20 results about "Fast optimization" patented technology

The application relates to a robust optimization design method of an airborne high-temperature superconducting generator, which comprises the following steps: a baseline design scheme is obtained by establishing a two-dimensional electromagnetic finite element baseline model and a total loss accounting model of the generator containing electromagnetic characteristics of superconducting coils; a feasible technical scheme set is obtained by performing scheme screening on pre-engineering hard constraints; an optimal candidate scheme is determined by performing fast optimization on the feasible domain by using a Taguchi orthogonal test, and the optimal candidate scheme is determined by main effect and range analysis or signal-to-noise ratio calculation; and a final design scheme is determined by taking the minimum performance fluctuation or the maximum signal-to-noise ratio as a criterion by introducing cold end temperature fluctuation, air gap assembly deviation, tape critical current dispersion and load and speed disturbance as noise factors for robust verification. The application can efficiently obtain an optimal design with high performance, strong engineering feasibility and operation robustness under limited simulation resources, and is especially suitable for a megawatt airborne high-temperature superconducting power generation system.

The application discloses a lens antenna multi-target optimization method based on prior knowledge neural network, takes INN as the main body of the algorithm, adopts a reverse neural network to perform reverse design on the lens antenna, and the input of the reverse neural network is electromagnetic response, and the output is a structure parameter, so that the inversion of the antenna structure parameter of multiple performance indexes is realized. Meanwhile, multiple sub-FNNs are introduced to provide prior knowledge corresponding to multiple performance indexes, respectively, the input of the forward neural network is the structure parameter, and the output is the electromagnetic response. Compared with the existing lens antenna design method, the design method has the following advantages: 1) the antenna design efficiency is high, and the designed antenna can realize good multi-target characteristics; 2) the proposed KBANN model relieves the problem of large neural network data requirement and provides a new solution. The application can realize the rapid optimization of the multi-target lens antenna, and relieves the problem of large neural network data requirement.

The application discloses a kind of circulator insertion loss quadratic response surface optimization methods.The method includes: obtaining the target performance of circulator, and determining the adjustable factor set of circulator according to target performance;Adopt experimental design method in adjustable factor set Significant factor is screened;According to significant factor, a quadratic response surface proxy model is constructed, and the minimum insertion loss is used as the optimization target to train the model;The trained model is used to execute multi-objective optimization with constraints, and the optimal parameter combination is obtained;After verifying the optimal parameter combination, the final design parameters are output.The application filters significant factors and constructs a quadratic response surface proxy model to minimize IL within the constraint space for fast optimization.The total amount of experiments is reduced by 70%, the verification error is less than 5%, the design cycle is significantly shortened, and the insertion loss is reduced, which is suitable for precise determination of low-loss process window for any-band self-biased circulator.

The application provides an Abaqus multivariable and multi-target fast optimization method and system based on a Bipod flexible support structure, and includes: five modules of Main_Optimizing.py, Sub_Parameter_Sensitivity_Analysis.py, Sub_Parameter_Range.py, Sub_Model_Bulider.py and Sub_Job_Submit.py work cooperatively, functions of coordinating a global process, identifying key design variables, determining an optimal search space, automatically generating a parameterized finite element model and realizing batch simulation calculation are realized, and therefore a finite element oriented multivariable and multi-target fast optimization is realized. The application can effectively coordinate conflicts among optimization targets, obtain a satisfactory parameter solution in a short time, solve problems of low efficiency of artificial experience parameter optimization and time-consuming and laborious large batch manual modeling, and is helpful to meet urgent needs of modern engineering fields for efficient and accurate structure optimization design.

The application discloses a fast Gaussian splash physical simulation method and related equipment, and relates to the technical field of physical model simulation generation. The method comprises the following steps: segmenting each object in a static three-dimensional Gaussian scene; constructing an occupancy field, and then dividing corresponding points into the interior of the object according to an occupancy probability; filling the points in the interior of the object by using a Monte Carlo importance probability sampling method; acquiring physical parameters of the object for simulation of a material point method according to a text description by using a large language model; and generating simulation results of each object according to the physical parameters. The application firstly segments an input static three-dimensional Gaussian scene, then obtains a preliminary filling result by using an occupancy field, and then fills a complex structure according to Gaussian importance by using the Monte Carlo importance probability sampling method proposed in the application. The text physical optimization based on the large language model proposed in the application can quickly optimize and determine the physical parameters of the object, and obtain stable simulation results.

The application discloses a kind of super low orbit spacecraft aerodynamic configuration optimization method, device, equipment and medium, it is related to spacecraft design field.The method comprises: obtaining target parameter;According to target parameter and NRLMSISE-00 model, calculate real-time atmospheric environment parameter;Based on real-time atmospheric environment parameter, construct multi-objective optimization function;With NSGA-III algorithm, with the minimum drag coefficient, the maximum working medium capture efficiency, the minimum structure weight and the optimal atomic oxygen erosion resistance performance as target, the multi-objective optimization function is solved, and the core geometric parameter of super low orbit spacecraft is obtained;Using constraint condition, core geometric parameter, derivative constraint parameter and material and coating parameter are filtered, and the optimal aerodynamic configuration of super low orbit spacecraft is obtained.The application can realize the rapid optimization of super low orbit spacecraft aerodynamic configuration, and has high reliability and long on-orbit life.

This invention discloses a multi-objective optimization method for the overall performance of aero-engines based on a surrogate model, belonging to the field of aero-engines. The method comprises the following steps: determining the overall performance parameters of the aero-engine; generating initial sample points for the training set and sample points for the test set; obtaining the actual performance parameter response values ​​of the initial and test sample points; normalizing the actual performance parameter response values ​​of the initial and test sample points to obtain a normalized dataset; establishing a PEHE-OP surrogate model; verifying the accuracy of the established PEHE-OP surrogate model; optimizing the overall performance parameters of the aero-engine using the MODE method; and outputting the optimized solution set of the overall performance parameters of the aero-engine using the MODE method upon completion of the optimization. This invention features low computational cost, fast optimization convergence speed, and the ability to comprehensively consider multiple performance parameters in the optimization problem, providing more options.

The application discloses a high-efficiency traveling wave tube focusing magnetic field fast optimization method under dynamic input, and belongs to the field of microwave vacuum electron technology.The method firstly constructs a high-frequency structure model in an electromagnetic simulation software, imports an initial PCM magnetic field to perform PIC simulation to obtain electron trajectories and an initial current distribution;then, the electron trajectories are simulated in combination with a three-dimensional electron motion and a space charge force equation, y-direction projection is taken, the outermost two trajectories are selected, and an optimized PCM magnetic field is obtained through an optimization algorithm iteration; subsequently, the magnetic field is imported to perform PIC simulation to obtain a new current distribution, and after convergence is judged, the iteration optimization is repeated, and finally, an optimal PCM magnetic field is obtained.The application realizes accurate optimization of the magnetic field under dynamic signal input, greatly shortens the magnetic system optimization period, effectively reduces the intercepted current, improves the electron flow rate, and guarantees the output power of the traveling wave tube and the overall tube efficiency.

The application provides a multi-region porous medium filter parameter fast optimization closed-loop simulation method and device, computer equipment and a storage medium, and belongs to the field of nuclear facility ventilation filter design and numerical simulation automation. The application solves the problems of low efficiency of manual trial and error of multi-region parameters, easy error of resistance parameter mapping, non-reproducible simulation results and poor batch operation stability in traditional filter design. The application is aimed at a multi-region porous medium filter, constructs a multi-region parameter space in a closed loop of "simulation-extraction-evaluation-updating", combines adaptive optimization and hierarchical screening, automatically maps resistance parameters, calls CFD solution, extracts indexes and iteratively optimizes, and quickly outputs an optimal design point, so that the number of manual trial and error and simulation times are greatly reduced, the design efficiency is improved, the results are traceable, and the needs of nuclear facilities and other engineering scenes are met.

This invention belongs to the field of electricity market trading and virtual power plant operation control technology. It discloses a rule-driven, fast-response trading strategy generation and simulation verification system for virtual power plants. This invention inputs the strategy into a verification environment that highly simulates the real market environment for closed-loop testing and performance evaluation, forming an iterative closed loop of "generation-verification-optimization". The system preferably combines a fast-solution variant of mixed-integer linear programming (MILP) with a rule engine, significantly improving response speed while ensuring strategy quality. By injecting domain knowledge into the decision-making process in the form of rules, combined with a fast optimization algorithm, this invention effectively solves the problems of slow response speed and poor interpretability of traditional methods, significantly improving the trading returns, strategy feasibility, and real-time response capabilities of virtual power plants participating in the electricity market.

This invention discloses a rapid optimization method for overheating prevention of generator slip rings based on multi-dimensional heat dissipation, specifically relating to the field of motors. The method includes: S1, quantifying the heat loss of all heat sources in the slip ring to determine the total heat load as the basic input for heat dissipation design; S2, designing a high thermal conductivity skeleton embedded within the ring to reduce internal thermal resistance and accelerate heat flow in the contact area; S3, optimizing the heat flow transition layer at the contact interface to uniformly distribute heat flow density and suppress local hot spots in the contact area; and S4, constructing a fractal tree-like rib structure on the outer ring surface to enhance convective heat dissipation efficiency during rotation. This invention, by constructing a five-dimensional synergistic heat dissipation system—comprising internal heat conduction, contact interface heat conduction, convective enhancement on the outer ring surface, axial end-face thermal expansion, and thermal isolation of the brush holder assembly—avoids operational failures such as accelerated carbon brush wear, poor contact, and oxidation erosion caused by overheating, significantly improving the operational reliability of the excitation system.

The application relates to a method, system and device for allocating a maintenance aircraft parking position and a storage medium, wherein the method comprises the following steps: acquiring a data set corresponding to the maintenance aircraft; encoding the arrangement order and the safety distance of the maintenance aircraft based on the data set through an encoding mechanism to obtain a corresponding target encoding mode; optimizing the arrangement order of the maintenance aircraft to obtain a candidate solution set and a target hangar utilization rate corresponding to the candidate solution set, with the maximum hangar utilization rate as an optimization target; optimizing the safety distance of the candidate solution set through a safety distance fast optimization algorithm based on a dichotomy search iteration to obtain an optimal safety distance and an arrangement order corresponding to the optimal safety distance; and inputting the optimal safety distance and the arrangement order corresponding to the optimal safety distance into an aircraft arrangement strategy based on a matching degree to arrange the maintenance aircraft and obtain a target arrangement scheme. The application is beneficial to improving the allocation efficiency of the aircraft parking position.

The application discloses an offshore crane anti-swing predictive control method, and belongs to the technical field of offshore engineering equipment control. The method aims to solve the problem of poor load swing suppression effect of the offshore crane caused by control lag and inaccurate model in the prior art. The scheme points are as follows: a state sensor group and a multi-source environment perception system are used to acquire the crane state and the predicted information of future waves in real time; a hybrid prediction model is used to predict the crane system state sequence under different control instructions in the future time domain through rolling prediction; based on the predicted sequence, a reference control trajectory is solved in the upper optimization, the reference control trajectory aims to suppress swing and reduce structural fatigue, an instant control instruction is solved in the lower fast optimization, and the instant control instruction meets the actuator constraint and is output; meanwhile, the system health management is independently executed, and the control mode is dynamically adjusted according to the evaluation. The method is mainly used for precise anti-swing control of the offshore crane under complex sea conditions, can effectively suppress the load swing in advance, and improves the operation accuracy and equipment safety.

The application provides a kind of distributed reactive power voltage fast optimization control method, it is related to power system optimization control technical field, the system data of initial time of period is obtained first in the application, call reactive power voltage optimization model in period to process, obtain period optimal operating point;Then based on the optimal operating point, measure the active power of new energy in each time in period, calculate new energy short-time fluctuation rate evaluation index and amplitude evaluation index;According to fluctuation rate evaluation index, judge whether to trigger new energy reactive power voltage optimization control;When triggering, according to amplitude evaluation index, select small fluctuation under distributed reactive power voltage fast optimization control based on linear sensitivity or large fluctuation under distributed reactive power voltage fast optimization control based on nonlinear sensitivity, realize the efficient dynamic tracking of system optimal operating point.The application can effectively improve the operation safety and economy of high proportion new energy power system.

This invention discloses a spatiotemporal decoupling prediction and feedforward compensation method for visual servoing of a robotic arm, comprising the following steps: S1, target TF transformation acquisition and dynamic spatiotemporal sliding window construction; S2, spatiotemporal decoupling and spatial dimensionality reduction mapping of multi-degree-of-freedom poses; S3, low-dimensional Fourier modeling and fast optimization based on adaptive fundamental frequency estimation; S4, heterogeneous system composite delay calculation, absolute timestamp feedforward compensation, and pose reconstruction; S5, trajectory cascade shaping and closed-loop distribution based on exponentially weighted moving average (EMA). The feedforward compensation method proposed in this invention, which does not rely on expensive computing power for spatiotemporal decoupling, dimensionality reduction fitting, and filtering reconstruction, can achieve millisecond-level computation on ordinary industrial control computers, completing long-delay feedforward compensation up to 250ms, ensuring the high-frequency smoothness and physical safety of the robotic arm.

This invention relates to the interdisciplinary field of artificial intelligence and welding process optimization, specifically disclosing a rapid optimization method for dissimilar metal welding process parameters based on transfer learning. This method aims to address the core problems of long optimization cycles and high costs in dissimilar metal welding due to scarce experimental data and reliance on trial and error in process development. Its core steps include: constructing a welding process knowledge base integrating multi-source data; generating a dynamic prediction model adapted to the target task using hierarchical transfer learning; establishing a digital twin integrating this model as a virtual optimization environment; applying multi-objective reinforcement learning in the twin for rapid optimization of process parameters; and finally achieving online dynamic control of the process strategy and system generalization across materials. This invention significantly reduces data dependence and achieves rapid and high-quality process optimization under small sample conditions.

The application discloses a heat pipe constraint component layout comprehensive optimization method based on mixed integer programming, relates to the technical field of component layout optimization, and comprises the following steps: determining component information and heat pipe information in a component layout area; determining the number of heat pipes required by the components; constructing an allocation relationship optimization constraint model of the components and the heat pipes; taking the center positions of the components as optimization variables, taking the minimization of the maximum actual heat conduction power of the heat pipes as an optimization target, taking non-interference constraints between the components and the component layout area, non-interference constraints between the components, component system centroid constraints and heat coupling constraints between the components as constraint conditions, constructing an optimization target model and a constraint model; constructing an intersection constraint model of the components and the heat pipes; simultaneously solving various models to obtain and solve a component layout optimization model, and obtaining the positions of the components in the layout area. The application can realize the fast optimization solution of the component layout under the consideration of the quality characteristic requirements and the heat pipe constraints, has low solution difficulty and high efficiency.

The present application relates to a kind of based on shap value's continuous casting process parameter optimization method, comprising: collecting historical process parameters and historical quality detection data in steel continuous casting production, and obtain historical training data set;Based on historical training data set, with historical process parameters as input, and with the defect identification in historical quality detection data as output, build billet defect prediction model;Real-time process parameters of target billet are collected, and after pre-processing, form real-time process parameter data set;According to real-time process parameter data set and the billet defect prediction model of training completion, the SHAP value corresponding to each process parameter of target billet is calculated;The quality control analysis is carried out to each SHAP value of target billet, and the process parameter recommended value interval of target billet is determined.The present application realizes the rapid optimization of process parameter, satisfies the on-line application demand of complex working condition, provides accurate guidance for the dynamic adjustment of process parameter, so that product quality stability is significantly improved.

The application provides a kind of dark field imaging simulation and illumination parameter optimization method and system based on machine learning, belongs to optical detection field. In order to solve the problems of low optimization efficiency caused by artificial experience in the illumination parameters of the existing dark field detection technology, huge search calculation amount of theoretical simulation exhaustive method and lack of engineering practicability, the application establishes a "defect-illumination-imaging" parameter and scattering intensity correlation data set through wave optics physical simulation, and trains a machine learning agent model based on this, which replaces the time-consuming and laborious direct simulation process, thereby realizing the instantaneous prediction of scattering intensity. The application realizes the rapid optimization of illumination parameters, and through the systematic data-driven method, it can find the globally optimal illumination parameter combination, improve the signal-to-noise ratio and sensitivity of detection, and has high flexibility, which is suitable for different application scenarios.

The application discloses a kind of underwater area of interest coverage path planning method and device of unmanned vehicle, solve the problems such as long operation path, multiple steering times, low coverage algorithm operation efficiency that are faced in large-scale ocean area coverage detection. Extract multiple interest areas in the user concerned depth range from the known information of topographic region;Multiple polygon boundary ranges are generated by clustering interest areas;Improved Bayazit algorithm is proposed to segment multiple polygons based on concave points to obtain multiple sub-convex polygons;Each two sub-convex polygons with common edge and consistent internal parallel line direction are merged to obtain multiple target interest areas;A discrete grouping teaching algorithm is proposed to plan the initial path of the target interest area;A multi-thread fast optimization method is constructed to optimize the initial path and achieve optimal coverage path planning. The application realizes autonomous and efficient detection and coverage planning of underwater topographic interest areas under the premise of limited endurance and limited computing power of unmanned vehicle detection force.