11 results about "Design objective" patented technology

This invention discloses a design method and apparatus for refractory high-entropy alloys based on a large model, relating to the field of alloy material design technology. The method includes: First, by generating and collecting data, pre-trained surrogate models for thermodynamics (covering phase diagrams, solidification processes, etc.) and mechanical properties are constructed, and calculation models for physical parameters such as valence electron concentration and density are established. Then, these models are integrated into a single ensemble model, and a large language model is introduced as the core of intelligent scheduling. The large model is responsible for parsing the design task, intelligently planning and calling the interfaces of each model based on a knowledge base and rule base. Finally, based on the set design objectives, a multi-objective genetic algorithm is initiated for iterative optimization and adaptive adjustment, efficiently outputting a final alloy design scheme that meets multiple performance requirements. This invention enables intelligent multi-objective design of refractory high-entropy alloys, solving the problem of high dependence on manual intervention.

This invention discloses a multi-domain adaptive design rule method based on EMC data assets. It includes: acquiring the target application scenario identifier, functional type, IC combination, and design objectives; retrieving matching typical basic products and their derivative product cases to obtain their feature sets and mapping relationships; constructing an initial design concept by combining IC-level and system-level data assets; retrieving the standard essential characteristics of the target application scenario; constructing an EMC domain adaptation model, establishing an adaptive mapping based on verified mapping relationships, and quantitatively correcting and generating design rule parameters; outputting design rules containing constraint objects, types, target values, source metadata, and protection layer priorities; and structurally storing and assigning multi-dimensional tags. This invention establishes cross-application scenario mappings through typical basic products and derivative products, achieving quantitative adaptive generation of design rules, supporting cross-domain reuse, test prediction, rule self-evolution, and automatic generation of design documents, shifting EMC design from experience-based to data-driven.

This invention discloses a passive component selection method based on EMC data assets. It includes: acquiring the application scenario, design objectives, housing structure, and EMC standard requirements of the target product; acquiring port and PCB interconnect information to determine the protection priorities of emission suppression, immunity enhancement, and transient protection; retrieving IC-level and system-level EMC data assets to identify interference source pins and sensitive pins; searching historical design cases to extract successful solution characteristics and failure modes; calculating the required emission suppression, immunity enhancement, transient protection level, housing shielding effectiveness, and common-mode suppression requirements; establishing an inside-out forward design method to determine IC-level, PCB-level, and system-level three-layer protection and dynamically allocate weights; establishing a weighted scoring model, introducing a multi-dimensional labeling system for filter topology, generating and ranking candidate solutions; and integrating selection records into a closed-loop protection selection data asset. This invention achieves quantitative selection of passive components.

This invention belongs to the field of flowmeter technology and discloses a method for optimizing the impeller of a turbine flowmeter. The method includes: S1: collecting data on the physical properties, operating range, and performance indicators of the medium through a system to establish clear design constraint data; S2: integrating streamlined and multi-lead design concepts, and determining the blade structural parameters through mathematical modeling and simulation analysis; S3: ensuring precise matching between the processing process and design objectives through parameter modeling, accuracy threshold setting, process coordination, and data linkage; S4: manufacturing an impeller prototype based on the processing technology, and calibrating the blade size and angle parameters using equipment; S5: verifying linearity and starting flow rate indicators through data acquisition and analysis, and iteratively optimizing design and processing parameters; S6: establishing a large-scale batch production process based on the optimized final parameters. This invention solves the problems of large starting flow rate, poor linearity, and insufficient consistency of metering accuracy in turbine flowmeters under low Reynolds number conditions.

This application discloses an intelligent design method, apparatus, device, and readable storage medium for adjacent tunnel spatial relationships, relating to the field of tunnel engineering technology. It includes: acquiring basic data on existing tunnels and the engineering environment; determining the feasible spatial domain of the new tunnel based on the basic data and the design objectives of the new tunnel, and establishing a spatial relationship constraint model for adjacent tunnels; generating a set of candidate spatial layout schemes for the new tunnel within the feasible spatial domain, based on the spatial relationship constraint model; constructing an intelligent evaluation model to score and rank the feasible schemes in the candidate spatial layout scheme set; and outputting a recommended design scheme that meets the scoring requirements and its key spatial relationship parameters with existing tunnels. This method significantly improves design efficiency, reduces reliance on manual experience, and ensures the quantifiability and reproducibility of the design results.

The application discloses a circuit automatic design method based on large language model distillation and self-evolution, comprising the following steps: inputting a circuit type and a design target specification; constructing a reference netlist set, and performing initial supervision fine-tuning on a generation strategy model; generating a candidate netlist and performing simulation, calculating a target accuracy and a standard mark of each candidate netlist; for the candidate netlist that does not meet the standard, calculating a constraint violation degree and attempting automatic repair; for the candidate netlist that meets the standard, calculating a comprehensive performance vector and performing a Pareto sorting and diversity screening; performing preference optimization training by using good and bad sample pairs; performing security strategy optimization; and outputting a finally generated netlist. The application can reduce invalid candidate netlists and invalid simulation, improve the output rate of candidate netlists meeting the design target specification, and enhance the training stability and the circuit automatic design effect under multi-index constraints.

This invention belongs to the field of microwave vacuum electronic device design technology, specifically relating to a strip-beam electron gun design method based on intelligent collaborative optimization. The method includes: S1, selecting an initial model from a database according to the design objectives; S2, establishing the model and extracting electron beam performance parameters through a CST and MATLAB co-simulation platform; S3, calculating the objective evaluation function; S4, optimizing the electron gun structural parameters using an intelligent collaborative optimization algorithm; S5, performing performance evaluation and redundancy analysis; and S6, outputting the final parameters and storing them in a database. This invention achieves full automation of the strip-beam electron gun design process. By intelligently adjusting the optimization strategy through algorithms, it significantly improves design efficiency and accuracy, effectively optimizes the laminar flow and uniformity of the electron beam, and supports both single-sided and double-sided compression strip-beam electron gun configurations.

The application belongs to the field of microwave technology and electromagnetic optimization design, and particularly discloses an electromagnetic device optimization design method based on a particle swarm optimization algorithm assisted by multi-view semi-supervised learning. The method disclosed by the application comprises the following steps: defining design variables and optimization objectives, and generating initial samples; updating particle positions by using an improved social learning particle swarm algorithm, selecting a prediction model to perform real simulation on potential particles, and storing the remaining particles in an unlabeled sample pool; screening high-confidence samples by using a multi-view model, generating pseudo-labels by using convex combination optimization, and expanding a training set; constructing a semi-supervised learning model, calculating a prediction error, and setting a tolerance interval; selecting a prediction model according to a three-mode decision rule, iteratively updating the model and the particle swarm, and stopping until the design objectives are met. The application can efficiently optimize the performance of electromagnetic devices with a small number of simulation times. The application is suitable for electromagnetic design problems with high-cost simulation evaluation.

This application discloses a full-link design method for an intelligent operation monitoring system in a nuclear power plant, comprising: acquiring monitoring characteristic parameters and design input parameters; the monitoring characteristic parameters including one or more of the monitoring frequency, monitored objects, anomaly identification methods, and intervention methods under manual monitoring conditions; and the design input parameters including the design objectives and constraints of the intelligent operation monitoring system; determining the application constraint parameters, overall configuration parameters, and functional configuration parameters of the intelligent operation monitoring system based on the monitoring characteristic parameters and design input parameters; obtaining the intelligent operation monitoring system based on the overall configuration parameters; calibrating and verifying the intelligent operation monitoring system; and iteratively optimizing the intelligent operation monitoring system based on the verification results. This application achieves standardized design and stable deployment of an intelligent operation monitoring system for nuclear power plants by constructing a full-link design method covering design input analysis, system configuration determination, core element development, and calibration iteration.

The application discloses a material library constraint-based radio frequency matching network design method and device, equipment and storage medium, and the method comprises the steps of obtaining a material index space, determining a target impedance and a target matching configuration according to a current matching demand; constructing a current search space based on the target matching configuration and the material index space, and running a differential evolution algorithm in the current search space to obtain an optimal nominal value combination; mapping the optimal nominal value combination to an integer index in the material index space, enumerating all candidate material combinations in the neighborhood of the integer index, determining impedance errors of the candidate material combinations, selecting the candidate material combination with the minimum error as a current optimal discrete solution, converting the current optimal discrete solution back to a continuous value, and returning to iteration until an optimal material combination for designing the target matching network is output. The application embeds the material library discrete constraint in the optimization process, adopts a double-layer search architecture of an outer continuous differential evolution and an inner discrete neighborhood enumeration, eliminates the disconnection between design and material selection, and thus improves the efficiency and accuracy.