Efficiency optimization of remote adjustment of electric energy meters based on the fusion of adaptive and flow processing

This paper focuses on the problem of congestion of front-end tasks in the collection system caused by a large number of penetration tasks, which affects system performance. It deeply studies the Flink flow processing mechanism and proposes an efficiency optimization scheme for remote adjustment of electric energy meters based on the fusion of adaptive and flow processing. By establishing separate message queues and task flows for each terminal, and dynamically adjusting task execution parameters through adaptive strategies, the efficiency of task execution can be improved. The paper constructs a task scheduling model, designs an adaptive adjustment algorithm, and elaborates on the scheme in detail through formula derivation and chart display. The experimental results show that this scheme can effectively alleviate task congestion and significantly improve the efficiency of remote adjustment of electric energy meters.