15 results about "Engineering optimization" patented technology

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 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.

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 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 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 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.

This invention provides a two-layer optimization method, system, and computer storage device for large-scale, expensive single-objective optimization problems. The method involves steps including initialization, subproblem construction, surrogate model construction, subproblem optimization and evaluation, a bottom-level population update stage, a top-level surrogate model selection stage, and a top-level optimization and global update stage. By constructing and evaluating multiple candidate subspaces at the lower layer, it automatically identifies key subspaces that significantly impact the global objective and focuses on optimizing these key subspaces at the upper layer. Under a finite expensive function evaluation budget, it achieves efficient solutions to large-scale single-objective optimization problems. By introducing a surrogate model to approximate the expensive objective function and combining it with a two-layer optimization structure, it organically combines subspace selection and fine-tuning, effectively improving search efficiency, accelerating convergence speed, and enhancing the quality of the final solution, thus meeting the application needs of complex engineering optimization scenarios.

The application discloses a kind of local vortex structure quantitative analysis methods for optimizing the performance of biomimetic submersible, belong to underwater biomimetic robot technical field.The method of the present application comprises, first based on the numerical calculation of flow field is carried out based on the immersed boundary method, obtains overall flow field data;Then according to the geometric characteristics and function of biomimetic body Preset division local analysis area;Finally, the contribution of each area to overall hydrodynamic force is calculated using quantitative formula based on vortex dynamics.The method solves the problem that the existing technology has space dimension reduction, grid is not suitable for large deformation motion and cannot complete time-varying analysis, quantifies the contribution of local vortex structure of key parts to water power at each moment, establishes the quantitative space-time mapping relationship between local vortex structure and stress, realizes the quantitative connection from flow field micro mechanism to engineering optimization design, provides accurate and reliable quantitative analysis tool and decision basis for deep understanding of biological propulsion mechanism and implementation of targeted performance optimization of biomimetic submersible.

The application discloses a kind of based on artificial intelligence's cross-basin water diversion engineering optimization scheduling method, belong to cross-basin water diversion optimization technical field.The problems that the traditional cross-basin water diversion engineering optimization scheduling method in prior art is difficult to generate optimal scheduling plan due to easy strategy deviation;The application collects the storage capacity, flow, operation constraint and historical scheduling record of each node of test area reservoir, forms global dynamic embedding, and constructs reservoir node and water flow pipeline into graph structure, generates global embedding matrix as state input, trains basic global strategy network, forms global water diversion strategy network, divides global water diversion strategy network into macroscopic layer and microscopic layer, jointly trains by sharing global embedding information, forms layered water diversion strategy, converts global multi-step return into short-term and medium-term weighted return, generates scheduling optimization strategy.The application improves the global optimization and fine control of cross-reservoir water diversion, and can be applied to cross-basin water diversion planning.

The application discloses a kind of based on artificial intelligence's cross-basin water diversion engineering scale demonstration and optimization method, belong to the water conservancy engineering optimization technical field based on deep learning;First, build water diversion scheme generation dataset and economic mapping dataset, provide basis for model training;Subsequently, a large number of candidate schemes are generated using conditional constraint type generation model, and risk quantification evaluation is carried out by extracting runoff variation and dry-wet ratio and other indexes through runoff regulation calculation and reservoir dispatching simulation;Then, through self-attention and gradient enhancement mechanism, economic indicators are predicted, and multidimensional economic evaluation is realized;Finally, based on hydrological variation and economic evaluation results, a multi-objective optimization function is constructed, and the optimal water diversion engineering scale parameters are selected by improved simulated annealing algorithm;The application realizes the scientific, systematic and intelligent demonstration and optimization of water diversion engineering scale through five links of multi-source dataset construction, conditional constraint scheme generation, hydrological variation index evaluation, engineering scale economic prediction and multi-objective optimization.

This invention discloses a novel swarm intelligence optimization algorithm integrating multiple strategies. The method steps are as follows: S1, initialize the Tibetan fox population and dynamic territory; S2, adaptively adjust the territory division using nonlinear dynamic parameters combined with population distribution; S3, integrate PSO, DE, and gradient search strategies to dynamically adjust the selection probability to achieve precise local development; S4, design an adaptive migration mechanism; S5, maintain population diversity through subpopulation co-evolution and periodic information exchange; S6, add a globally optimal guided pattern search to improve convergence accuracy; S7, complete population update using elite retention and dynamic elimination strategies. Compared with existing technologies, this invention's core parameters are dynamically adjusted nonlinearly, balancing exploration and development. Experiments verify that it has better accuracy and robustness in multi-function optimization, and its performance in practical applications is significantly better than PSO and GA. It can be widely used in engineering optimization, machine learning hyperparameter tuning, and other fields.

The present application relates to a kind of cantilever end torsion angle prediction method, control method, system and medium, it is related to end torsion angle prediction and control technical field.Prediction method, based on the stiffness of the obtained bridge, section inertia moment, section shear modulus, section torsion constant and cantilever curved bridge structure dead weight, the undetermined coefficient of the trigonometric series of cantilever end deflection and torsion angle is calculated, and further the cantilever end torsion angle and deflection are calculated.Compared with prior art, the relationship between structure parameters and cantilever end rotation angle can be directly reflected, so that parameter sensitivity analysis and engineering optimization design can be facilitated, and the applicability of the project is improved.