Research On Real-time Data Sensing And Processing Model In Internet Of Things Based Manufacturing

Using Internet of Things in manufacturing,a new technology comes into being-Internet of Things based Manufacturing(ITM),which is integrating different resources in the production environment,analyzing and using the real-time sensing data to make the optimal decision, dynamic scheduling and control in the procedure of production. Although the application of Things technology gradually developed and perfected in the manufacturing sector, there are still many challenges in Internet of Things based Manufacturing technology:Since the large-scale production environment, there may be different types of communication networks between different production or workshop areas, resulting in difficulty in real-time data exchange.In the manufacturing environment, the resulting data is massive level, so how to process these real-time data is very difficult.Therefore,according to characteristics of the data in Internet of Things based Manufacturing,a real-time data sensing and processing model in Internet of Things based Manufacturing is present, the contents are as follows:(1)Analyzing the present situation of manufacturing development and combining with the characteristics of the Internet of Things,this dissertation designs a real-time data sensing and processing model in Internet of Things based Manufacturing,with real-time data driven optimization method,the classification of terminal.(2)For the common heterogeneous networks in Internet of Things based Manufacturing,designing a ITM gateway as an access platform for integration of heterogeneous networks,analyzing network protocols and designing functional module analysis for the ITM gateway.(3)Based on the requirement of data flow events processing in real-time data sensing and processing model of ITM,combining with cloud computing,designing a partitioning and execution of real-time data flow events model.Through the use of partitioning multi-objective optimization algorithm in the data flow events,obtain the optimal decomposition event, and get the rational allocation of local resources, cloud resources and wireless network resources.(4)Test the ITM gate and use Matlab simulation environment to test load balancing of gateway and the effectiveness of partitioning algorithm of data flow events.Experimental results show that IMT gateway ensure the integration of heterogeneous network transmission, and can meet the dynamic load balancing among multiple gateways; through analysis the effect of different parameters of partitioning algorithm for the system performance, and compared with no partitioning method in system performance,it verify the validity of the partitioning algorithm.This dissertation present a real-time data sensing and processing model in Internet of Things based Manufacturing,which not only can transmit the data in heterogeneous networks,but also process the real-time data flow events,getting the rational allocation of resources.