Research On The Mapping Mechanism Of Energy Internet Digital Twin Based On Machine Learnin

The Internet of Energy is a new generation information and communication technology-based energy system that aims to integrate various energy types into an internet-like grid to maximize the use and efficient management of energy.The Internet of Energy generates a lot of data due to its wide range of devices,and it is important to know how quickly this data can be processed.Digital twin technology can update its own models in real time to help optimize physical objects and make decisions.The use of digital twin technology in energy interconnection systems can improve the efficiency of decision making in the Internet of Energy.However,in the ever-changing physical world,it is challenging for digital twin models to map real data collected by sensors into virtual space,with problems such as high volumes of mapped data and high resource consumption.Deep reinforcement learning is an emerging technique for solving the problem of limited environmental experience and low accuracy.The text uses deep reinforcement learning to construct an adaptive data mapping strategy that can dynamically adjust mapping points according to the state of the environment and user requirements.The main elements are as follows:First,this paper first provides a digital twin architecture in the Internet of Energy.Then an adaptive data mapping mechanism is proposed to solve the digital twin data mapping problem in the Internet of Energy.Combined with the actual scenarios of the Internet of Energy,this paper proposes a digital twin mapping mechanism for singlesensor scenarios and multi-sensor scenarios,respectively.It is able to reduce the amount of data mapped from the physical world to the digital space and ensure that physical entities can be reconstructed with high accuracy in the digital space.Second,the Internet of Energy is very vulnerable to cyber-attacks and how to detect these attacks becomes a major challenge affecting the security of the system.To ensure the security of the Internet of Energy,this paper investigates a scheme based on machine learning techniques to detect false data injection attacks after the mapping mechanism.Third,the digital twin mapping mechanism problem is described as a Markov decision process,and machine learning is used to minimize the number of mapping points and model mapping errors.Experimental results show that the proposed adaptive data mapping strategy can successfully achieve the dynamic selection of mapping points and effectively reduce the amount of mapped data and energy consumption,and with minimal impact on the detection of false data injection attacks.