河蟹池塘养殖智能支持系统关键技术研究
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摘要
随着人们生活水平的不断提高,对水产品的需求也在持续增长,我国已成为水产品生产和消费的大国,2012年全国水产品总产量达到5906万吨,取得了巨大的经济效益。河蟹是我国主要的水产品种之一,味道鲜美、营养丰富,除了食用之外,河蟹的甲壳等部分还可以使用在工业生产、医学治疗等诸多方面,已取得了良好的社会效益。河蟹在我国分布广泛,养殖方式有池塘养殖、湖泊养殖、河段围栏养殖、稻田养殖等多种形式,受地理环境特点的影响,池塘养殖是养殖户主要采取的养殖方式之一。池塘养殖虽然实施起来较为便利,投入较少,但也存在着养殖分散、多数养殖户不具备相应专业知识,缺乏规范化、标准化的指导,从而导致养殖过程自动化程度低、产品规格差异大、病害严重、产品安全无保障等诸多问题。
为了解决池塘养殖存在的这些问题,在江苏省“物联网示范工程”项目的支撑下,开展了河蟹池塘养殖智能支持系统一些关键技术的研究。本文主要围绕以下工作展开：
首先,针对大范围、多数据的参数获取技术展开研究。根据河蟹池塘养殖基地通常地势平坦、范围广等实际情况,引入了无线传感器网络技术,在养殖基地建立了一套无线传感器网络系统。通过对GAF拓扑控制算法的深入研究,提出了GAF-G改进拓扑算法,实现了固定分簇、固定簇头的拓扑结构,并实现了簇内节点与簇头通信,簇间簇头之间通信的路由关系。通过对池塘水体断面数据的分析,确定了池塘水体各主要指标的分布情况,从而为传感器节点的布置提供了依据,以实现优化配置。
其次,针对池塘各水质因子之间的相互关系展开研究,确定各因子之间的内在联系,并针对主要因子提出不同的调节措施。在深入分析各因子内在关系的基础上,利用基于模糊PID控制算法,对典型河蟹养殖池中溶氧量进行控制,并通过仿真与实际测试确定了控制效果。
接着,针对池塘水质评价工作展开研究,明确了水质评价的重要意义和方法。通过专家咨询的方式,确定了针对河蟹池塘养殖的最主要水质指标：溶解氧、酸碱度(pH值)、温度和盐度。并对这四项指标进行量化分级,分为Ⅰ-V级,共五级水质。详细介绍了目前较为常用的几种水质评价算法,并通过几种算法的应用实例,比较几种算法的优缺点及适用场合。
然后,针对河蟹养殖专家支持系统展开研究。介绍了专家系统在农业方面的应用及发展情况,和专家系统推理机的工作原理,建立了河蟹养殖病害知识库。通过深入研究Rete匹配算法的工作方式,提出了基于索引列表的改进策略。通过分析冲突集的工作方式,提出了基于指针链表的改进策略,并通过仿真确认了改进效果。
最后,针对智能系统的实现展开工作,利用现有的网络技术、数据库技术和通信技术实现了系统集成,完成了智能管理系统的建设。
With the improvement of people's living standard and the increasing demand for aquatic products, China has become one of the largest aquaculture and consumer countries. In2012, the national aquatic production reached59.06million tons. The huge economic benefits were brought. Crab is one of the major aquaculture species, and it is one kind of delicious and nutritious food. In addition to food, some parts of the crab can also be used in industrial production, medical treatment and other aspects. The social benefits are achieved significantly. Crab breeding widely distributes in China, and there are several breeding methods, such as pond breeding, lake breeding, and fence breeding. Due to the characteristics of the geographical environment, pond breeding is one of the mainly methods. Although pond breeding is more convenient and less investment, there are also some problems, such as scattered farming, lack of standardization guidance, and so on. All these disadvantages result in a low degree of automation of the breeding process, large differences in product specifications, serious diseases, lack of product safety and many other issues.
In order to solve these problems in crab breeding, some of the key technologies of intelligent support system for crab breeding were researched, which was based on the demonstration projects of internet of things in Jiangsu province. The work mainly focuses on the following aspects.
Firstly, research on technology of collecting large-scale, multi-data was carried out. According to the actual situation of crab breeding in ponds, the technology of wireless sensor network was introduced, and a breeding center based on the wireless sensor network system was established. Through in-depth study on topology algorithm of GAF, one kind of improved topology algorithm of GAF-G was proposed. The improved algorithm implemented the topology of fixed clustering, the fixed cluster head, and implemented communications in clusters. Based on study on distribution of the main water factors in crab ponds, the deployment of sensor nodes was determined and the optimal allocation was achieved.
Secondly, research on the intrinsic link between each water factor in crab ponds was carried out. Based on analysis of the intrinsic relationship of each factor, one kind of fuzzy PID algorithm was proposed to control dissolved oxygen in crab ponds. The results of simulation and actual testing confirmed the control effects.
Thirdly, research on the evaluation system for pond water quality, and evaluation methods were carried out. According to experts'advice, the most important indicators for water quality of crab ponds were determined:dissolved oxygen, pH, temperature and salinity. In addition, quantitative classification of four indicators was divided into Ⅰ～Ⅴ level. Several evaluation algorithms'for water quality were introduced in details in the evaluation system, and through several application examples, the advantages and disadvantages of different algorithms were described.
Fourthly, research on crab breeding expert support system was carried out. The development and applications of expert system in agriculture were described, and the reasoning mechanism of expert system was discussed in details. One kind of knowledge base for crab breeding was established. Through in-depth research on Rete algorithm, one kind of improved matching algorithm of Rete was proposed based on method of index. By analyzing the working process of conflict set, one method based on chain was introduced. The improved effectiveness of new method was confirmed by simulation.
Finally, the system integration of intelligent system was implemented, the technology of network, database and communications were adopted to accomplish the intelligent system.
为了解决池塘养殖存在的这些问题,在江苏省“物联网示范工程”项目的支撑下,开展了河蟹池塘养殖智能支持系统一些关键技术的研究。本文主要围绕以下工作展开：
首先,针对大范围、多数据的参数获取技术展开研究。根据河蟹池塘养殖基地通常地势平坦、范围广等实际情况,引入了无线传感器网络技术,在养殖基地建立了一套无线传感器网络系统。通过对GAF拓扑控制算法的深入研究,提出了GAF-G改进拓扑算法,实现了固定分簇、固定簇头的拓扑结构,并实现了簇内节点与簇头通信,簇间簇头之间通信的路由关系。通过对池塘水体断面数据的分析,确定了池塘水体各主要指标的分布情况,从而为传感器节点的布置提供了依据,以实现优化配置。
其次,针对池塘各水质因子之间的相互关系展开研究,确定各因子之间的内在联系,并针对主要因子提出不同的调节措施。在深入分析各因子内在关系的基础上,利用基于模糊PID控制算法,对典型河蟹养殖池中溶氧量进行控制,并通过仿真与实际测试确定了控制效果。
接着,针对池塘水质评价工作展开研究,明确了水质评价的重要意义和方法。通过专家咨询的方式,确定了针对河蟹池塘养殖的最主要水质指标：溶解氧、酸碱度(pH值)、温度和盐度。并对这四项指标进行量化分级,分为Ⅰ-V级,共五级水质。详细介绍了目前较为常用的几种水质评价算法,并通过几种算法的应用实例,比较几种算法的优缺点及适用场合。
然后,针对河蟹养殖专家支持系统展开研究。介绍了专家系统在农业方面的应用及发展情况,和专家系统推理机的工作原理,建立了河蟹养殖病害知识库。通过深入研究Rete匹配算法的工作方式,提出了基于索引列表的改进策略。通过分析冲突集的工作方式,提出了基于指针链表的改进策略,并通过仿真确认了改进效果。
最后,针对智能系统的实现展开工作,利用现有的网络技术、数据库技术和通信技术实现了系统集成,完成了智能管理系统的建设。
With the improvement of people's living standard and the increasing demand for aquatic products, China has become one of the largest aquaculture and consumer countries. In2012, the national aquatic production reached59.06million tons. The huge economic benefits were brought. Crab is one of the major aquaculture species, and it is one kind of delicious and nutritious food. In addition to food, some parts of the crab can also be used in industrial production, medical treatment and other aspects. The social benefits are achieved significantly. Crab breeding widely distributes in China, and there are several breeding methods, such as pond breeding, lake breeding, and fence breeding. Due to the characteristics of the geographical environment, pond breeding is one of the mainly methods. Although pond breeding is more convenient and less investment, there are also some problems, such as scattered farming, lack of standardization guidance, and so on. All these disadvantages result in a low degree of automation of the breeding process, large differences in product specifications, serious diseases, lack of product safety and many other issues.
In order to solve these problems in crab breeding, some of the key technologies of intelligent support system for crab breeding were researched, which was based on the demonstration projects of internet of things in Jiangsu province. The work mainly focuses on the following aspects.
Firstly, research on technology of collecting large-scale, multi-data was carried out. According to the actual situation of crab breeding in ponds, the technology of wireless sensor network was introduced, and a breeding center based on the wireless sensor network system was established. Through in-depth study on topology algorithm of GAF, one kind of improved topology algorithm of GAF-G was proposed. The improved algorithm implemented the topology of fixed clustering, the fixed cluster head, and implemented communications in clusters. Based on study on distribution of the main water factors in crab ponds, the deployment of sensor nodes was determined and the optimal allocation was achieved.
Secondly, research on the intrinsic link between each water factor in crab ponds was carried out. Based on analysis of the intrinsic relationship of each factor, one kind of fuzzy PID algorithm was proposed to control dissolved oxygen in crab ponds. The results of simulation and actual testing confirmed the control effects.
Thirdly, research on the evaluation system for pond water quality, and evaluation methods were carried out. According to experts'advice, the most important indicators for water quality of crab ponds were determined:dissolved oxygen, pH, temperature and salinity. In addition, quantitative classification of four indicators was divided into Ⅰ～Ⅴ level. Several evaluation algorithms'for water quality were introduced in details in the evaluation system, and through several application examples, the advantages and disadvantages of different algorithms were described.
Fourthly, research on crab breeding expert support system was carried out. The development and applications of expert system in agriculture were described, and the reasoning mechanism of expert system was discussed in details. One kind of knowledge base for crab breeding was established. Through in-depth research on Rete algorithm, one kind of improved matching algorithm of Rete was proposed based on method of index. By analyzing the working process of conflict set, one method based on chain was introduced. The improved effectiveness of new method was confirmed by simulation.
Finally, the system integration of intelligent system was implemented, the technology of network, database and communications were adopted to accomplish the intelligent system.
引文
[1]林乐峰.河蟹生态养殖与标准化管理[M].北京：中国农业出版社,2007.
[2]农业部渔业局.2013中国渔业统计年鉴[M].北京：中国农业出版社,2013.
[3]赵德安,刘星桥,潘天红等.故障树分析在水产养殖监控系统可靠性设计中的应用[J].农业工程学报,2003,(19)：249-253.
[4]刘星桥,赵德安,全力等.水产养殖多环境因子控制系统的研究[J].农业工程学报,2003,(3)：205-208.
[5]Scharbarg J L, Boyer M, Fraboul C. CAN-Ethernet Architectures for Real-time Application[J].Emerging Technologies and Factory Automation,IEEE Press, 2005,9:245-252.
[6]Hermann Kopetx. Real Time System Design Principles for Distributed Embedded Application[M]. Kluwer Academic Publishers,1997.
[7]Wong Willliam.32-bit Microcontroller Combines CAN and Ethernet Interfaces[J]. Electronic Design,2002,11:51-55.
[8]Zin-Won Park, Myung-Kyun Kim. Design of an Integrated Fieldbus Gateway[C]. KORUS'2005, p843-846.
[9]J. B. Dugan, S. J, Bavuso, M. A. Boyd. Dynamic Fault Tree Models for Fault Tolerant Computer Systems [J]. IEEE Transaction on Reliability,1992,41 (3), p363-377.
[10]李道亮,傅泽田.智能化水产养殖信息系统的设计与初步实现[J].农业工程学报,2000,16(4)：135-138.
[11]王雪.无线传感网络测量系统[M].北京：机械工业出版社,2007.
[12]赵小强,程文.水质远程分析科学决策智能化环保系统[M].西安：西安电子科技大学出版社,2012.
[13]石纯一,李明树,钱跃良.农业专家系统入门[M].北京：清华大学出版社,1999.
[14]李少昆,赖军臣,明博.玉米病虫害诊断专家系统[M].北京：中国农业科学出版社,2009.
[15]王殊,阎毓杰,胡富平等.无线传感器网络的理论及应用[M].北京：北京航空航 天大学出版社,2007.
[16]谭励.无线传感器网络理论与技术应用[M].北京：机械工业出版社,2011.
[17]吴成东.智能无线传感器网络原理与应用[M].北京：科学出版社,2011.
[18]孙利民.无线传感器网络[M].北京：清华大学出版社,2005.
[19]陈林星.无线传感器网络技术与应用[M].北京：电子工业出版社,2009.
[20]曹文明,王瑞.传感器网络覆盖定位模糊信息处理方法[M].北京：电子工业出版社,2010.
[21]史兵,赵德安.面向规模化水产养殖的无线传感网络构建[J].机化研究.2012(11)：214-217.
[22]黄化吉.NS网络模拟和协议仿真[M].北京：人民邮电出版社,2010.
[23]于斌.NS2与网络模拟[M].北京：人民邮电出版社,2007.
[24]许文强,张国霞,王新红等.基于NS-2的网络半实物仿真平台实现[J].2010(12)：3285-3289.
[25]向明尚,陈素丽,曹茂俊等.NS-2网络仿真平台的探讨与实现[J].大庆石油学院学报[J],2005,(02)：75-80.
[26]陈玲,赵建夫.环境监测[M].北京：化学工业出版社,2004.
[27]杨若明,金军.环境监测[M].北京：化学工业出版社,2009.
[28]李青山,李怡庭.水环境监测实用手册[M].北京：中国水利水电出版社,2003.
[29]王瑞梅.池塘水质管理智能决策支持系统研究[D].北京：中国农业大学,2003.
[30]步青云.浅水湖泊溶解氧变化对沉积物磷、氮的影响[D].中国环境科学研究院,2006.
[31]张显久.养殖池塘生态环境的微生物修复[D].南京：南京农业大学,2003.
[32]郝芳华,李春晖,赵彦伟等.流域水质模型与模拟[M].北京：北京师范大学出版社,2008.
[33]王敦春.水质系统信息识别与评价方法研究[D].武汉：武汉水利水电大学,1996.
[34]刘利平,王武.水产养殖中水处理技术的现状与展望[J].水产科学,2002,(02)：35-37.
[35]陈敏,刘中柱.高密度水产养殖自控生态型大棚的水质净化技术[J].农业工程学报,2002,18(6)：95-97.
[36]殷勤业.模式识别与神经网络[M].北京：机械工业出版社,2001.
[37]Kevin M.passion, Stephen Yurkovich. Fuzzy Control[M].北京：清华大学出版社,2001.
[38]章正斌.模糊控制工程[M].重庆：重庆大学出版社,1999.
[39]廉小亲.模糊控制技术[M].北京：中国电力出版社,2003.
[40]王磊,王为民.模糊控制理论及应用[M].北京：国防工业出版社,2001.
[41]王立新.模糊系统与模糊控制教程[M].北京：清华大学出版社,2003.
[42]姜叶琴,姚健萍,杨万喜.水产养殖水体处理方法及应用前景综述[J].海洋湖沼通报,2004,(03)：99-104.
[43]唐玉朝,王坤,王健.某人工湖水污染现状及控制措施研究[J].安徽建筑工业学院学报(自然科学版),2006,(01)：100-104.
[44]Yamamoto, H., Furnhashi, T.. New Fuzzy Inference Method for Symbolic Stability Analysis of Fuzzy Control System[C]. Advanced Motion Control, Proceedings.6th International Workshop on 30 March-1 April 2000,p443-447.
[45]Sakai, S., Takahama, T. Learning Fuzzy Control Rules by Vector Simplex Method[C]. IFSA World Congress and 20th NAFIPS International Conference, 2001, Joint 9th, P25-28.
[46]刘星桥.水产养殖数字化监测与控制系统关键技术的研究[D].江苏大学2009.
[47]Jian-yu Xu,Shao-rong Cui.Behavioral Responses of Tilapia to Acute Fluctuations in Dissolved Oxygen Levels as Monitored by Computer Vision[J].Aquacultural Engineering,2006,35:207-217.
[48]Han-Xiong Li,Gatland, H.B. Conventional Fuzzy Control and its Enhancement[J]. Systems, Man and Cybernetics, Part B, IEEE Transactions on Volume 26,Issue 5, Oct.1996, p791-797.
[49]刘金英.灰色预测理论与评价方法在水环境中的应用研究[D].吉林大学,2004
[50]徐祖信.我国河流单因子水质标识指数评价方法研究[J].同济大学学报(自然科学版),2005(03)：321-325.
[51]潘恒健.主因子分析法在小清河(济南段)水质评价中的研究与应用[D].山 东师范大学,2005.
[52]毛兴华.常用水质评价方法的选择[J].水科学与工程技术,2006(1)：21-23.
[53]刘洪波,朱梦羚,高赛赛.南方某水源水库富营养化水质主控因子识别与评价研究[J].水资源与水工程学报,2013(4)：49-53.
[54]苗成林,周宏.基于灰色模型的水质预测[J].中国农村水利水电,2007,(02)：101-104.
[55]陈淼,陈豪立.灰色模型的模糊法在水质评价中的应用[J].贵州工业大学学报(自然科学版),2004,(06)：86-90.
[56]李云先,吴文金.地理信息系统在地下水环境质量评价中的应用[J].北京工业职业技术学院学报,2005,(02)：39-42.
[57]麦柳妍.基于GIS的地下水天然资源评价系统研究[D]长安大学,2006.
[58]王疆霞,李云峰,徐中华.GIS技术及其在水文学和水资源管理方面的应用[J]西北地质,2003,(01)：109-113.
[59]董志颖,李兵,孙晶.GIS支持下的吉林西部水质预警研究[J].吉林大学学报(地球科学版),2003,(01)：56-58.
[60]张勇.城市浅水景观湖水质模型研究[D].太原理工大学,2006.
[61]彭希珑.赣江南昌段水源地水质预报模型研究[D].南昌大学,2005.
[62]李胜海.基于人工神经网络和遗传算法的水污染控制规划方案优化研究[D].重庆大学,2002.
[63]王海霞.模糊神经网络在水质评价中的应用[D].重庆大学,2002.
[64]树锦.人工神经网络模型在黄河水质预测中的应用[D].河海大学,2006.
[65]叶巧文.河流水质管理决策支持系统应用研究[D].广东工业大学,2004.
[66]李如忠.河流水环境系统不确定性问题研究[D].河海大学,2004.
[67]邓涛.供水系统加氯模式优化理论与计算方法[D].哈尔滨工业大学,2007.
[68]Wang Sitong. Fuzzy Iterative-deepening Heuristic Search Algorithm and its Learning Investigation[J]. Fuzzy Sets and Systems 1996.
[69]Gupta M, Kiszka. J.B,Trojan G. M.Multivariable structure of fuzzy control system IEEE.Trans.SMC,1985(15):116-132.
[70]W.X.Xie and S.D.Bedrosian.An information measure for fuzzy sets, IEEE Trans.Systerm,man and cybernetics,1984(14):18-24.
[71]陈丽华,藏荣鑫,王宏伟.人工神经网络及其在水质信息检测中的应用[M].北京：国防工业出版社,2011.
[72]郭劲松,王红,霍国友等.四种水质综合评价方法的比较[J].重庆建筑大学学报,2000,(4)：6-12.
[73]程伟良.广义专家系统北京[M].北京：北京理工大学出版社,2005.
[74]史忠植.王文杰人工智能[M].北京：国防工业出版社,2007.
[75]王顺晃,舒迪前.智能控制系统及其应用[M].北京：机械工业出版社,2005.
[76]杨善林.智能决策方法与智能决策支持系统[M].北京：科学出版社,2005.
[78]张红燕,袁永明,贺艳辉等.水产养殖专家系统的设计与实现[J].中国农学通报,2011(1)101-104.
[79]郑蕉,杨珺,彭军等.基于INTERNET的农业杂草鉴别专家系统[J].农业网络信息,2007,(2)34-37.
[80]于复坤,李百胜,王开湘.专家系统应用于植物检疫领域初探[J].植物检疫,1991,(5)：27-29.
[81]胡为群,王一成,祝利莉等.猪病诊治专家系统的研制与应用[J].中国农学通报,2006,(08)：24-27.
[82]柳小妮.甘肃省草地蝗虫预测预报专家系统的研究与开发[D].甘肃农业大学,2004.
[83]高川.基于模糊特征的家畜疾病诊断专家系统的研究[D].重庆大学,2006.
[84]金胜男.基于多层关联规则的概念分层知识库中知识发现的研究[D].天津大学,2006.
[85]Prusinkiewice P, Hammel M, Hanan J, et al. Visual Model of Plant Development[M]. Springer-Verlag,1996.2-6.
[86]Blaise F. Simulation of the Growth of Metamorphosis and Spatial Interactions in the Architecture and Development of Plans. Springer-Verlag,1998:81-109.
[87]沈文武.因果图推理算法及专家系统应用研究[D].重庆大学,2005.
[88]王亚南.专家系统中推理机制的研究与应用[D].武汉理工大学,2006.
[89]Tom Hammergren, Data Warehousing:Building the Corporate Knowledge Base [M], Ventana Communications Group Inc.1997:207-312.
[90]吴源泉,刘江宁.人工智能与专家系统[M].长沙：国防科技大学出版社,2000.
[91]Zhao Chunjiang. Research and Application of Platform for Agricultural Intelligent Systems [C]. ISIAIT2000, Beijing China,2000.12:40-45.
[92]Welch S M, Jones J W, Brennan M W, et al. PCYield:model-based decision support for soybean production [J]. Agricultural Systems,2002,74:79-98.
[93]Stuth J W, Hamilton W T, Conner R. Insights in Development and deployment of the GLA and NUTBAL decision support systems for grazinglands[J].Agricultural Systems,2002,74:99-113.
[94]李道亮,傅泽田；田东.智能系统：基础,方法及其在农业中的应用[M].北京：清华大学出版社,2004.
[95]史兵,赵德安等.基于无线传感器网络的规模化水产养殖智能监控系统[J].农业工程学报.2011(9)：136-140.
[96]史兵,赵德安等.工厂化水产养殖智能监控系统设计[J].农业机械学报.2011(9)：191-196.
[97]史兵,赵德安等.面向规模化水产养殖的无线传感网络构建[J].农机化研究.2012(11)：214-217.
[98]张东波.基于CC2420的无线传感器网络系统设计与实现[D].成都：电子科技大学,2007.
[99]Shi Bing, Zhao Dean. Research on Wireless Communication Based on GPRS in Greenhouse. The 10th WCICA 2012.
[100]Shi Bing, Zhao Dean. Remote Monitoring System for Water Quality in River based on WSN. ICMSIT 2013.
[101]祁昕,陈海东,刘烨.水产养殖业用溶解氧检测仪[J].传感器技术,2001(11)：55-56.
[102]王红英.一种新的池塘溶解氧预测模型[J].农业工程学报,1997(4)：105-109.
[103]Phillip G.Lee. Process Control and Artificial Intelligence Software for Aquaculture[J]. Aquacultural Engineering,2000,23:13-36.
[104]Timmons M.B. A Mathematical Model of Low-head Oxygenators[J]. Aquacultural Engineering,2001,24:257-277.
[105]徐志祥等.控制网与智能型传感器[J].测控制技术,2001,20(2)：13-15.
[106]吴钦伟.工业仪表与装置智能化网络化的进展[J].自动化博览,2003(18)：1-6.
[107]冯莉,董桂梅.短距离无线通信技术及其在仪器通信中的应用[J].仪表技术与传感器,2007,2：31-33.
[108]J. M. Christenstn, J. M. Howerd and B. S. stevens, Field Experience in Maintenance, in Human Dtection and Diagnosis of System Failures [M]. New York:Plenum Press,1981, plll-133.
[109]于海晨,仲崇权.基于Internet的控制系统远程监控方案及实例[J].计算机自动测量与控制,2001(5)：14-16.
[110]K. B. Misra, G. G. Weber, A New Method for Fuzzy Tree Analysis [J]. Microelectron and Reliability,1989,29(2):p195-216.
[111]Jerschewski P. A flow system for calibration of dissolved oxygen sensors. Fresenius J AnalChenm,1997(6):667-682.
[112]宋协法,宋业.工厂化鱼类养殖智能系统的研究[J].中国海洋大学学报(自然科学版),2005,35(5)：95-100.
[113]胡俊.工业以太网和基Internet的远程监控系统[J].世界仪表与自动化,2002,6(2)：43-45.
[2]农业部渔业局.2013中国渔业统计年鉴[M].北京：中国农业出版社,2013.
[3]赵德安,刘星桥,潘天红等.故障树分析在水产养殖监控系统可靠性设计中的应用[J].农业工程学报,2003,(19)：249-253.
[4]刘星桥,赵德安,全力等.水产养殖多环境因子控制系统的研究[J].农业工程学报,2003,(3)：205-208.
[5]Scharbarg J L, Boyer M, Fraboul C. CAN-Ethernet Architectures for Real-time Application[J].Emerging Technologies and Factory Automation,IEEE Press, 2005,9:245-252.
[6]Hermann Kopetx. Real Time System Design Principles for Distributed Embedded Application[M]. Kluwer Academic Publishers,1997.
[7]Wong Willliam.32-bit Microcontroller Combines CAN and Ethernet Interfaces[J]. Electronic Design,2002,11:51-55.
[8]Zin-Won Park, Myung-Kyun Kim. Design of an Integrated Fieldbus Gateway[C]. KORUS'2005, p843-846.
[9]J. B. Dugan, S. J, Bavuso, M. A. Boyd. Dynamic Fault Tree Models for Fault Tolerant Computer Systems [J]. IEEE Transaction on Reliability,1992,41 (3), p363-377.
[10]李道亮,傅泽田.智能化水产养殖信息系统的设计与初步实现[J].农业工程学报,2000,16(4)：135-138.
[11]王雪.无线传感网络测量系统[M].北京：机械工业出版社,2007.
[12]赵小强,程文.水质远程分析科学决策智能化环保系统[M].西安：西安电子科技大学出版社,2012.
[13]石纯一,李明树,钱跃良.农业专家系统入门[M].北京：清华大学出版社,1999.
[14]李少昆,赖军臣,明博.玉米病虫害诊断专家系统[M].北京：中国农业科学出版社,2009.
[15]王殊,阎毓杰,胡富平等.无线传感器网络的理论及应用[M].北京：北京航空航 天大学出版社,2007.
[16]谭励.无线传感器网络理论与技术应用[M].北京：机械工业出版社,2011.
[17]吴成东.智能无线传感器网络原理与应用[M].北京：科学出版社,2011.
[18]孙利民.无线传感器网络[M].北京：清华大学出版社,2005.
[19]陈林星.无线传感器网络技术与应用[M].北京：电子工业出版社,2009.
[20]曹文明,王瑞.传感器网络覆盖定位模糊信息处理方法[M].北京：电子工业出版社,2010.
[21]史兵,赵德安.面向规模化水产养殖的无线传感网络构建[J].机化研究.2012(11)：214-217.
[22]黄化吉.NS网络模拟和协议仿真[M].北京：人民邮电出版社,2010.
[23]于斌.NS2与网络模拟[M].北京：人民邮电出版社,2007.
[24]许文强,张国霞,王新红等.基于NS-2的网络半实物仿真平台实现[J].2010(12)：3285-3289.
[25]向明尚,陈素丽,曹茂俊等.NS-2网络仿真平台的探讨与实现[J].大庆石油学院学报[J],2005,(02)：75-80.
[26]陈玲,赵建夫.环境监测[M].北京：化学工业出版社,2004.
[27]杨若明,金军.环境监测[M].北京：化学工业出版社,2009.
[28]李青山,李怡庭.水环境监测实用手册[M].北京：中国水利水电出版社,2003.
[29]王瑞梅.池塘水质管理智能决策支持系统研究[D].北京：中国农业大学,2003.
[30]步青云.浅水湖泊溶解氧变化对沉积物磷、氮的影响[D].中国环境科学研究院,2006.
[31]张显久.养殖池塘生态环境的微生物修复[D].南京：南京农业大学,2003.
[32]郝芳华,李春晖,赵彦伟等.流域水质模型与模拟[M].北京：北京师范大学出版社,2008.
[33]王敦春.水质系统信息识别与评价方法研究[D].武汉：武汉水利水电大学,1996.
[34]刘利平,王武.水产养殖中水处理技术的现状与展望[J].水产科学,2002,(02)：35-37.
[35]陈敏,刘中柱.高密度水产养殖自控生态型大棚的水质净化技术[J].农业工程学报,2002,18(6)：95-97.
[36]殷勤业.模式识别与神经网络[M].北京：机械工业出版社,2001.
[37]Kevin M.passion, Stephen Yurkovich. Fuzzy Control[M].北京：清华大学出版社,2001.
[38]章正斌.模糊控制工程[M].重庆：重庆大学出版社,1999.
[39]廉小亲.模糊控制技术[M].北京：中国电力出版社,2003.
[40]王磊,王为民.模糊控制理论及应用[M].北京：国防工业出版社,2001.
[41]王立新.模糊系统与模糊控制教程[M].北京：清华大学出版社,2003.
[42]姜叶琴,姚健萍,杨万喜.水产养殖水体处理方法及应用前景综述[J].海洋湖沼通报,2004,(03)：99-104.
[43]唐玉朝,王坤,王健.某人工湖水污染现状及控制措施研究[J].安徽建筑工业学院学报(自然科学版),2006,(01)：100-104.
[44]Yamamoto, H., Furnhashi, T.. New Fuzzy Inference Method for Symbolic Stability Analysis of Fuzzy Control System[C]. Advanced Motion Control, Proceedings.6th International Workshop on 30 March-1 April 2000,p443-447.
[45]Sakai, S., Takahama, T. Learning Fuzzy Control Rules by Vector Simplex Method[C]. IFSA World Congress and 20th NAFIPS International Conference, 2001, Joint 9th, P25-28.
[46]刘星桥.水产养殖数字化监测与控制系统关键技术的研究[D].江苏大学2009.
[47]Jian-yu Xu,Shao-rong Cui.Behavioral Responses of Tilapia to Acute Fluctuations in Dissolved Oxygen Levels as Monitored by Computer Vision[J].Aquacultural Engineering,2006,35:207-217.
[48]Han-Xiong Li,Gatland, H.B. Conventional Fuzzy Control and its Enhancement[J]. Systems, Man and Cybernetics, Part B, IEEE Transactions on Volume 26,Issue 5, Oct.1996, p791-797.
[49]刘金英.灰色预测理论与评价方法在水环境中的应用研究[D].吉林大学,2004
[50]徐祖信.我国河流单因子水质标识指数评价方法研究[J].同济大学学报(自然科学版),2005(03)：321-325.
[51]潘恒健.主因子分析法在小清河(济南段)水质评价中的研究与应用[D].山 东师范大学,2005.
[52]毛兴华.常用水质评价方法的选择[J].水科学与工程技术,2006(1)：21-23.
[53]刘洪波,朱梦羚,高赛赛.南方某水源水库富营养化水质主控因子识别与评价研究[J].水资源与水工程学报,2013(4)：49-53.
[54]苗成林,周宏.基于灰色模型的水质预测[J].中国农村水利水电,2007,(02)：101-104.
[55]陈淼,陈豪立.灰色模型的模糊法在水质评价中的应用[J].贵州工业大学学报(自然科学版),2004,(06)：86-90.
[56]李云先,吴文金.地理信息系统在地下水环境质量评价中的应用[J].北京工业职业技术学院学报,2005,(02)：39-42.
[57]麦柳妍.基于GIS的地下水天然资源评价系统研究[D]长安大学,2006.
[58]王疆霞,李云峰,徐中华.GIS技术及其在水文学和水资源管理方面的应用[J]西北地质,2003,(01)：109-113.
[59]董志颖,李兵,孙晶.GIS支持下的吉林西部水质预警研究[J].吉林大学学报(地球科学版),2003,(01)：56-58.
[60]张勇.城市浅水景观湖水质模型研究[D].太原理工大学,2006.
[61]彭希珑.赣江南昌段水源地水质预报模型研究[D].南昌大学,2005.
[62]李胜海.基于人工神经网络和遗传算法的水污染控制规划方案优化研究[D].重庆大学,2002.
[63]王海霞.模糊神经网络在水质评价中的应用[D].重庆大学,2002.
[64]树锦.人工神经网络模型在黄河水质预测中的应用[D].河海大学,2006.
[65]叶巧文.河流水质管理决策支持系统应用研究[D].广东工业大学,2004.
[66]李如忠.河流水环境系统不确定性问题研究[D].河海大学,2004.
[67]邓涛.供水系统加氯模式优化理论与计算方法[D].哈尔滨工业大学,2007.
[68]Wang Sitong. Fuzzy Iterative-deepening Heuristic Search Algorithm and its Learning Investigation[J]. Fuzzy Sets and Systems 1996.
[69]Gupta M, Kiszka. J.B,Trojan G. M.Multivariable structure of fuzzy control system IEEE.Trans.SMC,1985(15):116-132.
[70]W.X.Xie and S.D.Bedrosian.An information measure for fuzzy sets, IEEE Trans.Systerm,man and cybernetics,1984(14):18-24.
[71]陈丽华,藏荣鑫,王宏伟.人工神经网络及其在水质信息检测中的应用[M].北京：国防工业出版社,2011.
[72]郭劲松,王红,霍国友等.四种水质综合评价方法的比较[J].重庆建筑大学学报,2000,(4)：6-12.
[73]程伟良.广义专家系统北京[M].北京：北京理工大学出版社,2005.
[74]史忠植.王文杰人工智能[M].北京：国防工业出版社,2007.
[75]王顺晃,舒迪前.智能控制系统及其应用[M].北京：机械工业出版社,2005.
[76]杨善林.智能决策方法与智能决策支持系统[M].北京：科学出版社,2005.
[78]张红燕,袁永明,贺艳辉等.水产养殖专家系统的设计与实现[J].中国农学通报,2011(1)101-104.
[79]郑蕉,杨珺,彭军等.基于INTERNET的农业杂草鉴别专家系统[J].农业网络信息,2007,(2)34-37.
[80]于复坤,李百胜,王开湘.专家系统应用于植物检疫领域初探[J].植物检疫,1991,(5)：27-29.
[81]胡为群,王一成,祝利莉等.猪病诊治专家系统的研制与应用[J].中国农学通报,2006,(08)：24-27.
[82]柳小妮.甘肃省草地蝗虫预测预报专家系统的研究与开发[D].甘肃农业大学,2004.
[83]高川.基于模糊特征的家畜疾病诊断专家系统的研究[D].重庆大学,2006.
[84]金胜男.基于多层关联规则的概念分层知识库中知识发现的研究[D].天津大学,2006.
[85]Prusinkiewice P, Hammel M, Hanan J, et al. Visual Model of Plant Development[M]. Springer-Verlag,1996.2-6.
[86]Blaise F. Simulation of the Growth of Metamorphosis and Spatial Interactions in the Architecture and Development of Plans. Springer-Verlag,1998:81-109.
[87]沈文武.因果图推理算法及专家系统应用研究[D].重庆大学,2005.
[88]王亚南.专家系统中推理机制的研究与应用[D].武汉理工大学,2006.
[89]Tom Hammergren, Data Warehousing:Building the Corporate Knowledge Base [M], Ventana Communications Group Inc.1997:207-312.
[90]吴源泉,刘江宁.人工智能与专家系统[M].长沙：国防科技大学出版社,2000.
[91]Zhao Chunjiang. Research and Application of Platform for Agricultural Intelligent Systems [C]. ISIAIT2000, Beijing China,2000.12:40-45.
[92]Welch S M, Jones J W, Brennan M W, et al. PCYield:model-based decision support for soybean production [J]. Agricultural Systems,2002,74:79-98.
[93]Stuth J W, Hamilton W T, Conner R. Insights in Development and deployment of the GLA and NUTBAL decision support systems for grazinglands[J].Agricultural Systems,2002,74:99-113.
[94]李道亮,傅泽田；田东.智能系统：基础,方法及其在农业中的应用[M].北京：清华大学出版社,2004.
[95]史兵,赵德安等.基于无线传感器网络的规模化水产养殖智能监控系统[J].农业工程学报.2011(9)：136-140.
[96]史兵,赵德安等.工厂化水产养殖智能监控系统设计[J].农业机械学报.2011(9)：191-196.
[97]史兵,赵德安等.面向规模化水产养殖的无线传感网络构建[J].农机化研究.2012(11)：214-217.
[98]张东波.基于CC2420的无线传感器网络系统设计与实现[D].成都：电子科技大学,2007.
[99]Shi Bing, Zhao Dean. Research on Wireless Communication Based on GPRS in Greenhouse. The 10th WCICA 2012.
[100]Shi Bing, Zhao Dean. Remote Monitoring System for Water Quality in River based on WSN. ICMSIT 2013.
[101]祁昕,陈海东,刘烨.水产养殖业用溶解氧检测仪[J].传感器技术,2001(11)：55-56.
[102]王红英.一种新的池塘溶解氧预测模型[J].农业工程学报,1997(4)：105-109.
[103]Phillip G.Lee. Process Control and Artificial Intelligence Software for Aquaculture[J]. Aquacultural Engineering,2000,23:13-36.
[104]Timmons M.B. A Mathematical Model of Low-head Oxygenators[J]. Aquacultural Engineering,2001,24:257-277.
[105]徐志祥等.控制网与智能型传感器[J].测控制技术,2001,20(2)：13-15.
[106]吴钦伟.工业仪表与装置智能化网络化的进展[J].自动化博览,2003(18)：1-6.
[107]冯莉,董桂梅.短距离无线通信技术及其在仪器通信中的应用[J].仪表技术与传感器,2007,2：31-33.
[108]J. M. Christenstn, J. M. Howerd and B. S. stevens, Field Experience in Maintenance, in Human Dtection and Diagnosis of System Failures [M]. New York:Plenum Press,1981, plll-133.
[109]于海晨,仲崇权.基于Internet的控制系统远程监控方案及实例[J].计算机自动测量与控制,2001(5)：14-16.
[110]K. B. Misra, G. G. Weber, A New Method for Fuzzy Tree Analysis [J]. Microelectron and Reliability,1989,29(2):p195-216.
[111]Jerschewski P. A flow system for calibration of dissolved oxygen sensors. Fresenius J AnalChenm,1997(6):667-682.
[112]宋协法,宋业.工厂化鱼类养殖智能系统的研究[J].中国海洋大学学报(自然科学版),2005,35(5)：95-100.
[113]胡俊.工业以太网和基Internet的远程监控系统[J].世界仪表与自动化,2002,6(2)：43-45.