Total Mercury and Methyl Mercury in The Coastal Waters of The Northern Gulf of Mexico Using Oysters and Sediments From NOAA's Mussel Watch Program [J]. Marine Pollution Bulletin, 2012, 64(11): 2399–2408.
18	Sigler M, DeMaster D, Boveng P, et al. Advances in Methods for Marine Mammal and Fish Stock Assessments: Thermal Imagery and CamTrawl [J]. Marine Technology Society Journal, 2015, 49(2): 99–106.
19	Hartwell S I, Apeti A D, Pait A S, et al. Benthic Habitat Contaminant Status and Sediment Toxicity in Bristol Bay, Alaska [J]. Regional Studies in Marine Science, 24: 343–354.
20	Geronimo R C, Franklin E C, Brainard R E, et al. Mapping Fishing Activities and Suitable Fishing Grounds Using Nighttime Satellite Images and Maximum Entropy Modelling [J]. Remote Sensing, 2018, 10(10): 1604.
21	Mark Y A, Philip A, Nelson A W, et al. Safety Assessment on Microbial and Heavy Metal Concentration in Clarias Gariepinus (African Catfish) Cultured in Treated Wastewater Pond in Kumasi, Ghana [J]. Environmental Technology, 2019, 40(3): 302–311.
22	周国民. 我国农业大数据应用进展综述 [J]. 农业大数据学报, 2019, 1(1): 16–23.
	Zhou G M. Progress in the Application of Big Data in Agriculture in China. Journal of Agricultural Big Data, 2019, 1(1): 16–23.
23	赵瑞雪, 赵华, 朱亮. 国内外农业科学大数据建设与共享进展 [J]. 农业大数据学报, 2019, 1(1): 24–37.
	Zhao R X, Zhao H, Zhu L. Progress in the Development and Sharing of Big Data in Agricultural Science between China and Foreign Countries [J]. Journal of Agricultural Big Data, 2019, 1(1): 24–37.
24	科技部发展计划司, 财政部教科文司. 国家科技基础条件平台中心. 国家科技基础条件平台科技资源开放共享目录. [M]. 北京: 科技文献出版社, 2013.
	Department of Development and Planning for the Ministry of Science and Technology, Department of Education and Culture for the Ministry of Finance, National Science and Technology Infrastructure Center. Open and Shared Catalogue of Science and Technology Resources for National Science and Technology Infrastructure [M]. Beijing: Scientific and Technical Documentation Press, 2013.
25	巩沐歌. 国内外渔业信息化发展现状对比分析 [J]. 现代渔业信息, 2011, 26(12): 20–24.
	Gong M G. A Comparative Analysis of Development Status of Fisheries Informatization at Home and Abroad [J]. Modern Fisheries Information, 2011, 26(12): 20–24.
26	王欣, 高倩. 海洋国家实验室: 探索中国特色国家实验室建设道路 [J]. 走向世界,2017, 33: 30–33.
	Wang X, Gao Q. Qingdao National Laboratory For Marine Science and Technology Explore The Way to Build The National Laboratory With Chinese Characteristics [J]. Openings,2017, 33: 30–33.
27	Mills C M, Townsend S E, Jennings S, et al. Estimating high resolution trawl fishing effort from satellite-derived vessel monitoring system data[J]. ICES Journal of Marine Science, 2007, 64(2), 248–255.
28	Peel D, Good N M. A hidden Markov model approach for determining vessel activity from vessel monitoring system data[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2011, 68(7): 1252–1264.
29	Natale F, Gibin M, Alessandrini A, et al. Mapping Fishing Effort through AIS Data[J]. PLoS ONE, 2015, 10(6): e0130746.
30	Enever R, Lewin S, Reese A, et al. Mapping fishing effort: Combining fishermen’s knowledge with satellite monitoring data in English waters[J]. Fisheries Research,2017, 189: 67–76.
31	Palmer M C, Wigley S E. Using positional data from vessel monitoring systems to validate the logbook-reported area fished and the stock allocation of commercial fisheries landings[J]. North American Journal of Fisheries Management, 2009, 29(4): 928–942.
32	Bastardie F, Nielsen J R, Ulrich C, et al. Detailed mapping of fishing effort and landings by coupling fishing logbooks with satellite-recorded vessel geo-location[J]. Fisheries Research, 2010, 106(1): 41–53.
33	Stelzenmuller V, Rogers S I, Mills C M. Spatio-temporal patterns of fishing pressure on UK marine landscapes and their implications for spatial planning and management[J]. ICES Journal of Marine Science, 2008, 65(6): 1081–1091.
34	Geronimo R C, Franklin E C, Brainard R E. Mapping Fishing Activities and Suitable Fishing Grounds Using Nighttime Satellite Images and Maximum Entropy Modelling[J]. Remote Sensing, 2018, 10(10): 1604.
35	Kaiser M J, Hinz H, Hiddink J G. Quantification and prediction of the impact of fishing on epi-faunal communities[J]. Marine Ecology Progress Series,2011, 430: 71–86.
36	Bueno-Pardo J, Ramalho S P, García-Alegre A, et al. Deep-sea crustacean trawling fisheries in Portugal: quantification of effort and assessment of landings per unit effort using a Vessel Monitoring System (VMS)[J]. Scientific Reports, 2017, 7: 40795.
37	Marzuki M I, Gaspar P, Garello R, et al. Fishing Gear Identification From Vessel-Monitoring-System-Based Fishing Vessel Trajectories[J]. Journal of Oceanic Engineering, 2018, 43(3): 689–699.
38	Watson J T, Haynie A C. Paths to resilience: Alaska pollock fleet uses multiple fishing strategies to buffer against environmental change in the Bering Sea[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2018, 75(11): 1977–1989.
39	Xu L Q, Liu S Y, Li D L. Prediction of Water Temperature in Prawn Cultures Based on a Mechanism Model Optimized by an Improved Artificial Bee Colony[J]. Computers and Electronics in Agriculture,2017, 140: 397–408．
40	Ashoka Deepananda K H M, Amarasinghe U S, Jayasinghe-Mudalige U K, et al. Stilt Fisher Knowledge in Southern Sri Lanka as an Expert System: Astrategy towards Co-Management[J]. Fisheries Research,2016, 174: 288–297.
41	王斌, 徐建瑜, 王春琳. 基于计算机视觉的梭子蟹蜕壳检测及不同背景对蜕壳的影响[J]. 渔业现代化, 2016, 43(2): 11–16.
	Wang B, Xu J Y, Wang C L. Computer-Vision Based Molting Detection of Portunus Tritubercularus and Effects of Different Backgrounds on Molting[J]. Fishery Modernization, 2016, 43(2): 11–16.
42	段青玲, 张磊, 魏芳芳, 等. 基于时间序列GA-SVＲ的水产品价格预测模型及验证[J]. 农业工程学报, 2017, 33(1): 308–314.
	Duan Q L, Zhang L, Wei F F, et al. Forecasting Model and Validation for Aquatic Product Price Based on Time Series GA-SVR[J]. Transactions of the CSAE, 2017, 33(1): 308–314.
43	颜波, 石平, 黄广文. 基于RFID和EPC物联网的水产品供应链可追溯平台开发[J]. 农业工程学报, 2013, 29(15): 172–183.
	Yan B, Shi P, Huang G W. Development of Traceability System of Aquatic Foods Supply Chain Based on RFID and EPC Internet of Things[J]. Transactions of the CSAE, 2013, 29(15): 172–183.
44	Tai H J, Liu S Y, Li D L, et al. A Multi-Environmental Factor Monitoring System for Aquiculture Based on Wireless Sensor Networks[J]. Sensor Letters, 2012, 10(1): 265–270.
45	邢文明. 我国科研数据管理与共享政策保障研究 [D]. 武汉: 武汉大学, 2014.
	Xing W M. Research on Policy Guarantee of Scientific Data Management and Sharing in China [D]. Wuhan: Wuhan University, 2014.
46	GB 50174-2017, 数据中心设计规范[S].
	GB 50174-2017, Code For Design Of Data Centers[S].
47	赵彦云. 对大数据统计设计的思考 [J]. 统计研究, 2015, 32(6): 3–10．
	Zhao Y Y. Reflection on Statistical Design in Big Data [J]. Statistical Research, 2015, 32(6): 3–10．
48	Shehabi A, Smith S J, Masanet E, et al. Data Center Growth In The United States: Decoupling The Demand For Services From Electricity Use [J]. Environmental Research Letters, 2018, 13:124030.

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