基于云平台的大数据驱动的信息物理融合系统建模方法

大数据驱动的信息物理融合系统（CPS）不仅具有大数据的多“V”特征， 而且还具有CPS的典型特征， 并以云平台为实现平台。项目基于云平台的大数据驱动的CPS建模方法试图解决大数据驱动的CPS的数据的海量性，多样性，实时性、空间性及价值性， 所提出的模型要求避免传统建模孤立考虑连续与离散、时空特性和安全特性的弱点，能够充分表达时间约束和空间位置条件，一体化地实现大数据环境下复杂计算、网络化实时通信与控制等理念。主要的创新体现在：1. 提出一种多视图多领域的大数据驱动的CPS的建模方法。2.提出时空大数据的建模与分析方法, 特别是移动对象大数据的建模方法。 3.提出大数据驱动的实时通信方法。4.基于云平台的大数据驱动的CPS的时空调度方法。5.时空驱动的大数据多模计算方法。6. Modelicalml, AADL 及UML针对大数据驱动的CPS特性的扩展及模型的转换与集成方法。

Big data driven cyber physical systems not only have multiple "V " features, but also have typical features of cyber physical systems, and are implemented on cloud platform as target platform. This project "modeling methods for big data driven cyber physical systems based on cloud platforms" attempts to solve Volume, Variety, Real Time, Value and Spatial features of big data driven cyber physical systems, the proposed models can avoid the shortcomings of classical modeling methods considering continuous and discrete features, spatial-temporal features and safety features in isolation, can sufficiently express time constraints and space location condition, complete complex computing, real-time network communication and control in integration. The main originality of this project is as follows: 1.to propose multi-view and multi-domain modeling methods for big data driven cyber physical systems. 2. to propose modeling and analysis methods for spatial-temporal big data, especially modeling methods for moving object big data. 3. to propose big data real-time communication methods. 4. spatial-temporal scheduling methods for big data driven cyber physical systems based on cloud platforms. 5. Spatial-temporal requirement driven MapReduce computing methods for big data.6. according to the special features of big data driven cyber physical systems, to extend Modelicalml, AADL and UML, and realize model transformation and integration among them.