Intelligent data transmission system for digital power enterprise architecture: based on deep reinforcement learning

Under the framework of digital power enterprise, the research of data transmission system is crucial to improving the intelligent level of power data processing. In order to efficiently and safely transmit massive and scattered power data, while ensuring the security and integrity of the data. The paper proposes an intelligent data transmission system based on improving the deep reinforcement learning process. The transmission strategy is dynamically adjusted through the Actor-Critic framework, the data transmission path is optimized, and the data compression and decompression technology is introduced to reduce the transmission cost and improve the security. The experimental results show that the performance coverage of the algorithm is 94% and 97.8%; the case coverage remains above 90% and 92%. In the task time effect analysis, the data processing time of the system is at least 1h, and the maximum time is not more than 5h, indicating that the system has strong data generation ability. It proves that the research and designed intelligent data transmission system provides an effective solution for the data management and transmission of power enterprises, provides a solid foundation for the development of smart grid, and improves the efficiency and stability of data transmission.