2results about How to "Improve calculation efficiency" patented technology

The present application relates to a kind of fusion supply area level new energy consumption rate calculation method, comprising: the power balance analysis of provincial, municipal and supply area level power grid, determine whether there is new energy power abandonment risk in corresponding level power grid;Build power grid time sequence production simulation model;Actual wind and light output condition of province level is calculated;Optimize municipal theoretical wind and light output condition;Actual wind and light output condition of municipal considering provincial power abandonment is calculated;Optimize supply area level theoretical wind and light output condition;Actual wind and light output condition of supply area level considering provincial and municipal power abandonment is calculated;Get supply area level new energy consumption rate.The present application realizes the layer-by-layer refinement calculation from provincial, municipal to supply area level, realizes the analysis accuracy of consumption rate to be implemented to specific supply area range;Power balance analysis is placed in each level time sequence simulation simulation, quickly filters out the area without power abandonment risk, simplifies iterative optimization process, significantly improves new energy consumption rate measurement efficiency.

This specification discloses a model training method and apparatus, and a data processing method and apparatus. The training method includes: training a configuration prediction model based on model feature vectors and device feature vectors, capable of outputting configuration numbers for each layer of the model. The processing method includes: using the configuration prediction model to predict the configuration numbers of the model to be loaded and run on the device, thereby predicting the configuration numbers for each layer of the complete model. The device performs loading inference based on the predicted configuration numbers for each layer of the model, and can generate target content based on user input information.