流程工业生产系统TRF模型及方法研究
摘要
当前新兴的扩展PSE领域研究和流程工业MES(P-MES)系统实践之间有着相辅相成的紧密关联,两者既具有相同的对象,也有着共同的目标。由于扩展PSE领域的研究工作继承和延续了传统PSE的研究思路,使得当前大量的努力和成果集中于多层次的决策与优化方法方面。然而,作为最终面向工程应用的理论研究,这些努力和成果是否具有价值和意义则必须由系统化的应用成效决定,即当前及未来扩展PSE理论研究活动必须以类似于P-MES的系统技术的前沿需求为导向方能获得均衡及有目标的发展。本项研究首先以此为视角对扩展PSE及P-MES的现状进行了系统性的对比分析,从而指出了若干薄弱环节,进而明确了系统导向下的若干研究挑战,最终将其中最缺乏关注与成果的全局生产系统状态观测与反馈建模难题作为本项研究的主题。
通过分析空间多尺度的生产系统(工厂)和时间多尺度的CMS系统之间的信息流关系,透视了控制信息流的分解过程和反馈信息流的聚集过程。面向生产系统状态观测、反馈和追溯的目标,提出在反馈信息流中实现测量数据、基本信息单元和生产系统状态信息逐层聚集与融合的TRF模型理论及其4项关键方法。论证了TRF模型的机理和针对生产物流结构性动态的适应能力,给出了该模型及其关键方法在工厂级控制环中的架构方式。
以上述系统性分析结果为基础展开4项关键方法的研究,分别在TRF模型中实现事件跟踪、移动合成、模型表达和模型解析。其中,基于数据／信息之间的冗余关系将事件跟踪问题转化为一类在线的必要特征估计问题,从而解决了-般测量时序下的事件跟踪难题,以此为基础分别给出了自动的事件在线跟踪和移动实时合成方法。从TRF模型的时间表达原理入手,对比调度优化中广泛使用的连续时间原理及相关方法,通过分析STN/RTN在传统连续时间方法中的作用及其面向TRF模型表达需求的不足,创新地提出了连续时间网络(CTN)概念,基于CTN及其节点图式所提供的动态索引,给出了TRF模型的动态表达方法和动态解析方法。
由上述关键方法的原理支撑的TRF模型系统概念设计、关键方法仿真以及同比优势均表明本项研究的结果成立,且具有深入研究和实践的价值。
Current emerging research in extended PSE domain and system practice of process industry MES (P-MES), which have the same object and target, has a mutual relation in-between. Since researches in extended PSE area inherit and continue the research thought of traditional PSE, a majority efforts and results lie in aspects such as multi-hierarchy decision-making and optimization method. However, as an engineering application oriented theoretical research, value and significance of these efforts and results must be evaluated by systematical application effectiveness, that is to say, current and future theory research activities in extended PSE domain can only obtain balance and development by following future demand similar to that of P-MES. In this thesis, current situation and weak point of extended PSE and P-MES have been compared systematically first, then multiple weak points are pointed, research challenges under system direction are further identified, eventually, global process system feedback modeling problem that lacks focuses and achievements is taken as the topic of this research.
Decomposition process of control information flow and aggregation process of feedback information flow are demonstrated by analyzing process system (plant) of multi-dimensional space and CIMS system of multi-dimensional time. To fulfill the target concerning state tracing, feedback and tracking of process system, TRF model theory including aggregation and fusion realization of measurement data, basic information units and state information in process system based on feedback information flows and four other key methods. Mechanism of TRF model and adaptability subject to structural dynamic of process flows are verified, architecture modes of this model and relevant key method in plant-wide scale are given.
Research works on four key methods are conducted based on analysis results mentioned above to realize event-tracing, movement integration, model presentation and model analysis. Based on redundancy relation between data/information, event-tracing problem is transformed into a class of on-line necessary characteristic estimation problem to solve the difficult problem of on-line event-tracing in conventional measurement time series, then automatic event-tracing and movement integration methods are given respectively. Begin with the theory for time representation in TRF model, continuous time method widely adopted in scheduling optimization is analyzed by comparison. Concept of continuous time network (CTN) is proposed creatively though analyzing the role of STN/RTN in traditional continuous time method and their insufficiency to meet the model presentation demands in TRF. By following the node-graph dynamic index mechanism of CTN, dynamic description method and analytic method of TRF model are given.
Design of TRF model system concept, key method simulation and advantages by comparison supported by mechanism of the aforementioned key method demonstrate the correctness of this research, besides,this research deserves further research and has value in practical application.
通过分析空间多尺度的生产系统(工厂)和时间多尺度的CMS系统之间的信息流关系,透视了控制信息流的分解过程和反馈信息流的聚集过程。面向生产系统状态观测、反馈和追溯的目标,提出在反馈信息流中实现测量数据、基本信息单元和生产系统状态信息逐层聚集与融合的TRF模型理论及其4项关键方法。论证了TRF模型的机理和针对生产物流结构性动态的适应能力,给出了该模型及其关键方法在工厂级控制环中的架构方式。
以上述系统性分析结果为基础展开4项关键方法的研究,分别在TRF模型中实现事件跟踪、移动合成、模型表达和模型解析。其中,基于数据／信息之间的冗余关系将事件跟踪问题转化为一类在线的必要特征估计问题,从而解决了-般测量时序下的事件跟踪难题,以此为基础分别给出了自动的事件在线跟踪和移动实时合成方法。从TRF模型的时间表达原理入手,对比调度优化中广泛使用的连续时间原理及相关方法,通过分析STN/RTN在传统连续时间方法中的作用及其面向TRF模型表达需求的不足,创新地提出了连续时间网络(CTN)概念,基于CTN及其节点图式所提供的动态索引,给出了TRF模型的动态表达方法和动态解析方法。
由上述关键方法的原理支撑的TRF模型系统概念设计、关键方法仿真以及同比优势均表明本项研究的结果成立,且具有深入研究和实践的价值。
Current emerging research in extended PSE domain and system practice of process industry MES (P-MES), which have the same object and target, has a mutual relation in-between. Since researches in extended PSE area inherit and continue the research thought of traditional PSE, a majority efforts and results lie in aspects such as multi-hierarchy decision-making and optimization method. However, as an engineering application oriented theoretical research, value and significance of these efforts and results must be evaluated by systematical application effectiveness, that is to say, current and future theory research activities in extended PSE domain can only obtain balance and development by following future demand similar to that of P-MES. In this thesis, current situation and weak point of extended PSE and P-MES have been compared systematically first, then multiple weak points are pointed, research challenges under system direction are further identified, eventually, global process system feedback modeling problem that lacks focuses and achievements is taken as the topic of this research.
Decomposition process of control information flow and aggregation process of feedback information flow are demonstrated by analyzing process system (plant) of multi-dimensional space and CIMS system of multi-dimensional time. To fulfill the target concerning state tracing, feedback and tracking of process system, TRF model theory including aggregation and fusion realization of measurement data, basic information units and state information in process system based on feedback information flows and four other key methods. Mechanism of TRF model and adaptability subject to structural dynamic of process flows are verified, architecture modes of this model and relevant key method in plant-wide scale are given.
Research works on four key methods are conducted based on analysis results mentioned above to realize event-tracing, movement integration, model presentation and model analysis. Based on redundancy relation between data/information, event-tracing problem is transformed into a class of on-line necessary characteristic estimation problem to solve the difficult problem of on-line event-tracing in conventional measurement time series, then automatic event-tracing and movement integration methods are given respectively. Begin with the theory for time representation in TRF model, continuous time method widely adopted in scheduling optimization is analyzed by comparison. Concept of continuous time network (CTN) is proposed creatively though analyzing the role of STN/RTN in traditional continuous time method and their insufficiency to meet the model presentation demands in TRF. By following the node-graph dynamic index mechanism of CTN, dynamic description method and analytic method of TRF model are given.
Design of TRF model system concept, key method simulation and advantages by comparison supported by mechanism of the aforementioned key method demonstrate the correctness of this research, besides,this research deserves further research and has value in practical application.
引文
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