21results about How to "Reduce optimization time" patented technology

The invention discloses a wind power plant wind speed prediction method, system and device and a storage medium, and the method comprises the steps: optimizing the white noise amplitude weight and noise addition times in adaptive noise ensemble empirical mode decomposition CEEMDAN through employing an improved energy valley optimization algorithm IEVO, and carrying out the CEEMDAN decomposition of the historical data of a wind power plant; the decomposed data is preprocessed by adopting sample entropy SE, so that the calculation complexity of the data is reduced; for the preprocessed data, introducing a random forest algorithm RF feature selection of a heuristic strategy for optimization, and eliminating redundant features; converting the optimized data from one-dimensional time sequence data into a two-dimensional image by using a GASF (Grubby Amplitude Shift Function), and extracting image features; the extracted image features are sent to a time sequence block converter Patch TST model for wind speed prediction, and a training algorithm GPTuner based on a gradient boosting decision tree is introduced into the Patch TST model to adjust model parameters; compared with a traditional method, the accuracy of wind speed prediction can be effectively improved, and stable operation of a wind power plant is guaranteed.

The invention discloses a magnetic pole optimization method for reducing torque ripple of a permanent magnet synchronous motor. The magnetic pole optimization method comprises the following steps: step 1, analyzing a stator magnetomotive force and a stepped permanent magnet excitation magnetic potential during load operation by using an analytical method; step 2, obtaining air gap flux density harmonic amplitudes and orders respectively generated by the armature magnetic potential and the permanent magnet excitation magnetic potential; 3, solving a resultant magnetic field under the combined action of the permanent magnet and the armature magnetic potential, calculating the total output torque during load operation, and obtaining the relation between the working harmonic wave and the average output torque and the torque pulsation; and 4, based on the relationship between the working harmonic waves of the magnetic field and the output torque, the air gap flux density amplitude of the order related to the average torque is kept unchanged by adjusting the parameters of the stepped magnetic poles, and the air gap flux density amplitude of the order related to the torque pulsation is reduced. The motor obtained through the method can output the expected average torque, meanwhile, the torque pulsation is reduced, and the better torque performance is achieved; and good economic benefits can be generated.

The invention discloses a local parametric modeling and optimizing method, device and equipment for an automobile air conditioner pipeline, and relates to the technical field of automobile air conditioner system optimizing.The method comprises the steps that a pipeline model of an air conditioner system is obtained, a to-be-optimized area is deleted, and the pipeline model with the to-be-optimized area deleted is imported into simulation software; re-modeling the to-be-optimized area to obtain a pipeline local model, and fusing the pipeline local model with the pipeline model imported into the simulation software to obtain a fused model; selecting model parameters for optimizing the local model of the pipeline, and setting the model parameters to obtain a plurality of pipeline performance optimization schemes; and performing performance simulation calculation on the fused model based on a pipeline performance optimization scheme, and determining an optimal pipeline performance optimization scheme according to a simulation result. The optimization time of the automobile air conditioner pipeline can be shortened, and the optimal design scheme of the pipeline can be quickly obtained.

The invention belongs to the technical field of airdrop and special vehicles, and particularly discloses a wheel type airdrop vehicle parachute opening impact strength simulation evaluation and optimization method and system. Comprising the following steps: performing parachute opening dynamics simulation to obtain an accurate boundary of parachute opening strength impact simulation; performing whole vehicle parachute opening strength dynamic simulation analysis to obtain an analysis result of vehicle body strength and deformation, evaluating the analysis result, outputting design parameters if the analysis result meets requirements, and otherwise, performing failure reason analysis; air-drop system modal analysis is added, and failure reasons are judged; according to failure reasons, partial grids are intercepted or static simulation is adopted to replace dynamic simulation, and a reduction optimization model is obtained; and based on HyperStudy, carrying out multi-objective optimization according to the structural characteristics of the parts of the failure parts and failure reasons. According to the method, high-precision calculation of the re-loading air-drop process of multi-stage parachute opening of multiple parachutes is achieved, and scientific optimization of the parachute opening strength structure of the air-drop vehicle is achieved by optimizing the simulation means.

According to the interpretable intelligent flow control method, an automatic differential mechanism in a neural network is introduced into linear genetic programming, an explicit expression obtained by the linear genetic programming is automatically adjusted through a gradient descent algorithm, and the time needed by control law optimization can be greatly shortened; compared with a neural network control law, the explicit symbolic expression control law has higher interpretability, so that researchers can better understand a control mechanism and find new physical knowledge; the framework can set state sensors and different reward functions according to different control targets, and has very high universality.

The invention provides a method for automatically generating a pixelated resonant cavity of an oscillator based on knowledge-driven reverse design. The method comprises the following steps: determining a topological region range of VCO resonant cavity design; determining a reverse design target; a priori knowledge driving pixel matrix drawing strategy is established, pixelated inductor topological structure drawing is completed in the topological region based on the drawing strategy, and a primary pixelated inductor topological structure quality factor and a secondary pixelated inductor topological structure quality factor are calculated; according to a reverse design target, searching a Q value of a primary pixelated inductance topological structure from a binary n * n matrix assembly at a given target frequency, and meanwhile, achieving a maximized optimal solution to obtain an optimal pixel topological structure; and constructing the voltage-controlled oscillator in combination with the optimal pixel topological structure. According to the method, batch automatic simulation and optimization of the optimal resonant cavity structure can be realized, and compared with manual design, the time cost is saved, and meanwhile, the performance is further improved.

The application discloses a group target RCS optimization method based on space mapping and multi-stage electromagnetic modeling, and the method comprises the following steps: under a given RCS threshold, for initial group target space distribution positions and postures, a hummingbird optimization algorithm is combined with an electromagnetic scattering solver based on a physical optical method to perform parameter optimization; the optimal solution of a coarse model is used as an initial design value of a fine model, a mode reconstruction algorithm is combined with a fast multipole technique to accelerate electromagnetic coupling calculation between targets; if the fine model result meets the RCS threshold requirement, the optimization is terminated; if the threshold is not met, a space mapping relationship between the coarse model and the fine model is established through parameter extraction, the next fine model design value is predicted, and RCS is recalculated for threshold judgment until convergence is achieved. The space mapping algorithm is combined with the coarse model with high calculation efficiency and the fine model with high precision, RCS optimization of the group target formation is realized quickly and accurately under the premise of ensuring electromagnetic calculation precision.

The invention discloses a continuum structure topological optimization method and system based on a variable density method theory, and the method comprises the steps: carrying out the discretization of a continuum structure optimization region, and forming a plurality of middle density units; based on a variable density method theory, constructing a topological optimization model which takes the unit density of each intermediate density unit as a design variable, takes a preset volume fraction as a constraint condition and takes minimization of structural flexibility as a target; and adopting an interpolation model which punishes the unit density by an exponential function, and carrying out iterative optimization solution of eliminating gray units on the topological optimization model to obtain a topological optimization scheme. The method is higher in convergence speed and higher in capability of searching the optimal distribution of the material.

The invention discloses an energy storage power station charging and discharging optimization method based on lattice sampling and a Levy flight genetic algorithm, which is an energy storage power station charging and discharging optimization method taking minimum cost and maximum benefit of an energy storage power station as objective functions and taking charging and discharging of the energy storage power station and generating capacity of a conventional unit as constraints. A charge and discharge optimization problem of an energy storage power station is converted into a problem of solving a charge and discharge Pareto frontier solution set, an initial population generation method based on lattice sampling is provided, and it can be ensured that initial populations of charge and discharge of the energy storage power station and power generation of a conventional unit are uniformly distributed in a constraint space; a filial generation population generation method based on Levy flight variation is provided, the algorithm can be effectively prevented from falling into a local optimal solution, and finally an energy storage power station charging and discharging optimization strategy with the lowest cost and the maximum benefit can be obtained.

The invention discloses a nested watershed hydrological model parameter optimization method, which comprises the following steps of: determining a research area of a watershed, layering the research area by utilizing elevation in the research area, and obtaining n sub-areas with different elevation levels; constructing a hydrological model equation of each elevation level sub-region; constructing a vector about dynamic parameters, performing crossover variation on the dynamic parameters in the vector by using a fitness function, and screening an optimal offspring individual vector; and sequentially optimizing the dynamic parameters of the hydrological model equation of each elevation level sub-region from top to bottom from the elevation level sub-region with the maximum elevation until the optimization of the dynamic parameters of the hydrological model equation of the research region is completed. According to the nested type watershed hydrological model parameter optimization method provided by the invention, the optimization time required by watershed parameter optimization is greatly shortened, and the parameter optimization efficiency is effectively improved.

The present invention provides a method and system for adaptively adjusting mold temperature based on machine learning, which relates to the field of temperature adjustment technology, including collecting real-time temperature data and temperature-related process parameters in a mold production process, performing feature extraction on the real-time temperature data and the temperature-related process parameters to obtain multiple candidate features, and constructing a multi-objective optimization model of mold temperature uniformity and product quality according to the obtained target temperature adjustment key feature set to obtain the optimal solution of the multi-objective optimization model; constructing a temperature adjustment model, and obtaining an initial temperature adjustment strategy by solving the temperature adjustment model, and sending it to an edge computing node at a workshop site at the same time, and when an abnormality is detected, triggering a behavior cloning-based online strategy update mechanism, using the newly collected temperature data at the time of the abnormality to adaptively optimize the initial temperature adjustment strategy, and generating a target temperature adjustment strategy for guiding the real-time control of the mold temperature.

The invention relates to the technical field of cementitious material preparation, in particular to a low-carbon cementitious material preparation method and system utilizing solid waste resources, and the method comprises the steps: obtaining a solid waste component database, constructing a plurality of cementitious material reference data based on the solid waste component database and a plurality of historical cementitious materials, performing high-performance material selection in the original solid waste material group based on the multiple pieces of cementing material reference data and the performance weight group to obtain a target solid waste material group, performing response surface optimization on the target solid waste material group to obtain an optimal solid waste proportion group, and inputting the optimal solid waste proportion group and the target solid waste material group into a target random forest model to obtain a target random forest model; and preparing the target low-carbon gel on the basis of the optimal solid waste ratio group, the target solid waste material group, the curing conditions and the exciting agent ratio. The accuracy of performance prediction of the cementing material and the efficiency of the preparation process can be improved.

The present invention provides an intelligent optimization method for cylinder piston sealing grooves based on a new improved particle swarm algorithm, including the following steps: referring to a mechanical design manual and designing a plurality of sealing grooves of different sizes for the piston based on experience; building a cylinder motion trajectory tracking PID control test platform; preliminarily determining the value range of the PID controller parameters to be optimized to form a search space; setting the initial parameters and fitness function of the improved particle swarm algorithm; using the improved particle swarm algorithm to optimize the cylinder PID motion trajectory tracking control parameters when the sealing ring is located in different sealing grooves, and selecting the sealing groove with the best tracking accuracy. The new improved particle swarm algorithm proposed in the present invention can dynamically coordinate the relationship between the local search and the global search of the algorithm, and can effectively jump out when the particles fall into a local optimum. In addition, through the method proposed in the present invention, the traditional leakage prevention requirements in the design of the cylinder piston sealing groove are guaranteed, and the higher-precision motion servo control of the cylinder is also achieved.

The invention relates to an engineering machinery assembly workshop automatic layout and optimization method based on logistics simulation and a genetic algorithm, and belongs to the field of logistics simulation and genetic algorithms. According to the invention, the genetic algorithm module, the data processing and storage module, the simulation module, the evaluation analysis module and the visualization module are integrated on the logistics simulation platform, so that automatic layout and optimization of the discrete assembly workshop are realized. The method specifically comprises the steps that a genetic algorithm module is used for encoding functional areas and generating samples, a data processing and storage module is used for converting sample encoding information into coordinates needed by simulation, a simulation module is used for simulating operation and outputting index data, an evaluation analysis module is used for calculating sample fitness values, and a simulation result is obtained. And the genetic algorithm module performs selection, crossover and mutation operations according to the fitness value, and performs iterative optimization to obtain an optimal layout scheme. And the visualization module is used for displaying the simulation process and result. The optimization efficiency of the complex production system is improved, and the limitation of the prior art is overcome.

This invention discloses a method and apparatus for optimizing parameters of lightweight hydrogen storage cylinders based on adaptive optimization. The method includes: constructing a multi-agent collaborative optimization framework to define the functional boundaries of ant colony agents, NSGA-II agents, quantum annealing agents, and surrogate model agents, and clarifying the information interaction rules between agents; performing parameter collaborative optimization based on the multi-agent collaborative optimization framework according to collected design parameter samples and corresponding performance data of the hydrogen storage cylinder; dynamically adjusting the collaborative weights and optimization strategies of each agent by real-time monitoring of surrogate model prediction errors, population diversity, and algorithm convergence speed; selecting candidate solutions from the final Pareto front for real-world simulation verification; if the performance meets the design requirements, outputting the optimal parameter combination for the hydrogen storage cylinder; if not, feeding back the simulation results to the surrogate model agent for updating, and re-performing the iterative optimization process until the optimal parameter combination that meets the requirements is obtained.

The invention discloses a chip layout layer merging method, device and equipment and a computer storage medium. The method comprises the steps of obtaining a layout processing script file; according to a first operation command, determining a first layer which is operated by the first operation command, the first operation command being any one of the plurality of operation commands; executing operation corresponding to the first operation command on the first image layer to obtain a second image layer; according to the execution sequence of the operation command, judging whether the second layer is subjected to operation operation by other operation commands or not to obtain a judgment result; according to the category label of the first operation command and the judgment result, determining whether to perform merging operation on the target graphs in the second graph layer; and under the condition of determining to perform the merging operation on the target graphs in the second graph layer, performing the merging operation on the target graphs in the second graph layer. According to the embodiment of the invention, the layout optimization efficiency can be improved.

This invention relates to the field of semantic intelligence optimization technology, and particularly to a method for semantic intelligence optimization of official documents based on a domain knowledge base and a large-scale intelligent model agent. The method includes: acquiring the text to be optimized and a set of reference texts; constructing a semantic loss function based on the text to be optimized and the set of reference texts, the semantic loss function including an intent consistency loss component and an expression normativity loss component; and performing semantic optimization in two stages with the goal of minimizing the total semantic loss function: in the first stage, the core intent of the user text is fused and aligned with that of the superior text to reduce the intent consistency loss component; in the second stage, the user text is matched and adapted to the normative expressions of the superior text to reduce the expression normativity loss component. This invention constructs a semantic loss function that includes intent consistency loss and expression normativity loss, achieving high-fidelity semantic alignment between the generated text and the reference standard.

This invention relates to an adaptive magnetic field distribution determination method and system for a semi-iron-core reactor. The method includes: acquiring the structural parameters of the semi-iron-core reactor; automatically determining the optimal configuration parameters of the equivalent current using an adaptive equivalent current configuration algorithm; dividing the core into a central region, a transition region, and an edge region along its height direction based on the spatial non-uniformity of the magnetic field within the core, and applying differentiated equivalent current annular conductor density and diameter configurations to different regions; introducing a nonlinear permeability model, hysteresis effect correction, and eddy current effect correction to dynamically modify the boundary conditions; and solving an improved matrix equation based on the optimal configuration parameters, differentiated configuration, and comprehensively modified boundary conditions to obtain the current values ​​of each equivalent current annular conductor, and superimposing the magnetic fields generated by the winding coil and the equivalent current annular conductors to obtain the full-space magnetic field distribution. This invention improves computational accuracy and efficiency, and lowers the threshold for engineering applications.

The present disclosure relates to a program code optimization method, device, electronic device and storage medium. It includes: obtaining a program code file to be optimized; dividing the program code in the program code file to obtain cache type code and non-cache type code; determining a file call identifier according to the calling relationship between the target code in the non-cache type code and the cache type code, and obtaining a pre-stored first program optimization file corresponding to the file call identifier as the optimization file corresponding to the cache type code; obtaining an optimized program code file according to the first program optimization file and the second program optimization file obtained according to the target code in the non-cache type code. The present method improves the efficiency of program code optimization.

The application relates to the technical field of vehicle noise, in particular to a vehicle wind noise modeling optimization method and device and a storage medium, wherein the method comprises the following steps: obtaining multiple modeling parameters of a target modeling area of a vehicle, then constructing a design space of the vehicle according to the multiple modeling parameters, and determining a wind noise index of the vehicle according to the design space, so as to construct a proxy model meeting preset proxy conditions according to the multiple modeling parameters and the wind noise index, and based on an optimization target and a constraint condition of the vehicle, the proxy model is used to perform optimization processing in the design space, so as to obtain a wind noise modeling optimization strategy of the target modeling area. Therefore, the problem that in the related art, due to the dependence on physical sample vehicle testing and multiple high-time-consuming CFD and CAA simulation iteration schemes, the development cost of the vehicle wind noise modeling is high, the development cycle is long, and the development efficiency is low is solved.

This invention relates to the field of production line lifecycle management technology, specifically to an intelligent operation system and method for a glass production line based on a digital twin. The system includes a digital process package generation module, a digital twin construction module, and a monitoring and verification module. This application uses the obtained glass performance indicators as constraints for multiphysics simulation and optimization to obtain a digital process package for the glass production line. This provides a standardized and directly executable process basis for subsequent production line operation, improving the consistency and reproducibility of process solutions in actual production. Furthermore, by combining the acquired BIM model and collected raw multi-source sensor data with the digital process package, a digital twin is constructed, achieving a high-precision bidirectional mapping between the physical production line and the digital twin. Finally, the status monitoring and verification of the production line operation are performed based on the digital twin, achieving more accurate and efficient glass production line management and solving the problems of low accuracy and real-time performance in existing glass production line management technologies.