From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

A Dual Closed-Loop Digital Twin Construction Method for Optimizing the Copper Disc Casting Process

The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.

Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing rapidly,the competition is becoming increasingly fierce,and the digital transformation of the production line is imminent.As one of themost important components of heavy vehicles,the transmission front andmiddle case assembly lines have a high degree of automation,which can be used as a pilot for the digital transformation of production.To ensure the visualization of digital twins(DT),consistent control logic,and real-time data interaction,this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line.Firstly,theDT-based systemarchitecture is designed,and theDT model is created by constructing the visualization model,logic model,and data model of the assembly line.Then,a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line.Eventually,some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment.

Systematic review of digital twin technology and applications

As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application value.This phenomenon has been widely studied in academia and industry.In this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are summarized.The internal association between DT and related technologies is explained.The four stages of DT development history are identified.The fundamentals of the technology,evaluation indexes,and model frameworks are reviewed.Subsequently,a conceptual ternary model of DT based on time,space,and logic is proposed.The technology and application status of typical DT systems are described.Finally,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.

Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

基于TwinCAT3的一种开源机器人控制系统设计

目前主流工业机器人为封闭式控制结构,存在不开源、二次开发难的问题,因此设计一种基于TwinCAT3(the windows control and automation technology)的跨平台、可移植性好的机器人控制系统架构。该架构包含视觉、运动控制和算法集成与仿真控制模块,采用倍福自动化设备规范(automation device specification,ADS)通信技术和实时工业以太网总线技术(ethernet for control automation technology,EtherCAT),建立以计算机(personal computer,PC)和倍福控制器为EtherCAT主站,控制多组从站执行器的一主多从工作模式。该模式结合离线与在线控制、集成数字孪生技术,完成虚拟样机与物理样机的联动;采用开源可扩展架构,便于视觉算法、智能算法等算法集成。经实验验证,此架构具有拓展性好、实时性强的特点。

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.

Data-Centric Approach to Digital Twin Modeling of Production Lines

Digital twin(DT) is a virtual replica of a physical world that has become one of the most important ideas in the manufacturing industry’s digital revolution. DT modeling is a vital issue in building a DT of a production line. In this paper, a method is proposed to address the difficulties of complicated production line business and data heterogeneity. The method focuses on essential data in the production line and creates conceptual and information models based on the ArtiFlow model and AutomationML(AML). Conceptual models are mainly used to describe and analyze the business activities of the production line, and information models describe real production lines in the form of XML files. The proposed modeling approach has been applied to a real-world clothing production line to demonstrate its feasibility and effectiveness.

微电网孤岛运行工况下DT模型自适应迁移方法

微电网在孤岛运行工况变化情况下,会造成数字孪生(DT)模型迁移时无法准确匹配源域模型,从而导致迁移效率偏低等问题。为此,研究提出微电网孤岛运行工况变化场景下的数字孪生模型自适应迁移方法。通过分析微电网在孤岛运行工况下的运行特点,建立数字孪生模型,利用微电网孤岛运行工况的时变性,匹配计算源域模型,并通过缩小不同微电网孤岛运行工况下的源域数据分布差异,实现数字孪生模型的自适应迁移。实验结果表明,电流负载、实际电流等部分特征数据迁移效果较好,且具有较高迁移效率,验证了所提迁移方法可以适应不同微电网孤岛运行工况,具有较好的实用性。

Digital Twins and Cyber Physical Systems toward Smart Manufacturing and Industry 4.0:Correlation and Comparison

State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.

矿山数字孪生MiDT:模型架构、关键技术及研究展望

矿山数字孪生(Mine Digital Twin,MiDT)是智慧矿山建设的重要组成部分,是一个集成模型构建、智能控制、故障预测、人机交互等先进技术于一体的安全、高效、便捷的智慧系统,是推动矿山数字化和智能化转型发展的重要技术引擎。在矿山数字化与智能化建设的背景下,阐述了矿山数字孪生的基本内涵,对我国矿山数字孪生技术研究现状进行了总结与分析,并对其未来发展趋势进行了展望。首先,介绍了数字孪生的基本概念、级别划分、典型应用及最新发展,分析了矿山数字孪生存在的应用难点,表明了矿山数字孪生开发的必要性;其次,调研了数字孪生在矿山建设、开采、监控以及运维等方面的应用及发展现状,剖析了新一代信息技术与矿山数字孪生推进过程的深度融合;再次,阐述了矿山数字孪生模型的系统架构,分析了端层、边层、网层与云层等子系统的组成及功能,基于数字孪生五维模型的主要组成部分与运行机理构建了矿山数字孪生四边形系统模型;接着,阐述了矿山数字孪生模型系统架构与矿山数字孪生关键技术的内在关系,分析总结了矿山数字孪生在模型构建、智能控制、设备故障预测和人机交互等方面亟需突破的关键技术及研究内容;最后,从实际应用、模型系统架构、关键技术等方面归纳提出了矿山数字孪生技术未来的研究重点和发展方向。

Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis

This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training.

Investigation into the Independent Metering Control Performance of a Twin Spools Valve with Switching Technology-controlled Pilot Stage

In hydraulic area,independent metering control(IMC)technology is an effective approach to improve system efficiency and control flexibility.In addition,digital hydraulic technology(DHT)has been verified as a reasonable method to optimize system dynamic performance.Integrating these two technologies into one component can combine their advantages together.However,few works focused on it.In this paper,a twin spools valve with switching technologycontrolled pilot stage(TSVSP)is presented,which applied DHT into its pilot stage while appending IMC into its main stage.Based on this prototype valve,a series of numerical and experiment analysis of its IMC performance with both simulated load and excavator boom cylinder are carried out.Results showed fast and robust performance of pressure and flow compound control with acceptable fluctuation phenomenon caused by switching technology.Rising time of flow response in excavator cylinder can be controlled within 200 ms,meanwhile,the recovery time of rod chamber pressure under suddenly changed condition is optimized within 250 ms.IMC system based on TSVSP can improve both dynamic performance and robust characteristics of the target actuator so it is practical in valve-cylinder system and can be applied in mobile machineries.

Modeling and Analysis of Production Logistics Spatio-Temporal Graph Network Driven by Digital Twin

In the process of logistics distribution of manufacturing enterprises, the automatic scheduling method based on the algorithm model has the advantages of accurate calculation and stable operation, but it excessively relies on the results of data calculation, ignores historical information and empirical data in the solving process, and has the bottleneck of low processing dimension and small processing scale. Therefore, in the digital twin(DT) system based on virtual and real fusion, a modeling and analysis method of production logistics spatio-temporal graph network model is proposed, considering the characteristics of road network topology and time-varying data. In the DT system, the temporal graph network model of the production logistics task is established and combined with the network topology, and the historical scheduling information about logistics elements is stored in the nodes. When the dynamic task arrives, a multi-stage links probability prediction method is adopted to predict the possibility of loading, driving, and other link relationships between task-related entity nodes at each stage. Several experiments are carried out, and the prediction accuracy of the digital twin-based temporal graph network(DTGN) model trained by historical scheduling information reaches 99.2% when the appropriate batch size is selected. Through logistics simulation experiments, the feasibility and the effectiveness of production logistics spatio-temporal graph network analysis methods based on historical scheduling information are verified.

(Digital Twin）技术在船舶系统维护中的应用

本文以某2 300载重吨锚作船的推进系统为研究对象,利用数字孪生(Digital Twin)技术对其进行数字建模,预测系统在运行过程中可能出现的异常情况,并对系统的运行特性进行分析研究,以便于及时做出调整,避免对航行造成重大的损害。

元宇宙技术发展综述及其在建筑领域的应用展望

新冠疫情的暴发使得人们的活动越来越多地向线上转移,元宇宙概念热潮的出现使得基于虚拟数字世界的生产生活模式越来越多地受到人们关注。目前,元宇宙概念已经在教育、商业、工业等领域有所应用。随着建筑信息化、数字化技术的广泛应用,建筑行业智能化水平不断提高,虚拟数字技术在建筑行业发展中扮演着越来越重要的角色。对元宇宙概念的最新发展和应用进行介绍,重点阐述元宇宙与建筑信息化技术的关系以及元宇宙在建筑领域的潜在应用。分析结果表明,元宇宙的发展依赖于拓展现实、数字孪生、区块链等既有数字化技术的集成,作为一种新技术,元宇宙将为建筑领域的生产方式带来新的改变,也可能催生新的产业模式。

基于数字孪生的分段建造可视化系统设计

针对船舶分段生产过程中存在的数据直观性差、关键信息共享程度低,分段位置不明确等问题,综合考虑分段工艺流向、堆场资源等情况,等比例搭建船舶分段建造产线孪生模型;构建孪生数据驱动的分段建造管理体系和数据存储管理方法,为分段建造监控提供数据基础;应用建模软件和虚拟仿真软件,提供动态数据查询与决策依据的可视化平台,为船舶产品建造和管理中各类信息的集成化应用提供支持。

基于数字孪生的多自动驾驶车辆分布式协同路径规划算法

针对多辆自动驾驶车辆(AVs)在进行路径规划过程中存在的车辆之间协作难、协作训练出来的模型质量低以及所求结果直接应用到物理车辆的效果较差的问题,该文提出一种基于数字孪生(DT)的多AVs分布式协同路径规划算法,基于可信度加权去中心化的联邦强化学习方法(CWDFRL)来实现多AVs的路径规划。首先将单个AVs的路径规划问题建模成在驾驶行为约束下的最小化平均任务完成时间问题,并将其转化成马尔可夫决策过程(MDP),使用深度确定性策略梯度算法(DDPG)进行求解;然后使用联邦学习(FL)保证车辆之间的协同合作,针对集中式的FL中存在的全局模型更新质量低的问题,使用基于可信度的动态节点选择的去中心化FL训练方法改善了全局模型聚合质量低的问题;最后使用DT辅助去中心化联邦强化学习(DFRL)模型的训练,利用孪生体可以从DT环境中学习的优点,快速将训练好的模型直接部署到现实世界的AVs上。仿真结果表明,与现有的方法相比,所提训练框架可以得到一个较高的奖励,有效地提高了车辆对其本身速度的利用率,与此同时还降低了车辆群体的平均任务完成时间和碰撞概率。

基于智能分层切片技术的数字孪生传感信息同步策略

针对传感数据在无线接入网(RAN)中传输的不可靠性与不及时性造成数字孪生(DTs)同步信息的不精确问题,该文提出一种基于智能分层切片技术的DTs传感信息同步策略。该策略在双时间尺度下,以最大化传感信息满意度和最小化切片重配置及DTs同步成本为目标,联合优化切片无线资源配置以及DTs传感信息同步问题。首先,在大时间尺度,利用网络切片为有着不同服务质量(QoS)的DTs提供隔离以及解决部署问题;在小时间尺度,通过更加灵活的无线资源分配来提高DTs传感信息同步任务对动态环境的适应性,进一步提高通信性能,建立更逼近于物理实体的DTs。其次,为了求解不同时间尺度的优化问题,该文提出一种双层深度强化学习(DRL)框架实现高效的网络资源交互,其中下层控制算法利用优先经验放回(PER)机制加快收敛速度。最后,仿真结果验证了所提策略的有效性。

工业物联网中数字孪生辅助任务卸载算法

针对工业物联网(IIoT)设备资源有限和边缘服务器资源动态变化导致的任务协同计算效率低等问题,该文提出一种工业物联网中数字孪生(DT)辅助任务卸载算法。首先,该算法构建了云-边-端3层数字孪生辅助任务卸载框架,在所创建的数字孪生层中生成近似最佳的任务卸载策略。其次,在任务计算时间和能量的约束下,从时延的角度研究了计算卸载过程中用户关联和任务划分的联合优化问题,建立了最小化任务卸载时间和服务失败惩罚的优化模型。最后,提出一种基于深度多智能体参数化Q网络(DMAPQN)的用户关联和任务划分算法,通过每个智能体不断地探索和学习,以获取近似最佳的用户关联和任务划分策略,并将该策略下发至物理实体网络中执行。仿真结果表明,所提任务卸载算法有效降低了任务协同计算时间,同时为每个计算任务提供近似最佳的卸载策略。