60 results about "Engineering optimization" patented technology

The invention discloses a multi-agent cooperative cue word engineering optimization method, system and device and a medium, and relates to the technical field of cue word engineering, the method comprises the following steps: after initially receiving a user input task, starting a cue word optimization process; an initial cue word template is created through a cue word generation agent, historical optimal templates are retrieved from a knowledge base for fusion, and candidate cue word templates are generated. And the analysis agent inputs the candidate template into the second large language model to generate a candidate answer. And the evaluation agent performs multi-dimensional evaluation on the answers and gives scores and defect analysis. And according to an analysis result, updating the cue word to generate a candidate template of the intelligent agent. And arranging the intelligent agent to cyclically execute the steps until the stop condition is met, and outputting the optimal cue word template. And finally, storing the optimal template into a knowledge base and establishing a semantic index to support subsequent retrieval reuse. According to the method, manual intervention is not needed, and a complete automatic process of generating, evaluating and optimizing the cue word is realized.

The invention discloses a multi-strategy improved fox-monkey optimization algorithm for global optimization and application thereof, and belongs to the technical field of optimization algorithms, the method comprises the following steps: firstly, using Chebyshev chaotic mapping to initialize a population so as to improve the diversity and coverage range of the initial population; secondly, a difference population evolution strategy is introduced in the iteration process, and the algorithm is prevented from falling into local optimum in the later period; and finally, introducing a crisscross strategy to enhance global search capability and maintain population diversity. 20 benchmark test functions and a part of complex functions in a cec2017 function set are adopted for a simulation experiment, and the experiment result shows that the optimization precision, the convergence speed and the stability of the improved algorithm are obviously improved. The improved algorithm is applied to the engineering optimization problem, compared with an original algorithm, the convergence precision of the algorithm in three actual engineering problems is improved by 19.56%, 19.18% and 6.7% respectively, the standard deviation is reduced by 91.44%, 27.56% and 92.19% respectively, and the feasibility of the improved algorithm is further verified.

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 provides a jacket type offshore electrical platform optimization method based on a sequential quadratic programming algorithm. The jacket type offshore electrical platform optimization method comprises the following steps: S1, establishing a parameterized offshore electrical platform structure analysis model; s2, calculating an environmental load borne by the offshore electrical platform by adopting an equivalent static load method; s3, establishing an offshore electrical platform optimization model which comprises a target function and a constraint function; s4, performing optimization design by adopting a sequential quadratic specification algorithm; and S5, performing dynamic analysis on the optimal structure, judging whether the optimal structure meets a constraint condition or not under a dynamic load working condition, if so, ending optimization, and if not, manually adjusting the size of the component which does not meet the constraint until the constraint condition is met. According to the method, the sequential quadratic programming algorithm is adopted, the optimization efficiency is high, the engineering optimization requirement is met, the size optimization of the offshore structure considering the pile-soil interaction under wind, wave and flow loading can be achieved, and the cost is further reduced on the premise that the structural stability and safety are guaranteed.

The invention relates to an engineering optimization method and system based on a whale optimization algorithm, and a medium, and belongs to the technical field of intelligent optimization algorithms. Comprising the following steps: S1, initializing parameters and populations: setting a population scale, a maximum number of iterations, dimensions and upper and lower bounds of variables, generating an initial population, and initializing a global optimal solution and a current optimal solution; s2, executing an individual variation strategy: recording a global optimal individual position and a current iteration optimal individual position; s3, executing a unified search strategy; s4, executing a group communication strategy; and S5, judging whether the current number of iterations reaches the maximum number of iterations, if so, outputting a globally optimal solution, otherwise, adding 1 to the current number of iterations and returning to the step S2 to continue iteration. The algorithm provided by the invention aims to improve the global search capability and convergence precision of the algorithm and maintain the time complexity equivalent to that of the original WOA at the same time.

The invention discloses a high-speed aircraft aerodynamic configuration agile optimization method based on an artificial intelligence model. The method comprises the following steps: A1, establishing an aerodynamic force agent model; a2, performing a global optimization algorithm; a3, establishing an intelligent autonomous decision-making model; by constructing a multi-target adaptive weight distribution model, weight dynamic collaborative optimization of proxy model confidence, a constraint penalty function and target function key elements is achieved, the mathematical modeling problem of dynamic matching of weight parameters and optimization targets in the iteration process is solved, a closed-loop optimization system with the autonomous perception-decision-execution capacity is developed, and the optimization efficiency is improved. Therefore, the convergence speed is improved in a complex engineering optimization scene, local optimum is avoided, and the robustness of a global optimal solution is ensured; and A4 pneumatic optimization iteration: based on the proxy model for predicting indexes such as the lift-drag ratio, performing global optimization of design parameters by using an artificial intelligence optimization algorithm, performing verification through CFD, and after verification, putting a CFD calculation result into a training set of the model to re-train the model and continuing optimization until optimization design is converged.

The application discloses a kind of based on manifold constraint's elastic body erosion profile multimodal self-attention fusion prediction method, comprising: data set construction and pre-processing;Build multimodal self-attention fusion network model;Based on the geometric manifold characteristics and physical evolution law of elastic body erosion profile, build the dual compound constraint loss function including data loss, geometric manifold regularization loss and physical consistency loss;Model training and hyperparameter optimization;The microstructure image and physical parameter under the working condition to be predicted are input into the trained model, and the discrete coordinate points and continuous profile curve of the elastic body erosion profile are output, and the importance of different input features to the prediction result is quantified by the integrated explainability analysis module.The application realizes the high-precision and rapid prediction of the elastic body erosion profile, and has the advantages of strong generalization ability and explainability, which provides important technical support for the completion of ammunition warhead design, protection engineering optimization and damage efficiency evaluation tasks.

The invention relates to the technical field of structural engineering optimization design and intelligent model selection, in particular to a structured generative adversarial network-based pipe truss structural design and model selection method, which comprises the following steps of: obtaining pipe truss load data and corresponding structural design schemes under different load conditions; respectively coding the load data and the design scheme to obtain a multi-dimensional dynamic load feature vector and graph structure information; inputting the two schemes into a generator, and generating a design scheme and a model selection scheme by taking the minimum steel use amount as a target under the constraint of physical information; discriminating the generation scheme and the real scheme through a discriminator, calculating an adversarial loss, and alternately training the generator and the discriminator until convergence; and finally, inputting the preset load and the structure design scheme into a convergence generator, and outputting an optimal scheme. According to the method, the end-to-end mapping from the dynamic load to the optimal design and model selection scheme is realized, the design efficiency is remarkably improved, and the structural lightweight is realized while the engineering reliability is ensured.

The invention provides a construction project cost analysis method and device, a storage medium and a program product, and relates to the technical field of construction cost analysis. Performing budget reverse decomposition operation on the current construction project by using the cloud computing service center to obtain a sub-item project parameter set, geographic position data, sub-item budget constraint conditions and a sub-item project optimization strategy; a background database is used for executing missing parameter influence operation on the subentry engineering parameter set according to the geographic position data and the material supply chain data, and a cost influence weight is obtained; key parameter locking operation is executed on the front-end application software according to the cost influence weight, so that key parameters are obtained; and utilizing a background database to generate a cost decision analysis report according to the key parameters, the sub-item engineering parameter set, the sub-item engineering optimization strategy and the sub-item budget constraint conditions. The technical problem that the parameter processing efficiency is low in the construction project cost analysis process in the prior art is solved.

The application provides an engineering optimization method based on failure state variable routing and landscape discrimination, and relates to the technical field of engineering design optimization.The application comprises the following steps: establishing an engineering design constraint optimization model; constructing a fitness evaluation function; initializing a differential evolution algorithm population and parameters; according to individual historical failure states and population evolution states, self-adaptively selecting and generating a first mutation vector from a plurality of pre-defined mutation strategies; constructing a reference direction based on excellent individuals in the population, discriminating and repairing the first mutation vector for direction consistency, and obtaining a second mutation vector; discriminating the problem form of a current search area according to the correlation between population distribution information and fitness sequences, and adjusting the search behavior accordingly; updating the population through a crossover operation; iterating until a termination condition is met, and outputting optimal engineering design parameters.The application improves the optimization precision and convergence efficiency of complex engineering design problems through failure state driven variable routing and landscape structure discrimination.

The invention relates to the technical field of engineering optimization, in particular to an accurate sloping design and earth volume dynamic optimization method oriented to electric power engineering. According to the method, the technical problem that the slope boundary of the complex terrain is not accurately determined is solved through the steps of dynamically shifting the polyline, performing multi-stage encryption intersection, performing three-dimensional topology reconstruction and the like. According to the method, a gradient trigger encryption mechanism and combined integral earthwork calculation are innovatively introduced, in the scenes such as a transformer substation side slope and a power transmission line tower footing, the slope boundary precision is improved by 300% or above, the earthwork volume calculation error is controlled within 5%, and the reliability and economical efficiency of electric power engineering design are remarkably improved.

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

A reliability design optimization method suitable for separating impact components of a shedding connector belongs to the technical field of engineering optimization. In order to solve the problems that traditional reliability optimization design is highly dependent on physical test, and the test cycle is long and the cost is high, the present application proposes a spatial self-adaptive sampling strategy for Kriging surrogate model, which combines model prediction uncertainty and gradient information, dynamically identifies active constraints, thereby effectively reducing misclassification and redundant sampling; in view of the different response characteristics of the objective function and the constraint function, an anisotropic sampling space is constructed for each function, guiding the sample to focus on the area with higher optimization potential; based on the maximum chord deviation criterion, the sampling points are placed in the nonlinear significant area, so as to improve the accuracy of the local surrogate model and accelerate the convergence speed, thereby realizing reliability design optimization.

The invention discloses a method and equipment for improving engineering problem modeling quality and a storage medium, and belongs to the field of engineering optimization problem modeling by a large language model. The method comprises the following steps: step 1, organizing a series of engineering optimization problems according to hierarchical classification and complexity, and constructing a tree structure knowledge base; step 2, receiving natural language description of a target engineering problem to be modeled, performing recursive search on the tree structure knowledge base in the step 1, and identifying modeling sub-problems corresponding to nodes related to the target engineering problem until the most matched and most specific modeling sub-problem is found; 3, retrieving an advanced modeling thought corresponding to the modeling sub-problem in the step 2, and combining the advanced modeling thought with the description of the target engineering problem to generate a global modeling thought; and 4, automatically generating an optimization model and solver codes of the target engineering problem by using the large language model according to the global modeling thought in the step 3. According to the method, the automatic modeling quality of solving a complex engineering optimization problem by using a large language model can be improved.

The invention provides an interpretable knowledge mining system, method and device and a medium, and belongs to the technical field of interpretable artificial intelligence and intelligent data mining. The system comprises a data processing module, an algorithm and intelligent interpretation module, a visual analysis module and an operation management module. An explicit mathematical model which is simple in structure and easy to understand is generated through a multi-branch shallow symbol feature learning tree structure, a dynamic branch expansion strategy, LLM guide initialization and a staged grey wolf optimization selection strategy; and an LLM and retrieval enhancement generation (RAG) mechanism is introduced to perform automatic semantic analysis and intelligent interpretation on the generated model. According to the interpretable knowledge mining system, method and equipment and the medium, the technical threshold of interpretable modeling is effectively reduced, meanwhile, the prediction performance, the structure interpretability and the knowledge discovery efficiency of the model are improved, and the interpretable knowledge mining system, method and equipment and the medium are suitable for engineering optimization, scientific modeling and cross-domain data analysis.

The invention relates to the technical field of engineering optimization, in particular to an intelligent engineering scheme optimization method and system based on AI generative design, and the method comprises the steps: obtaining a historical engineering design scheme and engineering design feedback after the scheme demand information sent by a user terminal is detected, performing optimization processing on the historical engineering design schemes according to the scheme demand information and the engineering design feedback to obtain an optimized engineering design scheme set, sending the engineering design scheme set to the user terminal, and receiving a scheme check report sent by the user terminal, judging whether the project design scheme selected by the user terminal exists in the scheme check report, if yes, defining the project design scheme selected by the user terminal as a target project design scheme, and after it is detected that the target project design scheme is started and implemented, carrying out real-time potential scheme optimization on the target project design scheme, and controlling and displaying the optimized target engineering design scheme. The engineering implementation efficiency is improved.

This invention relates to an engineering optimization method, system, and medium based on the Whale Algorithm (WOA), belonging to the field of intelligent optimization algorithm technology. The method includes the following steps: S1: Initialize parameters and population: Set the population size, maximum number of iterations, dimension, and upper and lower bounds of variables; generate an initial population and initialize the global optimum and the current optimum; S2: Execute individual mutation strategy: Record the position of the global optimum and the position of the current iteration's optimum; S3: Execute a unified search strategy; S4: Execute a group communication strategy; S5: Determine if the current iteration count has reached the maximum number of iterations. If so, output the global optimum; otherwise, increment the current iteration count and return to step S2 to continue iteration. The algorithm proposed in this invention aims to improve the global search capability and convergence accuracy of the algorithm while maintaining a time complexity comparable to the original WOA.

The present application belongs to the field of marine renewable energy and floating structure engineering optimization design, and discloses a tension leg type floating wind turbine multi-parameter automatic collaborative optimization design method and system. The method realizes the integrated efficient optimization of the floating body and the anchoring system by fusing parameterized geometric modeling, open source frequency domain hydrodynamic calculation, fast load evaluation and genetic algorithm. The present application realizes efficient automatic design, avoids the unstable interface problem, and the process is reproducible and expandable. The present application can optimize in a larger design space, obtain a comprehensive design scheme with better performance matching of the floating body and the anchoring system and lower cost of the whole system, effectively balance efficiency and reliability, and stably handle complex constraints based on the genetic algorithm adaptive optimization mechanism of multi-constraint punishment.

The invention discloses an analysis method and device for transverse vibration of a variable-parameter drill column, and relates to the technical field of variable-parameter structural dynamics, and the method comprises the steps: obtaining drilling tool combination information of the drill column along the well depth, constructing a structure and mechanical parameter function changing along the well depth according to the information, and building a transverse vibration control equation and boundary conditions; performing spatial discretization on the control equation, constructing a correlation matrix, solving a generalized characteristic value to obtain a vibration characteristic parameter, and further constructing a generalized integral transformation relation; and establishing and solving a modal kinetic equation in combination with external excitation, recovering a result to a physical space to obtain a vibration response, and outputting an engineering decision basis through analysis. According to the method, the technical problems that the vibration analysis result is inaccurate and the calculation efficiency is insufficient due to the fact that an existing method is difficult to adapt to the change of the structure and mechanical properties of the drill string and the influence of a complex environment are solved, and the technical effects of accurately obtaining vibration related responses and key indexes and providing reliable support for engineering optimization and adjustment are achieved.

The application discloses a kind of arithmetic optimization algorithm based on multi-strategy improvement engineering optimization method, steps are as follows: first, engineering problem is formalized, and the parameter of input multi-strategy improved arithmetic optimization algorithm is determined.Second, compare r1 using sine factor improvement with mathematical optimizer acceleration function MOA, reconstruct the search phase of arithmetic optimization algorithm, to balance the proportion of exploration phase and development phase in arithmetic optimization algorithm.Then, the golden sine strategy is used to guide individual to approach optimal solution, to realize algorithm fine search.Using Runge-Kutta algorithm enhanced solution quality ESQ mechanism further improves the quality of current optimal solution, makes it jump out of local optimum, searches to optimal solution.The algorithm is further applied to benchmark test function and engineering optimization problem instance, and the test results show that the improved algorithm has strong search ability, high convergence precision, fast convergence speed, and good effect for the engineering optimization problem.

The invention belongs to the technical field of oil and gas field development, and provides a model library construction method based on digital twin injection-production engineering. Comprising the steps of integrating an injection-production engineering physical entity model, reconstructing a physical three-dimensional model of a physical entity, defining attributes of the physical three-dimensional model, embedding a formula algorithm, developing an interface, realizing data interaction and visual modeling, and displaying a production state. Boundary conditions are defined based on the physical three-dimensional model, simulation software is imported to form a simulation model, the accuracy of the simulation model is verified through comparison with actual data, and an interface is constructed for a user to operate and test the injection-production process; packaging to form a directly called engineering application component, a feature engineering component and a tool component supporting knowledge reuse; and integrating a plurality of injection-production engineering optimization algorithms by utilizing an algorithm interface, and constructing a water injection optimization and oil production quantity dynamic regulation and control model in combination with empirical rules and optimal practices of an injection-production process. According to the invention, an injection-production engineering digital twinborn model library which can realize efficient modeling, accurate simulation and optimization and a flexible and extensible system is established.

The invention discloses an efficient proxy optimization method for parallel modeling and sub-optimization. The method comprises the following steps: defining an optimization target and a constraint function and selecting a point adding criterion; initiating a plurality of processes through a message passing interface (MPI); the root process generates an initial sample point set through test design and broadcasts the initial sample point set; sample response values are calculated in parallel in all the processes and summarized through MPI full collection; proxy model modeling tasks are distributed to all processes to be executed in parallel, and model parameters are collected completely after execution is completed; sub-optimization problems of different point adding criteria are distributed to all processes for parallel solving, and new sample points are generated and then summarized; and circularly executing until the convergence standard is met. According to the method, the utilization rate of computing resources is remarkably improved, and the time cost of complex engineering optimization is greatly shortened.

The invention relates to the technical field of cloud computing and data center operation and maintenance, and provides a data center resource scheduling optimization method based on artificial intelligence, and the method comprises the following steps: collecting multi-dimensional data; preprocessing the data; constructing and training a hybrid intelligent scheduling model; and real-time scheduling decision generation, scheduling execution and closed-loop optimization are carried out. According to the artificial intelligence-based hybrid intelligent model-driven data center resource scheduling optimization method provided by the invention, through multi-dimensional data acquisition, feature engineering optimization, hybrid intelligent model construction and closed-loop iterative optimization, multi-target collaborative optimization of a resource utilization rate, an SLA standard-reaching rate and energy consumption is realized; and the intelligent level and the dynamic adaptability of resource scheduling of the data center are improved.

The present application relates to a kind of engineering cost data sensitivity index intelligent analysis method and system, belong to engineering cost intelligent analysis technical field, a kind of engineering cost data sensitivity index intelligent analysis method, comprising: collection historical project engineering data and constructs historical index library;Based on historical index library, constructs sensitivity analysis model;Using the model, the sensitivity analysis of historical project engineering data is carried out, and sensitive index is obtained;Based on sensitive index, the target value of new project limit is calculated;Based on sensitive index, the analysis of new project engineering data is carried out, and engineering optimization point is obtained;According to limit target value and engineering optimization point, generate intelligent analysis result.The problem that traditional engineering cost analysis is not accurate, and the rationality of cost prediction is insufficient is solved.

The invention relates to the technical field of computational modeling and numerical optimization, and particularly discloses a multi-parameter objective function comprehensive optimization method based on a response surface and multiple rounds of orthogonal tests, which comprises the following steps of: firstly, establishing a response surface relationship between parameters and an objective function by using a response surface method, solving constants in the response surface function through a sample library obtained by the orthogonal tests, and calculating a response surface relationship between the parameters and the objective function; solving a response surface function to obtain a preliminary optimal parameter combination; and then, performing round-by-round reduction and iterative optimization on a parameter value range by combining a multi-round orthogonal test method, and finally outputting an optimal parameter combination of an objective function by calculating an objective function value of each orthogonal scheme and performing comparison and screening. The method has the advantages of being high in optimization precision, high in calculation efficiency, wide in application range and high in expandability, the system or model performance can be remarkably improved, the test frequency and calculation cost are reduced, the optimization period is shortened, and the method is suitable for multi-parameter and multi-target optimization of physical models, engineering optimization and other scenes.

The application discloses a design variable optimization method and system fusing reinforcement learning and CMA-ES, and relates to the field of engineering optimization design. The method comprises the following steps: determining truss body design variables; constructing a Gaussian process proxy model, a policy network and an evaluation network; iteratively updating a population and state information by using a CMA-ES algorithm until a set number of iterations is reached, and then outputting state information sequences under the current optimization trajectory; training the policy network and the evaluation network by using reinforcement learning based on the state information sequences under the current optimization trajectory, a state value function and an immediate reward; determining whether the current optimization trajectory reaches a set number of optimization trajectory training times; if yes, iteratively updating the population and the state information by using the CMA-ES algorithm based on the trained policy network of the reinforcement learning until a set number of iterations is reached, and then generating an optimal population. The application improves the optimization efficiency of the truss body design variables.

The invention discloses a method for automatically generating a curled fillet curved surface based on virtual constraint and self-adaptive offset, and relates to the technical field of computer aided design, comprising the following steps: S1, constructing a virtual constraint wall: according to different constraint modes, along a parameter boundary of a basic curved surface, according to a certain height, direction and draft angle, constructing the virtual constraint wall; automatically generating an NURBS curved surface representing physical space limitation, namely a virtual wall; and S2, generating a self-adaptive offset curve: calculating a parallel space curve in a parameter domain according to the current offset near the target boundary of the basic curved surface. The system provided by the invention not only realizes automation of a whole process, but also introduces numerous key design parameters including a fillet radius, a flange height, a scanning angle, geometric attributes of a virtual wall and a constraint mode; in this way, the method can be easily integrated into a product life cycle management or computer-aided engineering optimization process to serve as a reliable link in design of an automation chain.

The invention relates to the field of geotechnical engineering intelligent calculation, and discloses a PINO-based multilayer sludge vacuum loading consolidation prediction method and system, and the method comprises the steps: defining a parameter space, generating a working condition combination, solving and calculating a high-fidelity numerical model in batches, and obtaining and making a data set; determining input and output data structures of a PINO model, taking the PINO as a reference, combining a high-fidelity numerical solution with the PINO, carrying out offline learning pre-training of the PINO based on a data set, and realizing basic operator learning under physical law constraint; pINO instantiation fine tuning is carried out on the basic operator by using a PINN, so that the general solid operator is rapidly adapted to specific engineering parameters, natural transition from operator-level prediction to engineering-level application is realized, and real-time prediction and engineering optimization are carried out. According to the method, millisecond-level consolidation prediction can be realized in a hundred million-level parameter space.