124 results about "Hypercube" patented technology

The invention discloses an aircraft flow field prediction method and system of a multi-region physical drive neural network, and the method comprises the steps: constructing a continuous region mask and high-dimensional physical parameter sampling system, carrying out the global sampling of high-dimensional physical parameters through employing a Latin hypercube sampling method, and carrying out the space division through combining with a KMeans clustering algorithm; inputting the space coordinates, the continuous area mask, the wall surface distance and the physical condition parameters into an AMPD model, and generating a boundary layer mask, an eddy current mask and a physical residual error; inputting the boundary layer mask and the eddy current mask into a physical constraint driven loss function system, and establishing a multi-target residual minimization loss function for training an AMPD model; based on the multi-target residual error minimization loss function and the physical residual error, training an AMPD model by adopting a course learning training strategy; wing surface flow field reconstruction is carried out through the trained AMPD model, aircraft flow field prediction is completed, and high-precision and high-efficiency intelligent prediction of wing streaming is achieved.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention discloses a Bayesian algorithm-based swivel bridge spherical hinge structure optimization design method, which is characterized in that a parameterized model of a swivel bridge spherical hinge structure is constructed, and a finite element simulation technology and a Bayesian optimization algorithm are combined, so that multi-target global optimization design is realized. The method specifically comprises the following steps: establishing a refined finite element model of the swivel bridge spherical hinge; defining input design variables (spherical radius, supporting radius, pin roll radius and the like) and output optimization targets (maximum contact stress, horizontal and vertical friction moment); adopting Latin hypercube sampling (LHS) to generate a plurality of groups of initial parameter combinations; dynamically selecting a high-value parameter combination through a Bayesian optimization framework to carry out finite element simulation; training a Gaussian process agent model and carrying out iterative optimization; and quantizing the parameter sensitivity and outputting a Pareto optimal solution set. According to the method, the simulation frequency can be remarkably reduced, the design efficiency is effectively improved, and the problem that traditional experience design is prone to falling into local optimum is solved.

The invention discloses a flow field prediction-oriented full-process automatic joint reduced-order modeling method, which comprises the following steps of: specifying a target physical field parameter space, and randomly generating a sample space according to a Latin hypercube sampling method; constructing a full-process automatic simulation tool chain, driving target physical field numerical calculation and generating a training data set; carrying out singular value decomposition-based intrinsic orthogonal decomposition on the output physical field data, and only retaining first r main feature components to construct a reduced-order data set; constructing a multi-input multi-output full-connection feedforward neural network, and modeling and training a nonlinear mapping relation between input parameters and reduced-order features; new working condition parameters are input, reduced-order features are predicted through the trained neural network, distribution of a target physical field is reconstructed according to a singular value decomposition reduction matrix, and more flexible and reliable technical support is provided for reducing the training cost of a reduced-order model and improving simulation efficiency.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention relates to an active optimization design method of an axial flow fan blade, which comprises the following steps of: defining a blade profile construction line by adopting seven parameters such as a leading edge radius, and parameterizing and representing two-dimensional blade profile geometric characteristics based on NURBS curve control points; a two-dimensional blade profile and three-dimensional fan coordinate system is constructed, and control point coordinate mapping is achieved through matrix transformation; the method comprises the following steps: acquiring an initial sample based on Latin hypercube sampling, and establishing a Gaussian process proxy model by combining two-dimensional CFD calculation data; a K-RVEA algorithm is applied, maximization of a worst attack angle lift-drag ratio and minimization of a resistance coefficient are taken as double targets, Pareto optimization is carried out in combination with geometry and performance constraints, and iteration is terminated through a hyper-volume convergence criterion; and the optimized two-dimensional blade profile parameters are converted into NURBS description, and stacking is carried out in the spanwise direction to generate a three-dimensional blade entity. The grid sensitive effect in three-dimensional optimization is overcome, the flow field analysis precision is improved, invalid calculation is reduced, the blade profile curvature continuity and the process feasibility are guaranteed, and the design period of the axial flow fan is shortened.

The invention discloses a transformer temperature rise evaluation method and system based on a Stacking integrated learning framework, and the method comprises the steps: constructing a sample data set based on input variables including the size of an oil baffle plate, the width of an oil duct, the number of oil baffle plates and boundary temperature and response variables including the temperature rise of winding wire oil and the temperature rise of a hot spot through employing a Latin hypercube sampling principle and CFD simulation; constructing a Stacking integrated temperature rise model fusing the multi-model information in a layered manner, independently training a first layer by using the sample data set to generate a plurality of prediction results, and training a second layer after splicing the prediction results to obtain the Stacking integrated temperature rise model fusing the multi-model information; and after hyper-parameter joint optimization is carried out to determine hyper-parameters, a machine learning library is called to automatically complete training of the Stacking integrated temperature rise model fused with multi-model information, a trained temperature rise prediction model is obtained, temperature rise prediction is carried out on a to-be-predicted transformer configured by a new oil baffle plate and an oil duct structure, and predicted values of output line oil temperature rise and hot spot temperature rise values are obtained.

The invention relates to the technical field of extremely-small curved surface structures, in particular to an optimal design method, system and equipment for an extremely-small curved surface structure and a storage medium. The minimum curved surface structure optimization design method comprises the steps that a multi-objective optimization problem is determined according to design variables and optimization objectives of a three-period minimum curved surface lattice structure; constructing an approximate function relationship between the optimization target and the design variable by utilizing a self-adaptive multi-target engineering optimization method of a Kriging agent model; latin hypercube sampling and a sequential quadratic programming optimization algorithm are combined to carry out optimization design on the three-period minimal curved surface lattice structure. According to the design method, the light / bearing / wave-absorbing minimum curved surface lattice sandwich structure with good structure-performance dual characteristics can be designed, the wave-absorbing structure is filled with the minimum curved surface lattice structure in a shape follow-up mode, design and manufacturing of a wave-absorbing and bearing integrated structure are achieved, and rapid iteration and performance improvement of design of the wave-absorbing and bearing structure are supported.

The invention discloses a direct-current cable terminal structure optimization method and device based on a double-layer integrated stacking optimization proxy model and a medium. The method comprises the following steps: (1) carrying out trapezoid-chamfer parametric modeling on an outer side curve of an epoxy sleeve, and constructing a training sample by Latin hypercube sampling; (2) constructing a'base learner-meta learner 'double-layer integrated stacking agent model, and synchronously optimizing model configuration and hyper-parameters by using an improved sparrow search algorithm; (3) driving ISSA (International Standard Standard Architecture) to quickly optimize by using the trained proxy model to obtain an optimal geometric curve; and (4) through finite element-stream theory-thermal shock joint verification, it is confirmed that the maximum electric field intensity of the optimized terminal is reduced, the initial discharge voltage is increased, and the mechanical reliability meets the long-term operation requirement. The method solves the problem that a traditional agent model is insufficient in precision and insufficient in insulation margin of a gas-solid interface of a 550kV direct-current cable terminal, and is widely applied to localization design of + / -550kV GIS cable terminals.

The invention discloses a multi-dimensional power carbon emission reduction path optimization method based on machine learning, and the method comprises the following steps: 1, collecting and standardizing multi-source power carbon emission data to generate a power carbon emission data set, and determining a historical low-carbon scheduling path vector; 2, constructing an electric power carbon emission influence map based on the electric power carbon emission data set; 3, constructing an attribute vector matrix and carrying out principal component analysis; 4, adopting Latin hypercube sampling to generate a candidate scheduling path vector set; step 5, calculating a Soft-DTW distance to obtain a screened candidate scheduling path vector set; 6, constructing a power flow topological graph, calculating the structure entropy value of each candidate scheduling path, and generating an optimal carbon emission reduction scheduling path; and step 7, carrying out visual display on the optimal carbon emission reduction scheduling path, and generating a power scheduling instruction set. According to the method, Latin hypercube sampling and the Soft-DTW distance are fused, and intelligent screening and scheduling of power low-carbon paths are achieved.

The invention discloses a pulse compression radar accelerator generation method based on model driving, and relates to the technical field of pulse radars, and the method comprises the steps: based on a pulse compression radar signal processing flow, splitting operators according to calculation, interconnection and storage dimensions, and extracting features; defining 9 types of core components to form a parameterized component library; the components and the connection relation are combined, and an architecture description graph in a JSON format is generated; analyzing the ADG through an architecture interpreter to obtain a standardized parameter, and generating an RTL code based on a Chisel hierarchical architecture; performing simulation verification on the RTL code, pre-estimating the resource if the simulation verification is passed, and returning an error if the simulation verification is not passed; injecting expert configuration and combining with Latin hypercube sampling to obtain an initial solution set, iteratively searching a high-quality solution cluster by a TPE algorithm, resetting a parameter boundary and then switching to an ILS algorithm, and combining with a hardware rule to obtain a global optimal solution; and repeatedly executing the steps until the end conditions that the resources reach the standard and iteration reaches the upper limit are met, and outputting the accelerator architecture with the optimal resources under the target application constraint and the complete RTL code.

The invention discloses a front subframe structure MaOP method and device based on TPT, a medium and a program product, and the method comprises the steps: (1) constructing a MaOP design model capable of optimizing the weight, modal, strength and rigidity at the same time based on front subframe longitudinal beam and load analysis; (2) generating an elite population based on a diversity criterion and Latin hypercube, obtaining target values of the elite population, establishing a data set, and constructing a radial basis function model; (3) designing co-evolution operation driven by TPT to generate a candidate frame set; (4) an individual potential evaluation criterion is constructed based on the advantage and disadvantage target sets, and an optimal candidate frame is screened; and (5) obtaining each target value of the optimal candidate frame, updating the data set and the radial basis function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, a TPT-driven coevolution mechanism is adopted, radial basis function model prediction is combined, and the multi-performance index optimization process for the front auxiliary frame can be effectively balanced.

The invention relates to a building space layout optimization design method and device, equipment and a medium. The method comprises the following steps: analyzing a building base contour, a room type library and a building specification text to generate structured data; based on the geometric boundary, the hard constraint rule set and the spatial topological relation graph, an optimized sampling scheme set is obtained through Latin hypercube sampling, reinforcement learning strategy and supplementary sampling processing, and based on the spatial topological relation graph, the attribute matrix and the soft constraint target set, multi-target calculation and agent model training processing are conducted on the optimized sampling scheme set, so that the optimal sampling scheme set is obtained. Obtaining a full-scheme evaluation set; and based on the hard constraint rule set and the soft constraint target set, performing Pareto screening, hard constraint repair and soft constraint optimization processing on the whole scheme evaluation set to generate a final optimization layout scheme. According to the method, by means of building design information structured analysis, multi-strategy sampling, multi-target evaluation and the like, the design efficiency, scheme compliance and multi-target performance global optimality of building space layout are improved.

The invention provides an optimized design method for an impact type rotating wheel bucket blade structure based on entropy production analysis. The method comprises the following steps: firstly, generating a sample space through parametric modeling and optimal Latin hypercube experimental design of a rotating wheel; then, acquiring internal flow field data through numerical simulation; identifying a key loss area based on entropy yield flow loss; then, a Gaussian process regression agent model is constructed by taking the geometric dimension parameters of the bucket blade as input and hydraulic efficiency and entropy production as output, multi-objective optimization is carried out by adopting an improved NSGA-II algorithm, and the accuracy of the agent model is improved through a dynamic updating strategy; and finally, carrying out numerical simulation verification on the Pareto optimal solution, and screening out a runner design scheme with excellent comprehensive performance. The flow loss can be effectively reduced, the hydraulic efficiency and the operation stability of the impact type runner are improved, and technical support is provided for optimization of the hydraulic performance of the impact type runner.

The invention provides a foundation pit support structure lateral displacement prediction method based on dictionary learning fusion monitoring data. The foundation pit support structure lateral displacement prediction method comprises the steps that S1, a physical parameter database is established; s2, performing conversion to obtain weak prior distribution of a compressibility parameter ES; s3, sampling by using Latin hypercube to obtain N alternative parameter combinations of the soil body; s4, establishing a finite element model of the target foundation pit; s5, inputting a parameter combination to calculate corresponding side displacement data of the enclosure structure, and forming an over-complete dictionary D; s6, reading measured side displacement data of the enclosure structure to form a monitoring data matrix Y; S7, extracting corresponding data in the over-complete dictionary D, and forming a dimension transformation matrix A matched with the dimension of the monitoring data matrix Y; s8, obtaining an important atom set and a sparse solution vector x by using an orthogonal matching pursuit algorithm program; and S9, obtaining a side displacement prediction curve of the building envelope corrected by combining the monitoring data. The method has the advantages of high prediction precision, low calculation cost and high response speed.

This application discloses a method, device, medium, and product for optimizing the structure of a shaped charge shroud, relating to the field of structural design. The method includes: constructing an objective function with structural parameters as design variables and maximizing performance index values ​​as the objective; determining multiple initial sample points using a Latin hypercube sampling method based on the range of structural parameter values; constructing an initial sample library; constructing a surrogate model based on the initial sample library; determining candidate sample points and their corresponding performance index values ​​using a Bayesian optimization loop based on the surrogate model and the range of structural parameter values; updating the initial sample library and the surrogate model; continuing until a termination condition is met; and using the sample point corresponding to the maximum performance index value as the target structural parameter; and optimizing the design of the shaped charge shroud based on the target structural parameter. This application can reduce the computational cost of determining the structural parameters of a shaped charge shroud and improve the efficiency and intelligence of shaped charge shroud structure optimization.

The embodiment of the application provides a routing method and device of a multi-dimensional hypercube interconnected network, wherein P sending paths are arranged between a target network node and directly connected network nodes, the method is used for routing from a target sub-network to the target sub-network or a reference sub-network, the method comprises the following steps: acquiring a starting routing node on the target sub-network and a final routing node on the target sub-network or the reference sub-network; calculating a shortest routing path from the starting routing node to the final routing node to obtain an initial routing path; determining a target sending path of each hop in the initial routing path from the P sending paths according to the initial routing path to obtain a target routing path; and routing from the starting routing node to the final routing node according to the target routing path. Through the application, the problem that a deadlock phenomenon may occur in a routing process is solved, and the effect that the routing process avoids the deadlock phenomenon is achieved.

The present application relates to a kind of efficient parallel optimization method and device considering the actual engineering constraint of horizontal well, method includes the following steps: S1 constructs water drive reservoir production optimization mathematical control model;S2 based on horizontal well engineering application condition and reservoir numerical model, the constraint judgment method of horizontal well is constructed;S3 based on the population in Latin hypercube sampling initialization algorithm, using the constraint judgment method of horizontal well is handled, database is constructed;S4 using the excellent individual in database forms temporary population, based on temporary population, Gaussian model is constructed;S5 based on differential evolution algorithm with different strategies obtains three sub-populations, and using the constraint judgment method of horizontal well is handled;S6 based on the pseudo-update strategy of temporary population, the potential individual of sub-population is obtained;S7 based on Euclidean distance, select the individual closest to current optimal individual, and carry out parallel numerical simulation;S8 updates database.

The invention relates to a magnetic suspension centrifugal fan structure optimization method and system based on machine learning. According to the method, parametric modeling is carried out on a meridian plane profile, a blade setting angle and a torsion angle of an impeller, a sample set is generated by utilizing advanced Latin hypercube sampling (ALHS), and a performance database is constructed in combination with CFD simulation; 9 key geometric parameters which have the maximum influence on isentropic efficiency are screened out through adoption of MOP, and a high-precision Kriging approximation model is established; and with the maximum isentropic efficiency as the target and the inlet and outlet pressure difference as the constraint, iterative optimization is carried out by adopting a genetic algorithm (GA), and optimal impeller parameters are obtained. According to the invention, high-efficiency and low-cost automatic design is realized, and the method is suitable for improving the performance of the small-sized centrifugal blower.

The invention provides a tunnel blasting equivalent load prediction method and system based on an artificial neural network for tunnel blasting dynamic response rapid evaluation. The method comprises the following steps: firstly, establishing engineering parameters for describing a single-hole blasting working condition of a tunnel, and converting the engineering parameters into dimensionless input parameter vectors; aiming at continuity parameters and grading parameters, generating sample working conditions by adopting an orthogonal test and Latin hypercube combined sampling mode, carrying out numerical simulation on each working condition, extracting a blasting triangular wave speed-time history curve of a representative measuring point in the model, and establishing a blasting triangular wave speed-time history curve with engineering parameters of a single-hole blasting working condition as input; the database takes blasting triangular wave parameters as output; an artificial neural network is trained under the loss function, rapid prediction of triangular wave parameters is achieved, and triangular waves can serve as blasting equivalent load time history to be applied to the normal direction of the plane where representative measuring points are located and used for tunnel blasting dynamic response calculation. According to the method, the calculation cost and the modeling complexity can be remarkably reduced, and the numerical calculation stability and the engineering applicability are improved.

This invention discloses a heat sink structure optimization design method based on a non-dominated sorting genetic algorithm, belonging to the field of heat sink optimization design technology. The method first determines the structural parameters to be optimized (heat sink length, width, height, fin thickness, fin spacing, substrate thickness) and the optimization objective function (maximum junction temperature of power devices, heat sink mass, heat sink entropy productivity). Then, Latin hypercube sampling is used to sample parameters and establish a geometric model. Sample data is constructed through thermal simulation, and a surrogate model is established using response surface methodology. Finally, multi-objective optimization is performed based on the non-dominated sorting genetic algorithm to obtain the Pareto optimal solution set, and the best solution is selected through comprehensive performance evaluation indicators. This invention effectively reduces heat sink mass and cost while ensuring heat dissipation performance, shortens the R&D cycle, and is applicable to the heat dissipation optimization design of power devices in power electronic systems.

Embodiments of the present application disclose a training text data acquisition method and device, electronic equipment and a storage medium. The method comprises: establishing a text vector corresponding to each candidate document, and drawing a hypercube comprising each text vector; dividing the hypercube into a plurality of sub-cubes evenly; determining the number of clusters for clustering and determining initial centroids based on the number of clusters and the number of text vectors in each sub-cube; and clustering each text vector based on the number of clusters and the initial centroids to obtain a plurality of clustering result clusters, and determining training text data based on the plurality of clustering result clusters. Embodiments of the present application can generate professional and high-quality training text data.

A multi-objective optimization method adaptive to a U-shaped channel rib plate perforation matrix cooling structure comprises the steps that firstly, a parameterized finite element model of the U-shaped channel rib plate perforation matrix cooling structure of an inner cooling channel of a turbine blade is established, and heat exchange and flow resistance analysis of the matrix cooling structure are achieved in a fluid-solid coupling mode; performing a DOE experiment by using Latin hypercube sampling, generating a parameter set, importing the parameter set into a parameterized matrix cooling structure finite element model, and performing numerical simulation by combining a Reynolds average Navier-Stokes equation to obtain a data set of a training agent model; in a reliable precision range, a response surface agent model about structural heat exchange and flow resistance is established; performing multi-objective optimization on a data set derived by the proxy model through a non-dominated sorting genetic algorithm-II, and finding a matrix cooling optimal structure in a Pareto frontier solution set in combination with optimization of an ideal solution similarity sorting method; according to the method, the performance of the matrix cooling structure can be improved, and the calculation time and cost of parameterized multi-objective optimization of the matrix cooling structure are greatly reduced.

The invention discloses a transient multi-physics field reduced-order reconstruction method and device driven by a time sequence working condition, and relates to the technical field of digital twinning and calculation simulation acceleration. The method comprises the following steps: performing Latin hypercube sampling on monitoring working condition parameters to obtain a working condition parameter combination covering a sample space; establishing a high-fidelity finite element model to obtain geometric topology information; establishing a working condition parameter amplitude curve, performing batch finite element calculation in combination with the high-fidelity finite element model to obtain transient multi-physics field response data, and constructing a graph data structure; the training graph neural network auto-encoder comprises an encoder and a decoder; and training a time sequence neural network, establishing a mapping relation from time sequence working condition parameters to low-dimensional latent variables, and realizing rapid prediction and reconstruction from the time sequence working condition parameters to transient multi-physical field whole-field distribution in combination with a graph neural network decoder. According to the method, online rapid prediction of the transient physical field of the high-temperature turbine component can be realized, and the method has the advantages of high prediction precision, fast calculation response, strong cross-working-condition generalization capability and the like.

The application discloses a method and device for constructing a folded hypercube edge-disjoint Hamiltonian cycle, and relates to the technical field of interconnection network topology. The method comprises the following steps: recursively constructing a first Hamiltonian cycle on an n-dimensional folded hypercube; applying a mapping f to each vertex u in the first Hamiltonian cycle, and sequentially connecting the mapped vertices in the original order to obtain a second Hamiltonian cycle; and the first Hamiltonian cycle and the second Hamiltonian cycle do not have any common edge in the n-dimensional folded hypercube. The above method provides key technical support for the topology design and routing protocol of a high-performance parallel computing system.

The invention discloses an intelligent optimization design method and system for a hydrodynamic energy-saving device in front of a propeller of a ship, and the method comprises the steps: carrying out the parametric modeling of the hydrodynamic energy-saving device in front of the propeller of a target ship body, and selecting key geometric parameters; acquiring specific values of the multiple groups of key geometric parameters by adopting an optimal Latin hypercube sampling method to serve as multiple groups of configuration parameters of the target ship body; hydrodynamic performance CFD predicted values of each group of configuration parameters are obtained, and a data set is constructed by the predicted values and the corresponding configuration parameters; constructing a proxy model by utilizing the data set, performing interpolation optimization on the proxy model, screening out an optimal configuration parameter combination meeting the maximum energy-saving rate, obtaining a hydrodynamic performance proxy model predicted value, comparing the hydrodynamic performance proxy model predicted value with a hydrodynamic performance CFD predicted value of the optimal configuration parameter combination obtained by CFD software, and if an error is within a preset range, determining that the hydrodynamic performance of the optimal configuration parameter combination is not within the preset range. And if so, taking the scheme as an optimal design scheme. According to the method, the prediction efficiency and precision of the optimal design can be improved while the resource consumption is reduced.

This invention belongs to the field of electromagnetic device design technology, and relates to a method and apparatus for inverse design of electromagnetic devices based on bijective differentiable projection and neural networks. The method includes: determining the structural variables to be designed in the electromagnetic device and their inequality constraints; constructing a convex feasible region space composed of the inequality constraints; establishing a bijective mapping function from a hypercube domain to the convex feasible region space; establishing a neural network for generating the structural variables of the electromagnetic device; concatenating the bijective mapping function in the output layer of the neural network to form a neural network with a bijective differentiable projection layer and training it; inputting the target electromagnetic parameters into the trained neural network with the bijective differentiable projection layer to generate structural variables that satisfy the inequality constraints, thereby realizing the inverse design of the electromagnetic device. This invention accurately projects the output of the neural network into a preset convex feasible region, achieving rapid inverse design of electromagnetic devices that satisfy complex constraints.

This invention discloses a UAV 3D path planning method based on a multi-strategy improved lemming optimization algorithm, comprising the following steps: S1, constructing a 3D task space model; S2, constructing a flight cost function; S3, initializing the population using Latin hypercube sampling; S4, iterative global exploration and local exploitation with adaptive parameters; S5, updating the population using a bidirectional population evolution strategy and a memory mechanism; S6, evaluating fitness and selecting the current optimal individual; S7, iterative judgment and outputting the optimal path. This invention improves upon the UAV 3D path planning problem in complex environments by employing multi-strategy collaborative improvement, using Latin hypercube sampling to initialize the population, designing an adaptive parameter adjustment strategy to balance exploration and exploitation, and introducing a bidirectional population evolution strategy and a memory mechanism to enhance optimization performance. Through boundary reflection processing and flight cost function evaluation, the optimal flight path is obtained from the population, thus effectively solving the UAV 3D path planning problem in complex environments.

The invention relates to the technical field of high-speed micro pumps, and discloses a high-speed micro centrifugal pump impeller profile pre-compensation method based on fluid-solid coupling analysis, which comprises the following steps: constructing an impeller parameterized geometric model and extracting design variables; an optimized Latin hypercube sampling strategy is adopted to construct a fluid-solid coupling sample library, and an intrinsic orthogonal decomposition technology is combined with Gaussian process regression to establish a high-precision full-field deformation prediction model; establishing a multi-objective optimization function based on a full-field deformation prediction model, and performing optimization by using a non-dominated sorting genetic algorithm to obtain a target thermal state geometry considering both hydraulic performance and structural safety; and finally, carrying out reverse iteration solution on the target thermal-state geometry by utilizing a geometry-load mixed correction algorithm to obtain the cold-state manufacturing geometry in a static state. According to the method, the calculation cost is remarkably reduced through the agent model, the adverse effect of fluid-structure interaction deformation on the impeller performance is eliminated from the manufacturing end through the reverse pre-compensation strategy, and the actual operation efficiency and lift stability of the high-speed micro centrifugal pump are effectively improved.

The invention relates to the technical field of fluid conveying, in particular to a collaborative design optimization method for a guide vane type mixed-flow pump. Preliminarily designing the impeller according to the operating parameters of the design points, acquiring main geometric parameters, parameterizing the axial surface projection shapes of the impeller and the guide vane by adopting two constraints of geometry and size, and associating the axial surface projection parameters of the guide vane with the design parameters of the outlet of the impeller; according to the axial surface projection boundaries of an impeller and a guide vane, in combination with the structural characteristics and constraint conditions of the pumping chamber of the guide vane type mixed-flow pump, two constraints of geometry and size are adopted to carry out collaborative design on the pumping chamber; taking the initial design parameters and the geometric constraint parameters as optimization variables, determining a variable range, sampling by utilizing Latin hypercube sampling, and screening out key parameters by utilizing sensitivity to construct an error back-propagation neural network agent model; and a non-dominated sorting genetic algorithm with a penalty mechanism and an adaptive crossover variation attenuation strategy is introduced to carry out multi-target optimization on design parameters.