基于数字孪生的云网智能运维技术研究

云网融合的加速发展,既推动着通信网络数字化和智能化转型升级,也带来了云网运维复杂性不断提高的问题。尽管近年来通过各种智能化技术手段取得了一定进展,使网络管理控制变得更加敏捷和高效,但大规模云网设施仍然面临着运行维护过程中效率低、周期长和成本高等挑战。针对上述挑战,该文提出基于数字孪生的自适应探测、双重评估、优化调整三种智能运维的技术,旨在提高云网运维的效率并帮助预测网络异常。在自适应探测技术中,利用数据统计方法构建历史时序数据样本,通过算法选择适应的概率分布,预测故障发生的概率。双重评估技术中,通过对孪生系统和物理系统进行双重评估,验证故障原因并进行故障朔源。优化调整技术中,通过张量分解处理大数据,优化数据样本,并通过机器学习训练样本数据来优化调整智能运维模型。实验验证表明,该技术能够预测网络异常、快速定位故障,并优化调整系统,从而实现智能运维的目标。     

数字孪生流域建设监督检查实践及问题分析

当前,水利部正在大力推进智慧水利建设,数字孪生流域是智慧水利建设的核心与关键。水利部在推进数字孪生流域建设工作会议和全国水利工作会议上明确提出要加快数字孪生流域建设。为保障数字孪生流域建设高效、顺利开展,水利部组织对数字流域建设先行先试单位建设情况进行了监督检查,检查内容包括组织推进、工作进展、项目成果、应用成效、共建共享、其他等五方面。本文详细列举了数字孪生流域建设监督检查中发现的典型问题,从项目管理、需求调研、数据共享、专业模型、标准规范、监督检查等方面深入分析了问题发生的原因,有针对性地提出了改进工作的建议,对数字孪生流域、数字孪生工程、数字孪生水网建设全过程管理具有重要的参考价值和指导意义。     

基于数字孪生的跨营区数据融合应用研究

稿件智慧营区建设与之前的数字营区相比,主要区别在于对营区及相关系统多元化感知数据获取及融合处理.在解决单营区数据融合的基础上,要考虑跨营区多级协同营区的数据融合应用.结合智慧营区特征以及跨营区数据融合需求,给出基于数字孪生技术的数据融合架构设计,并提出有关数据融合应用构想.通过数字孪生技术提供底层技术支撑,力图取得营区数据融合信息模型赋能优势,通过建立物理/虚拟模型的数据融合模型和交互关系,确保跨营区的协同化运用,对未来跨营区数据融合应用具有一定的参考意义.     

数字孪生甬江流域水资源“四预”应用研究

基于当前数字孪生流域发展背景及宁波市水资源利用特点，为满足宁波市水资源在不同时空下合理分配的需求，以甬江流域水资源管理与调配“四预”流程为切入点，深度融合数字孪生、BIM 建模等现代化信息技术，在建立来水预报分析、需水预测、水资源优化调配、水资源实时分析评价及水资源预警等各类模型的基础上， 构建具有“预报分析—监测预警—调配预演—调度预案—动态评价”功能的水资源管理与调配业务应用系统，以达到及时准确预报、全面精准预警、同步仿真预演、精细数字预案、多维动态评价的目标，最终实现水资源的智能优化调配与管理。     

数字孪生机场总体架构研究

目前,机场信息资源的集约化程度和业务流程的数字化程度普遍较低,传统的机场运行管理模式进入瓶颈期。基于数字孪生的基础理论,开展了数字孪生机场运维的理论模型和总体架构研究。首先,分析了数字孪生机场五维结构化本体模型,在此基础上提出了符合机场运维需求的技术体系架构和孪生机场建设的三个层级。然后,阐述了数字孪生机场感知模型构建的三种常用方法,对数字孪生机场的应用服务功能进行了划分。最后,总结了数字孪生机场的应用方案和价值。该研究可为数字孪生机场建设提供理论基础和技术支撑。     

数字孪生水利建设保障体系应用与思考

推进数字孪生水利建设是贯彻落实“节水优先、空间均衡、系统治理、两手发力”治水思路和《数字中国建设整体布局规划》的明确要求,为保障数字孪生水利建设有力有序进行,亟须构建一套结构合理、系统全面的保障体系予以支撑。紧扣“需求牵引、应用至上、数字赋能、提升能力”的总体要求,提出数字孪生水利建设保障体系,通过2022年水利部开展的数字孪生流域建设先行先试予以实践验证,对进一步做好数字孪生水利建设保障工作提出建议,为数字孪生水利建设全过程管理提供有力支撑。通过实践应用,有效保障数字孪生流域建设先行先试中期评估工作顺利完成,并形成一批可复制和可推广的应用案例,成效良好,可为有关单位开展先行先试或其他专项工作提供参考借鉴。     

数字孪生水网(杭嘉湖平原)综合应用研究

嘉兴市位于太湖流域下游杭嘉湖平原,针对区域内水情多变、洪涝多发、水资源供需矛盾突出等问题,为加强洪涝灾害风险防控,强化水资源监管,优化水资源利用,开展数字孪生平原水网综合应用研究,通过模型分析、功能调用、服务调用、数据融合共享等方式,对雨水情监测、水利工程管理、防汛形势研判、联合调度等模块进行集成, 实现对全域水灾害、水环境的预报预警和预演预案功能。系统运行结果有效提高区域内水灾害监控和态势分析能力,加强洪水风险的统筹管控,增强市、区、县水利工程的协同调度能力。实现由经验防御到智慧防御的转变,最大化地发挥水利工程的排涝调度效益,提升区域整体的防洪排涝和水环境改善能力。研究具有平原区河网数字化改革建设的典型性、代表性,可为全国水利数字化改革提供可复制、参考的经验。     

数字孪生流域数据安全问题探究及对策

当前,水利部大力推进智慧水利建设,特别是数字孪生流域、数字孪生水网和数字孪生水利工程建设,他们都面临着大量的数据汇集,保障数据安全对推动新阶段水利高质量发展具有十分重要的意义。为提高数字孪生流域建设中的数据安全保障水平,在分析数字孪生流域数据特征的基础上,针对数字孪生流域建设过程中面临的数据安全管理制度体系不健全、大范围数据共享的安全风险、多样业务场景的安全保障挑战及大数据安全隐患等问题进行探讨,结合相关研究成果和作者认识,提出数字孪生流域的数据安全治理框架和措施,为解决数字孪生流域的数据安全问题提供对策。     

数字孪生潭江流域先行先试建设与实践

为提高潭江流域水利工程防洪调度能力,开展数字孪生潭江流域建设。数字孪生潭江面向流域防洪调度需求,围绕“四预”核心功能,以22座大中型水库、7座梯级闸陂为精细化调度目标,建设数据底板、模型平台、业务应用等,构建流域L1级数据底板,流域重点区域及工程L2级数据底板,开发六大类水利专业模型,构建具有“四预”功能的预报调度系统平台,实现流域多源监测感知与数据高效能管理,江河水库闸坝集中管理、联合调度、科学决策,最大程度保障流域内防洪（潮）的安全,完善预报监测薄弱环节,提升工程调度反馈时效性,同时为河湖规范化管理提供依据。项目成果在2022年“龙舟水”、台风暴雨防御期间投入实战,助力潭江流域水库、水闸群的联合调度,取得防洪不受淹、水资源不浪费的最佳效益。     

From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin

In recent years, the digital twin has attracted widespread attention as an important means of digitalization and intelligence. However, the digital twin is becoming more and more complex due to the expansion of need on the simulation of multi-scale and multi-scenario in reality. The instance of digital twin in references mostly concentrates a particular application, while it is still a lack of a method for constructing the complex digital twin in the total elements, the variable scale of working environments, changeable process, not even the coupling effects. In this paper, a novel modeling method for such a complex digital twin is proposed based on the standardized processing on the model division and assembly. Firstly, the complex model of digital twin is divided into several simple models according to the composition, context, component, and code in 4C architecture. Composition and context make the digital twin focus on the effective elements in a specific scale and scenario. Component and code develop the digital twin in standard-based modularization. Secondly, assemble the simple models of digital twins into the complex model through information fusion, multi-scale association and multi-scenarios iterations. Ontology establishes the complete information library of the entities on different digital twins. Knowledge graph bridges the structure relationship between the different scales of digital twins. The scenario iterations realize the behavior interaction and the accuracy calculation results. It provides an implementable method to construct a complex model of digital twin, and the reuse of components and code also enables rapid development of digital twins.     

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.     

Secure sharing of big digital twin data for smart manufacturing based on blockchain

With the rapid development of digital twin technology, a large amount of digital twin data named as big digital twin data (BDTD), is generated in the lifecycle of equipment, which is supposed to be used in digital twin enabled applications. However, in the implementation of these applications, data sharing problem which is caused by the lack of data security as well as trust among stakeholders of equipment, limits data using value. It is a novel way to introduce blockchain technology into digital twin to solve the problem. However, current methods cannot fulfill the requirements of exponential growth and timely sharing of BDTD. Therefore, a blockchain-based framework for secure sharing of BDTD is proposed to solve the problems. Cloud storage is integrated into the framework, with which, BDTD is encrypted and stored in Cloud, while the hash of BDTD and transaction records are stored in blockchain. Some rules of generating new block are designed to improve the processing speed of blockchain. An algorithm for optimal sampling rate selection is presented to maximize total social benefits of the participants of BDTD sharing. Simulation results show that the algorithm is better than traditional method for maximizing the total social benefits. Furthermore, a protype system is developed and evaluated based on Fabric test network. Evaluation results show that BDTD can be shared securely multiple times per second through the framework, which demonstrates the feasibility of the framework in supporting timely sharing of BDTD.     

Mutual information-enhanced digital twin promotes vision-guided robotic grasping

Vision-guided learning for autonomous robotic manipulations is a wide-ranging and high-impact topic in the context of smart manufacturing. Most learning strategies are object-centered or prior information-dependent, which likely lead to the problems of generalization across objects or scenes. To alleviate this, this work presented an embodiment decision-making method by the marriage between the digital twin epistemology and information-theoretic approach. The initial insight was that the mutual information generated in the interactions between the available vision models and real-world perceptions could decrease the uncertainty of sensing-action processes. Further, the real-time interactive information gains and visual templates constitute the digital twin through bidirectional data flowing and real-time optimization. As a demonstration of concept, on the conveyor-based and vision-guided robotic grasping platform, the robotic grasping experiments of freely placed and moving parts were performed. Experimental results indicated that the autonomous and real-time optimization of the conveyor-based and vision-guided robotic grasping system happens and the adaptability to the real-world changes had been clearly increased. This research suggested that the representation and dynamic capture of the complex interactions between both sides of cyber-physical system could generate new possibilities to the evolution of decision-making paradigm in more complex industrial processes.     

Integrating crowd-/service-sourcing into digital twin for advanced manufacturing service innovation

In order to support advanced collaborations among smart products, services, users and service providers in a smart product and service ecosystem (S-PSS), this paper proposed a service-oriented hybrid digital twin (DT) and digital thread platform-based approach with embedded crowd-/service-sourcing mechanism for enabling advanced manufacturing services. This approach is well supported by the ecosystem interaction intelligence of digitally connected products, services, users, and service providers via Internet of Beings (IoB) (Things, Users and Service providers). First, driven by industrial application needs in heating industry, a conceptual model of the service-oriented hybrid platform integrated with crowdsourcing mechanism is developed, which supports the concepts of product DT, service DT and human user DT. Second, the key system realization techniques are developed to integrate service crowdsourcing and service recommendation for realizing smart services. Finally, a case study is carried out for evaluating and confirming its feasibility.     

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents.Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical.The proposed architecture has been tested to illustrate its feasibility and practicality.     

Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control

This paper presents an innovative digital twin to monitor and control complex manufacturing processes by integrating deep learning which offers strong feature extraction and analysis abilities. Taking welding manufacturing as a case study, a deep learning-empowered digital twin is developed as the visualized digital replica of the physical welding for joint growth monitoring and penetration control. In such a system, the information available directly from sensors including weld pool images, arc images, welding current and arc voltage is collected in pulsed gas tungsten arc welding (GTAW-P). Then, the undirect information charactering the weld joint geometry and determining the welding quality, including the weld joint top-side bead width (TSBW) and back-side bead width (BSBW), is computed/estimated by traditional image processing methods and deep convolutional neural networks (CNNs) respectively. Compared with single image source, weld pool image or arc image, the CNN model performs better when taking the 2-channel composite image combined by both as the input and the state-of-the-art accuracy in BSBW prediction with mean square error (MSE) as 0.047 mm² is obtained. Then, a decision-making strategy is developed to control the welding penetration to meet the quality requirement and applied successfully in various welding conditions. By modeling the weld joint cross section as an ellipse, the developed digital twin is visualized to offer a graphical user interface (GUI) for users perceiving the weld joint growth intuitively and effectively.     

From simple digital twin to complex digital twin part II: Multi-scenario applications of digital twin shop floor

The shop floor has always been an important application object for the digital twin. It is well known that production, process, and product are the core business of the shop floor. Therefore, the digital twin shop floor covers multi-dimensional information and multi-scale application scenarios. In this paper, the digital twin shop floor is constructed according to the modeling method of the complex digital twin proposed in Part I. The digital twin shop floor is firstly divided into several simple digital twins that focus on scenarios of different scales. Two simple application scenarios are constructed, including tool wear prediction and spindle temperature prediction. Main functions in different application scenarios, such as data acquisition, data processing, and data visualization, are implemented and encapsulated as components to construct simple digital twins. Secondly, ontology models, knowledge graphs, and message queues are used to assemble these simple digital twins into the complex digital twin shop floor. And two complex application scenarios are constructed, including machining geometry simulation considering spindle temperature and production scheduling considering tool wear. The implementation of the complex digital twin shop floor demonstrates the feasibility of the proposed modeling method.     

Enabling technologies and tools for digital twin

Digital twin is revolutionizing industry. Fired by sensor updates and history data, the sophisticated models can mirror almost every facet of a product, process or service. In the future, everything in the physical world would be replicated in the digital space through digital twin technology. As a cutting-edge technology, digital twin has received a lot of attention. However, digital twin is far from realizing their potential, which is a complex system and long-drawn process. Researchers must model all the different parts of the objects or systems. Varied types of data needed to be collected and merged. Many researchers and participators in engineering are not clear which technologies and tools should be used. 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin. From the perspective of 5-dimension digital twin model, this paper tries to investigate and summarize the frequently-used enabling technologies and tools for digital twin to provide technologies and tools references for the applications of digital twin in the future.     

数字孪生乐安河助力提升小流域防洪决策支撑能力

江西省乐安河流域防汛工作具有小流域防洪的典型特征，针对小流域防洪存在的“四预”体系不完备、决策支持能力不能满足业务需要等问题，开展数字孪生乐安河流域的建设试点。将 WebGIS 引擎和游戏引擎相融合， 结合使用新型水文监测感知体系、BIM + GIS + IoT、多源数据融合等先进技术，搭建视觉真实、数据准确的三维数字孪生场景，构建未来降雨、水文预报、水工程调度、一维/二维演算等多模型耦合的流域防洪智能模拟仿真平台，实现乐安河上游暴雨集中区（婺源）洪水预报全过程的精准化模拟和预报调度一体化，为决策部门提供受灾影响范围这一关键性决策依据研究成果，对构建中小流域（县域）防洪“四预”体系，助力提升小流域（县域）防洪决策支撑能力具有示范性作用。