| In discrete manufacturing workshops,there are many product models,flexible process routes,and various production factors.However,the workshop lacks effective integration and utilization methods for the multi-source replacement data generated by the production factors,resulting in low data utilization.At the same time,the physical flow and data flow of the production site are merged and synchronized.It is difficult for managers to retrieve the overall operating status of the workshop in real time based on the data.As a result,the workshop is called a "black box" during the production process.Plan and make effective planning of manufacturing resources.Here,without changing the internal equipment,layout,network architecture and process of the internal discrete manufacturing workshop,the discrete manufacturing workshop visualization platform is designed and developed.Through the integrated storage,fusion analysis and visual display of the internal data of the discrete manufacturing workshop,Provide data support for workshop managers to make scientific decisions;through the discrete manufacturing workshop visualization scenarios,it is convenient for workshop managers to fully control the overall operating status of the workshop.The main research contents and contributions of this thesis are as follows:(1)In view of the problem that the data of discrete manufacturing workshop are scattered in various business systems and equipment,which hinders the integration and utilization of manufacturing data,the traditional big data technology is used to realize the integration and centralized storage of the data of discrete manufacturing workshop;The object-oriented spatiotemporal data model is designed,which is helpful for data analysis and utilization.(2)The construction method of the visual scene of the discrete manufacturing workshop based on Demo3D software is studied.The visual model of the discrete manufacturing workshop is built to reflect the physical spatial attributes of the workshop.The visual model of the discrete manufacturing workshop is driven by real-time data so that the visual scene can reflect the production running state of the real workshop.(3)Aiming at the problems such as uneven sparse location coordinates and serious pulsation of moving elements in the collected discrete manufacturing workshop,a Kalman filter based on Spark Streaming was designed to realize real-time smoothing processing of the trajectory and improve the visualization effect.Aiming at the problem that massive historical track data is difficult to store due to the redundancy of track data of moving elements,Douglas-peucker(DP)track compression algorithm based on Map Reduce is designed to compress historical track data and improve the efficiency of storage,query and analysis of historical track data.(4)Based on the above algorithm and technology research,the design and implementation of the visual platform for discrete manufacturing workshop is carried out,and the feasibility of the platform is verified. |
