制造物联海量实时数据处理方法研究
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摘要
制造技术与迅猛发展的互联网、云计算、物联网等新一代信息技术相融合，不断形成制造业信息化新的核心技术，推动着制造业的进步和发展，也为我国制造业的跨越式发展提供了难得的历史机遇。应用物联网技术提升传统制造业，形成推动制造业信息化发展的制造物联技术，是先进制造技术、信息技术和智能技术的集成和深度融合，体现了制造技术从机械化、自动化、数字化走向智能化的发展趋势。大力推进制造物联技术与生产过程的融合，促进制造过程的智能化提升和改造，催生高端制造先进的生产模式，形成我国制造业的“智慧制造”，是当前制造行业研究的一个重点领域。在制造物联关键技术中，研究制造过程产生的海量数据的实时感知、实时传输与分发、实时处理与融合等，对制造过程的实时决策及实时控制，确保企业生产安全有序进行、及时决策、提高效率、减少损失非常重要，也为我国制造业改变当前过于依赖国外实时数据处理产品打下基础。
本文对现有信息技术、制造业信息化及物联网等相关领域研究进展进行分析，研究制造物联海量实时感知数据的建模及处理方法，针对制造系统的特性、制造物联关键技术及制造环节实时监控需求等方面的特点，深入分析所涉及到的若干关键问题，研究新的或改进的方法，并将这些方法应用到具体的制造环境中。论文主要研究工作及成果如下：
①提出了制造物联实时数据的定义，对实时数据进行基础理论建模。对实时数据的采集过程进行了研究，分析探讨了实时数据获取和存储的关键技术，提出了制造物联实时数据的获取模型。分析了实时数据传输和分发中的挑战，提出了海量实时数据连续查询模型，并通过优化访问方法，给出了自适应分布式制造物联实时数据的访问模型，以解决突发数据和非匀速实时数据的传输访问问题。
②基于内存的实时数据库的存储机制研究。分析了内存数据库的特点、相关概念及技术，针对内存数据库的数据组织、查询技术与优化、并发控制和恢复机制等方面进行研究，采用基于CSB+树的索引方法，以快速定位索引实时数据；提出了一种新型的基于虚拟单元可智能增长的内存池技术，满足内存数据库系统对空间利用率和系统健壮性要求；基于智能算法，提出了海量连续实时数据的查询技术与优化算法；为解决实时事务在并发执行过程中所发生的各种冲突，采用了并行实时事务标记排序法，并利用定义优先级算法对排序算法进行优化，解决排序算法中的优先级颠倒的问题。论文提出了一种基于元数据层次化结构的实时系统数据模型，以便更快捷地访问实时数据对象。该模型对制造物联实时感知数据进行层次划分，通过元数据映射，完成实时数据的有效组织，有效实现实时数据检索，提高效率。基于元数据的层次结构，制定了实时感知数据与超过时限的历史数据之间互相迁移的策略，并对该策略进行性能评估。各种仿真及测试运行结果表明，基于内存的实时数据库分层存储机制能有效进行数据组织，满足制造物联中海量实时数据的存储需求。
③设计并实现一种新的实时数据访问协议，以提高制造物联实时数据系统的相关性能。设计了基于双缓冲区和推进发送数据的数据存储和发送模型。双缓冲区模型中的两个缓冲区交替接收感知数据，将实时数据并发进行处理，有效利用内存接收处理海量数据。数据的连续推进发送模式，同双缓冲区结合，可以在不同网络阻塞环境下保证数据的完整性。建立性能模型验证实时数据分发模型的性能，并实现了实时数据检测与处理系统的原型系统。仿真实验结果证明模型在保证数据完整性方面有着非常出色的表现。基于双缓冲区和推进发送数据的模型解决了数据在采集和发送过程中出现的数据丢失问题，有效地保证了数据传输处理的完整性。为了优化制造物联海量实时数据分发效率，研究并提出了一种基于智能多代理模型和优先级排序算法的实时数据分发策略，性能分析证明该方法能进一步提高海量实时数据分发效率。
④分析了制造物联海量实时数据融合的功能模型和通用处理结构，提出了在资源受限网络环境下对实时感知数据进行融合的方法，研究了基于u检验法的剔除冗余无效数据的融合系统，并将融合算法应用在分布式检测环境下，对融合模型进行优化。基于形态-小波滤波融合方法，对旋转机械的振动信号检测过程中产生的脉冲噪声和白噪声进行去噪研究，实验证明该方法有很好的去噪效果，在实际振动信号处理中取得了满意的效果。
⑤提出了制造物联海量实时数据处理方法在石化行业运营管理及安全监控系统中的应用方案。对实时感知的海量数据，以分层的实时数据模型进行组织和管理，并以双缓冲区数据持续推送模型对实时数据传输分发，对本地感知数据实时融合处理，形成石化企业加气站生产运营实时监管物联平台，实现了对监管平台的性能优化和改进。
综上，本文分析并研究了当前制造物联海量实时数据处理中面临的若干关键问题，设计并改进了一系列模型和算法。理论分析、实验及应用结果表明：相关处理方法是实时、正确的，能对制造物联海量实时数据进行有效处理，为我国制造业过于依赖国外实时数据库及实时数据传输与处理系统产品提供一种新选择。
The mixture of manufacturing technology and the rapid development of newgeneration of information techniques, including internet, cloud computing, and internetof things, is continuously forming new core technologies of the manufacturinginformatization, promoting the progress and development of manufacturing, which alsooffers great opportunity to the great-leap-forward development of manufacturing in ourcountry. Using the internet of things to promote traditional manufacturing industry, andto form manufacturing content group technology which promotes the manufacturinginformatization, is the integration and deeper infusion of advanced manufacturingtechnology, information technology and intellectual technology, and it also reflects thetrend from mechanization, automation, digitization to intellectualization of themanufacturing industry. It is currently the key area of industrial research to carryforward the integration of technology in the internet of manufacturing elements andmanufacturing procedure, to promote and reform the automation during manufacturingprocedure, and to create high-level production mode, finally reaching the “IntelligentManufacturing” in China’s manufacturing industry. Among those key technologies inthe internet of manufacturing elements, real-time monitoring, transmission anddistribution, processing and mixing toward massive data produced during manufactur-ing procedure, and so on, are essential to the real-time decision and control, and ensuresall the manufacturing procedure be made orderly, immediately, efficiently, and withminimum losses.
This paper analyses the related fields such as existing information technology,manufacturing informatization and internet of things and so on, studies real-timesensing data modeling and processing method in manufacturing elements system.Aiming at manufacturing system’s features, key technologies in the internet ofmanufacturing elements and real-time monitoring requirements during producingprocedure, this paper does a thorough research on some involved problem, proposesnew or optimized methods, and applies them to specific production environments. Hereare the main research work and achievements of this paper:
①Proposes the definition of real-time data in the internet of manufacturingelements, builds basic theoretical model to real-time data. This paper studies thereal-time data collecting process, proposes the collecting model, and analyses the key technologies in real-time data acquisition and storage. This paper analyses thechallenges during real-time data transition and distribution, proposes continuousmassive real-time data searching module, and comes up with the access model throughoptimized data access method, in order to deal with burst data and non-uniformreal-time data’s transition access problem.
③Proposes a new real-time database storage mechanism based on main memory.This paper analyses the development, feature, related concepts and technologies of themain memory database, studies data organization of the memory-based database, querytechniques and its optimization, concurrency control and recovery mechanism, putsforward the CSB+tree-based index method to quickly locate the indexed real-time data.This paper also raises a new virtual memory pool technology based on virtual unit withsmart growth to meet the requirements of space utilization and system robustness ofmain memory database system. Based on the intelligent algorithm, this paper proposedcontinuous massive real-time data query techniques and its optimized algorithm. Tosolve conflicts while real-time transactions is dealing with concurrencies, this paperdesigns a time sorting algorithm, optimizes sorting algorithm by defining priorityalgorithm, solving the problem of priority-reversion in time sorting algorithm. Thispaper puts forward a real-time system data model in order to access real-time data morequickly based on the metadata hierarchical structure. This model divides real-timesensing data under the internet of manufacturing elements into levels, realizing thedata’s efficient organization based on metadata mapping. Metadata hierarchicalstructure realizes real-time data searching, and improves efficiency. Based on thestructure, this paper develops transfer strategy between real-time data and historical data,and can also accesses strategy performance. Various simulation and experimental resultsshow that this method could organize data efficiently and meet the requirements ofmassive real-time data storage for manufacturing elements.
③Designs and realizes a practical real-time data access protocol to improvereal-time data system’s performance in the internet of manufacturing elements. Thispaper designs a data storage and delivery model based on double-buffer and promotingdata sending. Double-buffer model is in charge of receiving data from systemalternatively, storing these data into real-time database, ensuring the data integrity underdifferent network environments. To validate model’s performance, this paper proposedthe performance model and the prototype system of real-time data monitoring system,conducts various experiments which proves that this model is very strong in protecting data integrity. The model based on double-buffer and promoting data sending tackles theproblem of data loss during data collecting and sending process, and ensures dataintegrity. In order to optimize massive real-time data distributing efficiency, this paperstudies and proposes a real-time data distribution strategy based on smart multi-agentmodel and priority sorting algorithm, the performance analyze proves that this methodadvances the efficiency of massive real-time data distribution.
④Analyses the model and general processing structure of massive real-time datafusion, uses u test method under resource-constrained network condition, andoptimizes fusion model under distributed testing environment. An integrated filtermethod that combined morphological filter and wavelet threshold filtering is proposedin this paper. This method combined the advantages of the two filtering methods to filterimpulse and white noise effectively. The comparative experiments with waveletthreshold filter and morphological filter show that this method not only couldeffectively filter out impulse noise but also white noise.
⑤Proposes massive real-time data processing method’s application in theoperation management and safety monitoring system of petrochemical industry. Thisapplication organizes and manages real-time sensing data and leveled data model,transmits and distributes real-time data using distributed propulsion model, conductsreal-time fusion to local perception data in the terminals, and finally develops into theoptimized production and real-time monitoring platform, realizing performanceoptimization and improvements of the monitoring and management platform.
In conclusion, this paper analyses and studies the key problems of the existing dataprocessing methods for the massive real-time data in the internet of manufacturingelements, designs and optimizes a serious of model and algorithm. Theoretical analysisand experiment results have shown that these related processing methods are real-timeand correct, they could process massive real-time data efficiently, and providespowerful guarantee for the real-time producing monitoring and management inmanufacturing industry.
本文对现有信息技术、制造业信息化及物联网等相关领域研究进展进行分析，研究制造物联海量实时感知数据的建模及处理方法，针对制造系统的特性、制造物联关键技术及制造环节实时监控需求等方面的特点，深入分析所涉及到的若干关键问题，研究新的或改进的方法，并将这些方法应用到具体的制造环境中。论文主要研究工作及成果如下：
①提出了制造物联实时数据的定义，对实时数据进行基础理论建模。对实时数据的采集过程进行了研究，分析探讨了实时数据获取和存储的关键技术，提出了制造物联实时数据的获取模型。分析了实时数据传输和分发中的挑战，提出了海量实时数据连续查询模型，并通过优化访问方法，给出了自适应分布式制造物联实时数据的访问模型，以解决突发数据和非匀速实时数据的传输访问问题。
②基于内存的实时数据库的存储机制研究。分析了内存数据库的特点、相关概念及技术，针对内存数据库的数据组织、查询技术与优化、并发控制和恢复机制等方面进行研究，采用基于CSB+树的索引方法，以快速定位索引实时数据；提出了一种新型的基于虚拟单元可智能增长的内存池技术，满足内存数据库系统对空间利用率和系统健壮性要求；基于智能算法，提出了海量连续实时数据的查询技术与优化算法；为解决实时事务在并发执行过程中所发生的各种冲突，采用了并行实时事务标记排序法，并利用定义优先级算法对排序算法进行优化，解决排序算法中的优先级颠倒的问题。论文提出了一种基于元数据层次化结构的实时系统数据模型，以便更快捷地访问实时数据对象。该模型对制造物联实时感知数据进行层次划分，通过元数据映射，完成实时数据的有效组织，有效实现实时数据检索，提高效率。基于元数据的层次结构，制定了实时感知数据与超过时限的历史数据之间互相迁移的策略，并对该策略进行性能评估。各种仿真及测试运行结果表明，基于内存的实时数据库分层存储机制能有效进行数据组织，满足制造物联中海量实时数据的存储需求。
③设计并实现一种新的实时数据访问协议，以提高制造物联实时数据系统的相关性能。设计了基于双缓冲区和推进发送数据的数据存储和发送模型。双缓冲区模型中的两个缓冲区交替接收感知数据，将实时数据并发进行处理，有效利用内存接收处理海量数据。数据的连续推进发送模式，同双缓冲区结合，可以在不同网络阻塞环境下保证数据的完整性。建立性能模型验证实时数据分发模型的性能，并实现了实时数据检测与处理系统的原型系统。仿真实验结果证明模型在保证数据完整性方面有着非常出色的表现。基于双缓冲区和推进发送数据的模型解决了数据在采集和发送过程中出现的数据丢失问题，有效地保证了数据传输处理的完整性。为了优化制造物联海量实时数据分发效率，研究并提出了一种基于智能多代理模型和优先级排序算法的实时数据分发策略，性能分析证明该方法能进一步提高海量实时数据分发效率。
④分析了制造物联海量实时数据融合的功能模型和通用处理结构，提出了在资源受限网络环境下对实时感知数据进行融合的方法，研究了基于u检验法的剔除冗余无效数据的融合系统，并将融合算法应用在分布式检测环境下，对融合模型进行优化。基于形态-小波滤波融合方法，对旋转机械的振动信号检测过程中产生的脉冲噪声和白噪声进行去噪研究，实验证明该方法有很好的去噪效果，在实际振动信号处理中取得了满意的效果。
⑤提出了制造物联海量实时数据处理方法在石化行业运营管理及安全监控系统中的应用方案。对实时感知的海量数据，以分层的实时数据模型进行组织和管理，并以双缓冲区数据持续推送模型对实时数据传输分发，对本地感知数据实时融合处理，形成石化企业加气站生产运营实时监管物联平台，实现了对监管平台的性能优化和改进。
综上，本文分析并研究了当前制造物联海量实时数据处理中面临的若干关键问题，设计并改进了一系列模型和算法。理论分析、实验及应用结果表明：相关处理方法是实时、正确的，能对制造物联海量实时数据进行有效处理，为我国制造业过于依赖国外实时数据库及实时数据传输与处理系统产品提供一种新选择。
The mixture of manufacturing technology and the rapid development of newgeneration of information techniques, including internet, cloud computing, and internetof things, is continuously forming new core technologies of the manufacturinginformatization, promoting the progress and development of manufacturing, which alsooffers great opportunity to the great-leap-forward development of manufacturing in ourcountry. Using the internet of things to promote traditional manufacturing industry, andto form manufacturing content group technology which promotes the manufacturinginformatization, is the integration and deeper infusion of advanced manufacturingtechnology, information technology and intellectual technology, and it also reflects thetrend from mechanization, automation, digitization to intellectualization of themanufacturing industry. It is currently the key area of industrial research to carryforward the integration of technology in the internet of manufacturing elements andmanufacturing procedure, to promote and reform the automation during manufacturingprocedure, and to create high-level production mode, finally reaching the “IntelligentManufacturing” in China’s manufacturing industry. Among those key technologies inthe internet of manufacturing elements, real-time monitoring, transmission anddistribution, processing and mixing toward massive data produced during manufactur-ing procedure, and so on, are essential to the real-time decision and control, and ensuresall the manufacturing procedure be made orderly, immediately, efficiently, and withminimum losses.
This paper analyses the related fields such as existing information technology,manufacturing informatization and internet of things and so on, studies real-timesensing data modeling and processing method in manufacturing elements system.Aiming at manufacturing system’s features, key technologies in the internet ofmanufacturing elements and real-time monitoring requirements during producingprocedure, this paper does a thorough research on some involved problem, proposesnew or optimized methods, and applies them to specific production environments. Hereare the main research work and achievements of this paper:
①Proposes the definition of real-time data in the internet of manufacturingelements, builds basic theoretical model to real-time data. This paper studies thereal-time data collecting process, proposes the collecting model, and analyses the key technologies in real-time data acquisition and storage. This paper analyses thechallenges during real-time data transition and distribution, proposes continuousmassive real-time data searching module, and comes up with the access model throughoptimized data access method, in order to deal with burst data and non-uniformreal-time data’s transition access problem.
③Proposes a new real-time database storage mechanism based on main memory.This paper analyses the development, feature, related concepts and technologies of themain memory database, studies data organization of the memory-based database, querytechniques and its optimization, concurrency control and recovery mechanism, putsforward the CSB+tree-based index method to quickly locate the indexed real-time data.This paper also raises a new virtual memory pool technology based on virtual unit withsmart growth to meet the requirements of space utilization and system robustness ofmain memory database system. Based on the intelligent algorithm, this paper proposedcontinuous massive real-time data query techniques and its optimized algorithm. Tosolve conflicts while real-time transactions is dealing with concurrencies, this paperdesigns a time sorting algorithm, optimizes sorting algorithm by defining priorityalgorithm, solving the problem of priority-reversion in time sorting algorithm. Thispaper puts forward a real-time system data model in order to access real-time data morequickly based on the metadata hierarchical structure. This model divides real-timesensing data under the internet of manufacturing elements into levels, realizing thedata’s efficient organization based on metadata mapping. Metadata hierarchicalstructure realizes real-time data searching, and improves efficiency. Based on thestructure, this paper develops transfer strategy between real-time data and historical data,and can also accesses strategy performance. Various simulation and experimental resultsshow that this method could organize data efficiently and meet the requirements ofmassive real-time data storage for manufacturing elements.
③Designs and realizes a practical real-time data access protocol to improvereal-time data system’s performance in the internet of manufacturing elements. Thispaper designs a data storage and delivery model based on double-buffer and promotingdata sending. Double-buffer model is in charge of receiving data from systemalternatively, storing these data into real-time database, ensuring the data integrity underdifferent network environments. To validate model’s performance, this paper proposedthe performance model and the prototype system of real-time data monitoring system,conducts various experiments which proves that this model is very strong in protecting data integrity. The model based on double-buffer and promoting data sending tackles theproblem of data loss during data collecting and sending process, and ensures dataintegrity. In order to optimize massive real-time data distributing efficiency, this paperstudies and proposes a real-time data distribution strategy based on smart multi-agentmodel and priority sorting algorithm, the performance analyze proves that this methodadvances the efficiency of massive real-time data distribution.
④Analyses the model and general processing structure of massive real-time datafusion, uses u test method under resource-constrained network condition, andoptimizes fusion model under distributed testing environment. An integrated filtermethod that combined morphological filter and wavelet threshold filtering is proposedin this paper. This method combined the advantages of the two filtering methods to filterimpulse and white noise effectively. The comparative experiments with waveletthreshold filter and morphological filter show that this method not only couldeffectively filter out impulse noise but also white noise.
⑤Proposes massive real-time data processing method’s application in theoperation management and safety monitoring system of petrochemical industry. Thisapplication organizes and manages real-time sensing data and leveled data model,transmits and distributes real-time data using distributed propulsion model, conductsreal-time fusion to local perception data in the terminals, and finally develops into theoptimized production and real-time monitoring platform, realizing performanceoptimization and improvements of the monitoring and management platform.
In conclusion, this paper analyses and studies the key problems of the existing dataprocessing methods for the massive real-time data in the internet of manufacturingelements, designs and optimizes a serious of model and algorithm. Theoretical analysisand experiment results have shown that these related processing methods are real-timeand correct, they could process massive real-time data efficiently, and providespowerful guarantee for the real-time producing monitoring and management inmanufacturing industry.
引文
[1]中华人民共和国科学技术部.“十二五”制造业信息化科技工程规划[EB/01].http://www.most.gov.cn/tztg/201205/t20120511_94337.htm,2012-5-11.
[2]李伯虎,张霖,王时龙,等.云制造—面向服务的网络化制造新模式[J].计算机集成制造系统,2010,16(1):1-7.
[3]郑力,蒋平宇,乔立红,等.制造系统研究的前沿和挑战[J].机械工程学报,2010,46(21):124-136.
[4] Zhou Zude, Yu Wen yong,Chen Youping. Concept and related scientific problems of digitalmanufacturing[J]. China Mechanical Engineering,2001,12(1):100-104..
[5] Thomas, Kelly. MES in the Age of Agile Manufacturing. A Presentation at MESA Roundtable4, Chicago,Sep.13,1995.
[6] Michael McClellan. Applying Manu facturing Execution Systems. CRC Press,1997.
[7] Brandl D. MES Software: A Critical Element in the Enterprise Integration Process, I&CS(USA),1999,72(10):51-56.
[8] Vijayan, Jaikumar. Manufacturing Execution Systems. Computer World,2000,34(31):38-42.
[9]于海斌,朱云龙.可集成的制造执行系统.计算机集成制造系统CIMS[J],2000,6(6):1-6.
[10] MESA International. MES Functionalities&MRP to MES Data Flow Possibilities. MESAInternational White Paper Number2,1997.
[11] MESA International. MES Software Evaluation Selection. MESA International White PaperNumber4,1997.
[12] Barkmeyer Ed, Nenno Peter, Feng Shan, et al. NIST Res ponse to MES Request forInformation. NIST Response to RFI-3: MESM odels,1999.
[13] Scott D. Comparative Advantage through Manufacturing Execution Systems. Proceedings ofthe SEMICON Taiwan96IC Seminar, September1996:227-236.
[14] Cheng FT, Shen E, Deng JY, et al. Development of a System Framework for theComputer-Integrated MES: A Distributed Object-Oriented Approach. International Journal ofCIM,1999,12(5):384-402.
[15]刘卫宁,黄文雷,孙棣华等.基于射频识别的离散制造业制造执行系统设计与实现[J].计算机集成制造系统,2007,13(10):1886-1890.
[16] Ercan Oztemel, Esra Kurt Tekez.A general framework of a Reference Model for IntelligentIntegrated Manufacturing Systems (REMIMS)[J]. Engineering Applications of ArtificialIntelligence,2009,22(6):855-864.
[17] Wang S.L., Xia H., Liu F., Tao G.B., Zhang Z. Agent-based Modeling and Mapping ofManufacturing System[J], Journal of Materials Processing Technology,2002,12(9):518-523.
[18] Wang Shilong,Jian Yi,Liu Fei，Robert Young,Novel Structure for Distributed Numric Control,Chinese Journal of Mechanical Engineering (English Edition),2003,16(4):437-440.
[19] Luigi Atzori, Antonio Iera, et al. The Social Internet of Things (SIoT)–When social networksmeet the Internet of Things: Concept, architecture and network characterization[J]. ComputerNetworks,2012,56（16）:3594-3608.
[20] Luigi Atzori, Antonio Iera, Giacomo Morabito. The Internet of Things: A survey[J]. ComputerNetworks,2010,54(15):2787-2805.
[21] Daniele Miorandi, Sabrina Sicari, et al. Internet of things: Vision, applications and researchchallenges[J]. Ad Hoc Networks,2012,10(7):1497-1516.
[22] Qin Xiaotie, Gu Yuesheng. Data fusion in the Internet of Things[J]. Procedia Engineering,2011,15:3023-3026.
[23] Kiev Gama, Lionel Touseau, Didier Donsez. Combining heterogeneous service technologiesfor building an Internet of Things middleware[J]. Computer Communications,2012,35(4):405-417.
[24] Xu Xiaoli, Chen Tao, Minami Mamoru. Intelligent fault prediction system based on internet ofthings[J]. Computers&Mathematics with Applications,2012,64(5):833-839.
[25] Mari Carmen Domingo. An overview of the internet of underwater things[J]. Journal ofNetwork and Computer Applications,2012,35(6):1879-1890.
[26] Shuai Xiaoying, Qian Huanyan. Design of Wetland Monitoring System Based on the Internetof Things[J]. Procedia Environmental Sciences,2011,10, Part B:1046-1051.
[27] Dimitris Kiritsis. Closed-loop PLM for intelligent products in the era of the Internet ofthings[J]. Computer-Aided Design,2011,43(5):479-501.
[28] Detlef Zuehlke. SmartFactory—Towards a factory-of-things[J]. Annual Reviews in Control,2010,34(1):129-138.
[29] Chen Xian-Yi, Jin Zhi-Gang. Research on Key Technology and Applications for Internet ofThings[J]. Physics Procedia,2012,33:561-566.
[30]黄沈权,顾新建,等.云制造环境下支持演化的制造云服务元建模[J].计算机集成制造系统,2012,18(6):1327-1336.
[31]李培根,张洁.敏捷化智能制造系统的重构与控制[M].北京:机械工业出版社,2003.
[32] Kiritsis D, Nguyen VK, Stark J. How closed-loop PLM can improve knowledge managementover the complete product lifecycle[J]. International Journal of Product Lifecycle Management2008.
[33] Meyer GG, Framling K, Holmstrom J. Intelligent products: A survey In: Intelligent Products[J].Computers in Industry,2009,60(3):137-148.
[34] Fu Xing, Wang Xiaorui, Eric Puster.Simultaneous thermal and timeliness guarantees indistributed real-time embedded systems[J].Journal of Systems Architecture,2011,57(6):584-596.
[35] Maurizio Faccio, Alessandro Persona, Giorgia Zanin. Waste collection multi objective modelwith real time traceability data[J]. Waste Management,2011,31(12):2391-2405.
[36] Galmarini S., Bianconi R., Vries G. de, Bellasio R. Real-time monitoring data for real-timemulti-model validation: coupling ENSEMBLE and EURDEP[J]. Journal of EnvironmentalRadioactivity,2008,99(8):1233-1241.
[37] Roberto Golinelli, Giuseppe Parigi. Real-time squared: A real-time data set for real-time GDPforecasting[J]. International Journal of Forecasting,2008,24(3):368-385.
[38] Martin Naedele.An approach to modeling and evaluation of functional and timing specifica-tions of real-time systems[J]. Journal of Systems and Software,2001,57(2):155-174..
[39] Domenico Giannone, Lucrezia Reichlin, David Small. Nowcasting: The real-time information-al content of macroeconomic data[J]. Journal of Monetary Economics,2008,55(4):665-676.
[40] Thanasis Vafeiadis, Alexandros Papanikolaou, et al. Real-time network data analysis using timeseries models[J]. Simulation Modelling Practice and Theory,2012,29(12):173-180.
[41] Ousmane Diallo, et al. Real-time data management on wireless sensor networks: A survey[J].Journal of Network and Computer Applications,2012,35(3):1013-1021.
[42] Xiao Zhiqiang, Liang Shunlin, et al. Real-time retrieval of Leaf Area Index from MODIS timeseries data[J]. Remote Sensing of Environment,2011,115(1):97-106.
[43] Neil A. Duffie, Vittaldas V. Prabhu. Real-time distributed scheduling of heterarchicalmanufacturing systems[J]. Journal of Manufacturing Systems,1994,13(2):94-107.
[44] Paul Valckenaers, Paul Valckenaers, et al. Schedule execution in autonomic manufacturingexecution systems[J]. Journal of Manufacturing Systems,2007,26(2):75-84.
[45]饶运清，EFSTATHIOU J.基于信息熵的制造系统复杂性测度及其在调度中的应用[J].机械工程学报,2006,42(7):8-13.
[46]段建国,李爱平等.基于状态熵的制造系统结构复杂性建模与评价[J].机械工程学报,2012,48(5):92-100.
[47]黄刚,李晋航,等.离散制造业可适应制造执行系统的研究与实现[J].计算机集成制造系统,2011,17(10):2137-2143.
[48]杨善林,刘心报,等.敏捷型生产过程优化和制造执行系统[J].系统工程理论与实践,2011,31(S1):181-186.
[49]刘云生.实时数据库系统[M].北京：科学出版社,2012.
[50] Yang Zhengyi, Wang Shilong. Introducing A Novel Data Organization and Access Model forReal-Time Monitoring System[J]. International Journal of Advancements in ComputingTechnology.2012,4(18):236-243.
[51] Yang Zhengyi, Wang Shilong. An efficient modeling approach for real-time monitoring systemwith metadata hierarchy[C]. Lecture Notes in Electrical Engineering.(in press)
[52]丁治明,高需.面向物联网海量传感器采样数据管理的数据库集群系统框架[J].计算机学报,2012,35(6):1175-1191.
[53]胡永利,孙艳丰,尹宝才.物联网信息感知与交互技术[J].计算机学报,2012,35(6):1147-1163.
[54] Hui Fei, Zhao Xiang-mo, Shi Xin, Zhang Jiang-yang. A Multi-interface WSNs BasedHazardous Materials Transportation Monitoring System[J]. International Journal of DigitalContent Technology and its Applications,2012,6(5):255-263.
[55] Andriansyah R., Woensel T V, et al. Performance optimization of open zero-buffer multi-serverqueueing networks[J]. Computers&Operations Research,2010,37(8):1472-1487.
[56] Grosso C. Del, Antoniol G., Merlo E., Galinier P. Detecting buffer overflow via automatic testinput data generation[J]. Computers&Operations Research,2008,35(10):3125-3143.
[57] Satish Chand, Hari Om. Buffer storage for continuous delivery of video data[J]. AppliedMathematical Modelling,2009,33(10):3936-3942.
[58] Zhou Yanhong, et al. Research of Online Water Quality Monitoring System Based on ZigbeeNetwork[J].Advances in Information Sciences and Service Sciences,2012,4(5):255-261.
[59] Eric He, Javid Alimohideen, et al. Quanta: a toolkit for high performance data delivery overphotonic networks[J]. Future Generation Computer Systems,2003,19(6):919-933.
[60] Fortino G., Guerrieri A., et al. A flexible building management framework based on wirelesssensor and actuator networks[J]. Journal of Network and Computer Applications,2012,35(6):1934-1952.
[61]王宏安,荣冈,等.化工生产执行系统MES[M].北京：化学工业出版社,2006.
[62]焦洪峰,艾秀娟.压缩天热气(CNG)加气站网络监控系统设计[J].电气技术,2011,(2):63-65.
[63]邱东利,黄虎成,等. RFID技术在CNG加气站网络化监控系统中的应用[J].现代电子技术,2011,34(4):181-183.
[64] Wang Hui, Hu Shixin. Manufacturing complexity in assembly systems with hybrid configu-rations and its impact on throughout [J]. CIRP Annals-Manufacturing Technology,2010,59(1):53-56.
[65] Huang G Q, Zhang Y F, Newman S. RFID-enabled real-time wireless manufacturing foradaptive assembly planning and control [J]. Journal of Intelligent Manufacturing,2008,19(6):701-713.
[66]吕炳潮,杨扬,伍民友.实时信息的理论研究及应用[J].计算机工程与应用,2010,31(18):4012-4014.
[67] Chowh K H, Choy K L, Lee W B. Design of a RFID case-based resource management systemfor warehouse operations[J]. Expert Systems with Applications,2006,30(4):561-576.
[68] Zhou Guanghui, Jiang Pingyu,Huang Guoquan. A game-theory approach for job scheduling innetworked manufacturing[J]. International Journal of Advanced ManufacturingTechnology,2009,41(9/10):972-985.
[69] SAKAWAM, MORIT. An efficient genetic algorithm for job-shop scheduling problems withfuzzy processing time and fuzzy due date[J]. Computers and Indust rial Engineering,1999,36(2):325-341.
[70]尹胜,尹超,刘飞,等.云制造环境下外协加工资源集成服务模式及语义描述[J].计算机集成制造系统,2011,17(3):525-532.
[71]詹青龙,刘建卿.物联网工程导论[M].北京：清华大学出版社,2012.
[72] Mattfeld D C, et al. An efficient genetic algorithm for job shop scheduling with tardinessobjectives[J].European Journal of Operational Research,2004,155(3):616-630.
[73] Yun Y S. Genetic algorithm with fuzzy logic controller for preemptive and non-preemptivejob-shop scheduling problems[J]. Computers and Industry Engineering,2002,43(3):623-644.
[74] Vinod V, Sridharan R. Scheduling a dynamic job shop production system withsequence-dependent setup: an experimental study[J]. Robotics and Computer-IntegratedManufacturing,2008(24):435-449.
[75] Zhou Guanghui, Jiang Pingyu, Huang Guoquan. Non-cooperation game for customer’scompetition driven job schedule[J]. Chinese Journal of Mechanical Engineering,2006,42(7):56-61.
[76] Chowh K H, Choy K L, Lee W B. A dynamic logistics process knowledge-based system-anRFID multi-agent approach[J]. Knowledge-Based Systems,2007,20(4):357-372.
[77] Aliry. An effective hybrid immune-hill climbing optimization approach for solving design andmanufacturing optimization problems in industry[J]. Journal of Materials ProcessingTechnology,2009,209(6):2773-2780.
[78] Nagesh Sukhi, Ward Nick. Intelligent Second-Generation MES Solutions for300mm Fabs.Solid State Technology, Jun2000,43(6):133-137.
[79]王信义,等.生产系统中的监控检测技术[M].北京：北京理工大学出版社,1998.
[80]吴宏钢.风力发电机组振动检测及噪声评估研究[D].重庆大学,博士研究生学位论文,2010.
[81]胡友民，杜润生，杨叔子．制造系统数据采集技术研究[J]．制造业自动化，2002，24(3):23-27.
[82] Pintelon L, Wassenhove L V. A maintenance management tool[J]. International Journal ofManagement Science,2002,(18):59-70.
[83] Ivancic I. The development of maintenance in modern production[C]//Dubrovnik, Croatia:Conference Proceedings, Euromaintenance’2001,17-35.
[84] Jun H B, Kiritsis D, Xirouchakis P. A primitive ontology model for product lifecycle meta datain the closed-loop PLM[C]. Springer-Verlag London Limited;2007:729-740.
[85] Matsokis A, Kiritsis D. An advanced method for time treatment in product lifecyclemanagement models[C]. In:WCEAM2009.
[86] Yang Zhengyi, Wang Shilong. Model and Performance Analysis of Real-time Monitoring DataProcessing[J]. International Journal of Digital Content Technology and its Applications.2012,6(17):595-602.
[87]张伯鹏.制造信息学[M].北京:清华大学出版社,2003.
[88]张伯鹏,郁鼎文.制造工程中的自演进系统[J].中国机械工程,2008,19(2):178-182.
[89] Liu Yanni Ellen, Wong Johnny W. Deadline-based scheduling in support of real-time datadelivery[J]. Computer Networks,2008,52(3):514-530.
[90] Kevin Dominic Delaney, Pat Phelan. Design improvement using process capability data[J].Journal of Materials Processing Technology,2009,209(1):619-624.
[91] Ge Zhiqiang, Gao Furong, Song Zhihuan. Batch process monitoring based on support vectordata description method[J]. Journal of Process Control,2011,21(6):949-959.
[92] Plamen Z. Zahariev, Georgi V. Hristov. Performance evaluation of data delivery approaches forwireless sensor networks[J]. Procedia Computer Science,2011,3:714-720.
[93] Chen W, Chen Y, Guo D. Design and implementation of monitoring and management systemfor Tank Truck Transportation[J]. WSEAS Transactions on information science andapplications,2010,7(1):26-35.
[94] Li Gang, Che XiLong. The Remote Monitoring System Based on GPRS for the Health State ofCivil Engineering[J]. International Journal of Digital Content Technology and its Applications,2012,6(8):26~33.
[95] Chen Lanxiang, Guo Gongde. An efficient remote data possession checking in cloud storage[J].International Journal of Digital Content Technology and its Applications,2011,5(4):43-50.
[96] Sun HaiPing, Zeng Hong. Bus data acquisition and remote monitoring system based on CANbus and GPRS[J]. IEEE International Conference on Consumer Electronics, Communicationsand Networks(CECNet),2011:1094-1097.
[97] Zhong Weisheng, Li Yijun. Design of Water-Quality Monitoring System Based on GSM[J].International Journal of Advancements in Computing Technology,2011,3(11):375-380.
[98]胡侃,刘云生.传感器网络中协作实时数据库事务的提交控制[J].计算机学报,2007,30(6):916-923.
[99]郭超,李坤,等.多核处理器环境下内存数据库索引性能分析[J].计算机学报,2010,33(8):1512-1522.
[100]苏颖,于明,张伯鹏.一种衡量中小制造企业管理信息质量的方法[J].计算机集成制造系统,2004,10(2):176-182.
[101]Zhang Y F, Huang G Q, Qu T, et al. Agent-based workflow management for RFID-enabledreal-time reconfigurable manufacturing[J]. International Journal of Computer IntegratedManufacturing,2010,23(2):101-112.
[102]Shen W M, Hao Q. An Agent-based service-oriented integration architecture for collaborativeintelligent manufacturing[J]. Robotics and Computer-Integrated Manufacturing,2007,23(3):315-325.
[103]Weng M, Wu Z, Qi G, et al. Multi-Agent based workload control for make-to-ordermanufacturing[J]. International Journal of Production Research,2008,46(8):2197-2213.
[104]冯玉琳,黄涛,金蓓弘.网络分布计算和软件工程[M].北京：科学出版社,2003.
[105]许贵平,刘云生,徐长醒.分布式主动实时事务预分析与预测的集成框架[J].华中科技大学学报（自然科学版）,2005,33(1):1-3.
[106]Gedik B, Wu K L, Liu L, et al. CPU load shedding for binary stream joins[J]. Knowledge andInformation Systems,2007,13(3):271-303.
[107]Yan Ying, Jin Cheqing, Cao Feng, et al. Load shedding for shared window joins over datastreams[J]. Journal of Computer Research and Development,2004,41(10):1836-1841.
[108]Chen J, DeWitt D, Tian F, et al. NiagaraCQ: A scalable continous query system for internetdatabases[C].//Proc of the19th ACM SIGMOD Conf. New York: ACM,2000:379-390.
[109]Chandrasekaran S, Deshpande A, Franklin M, et al.TelegraphCQ: Continuous dataflowprocessing for an uncertain world[C].//Proc of the1st CIDR Conf. New York: ACM,2003:668-668.
[110]Tatbul N, Cetint emel U, Zdonik S, et al. Load shedding in a data stream manager[C]//Proc ofthe29th ACM VLDB Conf. San Francisco: Margan Kaufmann,2003:309-320.
[111]Ayad A, Naught on J. Static optimizat ion of conjunct ive queries with sliding windows overinfinite streams[C]//Proc of the23th ACM SIGMOD Conf. New York: ACM,2004:419-430.
[112]方旺盛，郑剑，邵利平.一种基于文件缓冲方式的操作大数据量数据的方法[J].计算技术与自动化,2004(12).
[113]Daniel Barbar. Requirements for Clustering Data Streams[J]. ACM SIGKDD ExplorationsNewsletter,2003,3(2):23-27.
[114]Tan Zdonik. The Aurora and Medusa Projects[J]. Bulletin of the IEEE Computer SocietyTechnical Committee on Data Engineering,2003,26(1):3-10.
[115]Chuck Cranor. The Gigascope Stream Database[J]. Bulletin of the IEEE Computer SocietyTechnica l Committee on Data Engineering,2003,26(1):27-32.
[116]Abhinandan Das. Approximate Join Processing Over Data Streams[J]. ACM SIGMOD Record,SIGMOD,2003,(6):40-51.
[117]桂浩等.面向流数据的数据管理系统的研究[J].计算机应用研究,2005,(1):88-90.
[118]Varanon Uraikul,et al. Artificial intelligence for monitoring and supervisory control of proeesssystems. Engineering Applications of Artificial Intelligence[J],2007,20:115-131.
[119]Manabu Kano,Yoshiaki Nakagawa. Data-based proeess monitoring, proeess control, andquality improvement: Recent developments and applications in steel industry[J].Cornputersand Chemical Engineering,2008,32:12-24.
[120]Shen Zhijun, Zeng Huashen. A load balanced switch architecture based on implict flowsplitter[J]. Journal of Computer Research and Development,2012,49(6):1220-1227.
[121]Hankins R. A., Patel J. M. Efect of node size on the performance of cache-conscious B+-trees
[C]. in SIGMETRICS,2003:283-294.
[122]冯卫兵,李战怀.流数据的连续查询优化技术[J].计算机应用研究,2008,25(1):131-133.
[123]Luis Nogueira, Luis Miguel Pinho. A capacity sharing and stealing strategy for open real-timesystem[J]. Journal of Systems Architecture,2010,56:163-179.
[124]Manegold S, Boncz P, Kerstenm L. Generic database cost models for hierarchical memorysystems[C]//Proceedings of28th International conference on Very Large Data Bases(VLDB02). HongKong:[s.n.],2002:191-200.
[125]Suei Pei-Lun, Lu Yung-Feng, et al. A signature-based Grid index design for main-memoryRFID database applications[J]. Journal of Systems and Software,2012,85(5):1205-1212.
[126]Byun Siwoo, Hur Moonhaeng. An index management using CHC-cluster for flash memorydatabases[J]. Journal of Systems and Software,2009,82(5):825-835.
[127]Penaflor B.G., Ferron J.R., Piglowski D.A., et al. Current status of the upgraded DIII-Dreal-time digital plasma control system[J]. Fusion Eng. Des.2006,81:1923-1926.
[128]Guillemin D, Robert P, Zwart B. AIMD algorithms and exponential functionals[J]. Ann ApplProbab,2004,14(1):90-117.
[129]Ronell M. A C++pooled, shared memory allocator for simulator development[C]. SimulationSymposium, Proceedings.37th Annual,2004:187-195.
[130]余俊良,杨正益.基于虚拟单元可智能增长的内存池研究[J].计算机工程与应用.(in press)
[131]肖迎元,刘云生.确保时态一致性的实时并发控制协议[J].2008,(11):2102-2106.
[132]刘云生,何冰.混合实时事务的延迟单调速率调度算法及其可调度性分析[J].计算机学报,2004,27(3):289-294.
[133]申志军,曾华燊.基于隐式Flow Splitter的负载均衡交换结构[J].计算机研究与发展,2012,49(6):1220-1227.
[134]张小嘎,伍行健,李誉. CPLD在高速信号处理系统中的应用[J].振动、测试与诊断,1999,19(2):139-143.
[135]Qi H,Iyengar S, Chkrabarty K. Multiresolution data integration using mobile agents indistributed sensor networks[J]. IEEE Trans on System, Man and Cybernetics,2001,31(3):283-291.
[136]Fok C, Roman G, Lu C. Agilla: A mobile agent middleware for self-adaptive wireless sensornetworks[J]. ACM Trans on autonomous and adaptive systems,2009,4(3):1-26.
[137]韩崇昭,朱洪艳,段战胜,等.多源信息融合[M].北京：清华大学出版社,2006.
[138]罗志增,蒋静坪.机器人感觉与多信息融合[M].北京：机械工业出版社,2002.
[139]Sandsmark N, Mehta S. Integrated information platform for reservoir and subsea productionsystems, In: Proceedings of the13th product data technology europe symposium,2004.
[140]Gulla J A, Tomassen S L, Strasunskas D. Semantic interoperability in the norwegian petroleumindustry[C]. In:5th international conference on information systems,2006.
[141]Yang Zhengyi, Yin Aijun. A Novel Approach for End Effect Processing of Empirical ModeDecomposition[J]. International Journal of Convergence Information Technology,2012,7(19):21-29.
[142]杨正益,尹爱军,赵磊.形态-小波阈值降噪方法及其应用[J].重庆大学学报（自然科学版）.
[143]潘立强,李建中,骆吉洲.传感器网络中一种基于时-空相关性的缺失值估计算法.计算机学报，2010,33(1):1-10.
[144]邓林峰，赵荣珍，龚俊.一种改进的转子振动信号消噪方法研究[J].仪器仪表学报,2011,32（9）:1961-1966.
[145]滕军，朱焰煌，周峰，等.自适应分解层数的小波域中值滤波振动信号降噪法[J].振动与冲击,2009,28（12）:58-62.
[146]Sun Yan, Chan Kap Luk, Krishnan Shankar Muthu. ECG signal conditioning by morphologicalfiltering [J]. Computers in Biology and Medicine,2002,32(6):465-479.
[147]Wang Jing, Xu Guanghua, Zhang Qing. Application of improved morphological filter to theextraction of impulsive attenuation signals [J]. Mechanical Systems and Signal Processing,2009,23(1):236-245.
[148]沈路，周晓军，张文斌，等.广义形态滤波器的旋转机械振动信号降噪[J].振动与冲击,2009,28（9）:70-73.
[149]Zhang Wenbin, Wang Hongjun, Teng Rujing.Application of Rank-order Morpho-logical Filterin Vibration Signal De-noising[D].20103rd International Congress on Image and SignalProcessing:4025-4027.
[150]胡爱军，唐贵基，安连锁.基于数学形态学的旋转机械振动信号降噪方法[J].机械工程学报,2006,42（4）:127-130.
[151]张志鹏，王伟平，郭明威.基于小波-中值降噪的经验模式分解及应用[J].振动、测试与诊断,2011,31（6）:779-782.
[152]杜必强，王松岭，唐贵基.形态降噪在转子分形故障状态识别中的应用[J].中国电机工程学报,2008,28（26）:71-76.
[153]向东阳，吴正国，胡文彪等.改进的小波变换系数相关去噪方法[J].振动、测试与诊断2010,3（5）:561-565.
[154]唐进元，陈维涛，陈思雨等.一种新的小波阈值函数及其在振动信号去噪分析中的应用[J].振动与冲击,2009,28(7):118-121.
[155]蒋克荣，唐向清，朱德泉.基于改进阈值算法的汽车轮速信号处理[J].仪器仪表学报,2010,31(4):736-740.
[2]李伯虎,张霖,王时龙,等.云制造—面向服务的网络化制造新模式[J].计算机集成制造系统,2010,16(1):1-7.
[3]郑力,蒋平宇,乔立红,等.制造系统研究的前沿和挑战[J].机械工程学报,2010,46(21):124-136.
[4] Zhou Zude, Yu Wen yong,Chen Youping. Concept and related scientific problems of digitalmanufacturing[J]. China Mechanical Engineering,2001,12(1):100-104..
[5] Thomas, Kelly. MES in the Age of Agile Manufacturing. A Presentation at MESA Roundtable4, Chicago,Sep.13,1995.
[6] Michael McClellan. Applying Manu facturing Execution Systems. CRC Press,1997.
[7] Brandl D. MES Software: A Critical Element in the Enterprise Integration Process, I&CS(USA),1999,72(10):51-56.
[8] Vijayan, Jaikumar. Manufacturing Execution Systems. Computer World,2000,34(31):38-42.
[9]于海斌,朱云龙.可集成的制造执行系统.计算机集成制造系统CIMS[J],2000,6(6):1-6.
[10] MESA International. MES Functionalities&MRP to MES Data Flow Possibilities. MESAInternational White Paper Number2,1997.
[11] MESA International. MES Software Evaluation Selection. MESA International White PaperNumber4,1997.
[12] Barkmeyer Ed, Nenno Peter, Feng Shan, et al. NIST Res ponse to MES Request forInformation. NIST Response to RFI-3: MESM odels,1999.
[13] Scott D. Comparative Advantage through Manufacturing Execution Systems. Proceedings ofthe SEMICON Taiwan96IC Seminar, September1996:227-236.
[14] Cheng FT, Shen E, Deng JY, et al. Development of a System Framework for theComputer-Integrated MES: A Distributed Object-Oriented Approach. International Journal ofCIM,1999,12(5):384-402.
[15]刘卫宁,黄文雷,孙棣华等.基于射频识别的离散制造业制造执行系统设计与实现[J].计算机集成制造系统,2007,13(10):1886-1890.
[16] Ercan Oztemel, Esra Kurt Tekez.A general framework of a Reference Model for IntelligentIntegrated Manufacturing Systems (REMIMS)[J]. Engineering Applications of ArtificialIntelligence,2009,22(6):855-864.
[17] Wang S.L., Xia H., Liu F., Tao G.B., Zhang Z. Agent-based Modeling and Mapping ofManufacturing System[J], Journal of Materials Processing Technology,2002,12(9):518-523.
[18] Wang Shilong,Jian Yi,Liu Fei，Robert Young,Novel Structure for Distributed Numric Control,Chinese Journal of Mechanical Engineering (English Edition),2003,16(4):437-440.
[19] Luigi Atzori, Antonio Iera, et al. The Social Internet of Things (SIoT)–When social networksmeet the Internet of Things: Concept, architecture and network characterization[J]. ComputerNetworks,2012,56（16）:3594-3608.
[20] Luigi Atzori, Antonio Iera, Giacomo Morabito. The Internet of Things: A survey[J]. ComputerNetworks,2010,54(15):2787-2805.
[21] Daniele Miorandi, Sabrina Sicari, et al. Internet of things: Vision, applications and researchchallenges[J]. Ad Hoc Networks,2012,10(7):1497-1516.
[22] Qin Xiaotie, Gu Yuesheng. Data fusion in the Internet of Things[J]. Procedia Engineering,2011,15:3023-3026.
[23] Kiev Gama, Lionel Touseau, Didier Donsez. Combining heterogeneous service technologiesfor building an Internet of Things middleware[J]. Computer Communications,2012,35(4):405-417.
[24] Xu Xiaoli, Chen Tao, Minami Mamoru. Intelligent fault prediction system based on internet ofthings[J]. Computers&Mathematics with Applications,2012,64(5):833-839.
[25] Mari Carmen Domingo. An overview of the internet of underwater things[J]. Journal ofNetwork and Computer Applications,2012,35(6):1879-1890.
[26] Shuai Xiaoying, Qian Huanyan. Design of Wetland Monitoring System Based on the Internetof Things[J]. Procedia Environmental Sciences,2011,10, Part B:1046-1051.
[27] Dimitris Kiritsis. Closed-loop PLM for intelligent products in the era of the Internet ofthings[J]. Computer-Aided Design,2011,43(5):479-501.
[28] Detlef Zuehlke. SmartFactory—Towards a factory-of-things[J]. Annual Reviews in Control,2010,34(1):129-138.
[29] Chen Xian-Yi, Jin Zhi-Gang. Research on Key Technology and Applications for Internet ofThings[J]. Physics Procedia,2012,33:561-566.
[30]黄沈权,顾新建,等.云制造环境下支持演化的制造云服务元建模[J].计算机集成制造系统,2012,18(6):1327-1336.
[31]李培根,张洁.敏捷化智能制造系统的重构与控制[M].北京:机械工业出版社,2003.
[32] Kiritsis D, Nguyen VK, Stark J. How closed-loop PLM can improve knowledge managementover the complete product lifecycle[J]. International Journal of Product Lifecycle Management2008.
[33] Meyer GG, Framling K, Holmstrom J. Intelligent products: A survey In: Intelligent Products[J].Computers in Industry,2009,60(3):137-148.
[34] Fu Xing, Wang Xiaorui, Eric Puster.Simultaneous thermal and timeliness guarantees indistributed real-time embedded systems[J].Journal of Systems Architecture,2011,57(6):584-596.
[35] Maurizio Faccio, Alessandro Persona, Giorgia Zanin. Waste collection multi objective modelwith real time traceability data[J]. Waste Management,2011,31(12):2391-2405.
[36] Galmarini S., Bianconi R., Vries G. de, Bellasio R. Real-time monitoring data for real-timemulti-model validation: coupling ENSEMBLE and EURDEP[J]. Journal of EnvironmentalRadioactivity,2008,99(8):1233-1241.
[37] Roberto Golinelli, Giuseppe Parigi. Real-time squared: A real-time data set for real-time GDPforecasting[J]. International Journal of Forecasting,2008,24(3):368-385.
[38] Martin Naedele.An approach to modeling and evaluation of functional and timing specifica-tions of real-time systems[J]. Journal of Systems and Software,2001,57(2):155-174..
[39] Domenico Giannone, Lucrezia Reichlin, David Small. Nowcasting: The real-time information-al content of macroeconomic data[J]. Journal of Monetary Economics,2008,55(4):665-676.
[40] Thanasis Vafeiadis, Alexandros Papanikolaou, et al. Real-time network data analysis using timeseries models[J]. Simulation Modelling Practice and Theory,2012,29(12):173-180.
[41] Ousmane Diallo, et al. Real-time data management on wireless sensor networks: A survey[J].Journal of Network and Computer Applications,2012,35(3):1013-1021.
[42] Xiao Zhiqiang, Liang Shunlin, et al. Real-time retrieval of Leaf Area Index from MODIS timeseries data[J]. Remote Sensing of Environment,2011,115(1):97-106.
[43] Neil A. Duffie, Vittaldas V. Prabhu. Real-time distributed scheduling of heterarchicalmanufacturing systems[J]. Journal of Manufacturing Systems,1994,13(2):94-107.
[44] Paul Valckenaers, Paul Valckenaers, et al. Schedule execution in autonomic manufacturingexecution systems[J]. Journal of Manufacturing Systems,2007,26(2):75-84.
[45]饶运清，EFSTATHIOU J.基于信息熵的制造系统复杂性测度及其在调度中的应用[J].机械工程学报,2006,42(7):8-13.
[46]段建国,李爱平等.基于状态熵的制造系统结构复杂性建模与评价[J].机械工程学报,2012,48(5):92-100.
[47]黄刚,李晋航,等.离散制造业可适应制造执行系统的研究与实现[J].计算机集成制造系统,2011,17(10):2137-2143.
[48]杨善林,刘心报,等.敏捷型生产过程优化和制造执行系统[J].系统工程理论与实践,2011,31(S1):181-186.
[49]刘云生.实时数据库系统[M].北京：科学出版社,2012.
[50] Yang Zhengyi, Wang Shilong. Introducing A Novel Data Organization and Access Model forReal-Time Monitoring System[J]. International Journal of Advancements in ComputingTechnology.2012,4(18):236-243.
[51] Yang Zhengyi, Wang Shilong. An efficient modeling approach for real-time monitoring systemwith metadata hierarchy[C]. Lecture Notes in Electrical Engineering.(in press)
[52]丁治明,高需.面向物联网海量传感器采样数据管理的数据库集群系统框架[J].计算机学报,2012,35(6):1175-1191.
[53]胡永利,孙艳丰,尹宝才.物联网信息感知与交互技术[J].计算机学报,2012,35(6):1147-1163.
[54] Hui Fei, Zhao Xiang-mo, Shi Xin, Zhang Jiang-yang. A Multi-interface WSNs BasedHazardous Materials Transportation Monitoring System[J]. International Journal of DigitalContent Technology and its Applications,2012,6(5):255-263.
[55] Andriansyah R., Woensel T V, et al. Performance optimization of open zero-buffer multi-serverqueueing networks[J]. Computers&Operations Research,2010,37(8):1472-1487.
[56] Grosso C. Del, Antoniol G., Merlo E., Galinier P. Detecting buffer overflow via automatic testinput data generation[J]. Computers&Operations Research,2008,35(10):3125-3143.
[57] Satish Chand, Hari Om. Buffer storage for continuous delivery of video data[J]. AppliedMathematical Modelling,2009,33(10):3936-3942.
[58] Zhou Yanhong, et al. Research of Online Water Quality Monitoring System Based on ZigbeeNetwork[J].Advances in Information Sciences and Service Sciences,2012,4(5):255-261.
[59] Eric He, Javid Alimohideen, et al. Quanta: a toolkit for high performance data delivery overphotonic networks[J]. Future Generation Computer Systems,2003,19(6):919-933.
[60] Fortino G., Guerrieri A., et al. A flexible building management framework based on wirelesssensor and actuator networks[J]. Journal of Network and Computer Applications,2012,35(6):1934-1952.
[61]王宏安,荣冈,等.化工生产执行系统MES[M].北京：化学工业出版社,2006.
[62]焦洪峰,艾秀娟.压缩天热气(CNG)加气站网络监控系统设计[J].电气技术,2011,(2):63-65.
[63]邱东利,黄虎成,等. RFID技术在CNG加气站网络化监控系统中的应用[J].现代电子技术,2011,34(4):181-183.
[64] Wang Hui, Hu Shixin. Manufacturing complexity in assembly systems with hybrid configu-rations and its impact on throughout [J]. CIRP Annals-Manufacturing Technology,2010,59(1):53-56.
[65] Huang G Q, Zhang Y F, Newman S. RFID-enabled real-time wireless manufacturing foradaptive assembly planning and control [J]. Journal of Intelligent Manufacturing,2008,19(6):701-713.
[66]吕炳潮,杨扬,伍民友.实时信息的理论研究及应用[J].计算机工程与应用,2010,31(18):4012-4014.
[67] Chowh K H, Choy K L, Lee W B. Design of a RFID case-based resource management systemfor warehouse operations[J]. Expert Systems with Applications,2006,30(4):561-576.
[68] Zhou Guanghui, Jiang Pingyu,Huang Guoquan. A game-theory approach for job scheduling innetworked manufacturing[J]. International Journal of Advanced ManufacturingTechnology,2009,41(9/10):972-985.
[69] SAKAWAM, MORIT. An efficient genetic algorithm for job-shop scheduling problems withfuzzy processing time and fuzzy due date[J]. Computers and Indust rial Engineering,1999,36(2):325-341.
[70]尹胜,尹超,刘飞,等.云制造环境下外协加工资源集成服务模式及语义描述[J].计算机集成制造系统,2011,17(3):525-532.
[71]詹青龙,刘建卿.物联网工程导论[M].北京：清华大学出版社,2012.
[72] Mattfeld D C, et al. An efficient genetic algorithm for job shop scheduling with tardinessobjectives[J].European Journal of Operational Research,2004,155(3):616-630.
[73] Yun Y S. Genetic algorithm with fuzzy logic controller for preemptive and non-preemptivejob-shop scheduling problems[J]. Computers and Industry Engineering,2002,43(3):623-644.
[74] Vinod V, Sridharan R. Scheduling a dynamic job shop production system withsequence-dependent setup: an experimental study[J]. Robotics and Computer-IntegratedManufacturing,2008(24):435-449.
[75] Zhou Guanghui, Jiang Pingyu, Huang Guoquan. Non-cooperation game for customer’scompetition driven job schedule[J]. Chinese Journal of Mechanical Engineering,2006,42(7):56-61.
[76] Chowh K H, Choy K L, Lee W B. A dynamic logistics process knowledge-based system-anRFID multi-agent approach[J]. Knowledge-Based Systems,2007,20(4):357-372.
[77] Aliry. An effective hybrid immune-hill climbing optimization approach for solving design andmanufacturing optimization problems in industry[J]. Journal of Materials ProcessingTechnology,2009,209(6):2773-2780.
[78] Nagesh Sukhi, Ward Nick. Intelligent Second-Generation MES Solutions for300mm Fabs.Solid State Technology, Jun2000,43(6):133-137.
[79]王信义,等.生产系统中的监控检测技术[M].北京：北京理工大学出版社,1998.
[80]吴宏钢.风力发电机组振动检测及噪声评估研究[D].重庆大学,博士研究生学位论文,2010.
[81]胡友民，杜润生，杨叔子．制造系统数据采集技术研究[J]．制造业自动化，2002，24(3):23-27.
[82] Pintelon L, Wassenhove L V. A maintenance management tool[J]. International Journal ofManagement Science,2002,(18):59-70.
[83] Ivancic I. The development of maintenance in modern production[C]//Dubrovnik, Croatia:Conference Proceedings, Euromaintenance’2001,17-35.
[84] Jun H B, Kiritsis D, Xirouchakis P. A primitive ontology model for product lifecycle meta datain the closed-loop PLM[C]. Springer-Verlag London Limited;2007:729-740.
[85] Matsokis A, Kiritsis D. An advanced method for time treatment in product lifecyclemanagement models[C]. In:WCEAM2009.
[86] Yang Zhengyi, Wang Shilong. Model and Performance Analysis of Real-time Monitoring DataProcessing[J]. International Journal of Digital Content Technology and its Applications.2012,6(17):595-602.
[87]张伯鹏.制造信息学[M].北京:清华大学出版社,2003.
[88]张伯鹏,郁鼎文.制造工程中的自演进系统[J].中国机械工程,2008,19(2):178-182.
[89] Liu Yanni Ellen, Wong Johnny W. Deadline-based scheduling in support of real-time datadelivery[J]. Computer Networks,2008,52(3):514-530.
[90] Kevin Dominic Delaney, Pat Phelan. Design improvement using process capability data[J].Journal of Materials Processing Technology,2009,209(1):619-624.
[91] Ge Zhiqiang, Gao Furong, Song Zhihuan. Batch process monitoring based on support vectordata description method[J]. Journal of Process Control,2011,21(6):949-959.
[92] Plamen Z. Zahariev, Georgi V. Hristov. Performance evaluation of data delivery approaches forwireless sensor networks[J]. Procedia Computer Science,2011,3:714-720.
[93] Chen W, Chen Y, Guo D. Design and implementation of monitoring and management systemfor Tank Truck Transportation[J]. WSEAS Transactions on information science andapplications,2010,7(1):26-35.
[94] Li Gang, Che XiLong. The Remote Monitoring System Based on GPRS for the Health State ofCivil Engineering[J]. International Journal of Digital Content Technology and its Applications,2012,6(8):26~33.
[95] Chen Lanxiang, Guo Gongde. An efficient remote data possession checking in cloud storage[J].International Journal of Digital Content Technology and its Applications,2011,5(4):43-50.
[96] Sun HaiPing, Zeng Hong. Bus data acquisition and remote monitoring system based on CANbus and GPRS[J]. IEEE International Conference on Consumer Electronics, Communicationsand Networks(CECNet),2011:1094-1097.
[97] Zhong Weisheng, Li Yijun. Design of Water-Quality Monitoring System Based on GSM[J].International Journal of Advancements in Computing Technology,2011,3(11):375-380.
[98]胡侃,刘云生.传感器网络中协作实时数据库事务的提交控制[J].计算机学报,2007,30(6):916-923.
[99]郭超,李坤,等.多核处理器环境下内存数据库索引性能分析[J].计算机学报,2010,33(8):1512-1522.
[100]苏颖,于明,张伯鹏.一种衡量中小制造企业管理信息质量的方法[J].计算机集成制造系统,2004,10(2):176-182.
[101]Zhang Y F, Huang G Q, Qu T, et al. Agent-based workflow management for RFID-enabledreal-time reconfigurable manufacturing[J]. International Journal of Computer IntegratedManufacturing,2010,23(2):101-112.
[102]Shen W M, Hao Q. An Agent-based service-oriented integration architecture for collaborativeintelligent manufacturing[J]. Robotics and Computer-Integrated Manufacturing,2007,23(3):315-325.
[103]Weng M, Wu Z, Qi G, et al. Multi-Agent based workload control for make-to-ordermanufacturing[J]. International Journal of Production Research,2008,46(8):2197-2213.
[104]冯玉琳,黄涛,金蓓弘.网络分布计算和软件工程[M].北京：科学出版社,2003.
[105]许贵平,刘云生,徐长醒.分布式主动实时事务预分析与预测的集成框架[J].华中科技大学学报（自然科学版）,2005,33(1):1-3.
[106]Gedik B, Wu K L, Liu L, et al. CPU load shedding for binary stream joins[J]. Knowledge andInformation Systems,2007,13(3):271-303.
[107]Yan Ying, Jin Cheqing, Cao Feng, et al. Load shedding for shared window joins over datastreams[J]. Journal of Computer Research and Development,2004,41(10):1836-1841.
[108]Chen J, DeWitt D, Tian F, et al. NiagaraCQ: A scalable continous query system for internetdatabases[C].//Proc of the19th ACM SIGMOD Conf. New York: ACM,2000:379-390.
[109]Chandrasekaran S, Deshpande A, Franklin M, et al.TelegraphCQ: Continuous dataflowprocessing for an uncertain world[C].//Proc of the1st CIDR Conf. New York: ACM,2003:668-668.
[110]Tatbul N, Cetint emel U, Zdonik S, et al. Load shedding in a data stream manager[C]//Proc ofthe29th ACM VLDB Conf. San Francisco: Margan Kaufmann,2003:309-320.
[111]Ayad A, Naught on J. Static optimizat ion of conjunct ive queries with sliding windows overinfinite streams[C]//Proc of the23th ACM SIGMOD Conf. New York: ACM,2004:419-430.
[112]方旺盛，郑剑，邵利平.一种基于文件缓冲方式的操作大数据量数据的方法[J].计算技术与自动化,2004(12).
[113]Daniel Barbar. Requirements for Clustering Data Streams[J]. ACM SIGKDD ExplorationsNewsletter,2003,3(2):23-27.
[114]Tan Zdonik. The Aurora and Medusa Projects[J]. Bulletin of the IEEE Computer SocietyTechnical Committee on Data Engineering,2003,26(1):3-10.
[115]Chuck Cranor. The Gigascope Stream Database[J]. Bulletin of the IEEE Computer SocietyTechnica l Committee on Data Engineering,2003,26(1):27-32.
[116]Abhinandan Das. Approximate Join Processing Over Data Streams[J]. ACM SIGMOD Record,SIGMOD,2003,(6):40-51.
[117]桂浩等.面向流数据的数据管理系统的研究[J].计算机应用研究,2005,(1):88-90.
[118]Varanon Uraikul,et al. Artificial intelligence for monitoring and supervisory control of proeesssystems. Engineering Applications of Artificial Intelligence[J],2007,20:115-131.
[119]Manabu Kano,Yoshiaki Nakagawa. Data-based proeess monitoring, proeess control, andquality improvement: Recent developments and applications in steel industry[J].Cornputersand Chemical Engineering,2008,32:12-24.
[120]Shen Zhijun, Zeng Huashen. A load balanced switch architecture based on implict flowsplitter[J]. Journal of Computer Research and Development,2012,49(6):1220-1227.
[121]Hankins R. A., Patel J. M. Efect of node size on the performance of cache-conscious B+-trees
[C]. in SIGMETRICS,2003:283-294.
[122]冯卫兵,李战怀.流数据的连续查询优化技术[J].计算机应用研究,2008,25(1):131-133.
[123]Luis Nogueira, Luis Miguel Pinho. A capacity sharing and stealing strategy for open real-timesystem[J]. Journal of Systems Architecture,2010,56:163-179.
[124]Manegold S, Boncz P, Kerstenm L. Generic database cost models for hierarchical memorysystems[C]//Proceedings of28th International conference on Very Large Data Bases(VLDB02). HongKong:[s.n.],2002:191-200.
[125]Suei Pei-Lun, Lu Yung-Feng, et al. A signature-based Grid index design for main-memoryRFID database applications[J]. Journal of Systems and Software,2012,85(5):1205-1212.
[126]Byun Siwoo, Hur Moonhaeng. An index management using CHC-cluster for flash memorydatabases[J]. Journal of Systems and Software,2009,82(5):825-835.
[127]Penaflor B.G., Ferron J.R., Piglowski D.A., et al. Current status of the upgraded DIII-Dreal-time digital plasma control system[J]. Fusion Eng. Des.2006,81:1923-1926.
[128]Guillemin D, Robert P, Zwart B. AIMD algorithms and exponential functionals[J]. Ann ApplProbab,2004,14(1):90-117.
[129]Ronell M. A C++pooled, shared memory allocator for simulator development[C]. SimulationSymposium, Proceedings.37th Annual,2004:187-195.
[130]余俊良,杨正益.基于虚拟单元可智能增长的内存池研究[J].计算机工程与应用.(in press)
[131]肖迎元,刘云生.确保时态一致性的实时并发控制协议[J].2008,(11):2102-2106.
[132]刘云生,何冰.混合实时事务的延迟单调速率调度算法及其可调度性分析[J].计算机学报,2004,27(3):289-294.
[133]申志军,曾华燊.基于隐式Flow Splitter的负载均衡交换结构[J].计算机研究与发展,2012,49(6):1220-1227.
[134]张小嘎,伍行健,李誉. CPLD在高速信号处理系统中的应用[J].振动、测试与诊断,1999,19(2):139-143.
[135]Qi H,Iyengar S, Chkrabarty K. Multiresolution data integration using mobile agents indistributed sensor networks[J]. IEEE Trans on System, Man and Cybernetics,2001,31(3):283-291.
[136]Fok C, Roman G, Lu C. Agilla: A mobile agent middleware for self-adaptive wireless sensornetworks[J]. ACM Trans on autonomous and adaptive systems,2009,4(3):1-26.
[137]韩崇昭,朱洪艳,段战胜,等.多源信息融合[M].北京：清华大学出版社,2006.
[138]罗志增,蒋静坪.机器人感觉与多信息融合[M].北京：机械工业出版社,2002.
[139]Sandsmark N, Mehta S. Integrated information platform for reservoir and subsea productionsystems, In: Proceedings of the13th product data technology europe symposium,2004.
[140]Gulla J A, Tomassen S L, Strasunskas D. Semantic interoperability in the norwegian petroleumindustry[C]. In:5th international conference on information systems,2006.
[141]Yang Zhengyi, Yin Aijun. A Novel Approach for End Effect Processing of Empirical ModeDecomposition[J]. International Journal of Convergence Information Technology,2012,7(19):21-29.
[142]杨正益,尹爱军,赵磊.形态-小波阈值降噪方法及其应用[J].重庆大学学报（自然科学版）.
[143]潘立强,李建中,骆吉洲.传感器网络中一种基于时-空相关性的缺失值估计算法.计算机学报，2010,33(1):1-10.
[144]邓林峰，赵荣珍，龚俊.一种改进的转子振动信号消噪方法研究[J].仪器仪表学报,2011,32（9）:1961-1966.
[145]滕军，朱焰煌，周峰，等.自适应分解层数的小波域中值滤波振动信号降噪法[J].振动与冲击,2009,28（12）:58-62.
[146]Sun Yan, Chan Kap Luk, Krishnan Shankar Muthu. ECG signal conditioning by morphologicalfiltering [J]. Computers in Biology and Medicine,2002,32(6):465-479.
[147]Wang Jing, Xu Guanghua, Zhang Qing. Application of improved morphological filter to theextraction of impulsive attenuation signals [J]. Mechanical Systems and Signal Processing,2009,23(1):236-245.
[148]沈路，周晓军，张文斌，等.广义形态滤波器的旋转机械振动信号降噪[J].振动与冲击,2009,28（9）:70-73.
[149]Zhang Wenbin, Wang Hongjun, Teng Rujing.Application of Rank-order Morpho-logical Filterin Vibration Signal De-noising[D].20103rd International Congress on Image and SignalProcessing:4025-4027.
[150]胡爱军，唐贵基，安连锁.基于数学形态学的旋转机械振动信号降噪方法[J].机械工程学报,2006,42（4）:127-130.
[151]张志鹏，王伟平，郭明威.基于小波-中值降噪的经验模式分解及应用[J].振动、测试与诊断,2011,31（6）:779-782.
[152]杜必强，王松岭，唐贵基.形态降噪在转子分形故障状态识别中的应用[J].中国电机工程学报,2008,28（26）:71-76.
[153]向东阳，吴正国，胡文彪等.改进的小波变换系数相关去噪方法[J].振动、测试与诊断2010,3（5）:561-565.
[154]唐进元，陈维涛，陈思雨等.一种新的小波阈值函数及其在振动信号去噪分析中的应用[J].振动与冲击,2009,28(7):118-121.
[155]蒋克荣，唐向清，朱德泉.基于改进阈值算法的汽车轮速信号处理[J].仪器仪表学报,2010,31(4):736-740.