大闸蟹养殖大数据分析模型和应用进展

doi:10.19788/j.issn.2096-6369.210106

农业大数据学报 ›› 2021, Vol. 3 ›› Issue (1): 56-65.doi: 10.19788/j.issn.2096-6369.210106

• 专题——农产品单品种大数据 • 上一篇下一篇

大闸蟹养殖大数据分析模型和应用进展

段青玲^1,^2,³(), 刘怡然^1,^2,³, 周新辉^1,^2,³, 任妮⁴, 李道亮^1,^2,³()

^1.中国农业大学国家数字渔业创新中心，北京 100083
^2.中国农业大学信息与电气工程学院，北京 100083
^3.北京市农业物联网工程技术研究中心，北京 100083
^4.江苏省农业科学院农业信息研究所，南京 210014

收稿日期:2021-02-18 出版日期:2021-03-26 发布日期:2021-05-18
通讯作者: 李道亮 E-mail:dqling@ cau.edu.cn;dliangl@ cau.edu.cn
作者简介:段青玲，女，博士，教授，研究方向: 主要从事智能信息处理和农业大数据研究；E-mail: dqling@ cau.edu.cn
基金资助:
江苏省农业科技自主创新资金项目(CX(19)1003);宁波市公益类科技计划项目(202002N3034)

Progress in Analysis Models and the Application of Big Data in Chinese Mitten Crab Culturing

Qingling Duan^1,^2,³(), Yiran Liu^1,^2,³, Xinhui Zhou^1,^2,³, Ni Ren⁴, Daoliang Li^1,^2,³()

^1.China National Innovation Center of Digital Fishery, Beijing 100083, China
^2.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
^3.Beijing Engineering and Technology Research Center for the Internet of Things in Agriculture, Beijing 100083, China
^4.Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China

Received:2021-02-18 Online:2021-03-26 Published:2021-05-18
Contact: Daoliang Li E-mail:dqling@ cau.edu.cn;dliangl@ cau.edu.cn

摘要/Abstract

摘要：

大闸蟹是我国特有的名优水产养殖品种，随着大闸蟹单品种大数据建设的推进，利用大数据分析技术挖掘数据潜在价值，成为促进大闸蟹产业链升级的重要手段。大数据分析模型是应用大数据技术理清数据相互关系，发掘事物内在规律的重要工具，对大数据技术能否在大闸蟹养殖领域成功应用，促进大闸蟹养殖产业升级有决定性影响。本文重点梳理了大闸蟹养殖大数据分析模型在水质预测预警、水质调控、投喂策略、病害防治、行为分析和品质鉴别等方面的应用进展，介绍了当前大闸蟹养殖大数据的平台建设现状，分析和讨论了大闸蟹养殖大数据分析模型当前面临的问题和发展方向。综述结果表明，大数据技术与大闸蟹全产业链深度融合有其基础和优势，但在利用大数据分析模型解决实际问题时，仍然面临针对性差、应用面窄和关联性低的问题。应充分考虑产业特点，深度挖掘现实需求，从提供智能化数据分析服务并建设多功能的大数据平台方面深化大数据技术在大闸蟹全产业链的应用，以期形成可借鉴、可移植的建设模式，为其他农产品大数据的建设提供参考。

关键词: 大闸蟹养殖, 单品种大数据, 数据分析, 数据模型, 水产大数据, 智慧水产养殖, 农业大数据, 智慧农业

Abstract:

The Chinese mitten crab is a well-known aquaculture product in China. With the development of a big data platform supporting the Chinese mitten crab industry chain, big data technologies can be used to generate insights and value from the data produced during crab culturing, becoming an important way to promote the upgrade of the hairy crab industry chain. Analysis models using big data can clarify the relationships among vast amounts of data and reveal the internal laws of crab farming. A study of these models examines whether big data technologies can be applied to crab culturing to successfully upgrade the crab industry. This paper summarizes the status and performance of several big data models focused on important problems in crab farming. These problems include water quality prediction and early warning, water quality control, feeding strategy, disease detection, behavior recognition, and product quality identification. Several big data platforms currently used in the crab industry are reviewed and the challenges and opportunities of big data analytic models in crab farming are discussed. The review highlights the foundations and advantages in thoroughly applying big data technologies in the crab farming industry, but also highlights challenges in solving practical problems, and the lack of targeted and associated research findings. To reinforce the application of big data technologies, the special characteristics and practical requirements of the industry should be considered when building analysis models, and more intelligent services should be provided on a big data platform for the crab industry. The construction of an effective big data platform on crabs can guide the construction of big data platforms for other agricultural products.

Key words: chinese mitten crab, big data of specific product, data analysis, data model, aquatic big data, smart aquaculture, agricultural big data, smart agriculture

中图分类号:

TP311.1

段青玲, 刘怡然, 周新辉, 任妮, 李道亮. 大闸蟹养殖大数据分析模型和应用进展[J]. 农业大数据学报, 2021, 3(1): 56-65.

Qingling Duan, Yiran Liu, Xinhui Zhou, Ni Ren, Daoliang Li. Progress in Analysis Models and the Application of Big Data in Chinese Mitten Crab Culturing[J]. Journal of Agricultural Big Data, 2021, 3(1): 56-65.

图/表 3

参考文献 64

1	S.Galit. To Explain or to Predict? [J]. Statistical Science, 2010, 25(3):289-310.
2	Xie K, Han L S, Jing M H, et al. Review of Intelligent Data Analysis and Data Visualization [C]. Proceedings of the 15th International Conference on Broad-Band and Wireless Computing, Communication and Applications, 2020: 365-375.
3	Liu Y R, Duan Q L, Zhang L. Evaluation model for water environment of Eriocheir sinensis ponds based on AdaBoost classifier [J]. International Agricultural Engineering Journal, 2017, 26(3): 340-348.
4	Cao X K, Liu Y R, Wang J P, et al. Prediction of dissolved oxygen in pond culture water based on K-means clustering and gated recurrent unit neural network[J]. Aquacultural Engineering, 2020, 91: 102-122.
5	Zhu P Y, Zhang Y L, Chou Y X, et al. Recognition of the storage life of mitten crab by a machine olfactory system with deep learning[J]. Journal of Food Process Engineering, 2019, (14): 1-13.
6	Lecun Y., Bengio Y., Hinton G.. Deep learning[J]. Nature, 2015, 521(7553):436-444.
7	中国信息通信研究院.大数据白皮书[EB/OL].[2020-12-18] .
	China Academy of Information and Communications Technology Big data white book[EB/OL].[2020-12-18] .
8	吴飞. 人工智能导论: 模型与算法[M] 北京: 高等教育出版社, 2020.
	Wu F. Introduction to Artificial Intelligence: Models and Algorithms [M]. Beijing: Higher Education Press, 2020
9	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 & Electronics in Agriculture, 2017, 140:397-408.
10	Chen Y Y, Cheng Y J, Cheng Q Q, et al. Short-Term prediction model for ammonia nitrogen in aquaculture pond water based on optimized LSSVM [J]. International Agricultural Engineering Journal, 2017, 26(3):416-427.
11	徐龙琴, 李乾川, 刘双印,等. 基于集合经验模态分解和人工蜂群算法的工厂化养殖pH值预测[J]. 农业工程学报, 2016(3):202-209.
	Xu L Q, Li Q C, Liu S Y, et al. Prediction of pH value in industrialized aquaculture based on ensemble empirical mode decomposition and improved artificial bee colony algorithm[J]. Transactions of the CSAE, 2016(3):202-209. (in Chinese with English abstract)
12	Das M., Ghosh S. K.. Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts[J]. Journal of Computer Science and Technology, 2020, 35(3):665-696.
13	Liu S Y, Xu L Q, Jiang Y, et al. A hybrid WA–CPSO-LSSVR model for dissolved oxygen content prediction in crab culture[J]. Engineering Applications of Artificial Intelligence, 2014, 29(3):114-124.
14	Culberson S.D., Piedrahita R.H.. Aquaculture pond ecosystem model: temperature and dissolved oxygen prediction-mechanism and application [J]. Ecological Modelling, 1996, 89: 231-258.
15	陈英义，程倩倩，方晓敏，等. 主成分分析和长短时记忆神经网络预测水产养殖水体溶解氧[J]. 农业工程学报，2018，34(17)：183-191.
	Chen Y Y, Cheng Q Q, Fang X M, et al. Principal component analysis and long short-term memory neural network for predicting dissolved oxygen in water for aquaculture[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(17): 183-191. (in Chinese with English abstract)
16	Shi P, Li G, Yuan Y, et al. Prediction of dissolved oxygen content in aquaculture using Clustering-based Softplus Extreme Learning Machine [J]. Computer and Electronics in Agriculture, 2019, 157, 329-338.
17	施珮，匡亮，袁永明，等. 基于改进极限学习机的水体溶解氧预测方法[J]. 农业工程学报，2020，36(19)：225-232.
	Shi P, Kuang L, Yuan Y M, et al. Dissolved oxygen prediction for water quality of aquaculture using improved ELM network [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(19): 225-232. (in Chinese with English abstract)
18	Huan J, Li H, Li M B, et al. Prediction of dissolved oxygen in aquaculture based on gradient boosting decision tree and long short-term memory network: A study of Chang Zhou fishery demonstration base [J], Computers and Electronics in Agriculture, 2020, 175:105530.
19	宦娟, 刘星桥. 基于K-means聚类和ELM神经网络的养殖水质溶解氧预测[J]. 农业工程学报, 2016, 32(17):174-181.
	Huan J, Liu X Q. Dissolved oxygen prediction in water based on K-means clustering and ELM neural network for aquaculture[J]. Transactions of the CSAE, 2016, 32(17):174-181. (in Chinese with English abstract)
20	Liu Y Q, Zhang Q, Song L H, et al. Attention-based recurrent neural networks for accurate short-term and long-term dissolved oxygen prediction [J], Computers and Electronics in Agriculture, 2019,165: 104964.
21	樊宇星，任妮，田港陆，等．基于DeepAR-RELM的池塘溶解氧时空预测方法研究[J]．农业机械学报，2020，51(1)：405-412.
	Fan Y X, Ren Ni, Tian G L, et al. Spatio-temporal Prediction Method of Dissolved Oxygen in Ponds Based on DeepAR-RELM [J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(1):405-412.(in Chinese with English abstract)
22	Cao X K, Ren N, Tian G L, et al. A three-dimensional prediction method of dissolved oxygen in pond culture based on Attention-GRU-GBRT [J]. Computer and Electronics in Agriculture, 2021, 181, 105955.
23	Chen Y Y, Xu J, Yu H H, et al. Three-dimensional short-term prediction model of dissolved oxygen content based on PSO-BPANN algorithm coupled with kriging interpolation[J]. Mathematical Problems in Engineering, 2016:1-10.
24	徐龙琴，刘双印，张垒，等. 基于DBN-LSSVR的南美白对虾养殖溶解氧预测[J]. 仲恺农业工程学院学报，2017(4)：1-7.
	Xu L Q, Liu S Y, Zhang L, et al. Prediction ofdissolved oxygen in Litopenaeus vannamei culture based on deep belief network and least squares support vector regression[J]. Journal of Zhongkai University of Agriculture and Engineering, 2017(4): 1-7. (in Chinese with English abstract)
25	朱成云, 刘星桥, 李慧,等. 工厂化水产养殖溶解氧预测模型优化[J/OL]. 农业机械学报, 2016, 47(1):273-278.
	Zhu C Y, Liu X Q, Li H, et al. Optimization of prediction model of dissolved oxygen in industrial aquaculture [J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(1):273-278. (in Chinese with English abstract)
26	胡金有, 王靖杰, 张小栓,等. 水产养殖信息化关键技术研究现状与趋势[J]. 农业机械学报, 2015, 46(7):251-263.
	Hu J Y, Wang J J, Zhang X S, et al. Research Status and Development Trends of Information Technologies in Aquacultures[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(7):251-263. (in Chinese with English abstract)
27	刘双印, 徐龙琴, 李道亮. 基于粗糙集融合支持向量机的水质预警模型[J]. 系统工程理论与实践, 2015, 35(6):1617-1624.
	Liu S Y, Xu L Q, Li D L. Water quality early-warning model based on support vector machine optimized by rough set algorithm[J]. Xitong Gongcheng Lilun Yu Shijian/system Engineering Theory & Practice, 2015, 35(6):1617-1624. (in Chinese with English abstract)
28	马从国，王建国，周恒瑞．国内养殖池塘溶解氧智能监测与调控研究进展[J]．中国农机化学报，2016，37(3)：261-264.
	Ma C G, Wang J G, Zhou H G. Research advance on intelligent monitoring and regulation for dissolved oxygen in domestic ponds [J]. Journal of Chinese Agricultural Mechanization, 2016, 37(3):261-264. (in Chinese with English abstract)
29	蒋建明，乔增伟，朱正伟，等．水产养殖复合式自动增氧系统设计与试验[J]．农业机械学报，2020，51(10)：278-283．
	Jiang J M, Qiao Z W, Zhu Z W, et al．Design and Test of Compound Mechanical Automatic Aeration in Aquaculture [J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(10): 278-283. (in Chinese with English abstract)
30	刘雨青，李志浩，曹守启，等. 基于模糊控制的水产养殖环境智能监控系统设计[J]. 渔业现代化，2020，47（2）：25-32.
	Liu Y Q, Li Z H, Cao S Q, et al. Design of intelligent monitoring system for aquaculture environment based on fuzzy control [J]. Fishery Modernization, 2020, 47(2):25-32. (in Chinese with English abstract)
31	王德望，赵敏．污水处理系统溶解氧的BP-PID控制算法[J]．软件导刊，2018，17(2)：68-70．
	Wang D W, Zhao M. BP-PID Control Algorithm for Dissolved Oxygen in Sewage Treatment System [J]. Software Guide, 2018, 17(2):68-70. (in Chinese with English abstract)
32	马从国，赵德安，王建国，等．基于无线传感器网络的水产养殖池塘溶解氧智能监控系统[J]．农业工程学报，2015，31(7)：193-200．
	Ma C G, Zhao D A, Wang J G, et a1. Intelligent monitoring system for aquiculture dissolved oxygen in ponds based on wireless sensor network [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(7):193-200. (in Chinese with English abstract)
33	掌晓峰，虞丽娟，毛文武，等．基于Zigbee网络的中华绒螯蟹养殖中溶氧量智能控制系统研究与应用[J].海海洋大学学报，2016，25(6)：866-872．
	Zhang X F, Yu L J, Mao W W, et al. The research and application of oxygen intelligent control based on Zigbee in Eriocheir sinensis aquaculture [J]. Journal of Shanghai Ocean University, 2016, 25(6):866-872. (in Chinese with English abstract)
34	贾二腾, 闫明军, 赖起铖,等. 中华绒螯蟹的摄食节律[J]. 中国水产科学, 2018, 25(3): 546-554.
	Jia E T, Yan M J, Lai Q C, et al. Feeding rhythm of the Chinese mitten crab (Eriocheir sinensis)[J]. Journal of Fishery Sciences of China, 2018, 25(3): 546-554. (in Chinese with English abstract)
35	Yong L, Sun Y F, Wang X D., et al., Effect of dietary phosphorus on growth performance, composition body, antioxidant activities and lipid metabolism of juvenile Chinese mitten crab (Eriocheir sinensis). Aquaculture, 2021. 531(30): 1-10.
36	冯伟, 李辉，唐永凯. 配合饲料和冰鱼对单体养殖中华绒螯蟹生长、性腺发育及其肌肉品质的影响[J/OL]. [2020-09-16].: .
	Feng W, Li H, Tang Y K, et al. Effects of the growth, gonadal development and muscle quality on Eriocheir sinensis under the monomer culture with formula feed and frozen fish[J]. Journal of Fisheries of China: https://kns.cnki.net/kcms/detail/31.1283.S.20200916.1517.002.html, 2020-09-16. (in Chinese with English abstract)
37	Cui Y, Ma Q, Limbu S M, et al. Effects of dietary protein to energy ratios on growth, body composition and digestive enzyme activities in Chinese mitten-handed crab, Eriocheir sinensis[J]. Aquaculture Research, 2016, 48(5):1-10.
38	Chen Y, Chen L, Qin J G, et al. Growth and immune response of Chinese mitten crab (Eriocheir sinensis) fed diets containing different lipid sources[J]. Aquaculture Research, 2016, 47(6):1984-1995.
39	Long X W, Sun Y F, Wade N M, et al., Key metabolic and enzymatic adaptations underlie the benefits of formulated diets in the adult female Chinese mitten crab (Eriocheir sinensis). Aquaculture Research, 2020. 51(12): 5125-5140.
40	Chen Y, Chen L, Qin J G, et al. Growth and immune response of Chinese mitten crab (Eriocheir sinensis) fed diets containing different lipid sources[J]. Aquaculture Research, 2016, 47(6):1984-1995.
41	赵德安, 刘晓洋, 孙月平,等. 基于机器视觉的水下河蟹识别方法[J]. 农业机械学报, 2019, 50(3):151-158.
	Zhao D A, Liu X Y, Sun Y P, et al. Detection of Underwater Crabs Based on Machine Vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(3):151-158. (in Chinese with English abstract)
42	Wang X, Hong J, Sun Y, et al. Design of Trajectory Planning System for River Crab Farming with Automatic Feeding Boat[J]. Journal of Physics: Conference Series, 2020, 1575(1): 1-7.
43	Zhang M, Xue H, Wang L, et al. A Decision Support System for Fish Feeding Based on Hybrid Reasoning[J]. Ifip Advances in Information & Communication Technology, 2016, 392:19-26.
44	王志勇, 谌志新, 汤涛林, 等. 基于.NET的池塘养殖数字化管理系统[J]. 南方水产科学, 2013, 9(1): 58-62.
	Wang Z Y, Chen Z X, Tang T L, et al. Digital management system for pond culture based on .NET[J]. South China Fisheries Science, 2013, 9(1): 58-62. (in Chinese with English abstract)
45	徐丽英, 于承先, 邢斌,等. 基于PDA的集约化水产饲料投喂决策系统[J]. 农业工程学报, 2008(s2):250-254.
	Xu L, Yu C, Xing B, et al. PDA-based aquaculture feeding decision support system[J]. Transactions of the Chinese Society of Agricultural Engineering, 2008(s2):250-254. (in Chinese with English abstract)
46	刘肖汉, 方苹, 陈静, 等. 2019年江苏省河蟹养殖病情监测分析[J].科学养鱼, 2020(08):48-50.
	Liu X H, Fang P, Chen J, et al. Monitoring and analysis of disease spread of Chinese mitten crab in Jiangsu province in 2019[J]. Scientific Fish Farming, 2020(08):48-50. (in Chinese)
47	陆军, 董娟, 冯子慧, 等. 中华绒螯蟹病害应急预警系统设计[J]. 电脑与信息技术, 2017, 25(3):33-35.
	Jun L U, Dong J, Feng Z H, et al. Design of Emergency Disease Warning System for Eriocheirsisensis[J]. Computer & Information Technology, 2017. (in Chinese)
48	盖之华, 施连敏, 陈志峰. 基于无线传感器网络的河蟹病原体监测系统[J]. 江苏农业科学, 2015, 43(11):510-512.
	Gai Z H, Shi L M, Chen Z F. Design of crabs pathogen monitoring system based on wireless sensor networks[J]. Jiangsu Agricultural Sciences,2015,43(11):510-512. (in Chinese with English abstract)
49	Zhang X, Fu Z, Cai W, et al. Applying evolutionary prototyping model in developing FIDSS: An intelligent decision support system for fish disease/health management[J]. Expert Systems with Applications An International Journal, 2009, 36(2):3901-3913.
50	Yuan H, Yang Y, Chen Y. Crab-Expert: a Web-Based ES for Crab Farming[C]. International Conference on Control, Automation, Robotics and Vision. IEEE, 2007:1-5.
51	阎笑彤, 徐翔, 郭显久, 等. 基于WEB的水产养殖病害诊断专家系统[J]. 大连海洋大学学报, 2016,31(02):225-230.
	Yan X T, Xu X, Guo X J, et al. An expert system of disease treatment in aquaculture based on WEB[J]. Journal of Dalian Ocean University, 2016,31(02):225-230. (in Chinese with English abstract)
52	Malik S., Kumar T., Sahoo A. K.. Image processing techniques for identification of fish disease[C]. IEEE International Conference on Signal & Image Processing, 2017: 55-59.
53	Hanafiah N., Sugiarto K., Ardy Y., et al. Expert system for diagnosis of Discus fish disease using fuzzy logic approach[C]. IEEE International Conference on Computer and Communications, 2016:56-61.
54	Yuan Q, Wang Q, Zhang T, et al. Effects of water temperature on growth, feeding and molting of juvenile Chinese mitten crab Eriocheir sinensis[J]. Aquaculture, 2017, 468:169-174.
55	Long X, Wu X, Zhao L, et al. Effects of salinity on gonadal development, osmoregulation and metabolism of adult male Chinese mitten crab, Eriocheir sinensis.[J]. Plos One, 2017, 12(6):e0179036.
56	Wang X D, Huang Z P, Wang C L, et al. A Comparative Study on Growth and Metabolism of Eriocheir sinensis Juveniles Under Chronically Low and High pH Stress. 2020, 11:885-885.
57	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. (in Chinese with English abstract)
58	Jiang Y, Li Z B, Fang J J. Automatic video tracking of Chinese mitten crabs based on the particle filter algorithm using a biologically constrained probe and resampling[J]. Computers & Electronics in Agriculture, 2014, 106:111-119.
59	Hong H, Yang X, You Z, et al. Visual quality detection of aquatic products using machine vision[J]. Aquacultural Engineering, 2014, 63(63):62-71.
60	朱艳, 曹元军, 李曙生. 基于图像识别的螃蟹自动分级系统及其控制程序[J]. 食品与机械, 2015(6):127-129.
	Zhu Y, Cao Y J, Li S S. Design of automatic grading control system of crab and its control program based on image recognition[J]. Food & Machinery, 2015(6):127-129. (in Chinese with English abstract)
61	Han K J, Tewfik A H. Expert computer vision based crab recognition system[C] International Conference on Image Processing, 1996. Proceedings. IEEE, 2001, 2: 649-652.
62	侍国忠, 陈明, 张重阳. 基于改进深度残差网络的河蟹精准溯源系统[J]. 液晶与显示, 2019, 34(12):1202-1209.
	Shi G Z, Chen M, Zhang C Y. Accurate traceability system of crab based on improved deep residual network[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(12):1202-1209. (in Chinese with English abstract)
63	段青玲, 刘怡然, 张璐,等. 水产养殖大数据技术研究进展与发展趋势分析[J]. 农业机械学报, 2018, 49(06): 8-23.
	Duan Q L, Liu Y R, Zhang L, et al. State-of-the-art Review for Application of Big Data Technology in Aquaculture [J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(06): 8-23.
64	刘雨青,李佳佳,曹守启, 等.基于物联网的螃蟹养殖基地监控系统设计及应用[J].农业工程学报,2018,34(16): 205-213.
	Liu Y Q, Li J J, Cao S Q, et al. Design and application of monitoring system for crab breeding base based on internet of things[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(16):205-213. (in Chinese with English abstract)

分类方法	依据	特点	代表参考文献	代表参考文献预测精度
影响因素个数	单一	构建方法简单	[13]	RMSE=0.5697^[13] MAPE=6.35%^[13]
影响因素个数	多个	考虑更多影响因素	[15-17]	RMSE=0.3232^[17]
监测点个数和分布	一维	数据采集容易	[18-20]	RMSE=0.1973^[18] MAPE=9.22%^[18]
监测点个数和分布	三维	能准确全面反映出溶解氧的空间分布规律	[21-23]	RMSE=0.348^[22]
模型构建方法	模型驱动	考虑水质参数间互相作用机理	[14]	AE<1.13mg/L^[14]
模型构建方法	数据驱动	考虑数据本身的特点	[24-25]	RMSE=0.2570^[24] MAPE=2.99%^[24]

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大闸蟹养殖大数据分析模型和应用进展

Progress in Analysis Models and the Application of Big Data in Chinese Mitten Crab Culturing

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