Identifying how a digital entity could best represent a physical entity within a heavily constrained network in the operational stage of a Digital twi

我的论文旨在帮助那些使用数字孪生兄弟的企业，他们希望通过确定数据降级对物理实体和数字实体之间的连接的影响，并创建解决方案来纠正任何性能下降，从而提高实体资产的性能。这个项目的初始目标是开发一个环境，能够进一步分析数字双胞胎中的数据采集和处理问题。这种环境包括模拟数字双胞胎，并了解数据降级如何影响物理实体和数字实体之间的通信。一旦从中获得了足够的知识，就会开发出一种适当的方法来纠正由于数据降级而导致的性能低下。该环境中的物理实体将是模拟器中的汽车对象。使用模拟汽车进行数据降级实验开发的简便性、简单性和速度将大大超过研究、开发和构建物理汽车模型所需的时间。已经对模拟器进行了大量的开发，特别是Microsoft AirSim和Carla开源模拟器。从最初的展望来看，CALA模拟器是该项目的首选。模拟器的开发是为了支持自主系统的开发、培训和验证。这个模拟器有一套传感器、环境条件和动态行为者来模拟数字双胞胎。这个项目的数字实体将是一个使用强化学习开发的人工智能控制器，它将从与模拟环境的车辆模型相关的传感器接收数据。控制器还将在模拟环境中向车辆实体提供命令。物理车辆和人工智能控制器之间的关系将是一个没有人工干预的闭环系统。管理数据降级的策略将包括三种不同的方法：幼稚方法、数据压缩方法和数据驱动方法。幼稚的方法将涉及简单化的政策，例如在物理和数字实体的通信中引入延迟或任意减少两个实体之间传输的信息量。数据压缩方法将涉及利用镓数据压缩算法来确定压缩百分比与物理实体性能之间的关系。最后，数据驱动方法旨在识别对物理实体的性能影响最大的数据。一旦开发出单独的组件，将进行性能评估，这将涉及物理汽车在赛道上行驶，测量检查点之间花费的时间，并使用不同的数据降级策略来确定对物理实体性能的影响。在完成对数据降级对数字孪生兄弟的影响的评估后，将进行充分的研究，以纠正对数据降级的影响。

My thesis aims to helps businesses that employ digital twins who want to improve a physical asset's performance by identifying the impact of data degradation upon the connection between the physical and digital entity and creating solutions to rectify any performance reduction.This project's initial aim is to develop an environment that would enable further analysis of data acquisition and processing problems within digital twin. This environment involves simulating a digital twin and understanding how data degradation affects communication between a physical and a digital entity. Once ample knowledge is derived from this, an appropriate method of rectifying poor performance due to data degradation will be developed.The physical entity in this environment will be a car object within a simulator. The ease, simplicity, and speed at which development would occur using a simulated car for data degradation experimentation would drastically outweigh the time taken to research, develop, and construct a physical car model. Much thought has been conducted into the simulator for development, specifically into Microsoft AirSim, and CARLA open-source simulator. From the initial outlook, the CARLA simulator is preferred for this project. The simulator was developed to support the development, training, and validation of the autonomous system. This simulator has a suite of sensors, environmental conditions, and dynamic actors to simulate a digital twin.This project's digital entity will be an AI controller developed with reinforcement learning, which will receive data from sensors associated with the simulated environment's vehicle model. The controller will also provide commands to the vehicle entity in the simulated environment. The relationship between the physical vehicle and the AI controller will be a closed-loop system without human interference.The policies that govern data degradation will encompass three separate approaches: a naive approach, a data compression approach, and a data-driven approach. The naïve approach will involve simplistic policies such as introducing latency into the physical and digital entities' communication or arbitrarily reducing the amount of information transmitted between the two entities. The data compression approach will involve utilizing gallium data compression algorithm to identify the relationship between the compression percentage and the physical entity's performance. Finally, the data-driven approach aims to identify data that has the highest impact on the physical entity's performance.Once the separate components are developed, an evaluation of the performance will be conducted, which will involve the physical car driving in a circuit, measure the time taken between checkpoints, and with different data degradation policies to ascertain the impact on the performance of the physical entity.Upon completion of the evaluation regarding the impact the data degradation has on the digital twin, ample research a will be conducted into rectifying the impact on the data degradation.