4results 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.

The application provides a production quality monitoring method and system of a dihydroetorphine hydrochloride sublingual tablet composition, and belongs to the technical field of drug quality monitoring. The method comprises the following steps: collecting tablet image data and performing pretreatment to obtain a target tablet; collecting near-infrared spectrum data of the target tablet and inputting the data into a quantitative correction model to obtain a predicted content and mark an abnormal tablet; imaging the abnormal tablet and qualified tablets obtained through sampling by using a microfocus X-ray imaging instrument to obtain X-ray images; analyzing the gray value distribution of the X-ray images and evaluating the component distribution of dihydroetorphine hydrochloride in the tablet to obtain a uniformity qualification judgment result; if the uniformity qualification judgment result is doubtful or the tablet needs to be confirmed, inputting the corresponding X-ray image and image data into a pre-trained deep learning model to obtain a judgment result of the content uniformity of the tablet; and arranging the uniformity qualification judgment result and the judgment result and sending a production monitoring interface.

The invention provides a drying simulation method and system for a battery pole piece and a storage medium, and the method comprises the steps: S1, building a drying dynamic model of the battery pole piece, and setting a boundary condition and an initial value; s2, temperature and humidity data of a hot air inlet and a hot air outlet of the drying oven are measured, absolute humidity of an air inlet is obtained, and a pole piece-hot air coupling heat and mass transfer model is established based on energy conservation and mass conservation; and S3, the drying kinetic model and the heat and mass transfer model are combined, and a parameter sum is solved by using actually measured data Yair, test. The drying process of the battery pole piece is described in stages, a drying kinetic model is established, and in combination with a pole piece-hot air coupling heat and mass transfer model based on energy conservation and mass conservation, real-time tracking of the agent containing rate and the surface temperature of the pole piece is achieved, the drying process of the battery pole piece is optimized, and the manufacturing efficiency of the battery pole piece is improved.

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.