168 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 relates to the technical field of model construction, in particular to a method and a system for establishing a parameterized tunnel model based on cloud computing. The method comprises the following steps: acquiring a tunnel section type, and extracting structural semantics to obtain a section feature description set; constructing a parameter dictionary of the tunnel section based on the section feature description set to obtain a dynamic parameter dictionary data set; and modeling a tunnel section geometric structure based on the dynamic parameter dictionary data set to obtain an initial three-dimensional tunnel geometric model. Through dynamic parameter management, a self-correction mechanism and a Latin hypercube sampling technology, efficient automatic modeling and precise simulation optimization of the tunnel section three-dimensional geometric model are achieved, meanwhile, through unified packaging and authority control, safe and efficient management and collaborative sharing of cloud model resources are achieved, and the method is suitable for large-scale popularization and application. And the intelligent level and the engineering efficiency of tunnel model construction and application are comprehensively improved.

The invention provides a digital twinning application-oriented electrical equipment electromagnetic heat flow coupling rapid calculation method, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, a three-dimensional model of electrical equipment is acquired and preprocessed, and a full-order electromagnetic heat flow coupling calculation model is established and verified through a temperature rise test; generating an experimental point matrix by using a Latin hypercube sampling method, and constructing a current temperature power density relational data set; performing regional division on the power density field by applying a K-means clustering algorithm, and constructing an electromagnetic response surface model through a radial basis function; establishing a heat flow field order reduction model based on an intrinsic orthogonal decomposition technology, and extracting a dominant mode primary function; bidirectional coupling of an electromagnetic field and a heat flow field reduced-order model is achieved, an improved Lagrange multiplier method and a fixed point iteration method are adopted for processing the nonlinear coupling problem, finally, a software development kit supporting an open platform communication unified architecture protocol is packaged, and the electromagnetic heat flow coupling rapid calculation capacity needed by digital twinning application 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 relates to the technical field of storage and retrieval, in particular to a large-model knowledge persistent storage and retrieval method, which comprises the following steps of: capturing version evolution paths of large-model knowledge units in real time, and constructing a four-dimensional knowledge manifold comprising an ontology feature vector and a version association weight for each knowledge unit; extracting a core knowledge skeleton through a topology preserving layer, separating version difference characteristics through an evolution path layer, and generating compressed skeleton-path double codes; constructing a multi-level index structure based on the skeleton-path double coding, wherein the multi-level index structure comprises a static knowledge ontology layer and a dynamic path redirection layer; writing the knowledge unit into a storage device by adopting a hypercube mapping strategy, and establishing a path attenuation model to dynamically recycle a waste version space; according to the method, the long-term storage availability is improved, and the retrieval accuracy and the storage resource utilization rate are improved while efficient evolution management of the knowledge units is guaranteed.

The invention relates to a multi-modal multifunctional composite metasurface design method and device and a medium, and the method comprises the steps: carrying out the parameterized sampling of the key size of a unit structure through Latin hypercube sampling, and generating a training sample set; electromagnetic simulation is carried out on the sampled structure to obtain two-state electromagnetic response data, and a mixed variable database is obtained; according to the target response, generating a plurality of groups of candidate structures through a generator in the improved conditional variation auto-encoder generative adversarial network, screening the candidate structures through a predictor, calculating a prediction comprehensive error, and selecting a preset number of candidate structures with the minimum prediction comprehensive error to perform full-wave simulation verification; and obtaining a simulation double-state response, calculating a simulation comprehensive error, and judging the current candidate structure according to a preset comprehensive error threshold value. Compared with the prior art, the method has the advantages of multi-state collaboration, low calculation cost, high degree of freedom and the like.

The invention discloses a regional distributed energy storage optimization scheduling method, which comprises the following steps: S1, model construction: constructing a mixed integer nonlinear programming model containing a multi-objective optimization function according to physical characteristics, operation constraints and economic objectives of a regional power distribution network, a micro-grid, an energy storage system and renewable energy; s2, scene generation and processing: adopting a Latin hypercube sampling method to generate a multi-scene data set of renewable energy output, load demand and electricity price fluctuation, performing clustering analysis on scene data, and screening out representative typical scenes so as to reduce calculation complexity; and S3, carrying out optimization solution. In the application, multi-dimensional constraints such as energy storage life attenuation, network loss, carbon emission and the like are added in constraint conditions, the practicability of the model is improved, and the limitation of traditional single-target scheduling is broken through through multi-target optimization and multi-constraint coordination.

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.

Disclosed are a method and devices of an efficient Gaussian Mixture Model (GMM) distribution based approximation of a data set including a collection of multi-dimensional numeric arrays in a computing environment. In accordance therewith, the data set is distributed across a multi-dimensional grid having integer coordinates associated therewith, and a hypercube is assigned to each constituent Gaussian distribution of constituent Gaussian distributions of the GMM distribution as a subspace of the multi-dimensional grid to form a number of hypercubes. A data footprint of the data set is reduced through the GMM distribution based on assigning the hypercube to the each constituent Gaussian distribution of the constituent Gaussian distributions of the GMM distribution.

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 provides a multi-objective optimization method based on an adaptive point adding criterion and an agent model, and relates to the technical field of multi-objective optimizing.The method comprises the steps that a Latin hypercube sampling method is used for sampling an optimization algorithm, and initial sample points are obtained; constructing an initial agent model by using the initial sample points; based on an error standard and a self-adaptive point adding criterion, updating the initial agent model to obtain an updated agent model; and calculating the optimization algorithm by using the updated agent model to obtain a multi-target optimization result, and completing multi-target optimization. According to the method, the problem that multi-objective optimization is difficult to balance precision and convergence speed is solved.

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 provides a mechanical equipment structure key pressure-bearing component real-time simulation method based on digital twinning, which takes a cubic press hinge beam as an example, and combines finite element analysis, Latin hypercube sampling, a K nearest neighbor algorithm, Gaussian interpolation and an RBF (Radial Basis Function) proxy model to realize stress-strain rapid prediction and three-dimensional visualization. According to the method, an efficient prediction model is established through structure database construction, dimension reduction processing, neighbor search and interpolation calculation, a simulation result is presented in real time by utilizing Python and Unity interaction, the design efficiency and accuracy are improved, and the method is suitable for structure optimization analysis under complex working conditions.

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 graph neural network-based equivalent mechanical performance prediction method for a truss-like unit cell structure, and the method employs a graph form to carry out the unified representation of the geometric structure of the truss-like unit cell according to the geometric structure characteristics of the truss-like unit cell. Then, a truss-like unit cell geometric configuration database containing different configurations is constructed through a Latin hypercube sampling method, and equivalent macroscopic mechanical properties corresponding to unit cells are solved by adopting a homogenization theory; based on the data set, a prediction model of truss-like unit cell equivalent mechanical performance is constructed based on a graph neural network, and the accuracy of the constructed neural network prediction model is evaluated through a verification set. Compared with a traditional method for solving equivalent mechanical performance based on finite element calculation, the truss-like unit cell equivalent mechanical performance solving method based on the neural network is constructed, the solving speed for large-scale heterogeneous unit cell configuration is effectively increased, and the method can be further used in heterogeneous dot matrix cross-scale structure optimization design based on truss-like unit cells.

The invention discloses a structure optimization method for prolonging the fatigue life of a crankshaft of a marine diesel engine. The structure optimization method comprises the following steps: developing a single-throw crankshaft modeling analysis plug-in; design parameters are selected, a three-dimensional geometric model of the single-throw crankshaft is dynamically constructed, and finite element analysis is carried out; developing a graphical interface for the dialog box script; seamless connection between the plug-in and the ABAQUS software is realized through development of the registration script; adopting optimal Latin hypercube sampling in a defined range to obtain a pre-calculation sample, developing a batch processing file and a Python script, and using an instruction to drive a single-throw crankshaft modeling analysis plug-in; a Pareto graph is adopted to analyze parameter sensitivity, a pre-calculation sample is screened, a high-precision Kriging proxy model is built, and then the optimal maximum bending stress value of the single-throw crankshaft and the corresponding optimal structure size are searched in combination with a particle swarm algorithm. According to the invention, the problem of low efficiency caused by frequent man-machine interaction when large samples are processed in the prior art is solved.

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 topological structure, a routing method and a routing device of a multi-dimensional hypercube interconnection network, and the multi-dimensional hypercube interconnection network comprises 2N network nodes. Target network nodes included by each sub-network in 2N-3 sub-networks of the multi-dimensional hypercube interconnection network as a target sub-network are divided into a first node set and a second node set, and four target network nodes in the first node set are connected with four target network nodes in the second node set in a one-to-one correspondence manner; the four target network nodes in the first node set are connected into a ring, the first network node in the second node set is connected with the third network node and the fourth network node, and the second network node is connected with the third network node and the fourth network node. According to the method and the device, the problem that the network communication time delay of the multi-dimensional hypercube interconnection network is relatively large is solved, and the effect of reducing the network communication time delay of the multi-dimensional hypercube interconnection network is achieved.

The invention relates to a multi-fidelity fluid data fusion simulation modeling method, device and equipment. The method comprises the following steps: acquiring factory fluid data of equipment parts stored in a multi-fidelity file and use fluid data of the equipment parts in a simulation environment; sampling the two pieces of data by adopting a Latin hypercube algorithm, outputting low-fidelity wall surface grid data and high-fidelity wall surface grid data, and constructing a kernel coupling matrix according to the two pieces of grid data; and taking multi-fidelity data obtained by combining the two pieces of grid data as a sample data set, inputting the sample data set into a multi-fidelity Gaussian regression simulation model constructed according to a kernel coupling matrix, and solving the kernel coupling matrix by adopting a radial basis function kernel function to obtain a to-be-evaluated sample data set. And verifying the to-be-evaluated sample data set through a preset error evaluation index to obtain real fluid data. By adopting the method, the production efficiency of high-performance equipment parts can be improved.

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.