1828 results about "Numerical models" patented technology

The invention discloses a slope rainfall infiltration stability dynamic evaluation and landslide prediction method. The method comprises the following steps: constructing a numerical model coupling rainfall infiltration and slope stress field response; integrating multi-source data, and updating model boundary conditions in real time; performing dynamic stability evaluation based on a local safety coefficient method; and training a landslide prediction model and realizing intelligent early warning identification. The seepage-stress-strength three-field coupling numerical model is provided, the physical response process of the unsaturated soil body is dynamically simulated, the precursor mechanism of rainfall-induced landslide can be truly restored, and prediction scientificity and accuracy are improved; remote sensing topographic data, soil property survey parameters and real-time meteorological monitoring information are integrated, model boundary conditions and initial states are dynamically updated through a system interface, real-time response to environmental changes is achieved, and simulation reliability is improved; a local safety coefficient evaluation mechanism is introduced, and a continuous weak region discrimination algorithm is combined, so that space identification and time sequence tracking of a potential slip region and a damage zone are realized, and the slope stability analysis refinement level is improved.

The invention relates to the technical field of coast engineering, in particular to a beach change evolution inversion method combining numerical simulation and actually measured data, which comprises the following steps: S1, acquiring an actually measured shoreline data set of a current period; s2, establishing a wave-tidal current-sediment coupling numerical model; s3, outputting a simulated shoreline data set; s4, performing spatial matching on the actually measured shoreline data set and the simulated shoreline data set to generate a spatial discrete residual field; s5, taking the spatial discrete residual field as a constraint condition, and reversely solving an optimal hydrodynamic parameter combination; and S6, inputting the optimal hydrodynamic parameter combination into the wave-tide-sediment coupling numerical model, and outputting the beach evolution form in the future period. According to the method, dynamic coupling of the hydrodynamic parameters and the beach evolution process is realized by constructing an inversion optimization mechanism based on actual measurement data driving, and the accuracy of shoreline change simulation and the reliability of prediction are greatly improved.

The invention provides a soft rock tunnel surrounding rock parameter dynamic identification method and system based on data driving, and relates to the technical field of underground tunnel mechanical parameter dynamic identification, and the method comprises the steps: obtaining multi-element tunnel surrounding rock parameters, and building a joint probability distribution model of the multi-element surrounding rock parameters based on a Copula theory; performing Monte Carlo simulation, and generating a high-dimensional parameter sample library meeting physical constraints in the parameter constraint space based on the joint probability distribution model; establishing a tunnel three-dimensional numerical model and performing automatic numerical simulation to generate multivariate response data; constructing a Kriging agent model of a Gaussian kernel function based on multivariate response data training, establishing a nonlinear mapping relation between parameter input and deformation output, constructing an inversion objective function by taking the minimum root-mean-square error of multi-measurement-point displacement as an objective, and performing inversion solution by using an adaptive particle swarm optimization algorithm to obtain inversion identification parameters, and a dynamic feedback mechanism is constructed to realize adaptive tracking of the time-varying characteristics of the surrounding rock parameters.

The invention provides a mine water disaster monitoring and early warning method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting multi-source data of a mining area, carrying out the time-space alignment of the multi-source data, and generating a data set, the multi-source data comprising remote sensing data; preprocessing the data, inputting the preprocessed data into a multi-modal fusion network, and extracting surface water body distribution, lithologic permeability, underground water level and structural fracture characteristics to obtain a three-dimensional hydrogeological static model; the hydrological numerical model based on physical driving is coupled with the static model, and the dynamic model is used for simulating the dynamic change of an underground water flow field and a pollutant diffusion path; based on the dynamic model updated in real time, multi-target collaborative evaluation is carried out to evaluate the mining area water resource, ecological and social collaborative effect; and calling an unmanned aerial vehicle to inspect a leakage position or a settlement position in the hydrogeological risk map based on a multi-target collaborative evaluation result. According to the method, the prediction accuracy is improved through the multi-source data.

The present invention relates to the technical field of coal mine water inrush disaster prevention and control, and relates to an artificial intelligence-based method for identifying a water inrush point in a mine and parameters of a simulation model, comprising the following steps: step 1: establishing a numerical model on the basis of observation data, and determining prior information of parameters to be identified including position coordinates of a water inrush point; step 2: on the basis of the numerical model and the prior information of the parameters, generating a training sample dataset and a test sample dataset for an alternative model; step 3: constructing and training an alternative model neural network; step 4: testing the accuracy of the alternative model; and step 5: executing a simulated annealing algorithm to identify the position of the water inrush point and parameters of a simulation model. By using the method provided by the present invention, the problem in accurate positioning of water inrush points in underground coal mines can be solved, which is of great significance in carrying out scientific disaster management and rescue efforts in a timely manner.

The invention discloses an internal wave characteristic field information rapid prediction method based on deep learning, and belongs to the technical field of ocean internal wave detection. According to the method, rapid prediction is carried out on the basis of traditional numerical simulation data by using deep learning, so that the efficiency of inner solitary wave prediction is remarkably improved; through multi-channel coupling learning and an iterative prediction strategy, the inner wave field prediction achieves the effect of high precision and high efficiency. Meanwhile, in combination with high-resolution numerical mode data, the consistency of a model prediction result and a physical process is ensured, and rapid extension period prediction of a solitary wave characteristic field in the ocean is realized while low error and high relevancy are kept. The method is suitable for application scenes of real-time ocean internal wave monitoring and early warning and the like, and can be widely applied to the technical fields of ocean environment monitoring and early warning, intelligent ocean observation, ocean engineering safety and the like.

The invention belongs to the technical field of mine safety monitoring and geotechnical engineering stability evaluation, and relates to a mine filling body performance detection and stability evaluation method and system.The method comprises the steps that S1, a first type of monitoring signals are collected in real time through a distributed optical fiber sensor array, and meanwhile vibration waveform signals serve as second type of monitoring signals; s2, inputting the first type of monitoring signals and the second type of monitoring signals into a multi-source data fusion model, and generating fusion state signals representing the three-dimensional damage degree and the real-time deformation state of the filling body; s3, inputting the fusion state signal into a parameter dynamic identification module to generate an updated numerical model parameter signal; and S4, based on the updated numerical model parameter signal, executing stability quantitative calculation to generate a risk level signal. According to the mine filling body performance detection and stability evaluation method and system, the problem of inaccurate psychological state recognition such as large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

The invention relates to the technical field of underground engineering and geotechnical mechanics, and provides a marine facies soft soil tunnel construction disturbance induced surrounding rock energy dissipation grading evaluation method which comprises the following steps: step 1, establishing a marine facies soft soil energy dissipation constitutive model based on thermodynamics; 2, determining mechanical parameters of the constitutive model; 3, establishing a tunnel construction mechanical model, embedding the constitutive model in the step 1 and the mechanical parameters in the step 2 into the tunnel construction mechanical model, and outputting an energy subitem output quantity in real time; step 4, establishing a damage unit identification system; 5, arranging a monitoring system to obtain monitoring data and feeding the monitoring data back to the numerical model for verification; and step 6, performing graded early warning according to the closeness degree of the structural unit to the instability failure energy threshold value. According to the scheme, a surrounding rock damage grading system is constructed through an energetic perspective, rapid risk identification is realized in combination with multi-source monitoring data, and a new theoretical and engineering means is provided for safe construction of the marine soft soil tunnel.

The invention relates to the technical field of wind power generation, and discloses a cross-scale wind power plant wind resource evaluation method based on a numerical mode, comprising the following steps: step 1, acquiring multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme adaptive to the regional terrain; step 3, through a WRF power downscaling method, generating annual scale wind resource data as initial wind resource data; 4, performing partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling the WRF mode and the wind power plant parameterized model; step 6, optimizing WRF lower boundary conditions; 7, simulating the atmospheric flow of the target area, and quantifying the wake flow evolution characteristics of the wind power plant; 8, constructing a wake flow analysis model; and step 9, formulating a collaborative productivity plan of the wind power plant group. The power generation capacity of the wind power plant to be developed can be accurately predicted, and a reliable basis is provided for scientific decision-making of resource distribution development and planning of the million-kilowatt wind power plant.

The invention discloses a slope settlement data processing method under an infiltration surface effect based on a water-soil coupling model, and relates to the technical field of civil engineering geomechanical analysis. And establishing a water-soil coupling numerical model containing a mutual coupling relationship between water flow permeation and soil deformation. According to the method, the multi-source monitoring data and the sudden change recognition algorithm are fused, so that the recognition precision and real-time performance of the sudden change point of the slope infiltration surface are improved. And a parameter adaptive correction mechanism is introduced, so that the model can dynamically adjust key parameters, and the adaptability to a complex geological environment and the prediction stability are enhanced. Meanwhile, a dynamic processing flow integrating data driving, model calculation and risk identification is constructed, intelligent linkage early warning of the side slope settlement risk is achieved, the landslide accident risk is effectively reduced, and the scientificity and safety of geological disaster monitoring are improved.

The invention discloses a KCBMA algorithm-based windmill bridge system safety assessment method, which relates to the technical field of windmill bridge coupling systems, combines a Kepler optimization algorithm (KOA) with a convolutional neural network-bidirectional long-short-term memory network (CNN-Bi-LSTM-MA) under a multi-head attention mechanism, adaptively searches an optimal hyper-parameter combination of the CNN-Bi-LSTM-MA network through the KOA, and provides a safety assessment method for a windmill bridge system. Constructing a neural network prediction model with dynamic adaptive capacity; the Bi-LSTM unit performs modeling by fusing the randomness characteristics of wind load excitation and system parameters, so that a numerical model can effectively represent the randomness of a wind-vehicle-bridge system and predict the random response of the wind-vehicle-bridge system. According to the method, the dynamic interaction among the wind-vehicle-bridge structures is integrated into the neural network model, the modeling time is remarkably shortened by using the KOA, the modeling accuracy is improved, the calculation cost is reduced, and the system response is more accurately predicted.

The invention relates to the technical field of geotechnical engineering safety monitoring, in particular to a river valley area bedding slope stability monitoring method and related products, and the method comprises the steps: building a three-dimensional geology-mechanics numerical model of a slope; acquiring monitoring data of the slope state in real time; dynamically correcting mechanical parameters in the three-dimensional geology-mechanical numerical model through inversion calculation; evaluating the stability of the slope and outputting early warning information; according to the method, multi-source monitoring data are fused into the numerical model in real time, dynamic inversion and tracking of internal mechanical parameters of the slope are achieved, comprehensive indexes such as the real-time safety coefficient, the main sliding direction deviation angle and the damage evolution rate are calculated, graded early warning is triggered according to the indexes, and the perspectiveness and reliability of early warning are improved.

The invention provides a ground source heat pump buried pipe system design method based on building load change, comprising the following steps: S1, acquiring geological survey data, meteorological data and building load data, and presetting buried pipe heat exchange system parameters; s2, establishing a multi-physics field coupling numerical model; s3, transient simulation is carried out through a multi-physics field coupling numerical model, and system short-term thermal response characteristic data corresponding to each candidate length are acquired and stored; s4, constructing and training a long-term dynamic performance prediction model through the long and short-term memory network model; and S5, based on a multi-objective optimization algorithm, solving a multi-objective optimization problem, obtaining a group of Pareto optimal buried pipe total length solution sets, and selecting a final buried pipe optimal total length from the optimal solution sets. According to the method, the design precision of the ground source heat pump buried pipe can be remarkably improved, the long-term dynamic prediction capacity is achieved, the reliability, economical efficiency and environment friendliness of long-term operation of a ground source heat pump system can be guaranteed, and the design efficiency and reliability are improved.

The invention discloses a low-carbon stability conjoint analysis method and system for underground engineering, and particularly relates to the technical field of engineering modeling optimization, and the method comprises the steps: obtaining rock-soil mechanical parameters, construction parameters and full-life-cycle carbon emission factor data of a target area; dividing a collaborative optimization region based on parameter fusion and identifying a conflict section; constructing a mechanical stability numerical model and a carbon emission quantitative model, outputting double indexes, and generating a double-target optimization decision space through dynamic weight distribution; traversing and screening candidate schemes meeting a safety threshold and carbon emission constraints, and extracting an anti-interference Pareto solution set in combination with geological sensitivity simulation; a low-carbon-stability balance scheme is generated through reverse coupling verification, and full-life-cycle low-carbon stability optimization support is provided for complex geological engineering.

The invention provides a rainfall and earthquake coupled slope simulation method and system. The method comprises the following steps: constructing a coupling numerical model through multi-physics field data of a slope; the dynamic coupling effect of rainfall and earthquake is simulated by initializing a seepage field and loading earthquake power; the stability of the simulation result is comprehensively evaluated through iteration of a strength reduction method, and whole-process coupling from seepage to dynamic response of the slope is achieved. SeismoSignal is used for processing seismic waves, and the problem that the value of kinematics parameters is not zero when the seismic action is finished is avoided; the allowable maximum negative pore water pressure boundary is assigned to the slope surface of the side slope, the actual steady-state seepage field of the side slope is restored, and the seepage field in the side slope under different rainfall conditions is accurately calculated; by adopting an unsaturated soil dynamic seepage model for a seepage field, Rayleigh damping is introduced and an earthquake load is applied, so that cross-time scale coupling is realized. The method is simple and convenient to operate, high in calculation efficiency and accurate in calculation result, and has practical significance and value in research of the problem in the rock-soil field.

The present disclosure provides an artificial intelligence-based method for enhancing mine safety by identifying and predicting locations of water inrush points in a mine, including the following steps: S1: constructing a numerical model to determine priori information of parameters to be recognized based on observation data, including coordinates of locations of water inrush points; S2: generating a training sample dataset and a test sample dataset of an alternative model based on the numerical model and the priori information of the parameters; S3: constructing and training a neural network of the alternative model; S4: testing an accuracy of the alternative model; and S5: performing a simulated annealing algorithm to identify the locations of water inrush points and simulation model parameters.

A wellbore temperature optimization and predication method integrating numerical models and machine learning includes the following steps: establishing a wellbore-formation transient heat transfer model, obtaining an initial data set composed of relevant parameters, normalizing the initial data set, training a wellbore temperature prediction model by using a random forest algorithm, then optimizing the hyperparameters of the random forest algorithm by using a genetic algorithm, performing global optimization by using an annealing algorithm to obtain the optimized wellbore temperature and related parameters, calculating the wellbore temperature by substituting the optimized parameters into the wellbore-formation transient heat transfer model, and performing comparative verification on the optimized wellbore temperature and the calculated wellbore temperature.

The invention relates to the technical field of nuclear power internal disaster analysis, and discloses a nuclear power plant internal flooding experiment verification method, device, equipment and medium, and the method comprises the steps: carrying out the modeling analysis of a target plant prototype, and determining a plurality of model parameter values of an experiment scaling model; constructing an experiment scaling model according to the plurality of model parameter values, and determining experiment parameters of an internal water logging experiment; performing an internal water logging experiment on the experimental scale model by using the experimental parameters to obtain an internal water logging experiment result of the experimental scale model; and obtaining an internal water logging simulation result of the numerical model, comparing the internal water logging simulation result with an internal water logging experiment result, and verifying the accuracy of the internal water logging simulation result according to a comparison result. According to the method, the experiment scale model of the nuclear power plant prototype is constructed, the flooding spreading conditions under different working conditions are simulated and compared with the flooding simulation result, the accuracy of flooding simulation calculation is verified, and more accurate guidance is provided for follow-up nuclear power project internal flooding analysis.

The invention provides a method and equipment for predicting the heat exchange performance of a buried pipe ground source heat pump thermal camouflage system. The method comprises the steps that S1, a multi-physics field coupling heat exchange mathematical model of a buried pipe heat exchange system is established; s2, based on the multi-physics field coupling heat exchange mathematical model, a control equation and boundary conditions are constructed; s3, on the basis of the control equation and the boundary conditions, a multi-physics field numerical model is constructed, and simulation and calibration are carried out; and S4, based on the calibrated numerical model, designing a simulation test and carrying out main control factor analysis to obtain a prediction effect of the heat exchange performance. By establishing a rock-soil body-fluid multi-physical field coupling model, the interaction mechanism of underground heat conduction, working medium flow and ground surface heat radiation is completely represented, and the hiding efficiency of the thermal camouflage system is remarkably improved.

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.

According to the offshore wind turbine single pile rigidity numerical simulation method considering long-term circulation and scouring, the accuracy and the calculation convenience are both considered in model building through the linear elastic steel pipe pile model and the Mohr-Coulomb soil body model. By calculating the horizontal ultimate bearing capacity of the single pile, combining the ultimate analysis method and the p-y curve calculation method, the influence of different scouring depths on the fan is considered, the horizontal load bearing capacity and inherent frequency evolution of the single pile are comprehensively measured, and a basis is provided for design and safety evaluation. A sandy soil rigidity attenuation model is established for secondary development of Abaqus and verified, and the rigidity change of sandy soil under long-term cyclic loading is considered, so that the simulation precision is improved. And the numerical model is tested and verified to ensure the reliability. And the accumulated lateral displacement of the steel pipe pile can be accurately predicted, the influence rule of cyclic load times, soil elasticity modulus and embedding depth factors on displacement development is disclosed, wind power engineering design optimization is assisted, and economical efficiency and safety are improved.

The invention discloses a numerical simulation-based roadway surrounding rock deformation monitoring method and device. The method comprises the following steps of: constructing a three-dimensional geologic model of a roadway area; based on the three-dimensional geologic model, establishing a numerical model of heat conduction, stress action and seepage multi-physics field coupling, and based on elastic-plastic characteristics of rocks in the roadway area, simulating simulation data of surrounding rocks in the roadway area under the multi-physics field action by the numerical model; acquiring real-time monitoring data of various types of sensors arranged in a risk monitoring area of surrounding rock deformation corresponding to the simulation data; denoising, filtering and abnormal value processing are carried out on the real-time monitoring data, and then multi-source data fusion is carried out to calibrate the numerical model; and predicting the deformation trend of the surrounding rock based on the calibrated target numerical model and the real-time monitoring data. Therefore, by means of geological modeling, numerical model simulation, sensor arrangement optimization, numerical model optimization and surrounding rock deformation trend prediction, roadway surrounding rock monitoring precision is improved, and safe operation of a roadway is guaranteed.

The invention discloses a spherical crown dome horizontal circumference drilling and blasting parameter model optimization construction method, and the method comprises the steps: collecting geological, crustal stress, hydrological and structural plane data, building a three-dimensional geological information model, carrying out the working condition partitioning based on a surrounding rock quality evaluation index, and constructing a parameter probability distribution model and an uncertainty boundary of each partition. Therefore, precise space description and parameter variability quantification of the spherical crown dome construction area are realized. The method comprises the following steps: establishing a three-dimensional numerical model of a cavern group, integrating blasting power, seepage and stress multi-physical field effects by adopting a finite element-discrete element coupling method, introducing a surrounding rock deterioration constitutive model considering cumulative damage and unloading aging, calibrating key parameters through an indoor test and field trial explosion, and establishing a three-dimensional model of the cavern group. And the numerical model can truly reflect the mechanical property degradation track of the surrounding rock under repeated blasting disturbance.

The invention discloses an underground water exploitable quantity evaluation method and system based on coupling of a physical mechanism and a neural network, and relates to the technical field of water resource management, meteorological data, surface water hydrological monitoring data, geological survey data, underground water resource development and utilization data and hydrogeological parameter data are integrated and preprocessed, and a basic data set is obtained; a three-dimensional geological model of a heterogeneous structure is constructed by utilizing a digital twin physical engine and combining drilling geological data. By introducing a digital twin technology and a numerical model, the underground water flowing process can be simulated more accurately, a digital twin physical engine is utilized to construct a heterogeneous aquifer digital model, a dynamic interaction process of surface water and underground water is embedded, and the model is enhanced through a graph neural network and a physical information neural network, so that the underground water flowing process is simulated more accurately. Errors caused by hypothesis of aquifer homogeneity, neglecting of surface water-underground water coupling process and the like of a traditional model are effectively reduced, and the accuracy of underground water exploitable quantity evaluation is improved.

The invention discloses an ocean three-dimensional temperature field reconstruction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous ocean observation data and a numerical model background field, and generating an input feature group; performing feature extraction on the input feature group by using a double-branch encoder; the extracted features are input to a multi-head space-time channel attention fusion module for dynamic calibration and fusion, and deep fusion hidden variables are obtained; jointly inputting the deeply fused hidden variables and the numerical model background field into a decoder based on a conditional variation auto-encoder, generating high-resolution three-dimensional temperature field grid data, and synchronously outputting a three-dimensional uncertainty field; according to the method, the continuous, complete and high-precision ocean three-dimensional temperature field in the whole research area is reconstructed through a mathematical method and a physical method by utilizing limited, sparse, multi-source and heterogeneous ocean observation data, and the problem that the ocean three-dimensional temperature field generated in the prior art is not accurate enough is solved.

The invention relates to the technical field of coast engineering, in particular to an estuary sandy coast erosion and deposition simulation system based on the coupling effect of flood peak runoff, waves and tide, which comprises a training data generation module, an intelligent agent model construction module, a dynamic prediction engine module and a scene evaluation module. The training data generation module is used for outputting a hydrodynamic state field, a wave characteristic field, a bed surface shear stress field and a bed surface elevation variable quantity by utilizing a traditional numerical model to construct a training data set; the intelligent agent model building module is based on a deep learning network and introduces physical constraint loss function training to obtain an intelligent agent model; the dynamic prediction engine module loads the trained deep learning network to realize bed surface elevation variation prediction and update terrain boundary conditions; and the scene evaluation module executes erosion and deposition evolution simulation according to different hydrological boundary condition combinations and extracts terrain evolution data to quantitatively evaluate the coast stability and the channel deposition risk. The invention relates to the field of estuary dynamic landform simulation and coast engineering.

The invention discloses a wind wave-seabed-pile foundation-photovoltaic array multi-physics field coupling calculation system for an ocean photovoltaic power station. The system comprises a wave model construction module, a photovoltaic array wind load numerical simulation module and a multi-physics field coupling numerical model integration module. The wave model construction module is used for extracting wave elements and constructing a wave model; the photovoltaic array wind load numerical simulation module is used for performing photovoltaic array wind load numerical simulation based on the extracted wave elements and the constructed wave model; and the multi-physics field coupling numerical model integration module is used for carrying out multi-physics field coupling numerical model integration, solving and verification based on a numerical simulation structure. According to the method, the wind wave load, the seabed seepage field, the pile foundation stress field and the photovoltaic array wind field are cooperatively calculated by building the multi-field dynamic coupling model, and therefore the accuracy and reliability of offshore photovoltaic power station pile foundation bearing performance evaluation are improved.

The invention relates to a temperature forecast correction method based on a PSD-Net and a PSD-Net, and belongs to the technical field of weather forecast, and the method comprises the steps: obtaining a dynamic meteorological variable based on the PSD-Net, and loading topographic data and a report starting time observation truth value at the same time; preprocessing the dynamic meteorological variable, the topographic data and the report starting time observation truth value; inputting the preprocessed data into a temperature forecast correction model to obtain temperature correction field data; wherein the temperature forecast correction model is obtained by training a PSD-Net model based on a training data set, and the training data set comprises historical dynamic meteorological variables, topographic data and a report starting time observation true value. Compared with the traditional numerical mode and the original output of the archaeological model, the temperature forecast MAE corrected by the method is reduced by more than 37.6%, the accuracy rate within 2 DEG C is improved by 14%-19%, and the precision advantage is more prominent especially in complex terrain areas and extreme weather events.

The invention provides an arch dam shape efficient intelligent optimization method and system based on an agent model. The method comprises the steps that an arch dam physical-numerical model is constructed, a double optimization target with structural safety and economical efficiency as the core is determined, design parameters are selected, a constraint function is set, and an evaluation index system is established; samples are generated through Latin hypercube sampling, a data set is constructed in combination with finite element calculation, and a multi-task learning architecture is adopted to train a high-precision agent model. And then, coupling the proxy model with a multi-objective optimization algorithm, quickly searching a Pareto optimal solution set, and screening out a comprehensive optimal figure by using a multi-attribute decision-making method. And finally, through a finite element simulation verification result, prediction precision and performance improvement are ensured. The method has the advantages of lightweight modeling, efficient prediction and accurate search, and provides an effective tool for intelligent optimization and rapid decision making of hydraulic structures such as arch dams and the like.

The invention discloses a particle mass transfer characteristic prediction method and system based on a machine learning algorithm, and relates to the technical field of particle movement in a multiphase flow system. The method comprises the following steps: constructing a bidirectional coupling numerical model, simulating a particle stirring process of a stirring tank by using the bidirectional coupling numerical model, and analyzing particle mass transfer characteristics and motion characteristics; constructing a machine learning model set, determining a neural network model, training the neural network model, and predicting the behavior of particles in the stirring tank by using the trained neural network to obtain a predicted value of the mass transfer coefficient of the stirring tank; and carrying out combined analysis on the analysis result of the interaction force between the particles and the fluid and the predicted value of the mass transfer coefficient of the stirring tank to obtain an inter-phase mass transfer rule in the stirring tank. The problems that the strong nonlinearity of the stirring tank is difficult to analyze, the mixing efficiency in the solid-liquid suspension process is low, the real-time performance based on dynamic model regulation and control is poor, and industrialization cannot be completely adapted are solved.