15 results about "Model building process" patented technology

The invention provides a method for automatically deploying artificial intelligence models, which simplifies a model building process through systematic data preprocessing, model selection, parameter optimization and performance monitoring mechanisms, and dynamically updates or switches models in an application environment to maintain overall prediction performance at the best state while improving the performance of the model in multiple application environments.

The invention discloses a building information model automatic construction method and system and a storage medium. Multi-source data are collected and preprocessed to form input data in a unified format; respectively extracting two-dimensional and three-dimensional features by using a multi-branch architecture of a convolutional neural network, and comprehensively outputting feature information of the building component in a feature fusion layer; and calling a modeling software interface to convert an identification result into corresponding components, and sequentially assembling the corresponding components into a complete building information model. In order to ensure the accuracy of the model, the method introduces a repairing mechanism based on geometric constraint to adjust gaps and overlapping between components, meanwhile, the volume error rate and the position deviation of the model are verified through statistical analysis, if the volume error rate and the position deviation exceed threshold values, error data are fed back to a network to be retrained, and closed-loop optimization of the model construction process is achieved. And the automatic feature extraction capability of deep learning is combined with a BIM software modeling mechanism, so that the modeling efficiency and precision in a complex scene can be remarkably improved.

The embodiment of the invention provides construction of a multi-working-condition tool wear monitoring model and a wear monitoring method, system, equipment and medium thereof, and belongs to the field of neural networks. The method comprises the steps that based on a Pytorch deep learning framework, an attention mechanism ECA is embedded into a two-dimensional convolutional neural network, and a subject network of a multi-working-condition tool wear monitoring model is constructed; performing training and fine tuning on the subject network through a multi-source domain transfer learning pre-training and fine tuning strategy; bayesian optimization is applied to the convolution kernel size in the pre-training stage and the hyper-parameter optimization process in the fine tuning stage, and a multi-working-condition tool wear monitoring model is obtained. The generalization ability of the tool wear monitoring model under variable working conditions can be improved by learning information related to the tool state in signals under different working conditions by using transfer learning. And a pre-training-fine tuning strategy is introduced into a model construction process, so that the adaptability of the model to a new environment and the construction efficiency of the model are improved.

This invention discloses a method, system, and medium for inference offloading of large model agents in edge computing scenarios. The method includes inputting the target large model task to be processed into a pre-constructed Transformer-PPO model to obtain a corresponding target offloading scheme for decomposing the target large model task into multiple target sub-tasks and determining the corresponding offloading decisions. The model building process includes using a Transformer network to obtain an inference chain of a directed acyclic graph of the large model task; generating an offloading scheme based on the PPO algorithm, which includes offloading strategies for each sub-task, where the offloading strategy is the offloading decision and the probability of the offloading decision for each sub-task; and obtaining the offloading scheme for the current training round by penalizing and / or rewarding the offloading strategy that increases and / or decreases the total latency in the previous training round. This invention can obtain a better inference offloading scheme for large model tasks, improve the processing efficiency of large model tasks, and reduce device energy consumption.

The invention relates to a multi-axial fatigue prediction method and medium based on non-linear coupling of normal and shear strain energy, and the method comprises the steps: calculating the normal strain and tangential strain of each group of experimental results at each angle under a loaded condition by using the fatigue experimental results of a to-be-predicted material, finding out the plane where the maximum shear strain amplitude of the maximum normal strain is located, and calculating the maximum shear strain amplitude of the maximum normal strain; taking as a critical surface; calculating each stress-strain component on the critical surface; acquiring material and shear performance parameters of a to-be-predicted material, and predicting by adopting the fatigue prediction model in combination with each stress-strain component; the model building process comprises the following steps: performing nonlinear coupling on normal strain energy and shear strain energy, obtaining a fatigue damage parameter by combining an average normal stress correction coefficient and an average tangential stress sensitivity coefficient, and obtaining a fatigue prediction model by combining the fatigue damage parameter with a fatigue damage equation in a shear form. Compared with the prior art, the method has the advantages of being high in fatigue life prediction precision, suitable for proportional and non-proportional loading, high in safety and the like.

This invention provides an intelligent modeling method and system for steel performance prediction based on LLM (Liquidity Management Model). The system includes: receiving and parsing user instructions through a natural language understanding module to generate a standardized task description; automatically generating or matching an automated model building process based on the task description through a planning and execution module; providing decision support based on a knowledge base through a knowledge recommendation module during task planning or execution; and automatically scheduling functional modules such as data access, feature engineering, and model building to complete the modeling task and output the results, based on the recommended information, through the planning and execution module. The system accordingly includes various functional modules implementing the above method. This application achieves end-to-end automated modeling through natural language interaction, automated task planning, and intelligent knowledge recommendation, significantly improving modeling efficiency, lowering the technical threshold, and enhancing model quality and versatility.

The invention provides a maintenance tool for a future state power grid model and a construction method thereof. The maintenance tool comprises a ground state model storage module, a time sequence topology change operator library, a model construction engine and a simulation file generation module, wherein the ground state model storage module is used for storing a ground state detailed model of a power grid. According to the method, a linkage mechanism of analysis context and sequential topology change operators is established, firstly, a research area is determined according to the analysis context, operators outside the research area are pre-screened based on electrical distance and sensitivity analysis, and invalid changes with the influence on the research area lower than a threshold value are eliminated. According to the method, unnecessary detailed modeling of the whole network topology irrelevant to the analysis target is avoided, the target performance of the model construction process is remarkably enhanced, it is ensured that the computing resources are accurately used for constructing the model part strongly relevant to the analysis intention, and the pertinence and the computing efficiency of model construction are improved.

The application discloses a descriptor screening and crystal material physical property prediction method based on material genetic engineering, and the method comprises the following steps: collecting crystal descriptor by calculating and collecting part of crystal material target property data, then carrying out cleaning and comprehensive screening on the descriptor by virtue of variance information, four correlation coefficients and a random sequential feature selection method, then automatically optimizing and adjusting the hyperparameter setting in the model building process by using intelligent optimization thinking, finally applying the trained optimal model to the physical property prediction of the crystal material to obtain the prediction result, and overcoming the non-standard in the model building process of the material physical property prediction model, and improving the learning efficiency and prediction effect of the model.

The invention discloses a method and device for establishing a specific field modeling environment. The method comprises the following steps: constructing a three-layer abstract system for specific field modeling based on a meta-meta model; generating a domain meta-model containing entity class definition, relation class definition and enumeration class definition through an automatic instantiation rule, and configuring attributes, representation forms and constraint relations; packaging the meta-model and the constraint thereof into standardized framework data with a unique identifier; loading the frame data by a runtime analysis module, dynamically generating a visual domain model editor, and enabling a user to complete model construction conforming to constraints; triggering increment verification based on an operation-level event in a model construction process; when the meta-model is updated, executing difference comparison to identify affected model elements and giving repair suggestions; and the runtime expansion of the modeling capability is realized through a plug-in mechanism.

The invention discloses a small target detection model and method based on anchor frame focusing loss distribution correction, and relates to the technical field of target detection. According to the method, an overall framework of an AFL-DETR model for end-to-end small target detection is provided, a novel loss function specially customized for small targets is provided in the model building process, the area can be deduced by extracting position information from an object query, the area is incorporated into the loss function, contribution of the size of each target is adjusted, and the detection accuracy of the AFL-DETR model is improved. Therefore, the accuracy of small target detection is improved. In addition, an information enhancement module is also introduced, and the module fuses the preprocessed image details and the shallow feature information. And then, the features are further extracted and fused to obtain cross-fusion information, so that context semantic information suitable for the small target is generated.

The invention relates to the technical field of building three-dimensional data processing, in particular to a BIM-based building model data accurate conversion processing method. According to the method, the local density feature and the local curvature of each voxel point in the three-dimensional point cloud are analyzed, the influence degree of construction site noise on each voxel point is obtained based on the two features, and the filling necessity of each empty voxel block is obtained based on the overall influence degree distribution of neighborhood non-empty voxel blocks in a second neighborhood range of the empty voxel blocks. Whether the empty voxel blocks need to be filled or not is judged according to the filling necessity, and then a final filling result is obtained for BIM model construction and data conversion. According to the method, the finally constructed BIM building model is more accurate, the accuracy and adaptability of the model construction process under the interference of construction site environmental factors are improved, and data conversion is more accurate.

The invention provides a decision model correction method and device, equipment, a storage medium and a program product, relates to the technical field of computers, and is used for reducing the subjectivity of decision model correction, reducing the complexity of a model construction process and ensuring the consistency of decision model correction. The method comprises the following steps: under the condition that a new feature is introduced into a decision scene, performing correlation analysis on the new feature and at least one decision index of a decision model to obtain at least one correlation coefficient; constructing a target weight matrix based on preset adjustment intensity, the at least one correlation coefficient and an original weight matrix of the decision model; and correcting the decision model based on the target weight matrix.

This invention relates to the field of computer-aided design technology, specifically to modeling methods, devices, and electronic devices. The method includes acquiring features and constraints from a target model document and obtaining the dependencies between features, where the features are geometric bodies in the target model involved in constraint solving; establishing a directed graph of the dependencies between features; grouping the constraints based on the directed graph to determine a sequence of constrained features; and performing constraint solving sequentially according to the order of the constrained feature sequence to determine the solution results, thereby establishing the target model. By establishing a directed graph of dependencies between features and grouping constraints, a hybrid solution of two-dimensional and three-dimensional constraints is achieved. This supports both two-dimensional sketch constraints and three-dimensional rigid body constraints during the target model building process, thus avoiding switching between the two environments during modeling and improving modeling efficiency.

The invention discloses a modeling and predicting method based on machine learning azeotrope system judgment, which is used for predicting whether a binary mixture forms an azeotrope system or not. The method comprises the steps of data set feature engineering construction and azeotrope system prediction model construction, the data set feature engineering construction comprises the steps of data cleaning, mixture input feature construction, feature conversion and the like, and the prediction model construction comprises the steps of data set division, model selection, model hyper-parameter optimization and the like. The model hyper-parameters needing to be optimized comprise the number of hidden layer nodes, the Dropout probability, the number of iterations, the batch size and the like. In the model construction process, through data preprocessing, feature construction, model parameter optimization and model evaluation, the prediction accuracy and the model generalization ability are improved. The method solves the problems of long time consumption, high cost, limited prediction range and the like of a traditional experimental analysis method, and has a wide application prospect.