17 results about "Gene expression programming" patented technology

This application provides a method for predicting the fatigue life of turbine blades based on adaptive gene expression programming. The method includes: establishing a finite element model of the turbine blade and determining key parameters affecting its fatigue life; extracting sample points, calculating fatigue life using the finite element model to form an initial dataset, which is then divided into a training set and a validation set; performing adaptive gene expression initialization programming, mutating and crossovering the population, and selecting and iterating based on fitness to obtain a surrogate model; evaluating the accuracy of the surrogate model; if the accuracy does not meet the requirements, adding the worst-performing points from the validation set to the training set for retraining until the accuracy meets the requirements; and using the obtained surrogate model to predict the expected value and standard deviation of the upper and lower bounds of the fatigue life. This method aims to handle complex, uncertain, coupled problems and improve the efficiency of fatigue life prediction while ensuring computational accuracy.

The invention belongs to the technical field of artificial intelligence, discloses a rusted RC beam shear strength prediction method and system fused with domain knowledge, and solves the problems of low accuracy, insufficient transparency and poor interpretability in rusted RC beam shear strength prediction in the prior art. According to the specific scheme, the rusted RC beam shear strength prediction method fusing domain knowledge comprises the steps that input parameters related to the rusted RC beam shear strength are determined, and the input parameters serve as input to generate a rusted RC beam shear strength explicit calculation formula through symbolic regression analysis on the basis of a gene expression programming algorithm; taking an existing empirical model as domain knowledge, fusing the existing empirical model with an explicit calculation formula based on symbolic regression through a decision tree algorithm, and constructing a rusted RC beam shear strength prediction model based on physical information machine learning; and carrying out visual analysis on the decision-making process of the rusted RC beam shear strength prediction model based on physical information machine learning.

The invention provides an intelligent design method for a composite beam bridge deck slab based on GEP + TCN + GA, and relates to the technical field of composite beam bridge deck slab design. An improved gene expression programming algorithm is innovatively combined with finite element simulation and a static force loading test, high-precision mathematical modeling of boundary conditions of the composite beam bridge deck slab is achieved, and the design efficiency of the composite beam bridge deck slab is improved. The limitation of a traditional empirical formula is broken through, and the automation and accuracy of boundary condition expression are remarkably improved. A closed-loop intelligent design system is formed by combining the efficient time sequence prediction capability of the time convolutional network algorithm and the intelligent optimization framework of the genetic algorithm, and the panel mechanical response can be rapidly and accurately predicted and the design scheme can be optimized under the complex multi-parameter working condition. The design efficiency and precision are improved, the consumption of simulation calculation resources is reduced, the generalization ability and the practical engineering applicability of the model are enhanced, and the requirements of the modern composite beam bridge deck slab structure design for intellectualization, refinement and high efficiency are fully met.

The invention relates to a turbulence model optimization method based on gene expression programming, which comprises the following steps: selecting a numerical simulation method in a CFD solver, and calculating vortex viscosity coefficient distribution; obtaining flow field characteristic variables through an RANS turbulence model; inputting a GEP algorithm by taking a vortex viscosity coefficient as a training truth value and a flow field characteristic variable as an input characteristic, carrying out GEP algorithm training work, and generating an initial population by combining three means of chaotic sequence generation, a gene pool predefined structure and random generation; changing individual crossover and mutation probability in real time to obtain a correction formula of the vortex viscosity coefficient; all coefficients in the vortex viscosity coefficient correction formula are optimized to obtain a vortex viscosity coefficient final expression, and the vortex viscosity coefficient final expression is loaded into a CFD solver; in the loaded CFD solver, calculation is carried out through an RANS turbulence model, and needed flow field information is obtained. The method has the capability of giving an explicit model equation, and the calculation precision of the RANS turbulence model is improved.

This invention provides an intelligent design method for composite beam bridge decks based on GEP+TCN+GA, belonging to the field of composite beam bridge deck design technology. This invention innovatively combines an improved gene expression programming algorithm with finite element simulation and static loading tests to achieve high-precision mathematical modeling of the boundary conditions of composite beam bridge decks, breaking through the limitations of traditional empirical formulas and significantly improving the automation and accuracy of boundary condition expression. By combining the efficient temporal prediction capability of the temporal convolutional network algorithm with the intelligent optimization framework of the genetic algorithm, a closed-loop intelligent design system is formed, capable of quickly and accurately predicting the mechanical response of the deck and finding the optimal design scheme under complex multi-parameter conditions. This not only improves design efficiency and accuracy and reduces simulation computational resource consumption, but also enhances the model's generalization ability and practical engineering applicability, fully meeting the demands of modern composite beam bridge deck structure design for intelligence, refinement, and efficiency.

This application relates to a method, device, and medium for planning multi-task collaborative reconnaissance for a swarm of unmanned aerial vehicles (UAVs). The method includes: utilizing a swarm of UAVs to conduct reconnaissance of a mission scenario; setting a benefit objective function based on the number of mission areas to be reconnaissanced in the mission scenario, the number of UAVs involved in decision-making, the reconnaissance value coefficient of the mission area, and the reconnaissance value coefficient of the mission area; setting a time objective function based on the number of mission areas actually reconnaissanced by the UAVs, the time available for the UAVs to transfer road sections, and the reconnaissance time allocated by the UAVs in the mission areas; constructing a multi-task collaborative reconnaissance model based on the benefit objective function, the time objective function, and pre-set constraints; and solving the multi-task collaborative reconnaissance model using genetic expression programming to obtain the UAVs' mission sequence and the reconnaissance time in each mission area. This method can improve the efficiency of multi-task reconnaissance.

The invention discloses an electric network car-hailing online car-sharing scheduling method and system under dual-network integration, and the method comprises the following steps: obtaining region information at different times in a preset region according to historical data, and carrying out the sampling of the region information to generate training data; according to the regional information, carrying out modeling on a dual-network integration electric network car-hailing online car-sharing scheduling problem, determining an optimization target of a model, and constructing a target function and a constraint condition; according to the optimization target and the training data, programming a co-evolution charging rule pool and an order receiving rule pool by adopting a gene expression; and taking the charging rule pool and the order receiving rule pool as action spaces of a deep reinforcement learning hyper-heuristic algorithm, solving the model based on the deep reinforcement learning hyper-heuristic algorithm, selecting actions from the action spaces for idle vehicles, and updating states of the vehicles according to the selected actions. According to the method, the convergence speed is improved through co-evolution of the charging rule and the order receiving rule population, and meanwhile, the robustness is improved by taking the rule pool as an action space of deep reinforcement learning.

The invention relates to the technical field of coal mine safety monitoring, in particular to a coal mining subsidence prediction method based on gene expression programming and artificial bee colony, which uses a gene expression programming (GEP) algorithm to establish a basic prediction model to simulate a subsidence event, and uses an artificial bee colony (ABC) algorithm to optimize the basic prediction model to simulate the subsidence event. GEP parameters are obtained from a GEP algorithm, the GEP parameters are optimized through an artificial bee colony (ABC) algorithm, a subsidence prediction model is established, and the subsidence prediction accuracy is improved; the optimal model is selected through the square correlation coefficient, the root-mean-square error, the square absolute error and the Nash efficiency, the indexes quantify the prediction accuracy of each model, and the selection process is guided. The model is subjected to thorough cross validation so as to evaluate the universality and the robustness of the model; the influence of each parameter on land subsidence is clarified through multi-parameter sensitivity analysis, and valuable insights are provided for mining engineers and stakeholders.

The present invention provides a project scheduling rule mining method and system based on gene expression programming (GEP), which is used to solve the multi-objective optimization problem of multi-skill, resource-constrained project scheduling. During the solution process, previous project information is used as training set data. Project information and resource information are integrated to extract various feature attributes with decision-making value. These attributes are combined with several basic mathematical operators to form the genetic source of soft chromosomes. Each soft chromosome represents a hybrid scheduling rule, which is used as a decision-making method for the order of task execution to obtain a specific scheduling solution. The solution process uses an improved GEP algorithm, which designs a backward traversal decoding method, incorporates four neighborhood structure operators and a rule mining perturbation mechanism, and improves the ENS ranking method used in the solution evaluation process. This greatly improves the algorithm performance and the efficiency of exploring the frontier solution set. The resulting metaheuristic rule set can be easily applied to real projects and production environments.

The invention is suitable for the technical field of workshop scheduling, and provides a reconfigurable flexible job workshop scheduling method based on double-layer optimization, and the method comprises the following steps: constructing a double-layer scheduling framework of an upper-layer production scheduling model and a lower-layer logistics scheduling model; adopting a lightweight double-layer optimization algorithm, and cooperatively solving the double-layer scheduling model through a high-precision agent model and an improved meta-heuristic algorithm; a transport scheduling rule is mined through gene expression programming and reinforcement learning, characteristic variables of an agent model are constructed, a double-layer scheduling model is constructed, the upper layer starts from the angle of production scheduling, production flexibility and reconfigurability are fully considered, and the production cost is reduced; and the lower layer focuses on logistics scheduling of products in the workshop, and the transportation cost is minimized by reasonably deploying the automatic guided vehicles.

The application discloses a kind of power information physical system active safety whole life cycle evaluation and verification platform.There is the problem that existing evaluation system lacks whole life cycle coverage, reliability evaluation and efficiency evaluation are mixed together.The application realizes data fusion compensation and whole life cycle dynamic index reduction through multi-source heterogeneous data and dynamic index system management module;Reliability and efficiency double-driven evaluation engine is built, the reliability level under small sample attack is quantified using AHP and grey prediction model, and explicit analytical expression of defense efficiency is mined relying on gene expression programming;And the significance of the evaluation results is verified and closed-loop correction using Monte Carlo mixed sampling and Friedman nonparametric test, directional trigger physical layer emergency control or information layer adaptive correction control, form whole life cycle active safety closed loop.The application improves the robustness, explainability and closed-loop response capability of safety evaluation.

The invention provides a method and device for calculating the sand concentration at a hole inlet and a storage medium, and belongs to the field of oil and gas exploitation. The method comprises the following steps: firstly, establishing a training sample; then, a prediction model used for predicting the sand concentration of the sand-carrying fluid of a single hole is established based on a gene expression programming algorithm by utilizing the training sample, and finally, the sand concentration of the sand-carrying fluid of each hole under different flow distribution conditions is obtained through calculation. According to the method, the influence of the following property of the proppant and the flow change in the shaft on the concentration of the proppant can be considered, the technical limitation of a traditional model is avoided, and the method is suitable for calculating the sand concentration at the hole under the horizontal well multi-cluster perforation condition so as to improve the rationality of fracturing design.

The application discloses a data processing method and device, equipment and storage medium, and relates to the technical field of computers, and comprises the following steps: initializing a population to obtain a current population with a function to be evaluated as an individual; a gene expression programming is improved by using a CUDA architecture and a linear table-based coding mode in advance to obtain a target population evolution algorithm; an ADF gene for enhancing individual expression is used in the algorithm; all individuals are subjected to parallel evaluation processing based on the CUDA architecture in the algorithm and a predefined kernel function to determine the current fitness of each individual, and it is judged whether the preset evolution termination condition is met at present; if not, the individual whose current fitness meets the preset elimination condition is eliminated, the remaining individuals are processed based on the preset genetic operator in the algorithm to generate a new current population, and the step of parallel evaluation processing is rejumped; and if yes, the individual solution is output. The application improves the population evolution speed and reduces the error of the found function.

The invention discloses a multi-factor shale gas flow-back rate calculation method considering geological engineering flow-back, which comprises the following steps: S1, on the basis of on-site data acquisition, taking an acquired on-site fractured well as a data set for calculating a flow-back rate value after shale gas pressure; s2, data preprocessing is performed on the collected data samples, and geological engineering flowback variables required for establishing a flowback rate mathematical model are screened out; s3, on the basis of the geological engineering flowback variable data processing result in the step S2, establishing a variable comprehensive calculation model, and sequencing variable importance; variable reconstruction is carried out through independence judgment, principal component analysis is carried out on reconstructed variables, and processed independent principal components are adopted as input variables of a prediction model; s4, based on gene expression programming, establishing a shale gas flowback rate prediction model under the influence of multiple factors of geological engineering flowback; and S5, fitting a shale gas flowback rate calculation formula based on shale gas well geological engineering flowback data by applying the established shale gas flowback rate prediction model.

This invention provides a method and apparatus for predicting the adhesion of aggregate-asphalt interfaces. The method includes: acquiring multiple sets of sample data of the aggregate-asphalt interface; each set of sample data contains multiple independent variables and corresponding dependent variables. The independent variables include the wetting work of high-temperature molten asphalt-aggregate during the spreading process of high-temperature molten asphalt on the aggregate surface, the adhesion work of room-temperature solid asphalt-aggregate during the contact process between room-temperature solid asphalt and aggregate, and the depth of asphalt filling the aggregate pores during the interlocking-anchoring process of room-temperature solid asphalt and aggregate; the dependent variable is the pull-out strength of the aggregate-asphalt interface; determining an interface adhesion prediction model based on gene expression programming, multiple independent variables, and dependent variables; and predicting the adhesion of the aggregate-asphalt interface based on the interface adhesion prediction model. This invention improves the accuracy of aggregate-asphalt interface adhesion prediction.

The invention relates to a creep book modeling method based on GEP tensor evolution and neural coefficient separation. Comprising the following steps: carrying out a creep experiment to obtain a training set and a verification set; constructing a gene expression programming model containing a nonlinear function set, and defining a primary population model; generating a primary population; screening and replacing the individuals, so that each individual has iteration and physical coefficients, and obtaining an updated population; based on the training set, a neural network learning rate is set, a loss function is defined, a neural network is adopted to optimize the physical coefficient in the updated population, and the optimal physical coefficient is output; and based on the verification set, performing mean square error fitness evaluation according to an individual optimal physical coefficient, screening out an optimal individual, performing operation on the remaining individuals, then performing screening replacement, and performing iteration to output the optimal individual. According to the method, tensor evolution operation, GEP and a neural decoupling mechanism are combined, and a new normal form is provided for cross-scale mechanical modeling of the electronic packaging material.

The embodiment of the invention discloses a cold-rolled strip steel moment arm coefficient prediction method and device based on gene expression programming. The method comprises the steps that equipment parameter sets of multiple sets of target cold rolling equipment and corresponding labeled moment arm coefficients are obtained; and screening at least one equipment parameter related to the force arm coefficient from the equipment parameter set as a related equipment parameter. And parameter fitting is carried out according to the related equipment parameters in the multiple groups of equipment parameter sets and the corresponding labeling force arm coefficients, and candidate prediction coefficients corresponding to each related equipment parameter are obtained. And executing a gene expression programming algorithm by taking the candidate prediction coefficient as an initial population to obtain a force arm coefficient prediction function. And further determining a predicted force arm coefficient according to the force arm coefficient prediction function. The moment arm coefficient prediction function capable of visualizing the relation between the parameters and the moment arm coefficient is accurately determined through the gene expression change algorithm, then accurate moment arm coefficient prediction is achieved through the function, and the prediction result has interpretability.