4 results about "Model composition" patented technology

The application discloses an alumina substance content detection device and method, and belongs to the technical field of alumina substance content detection. The method of the application is as follows: 1, the alumina substance content detection device is used to configure the spectrum collection on the surface of a sample; 2, a spectrum matrix M of an nth sample is constructed n ; 3, the spectrum data obtained by the alumina sample is preprocessed; 4, a prediction model for detecting the alumina substance content of the in-model sample composed of 75% training spectrum data and 25% training spectrum data is constructed by a PCA-PLS linear regression sub-model and a preliminary model, the prediction model for detecting the alumina substance content is compared and verified by using out-of-model samples, and a trained prediction model for detecting the alumina substance content is obtained; 5, test data is input into the trained prediction model for detecting the alumina substance content, and a prediction result of the alumina substance content is obtained; compared with the prior art, the application improves the efficiency of the existing alumina detection.

This invention discloses a method and apparatus for predicting passenger cabin temperature based on a stacked regression model. The method includes: collecting key operating parameters affecting passenger cabin temperature and corresponding temperature values ​​for different areas of the passenger cabin; selecting a preset number of samples from the key operating parameters and corresponding temperature values ​​according to preset rules to obtain a training dataset; constructing a stacked regression model architecture, wherein the stacked regression model includes a base regressor layer composed of multiple regression models and a meta regressor layer for fusing the prediction results of multiple regression models; training the stacked regression model architecture using the training dataset to obtain a passenger cabin temperature prediction model; obtaining the current key operating parameters of the passenger cabin; and inputting the current key operating parameters of the passenger cabin into the passenger cabin temperature prediction model to obtain the corresponding temperature values ​​for different areas of the passenger cabin. This invention solves the problem of insufficient accuracy in passenger cabin temperature simulation and prediction in existing technologies.

This invention discloses an electricity load forecasting method based on a hybrid expert architecture, relating to the field of electricity load forecasting technology, and aimed at solving the problem of inaccurate forecasts in existing methods. The method includes the following steps: constructing a forecasting framework composed of multiple electricity load forecasting expert models; pre-training a general electricity load forecasting expert model and saving the model parameters obtained after training as initialization parameters; fine-tuning based on the initialization parameters; generating routing embeddings through class-aware contrastive learning, and dynamically assigning input electricity load samples to corresponding partial-specialized expert models based on the routing embeddings; and forecasting the electricity load time series using the assigned partial-specialized electricity load forecasting expert models. This invention also discloses an electricity load forecasting device, electronic device, and computer storage medium based on a hybrid expert architecture. This invention improves the forecasting accuracy of electricity load by introducing a partial-specialized expert architecture and a class-aware contrastive routing mechanism.