针对传统服务组织高耦合、低透明、变更复杂等问题，提出了以物联大数据为中心的基于流引擎的微服务动态交互平台构造方案。将服务流程拆解为细粒度的微服务模型，以统一的表述方式明确服务的边界，使服务在开发过程中不再依赖于其他服务的接口也可以实现。用流数据通道将微服务连通起来，在生产者侧基于数据时空特征进行服务封装以构建统一的信息表示，在消费者侧进行服务解析并重新划分组织数据，从而在数据驱动下形成对应完整业务流程的服务。该方案实现了可视化的微服务动态交互管理平台，可用于发动机制造的旋压检测工艺流程。与传统的面向服务架构的设计相比，该平台设计耦合度低，能实现灵活的服务变更、拓展和演化，且在服务监控和故障处理等方面也有更好的表现。
In view of problems of traditional service design, such as high coupling, low transparency and complex change, a dynamic microservice interaction platform design based on stream engine is proposed. Service process is decomposed into fine-grained microservice models whose boundaries are defined in a unified model representation, so that a service can be implemented independently of the interfaces of other services. Microservices are connected through stream channels. Service encapsulation is carried out on the producer side based on temporal and spatial features of data to construct unified information representation. Service analysis is carried out on the consumer side to divide and reorganize data. A complete service process for business process is constructed, driven by streaming data. Based on this design, a visual microservice interaction management platform is realized and applied to spinning detection process of engine manufacturing. Compared with traditional service systems, this platform design features in lower coupling, more flexibility in service change, expansion and evolution, and improved performance in service monitoring and fault handling.
[1] Zhao Q Y, Wei L, Sheng Z W, et al. Research on service-oriented application support software platform of manufacturing informatization[C]//IEEE International Conference on Computer & Communications, 2016:1402-1406.
[2] 延建林, 孔德婧. 解析"工业互联网" 与"工业4.0" 及其对中国制造业发展的启示[J]. 中国工程科学, 2015, 17(7):141-144. Yan J L, Kong D J. Study on "industrial Internet" and "industry 4.0"[J]. Strategic Study of Chinese Academy of Engineering, 2015, 17(7):141-144. (in Chinese)
[3] Tao F, Sui F, Liu A, et al. Digital twin-driven product design framework[J]. International Journal of Production Research, 2019, 57(12):3935-3953.
[4] 张曙. 工业4.0和智能制造[J]. 机械设计与制造工程, 2014(8):1-5. Zhang S. Industry 4.0 and smart manufacturing[J]. Machine Design and Manufacturing Engineering, 2014(8):1-5. (in Chinese)
[5] Siderska J, Jadaan K S. Cloud manufacturing:a service-oriented manufacturing paradigm. A review paper[J]. Engineering Management in Production and Services, 2018, 10(1):22-31.
[6] Aladwan F, Alzghoul A, Ali E M M, et al. Service composition in service oriented architecture:a survey[J]. Modern Applied Science, 2018, 12(12):18-27.
[7] Francesco P D, Lago P, Malavolta I. Migrating towards microservice architectures:an industrial survey[C]//2018 IEEE International Conference on Software Architecture, 2018:29-38.
[8] Abdullah M, Iqbal W, Erradi A. Unsupervised learning approach for Web application auto-decomposition into microservices[J]. Journal of Systems and Software, 2019, 151:243-257.
[9] Kookarinrat P, Temtanapat Y. Design and implementation of a decentralized message bus for microservices[C]//International Joint Conference on Computer Science & Software Engineering, 2016:1-6.
[10] Schuler R, Kesselman C, Czajkowski K. Data centric discovery with a data-oriented architecture[C]//Workshop on the Science of Cyberinfrastructure Research, 2015:37-44.
[11] Fang Z, Computer D O. Mass data mining system for Internet of things based on association rules Apriori algorithm[J]. Journal of Hebei North University (Natural Science Edition), 2015:15-18.
[12] Boubiche S, Boubiche D E, Bilami A, et al. Big data challenges and data aggregation strategies in wireless sensor networks[J]. IEEE Access, 2018, 6:20558-20571.
[13] Begoli E, Camacho-Rodriguez J, Hyde J, et al. Apache calcite:a foundational framework for optimized query processing over heterogeneous data sources[C]//Proceedings of the 2018 International Conference on Management of Data, 2018:221-230.
[14] Shi X, Chen Z, Wang H, et al. Convolutional LSTM network:a machine learning approach for precipitation nowcasting[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015, 1:802-810.
[15] Goyal H, Sharma C, Joshi N. An integrated approach of GIS and spatial data mining in big data[J]. International Journal of Computer Applications, 2017, 169(11):1-6.
[16] Li L, Zhang J, Wang Y, et al. Missing value imputation for traffic-related time series data based on a multi-view learning method[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(8):2933-2943.
[17] 段红超. 基于SSH框架的MES生产制造管理系统设计与实现[D]. 杭州:浙江工业大学, 2017:1-60.
[18] 高媛媛. 基于SOA架构的生产管理系统的研发[D]. 北京:北京工业大学, 2018:1-86.
