2283 results about "Optimal design" patented technology

Disclosed is a method for evaluating the fatigue damage and life of a bridge structure under a multi-factor coupling effect. The method comprises the following steps: S1, reproducing complex service environment action values; S2, analyzing performance values of a bridge structure; S3, calculating coupling fatigue damage of a vulnerable part; and S4, predicting a remaining life of the bridge structure. By adopting the method, the fatigue damage condition and remaining life of an in-service bridge under a multi-factor coupling effect can be accurately evaluated, and the effect of each factor / action and the coupling effect the factors / actions in fatigue damage of a vulnerable part are quantitatively analyzed, thereby providing data support and decision-making reference for the operation and maintenance management, reinforcement and repair, and optimal design of bridges.

The invention discloses an optimal design method and system for foundation pit supporting, and aims to improve the design efficiency and safety of a supporting structure, collect on-site landform and soil physical and mechanical parameters, evaluate the influence of foundation pit excavation on the surrounding environment, analyze geological disaster risks, formulate preventive measures and select a proper supporting structure type. Finite element analysis software is used for simulating the performance of different supporting schemes, monitoring equipment is installed to monitor the state of a foundation pit in real time, a machine learning algorithm is used for establishing a prediction model, a design scheme is adjusted according to monitoring data, the final performance of a supporting system is evaluated, abnormal conditions are recorded, and the design process and the construction method are continuously improved according to project feedback. According to the method, a data acquisition and processing module, a design decision support module, a numerical simulation and optimization module, a monitoring and feedback control module, a construction management module and a later evaluation and feedback module are integrated, a closed-loop optimization design system is formed, key problems in support design are solved, and engineering efficiency and safety are remarkably improved.

The invention discloses a switching loss evaluation method and system based on a semi-soft switching modulation technology, and relates to the technical field of information. According to the method, parasitic inductance parameters and buffer capacitance parameters of a device are obtained, a resonant circuit working model is established, an initial voltage waveform and an initial current waveform are obtained by solving the model, and corresponding voltage stress and current stress are calculated; a parasitic inductance parameter and a buffer capacitance parameter of the device are optimized by using a genetic algorithm; adjusting the design of the buffer circuit according to the optimized device parameters, carrying out the simulation of the resonance process again, carrying out the frequency domain analysis of the optimized resonance voltage waveform and resonance current waveform, and optimizing the device parameters and the design of the buffer circuit. And finally, semi-soft switching modulation simulation is carried out, a temperature coefficient mapping relation between simulation switching loss and actually measured switching loss is constructed, an optimal design scheme of the device is determined, the switching loss of the device is effectively reduced, and it is ensured that the device works in a safe working area.

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The present invention relates to the technical field of machine tool structure optimized design. Disclosed is a method for the optimized design of a headstock structure of a vertical machining center. The method mainly comprises: parameterizing to establish a headstock three-dimensional model; establishing a finite element model of a headstock structure; according to limit working conditions in an actual machining process, performing static and dynamic characteristic analysis on the headstock structure; performing modal test testing on a headstock, carrying out comparative analysis with a simulation result, and correcting a finite element model of the headstock; using a sensitivity analysis method to select a key dimension of the headstock as an optimized design input parameter, while stress strain, low-order inherent frequency, and quality are target output parameters, and establishing a mathematical model for the optimized design of the headstock; and on the basis of a response surface model constructed by a second-order polynomial and a genetic algorithm, performing optimized design of the headstock until an optimal headstock structure which has higher performance and is lighter is obtained. According to the present invention, design efficiency is improved, prototype testing costs are reduced, and a better headstock structure is obtained.

The invention belongs to the technical field of automobile structure design, and particularly relates to a small sample multi-target integrated optimization design method and system for an automobile body structure, and the method comprises the steps: building an automobile body structure parameterized model, defining design variables, and obtaining a design variable combination; obtaining vehicle body structure performance data by utilizing finite element simulation, and constructing an integrated learning agent model; vehicle body structure design variables are optimized through a multi-objective optimization algorithm, and a Pareto leading edge and a solution set are obtained; and circularly adding a proxy model training data set, and iterating the whole optimization process to a termination condition. The technical problems that in the prior art, simulation calculation is long in time consumption, a single machine learning model is insufficient in prediction precision, and the multi-objective optimization effect is not ideal are solved, and the small sample integration optimization design method of the multi-objective optimization algorithm including the adaptive integration model and the fusion multi-strategy is constructed. And the multi-objective optimization efficiency of the vehicle body structure can be effectively improved.

The invention relates to the technical field of data processing, and discloses a coal mine area gas control and safety guarantee method and system under complex geological conditions. The method comprises the steps that geophysical prospecting, drilling and logging exploration are conducted on a coal mine area, and geological parameters are obtained; performing statistical analysis on the data, and constructing a three-dimensional gas model; identifying a high-gas area based on the model, and arranging a directional drilling network; differential extraction and anti-reflection measures are implemented according to the drilling network; fusing extraction and sensor data, and predicting a gas change trend by using a neural network; and performing intelligent diagnosis based on the early warning matrix to form a disposal scheme. Under the complex geological condition, the gas distribution rule is accurately mastered, scientific arrangement of directional drilling and optimization design of differential extraction parameters are achieved, meanwhile, a gas early warning decision support system based on artificial intelligence is established, the accuracy of gas treatment and the timeliness of early warning are improved, and therefore safe production of a coal mine is guaranteed.

The invention provides an airfoil profile adaptive design and optimization method based on reinforcement learning and a conditional generative adversarial network, and belongs to the field of airfoil profile optimization design. The method aims at achieving the two core functions of self-adaptive optimal airfoil generation and rapid optimization. According to the first function, through interaction of a reinforcement learning agent and a conditional generative adversarial network generator, generation strategies for generating optimal airfoil profile potential variables under different design conditions are continuously learned, so that the airfoil profile with the optimal aerodynamic performance is adaptively generated, intelligent and adaptive design of the airfoil profile is achieved, and the optimization period is remarkably shortened. Secondly, a self-adaptive agent model based on meta-learning is introduced, the model not only establishes a mapping relation among design conditions, aerodynamic performance and potential variables and directly predicts the optimal potential variables of a specific airfoil profile, but also can quickly adapt to an updated database through simple adjustment; the agent model predicts the optimal potential variable corresponding to the specific airfoil profile for the self-adaptive generation framework, so that the agent can quickly optimize the specific airfoil profile under the condition of fine adjustment.

The invention relates to the technical field of three-dimensional reconstruction and simulation, and discloses a foam material porous structure three-dimensional reconstruction and simulation system, which comprises a scanning imaging module used for carrying out scanning imaging on a foam material sample to obtain pore image data; the pore boundary recognition module is used for performing pore boundary recognition based on the pore image data to obtain a pore boundary recognition result; the three-dimensional reconstruction module is used for executing three-dimensional reconstruction of bubble evolution physical constraints according to the pore boundary recognition result to obtain a three-dimensional reconstruction model; the coupling simulation module is used for performing grid division and coupling simulation on the three-dimensional reconstruction model to obtain mechanical simulation data; and the parameter solving module is used for carrying out density field optimization and parameter solving based on the mechanical simulation data to obtain optimal gap design parameters, so that seamless connection from simulation analysis to optimization design is realized, the structural design period of the foaming material is shortened, and the design precision and consistency are improved.

The invention belongs to the technical field of industrial waste heat recovery, and discloses a flue gas waste heat recovery system optimization design method considering external parameter changes. The method comprises the steps of system monitoring data acquisition and feature extraction, thermodynamic modeling and parameter sensitivity analysis, external environment parameter and system performance coupling mechanism modeling, equipment failure prediction and reliability constraint determination, multi-objective optimization and self-adaptive control strategy generation and the like. Optimized operation of the flue gas waste heat recovery system under the condition of dynamic change of external environment parameters is achieved. According to the method, a dynamic influence matrix of external environment parameters and system performance is initiatively established, the reliability of the system is evaluated in combination with a stress-life analysis technology, efficiency and cost targets are balanced through a multi-target optimization algorithm, and a self-adaptive control strategy library for different working conditions is generated. The adaptability and stability of the system under complex working conditions are improved, the service life of equipment is prolonged, and the operation cost is reduced.

Systems and methods for asset life cycle optimization and management are provided. A probabilistic, physics-based, causal method for predicting the evolution of damage and failure time of an aging asset. The method comprises providing a probabilistic, physics-based, causal network, comprising a plurality of random-variable nodes, wherein the nodes represent at least one of: damage initiation time, damage state, damage rate, damage causal factors, observations, human expert knowledge, failure state, and failure time. The method further comprises applying the probabilistic physics-based causal network to an aging asset; predicting the evolution of damage and failure time of the aging asset; and using this knowledge to make optimal design, inspection, maintenance, and operational life cycle decisions for the aging asset.

The invention belongs to the cross technical field of the fusion graph attention network and the ship resistance field, and discloses a ship resistance intelligent forecasting method and system, and the method comprises the steps: constructing nodes and edges in a graph attention network, and carrying out the preprocessing of data; designing and improving a loss function; a dynamic weight multi-attention mechanism is established, and attention weights are automatically distributed according to different ship type features; applying a training strategy of an Adam and SGD combined optimizer to obtain a graph attention network model; and carrying out ship resistance prediction evaluation through the dynamic weight map attention network model. By introducing the ship resistance intelligent forecasting method based on the dynamic weight map attention network and the physical constraint, the ship resistance intelligent forecasting method has the advantages of simple structure and clear algorithm, is specially designed for forecasting resistance under different ship types and solving the matching problem of propellers and power devices, shows remarkable innovativeness and practical value, and is suitable for popularization and application. Powerful support is provided for optimization design, energy conservation and emission reduction and safe navigation of the ship.

The invention discloses a personalized design intelligent interaction method based on topological optimization, and the method comprises the steps: S1, constructing a user behavior monitoring system based on multi-modal data collection, and generating a high-dimensional data set containing time sequence features; s2, based on an adaptive deep neural network model, performing dynamic prediction and hierarchical modeling on the personalized demand of the user; s3, generating an optimal solution of the design parameters in real time through a dynamic topology reconstruction algorithm; s4, iteratively updating a design scheme generation rule through a reinforcement learning mechanism; s5, constructing a multi-objective design space exploration and optimization framework supported by the neural network, and rapidly screening and optimizing design parameters; s6, dynamic updating and local optimization of the design scheme are achieved through multi-dimensional data mapping and parameterization regulation and control means; and S7, establishing a data-driven continuous learning and evolution mechanism, continuously optimizing the user demand prediction model and designing an optimization algorithm. The method has the advantages of being high in dynamic adaptability, high in intelligent level and high in personalized meeting precision.

The invention discloses an indoor environment performance optimization design method and system for a tall and large space building based on CFD and a multi-objective optimization algorithm, and the method comprises the steps: building a parameterized analysis model through combining with a BIM model, carrying out the digital simulation analysis of an indoor light environment and energy consumption through employing Honeybee Radio and Honeybee Entry plug-ins in Rhino and Grasshopper environments, determining optimization parameters and objectives, and carrying out the optimization of the indoor environment and energy consumption. And a BIM model and an IDF file corresponding to the optimal schemes are exported, the uniformity of the indoor airflow organization is evaluated based on FFD software, and parameters of the optimal schemes are determined. Compared with a traditional indoor environment optimization method, the high and large space building indoor environment performance optimization and evaluation method is efficient, accurate and economical, and has higher accuracy, higher adaptability and wider applicability. Natural lighting, building energy consumption and indoor three-dimensional thermal comfort can be considered at the same time, and optimization of the indoor environment performance of the tall and large space building is achieved.

The invention relates to the technical field of slurry valve design, and discloses a large-diameter slurry valve sealing structure design method and device suitable for a high-wear working condition, and the method comprises the steps: firstly obtaining key geometric parameters of a sealing structure, constructing a three-dimensional topological basic model and a mechanical property simulation model, and then carrying out the fusion to form a digital twin model; then constructing a wear morphology time sequence data set; meanwhile, three-level early warning threshold values are set for the key working condition parameters, a working condition risk grade division rule is established, and a real-time working condition risk grade is obtained according to the working condition risk grade division rule; and finally, based on the wear morphology time sequence data set and the real-time working condition risk level, constructing a wear evolution prediction model, and optimizing key geometric parameters of the sealing structure in combination with a digital twin model to obtain an optimal geometric parameter combination. Accurate prediction and optimal design of the abrasion state of the sealing structure of the large-diameter slurry valve can be achieved, the service life of the sealing structure under the high-abrasion working condition is remarkably prolonged, and the reliability of the sealing structure under the high-abrasion working condition is remarkably improved.

The invention discloses an optimization design method of a large-model-driven mechanical part, which comprises the following steps of: performing knowledge retrieval according to a design task and a constraint condition, and generating text description of a structure design scheme by fusing the retrieved knowledge through a large-scale language model (LLM); the LLM performs task decomposition and modeling code mapping on the structure design scheme described by the text, and generated modeling codes are directly executed by modeling software to generate a three-dimensional model; the LLM generates a reasoning track and calls a finite element analysis tool set to perform performance analysis on the generated three-dimensional model; and according to optimization requirements and design constraints, the LLM optimizes the three-dimensional model to generate an optimal three-dimensional model. The invention discloses an optimization design system of a large model driven mechanical part. The optimization design system comprises a design scheme generation module, a parametric modeling module, a design scheme verification module and a structure parameter optimization module. The method and the system provided by the invention can save code quantity, reduce manual intervention and improve design efficiency.

The invention relates to the technical field of phase change heat storage, in particular to a test method of a composite nanoparticle latent heat system based on a Fibonacci fractal tree-shaped fin. The test method comprises the following steps: firstly, establishing a physical model of a nanoparticle three-sleeve vertical latent heat system based on the Fibonacci fractal tree-shaped fin; secondly, performing two-dimensional simulation analysis on the physical model by adopting a two-dimensional model, simulating the melting and solidification process of the PCM, analyzing the heat transfer performance and energy storage efficiency of the system, building an experimental device of a nano-particle three-sleeve vertical latent heat system based on the Fibonacci fractal tree-shaped fins, selecting proper PCM and nano-particles, simulating different working conditions, and establishing a three-sleeve vertical latent heat system based on the nano-particle three-sleeve vertical latent heat system based on the Fibonacci fractal tree-shaped fins. And finally, comparing a simulation result with an experimental result, adjusting parameters of a simulation model, improving the accuracy of the simulation result, analyzing the influence of different fin bifurcation angles, nanoparticle types, wall surface temperatures and fin materials on the heat transfer performance and energy storage efficiency of the system, and providing guidance for optimal design of the system.

The invention provides a perceptual concrete mix proportion optimization method based on interpretable machine learning, which comprises the following steps: constructing an inductive concrete compressive strength database and performing preprocessing to determine model input characteristics, constructing an interpretable machine learning model, and performing performance comparison with other mainstream machine learning models; a feature circulation method is adopted to reduce the collinearity influence, model input features are trained based on an optimal feature function, and the contribution of the model input features to a prediction result is quantified from the overall aspect and the local aspect; preliminarily drawing up a mix proportion, substituting the mix proportion into the interpretable machine learning model after the characteristic contribution quantification, and guiding the optimal design of the mix proportion of the inductive concrete mixture according to an interpretable analysis framework. According to the method, the optimal design of the inductive concrete mixture mix proportion is guided according to an interpretability analysis framework, the contribution degree of each parameter in the mix proportion to the material performance can be quantified, and technical support is provided for rapid and intelligent design of the self-sensing concrete mix proportion based on the performance.

The invention discloses a common concrete and ultra-high performance concrete performance prediction and mix proportion multi-objective optimization design method and system, and the method comprises the steps: obtaining a comprehensive data set which covers the mix proportion, performance data and cost data of common and ultra-high performance concrete; correlation analysis is carried out, and influence factors strongly correlated with the target variable are determined; constructing a concrete performance prediction model by using a machine learning algorithm, training and testing, then carrying out interpretability analysis, and selecting an optimal concrete performance prediction model; constructing a mix proportion optimization design objective function and an ultra-high performance concrete cost function; establishing a multi-objective optimization design mathematical model in combination with constraint conditions, a cost function and an optimization objective function; and solving by adopting a multi-objective evolutionary algorithm, generating an approximate Pareto frontier solution set, and determining an optimal mix proportion optimization design scheme. According to the method, the concrete performance can be predicted, the mix proportion with the optimal comprehensive performance and cost effectiveness is obtained, and multi-objective optimization design of the concrete mix proportion is achieved.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention belongs to the technical field of pipeline arrangement optimization, and particularly relates to a pipeline optimization design auxiliary platform based on a BIM (Building Information Modeling) technology, which comprises a preprocessing module, a BIM modeling module, a collision detection module, a simulation module, a collaborative design module and a feedback module, comprising design drawings, landforms and existing pipe network distribution parameters. The BIM modeling module utilizes BIM software, the pipeline optimization design auxiliary platform based on the BIM technology can fully combine specifications or design requirements, input requirements for pipeline space arrangement, on-way resistance, construction cost and the like in advance, automatically provide various optimization schemes for comparison and selection, input according to parameters of the space arrangement, the on-way resistance and the construction cost, reduce the resistance of a pipeline system, and improve the pipeline optimization design efficiency. And the material cost in construction is reduced, so that the construction cost and the construction period are comprehensively controlled, and energy waste caused in the operation process of the pipeline system is prevented.

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention discloses a building facade optimization method and system based on sustainable analysis, and relates to the technical field of data processing. According to the method, parametric modeling is carried out on the external facade of a target building, and a design space is defined; training a machine learning model integrating energy consumption, carbon emission, cost and lighting prediction functions by using historical data; building an optimization problem by taking external facade parameters as decision variables and taking minimization of energy consumption, carbon emission and cost and maximization of lighting as targets, and automatically generating an optimal scheme set by adopting a multi-target optimization algorithm; a decision maker is assisted to select a final scheme through a visual interface, and a corresponding BIM model is output; the method effectively solves the problems that a traditional design method is difficult to coordinate multi-target conflicts, depends on experience and is low in efficiency, and can automatically and intelligently generate an optimal design scheme which is energy-saving, low-carbon, low-cost and good in lighting performance.

The invention relates to an equipment performance intelligent detection technology, and particularly discloses a circuit breaker arc extinguish chamber arc extinguish performance detection method, which realizes optimal design of arc extinguish performance parameters of an arc extinguish chamber and reduces possible safety risks in an arc extinguish process. Comprising the steps that a magnetofluid simulation model based on magnetofluid dynamics is built, historical arc parameters in an arc extinguishing chamber are collected to serve as test sample input, and an arc extinguishing simulation model is obtained through updating; carrying out analogue simulation according to the arc extinguishing simulation model and outputting simulation images of magnetic field changes and current changes of different monitoring points in the arc extinguishing chamber; analyzing the simulation images of the magnetic field change and the current change of different monitoring points in the arc extinguish chamber to judge whether the simulation arc form change state value meets the requirement or not; performing matching detection on the simulated arc form change state value meeting the requirement and a preset standard arc chamber structure parameter; performing optimization and simulation parameter correction on the arc extinguishing simulation model, and adjusting physical parameters and boundary condition setting in the model.

The invention discloses an ML-based rear subframe structure lightweight and modal optimization target design method, which comprises the following steps: (1) through three-dimensional modeling, statics analysis and modal analysis, constructing a mathematical model corresponding to the weight of a rear subframe and first-order inherent frequency simulation; (2) generating a population based on a Maximin criterion and a Latin hypercube, and establishing a radial basis function machine learning model; (3) generating candidate offspring individual vectors by adopting a Pbest-driven DPM evolutionary strategy based on an Eplison function; (4) constructing a minimum and maximum Pareto front lifting function to screen real offspring individual vectors; and (5) performing simulation evaluation on a real offspring individual vector, adaptively switching the reference vector type based on a reverse generation distance updating condition, returning to the step (3) until an optimization target meets a design requirement, and outputting an optimal optimization design parameter value. According to the method, the evolution direction is adaptively adjusted according to the population simulation result, and the optimization design effect on the two targets of lightweight and modal optimization is good.

The invention discloses a multi-working-condition multi-objective optimization design method for a water bucket structure of an impulse water turbine, which comprises the following steps: constructing a three-dimensional numerical model of an original water bucket structure of the impulse water turbine, and obtaining parameterized optimization design variables; modeling is conducted through the parameterized optimization design variables, a three-dimensional model of the impulse turbine runner bucket subjected to preliminary optimization design is obtained, the coordinate change direction and range of a control point of the three-dimensional model are restrained, and the restrained optimization design variables are obtained; according to the parameterized optimization design variable and the constrained optimization design variable, constructing a sample space of optimization design, and constructing a response surface model between the constrained optimization design variable and the optimization objective function; and performing global optimization on the response surface model to obtain an optimal solution set, and obtaining an optimal solution from the optimal solution set. According to the method, actual engineering requirements are considered when the optimization parameter range is set, and the phenomenon that the bucket blade is excessively deformed in the multi-working-condition optimization design process is effectively avoided.

The invention belongs to the technical field of reliability-based design optimization, and particularly relates to a reliability design optimization method combining transfer learning and a physical information neural network. Comprising the following steps: constructing a physical information neural network model by adopting training data of a source domain, and evaluating the reliability of the source domain. A physical information neural network model constructed by adopting training data of a source domain is updated by utilizing a layer freezing and fine tuning technology in transfer learning, so that the reliability of a target domain with different statistical attributes is evaluated, the calculation overhead is saved, and the calculation efficiency is improved. A first-order scoring function is utilized to carry out random sensitivity analysis on the failure probability so as to ensure normal operation of design optimization. And finally, providing estimation reliability, an objective function value and corresponding sensitivity information for an optimizer to obtain a corresponding updated design point. And repeating the iterative design process until the optimal design is realized. The training speed of the physical information neural network model is increased, and the precision of the physical information neural network model is improved.

The invention discloses a multi-objective optimization method for injection molding process parameters. The method comprises the following steps: firstly, obtaining a simulation result data set based on Moldflow simulation analysis and CCF; then, an NGO-XGBoost is adopted to construct an injection molding quality optimization model; then explaining and analyzing the NGO-XGBoost model by using an SHAP method to obtain importance and positive and negative influences of each optimization design variable on a prediction quality optimization target, and performing multi-target optimization through MOMVO to obtain a Pareto leading edge; and finally, determining an optimal combination of process parameters through a CRITIC-TOPSIS evaluation method. According to the method, the buckling deformation and the volume shrinkage of the ABS plastic injection molding plastic part can be effectively reduced, so that a high-quality ABS plastic part product is obtained, and a theoretical basis and data support can be provided for obtaining an optimal technological parameter combination of ABS plastic injection molding.

The invention provides a computer-aided slide-resistant pile and anchor cable build-in depth design method, and relates to the technical field of slide-resistant pile and anchor cable design, and the method comprises the following steps: collecting drilling data of an engineering site, and establishing a three-dimensional geologic model containing spatial distribution of each soil layer; determining a feasible range of design parameters; according to the soil layer space distribution and parameters, a slope stability analysis model considering the supporting effect of the slide-resistant piles and the anchor cables is established and used for calculating the slope stability safety coefficient under the given design parameters; and taking the minimum construction cost as a target function, determining an optimization constraint condition according to the slope stability analysis model, carrying out iterative optimization on the design parameters in a feasible range by adopting an optimization algorithm until an optimization convergence condition is met, and outputting an optimal design parameter. The problems that a traditional design method excessively depends on experience, multiple influence factors are difficult to consider, and the calculation efficiency is low can be solved, and the design level of the built-in depth of the slide-resistant pile and the anchor cable is improved.

The invention discloses a metasurface reverse design method of a generative adversarial network based on anchor point network control. The method comprises the following steps: collecting metasurface design parameters and spectral response data, preprocessing, and encoding the data into a format which can be processed by a deep learning model; and analyzing the training data by using a K-means clustering algorithm to generate a structured control vector. And constructing an AcGAN framework, and integrating the spectrum similarity measurement SOC into a loss function of a generator and a discriminator to enhance the spectrum matching precision. Through an adversarial training method, a generator is trained to generate a design conforming to target spectral characteristics, and meanwhile, a discriminator and an anchor network AncorNet are optimized to quickly predict and evaluate spectral response. Candidate designs are generated using the trained generator in combination with control vectors and random noise. And screening the candidate design to select the optimal design. According to the method, the spectrum regulation and control precision and the design efficiency of the metasurface design are effectively improved, and the challenge faced by a traditional method when a complex spectrum requirement is processed is solved.