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

我的论文旨在通过识别数据退化对物理和数字实体之间连接的影响，并创建解决方案来纠正任何性能降低，帮助那些希望提高物理资产性能的数字孪生企业。该项目的最初目标是开发一个环境，能够进一步分析数字孪生中的数据采集和处理问题。该环境涉及模拟数字孪生，并了解数据退化如何影响物理实体和数字实体之间的通信。一旦从中获得了充分的知识，就会开发出一种适当的方法来纠正由于数据退化而导致的不良性能。这个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退化实验的轻松、简单和快速的开发将大大超过研究、开发和构建物理汽车模型所花费的时间。我们对模拟器的开发进行了大量的思考，特别是对微软AirSim和CARLA开源模拟器的开发。从最初的观点来看，CARLA模拟器是这个项目的首选。开发模拟器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、环境条件和动态演员来模拟数字双胞胎。该项目的数字实体将是一个采用强化学习技术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。控制器还将向模拟环境中的车辆实体提供命令。实体车辆与人工智能控制器之间的关系将是一个没有人为干扰的闭环系统。控制数据退化的策略将包括三种不同的方法：原始方法、数据压缩方法和数据驱动方法。naïve方法将涉及简单的策略，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，数据驱动的方法旨在识别对物理实体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电路中行驶，测量检查点之间所需的时间，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。在完成关于数据退化对数字孪生的影响的评估后，将进行充分的研究来纠正对数据退化的影响。

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.