水利业务信息化及综合集成应用模式研究
|
摘要
现代水利需要信息技术。水信息应用问题突显,但有其特点。要共享资源、整合应用,就要水信息综合集成：大手笔的服务平台、组件化的信息处理、创新的应用模式。深入理解需求,用知识图关联信息、组织应用过程、描述事件和主题,把数据、信息、知识可视化,用图来存贮经验、用事例推理来延长应用；把业务处理方法和模型组件化、规范化；按主题提供信息服务、按需要提供计算服务、按个性化提供决策服务；从高性能计算和可视化表现,创建平行系统,开展计算实验；把卫星遥感图片及实景拍摄照片组合应用,由多元信息及全局影像的发展变化,挖掘信息价值；以人为主,实现“人机结合”,在综合集成服务平台下提供信息、知识、决策服务。由平台、组件、主题、知识图、可视化工具组成新模式：由平台支持应用；由组件、主题、知识图快速组织应用；由丰富的多元信息可视化直观表现应用。在个性化定制应用和相关行业标准制订中,发挥行业导向作用,逐步推广新的应用模式。论文取得的主要成果如下：
(1)采用知识图实现知识的可视化表达,并把知识图著作工具产品化。
①以基于过程的知识获取、表达为手段,建立水信息与知识的知识图,把应用业务知识图化。采用知识图来关联信息、组织应用过程中的信息、描述事件和应用主题。
②研究知识图方法支持下的人—机结合机理。从信息感知、融合的角度,运用实证和经验总结的方法,研究水信息应用过程中专家运用知识及知识图的过程,实现知识共享与传递的机制、规律,并研究提高知识传递效率的途径。
③研究知识图方法支持的群体智慧形成机理。运用实证的方法,研究基于知识图的个体智慧转变为群体智慧的机制、规律,并支持群体创意,引导专家群体进行深入的分析与论证。通过群体专家之间进行知识传递,形成“群体记忆”,促进群体智慧的产生。
(2)与水信息应用中具体业务适应,按照组件开发标准,开发表现层和业务层组件。扩大传统模型对信息的依赖,发展新模型,并逐步组件化。不断丰富,建成应用组件库。利用组件库(已有了一定基础),解决应用系统构造、知识资源共享问题,规范组件应用的流程及服务组合,为快速集成和组建不同应用,创建人机结合综合集成平台打基础,并结合平台促进新模式的推广,逐步构建一个支持专家群体研讨的“知识场”。
(3)采用中间件、网格、综合集成研讨厅等技术构建综合服务平台体系。采用平台提供数据、信息、知识的综合集成；用平台提供三个服务：按照主题提供信息服务、按照需要提供计算服务、按照个性化组织应用提供决策服务；用平台建立具有开放的可以增长的知识体系,使系统具有方便服务、切近实用、长久生命力；在平台上用知识图来存贮经验、用事例推理来延长应用；通过决策知识集成与评价,发掘优秀决策知识,总结、提炼规律,从定性到定量,更好地提供服务。
(4)对具体应用主题,采用平台支持的模式,开展个性化的应用。以基于平台的洪水预报、水库调度和应急管理为实例,把主题用一系列的知识图来表达,知识图、平台、用有机结合,在应用过程中,检验信息、知识、决策服务的有效性和实用性。
(5)随着业务应用组件库(解决问题的过程或方法组件化)、主题服务标准库(由事件驱动,形成应用主题)、应用知识图库(解决问题的过程或方法、信息融合、知识形成等的图形化)的不断丰富,数据中心就成为了面向服务的主题服务中心,由此提出实用的数据中心建设方案。就目前多分布式数据源,分布存放、相对抽象,在应用中单独提供数据、没有语义,很难理解。只有给数据加以语义,变为信息才能提高应用效率、才有价值。所以,设计可行、可操作的数据中心,就有着重大的实用意义。
(6)探讨从主题到知识图形成信息集成,由平台、组件、主题、知识图、可视化工具组成新的应用模式。由平台可以支持应用；由组件、主题、知识图可以快速组织应用；由丰富的多元信息可视化可以表现出更直观应用。把多元信息融合、用知识表达决策过程、用平台提供服务、方便组织应用作为近期应用模式,并逐步加以推广
(7)基于平台的MODIS遥感信息分析、处理、应用。在遥感技术的支持下,提高多元信息的利用率,以信息融合和MODIS遥感信息的应用为重点,由多元信息及全局影像的发展变化,挖掘信息价值,通过对MODIS信息的集成,可将点信息、线信息和面信息结合起来,实现三位一体的洪水预报。
(8)结合网格技术、可视化技术,创建水信息应用的人工平行系统。在高性能计算和可视化表现下,从主动、被动两方面,提供计算服务,并开展计算实验。以洪水预报为例进行分析和论证。
(9)构建面向服务的水利业务应用服务中心。通过组件实现数据与业务集成,通过知识图和服务组合实现应用集成,通过平台实现综合集成,通过水利应用中心实现水利业务应用集成服务体系。
Modern water needs information technology. Water information application issues highlighted, however, it has characteristics. To share resources, integration of applications, it is necessary to integrated water information:generous service platform, components of information processing, and the application of innovative models. Depth understanding of the needs, information associated with the knowledge mapping, organized the application process, description of the events and themes, The data, information and knowledge visualization were store experience with maps and extend the application with case-based reasoning; The business methods and models were component-based and standardized; Provide information services by subject, provide computing services by the need, provide the decision-making by personalized services; from high-performance computing and visualization performance create a parallel system and carried out the calculation experiments. With the satellite remote sensing images and real photographs of composite applications, from multi-image information and the development changes of the overall in the value to mining the value of information; Mainly to achieve "human-computer connections" in the integrated service platform to provide information, knowledge, decision-making services. By the platform, components, themes, knowledge mapping, visualization tools to form a new model:Application by the platform support; By the components, the subject and knowledge mapping organized the application rapidly; The showed by multi-information visualization. In the Personalized custom applications and related industries standard-setting, Play the role of industry-oriented, gradually extend the application of new model. This paper's main results are as follows:
(1) Using knowledge mapping to achieve the expression of knowledge visualization, and knowledge mapping book products.
①Take the knowledge acquisition and expression based the process as a means, to establishment the knowledge mapping of water information and knowledge, Using knowledge mapping to link the information, organize the information in the application process, describe the events and the application of the theme.
②Research the human-machine mechanism of supported by the knowledge mapping. From the information perception, integration point of view, use of empirical methods and lessons learned, research the application of water information using expert knowledge and process knowledge mapping, achieve knowledge sharing and transfer mechanisms, laws, and to improve the efficiency of knowledge transfer channels.
③Research the mechanism of groups wisdom on knowledge mapping indorsation. Use the empirical, based on the knowledge mapping; research the individual knowledge into collective wisdom's mechanism, laws, and support groups, creative, guide groups of experts to conduct in-depth analysis and demonstration. Through the groups of experts knowledge transfer, form a "group memory", to promote the emergence of collective wisdom.
(2) Adapt to the water information business applications, in accordance with the components development standards, development of presentation layer and business layer components. Expand the traditional model's reliance on the information, development of new models, and gradually component. Continuously enriched, completed the application component library. Use the component library (have a certain foundation), to resolve structural applications, knowledge resources sharing, standardize the application of the process components and services, For rapid integration and the formation of different applications to create a comprehensive integrated platform combining human-computer foundation, combined with a platform to promote the new model, and gradually build a support group of experts discuss the "knowledge market".
(3) Use the middleware, grid, meta-synthetic engineering office technologies to build multi-service platform system. Using the platform to provide data, information, and knowledge integration; Platform provides three services:In accordance with the theme provide the information services, the need to provide computing services and the personalized service organizations provide the decision-making; Established an open growth knowledge system With the platform,it can make the system convenient service closest practical, long-term vitality; In the platform, store experience with knowledge mapping and extend the application with case-based reasoning; Through the Knowledge integration and evaluation, excavation good decision-making knowledge, from qualitative to quantitative, summed up and refined the law to delivery services better.
(4) For the concrete application themes, use the model of platform supported, develop individualized applications. Using flood forecasting、reservoir dispatching and emergency management which are based on platform as examples, use a series of knowledge graphs to express the themes, organically combined the knowledge graphs、platform and application, test the efficiency and practicality of information、knowledge and decision service.
(5) With the continuous enrichment of business application components library (componentization of the process and solution for solving the problem)、theme service standards library(form application themes driven by events)、application knowledge graphs library(the process or method for solving the problem、information fusion、formation of knowledge being graphical). Data center became themes service center facing service, thus propose practical data center construction scheme. Now, multi-distributed data resource was distributed stored and relatively abstract, provide data individually in the application, it is no semantic and hard to understand. Only change the data to be information by adding semantic can improve the application efficiency and value. So there is great practical significance to design the available and operable data center.
(6) Discuss the formation of information integration from themes to knowledge graphs, compose a new application model by platform、component、theme、knowledge graphs and Visualization tools. The platform can support the application; using the component、themes and knowledge graphs can organize application fast; using the multivariate information visualization can display application more intuitively; take the multivariate information combination、display the decision process by knowledge、using platform to support services、convenient organize application as the recent application model, and popularize it continuous.
(7) The analysis and application of MODIS remote sensing informations based on platform. Under the support of remote sensing technique, prove the utilization rate of multivariate information, Take the information combination and the application of MODIS remote sensing informations as the emphasis, excavate information value by the variation of multivariate information and Overall image, could combine the point type information、linear information and Surface information by integrating the MODIS information, realize the trinity flood forecasting.
(8) Combining the grid technique with visualization technique, to create artificial parallel systems for water Information application. Under the high-performance calculation and visualization, support calculating services from the active and passive aspects, and carry on computational experiments. Analyze and demonstrate the flood dispatching.
(9) Development of the water resources business application service center faced to service. Realize the integration of data and business by components, realize application integration by combining the knowledge graphs and services, realize meta-synthesis by platform, realize the water resources business application integrated service system by water resources application center.
(1)采用知识图实现知识的可视化表达,并把知识图著作工具产品化。
①以基于过程的知识获取、表达为手段,建立水信息与知识的知识图,把应用业务知识图化。采用知识图来关联信息、组织应用过程中的信息、描述事件和应用主题。
②研究知识图方法支持下的人—机结合机理。从信息感知、融合的角度,运用实证和经验总结的方法,研究水信息应用过程中专家运用知识及知识图的过程,实现知识共享与传递的机制、规律,并研究提高知识传递效率的途径。
③研究知识图方法支持的群体智慧形成机理。运用实证的方法,研究基于知识图的个体智慧转变为群体智慧的机制、规律,并支持群体创意,引导专家群体进行深入的分析与论证。通过群体专家之间进行知识传递,形成“群体记忆”,促进群体智慧的产生。
(2)与水信息应用中具体业务适应,按照组件开发标准,开发表现层和业务层组件。扩大传统模型对信息的依赖,发展新模型,并逐步组件化。不断丰富,建成应用组件库。利用组件库(已有了一定基础),解决应用系统构造、知识资源共享问题,规范组件应用的流程及服务组合,为快速集成和组建不同应用,创建人机结合综合集成平台打基础,并结合平台促进新模式的推广,逐步构建一个支持专家群体研讨的“知识场”。
(3)采用中间件、网格、综合集成研讨厅等技术构建综合服务平台体系。采用平台提供数据、信息、知识的综合集成；用平台提供三个服务：按照主题提供信息服务、按照需要提供计算服务、按照个性化组织应用提供决策服务；用平台建立具有开放的可以增长的知识体系,使系统具有方便服务、切近实用、长久生命力；在平台上用知识图来存贮经验、用事例推理来延长应用；通过决策知识集成与评价,发掘优秀决策知识,总结、提炼规律,从定性到定量,更好地提供服务。
(4)对具体应用主题,采用平台支持的模式,开展个性化的应用。以基于平台的洪水预报、水库调度和应急管理为实例,把主题用一系列的知识图来表达,知识图、平台、用有机结合,在应用过程中,检验信息、知识、决策服务的有效性和实用性。
(5)随着业务应用组件库(解决问题的过程或方法组件化)、主题服务标准库(由事件驱动,形成应用主题)、应用知识图库(解决问题的过程或方法、信息融合、知识形成等的图形化)的不断丰富,数据中心就成为了面向服务的主题服务中心,由此提出实用的数据中心建设方案。就目前多分布式数据源,分布存放、相对抽象,在应用中单独提供数据、没有语义,很难理解。只有给数据加以语义,变为信息才能提高应用效率、才有价值。所以,设计可行、可操作的数据中心,就有着重大的实用意义。
(6)探讨从主题到知识图形成信息集成,由平台、组件、主题、知识图、可视化工具组成新的应用模式。由平台可以支持应用；由组件、主题、知识图可以快速组织应用；由丰富的多元信息可视化可以表现出更直观应用。把多元信息融合、用知识表达决策过程、用平台提供服务、方便组织应用作为近期应用模式,并逐步加以推广
(7)基于平台的MODIS遥感信息分析、处理、应用。在遥感技术的支持下,提高多元信息的利用率,以信息融合和MODIS遥感信息的应用为重点,由多元信息及全局影像的发展变化,挖掘信息价值,通过对MODIS信息的集成,可将点信息、线信息和面信息结合起来,实现三位一体的洪水预报。
(8)结合网格技术、可视化技术,创建水信息应用的人工平行系统。在高性能计算和可视化表现下,从主动、被动两方面,提供计算服务,并开展计算实验。以洪水预报为例进行分析和论证。
(9)构建面向服务的水利业务应用服务中心。通过组件实现数据与业务集成,通过知识图和服务组合实现应用集成,通过平台实现综合集成,通过水利应用中心实现水利业务应用集成服务体系。
Modern water needs information technology. Water information application issues highlighted, however, it has characteristics. To share resources, integration of applications, it is necessary to integrated water information:generous service platform, components of information processing, and the application of innovative models. Depth understanding of the needs, information associated with the knowledge mapping, organized the application process, description of the events and themes, The data, information and knowledge visualization were store experience with maps and extend the application with case-based reasoning; The business methods and models were component-based and standardized; Provide information services by subject, provide computing services by the need, provide the decision-making by personalized services; from high-performance computing and visualization performance create a parallel system and carried out the calculation experiments. With the satellite remote sensing images and real photographs of composite applications, from multi-image information and the development changes of the overall in the value to mining the value of information; Mainly to achieve "human-computer connections" in the integrated service platform to provide information, knowledge, decision-making services. By the platform, components, themes, knowledge mapping, visualization tools to form a new model:Application by the platform support; By the components, the subject and knowledge mapping organized the application rapidly; The showed by multi-information visualization. In the Personalized custom applications and related industries standard-setting, Play the role of industry-oriented, gradually extend the application of new model. This paper's main results are as follows:
(1) Using knowledge mapping to achieve the expression of knowledge visualization, and knowledge mapping book products.
①Take the knowledge acquisition and expression based the process as a means, to establishment the knowledge mapping of water information and knowledge, Using knowledge mapping to link the information, organize the information in the application process, describe the events and the application of the theme.
②Research the human-machine mechanism of supported by the knowledge mapping. From the information perception, integration point of view, use of empirical methods and lessons learned, research the application of water information using expert knowledge and process knowledge mapping, achieve knowledge sharing and transfer mechanisms, laws, and to improve the efficiency of knowledge transfer channels.
③Research the mechanism of groups wisdom on knowledge mapping indorsation. Use the empirical, based on the knowledge mapping; research the individual knowledge into collective wisdom's mechanism, laws, and support groups, creative, guide groups of experts to conduct in-depth analysis and demonstration. Through the groups of experts knowledge transfer, form a "group memory", to promote the emergence of collective wisdom.
(2) Adapt to the water information business applications, in accordance with the components development standards, development of presentation layer and business layer components. Expand the traditional model's reliance on the information, development of new models, and gradually component. Continuously enriched, completed the application component library. Use the component library (have a certain foundation), to resolve structural applications, knowledge resources sharing, standardize the application of the process components and services, For rapid integration and the formation of different applications to create a comprehensive integrated platform combining human-computer foundation, combined with a platform to promote the new model, and gradually build a support group of experts discuss the "knowledge market".
(3) Use the middleware, grid, meta-synthetic engineering office technologies to build multi-service platform system. Using the platform to provide data, information, and knowledge integration; Platform provides three services:In accordance with the theme provide the information services, the need to provide computing services and the personalized service organizations provide the decision-making; Established an open growth knowledge system With the platform,it can make the system convenient service closest practical, long-term vitality; In the platform, store experience with knowledge mapping and extend the application with case-based reasoning; Through the Knowledge integration and evaluation, excavation good decision-making knowledge, from qualitative to quantitative, summed up and refined the law to delivery services better.
(4) For the concrete application themes, use the model of platform supported, develop individualized applications. Using flood forecasting、reservoir dispatching and emergency management which are based on platform as examples, use a series of knowledge graphs to express the themes, organically combined the knowledge graphs、platform and application, test the efficiency and practicality of information、knowledge and decision service.
(5) With the continuous enrichment of business application components library (componentization of the process and solution for solving the problem)、theme service standards library(form application themes driven by events)、application knowledge graphs library(the process or method for solving the problem、information fusion、formation of knowledge being graphical). Data center became themes service center facing service, thus propose practical data center construction scheme. Now, multi-distributed data resource was distributed stored and relatively abstract, provide data individually in the application, it is no semantic and hard to understand. Only change the data to be information by adding semantic can improve the application efficiency and value. So there is great practical significance to design the available and operable data center.
(6) Discuss the formation of information integration from themes to knowledge graphs, compose a new application model by platform、component、theme、knowledge graphs and Visualization tools. The platform can support the application; using the component、themes and knowledge graphs can organize application fast; using the multivariate information visualization can display application more intuitively; take the multivariate information combination、display the decision process by knowledge、using platform to support services、convenient organize application as the recent application model, and popularize it continuous.
(7) The analysis and application of MODIS remote sensing informations based on platform. Under the support of remote sensing technique, prove the utilization rate of multivariate information, Take the information combination and the application of MODIS remote sensing informations as the emphasis, excavate information value by the variation of multivariate information and Overall image, could combine the point type information、linear information and Surface information by integrating the MODIS information, realize the trinity flood forecasting.
(8) Combining the grid technique with visualization technique, to create artificial parallel systems for water Information application. Under the high-performance calculation and visualization, support calculating services from the active and passive aspects, and carry on computational experiments. Analyze and demonstrate the flood dispatching.
(9) Development of the water resources business application service center faced to service. Realize the integration of data and business by components, realize application integration by combining the knowledge graphs and services, realize meta-synthesis by platform, realize the water resources business application integrated service system by water resources application center.
引文
【1】 吴季松.现代水资源管理概论[M].中国水利水电出版社,2002：54-55.
【2】 刘树坤.国外防洪减灾发展趋势分析[J].水利水电科技进展,2000,20(1)：2-9.
【3】 郭生练,戴自述,宋星原,等.水文水资源决策支持系统研究的新进展[J].水电能源科学,1996,14(4)：229-233.
【4】 唐锡晋,顾基发.水资源管理决策支持系统的发展动态及特点[J].决策与决策支持系统,1994,4(4)：12-22.
【5】 D. Han, I. D. Cluckie, D. Karbassioun, et al. River Flow Modeling Using Fuzzy Decision Trees [J]. Water Resources Management,2002,16(10):431-445.
【6】 V. Chandramouli. Paresh Deka. Neural Network Based Decision Support Model for Optimal Reservoir Operation [J]. Water Resources Management,2005,19(11):447-464.
【7】 解建仓.水资源调度管理决策支持系统的理论与实践[M].陕西科学技术出版社,1997.
【8】 D. Nagesh Kumar, Falguni Baliarsingh. Folded Dynamic Programming for Optimal Operation of Multireservoir System [J]. Water Resources Management,2003,17(9):337-353.
【9】 D. Nagesh Kumar, K. Srinivasa Raju, T. Sathish. River Flow Forecasting using Recurrent Neural Networks [J]. Water Resources Management,2004,18(10):143-161.
【10】 V. Durga Rao, D. Satish Kumar. Spatial Decision Support System for Watershed Management [J]. Water Resources Management,2004,18(10):407-423.
【11】 Scholten, H., R. H. Van Waveren, S. Groot, F. VanGeer, et al. Improving the quality of model based decision support:Good Modelling Practice in water management [J]. Proceedings of Symposium S2 of the 6th Scientific Assembly of the IAHS, Maastricht, Netherlands,2001:223-230.
【12】 Kassahun, A., H. Scholten, G. Zompanakis, C.Gavardinas. Support for model based water management with the HarmoniQuA toolbox [J]. Proceedings iEMSs,2004.
【13】 Bruggemann, R, Drescher-Kaden, U. Introduction into Model-supported Evaluation of Environmental Chemicals (Einfuhrung in die modellgestutzte Bewertung von Umweltchemikalien), Springer, Berlin,2003:1-513.
【14】 Bruggemann, R, Drescher-Kaden, U. Introduction into Model-supported Evaluation of Environmental Chemicals (Einfuhrung in die modellgestutzte Bewertung von Umweltchemikalien), pp.1-513, Springer, Berlin,2003.
【15】张建云,水利信息化的发展思路和建设任务,中国水利,(9),2000：81
【16】 Liu ShuZhou, Angela Goh Eck Soong. A Formal Framework To Support Workflow Adaption. International Journal of Software Engineering and Knowledge Engineering, Vol.12 (3),2002: 245-267.
【17】 Jaroslav Mysiaka, Carlo Giupponib, Paolo Rosato. Towards the development of a decision support system for water resource management, Environmental Modelling & Software, Vol 20, pp.203-214, 2005.6.
【18】戴汝为,沙飞.复杂性问题研究综述：概念及研究方法[J].自然杂志,1995,17(2)：73-78.
【19】钱学敏.钱学森关于复杂系统与大成智慧的理论.西安交通大学学报(社会科学版),Vol.24, pp.51-57,2004年12月.
【20】钟荣丙.论系统科学近期发展史上的三颗灿烂明珠.系统辩证学学报,Vol 13, No. 1,pp.26-29,2005年1月.
【21】 Shima J P, et al. Past, present, and future of decision support technology [J]. Decision Support Systems,2002,33(2):111-126.
【22】李耀东,崔霞,戴汝为.综合集成研讨厅的理论框架、设计与实现.复杂系统与复杂性科学,Vol.1 No.1, pp.27-32,2004年1月
【23】戴汝为.支持科学决策和咨询的技术—思维系统工程[J].中国工程科学,2005,7(1)：17-20.
【24】司光亚,胡晓峰.战略决策模拟环境中XOD综合集成机制的研究与实现[J].小型微型计算机系统,2002,23(2)：242—245
【25】 Wallas G. The Art of Thought[M]. New York:Harcoxt Brace,1926:80 — 82.
【26】 Smoliar S W. Book review of creative cognition[J]. Artificial Intelligence,1995,79(1):183 — 196.
【27】 Boden M A. Modelling creativity:reply to reviewers[J]. Artificial Intelligence,1995,79(1):161 182.
【28】 ugiyama K, Nagata A, Shimojima A. Knowledge Science[M]. Kinokuniya Company Ltd, 2002:31-33.
【29】 http://catcode.com/ideafish.html
【30】 http://www.triz-journal.com/content/what_is_triz.asp
【31】 Mase K, Sumi Y, Nishimoto K. Informal conversation environment for collaborative concept formation[A]. Ishida T. Community Computing:Collaboration over Global Information Networks[C]. New York:John Wiley Sons, Inc,1998:165—205.
【32】 Aihara K, Hon K. Enhancing creativity through reorganizing mental space concealed in a research notes stack[J]. Knowledge-Based System.1998,11 (7-8):469-478.
【33】唐锡晋,刘怡君.从群体支持系统到创造力支持系统[J].系统工程理论与实践,2006,5：63—71.
【34】张兴学,张朋柱.群体决策研讨意见分布可视化研究—电子公共大脑视听室(ECBAR)的设计与实现[J].管理科学学报,2005,8(4)：15—25.
【35】戴汝为.从工程控制论到综合集成研讨厅体系—纪念钱学森归国50周年[J].复杂系统与复杂性科学,2006,3(2)：86-91.
【36】齐勇,马莉,赵季中,等.基于分布式构件技术的多层模式应用框架研究与实现[J].计算机工程,2001,27(1)：122-124.
【37】 Luis Iribame, J M Troya, Antonio Vallecillo. Trading for COTS components in Open
Environments[C]. In 14th International conference on Distributed Computing Systems, IEEE Computer Society Press,1998.
【38】左爱群,黄水松.基于组件的软件开发方法研究[J].计算机应用,1998,18(11)：4-7.
【39】王环,张建沛,张著.软件工程的新发展CBSE[J].应用科技,2002,29(6)：48-50.
【40】潘爱民.COM原理与应用[M].北京：清华大学出版社,1999.
【41】刘泽.面向业务领域框架的专用组件库的研究与实践[D].上海：复旦大学,2008：5-5.
【42】 C Szyperski. Component Software-Beyond Oriented-Object Programming [M]. New Jersey: Addison-Wesley Press,1997.
【43】 Kroeker K. Component Technology [J]. IEEE Computer,1998, (31):132-133.
【44]张世现,张文娟等.基于软件体系结构的可复用构件制作和组装[J].软件学报,2001,12(9)：1351-1359.
【45]战德臣,王忠杰.一种基于构件的复杂应用系统开发过程[J].哈尔滨工业大学学报,2002,34(6)：751-755.
【46】黄平.基于构件的软件开发方法在金融中间业务系统中的应用[J].浙江理工大学学报,2007,24(4)：453-456.
【47】 A.Brown. Large-Scale Component-Based DeveloPment. NewJersey:Prentice Hall, Inc.,2000.
【48】张驰.异构组件互操作技术研究[D].西安：西北工业大学,2006：2-5.
【49】陈博,许满武.基于Web的计算模型——Web Service[J].人文地理,2003(1).
【50】岳小婷,李森.深入理解WEB SERVICE[J].新疆师范大学学报(自然科学版),2003,22(1).
【51】 Stanislaw Ambroszkiewicz. A Simple Language for Web Service Description and Composition[C]. Proceedings of the IFIP TC6/WG6.4 Workshop on Internet Technologies, Applications and Social Impact,2002,232:289-306.
【52】 Werner Vogels. Web Services Are Not Distributed Objects[J]. IEEE Internet Computing,2003,7(6): 59-66.
【53】 Castro-Leon E. A perspective on Web Services [EB/OL]. http://www.webservices.org/index.php/a-rticle/113/1/61/,2004.
【54】杨涛,刘锦德.Web Services技术综述——一种面向服务的分布式计算模式[J].计算机应用,2004,24(8)：1-4.
【55】廖军,谭浩.新一代开放分布式处理技术——Web Services[J].计算机应用,2004,24(8)：5-9.
【56】 Tsalgatidou A, Pilioura T. An Overview'of Standards and Related Technology in Web Services[J]. Distributed and Parallel Databases,2002,12(23):135-162.
【57】 Tsur S, Abiteboul S, Agrawal R, et al. Are web service the next revolution in e-commerce[A]. In: Proceedings of the VLDB conference, Rome, September 2001,614-617.
【58】 Papazoglou M P. Service-oriented computing:concepts, characteristics and directions [A]. In: Proceedings of Fourth International Conference on Web Information Systems Engineering[C]. Los Alamitos, CA, USA:IEEE Computer Society Press,2003,3-12.
【59】 W3C Working Group. Web services architecture [DB/OL]. http://www.w3.org/tr/ws-arch/,2003.
【60】 Foster I. Service-oriented science [J]. Science,2005,308(5723):814-817.
【61】 The globus alliance. Globus toolkits 3.0 documentation 【DB/OL]. http://www-unix.globus.org/toolkitldocs/3.0/index.html,2004.
【62】毛新生.SOAP原理·方法·实践[M].北京：电子工业出版社,2007：112-121.
【63】 Xiaojie Dong, Weiqin Tong, Jin Feng. Using Categorization to Further Enhance the Utilization of Semantic Web in UDDI[J]. Computer and Computational Sciences,2006, (4):475-479.
【64】 E.Christensen, et al. Web Service Deseription Lauguage(WSDL)[EB/OL].http://www.w3.org/TR/2001/NOTE-wsdl-20010315,2001.
【65】梁越杰.区域性UDDI注册中心的设计与实现[D].浙江：浙江大学,2005.
【66】饶元,冯博琴,李尊朝.基于Web Services的服务合成技术研究综述[J].系统工程与电子技术,2005,27(8)：1481-1489.
【67】 Schahram Dustdar, wolfgang Schreiner. A survey on web services composition[J]. International Journal of Web and Grid Services,2005, (1):1-30.
【68】 Biplav Srivastava, Jana Koehler. Web Service Composition-Current Solutions and Open Problems[J]. ICAPS Workshop on Planning for Web Services,2003,28-35.
【69】 Patrick Gannon,王云,刘松,李安渝.SOA创新技术创新应用[A].IT技术创新论坛[C].数码科技广场主会场,2006,8.
【70】崔晓波.SOA概览[DB/OL]. http://articles.e-works.net.cn/471/Article35660.htm.
【71】杜彦斌.面向服务软件体系架构原理与范例研究[D].北京：首都经济贸易大学,2005.
【72】刘国静.基于SOA架构的企业应用研究[D].上海：华东师范大学,2006.
【73】万亮.基于SOA与EDA的综合架构应用研究[D].武汉：武汉理工大学,2006.
【74】雒文生,宋星原.洪水预报与调度[M].武汉：湖北科学技术出版社,1999.
【75】袁晓辉,张双全,张勇传,等.非线性马斯京根模型参数率定的新方法[J].水利学报,2001,32(5)：77-81.
【76】赵人俊.流域水文模拟——新安江模型与陕北模型[M].北京：水利电力出版社,1984.
【77】袁作新.流域水文模型[M].北京：水利电力出版社,1988.
【78】林三益.水文预报[M].北京：中国水利水电出版社,1986.
【79】包为民.水文预报[M].北京：中国水利水电出版社,2006.
【80】SL250—2000.水文情报预报规范[S].北京：中国水利水电出版社,2000.
【81】汪妮.基于中间件及智能Agent技术的防汛决策模式研究[D].西安：西安理工大学,2004.
【82】 De Castro LN, Von Zuben FJ. The Clonal Selection Algorithm with Engineering Applications[C]. In: Proceedings of 2000 GECCO, Workshop on Artificial Immune Systems and Their Applications. IEEE press,2000,36-37.
【83】 Kim J, Bentley PJ. Towards an Artificial Immune System for Network Intrusion Detection:An Investigation of Clonal Selection with a Negative Selection Operator[C]. In:Proceedings of 2001 Congress on Evolutionary Computation. IEEE press,2001,1244-1252.
【84】 Cutello, V., Nicosia, G., Pavone, M. Exploring the Capability of Immune Algorithms:A Characterization of Hypemutation Operators[C]. In Proceedings of Third International Conference on Artificial Immune Systems, ICARIS.2004,263-276.
【85】 Beyer HG, Schwefel HP. Evolution Strategies-A Comprehensive Introduction [J]. Natural Computing, 2002,1(2):3-52.
【86】长江流域规划办公室.水文预报方法[M].北京：水利出版社,1982,35-43.
【87】扬州水利学校.水文预报[M].北京：水利电力出版社,1979,34-35.
【88】翟国静.马斯京根模型参数估计方法探讨[J].水文,1977,(3)：40-43.
【89】杨晓华,金菊良,陈肇生,等.马斯京根模型参数估计的新方法[J].灾害学,1998,13(3)：1-6.
【90】杨晓华,郦建强.混沌高效遗传算法在马斯京根模型参数优选中的应用[J].水力发电学报,2008,27(2)：40-43.
【91】鲁帆,蒋云钟,王浩,等.多智能体遗传算法用于马斯京根模型参数估计[J].水利学报,2007,38(3)：289-294.
【92】 Chen J J, Yang X H. Optimal parameter estimation for Muskingum model based on Gray-encoded accelerating genetic algorithm [J]. Communications in Nonlinear Science and Numerical Simulation, 2007, (12):849-858.
【93】程银才,李明华,范世香.非线性马斯京根模型参数优化的混沌模拟退火法[J].水电能源科学,2007,25(1)：30-33.
【94】詹士昌,徐婕.蚁群算法在马斯京根模型参数估计中的应用[J].自然灾害学报,2005,14(5)：20-24.
【95】詹士昌.马斯京根洪水演算模型的改进—兼论其参数的蚁群算法率定[J].自然灾害学报,2006,15(2)：32-37.
【96】 Wilson E M. Engineering hydrology. Hampshire:MacMillan Education Ltd; 1974.
【97】 Gill M A. Flood routing by the Muskingum method. J Hydrol 1978; 36:353-363.
【98】 Tung Y K. River flood routing by nonlinear Muskingum method. J Hydraul Eng 1985; 111(12):1447-1460.
【99】 Yoon J, Padmanabhan G. Parameter estimation of linear and nonlinear Muskingum models. J Water Resour Plann Manage 1993; 119(5):600-610.
【100】 Mohan S. Parameter estimation of nonlinear Muskingum models using genetic algorithm. J Hydraul Eng 1997; 123(2):137-142.
【101】 Kim J H, Geem Z W, Kim E S. Parameter estimation of the nonlinear Muskingum model using harmony search. J Am Water Resour Assoc 2001; 37(5):1131-1138.
【102】 Das A. Parameter estimation for Muskingum models. J Irrig Drain Eng 2004; 130(2):140-147.
【103】 Zong W G. Parameter Estimation for the Nonlinear Muskingum Model Using the BFGS Technique. J Irrig Drain Eng 2006; 132(5):140-147.
【104】 Ivar Jacobson, Martin Griss, Patrik Jonsson.韩柯译.Software Reuse:Arehiteeture, Proeess and Orgnization of Business Sueeess[M].北京：机械工业出版社,2003.
【105】郑鹭亮.可复用组件库的研究设计[J].电脑知识与技术,2007,4(23)：1215-1223.
【106】杨芙清,王千祥,梅宏,等.基于复用的软件生产技术[J].中国科学E(辑),2001,31(4)：363-371.
【107】郭军.产品线架构中支持软件复用的若干关键技术的研究[D].沈阳：东北大学,2005.
【108】常继传,李克勤.青鸟系统中可复用软件组件的表示与查询[J].电子学报,2000,28(8)：20-23.
【109】张文祥,王新辉.分布式体系下组件库检索系统的设计与实现[J].计算机工程与设计,2006,27(10)：1879-1882.
【110】郑媖,陈昊,李伟斋,等.基于Web服务技术组件库的构建和实现[J].武汉理工大学学报,2004,26(2)：80-83.
【111】 Prieto Diaz R, Freeman P. Classifying Software for Reusability[M]. IEEE Computer Society Press,1987,4(1):6-16.
【112】 Mili A, Mili F. Reusing Software Issues and Research Direction[M]. IEEE Transaction on Software Engineering,1995,21 (6):56-58.
【113】 Prieto Diaz R, Swanson M. Parallel Development:A Case Study in Technology Transfer[A]. In: Przybylinski S, FowlerJ P. Proceedings of the Workshop on Transferring Software Engineering Tool Technology[C]. Calif:Santa Barbara,1987.112-113.
【114】 Apache JUDDI[EB/OL].http://ws.apache.org/juddi/releases.html.
【115】白扬.企业知识管理理论初探[J].情报科学,2000,(6)：34-38.
【116】 Bolloju, Khalifa, Turban. Integrating knowledge management into enterprise environments for the next generation decision support [J]. Decision Support System,2002(33):163-176.
【117】胡泊.现代企业知识管理系统设计研究[D].合肥：合肥工业大学,2006.
【118】王永庆.人工智能原理与方法[M].西安：西安交通大学出版社,1998.
【119】 EpplerM J, Burkard R A. Knowledge Visualization:Towards a New Discipline and its Fields ofApplication[R]. ICA Working Paper, University of Lugano,2004.
【120】赵国庆.黄荣怀,陆志坚,知识可视化的理论与方法[J].开放教育研究,2005,11(1)：23-27.
【121】龚建华,李亚斌,王道军,等.地理知识可视化中知识图特征与应用[J].遥感学报,2008,12(2)：355-361.
【122】 Eppler, M.J.& Burkard, R.A. Knowledge Visualization:Towards a New Discipline and its Fields of Application[R].ICA Working Paper, University of Lugano.Lugano.2004.
【123】 Chen Lina. Knowledge Mapping in Knowledge Management at Enterprises. LIBRARY AND INFORMATION SERVICE. NO.8,2003.58-78
【124】史忠植,蒋运承,张海俊,董明楷.基于描述逻辑的主体服务匹配.计算机学报.2004年5月.第27卷第5期.625-635
【125】 Martin J.E. (2002). Handbook on Knowledge Management 1, Springer Verlag.189～300.
【126】 Logan, D., Caldwell, F., "Knowledge Mapping:Five Key Dimensions to Consider, " GartnerGroup,2000.
【127】 Debra M. Amidon. Innovation Strategy for the Knowledge Economy. The Ken Awakening. Butterworth-Heinemann.1997.
【128】 Rita Kuo. Maiga Chang. Da-Xian Dong and Jia-Sheng Heh. "Applying Knowledge Map to Intelligent Agents in Problem Solving Systems, " World Conference on Educational Multimedia, Hypermedia & Telecommunications, Denver, Colorado, USA, Jun.24～29,2002.
【129】 Thomas R Gruber. Ontolingua:A Translation Approach to Potable Ontology Specification [J]. Knowledge Acquisition,1993,5(2):199～200.
【130】李亚芳,李霞.思维导图在学习者隐性知识管理中的应用[J].科技情报开发与经济,2008,18(34)：128-130.
【131】 De Mantaras, R L Plaza E. Case-based reasoning:an overview [J]. IEEE AI Commune,1997, 10(2):21-29.
【132】 Waston I, Marir F. Case-based reasoning:a review [J]. The Knowledge Engineering Review,1994, 9(4):355-381.
【133】 Cunningham P, Smyth B. Case-based reasoning in scheduling:reusing solution components [J].1996,35(11):2947-2960.
【134】史忠植.知识发现[M].北京：清华大学出版社,2002.
【135】胡良明,徐诚,方峻.CBR技术在枪械方案设计专家系统中的应用[J].系统仿真学报,2007,19(4)：772-775.
【136】 Stephen Slade. Case-based reasoning:a research paradigm [J]. AI Magazine,1991,31(1):42-55.
【137】 Deng P S. Using case-based reasoning approach to the support of ill-structured decisions [J]. European Journal of Operational Research,1996,93(3):511-521.
【138】常春光,汪定伟,胡琨元,等.基于两级CBR的钢铁企业动态调度系统研究[J].控制与决策,2004,19(10)：1109-1116.
【139】 Aamodt A, Plaza E. Case-based reasoning:foundational issues, methodological variations, and system approaches [J]. AI Communications,1994,7(1):39-59.
【140】王本德,于义彬,刘金禄,等.水库洪水调度系统的模糊循环迭代模型及应用[J].水科学进展,2004,15(2)：233-237.
【141】厨惠成,张改红,王国利.基于熵权的水库防洪调度多目标决策方法及应用[J].水利学报, 2007,38(1)：100-106.
【142】侯召成,陈守煜.水库防洪调度多目标模糊群决策方法[J].水利学报,2004,35(12)：106-119.
【143】郭生练,彭辉,王金星,等.水库洪水调度系统设计与开发[J].水文,2001,21(3)：4-7.
【144】王义民,黄强,畅建霞,等.安康水库洪水实时调度系统研究[J].水力发电学报,200],(4)：18.25.
【145】田峰巍,解建仓,颜竹丘.梯级水电站群的规划与调度[M].北京：科学技术文献出版社,1992.
【146】王义民,黄强,畅建霞,等.基于面向对象技术的水库洪水调度仿真模型研究[J].水力发电学报,2003,(4)：8-14.
【147】罗军刚,解建仓,汪妮.基于CBR的水库洪水调度模式[J].系统工程理论与实践,2008,28(9)：122-130.
【148】罗军刚,解建仓,陈田庆,汪妮.基于事例推理技术的水库洪水调度研究与应用[J].水科学进展,2009,20(1)：32-39.
【149】 Finnie G, Sun Z. R5 model for case-based reasoning [J]. Knowledge-Based Systems,2003, (16): 59-65.
【150】汤文宇,李玲娟.CBR方法中的案例表示和案例库的构造[J].西安邮电学院学报,2006,11(5)：75.78.
【151】 Smyth B, Keane M T, Cunningham P D. Hierarchical case-based reasoning integrating case-based and decompositional program-solving techniques for plant-control software design [J]. IEEE Transaction Knowledge and Data Engineering,2001,13(5):793-812.
【152】蒋亚军,娄臻亮.规则推理与事例推理在模具加工刀具智能化选择中的应用[J].上海交通大学学报,2005,39(7)：1066-1069.
【153】郭艳红,邓贵仕.基于事例的推理(CBR)研究综述[J].计算机工程与应用,2004,40(21)：1-5.
【154】张光前,邓贵仕,李朝晖.基于事例推理的技术及其应用前景[J].计算机工程与应用,2002,38(20)：52-55.
【155】王君,潘星,李静,等.基于案例推理的知识管理咨询系统[J].清华大学学报(自然科学版),2006,46(S1)：990-995.
【156】 Shin K S, Han I G. Case-based reasoning supported by genetic algorithms for corporate bond rating [J]. Expert Systems with Application,1999,16(2):85-95.
【157】黄继鸿,雷战波,李欣苗.基于遗传禁忌算法的案例检索策略[J].系统工程理论方法应用,2004,13(1)：10-13.
【158】黄继鸿,姚武,雷战波.基于案例推理的企业财务危机智能预警支持系统研究[J].系统工程理论与实践,2003,23(12)：46-52.
【159】 Chiu C. A case-based customer classification approach for direct marketing.[J]. Expert Systems with Applications,2002,22(2):163-168.
【160】王玉,邢渊,阮雪榆.基于事例的推理循环中人工神经网络和遗传算法的4种应用模型[J].上海交通大学学报,2003,37(2)：202-204.
【161】周惠成,张改红,王国利.基于熵权的水库防洪调度多目标决策方法及应用[J].水利学报,2007,38(1)：100-106.
【162】陈守煜.防洪调度多目标决策理论与模型[J].中国工程科学,2000,2(2)：47-52.
【163】赵卫东,李旗号,盛昭瀚.基于案例推理的决策问题求解研究[J].管理科学学报,2000,3(4)：29-36.
【164】常春光,崔建江,汪定伟,等.案例推理中案例调整技术的研究[J].系统仿真学报,2004,16(6)：1260-1265.
【165】 Chen S M, Tan J M. Handling multicriteria fuzzy decision-making problems based on vague set theory[J]. Fuzzy Sets and Systems,1994,67(2):163-172.
【166】 Maeda H, Murakami S. A fuzzy decision-making method and its application to a company choice problem[J]. Information Sciences,1998, (45):331-346.
【167】 Gau W L, Buehrer D J. Vague sets[J]. IEEE Transaction on Systems Man Cybernetics,1993, 23(2):610-614.
【168】 Zadeh L A, Fuzzy sets[J]. Information and Control,1965,8(3):338-356.
【169】 Atanassov K T. Intuitionistic fuzzy sets[J]. Fuzzy Sets and Systems,1986,20(1):87-96.
【170】 Hong D H, Choi C H. Multi-criteria fuzzy decision-making problems based on vague set theory[J]. Fuzzy Sets and Systems,2000,114(1):103-113.
【171】李凡,卢安,蔡立晶.基于Vague集的多目标模糊决策方法[J].华中科技大学学报,2001,29(7)：1-3.
【172】李凡,饶勇.基于Vague集的加权多目标模糊决策方法[J].计算机科学,2001,28(7)：60-65.
【173】刘华文.多目标模糊决策的Vague集方法[J].系统工程理论与实践,2004,24(5)：103-109.
【174】陈月映,张新波.基于三角模糊数Vague集的多目标决策方案[J].浙江工商大学学报,2007,85(4)：20-23.
【175】周珍,吴祈宗,刘福祥.基于区间值Vague集的多准则模糊决策方法[J].北京理工大学学报,2005,25(11)：1019-1023.
【176】王珏,刘三阳,张杰.基于Vague集的模糊多目标决策方法[J].系统工程理论与实践,2005,25(2)：119-122.
【177】黄松,黄卫来.基于熵权系数与Vague集的多目标决策方法[J].管理学报,2005,2(Sup)：120-123.
【178】程乾生.属性数学—属性测度和属性统计[J].数学的实践与认识,1998,28(2)：97-107.
【179】程乾生.质量评价的属性数学模型和模糊数学模型[J].数理统计与管理,1997,16(6)：18-23.
【180】罗军刚,解建仓.基于Vague集的模糊多目标决策方法及应用[J].数学的实践与认识,2008,
38(20)：114-122.
【181】罗晓沛,伯乐,昭原.数据库技术新进展[M].北京：清华大学出版社,1997.
【182】周竞涛.企业异构数据源集成[DB/OL]计算机世界,http://www.ccw.com.cn/htm/center/tech/02_8_30_2.asp,2002,8.
【183】张维明.信息系统集成技术[M].北京：电子工业出版社,2002.
【184】陈楠.多源空间数据集成的技术难点分析和解决策略.计算机应用研究[J],2005(10)：206-208.
【185】刘玉洁,杨忠东.MODIS遥感信息处理原理与算法[M].北京：科学出版社,2001：13-45.
【186】彭定志,熊立华,郭生练.MODIS在水文水资源中的应用与展望[J].水利学进展.2004,15(5)：683～688.
【187】蒋耿明,牛铮,阮伟利等.MODIS影像合成算法研究和实现[J].国土资源遥感,2004,(2)：11-14.
【188】 Jiang G M, Liu R G, Niew Z et al. An optimal geometric correction method for MODIS 1B data Collection and its software development[J]. Journal of Remote Sensing.2004,8(2):158-164.
【189】蒋艳凰,杨学军,易会战.卫星遥感图像并行几何校正算法研究[J].计算机学报,2004,27(7)：944-951.
【190】蒋耿明,牛铮,阮伟利等.MODIS影像条带噪声去除方法研究[J].遥感技术与应用,2003,18(6)：393-398.
【191】 Steven Platnick, Michael D, King Steven A. The MODIS cloud products algorithms and examples from Terra[J]. IEEE Transactions on Geoscience and Remote Sensing.2003,41(2):459～473.
【192】 Kerry Meyer, Yang Ping, Gao Bo-cai. Optical thickness of tropical circus clouds derived from the MODIS 0.66-and 1.375-channels[J]. IEEE Transactions on Geoscience and Remote Sensing.2004,42(4):833～841.
【193】齐述华,李贵才,王长耀,等.利用MODIS数据产品进行全国干旱监测的研究[J].水科学进展,2005,16(1)：56-61.
【194】刘良明,梁益同,马慧石.MODIS和AVHRR植被指数关系的研究[J].武汉大学学报(信息科学版),2004,29(4)：307-310.
【195】张立福,张良培,村松加奈子.利用MODIS数据计算陆地植被指数VIUPD[J].武汉大学学报,2005,30(8)：699-702.
【196】仝兆远,张万昌.基于MODIS数据的渭河流域土壤水分反演[J].遥感应用,2008,(1)：66-73.
【197】周艺,周伟奇等.遥感技术在内陆水体水质监测中的作用[J].水科学进展,2004,15(3)：312-317.
【198】鄢俊洁,刘良明,马浩录,等.MODIS数据在黄河凌汛监测中的应用[J].武汉大学学报(信息科学版),2004,29(8)：679-681.
【199】骆成凤.中国土地覆盖分类与变化监测遥感研究[D].北京：中国科学院遥感应用研究所,2005.
【200】刘勇洪,牛铮.基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究[J].遥 感技术与应用,2004,19(4)：217-224.
【201】王伟民,孙晓敏,张仁华.地物反射光谱对MODIS近红外通道水汽反演影像的模拟分析[J].遥感学报,2005,9(1)：8-15.
【202】刘良明,张红梅.基于MODIS数据的悬浮泥沙定量遥感方法[J].国土资源遥感,2006,2(6)：42-45.
【203】曾严,鄢俊洁.MODIS数据在积雪监测的中的应用[J].测绘与空间地理信息.2005,28(6)：97-99.
【204】李民,刘志辉,房世峰.基于MODIS数据的雪水当量监测模型研究[J].水土保持研究,2007,14(4)：74-81.
【205】尚建翠,吉久明,房宝金.知识集成理论发展及其应用研究[J].高校图书情报论坛,2007,6(3)：53-57.
【206】于景元,周晓纪.从定性到定量综合集成方法的实现和应用[J].系统工程理论与实践,2002,1(10)：26-32.
【207】于景元,周晓纪.综合集成方法与总体设计部[J].复杂系统与复杂性科学,2004,1(1)：20-26.
【208】王寿云,于景元等.开放复杂的巨系统[M].杭州：浙江科技出版社,1996.
【209】操龙兵,戴汝为.开放复杂智能系统[M].北京：人民邮电出版社,2008.
【210】张建云,水利信息化的发展思路和建设任务,中国水利,2000,(9)：81-84
【211】 H.H.Dewbler,etc., Introducing Object Orientation into Large and Complex Systems. IEEE Trans.Software Eng.,1994,20 (11):840-848.
【212】 Liu ShuZhou, Angela Goh Eck Soong. A Formal Framework To Support Workflow Adaption. International Journal of Software Engineering and Knowledge Engineering, Vol.12 (3),2002: 245-267.
【213】王飞跃.人工生命与自然生命—杂谈观念的演变与变化[J].科学中国人,2003,(8)：33.
【214】王飞跃.人工社会、计算实验、平行系统——关于复杂社会经济系统计算研究的讨论[J].复杂系统与复杂性科学,2004,1(4)：25-35.
【215】王飞跃,史帝夫·兰森.从人工生命到人工社会——复杂社会系统研究的现状和展望[J].复杂系统与复杂性科学,2004,1(1)：33-41.
【216】臧垒,蒋晓原,王钰,等.基于计算实验的探索性分析框架研究[J].系统仿真学报,2008,20(12)：3077-3081.
【217】王飞跃.计算实验方法与复杂系统行为分析和决策评估[J].系统仿真学报,2004,16(5)：893-897.
【218】李涛,杨秀月,郭齐胜.基于探索性计算实验的信息化武器装备体系优化方法[J].装甲兵工程学院学报,2008,22(1)：1-5.
【219】王飞跃.平行系统方法与复杂系统的管理和控制[J].控制与决策,2004,19(5)：485-489.
【220】罗军刚,解建仓,刘福朝.基于Web的电网线路和变电站预案管理系统.电网技术,2008, 32(1).93-96.
【2】 刘树坤.国外防洪减灾发展趋势分析[J].水利水电科技进展,2000,20(1)：2-9.
【3】 郭生练,戴自述,宋星原,等.水文水资源决策支持系统研究的新进展[J].水电能源科学,1996,14(4)：229-233.
【4】 唐锡晋,顾基发.水资源管理决策支持系统的发展动态及特点[J].决策与决策支持系统,1994,4(4)：12-22.
【5】 D. Han, I. D. Cluckie, D. Karbassioun, et al. River Flow Modeling Using Fuzzy Decision Trees [J]. Water Resources Management,2002,16(10):431-445.
【6】 V. Chandramouli. Paresh Deka. Neural Network Based Decision Support Model for Optimal Reservoir Operation [J]. Water Resources Management,2005,19(11):447-464.
【7】 解建仓.水资源调度管理决策支持系统的理论与实践[M].陕西科学技术出版社,1997.
【8】 D. Nagesh Kumar, Falguni Baliarsingh. Folded Dynamic Programming for Optimal Operation of Multireservoir System [J]. Water Resources Management,2003,17(9):337-353.
【9】 D. Nagesh Kumar, K. Srinivasa Raju, T. Sathish. River Flow Forecasting using Recurrent Neural Networks [J]. Water Resources Management,2004,18(10):143-161.
【10】 V. Durga Rao, D. Satish Kumar. Spatial Decision Support System for Watershed Management [J]. Water Resources Management,2004,18(10):407-423.
【11】 Scholten, H., R. H. Van Waveren, S. Groot, F. VanGeer, et al. Improving the quality of model based decision support:Good Modelling Practice in water management [J]. Proceedings of Symposium S2 of the 6th Scientific Assembly of the IAHS, Maastricht, Netherlands,2001:223-230.
【12】 Kassahun, A., H. Scholten, G. Zompanakis, C.Gavardinas. Support for model based water management with the HarmoniQuA toolbox [J]. Proceedings iEMSs,2004.
【13】 Bruggemann, R, Drescher-Kaden, U. Introduction into Model-supported Evaluation of Environmental Chemicals (Einfuhrung in die modellgestutzte Bewertung von Umweltchemikalien), Springer, Berlin,2003:1-513.
【14】 Bruggemann, R, Drescher-Kaden, U. Introduction into Model-supported Evaluation of Environmental Chemicals (Einfuhrung in die modellgestutzte Bewertung von Umweltchemikalien), pp.1-513, Springer, Berlin,2003.
【15】张建云,水利信息化的发展思路和建设任务,中国水利,(9),2000：81
【16】 Liu ShuZhou, Angela Goh Eck Soong. A Formal Framework To Support Workflow Adaption. International Journal of Software Engineering and Knowledge Engineering, Vol.12 (3),2002: 245-267.
【17】 Jaroslav Mysiaka, Carlo Giupponib, Paolo Rosato. Towards the development of a decision support system for water resource management, Environmental Modelling & Software, Vol 20, pp.203-214, 2005.6.
【18】戴汝为,沙飞.复杂性问题研究综述：概念及研究方法[J].自然杂志,1995,17(2)：73-78.
【19】钱学敏.钱学森关于复杂系统与大成智慧的理论.西安交通大学学报(社会科学版),Vol.24, pp.51-57,2004年12月.
【20】钟荣丙.论系统科学近期发展史上的三颗灿烂明珠.系统辩证学学报,Vol 13, No. 1,pp.26-29,2005年1月.
【21】 Shima J P, et al. Past, present, and future of decision support technology [J]. Decision Support Systems,2002,33(2):111-126.
【22】李耀东,崔霞,戴汝为.综合集成研讨厅的理论框架、设计与实现.复杂系统与复杂性科学,Vol.1 No.1, pp.27-32,2004年1月
【23】戴汝为.支持科学决策和咨询的技术—思维系统工程[J].中国工程科学,2005,7(1)：17-20.
【24】司光亚,胡晓峰.战略决策模拟环境中XOD综合集成机制的研究与实现[J].小型微型计算机系统,2002,23(2)：242—245
【25】 Wallas G. The Art of Thought[M]. New York:Harcoxt Brace,1926:80 — 82.
【26】 Smoliar S W. Book review of creative cognition[J]. Artificial Intelligence,1995,79(1):183 — 196.
【27】 Boden M A. Modelling creativity:reply to reviewers[J]. Artificial Intelligence,1995,79(1):161 182.
【28】 ugiyama K, Nagata A, Shimojima A. Knowledge Science[M]. Kinokuniya Company Ltd, 2002:31-33.
【29】 http://catcode.com/ideafish.html
【30】 http://www.triz-journal.com/content/what_is_triz.asp
【31】 Mase K, Sumi Y, Nishimoto K. Informal conversation environment for collaborative concept formation[A]. Ishida T. Community Computing:Collaboration over Global Information Networks[C]. New York:John Wiley Sons, Inc,1998:165—205.
【32】 Aihara K, Hon K. Enhancing creativity through reorganizing mental space concealed in a research notes stack[J]. Knowledge-Based System.1998,11 (7-8):469-478.
【33】唐锡晋,刘怡君.从群体支持系统到创造力支持系统[J].系统工程理论与实践,2006,5：63—71.
【34】张兴学,张朋柱.群体决策研讨意见分布可视化研究—电子公共大脑视听室(ECBAR)的设计与实现[J].管理科学学报,2005,8(4)：15—25.
【35】戴汝为.从工程控制论到综合集成研讨厅体系—纪念钱学森归国50周年[J].复杂系统与复杂性科学,2006,3(2)：86-91.
【36】齐勇,马莉,赵季中,等.基于分布式构件技术的多层模式应用框架研究与实现[J].计算机工程,2001,27(1)：122-124.
【37】 Luis Iribame, J M Troya, Antonio Vallecillo. Trading for COTS components in Open
Environments[C]. In 14th International conference on Distributed Computing Systems, IEEE Computer Society Press,1998.
【38】左爱群,黄水松.基于组件的软件开发方法研究[J].计算机应用,1998,18(11)：4-7.
【39】王环,张建沛,张著.软件工程的新发展CBSE[J].应用科技,2002,29(6)：48-50.
【40】潘爱民.COM原理与应用[M].北京：清华大学出版社,1999.
【41】刘泽.面向业务领域框架的专用组件库的研究与实践[D].上海：复旦大学,2008：5-5.
【42】 C Szyperski. Component Software-Beyond Oriented-Object Programming [M]. New Jersey: Addison-Wesley Press,1997.
【43】 Kroeker K. Component Technology [J]. IEEE Computer,1998, (31):132-133.
【44]张世现,张文娟等.基于软件体系结构的可复用构件制作和组装[J].软件学报,2001,12(9)：1351-1359.
【45]战德臣,王忠杰.一种基于构件的复杂应用系统开发过程[J].哈尔滨工业大学学报,2002,34(6)：751-755.
【46】黄平.基于构件的软件开发方法在金融中间业务系统中的应用[J].浙江理工大学学报,2007,24(4)：453-456.
【47】 A.Brown. Large-Scale Component-Based DeveloPment. NewJersey:Prentice Hall, Inc.,2000.
【48】张驰.异构组件互操作技术研究[D].西安：西北工业大学,2006：2-5.
【49】陈博,许满武.基于Web的计算模型——Web Service[J].人文地理,2003(1).
【50】岳小婷,李森.深入理解WEB SERVICE[J].新疆师范大学学报(自然科学版),2003,22(1).
【51】 Stanislaw Ambroszkiewicz. A Simple Language for Web Service Description and Composition[C]. Proceedings of the IFIP TC6/WG6.4 Workshop on Internet Technologies, Applications and Social Impact,2002,232:289-306.
【52】 Werner Vogels. Web Services Are Not Distributed Objects[J]. IEEE Internet Computing,2003,7(6): 59-66.
【53】 Castro-Leon E. A perspective on Web Services [EB/OL]. http://www.webservices.org/index.php/a-rticle/113/1/61/,2004.
【54】杨涛,刘锦德.Web Services技术综述——一种面向服务的分布式计算模式[J].计算机应用,2004,24(8)：1-4.
【55】廖军,谭浩.新一代开放分布式处理技术——Web Services[J].计算机应用,2004,24(8)：5-9.
【56】 Tsalgatidou A, Pilioura T. An Overview'of Standards and Related Technology in Web Services[J]. Distributed and Parallel Databases,2002,12(23):135-162.
【57】 Tsur S, Abiteboul S, Agrawal R, et al. Are web service the next revolution in e-commerce[A]. In: Proceedings of the VLDB conference, Rome, September 2001,614-617.
【58】 Papazoglou M P. Service-oriented computing:concepts, characteristics and directions [A]. In: Proceedings of Fourth International Conference on Web Information Systems Engineering[C]. Los Alamitos, CA, USA:IEEE Computer Society Press,2003,3-12.
【59】 W3C Working Group. Web services architecture [DB/OL]. http://www.w3.org/tr/ws-arch/,2003.
【60】 Foster I. Service-oriented science [J]. Science,2005,308(5723):814-817.
【61】 The globus alliance. Globus toolkits 3.0 documentation 【DB/OL]. http://www-unix.globus.org/toolkitldocs/3.0/index.html,2004.
【62】毛新生.SOAP原理·方法·实践[M].北京：电子工业出版社,2007：112-121.
【63】 Xiaojie Dong, Weiqin Tong, Jin Feng. Using Categorization to Further Enhance the Utilization of Semantic Web in UDDI[J]. Computer and Computational Sciences,2006, (4):475-479.
【64】 E.Christensen, et al. Web Service Deseription Lauguage(WSDL)[EB/OL].http://www.w3.org/TR/2001/NOTE-wsdl-20010315,2001.
【65】梁越杰.区域性UDDI注册中心的设计与实现[D].浙江：浙江大学,2005.
【66】饶元,冯博琴,李尊朝.基于Web Services的服务合成技术研究综述[J].系统工程与电子技术,2005,27(8)：1481-1489.
【67】 Schahram Dustdar, wolfgang Schreiner. A survey on web services composition[J]. International Journal of Web and Grid Services,2005, (1):1-30.
【68】 Biplav Srivastava, Jana Koehler. Web Service Composition-Current Solutions and Open Problems[J]. ICAPS Workshop on Planning for Web Services,2003,28-35.
【69】 Patrick Gannon,王云,刘松,李安渝.SOA创新技术创新应用[A].IT技术创新论坛[C].数码科技广场主会场,2006,8.
【70】崔晓波.SOA概览[DB/OL]. http://articles.e-works.net.cn/471/Article35660.htm.
【71】杜彦斌.面向服务软件体系架构原理与范例研究[D].北京：首都经济贸易大学,2005.
【72】刘国静.基于SOA架构的企业应用研究[D].上海：华东师范大学,2006.
【73】万亮.基于SOA与EDA的综合架构应用研究[D].武汉：武汉理工大学,2006.
【74】雒文生,宋星原.洪水预报与调度[M].武汉：湖北科学技术出版社,1999.
【75】袁晓辉,张双全,张勇传,等.非线性马斯京根模型参数率定的新方法[J].水利学报,2001,32(5)：77-81.
【76】赵人俊.流域水文模拟——新安江模型与陕北模型[M].北京：水利电力出版社,1984.
【77】袁作新.流域水文模型[M].北京：水利电力出版社,1988.
【78】林三益.水文预报[M].北京：中国水利水电出版社,1986.
【79】包为民.水文预报[M].北京：中国水利水电出版社,2006.
【80】SL250—2000.水文情报预报规范[S].北京：中国水利水电出版社,2000.
【81】汪妮.基于中间件及智能Agent技术的防汛决策模式研究[D].西安：西安理工大学,2004.
【82】 De Castro LN, Von Zuben FJ. The Clonal Selection Algorithm with Engineering Applications[C]. In: Proceedings of 2000 GECCO, Workshop on Artificial Immune Systems and Their Applications. IEEE press,2000,36-37.
【83】 Kim J, Bentley PJ. Towards an Artificial Immune System for Network Intrusion Detection:An Investigation of Clonal Selection with a Negative Selection Operator[C]. In:Proceedings of 2001 Congress on Evolutionary Computation. IEEE press,2001,1244-1252.
【84】 Cutello, V., Nicosia, G., Pavone, M. Exploring the Capability of Immune Algorithms:A Characterization of Hypemutation Operators[C]. In Proceedings of Third International Conference on Artificial Immune Systems, ICARIS.2004,263-276.
【85】 Beyer HG, Schwefel HP. Evolution Strategies-A Comprehensive Introduction [J]. Natural Computing, 2002,1(2):3-52.
【86】长江流域规划办公室.水文预报方法[M].北京：水利出版社,1982,35-43.
【87】扬州水利学校.水文预报[M].北京：水利电力出版社,1979,34-35.
【88】翟国静.马斯京根模型参数估计方法探讨[J].水文,1977,(3)：40-43.
【89】杨晓华,金菊良,陈肇生,等.马斯京根模型参数估计的新方法[J].灾害学,1998,13(3)：1-6.
【90】杨晓华,郦建强.混沌高效遗传算法在马斯京根模型参数优选中的应用[J].水力发电学报,2008,27(2)：40-43.
【91】鲁帆,蒋云钟,王浩,等.多智能体遗传算法用于马斯京根模型参数估计[J].水利学报,2007,38(3)：289-294.
【92】 Chen J J, Yang X H. Optimal parameter estimation for Muskingum model based on Gray-encoded accelerating genetic algorithm [J]. Communications in Nonlinear Science and Numerical Simulation, 2007, (12):849-858.
【93】程银才,李明华,范世香.非线性马斯京根模型参数优化的混沌模拟退火法[J].水电能源科学,2007,25(1)：30-33.
【94】詹士昌,徐婕.蚁群算法在马斯京根模型参数估计中的应用[J].自然灾害学报,2005,14(5)：20-24.
【95】詹士昌.马斯京根洪水演算模型的改进—兼论其参数的蚁群算法率定[J].自然灾害学报,2006,15(2)：32-37.
【96】 Wilson E M. Engineering hydrology. Hampshire:MacMillan Education Ltd; 1974.
【97】 Gill M A. Flood routing by the Muskingum method. J Hydrol 1978; 36:353-363.
【98】 Tung Y K. River flood routing by nonlinear Muskingum method. J Hydraul Eng 1985; 111(12):1447-1460.
【99】 Yoon J, Padmanabhan G. Parameter estimation of linear and nonlinear Muskingum models. J Water Resour Plann Manage 1993; 119(5):600-610.
【100】 Mohan S. Parameter estimation of nonlinear Muskingum models using genetic algorithm. J Hydraul Eng 1997; 123(2):137-142.
【101】 Kim J H, Geem Z W, Kim E S. Parameter estimation of the nonlinear Muskingum model using harmony search. J Am Water Resour Assoc 2001; 37(5):1131-1138.
【102】 Das A. Parameter estimation for Muskingum models. J Irrig Drain Eng 2004; 130(2):140-147.
【103】 Zong W G. Parameter Estimation for the Nonlinear Muskingum Model Using the BFGS Technique. J Irrig Drain Eng 2006; 132(5):140-147.
【104】 Ivar Jacobson, Martin Griss, Patrik Jonsson.韩柯译.Software Reuse:Arehiteeture, Proeess and Orgnization of Business Sueeess[M].北京：机械工业出版社,2003.
【105】郑鹭亮.可复用组件库的研究设计[J].电脑知识与技术,2007,4(23)：1215-1223.
【106】杨芙清,王千祥,梅宏,等.基于复用的软件生产技术[J].中国科学E(辑),2001,31(4)：363-371.
【107】郭军.产品线架构中支持软件复用的若干关键技术的研究[D].沈阳：东北大学,2005.
【108】常继传,李克勤.青鸟系统中可复用软件组件的表示与查询[J].电子学报,2000,28(8)：20-23.
【109】张文祥,王新辉.分布式体系下组件库检索系统的设计与实现[J].计算机工程与设计,2006,27(10)：1879-1882.
【110】郑媖,陈昊,李伟斋,等.基于Web服务技术组件库的构建和实现[J].武汉理工大学学报,2004,26(2)：80-83.
【111】 Prieto Diaz R, Freeman P. Classifying Software for Reusability[M]. IEEE Computer Society Press,1987,4(1):6-16.
【112】 Mili A, Mili F. Reusing Software Issues and Research Direction[M]. IEEE Transaction on Software Engineering,1995,21 (6):56-58.
【113】 Prieto Diaz R, Swanson M. Parallel Development:A Case Study in Technology Transfer[A]. In: Przybylinski S, FowlerJ P. Proceedings of the Workshop on Transferring Software Engineering Tool Technology[C]. Calif:Santa Barbara,1987.112-113.
【114】 Apache JUDDI[EB/OL].http://ws.apache.org/juddi/releases.html.
【115】白扬.企业知识管理理论初探[J].情报科学,2000,(6)：34-38.
【116】 Bolloju, Khalifa, Turban. Integrating knowledge management into enterprise environments for the next generation decision support [J]. Decision Support System,2002(33):163-176.
【117】胡泊.现代企业知识管理系统设计研究[D].合肥：合肥工业大学,2006.
【118】王永庆.人工智能原理与方法[M].西安：西安交通大学出版社,1998.
【119】 EpplerM J, Burkard R A. Knowledge Visualization:Towards a New Discipline and its Fields ofApplication[R]. ICA Working Paper, University of Lugano,2004.
【120】赵国庆.黄荣怀,陆志坚,知识可视化的理论与方法[J].开放教育研究,2005,11(1)：23-27.
【121】龚建华,李亚斌,王道军,等.地理知识可视化中知识图特征与应用[J].遥感学报,2008,12(2)：355-361.
【122】 Eppler, M.J.& Burkard, R.A. Knowledge Visualization:Towards a New Discipline and its Fields of Application[R].ICA Working Paper, University of Lugano.Lugano.2004.
【123】 Chen Lina. Knowledge Mapping in Knowledge Management at Enterprises. LIBRARY AND INFORMATION SERVICE. NO.8,2003.58-78
【124】史忠植,蒋运承,张海俊,董明楷.基于描述逻辑的主体服务匹配.计算机学报.2004年5月.第27卷第5期.625-635
【125】 Martin J.E. (2002). Handbook on Knowledge Management 1, Springer Verlag.189～300.
【126】 Logan, D., Caldwell, F., "Knowledge Mapping:Five Key Dimensions to Consider, " GartnerGroup,2000.
【127】 Debra M. Amidon. Innovation Strategy for the Knowledge Economy. The Ken Awakening. Butterworth-Heinemann.1997.
【128】 Rita Kuo. Maiga Chang. Da-Xian Dong and Jia-Sheng Heh. "Applying Knowledge Map to Intelligent Agents in Problem Solving Systems, " World Conference on Educational Multimedia, Hypermedia & Telecommunications, Denver, Colorado, USA, Jun.24～29,2002.
【129】 Thomas R Gruber. Ontolingua:A Translation Approach to Potable Ontology Specification [J]. Knowledge Acquisition,1993,5(2):199～200.
【130】李亚芳,李霞.思维导图在学习者隐性知识管理中的应用[J].科技情报开发与经济,2008,18(34)：128-130.
【131】 De Mantaras, R L Plaza E. Case-based reasoning:an overview [J]. IEEE AI Commune,1997, 10(2):21-29.
【132】 Waston I, Marir F. Case-based reasoning:a review [J]. The Knowledge Engineering Review,1994, 9(4):355-381.
【133】 Cunningham P, Smyth B. Case-based reasoning in scheduling:reusing solution components [J].1996,35(11):2947-2960.
【134】史忠植.知识发现[M].北京：清华大学出版社,2002.
【135】胡良明,徐诚,方峻.CBR技术在枪械方案设计专家系统中的应用[J].系统仿真学报,2007,19(4)：772-775.
【136】 Stephen Slade. Case-based reasoning:a research paradigm [J]. AI Magazine,1991,31(1):42-55.
【137】 Deng P S. Using case-based reasoning approach to the support of ill-structured decisions [J]. European Journal of Operational Research,1996,93(3):511-521.
【138】常春光,汪定伟,胡琨元,等.基于两级CBR的钢铁企业动态调度系统研究[J].控制与决策,2004,19(10)：1109-1116.
【139】 Aamodt A, Plaza E. Case-based reasoning:foundational issues, methodological variations, and system approaches [J]. AI Communications,1994,7(1):39-59.
【140】王本德,于义彬,刘金禄,等.水库洪水调度系统的模糊循环迭代模型及应用[J].水科学进展,2004,15(2)：233-237.
【141】厨惠成,张改红,王国利.基于熵权的水库防洪调度多目标决策方法及应用[J].水利学报, 2007,38(1)：100-106.
【142】侯召成,陈守煜.水库防洪调度多目标模糊群决策方法[J].水利学报,2004,35(12)：106-119.
【143】郭生练,彭辉,王金星,等.水库洪水调度系统设计与开发[J].水文,2001,21(3)：4-7.
【144】王义民,黄强,畅建霞,等.安康水库洪水实时调度系统研究[J].水力发电学报,200],(4)：18.25.
【145】田峰巍,解建仓,颜竹丘.梯级水电站群的规划与调度[M].北京：科学技术文献出版社,1992.
【146】王义民,黄强,畅建霞,等.基于面向对象技术的水库洪水调度仿真模型研究[J].水力发电学报,2003,(4)：8-14.
【147】罗军刚,解建仓,汪妮.基于CBR的水库洪水调度模式[J].系统工程理论与实践,2008,28(9)：122-130.
【148】罗军刚,解建仓,陈田庆,汪妮.基于事例推理技术的水库洪水调度研究与应用[J].水科学进展,2009,20(1)：32-39.
【149】 Finnie G, Sun Z. R5 model for case-based reasoning [J]. Knowledge-Based Systems,2003, (16): 59-65.
【150】汤文宇,李玲娟.CBR方法中的案例表示和案例库的构造[J].西安邮电学院学报,2006,11(5)：75.78.
【151】 Smyth B, Keane M T, Cunningham P D. Hierarchical case-based reasoning integrating case-based and decompositional program-solving techniques for plant-control software design [J]. IEEE Transaction Knowledge and Data Engineering,2001,13(5):793-812.
【152】蒋亚军,娄臻亮.规则推理与事例推理在模具加工刀具智能化选择中的应用[J].上海交通大学学报,2005,39(7)：1066-1069.
【153】郭艳红,邓贵仕.基于事例的推理(CBR)研究综述[J].计算机工程与应用,2004,40(21)：1-5.
【154】张光前,邓贵仕,李朝晖.基于事例推理的技术及其应用前景[J].计算机工程与应用,2002,38(20)：52-55.
【155】王君,潘星,李静,等.基于案例推理的知识管理咨询系统[J].清华大学学报(自然科学版),2006,46(S1)：990-995.
【156】 Shin K S, Han I G. Case-based reasoning supported by genetic algorithms for corporate bond rating [J]. Expert Systems with Application,1999,16(2):85-95.
【157】黄继鸿,雷战波,李欣苗.基于遗传禁忌算法的案例检索策略[J].系统工程理论方法应用,2004,13(1)：10-13.
【158】黄继鸿,姚武,雷战波.基于案例推理的企业财务危机智能预警支持系统研究[J].系统工程理论与实践,2003,23(12)：46-52.
【159】 Chiu C. A case-based customer classification approach for direct marketing.[J]. Expert Systems with Applications,2002,22(2):163-168.
【160】王玉,邢渊,阮雪榆.基于事例的推理循环中人工神经网络和遗传算法的4种应用模型[J].上海交通大学学报,2003,37(2)：202-204.
【161】周惠成,张改红,王国利.基于熵权的水库防洪调度多目标决策方法及应用[J].水利学报,2007,38(1)：100-106.
【162】陈守煜.防洪调度多目标决策理论与模型[J].中国工程科学,2000,2(2)：47-52.
【163】赵卫东,李旗号,盛昭瀚.基于案例推理的决策问题求解研究[J].管理科学学报,2000,3(4)：29-36.
【164】常春光,崔建江,汪定伟,等.案例推理中案例调整技术的研究[J].系统仿真学报,2004,16(6)：1260-1265.
【165】 Chen S M, Tan J M. Handling multicriteria fuzzy decision-making problems based on vague set theory[J]. Fuzzy Sets and Systems,1994,67(2):163-172.
【166】 Maeda H, Murakami S. A fuzzy decision-making method and its application to a company choice problem[J]. Information Sciences,1998, (45):331-346.
【167】 Gau W L, Buehrer D J. Vague sets[J]. IEEE Transaction on Systems Man Cybernetics,1993, 23(2):610-614.
【168】 Zadeh L A, Fuzzy sets[J]. Information and Control,1965,8(3):338-356.
【169】 Atanassov K T. Intuitionistic fuzzy sets[J]. Fuzzy Sets and Systems,1986,20(1):87-96.
【170】 Hong D H, Choi C H. Multi-criteria fuzzy decision-making problems based on vague set theory[J]. Fuzzy Sets and Systems,2000,114(1):103-113.
【171】李凡,卢安,蔡立晶.基于Vague集的多目标模糊决策方法[J].华中科技大学学报,2001,29(7)：1-3.
【172】李凡,饶勇.基于Vague集的加权多目标模糊决策方法[J].计算机科学,2001,28(7)：60-65.
【173】刘华文.多目标模糊决策的Vague集方法[J].系统工程理论与实践,2004,24(5)：103-109.
【174】陈月映,张新波.基于三角模糊数Vague集的多目标决策方案[J].浙江工商大学学报,2007,85(4)：20-23.
【175】周珍,吴祈宗,刘福祥.基于区间值Vague集的多准则模糊决策方法[J].北京理工大学学报,2005,25(11)：1019-1023.
【176】王珏,刘三阳,张杰.基于Vague集的模糊多目标决策方法[J].系统工程理论与实践,2005,25(2)：119-122.
【177】黄松,黄卫来.基于熵权系数与Vague集的多目标决策方法[J].管理学报,2005,2(Sup)：120-123.
【178】程乾生.属性数学—属性测度和属性统计[J].数学的实践与认识,1998,28(2)：97-107.
【179】程乾生.质量评价的属性数学模型和模糊数学模型[J].数理统计与管理,1997,16(6)：18-23.
【180】罗军刚,解建仓.基于Vague集的模糊多目标决策方法及应用[J].数学的实践与认识,2008,
38(20)：114-122.
【181】罗晓沛,伯乐,昭原.数据库技术新进展[M].北京：清华大学出版社,1997.
【182】周竞涛.企业异构数据源集成[DB/OL]计算机世界,http://www.ccw.com.cn/htm/center/tech/02_8_30_2.asp,2002,8.
【183】张维明.信息系统集成技术[M].北京：电子工业出版社,2002.
【184】陈楠.多源空间数据集成的技术难点分析和解决策略.计算机应用研究[J],2005(10)：206-208.
【185】刘玉洁,杨忠东.MODIS遥感信息处理原理与算法[M].北京：科学出版社,2001：13-45.
【186】彭定志,熊立华,郭生练.MODIS在水文水资源中的应用与展望[J].水利学进展.2004,15(5)：683～688.
【187】蒋耿明,牛铮,阮伟利等.MODIS影像合成算法研究和实现[J].国土资源遥感,2004,(2)：11-14.
【188】 Jiang G M, Liu R G, Niew Z et al. An optimal geometric correction method for MODIS 1B data Collection and its software development[J]. Journal of Remote Sensing.2004,8(2):158-164.
【189】蒋艳凰,杨学军,易会战.卫星遥感图像并行几何校正算法研究[J].计算机学报,2004,27(7)：944-951.
【190】蒋耿明,牛铮,阮伟利等.MODIS影像条带噪声去除方法研究[J].遥感技术与应用,2003,18(6)：393-398.
【191】 Steven Platnick, Michael D, King Steven A. The MODIS cloud products algorithms and examples from Terra[J]. IEEE Transactions on Geoscience and Remote Sensing.2003,41(2):459～473.
【192】 Kerry Meyer, Yang Ping, Gao Bo-cai. Optical thickness of tropical circus clouds derived from the MODIS 0.66-and 1.375-channels[J]. IEEE Transactions on Geoscience and Remote Sensing.2004,42(4):833～841.
【193】齐述华,李贵才,王长耀,等.利用MODIS数据产品进行全国干旱监测的研究[J].水科学进展,2005,16(1)：56-61.
【194】刘良明,梁益同,马慧石.MODIS和AVHRR植被指数关系的研究[J].武汉大学学报(信息科学版),2004,29(4)：307-310.
【195】张立福,张良培,村松加奈子.利用MODIS数据计算陆地植被指数VIUPD[J].武汉大学学报,2005,30(8)：699-702.
【196】仝兆远,张万昌.基于MODIS数据的渭河流域土壤水分反演[J].遥感应用,2008,(1)：66-73.
【197】周艺,周伟奇等.遥感技术在内陆水体水质监测中的作用[J].水科学进展,2004,15(3)：312-317.
【198】鄢俊洁,刘良明,马浩录,等.MODIS数据在黄河凌汛监测中的应用[J].武汉大学学报(信息科学版),2004,29(8)：679-681.
【199】骆成凤.中国土地覆盖分类与变化监测遥感研究[D].北京：中国科学院遥感应用研究所,2005.
【200】刘勇洪,牛铮.基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究[J].遥 感技术与应用,2004,19(4)：217-224.
【201】王伟民,孙晓敏,张仁华.地物反射光谱对MODIS近红外通道水汽反演影像的模拟分析[J].遥感学报,2005,9(1)：8-15.
【202】刘良明,张红梅.基于MODIS数据的悬浮泥沙定量遥感方法[J].国土资源遥感,2006,2(6)：42-45.
【203】曾严,鄢俊洁.MODIS数据在积雪监测的中的应用[J].测绘与空间地理信息.2005,28(6)：97-99.
【204】李民,刘志辉,房世峰.基于MODIS数据的雪水当量监测模型研究[J].水土保持研究,2007,14(4)：74-81.
【205】尚建翠,吉久明,房宝金.知识集成理论发展及其应用研究[J].高校图书情报论坛,2007,6(3)：53-57.
【206】于景元,周晓纪.从定性到定量综合集成方法的实现和应用[J].系统工程理论与实践,2002,1(10)：26-32.
【207】于景元,周晓纪.综合集成方法与总体设计部[J].复杂系统与复杂性科学,2004,1(1)：20-26.
【208】王寿云,于景元等.开放复杂的巨系统[M].杭州：浙江科技出版社,1996.
【209】操龙兵,戴汝为.开放复杂智能系统[M].北京：人民邮电出版社,2008.
【210】张建云,水利信息化的发展思路和建设任务,中国水利,2000,(9)：81-84
【211】 H.H.Dewbler,etc., Introducing Object Orientation into Large and Complex Systems. IEEE Trans.Software Eng.,1994,20 (11):840-848.
【212】 Liu ShuZhou, Angela Goh Eck Soong. A Formal Framework To Support Workflow Adaption. International Journal of Software Engineering and Knowledge Engineering, Vol.12 (3),2002: 245-267.
【213】王飞跃.人工生命与自然生命—杂谈观念的演变与变化[J].科学中国人,2003,(8)：33.
【214】王飞跃.人工社会、计算实验、平行系统——关于复杂社会经济系统计算研究的讨论[J].复杂系统与复杂性科学,2004,1(4)：25-35.
【215】王飞跃,史帝夫·兰森.从人工生命到人工社会——复杂社会系统研究的现状和展望[J].复杂系统与复杂性科学,2004,1(1)：33-41.
【216】臧垒,蒋晓原,王钰,等.基于计算实验的探索性分析框架研究[J].系统仿真学报,2008,20(12)：3077-3081.
【217】王飞跃.计算实验方法与复杂系统行为分析和决策评估[J].系统仿真学报,2004,16(5)：893-897.
【218】李涛,杨秀月,郭齐胜.基于探索性计算实验的信息化武器装备体系优化方法[J].装甲兵工程学院学报,2008,22(1)：1-5.
【219】王飞跃.平行系统方法与复杂系统的管理和控制[J].控制与决策,2004,19(5)：485-489.
【220】罗军刚,解建仓,刘福朝.基于Web的电网线路和变电站预案管理系统.电网技术,2008, 32(1).93-96.