8 results about "Optimal modeling" patented technology

The invention discloses an automatic land utilization optimization method and system based on a large-scale language model, and the method is characterized in that the method comprises the steps: inputting and preprocessing, structured prompting, code generation, optimization execution, evaluation and output, and the like, the inputting and preprocessing is used for analyzing a natural language planning target and geographic data of a user; the structured prompt retrieves most relevant information from a knowledge base according to a planning target of a user to form an enhanced structured prompt; the code generation is used for dynamically generating an optimization problem environment code according to prompt content; in the optimization execution, a standard interface is used for driving an optimization process, and an optimal solution or a Pareto optimal solution set is searched; and the evaluation and output submits the optimization scheme together with the initial demand of the user to LLM to analyze the result, and generates an evaluation report. Compared with the prior art, the method has the advantages that the difficulty and the time cost of optimization modeling are remarkably reduced, planning experts can focus on problems, and the professionality and the high quality of an optimization scheme are guaranteed.

The invention provides a laying hen feed raw material sample screening method and system based on multi-source variability, and relates to the technical field of data processing analysis, and the method comprises the steps: obtaining multi-source attribute data and conventional component content data of raw materials, mapping the attribute data into tensor modal dimensions through multi-source variability tensor construction processing, and obtaining a multi-source variation tensor model; the component data is used as a characteristic component, and a multi-source variability tensor is obtained through decoupling and compression. Variability spectrum decomposition processing is carried out, local rank spectrum decomposition is carried out along producing areas, time and component dimensions, and a variability spectrum vector set is obtained; and identifying a candidate modeling sample set through multi-scale extremum and sparsity screening. And evaluating the contribution degree and sensitivity of the sample to a standard ileum amino acid digestibility prediction equation through a leave-one-out method and sensitivity analysis, and screening out an optimal modeling sample. According to the method, the multi-source variation information of the raw materials can be integrated, the variation spectrum is comprehensively covered with the minimum sample size, and the precision and generalization ability of the prediction model are remarkably improved.

The application provides a laying hen feed raw material sample screening method and system based on multi-source variability, and relates to the technical field of data processing and analysis, which comprises obtaining multi-source attribute data and conventional component content data of raw materials, processing through a multi-source variability tensor, mapping attribute data into tensor modal dimensions, taking component data as characteristic components, and obtaining a multi-source variability tensor through decoupling and compression. Variability spectrum decomposition processing is performed, local rank spectrum decomposition is performed along the place of origin, time and component dimension, and a variability spectrum vector set is obtained. Through multi-scale extreme value and sparsity screening, a candidate modeling sample set is identified. Through leave-one-out method and sensitivity analysis, the contribution and sensitivity of the sample to the standard ileal amino acid digestibility prediction equation are evaluated, and the optimal modeling sample is screened out. The application can integrate multi-source variability information of raw materials, comprehensively cover the variability spectrum with the least sample amount, and significantly improve the precision and generalization ability of the prediction model.

The present application belongs to the field of computer aided design and manufacturing technology, and relates to a product parameterized surface modeling method based on parallel heuristic optimization: a user inputs a group of initial NURBS parameterized surfaces through a CAD system interface; the system constructs splicing surfaces meeting the requirements of geometric continuity between adjacent surface patches to form a complete surface model; on this basis, a comprehensive objective function is established with surface fairness as the core and integrating topological correctness constraints; a Runge-Kutta optimization algorithm is adopted to perform heuristic iterative optimization in the design variable space relying on GPU parallel computing until convergence, and the optimal modeling result of the product parameterized surface is output. The present application realizes automatic splicing, fairing optimization and topological guarantee of complex surfaces in a unified framework through heuristic global search and GPU parallel acceleration, and significantly improves the design efficiency and quality of parameterized surface modeling.

The invention provides an automobile modeling determination method, device and equipment based on adaptive multi-criterion evaluation, and the method comprises the steps: collecting the multi-modal data of an automobile modeling scheme, carrying out the feature extraction, enabling all modal features to be aligned and fused into a unified feature expression through a cross-modal fusion model, and carrying out the recognition of the multi-modal data. And performing multi-criterion evaluation on the fusion feature based on the initial criterion weight to obtain a sub-item score and a comprehensive score, and sorting the candidate schemes according to the sub-item score and the comprehensive score. And then feedback data of the target user group to the sorted schemes is obtained, and the response degree of each evaluation criterion to the user feedback is calculated according to the fusion feature representation and the feedback data, so that the weight configuration of each criterion is dynamically adjusted. The adjusted weight is used for re-executing evaluation and sorting, so that the criterion weight can be automatically optimized according to actual feedback of a specific user group, the evaluation model is converted from a static fixed weight to a dynamic adaptive weight, and the finally output optimal modeling scheme can be matched with differentiated aesthetic preferences of target market users.

The embodiment of the invention discloses a model training method and device, a parameter processing method and device, electronic equipment and a storage medium. The method comprises the steps that a training data set is acquired, the training data set comprises multiple pieces of training data, and each piece of training data comprises multiple target adjustable parameters and operation results corresponding to the multiple target adjustable parameters; and performing iterative training on a to-be-trained model based on the training data set until a training end condition is satisfied to obtain a target model, the to-be-trained model comprising a plurality of sub-modules determined based on a precision index and an interpretive index, and the plurality of sub-modules being used for performing different types of feature extraction on target adjustable parameters. According to the method, through the hybrid modeling framework based on parameter characteristic classification, different types of feature extraction is carried out on the target adjustable parameters through a plurality of sub-modules, optimal modeling of different types of parameters is realized, and the optimization capability of the target model on the target adjustable parameters is improved.

The invention relates to a compressor impeller automatic optimization method and system based on TUI control. The compressor impeller automatic optimization method comprises the steps that a three-dimensional modeling parameter script is used for calling the function of three-dimensional modeling software to complete compressor full-parametric modeling; the optimization algorithm database script is used for conducting algorithm optimization on the parameterized model of the compressor to obtain optimal modeling parameters, and modeling is conducted again; inputting the new model of the compressor into the grid drawing script to complete grid drawing of the new model of the compressor; the drawn grids are input into a simulation solving script, and the simulation solving script calls the function of simulation solving software according to a preset process to conduct simulation solving to obtain a numerical solution of simulation solving. According to the method, parameterization design, numerical simulation, an optimization algorithm and a data management module are integrated through the TUI script, automatic closed loop of the impeller design process is achieved, a user can control the whole optimization process through a text command, operation is simplified, and the use threshold is lowered.

The invention belongs to the technical field of computer aided design and manufacturing, and relates to a product parameterized curved surface modeling method based on parallel heuristic optimization. A user inputs a group of initial NURBS parameterized curved surfaces through a CAD system interface; the system constructs a spliced curved surface meeting the geometric continuity requirement between adjacent curved surface patches to form a complete curved surface model; on the basis, establishing a comprehensive objective function which takes the curved surface fairness as a core and integrates topology correctness constraints; and adopting a RungeKutta optimization algorithm, relying on GPU parallel calculation, executing heuristic iterative optimization in a design variable space until convergence, and outputting an optimal modeling result of the parameterized curved surface of the product. According to the method, through heuristic global search and GPU parallel acceleration, automatic splicing, fairing optimization and topology guarantee of complex curved surfaces are achieved under a unified framework, and the design efficiency and quality of parameterized curved surface modeling are remarkably improved.