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 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.

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 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.

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.

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.

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.

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.

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。

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。

基于数字孪生的加工生产线虚实交互技术研究

针对传统加工生产线运行过程中设备数据采集困难和可视化程度低的问题,研究了数字孪生(DT)技术在加工生产线虚实交互方面的应用问题。首先,以某电机外壳加工生产线为例,分析了生产线的加工流程,设计了虚实交互技术的总体框架,并提出了具体的方法;然后,对加工生产线进行了数字孪生体建模,对模型中的关键要素,几何、物理、行为属性及通讯接口的构建进行了详细的阐述;最后,采用过程控制的对象链接和嵌入(OPC)技术进行了加工生产线中多源异构数据的采集与处理,完成了实时数据间的映射。研究结果表明:采用虚实交互技术可以完成物理生产线与数字孪生体间的实时映射;电机外壳加工生产线中机械臂虚实交互的可靠性为99.95%,可满足精确性要求;采用服务模块能够更加直观、有效地反映生产线的实际加工状态,可满足加工过程信息动态可视化要求,证明了该方案具有可行性和有效性。

数字孪生技术在移动通信中的应用技术研究

针对我国移动通信互联终端用户越来越多,造成通信网络故障率较高,且检测难度大的问题,设计了一个基于数字孪生技术在移动通信中的故障检测系统。利用数字孪生技术实现对移动通信的各应用层创建数字世界;通过持续监控真实物理系统并使用大数据分析和机器学习来预测现实世界中发生的故障问题;利用生成对抗网络算法对转化到数字孪生技术内的移动通信数据进行计算检测,从而改善持续运营的问题。另外通过长短期记忆网络算法对整个故障识别模块进行改进,利用长短期记忆网络算法对历史数据智能存储的特点,达到对整个通信网络故障特征的提取,提高故障检测的效率和准确度。试验结果表明,系统技术核算的数据、误差率在可接受范围内,为其他技术研究奠定基础。

工业自动化领域机器可读标准研究

本文介绍了"机器可读标准"国内外的最新进展和定义,探讨了工业自动化领域机器可读标准的应用场景、关键技术和标准研制,为将机器可读标准用于智能制造设备和系统集成提供了参考依据。

基于数字孪生的智能车间系统仿真加速测试方法

为解决当前制造系统软件可靠性仿真测试时间长、测试环境难以搭建等问题,提出采用数字孪生技术与智能车间系统仿真加速测试相结合的方法;建立智能车间高保真数字孪生模型替代现实生产车间系统用于制造系统软件的可靠性仿真测试,首先要构建包含产品、设备资源、工艺流程等系统级仿真模型;同时,为仿真车间生产事件流程,在模型中,还需结合生产实际情况,设置设备间通信协议、通信数据以及生产线事件及队列顺序,真实模拟系统运行环境;通过构建步进电机产线数字孪生模型,仿真加工装配流程,运行智能车间系统软件,采用仿真时钟推进机制开展加速测试,验证了该方法的有效性和实用性,对开展工业系统软件高保真快速测试评估具有一定的借鉴意义。

数字孪生使能的智能超表面边缘计算网络任务卸载

针对新兴的计算密集型应用对移动用户高计算性能需求问题,该文提出一种数字孪生(DT)结合智能反射面(RIS)辅助的移动边缘计算(MEC)任务卸载方案。首先,在满足用户传输功率、用户和资源设备能耗、计算资源限制条件下,通过联合优化用户卸载决策、用户传输功率、RIS相移、波束成形矢量、计算资源分配,建立一个系统能耗最小化问题;其次,将该非凸组合优化问题分解为3个子问题,使用深度双Q网络(DDQN)方法确定用户卸载策略;然后对每个训练时间步进行一次求解,基于交替迭代方法得到问题的优化解。仿真结果表明,基于DDQN的算法训练速度较快,有效降低了系统总能耗。

数据驱动的复杂产品智能服务技术与应用

随着传感器、数据采集装置和其他具备感知能力的模块在复杂产品服务运行阶段的应用,复杂产品运维系统的数字化和智能化程度越来越高,具有实时、多源、异构、海量等特性的数据成为提高复杂产品系统可靠和低成本运行的决策依据,数字孪生技术提供了一种有效途径。介绍了数据驱动的复杂产品智能服务研究进展;分析了数据驱动的智能服务基本特征与框架模型;提出了数据驱动的复杂产品智能服务方法,主要包括面向服务的复杂产品建模与仿真方法、数据驱动的服务需求获取与精准分析预测方法、基于数字孪生的设备故障识别与动态性能预测方法、数据驱动的装备视情维修与备件库存联合多目标决策优化方法、基于数字孪生的复杂产品辅助维修技术、多要素协同的复杂装备能效精准分析预测方法、基于数据挖掘的复杂产品运行优化控制方法等;给出了智能服务系统的应用案例。所提出的框架和方法可为现代制造服务的智能化转型升级提供参考。

基于数字孪生的柔性直流电网纵联保护原理

随着柔性直流输电技术的发展,直流电网越来越受到关注。但是,直流电网故障电流上升速度快,与电力电子的弱过流能力形成突出矛盾。直流线路保护需在数毫秒级完成故障判别,同时还需兼顾选择性、可靠性、灵敏性,极具挑战性。该文提出了基于数字孪生的柔性直流输电系统纵联保护原理,在考虑直流线路参数频变的基础上,建立精确的直流线路数字孪生模型,利用状态估计的冗余特征提升了保护的可靠性,通过比较测量值与估计值之间的差异判别故障。仿真表明,所提保护方法可快速可靠地判别区内、外故障,并具有较好的耐受过渡电阻和抗干扰的能力。

数字孪生车间演化机理及运行机制

针对当前数字孪生车间演化机理不清楚造成其在制造领域技术路线图不明确的问题,从信息流、物料流、控制流三者的作用及关系剖析了当前生产车间所存在的问题和挑战,在总结数字孪生源起与发展现状后,提出从可视化、逻辑、数据三个维度构建可交互、可控制、可计算的虚拟车间,进而探讨从虚拟车间到数字模型车间、数字投影车间和数字孪生车间的演化机理。提出从数字化、智能化、智慧化三个阶段逐步构建数字孪生车间并阐述了各阶段的运行机制及使能技术,可为数字孪生车间在制造领域的推广与应用提供参考。