Carbon Assets Management Model Based on Virtual Network Cycle

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The improvement of the carbon asset management level of enterprises is a long-term and dynamic process, but lacks a scientific and systematic asset operation management system in business activities. To solve the problem of insufficient data response in business activities, a fast and efficient network architecture needs to be established and network management behavior needs to be improved to meet the increasing demand for carbon trading. A deep neural network least squares method is adopted to process the cyclic data changes in high-dimensional space, solving the problem of low efficiency in reinforcement training caused by the rapid growth of the virtualized network space. This paper addresses the market value attributes of carbon assets in the power system and solves the problem of inaccurate decision-making caused by poor data information collection. It uses a deep learning model to process complex nonlinear relationships and the connection process of carbon asset factor collection, transmission, and feedback, using the Internet of Things (IoT) for communication. It designs a virtualized data collection cycle using network virtualization. It optimizes virtual communication nodes with periodic change and normal data transmission from within the IoT using Software Defined Networking (SDN). After the simulation test, we conclude that the research results have improved the integrity of the management model data. The algorithm performance has been improved by 15% compared with traditional algorithms. Besides, the rapid response mechanism built by SDN network management can enjoy exclusive communication links to improve the reliability of data transmission. It has opened up a networked management innovation model for power system carbon asset management and can provide better technical references for different industries.