Research on Storage Performance Optimization of New Power Materials Based on MGF

The performance optimization of new power materials is of great significance for improving the efficiency of traditional power equipment and reducing energy consumption and environmental pollution. New power materials are widely used in the field of manufacturing, transportation, storage, and utilization of power, such as high-temperature superconductors, photovoltaic cells, and lithium-ion batteries. The performance of these materials has a direct impact on the development of the entire industry and the sustainable development of human society. Optimizing the performance of new electric power materials can improve its electrical conductivity, heat transfer, mechanical strength, corrosion resistance, and other characteristics, so as to improve the efficiency and safety of electric power equipment and reduce resource waste and environmental pollution. However, existing research on storage performance optimization of equipment is mostly based on traditional methods. Nowadays, with the rapid development of deep learning technology, traditional methods can no longer meet the needs of The Times. Therefore, this paper proposes a neural network-based performance optimization research method of MGF new power material equipment. First, we use deep learning method to complete the end-to-end power prediction. In the power-upgrading task, we propose a correction model based on multi-grain cascade forest and fuzzy control rules to correct the prediction results. Finally, the reliability of our model is proved by experiments.