Computer generated forces (CGFs) are autonomous or semi-autonomous actors within military, simulation

based, training and decision support applications. The CGF is often used to replace human role-players in

military exercises to, ultimately, improve training eff ciency. The modeling and development of CGFs is a

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complex, time-consuming and expensive endeavor where military domain expertise and doctrinal knowledge

are interpreted and programmed into the CGF by hand. Furthermore, CGFs often represent human actors

and behaviors (pilots, soldiers, manned systems, etc.) making it an even more challenging task.

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“新基建”与企业数字化转型

数据驱动型企业的成长路径分析

数据驱动的场景分析

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典型数据驱动服务提供商案例

未来趋势展望

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The importance of continuous and sustainable information exchange processes rises, due to the growing digitalisation in many fields of automotive area. By implementing the Digital Twin method, these challenges can be met in the future. In this paper, the Digital Twin method is discussed in the context of the automotive wiring harness. Different kinds of individual definitions of the Digital Twin are discussed and the methodical meaning is analysed. Furthermore the wiring harness specific requirements for the Digital Twin, caused by the enormous variety, is considered.

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consump-tion is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.

随着电网建设规模的扩大和数字经济的推动，电网数字化和智能化越来越成为电力行业发展的迫切需求。数字孪生技术基于数字化标识、自动化感知、网络化连接、普惠化计算、智能化控制和平台化服务等信息技术体系，为推进电网安全稳定运行、建设能源互联网企业提供了新思路和途径。该文基于数字孪生的概念、基本架构和特征，提出了数字孪生电网的内涵，构建了数字孪生电网的框架，阐释了数字孪生电网的运行模式，分析了数字孪生电网的关键技术，并从设备层、电网层、业务层和运营管理层4 个层面提出了数字孪生技术在电网企业可实现的典型应用，为数字孪生电网的建设提供了理论支撑，指明了发展方向和建设思路。

数据的驱动下基于物理设备与虚拟设备的同步映射与实时交互以及精准的PHM服务形成的设. 备健康管理新模式实现快速捕捉故障现象准确定位故障原因合理设计并验证

igital twin (DT) is one of the most promising enabling technologies for realizing smart grids. Characterized by seamless and active—data-driven, real-time, and closed-loop—integration between digital and physical spaces, a DT is much more than a blueprint, simulation tool, or cyber-physical system (CPS). Numerous state-of-the-art technologies such as internet of things (IoT), 5G, big data, and artificial intelligence (AI) serve as a basis for DT. DT for power systems aims at situation awareness and virtual test to assist the decision-making on power grid operation and management under normal or urgent condi-tions. This paper, from both science paradigms and engineering practice, outlines the backgrounds, challenges, framework, tools, and possible directions of DT as a preliminary exploration. To our best knowledge, it is also the first exploration on DT in the context of power systems. Starting from the fundamental and most frequently used power flow (PF) analysis, some typical application scenarios are presented. Our work is expected to contribute some novel discoveries, as well as some high-dimensional analytics, to the engineering community. Besides, the connection of DT with big data analytics and AI may has deep impact on data science.

Due to growing concerns regarding climate change and environmental protection, smart power genera-tion has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties. The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods. This paper reviews typical applications of ML and DDC at the level of monitor-ing, control, optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods can function in evaluating, counteracting, or withstanding the effects of the associated uncertainties. A holistic view is provided on the control techniques of smart power gen-eration, from the regulation level to the planning level. The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability, flexibility, profitability, and safety (abbre-viated as the ‘‘5-TYs”), respectively. Finally, an outlook on future research and applications is presented.