Key Technology Research On Digital Twin For Long-Distance Sand And Gravel Transportation System

With the emergence of emerging technologies such as cloud computing,the Internet of Things(Io T),artificial intelligence(AI),and virtual reality(VR),countries around the world are exploring strategies for transforming traditional manufacturing industries.Among them,the construction of physical information systems to achieve the interaction and integration of physical devices and information virtual space has become the core content of these strategies.As the cornerstone of modern industry,long-distance conveyor belts are widely used in the transportation of loose industrial materials due to their advantages such as high efficiency,low energy consumption,and high transmission capacity.In the process of intelligent transformation of the manufacturing industry,issues such as insufficient real-time monitoring of traditional long-distance sand conveyor monitoring systems,single monitoring methods,and inadequate data interaction have gradually emerged.These problems may lead to low operational efficiency and difficulty in timely detection of potential faults.Therefore,it has become an important topic of current research and practice to explore how to use advanced technologies to achieve more intelligent,convenient,and efficient monitoring management to improve the reliability and operational efficiency of the entire transmission system.This thesis aims to explore the key technologies and implementation approaches of the digital twin system for long-distance sand conveyor belts.Based on the digital twin architecture,key technologies such as physical-virtual coupling,virtual-physical mapping,visualization management,and interaction are studied to connect physical entities with virtual models and achieve data exchange and state mapping.The specific research content includes:(1)Overall design of key technologies for digital twins.Analyzing the problems faced by the research on key technologies for digital twins in long-distance sand conveyor systems and proposing specific research objectives.The digital twin architecture is determined and divided into five dimensions: physical entity,virtual model,twin data,service management,and data transmission.The corresponding content and functions of each dimension in the long-distance sand conveyor system are introduced.(2)Research on digital twin virtual model construction and data interaction technology.The Solidworks software is used to build a geometric model of the conveyor belt to accurately describe the three-dimensional structural information of the conveyor belt.The ADAMS dynamics analysis software and ANSYS finite element analysis software are used to construct physical models of the conveyor belt,which are used to characterize the motion displacement characteristics and deformation characteristics,respectively.The physical model and the behavioral model are combined for simulation to simulate the operating state of the physical entity under different conditions.The analysis results are compared and verified with the actual physical characteristics to verify the accuracy of the model.In addition,the interaction technology of digital twin data is studied,and suitable sensors are selected to describe the operation status of the conveyor belt.Combined with Simulink co-simulation technology,the behavior model is synchronized with external data to achieve the purpose of real-virtual control.(3)Research on key technologies for digital twin virtual-physical behavior mapping and visualization management at the service management layer.The traditional method of mapping virtual-physical behavior in digital twins usually uses entity sensor data to trigger specified actions in the virtual model.However,this method cannot reflect the flexible behavior characteristics of the conveyor belt,such as misalignment,slippage,and breakage.To address this issue,this thesis proposes a texture mapping-based virtual-physical behavior mapping technology.Based on the deformation videos of the virtual model of the conveyor belt,deformation images at different time points are captured.A series of operations such as point registration,feature creation,and batch processing are used to map the deformation results to the two-dimensional unfolded diagram of the geometric model and generate texture maps.By modifying the texture maps on the model surface,the dynamic virtual-physical behavior of the conveyor belt is mapped.The UE4(Unreal Engine 4)technology is used to build a digital twin scene in a virtual space,and combined with blueprint scripts and C++ scripts,it achieves the visualization display of the virtual model,dynamic mapping of virtual-physical behavior,and virtual roaming functions.In addition,web frontend technology is combined with UE4 to present digital twin management data in the form of charts,enabling functions such as data management,historical information preview,and real-time data display.This provides an intuitive and convenient interactive interface for digital twin applications.(4)Application of the research results of digital twin key technologies to misalignment fault detection in conveyor belts.By utilizing virtual model construction and data interaction technologies,the virtual model is driven by sensor data to generate twin data.The sensor data and twin data are fused to train a random forest algorithm optimized by genetic algorithms,in order to build a fault discrimination rule model for misalignment faults in conveyor belts.Combined with virtual-physical behavior mapping and visualization technologies,the visualization display of the conveyor belt’s operating status and the management of fault types are realized in the virtual space.In conclusion,this thesis focuses on exploring the key technologies and implementation approaches of the digital twin system for long-distance sand conveyor belts.By utilizing digital twin architecture,various key technologies such as physical-virtual coupling,virtual-physical mapping,visualization management,and interaction are studied to connect physical entities with virtual models and achieve data exchange and state mapping.The application of these research results to misalignment fault detection in conveyor belts demonstrates the potential of digital twin technology in improving monitoring and management in the manufacturing industry.