A high-definition map architecture for transportation digital twin system construction

With the development of digital twin, the research on the fusion of intelligent manufacturing system and digital twin technology attracts more attention. In this paper, the realization of digital twin is studied for geometric feature online inspection system of body-in-white (BIW). A three-level virtual modeling approach of Element-Behavior-Rule is proposed for the physical environment modeling in a digital environment. An element model, behavior model and rule model are defined, respectively, and the relationship among the three-layer model is given for real-time communication. A three-layer communication architecture of the digital twin system is designed for the real-time mapping between physical and virtual space, and the process of the online inspection digital twin system is analyzed. Finally, on the basis of the online inspection physical experimental platform and JHIM (Jiangheng Intelligent Manufacturing) software platform, the digital twin system for geometric feature online inspection of BIW is developed, which shows that the virtual model can be driven in real time by real-time physical inspection data. Digital twin technology provides a feasible way for real-time monitoring of the online inspection system.

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

Additive manufacturing is a promising manufacturing process with diverse applications, but ensuring the quality and reliability of the manufactured products is a key challenge. The digital twin has emerged as a technology solution to address this challenge, allowing real-time monitoring and control of the manufacturing process. This paper proposes a digital twin system framework for additive manufacturing that integrates machine learning models, employing Unity, OctoPrint, and Raspberry Pi for real-time control and monitoring. Particularly, the system utilizes machine learning models for defect detection, achieving an Average Precision (AP) score of 92%, with specific performance metrics of 91% for defected objects and 94% for non-defected objects, demonstrating high efficiency. The Unity client user interface is also developed for control and visualization, facilitating easy additive manufacturing process monitoring. This research article presents a detailed description of the proposed digital twin framework and its workflow for implementation, the machine learning models, and the Unity client user interface. It also demonstrates the effectiveness of the integrated system through case studies and experimental results. The main findings show that the proposed digital twin system met its functional requirements and effectively detects defects and provides real-time control and monitoring of the additive manufacturing process. This paper contributes to the growing field of digital twin technology and additive manufacturing, providing a promising solution for enhancing the quality and reliability in the field of additive manufacturing.

This study described the results of implementing the framework of the digital twin system for the digitization of plant factories. The purpose of this invention is to implement a system based on digital twins that can control and monitor the optimal environment for foliage containing functional components grown in plant factories. In this paper, for the realization of such a system, a digital twin framework that can be applied universally to plant factories was designed. This design structure was largely designed by dividing it into 1) the physical world layer, 2) the cyber and physical interaction layer, 3) the digital twin system layer, and 4) the application layer. In this study, the plant factory digital twin framework designed in this way was demonstrated in the test bed space of a company operating a plant factory in Chungcheongnam-do.

The increasing urban traffic problems have made the transportation system require a large amount of data. Aiming at the current problems of data types redundancy and low coordination rate of intelligent transportation systems (ITS), this paper proposes an improved digital twin architecture applicable to ITS. Based on the improved digital twin architecture, a framework for dynamic and static data collaboration in ITS is constructed. For various collaboration methods, this paper specifically describes the collaboration methods and scopes, and designs the framework and interfaces for data mapping. Finally, the effectiveness of the framework is verified by case studies to mine the spatiotemporal distribution characteristics of data, capture human travel characteristics, and visualize intersections using digital twins. This paper provides a new data fusion idea for digital twin systems in ITS, and the framework covers all data types in digital twin systems for cross-integration analysis.

Recently, more and more digital twin technologies are getting attention in managing and operating urban metros. The fact that integrating time-monitor data like a metro digital twin right now has chances to improve its operation and dependability. This essay is mostly about the applications of digital twins for the metro system: infrastructure monitoring and operation management. Digital twin, at the infrastructure level, can be utilized in observing the structure inside the metro tunnel to see if there is a dangerous situation. For example, a swelling on Earth. After applying our collected data to a predictive analysis, this will offer us an early warning and allow preventive maintenance. In terms of operation, a digital twin system would help people to monitor the operation of metro network, which means smoother passenger flow. Although the metro system's digital twin technology has great benefits, problems like data integration, reliability of the whole system and its security still need to be solved. More studies and practices need to take place to make sure that digital twin tech is employed safely and correctly on future metros.

In recent years, research on digital twins in the construction sector has grown significantly, particularly since 2018. However, most studies focus on isolated lifecycle phases and give limited attention to developing practices that address all stages of the building lifecycle. Current applications of digital twins in construction are largely concentrated on design and engineering, where Building Information Modeling (BIM) serves as the primary input. This dominance stems from BIM's role as an existing digital representation of built assets, which can be extended into a digital twin. One of the major barriers to wider implementation lies in data integration and interoperability—specifically, the challenge of consolidating information from diverse sources, formats, and lifecycle stages. Future research should prioritize the standardization of data formats, protocols, and application programming interfaces (APIs) to facilitate seamless exchange across digital twin systems. Additionally, the development of advanced data governance frameworks is essential to ensure accuracy, completeness, and security of information. Greater exploration into semantic data modeling and ontologies will also support the integration of heterogeneous data sources. Beyond technical issues, it is equally important to investigate the costs, benefits, and potential drawbacks of adopting digital twins across the entire building lifecycle. Expanding the scope of applications beyond design and engineering to include construction, operation and maintenance, as well as demolition and recovery phases, will be crucial. This paper therefore outlines the current state of digital twin research in construction, identifies critical challenges, and highlights key directions for future development.

A digital twin is a virtual representation of a physical system, which can be used for prediction and control. This representation can include predictive models and simulations, which are continuously updated with physical system data. Autonomous control using a digital twin can be considered as a cognitive loop, involving interpretation of sensor data (perception), integration of the data into predictive models (which can involve reasoning) and the planning and execution of actions on the physical system. In particular, meta-reasoning can be used to monitor and control the algorithms and models of the digital twin. This is important for autonomous problem detection and recovery, as well as for explanation and transparency.Our approach to cognitive digital twin systems is first to specify a high-level architecture, grounded in cognitive systems research (as is often done in robotics). We then provide a background and survey of meta-reasoning and include it as an additional layer in the architecture. Finally, we outline a roadmap for gradually increasing the autonomy and resilience of a digital twin system by iterative development in a real-world environment.

Lightweighting Process of Digital Twin Information Models for Smart City Services

The pointing accuracy of large radio telescopes is affected by antenna structure errors, installation errors, servo errors, gravity, and varying environmental factors. Although an on-line pointing model had been developed in the past for several large radio telescopes to correct these effects, it is also valuable to explore new ways to correct these effects. Therefore, in order to estimate the dynamic pointing error of large radio telescopes under solar radiation, a new way based on digital twin (DT) is used in this paper. Digital models of the Wuqing 70-m radio telescope (WRT70) are created based on the DT operational framework. Finally, the reliability of the WRT70’s DT system is verified, and the dynamic pointing error of WRT70 (staying at the position of the elevation angle of 90° and the azimuth angle of 180°) on two sunny days in different seasons is estimated by the created DT system. The research results show that the pointing error of large radio telescopes under solar radiation fluctuates greatly and the seasonal differences are very striking. The maximum elevation pointing error of WRT70 under solar radiation around the winter solstice is over 40 arcsec, which is equal to 4/11 of the beam width for WRT70 operating at 8 GHz. In this paper, it is feasible to estimate the time-varying pointing errors of large radio telescopes at rest under solar radiation by constructing a DT system, but it is not effective to estimate the dynamic pointing errors of large radio telescopes in operation. It is expected to establish a dynamic pointing error model and calibrate the dynamic pointing errors of large radio telescopes in operation by DT technology in the future.

In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing process, a digital twin framework of incremental learning driven by stream data is proposed. Additionally, a novel method of stream data-driven equipment operation state modeling and incremental anomaly detection is proposed based on the digital twin. Firstly, a hierarchical finite state machine (HFSM) for the manufacturing process was proposed to completely express the manufacturing process state. Secondly, the incremental learning detection method driven by stream data was used to detect the anomaly of the job process data, so as to change the job status in real time. Furthermore, the F1 value and time consumption of the proposed algorithm were compared and analyzed using a general dataset. Finally, the method was applied to the practical case development of a welding manufacturer’s digital twin system. The flexibility of the proposed model is calculated by the quantitative method. The results show that the proposed state modeling and anomaly detection method can help the system realize job state mapping and state change quickly, effectively, and flexibly.

Aiming at the problems of single inspection mode and poor real time performance in current substations, a substation inspection visualization system based on digital twins is proposed. The development process of substation twin system is introduced. Based on the digital twin five-dimensional model, the connection between physical entities and twin entities is established. The modeling of physical entities, real-time data mapping and result analysis are described respectively. At the same time, the verification and analysis of the system results can prove that the model based on the digital twin system can effectively carry out virtual real synchronization and prove the effectiveness of the system.

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

During the service process of active phased array antenna (APAA), factors such as array heating and wind loading cause significant deformation, which will seriously affect the electromagnetic (EM) performance. Obtaining the geometric parameters of the array through sensors and predicting the real-time EM performance based on the digital twin (DT) technique are crucial to guarantee the service quality of the array antenna (e.g., the detection accuracy of a target). Traditional offline simulation methods can calculate the real EM performance of APAA with geometric errors. However, it requires a large number of computational resources and computation time, which is difficult to meet the demand for real-time prediction in DT. In this article, a generalized DT framework based on the reduced-order model for APAA is proposed. First, we introduce a dynamic-static attention-enhanced convolutional network for real-time computation of key EM indicators. Then, a super-resolution generating network is proposed, which realizes the mapping of array geometrical errors to the 3-D far-field pattern, and provides support for comprehensive performance evaluation. The framework proposed in this article constructs a twin model of APAA, realizes the virtual-reality mirroring of the EM performance, and is deployed and applied in an APAA DT platform.

Based on the source of the processing production line twin data, the virtual and real interaction framework of the processing production line digital twin system was designed. Taking the data in the processing production line digital twin system as the research object, the equipment data acquisition transmission channel design was carried out through OPC UA communication technology, and the data acquisition function of the processing production line was realized by using the KEPServerEX 6 data acquisition platform. We chose the C# language and the Visual Studio platform to develop the monitoring module, and used TCP/IP protocol, S7.NET open source library, and motion control card dynamic link library, to achieve the control of the robotic arm, PLC, milling machine, monitoring and other functions. Utilizing the SQL Server database, the connection between the monitoring module and the digital twin system of the machining line was completed, and the basic data information of the whole system was studied. The virtual and real driving process of the whole system is studied to realize the function of this digital twin system of controlling the virtual with the real and controlling the real with the virtual.