8 results about "Model composition" patented technology

The invention discloses a polymer property prediction method and system, and relates to the technical field of high polymer material information, the polymer property prediction method comprises the following steps: S1, constructing a polymer multi-property standardized data set containing an active end group label; s2, preprocessing the data set and extracting multi-modal features; s3, constructing an integrated prediction model composed of a geometric graph neural network and a GPR model; and S4, optimizing model parameters and quantizing prediction uncertainty, and fusing multiple GPR model outputs through an integration strategy to obtain a final prediction result. According to the method, the characterization accuracy is improved through data standardization and feature fusion, polymerization sites and connection chemical environments are considered through active end group labeling and multi-modal feature screening fusion, the structural characterization discrimination is enhanced, the sensitivity of a model to different main chain configurations and end group substitution is improved, and the stability of the model is improved. And through integrated modeling and uncertainty quantification, the prediction reliability is enhanced, and the prediction precision and robustness are improved.

The invention relates to a target-driven digital model orthogonal analysis and dual-adaptation fusion method and system, and the method comprises the steps: obtaining an overall demand, and combining the overall demand with empirical data and a knowledge graph to generate a model demand; obtaining a model composition file and a model description file which is determined based on a model demand and is used for dividing model categories, generating a meta-model description file according to the model composition file and the model description file, and generating a meta-model based on the meta-model description file; analyzing input and output data logic of the meta-model, scheduling parameters according to meta-model interface specifications, and redefining an interface by adopting a nonlinear iteration and aggregation optimization method; correlation adaptation and proxy packaging are carried out on a result of transmission logic numerical value fitting, and numerical value verification is completed in combination with a precision constraint condition; and adapting the incidence relation of the meta-agent model, generating a business-level numeralization agent model, scheduling parameters to perform precision verification on the business-level numeralization agent model, and generating a unified and operable system model.

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.

The invention discloses a cold hydrogenation process parameter prediction method, an optimization method, a cold hydrogenation process parameter prediction device and cold hydrogenation process parameter optimization equipment. The cold hydrogenation process parameter prediction method comprises the following steps: acquiring initial process parameters of a cold hydrogenation process flow; obtaining a pre-created cold hydrogenation process parameter composite model, wherein the cold hydrogenation process parameter composite model comprises a front-section mechanism model, a reactor big data model and a rear-section mechanism model; and based on the initial process parameters, simulating the whole cold hydrogenation process flow by utilizing a cold hydrogenation process parameter composite model to obtain prediction parameters of the cold hydrogenation process flow. According to the technical scheme, the whole technological process can be simulated and predicted by obtaining the initial technological parameters and utilizing the composite model composed of the front-section mechanism model, the reactor big data model and the rear-section mechanism model, and accurate prediction and analysis of the technological performance are achieved.

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.

A system and processes address the anticipatory availability of complex, interdependent systems composed of multiple products and services related to the user and their body. These systems, known as mixed systems, utilize anticipatory digital modeling to facilitate user decision-making, aiming for both comfort and resource savings. A digital model for anticipatory modeling will consist of several partial models or sub-models, each with a fixed component modeling repetitive elements and a time-varying component specific to each availability model. Partial models of the user's body parts and their environment will be active within these models. The anticipatory modeling process, comprised of two phases—analysis and predictive modeling—will utilize these partial models.Variants of the Local Party Device (LPD) will be available to the user and, depending on their needs, interconnected by the central component of said device, the automatic Availability Supervisory Center (ASC), in communication with the interactive ASC and its human assistant to provide even more relevant advice. Figure to be published for the summary: [Fig. 5].

The invention discloses a multi-objective optimization method and system for an open thin-wall energy-absorbing stand column, and relates to the technical field of mechanical structure design, and the method comprises the steps: building an optimization model which aims at the minimization of peak compression force, the maximization of total energy absorption and the minimization of structural mass and comprises the minimum load threshold constraint; a high-precision agent model composed of a BP-Transform-LSTM auto-encoder hybrid neural network prediction model and a BP quality prediction model is constructed to replace expensive simulation to carry out millisecond-level performance evaluation; performing global optimization based on an NSGA-III algorithm, and applying punishment to a scheme violating constraints to obtain a Pareto non-dominated solution set; and finally, objectively screening out an optimal compromise design scheme from the solution set by adopting a multi-criterion decision strategy combining an entropy weight method and a TOPSIS method. Unification of optimization efficiency, prediction precision and engineering reliability is achieved, and the design quality and research and development speed of the energy absorption structure are remarkably improved.

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.