2587 results about "Simulation methods" patented technology

Disclosed in the present invention is a non-autoregressive Transformer-based modeling method for a 4-level pulse amplitude modulation high-speed transmitter. The method involves establishing a deep learning model having an encoder-decoder architecture to predict the behavior of a 4-level pulse amplitude modulation transmitter. An encoder processes unordered non-sequential inputs, including an input signal parameter and link parameters, to generate a context vector and then transmit same to a decoder. The decoder uses both the context vector generated by the encoder and a transmitter output signal sequence to generate a categorical probability distribution for each point in the sequence one by one. The model is trained using a random masking strategy, and inference is performed by means of non-autoregressive decoding and filtering, so that the model can perform parallel prediction on an output sequence, and perform a filtering process to predict an output signal. Compared to traditional simulation methods, the present invention achieves a significant acceleration effect, particularly when processing multi-link systems.

The invention discloses a PINN-based high-precision hydrodynamic numerical simulation method and system, and the method comprises the steps: firstly building a computational domain, setting reasonable geometric parameters and boundary conditions, and constructing a dimensionless Navier-Stokes control equation set; then designing a deep neural network architecture with space-time coordinate input and flow field variable output, and adopting a loss function combining physical constraint and data driving; the core innovation lies in providing a timing sequence sensing RAR-D adaptive sampling strategy, dividing a time domain into a plurality of time frames, performing residual error evaluation in each frame, constructing a probability density function related to residual errors, and balancing priority sampling and overall coverage of a high residual error region; adam and L-BFGS optimizers are adopted to carry out network training, the weight of a loss function is dynamically adjusted, and a sampling point set is periodically updated; and finally, the solution precision is verified through multi-dimensional flow field visualization analysis. Therefore, the prediction precision of the complex flow field is effectively improved, and the calculation efficiency is remarkably improved.

The invention discloses a process intelligent simulation method and system based on rational number fusion, and belongs to the technical field of high-end equipment manufacturing and artificial intelligence. The method comprises the following steps of: constructing a fusion database used for storing a mapping relationship between process parameters and organization characteristics by taking a mathematical model fusion normal form as a core, taking the mathematical model data as theoretical model data and taking the mathematical model data as industrial field data; constructing a multi-scale AI simulation agent model corrected by the industrial field data, and constructing an intelligent prediction model used for representing an association relationship between organization characteristics and service performance; and finally, receiving a target performance index, carrying out global optimization under double constraints of a physical boundary of a theory and a simulation environment which is corrected by a number by utilizing an AI reverse design engine, and carrying out reverse solution to obtain an optimal process parameter. Through a two-way closed-loop mechanism of a theoretic constraint number and a number correction theoretic, the fundamental problems of distortion of a theoretical model caused by a scale effect and lack of physical constraint with an AI model are solved.

The invention provides a five-axis series-parallel numerical control machine tool machining process virtual monitoring simulation method and system based on digital twinning, and the method comprises the steps: building an information interaction mechanism between a physical machine tool and a digital twinning model, enabling a server side to collect sensor data, and transmitting the real-time data to a client side through a communication protocol; the client processes the data and drives the digital twin model. And aiming at potential errors of twin system forecast, tool setting error compensation can be carried out in real time, so that the machining precision is improved. Meanwhile, the system supports an offline simulation function, combines an inverse kinematics algorithm and an NC code, fuses a material removal model, simulates a machining process, evaluates machining performance and errors, and provides an optimization basis for machining strategies of workpieces with different geometrical shapes and materials. According to the method, bidirectional interaction between the physical entity and the virtual model is achieved, the real-time monitoring and visualization capability of the machining process is enhanced, a reliable simulation evaluation and optimization means is provided for machining of complex parts, and the method has wide industrial application prospects.

The invention relates to the technical field of computer-aided manufacturing, and provides a staged processing real-time cutting simulation method and system.The method comprises the steps that S1, initialization modeling is conducted, specifically, a blank voxel grid model, a tool body voxel grid and a triangular grid are built; s2, performing a first simulation stage of low-precision real-time cutting dominated by voxel grids, judging a space overlapping relationship between the tool body voxel grids and the blank voxel grids in real time so as to determine a voxel region to be removed, and deleting blank voxels in the overlapping region through Boolean operation; s3, in the execution process of the first simulation stage, monitoring a preset multi-dimensional switching trigger condition in real time, and when any condition or a combination of multiple conditions is met, entering a second simulation stage; and S4, a second simulation stage of high-precision real-time cutting with the triangular network as the dominant is carried out, the voxel grid intermediate workpiece is converted into a transition triangular grid, and high-precision cutting calculation is carried out based on the triangular grid. And the efficiency-precision contradiction is broken through through a staged strategy.

The invention is applicable to the technical field of coal bed gas development and geological sequestration, and provides a THMC multi-field coupling simulation method and system for a process of displacing CH4 by coal bed CO2, and the method comprises the following steps: collecting multi-source parameter information of a target coal bed; according to the multi-source parameter information, constructing a coal seam structure model comprising a matrix and a crack system; constructing a control equation system based on the coal seam structure model, and introducing a multi-field coupling mechanism to obtain a fully-coupled mathematical model; performing numerical solution on the fully-coupled mathematical model to obtain a spatio-temporal evolution result of the key field variables; and according to the spatio-temporal evolution result of the key field variable, evaluating the index of the target coal seam. According to the invention, a heat-force-gas-chemical multi-physical field full-coupling simulation system is constructed, so that the CH4 recovery rate and the CO2 storage stability can be accurately predicted. The method is suitable for gas injection-storage coupling optimization of a multi-coal-rank and low-permeability coal seam, and has high precision, high adaptability and remarkable engineering guidance value.

The invention relates to the technical field of computational mechanics and geological disaster simulation, and discloses a full-process numerical simulation method suitable for simulating continuous medium damage to discontinuous medium movement. The invention provides a novel chain-type disaster simulation method for solving the problems that calculation efficiency and large deformation precision are difficult to consider at the same time and the evolution process of a slope from a continuum to a fragmented body cannot be dynamically reflected in the prior art. The core of the method is that a dynamic criterion is set based on a unit real-time damage variable, a failed finite element (FEM) unit is adaptively converted into smoothed particle hydrodynamics (SPH) particles, and the physical state of the particles is accurately mapped; and a virtual particle coupling algorithm is adopted to process a two-domain interface, so that bidirectional transmission of mechanical parameters is realized. The method is executed circularly, the whole process of'continuous deformation-fragmentation flow 'of the side slope is simulated dynamically, high-precision integrated simulation of a'seismic source-propagation-response-movement' disaster chain in the same frame is achieved, and an efficient tool is provided for disaster risk assessment and prevention and control.

A driving range simulation method and apparatus for a new energy vehicle, and a medium and a device. The method comprises: splitting a vehicle thermal management system model into four sub-models (110); establishing a training data set for each sub-model (120); establishing reduced-order sub-models for each sub-model (130); and integrating the model, and performing high- and low-temperature driving range simulation on a new energy vehicle (140).

The invention provides a deep energy reservoir multi-scale heat-fluid-solid coupling simulation method and system, and belongs to the field of deep energy reservoir numerical simulation. Acquiring spatial distribution characteristics and physical property parameters of different lithologic reservoir media under a micro-scale, and constructing a digital structure model of the reservoir media; a micro-observation action mechanism and a flow mechanism are embedded in the model to obtain a micro-observation numerical model; establishing a mapping relation by solving model structure parameters and physical property parameters of the microscopic numerical model under different thermal-fluid-solid coupling conditions; and on the basis of the mapping relationship, in combination with the macrogeological and engineering data of the target reservoir, establishing a macroscale heat-fluid-solid multi-field coupling control equation and solving, and outputting the multi-physics field response of the target reservoir under the given working condition. According to the method, fine simulation of physical property parameter dynamic transmission and a multi-field coupling evolution process from a micro view to a macro view is realized, and the accuracy and reliability of deep complex reservoir engineering behavior prediction are remarkably improved.

The invention provides an optical element surface type simulation method based on a physical information neural network, and relates to the technical field of element design and simulation, and the method comprises the steps: constructing a feedforward neural network; generating a deformation control equation model library; constructing a three-dimensional simulation database; forming a machine learning parameter library; training the PINN deformation simulation model in combination with a deformation control equation model library, a three-dimensional simulation database and a machine learning parameter library, and screening out a target PINN deformation simulation model meeting a preset condition; inputting the design parameters, the support structure parameters and the given external load parameters of different combinations into the target PINN model to obtain a key performance prediction result of the optical element; according to the method, the key performance of the element under various parameter combinations can be rapidly evaluated, the design iteration period is shortened, the trial and error cost is reduced, direct data support is provided for supporting structure optimization design, and the bottleneck problem that a traditional method restricts the research and development efficiency of a high-precision optical system is effectively solved.

The invention discloses a mechanical production practice virtual simulation method and system constructed based on a three-dimensional scene CAD model. Firstly, a target workshop is subjected to systematic aerial photography through an unmanned aerial vehicle to collect multi-view image data; a high-fidelity and interactive three-dimensional virtual scene model is automatically constructed by adopting a three-dimensional reconstruction technology fusing generative artificial intelligence and Gaussian splashing; and finally constructing an immersive virtual simulation platform. The platform not only can provide VR roaming and equipment simulation operation functions from a first person perspective, but also can collect user multi-dimensional operation behavior data in real time, and generates a visual intelligent comprehensive evaluation report through a preset multi-dimensional skill map and a quantitative scoring algorithm. According to the invention, high-efficiency and high-fidelity digital reproduction of a mechanical production practice scene is realized, a novel practical teaching mode which is safe, quantifiable and accurate in evaluation is created, and systematic pain points such as single scene, high cost, safety risk and subjective evaluation in traditional practice are effectively solved.

The invention provides a vehicle working condition virtual debugging simulation method based on multi-modal perception fusion, and relates to the technical field of digital twinning, and the method comprises the steps: achieving the precise fusion of multi-modal perception data through the synchronous collection of visual, radar and acoustic signals and the unification of a time reference and a coordinate system; constructing a vehicle dynamics state model based on mutual information weighting and recursive filtering, and obtaining dynamically consistent state vectors; combining space-time modeling and robust coding to generate driving characteristics capable of being physically explained, and inputting the driving characteristics into a simulation environment; in the digital twinborn model, parameter adaptive tracking is realized through online correction and joint recursive updating; and closed-loop feedback debugging is formed by using the health index and the fault probability, so that virtual and real consistent and dynamically adjustable vehicle working condition simulation and intelligent debugging are realized. According to the method, fusion of multi-mode sensing data can be realized, so that the simulation model dynamically tracks the working condition of the vehicle.

The invention relates to the technical field of metallurgical industry simulation and intelligent control, and discloses an intelligent metallurgical process virtual simulation method and system based on digital twinning. The method comprises the following steps: acquiring multi-modal high-dimensional data in a metallurgical process and reducing dimensions to obtain a feature vector set of a hidden layer space; performing disturbance injection simulation by using the feature vector set of the hidden layer space to obtain a multi-path state sequence with time dependence; predicting the abnormal path by using a recurrent neural network to obtain a predicted state evolution trajectory; utilizing a preset inverse mapping function to obtain multi-path state representation in the physical space; screening to obtain a risk path set; key evolution nodes are extracted from the set to be processed, and a virtual simulation scene is obtained; and performing optimization simulation on preset process adjustment parameters according to the virtual simulation scene, and determining optimized parameter configuration. The method can solve the problem that it is difficult to construct a comprehensive virtual simulation scene which truly restores the physical production rule.

The invention discloses a flood control toughness evolution simulation method and system based on natural-social element interaction, and the method comprises the steps: obtaining a natural element space-time sequence and a social element space-time sequence of a target region, and aligning the natural element space-time sequence and the social element space-time sequence to a unified space-time grid node, thereby obtaining a heterogeneous node sequence; calculating the time-varying interaction strength between the nodes along with the time and the interaction uncertainty of the time-varying interaction strength; compiling and generating a time-varying rule parameter between each pair of interaction nodes by using a preset rule compiler; and inputting the time-varying rule parameters into a pre-constructed toughness dynamic model, driving and updating the toughness state value of each node in continuous time steps, and obtaining a flood control toughness evolution track. According to the method, the problems of data driving and mechanism model splitting in the prior art are solved through a rule compiling mechanism, dynamic conversion of interaction characteristics from soft weights to hard rules is realized, and the accuracy and interpretability of flood control toughness evaluation in a complex time-varying scene are effectively improved.

The invention relates to a physical constraint fused graph structure UNet neural network for long-time time domain difference optical simulation method and system, and the method comprises the steps: 1, randomly constructing a device parameter data set through an FDTD method, obtaining FDTD uniform grid data, and carrying out the data preprocessing; 2, constructing a graph structure Unet model; 3, inputting the device parameter data set into the graph structure Unet model for end-to-end training; 4, predicting electromagnetic field scattering after a long-time light source encountering period is carried out on given device parameters and initial electromagnetic field distribution; and 5, comparing the electromagnetic field distribution of different device shapes, which is predicted for a long time, with the electromagnetic field value calculated by FDTD, and further judging the prediction accuracy of the graph structure Unet network model. According to the method, one-time long-time sequence prediction of electromagnetic scattering is completed by using the graph structure Unet model.

The present invention relates to the technical field of steel metallurgy process simulation, and specifically relates to a simulation method for particle motion and interaction behavior within a continuous casting mold. The simulation method comprises the following steps: acquiring mold parameters and establishing a model; performing fluid domain volume extraction and structured mesh division on the model; importing the mesh model into simulation software and performing iterative solution; establishing injection points for argon bubbles and inclusions within the mold, compiling and calculating the flotation removal of argon bubbles and inclusions at a steel-slag interface, the capture thereof at a solidification front, and collision-aggregation, and performing coupled computation; and post-processing the simulation results to obtain the flow field, temperature field, solidified-shell distribution, and discrete-phase motion behavior within the mold at different time points. The method of the present invention enables analysis of the motion and interactions of discrete-phase particles such as argon bubbles and inclusions within the mold, thereby accurately predicting the distribution of the particles in the molten steel and the solidified shell.

The invention discloses a high-speed rail vehicle body aluminum alloy material corrosion fatigue performance simulation method and system, and belongs to the technical field of material corrosion fatigue testing. The method comprises the following steps: acquiring multi-source data of a high-speed rail body aluminum alloy material on an actual service route; establishing a material corrosion conversion coefficient database; obtaining an equivalent mapping relation based on the service environment and the load input set; generating a laboratory equivalent accelerated environment spectrum; and executing the laboratory equivalent accelerated environment spectrum, obtaining material failure data, and if the comparison difference is not less than a preset threshold value, correcting the calculation parameters of the equivalent mapping relation and regenerating the spectrum system. Through synchronous acquisition and fusion analysis of the multi-source environmental data and the mechanical load and in combination with the corrosion conversion coefficient database construction and closed-loop verification optimization method, the problem of test result distortion caused by separation of a laboratory simulation environment and an actual service condition in the prior art is solved.

The invention relates to the technical field of virtual reality experiment teaching, and discloses an experiment teaching simulation method and system based on virtual reality. The method comprises the steps that a continuous behavior sequence generated by user operation is collected, and a teaching behavior chain is generated through discretization; constructing a state vector by identifying the space-time state of the behavior unit, and comparing the state vector with a standard template to mark an abnormal behavior unit; tracing the context to generate an abnormal behavior description block, and querying an error knowledge base to obtain error information; meanwhile, analyzing a state change event of the virtual experiment equipment; and finally, fusing the error information and the state event to generate a simulation teaching evaluation result. According to the method, refined multi-dimensional analysis of experimental operation behaviors and intelligent error influence quantitative evaluation are realized, and the accuracy and guidance of teaching feedback are improved.

The invention discloses a curved surface porous structure modeling simulation method and system for finite element analysis, and belongs to the technical field of mechanical engineering simulation, and the method mainly comprises the steps: carrying out the initial grid division of a target curved surface part, and obtaining a grid node set covering a curved surface; according to a preset hole center coordinate, three closest grid nodes are automatically searched in the grid node set, a local reference plane is established, and the hole axis direction is determined; generating an offset sketch plane in the normal direction of the reference plane, projecting the hole center to the sketch plane, drawing a circular sketch, and executing a stretching and cutting command to form a hole; and circularly executing the operation to realize automatic generation of multiple holes on the curved surface. And finally, the porous structure is subjected to grid division again, so that subsequent operation is facilitated. According to the method, the modeling process is combined with parameterization analysis and an automatic process, the efficiency and stability of porous modeling of the complex curved surface structure are remarkably improved, and a basis is provided for finite element pretreatment of the complex curved surface structure.

The invention relates to the technical field of twin models, in particular to a subway station flood prevention simulation method and system based on a three-dimensional model. According to the method, local important evaluation indexes are obtained through placename approximate analysis and concavity-convexity analysis of structures of point cloud data in a neighborhood space, and structural important evaluation indexes of single point cloud data are determined by combining and analyzing density distribution complexity in the neighborhood space; clustering the point cloud data and dividing the point cloud data into different regions; analyzing depth distribution of all the point cloud data in the regions; combining structural importance evaluation, determining an attention rate of each region, performing differential modeling and semantic processing to obtain a semantic three-dimensional model; and combining a hydrodynamic model to obtain a digital twinborn model. According to the method, differentiated three-dimensional modeling is carried out by evaluating the importance of each point cloud data in subway station flood prevention simulation, and flood prevention analysis is carried out more comprehensively and accurately by combining a digital twin model with a hydrodynamic model, so that the protection capability of subway flood prevention is improved.

The invention discloses a real-time ocean simulation method and system based on sensing data, and relates to the technical field of ocean information engineering and intelligent sailing. A graph neural network is combined with a multi-head attention mechanism to carry out data fusion, importance of different sensors and data time steps is dynamically weighted, and real-time ocean simulation is realized. According to the method, the quality difference and missing conditions of heterogeneous data are adaptively processed, the deviation and noise influence in the fusion process are reduced, meanwhile, physical consistency constraints such as mass conservation and boundary non-penetration are introduced, the physical rationality and smoothness of an output ocean current field are guaranteed, non-physical artifacts are avoided, and the reliability and practicability of a result are enhanced; a streaming updating mechanism allows a graph structure and model parameters to be rapidly and incrementally updated when new data arrives, data processing and assimilation delay are greatly reduced, and nearly real-time ocean current forecasting is realized, which is crucial to a ship autonomous navigation system, and accurate ocean current information can be timely provided to support dynamic path planning and risk avoiding decision making.

A simulation method for characterizing the spatiotemporal changes of groundwater levels, which belongs to the technical field of computer machine learning; includes the steps of: obtaining long-term groundwater level data based on long-term groundwater monitoring wells; superimposing multiple hydrogeological parameter information based on groundwater level data; and constructing a regional neural network model with physical mechanism by using a two-dimensional groundwater seepage equation serving as a physical mechanism, thereby simulating the temporal and spatial evolution of regional groundwater levels. The present invention can strengthen the rational use of groundwater resources, implement scientific and effective supervision, and provide a scientific basis for protecting regional groundwater safety and promoting the sustainable development of groundwater resources.

The invention relates to the technical field of geological engineering and underground water simulation, and discloses a regional underground water simulation method and system based on a physical mechanism and deep learning, and the method comprises the steps: converting the static attributes of an observation well into the embedding of nodes and edges through a graph neural network, and obtaining spatial features; according to the dynamic input of the observation well, modeling time sequence response of the underground water system under external dynamic driving through a long short-term memory neural network; deep fusion of spatial features and time dynamics is realized through a feature interaction mechanism, and a groundwater level simulation value is generated; combining a water balance constraint with a relative water level consistency constraint between observation wells to jointly construct a composite loss function for quantifying a difference between an analog value and a real observation value, and guiding parameter updating; the time-space dynamic and physical mechanism can be sensed synchronously, and the method is more reliable and efficient.

The embodiment of the invention relates to the technical field of geographic space platforms and visualization, and provides a forest fire spreading dynamic visualization simulation method and device, electronic equipment and a medium, and the method comprises the steps: obtaining image data and topographic data of a target region, and constructing a three-dimensional topographic scene; receiving a combustible point coordinate and a real-time meteorological parameter selected by a user, and configuring an initial fire behavior in the three-dimensional terrain scene based on the combustible point coordinate and the real-time meteorological parameter; performing periodic forest fire spreading calculation based on the configured initial fire behavior through a forest fire spreading algorithm to obtain simulated fire point data corresponding to each calculation period; generating flame particles and smoke particles corresponding to each calculation period based on the simulated fire point data corresponding to each calculation period; and sequentially rendering the flame particles and the smoke particles corresponding to each calculation period into a three-dimensional terrain scene, and carrying out real-time dynamic forest fire spreading visual simulation. Therefore, real-time forest fire spreading three-dimensional visualization with high precision and high trueness in the browser is realized.

The invention belongs to the technical field of near-roadside pollution diffusion simulation, and particularly relates to a road traffic pollution diffusion rapid simulation method based on a physical structured proxy model. Comprising the following steps: S1, meteorological characteristic engineering and sampling; s2, road network standardization decomposition: carrying out topology discretization decomposition on the complex urban road network into standardized unit line sources which can be independently calculated; s3, reconstructing spatial geometric features; s4, physical mechanism dual partitioning: constructing an orthogonal physical mechanism partitioning system according to the atmospheric stability level and the relative position of the receptor point; s5, proxy model training: respectively training an XGBoost regression model for each physical partition; and S6, area field rapid reconstruction: receiving real-time boundary conditions for parallel inference, and rapidly reconstructing an area pollutant concentration field through linear superposition. On the premise of ensuring the simulation precision highly consistent with that of a full-physical model, the calculation efficiency is remarkably improved, and the method is suitable for real-time air quality simulation of a large-scale urban road network.

The invention relates to the technical field of metal composite materials, in particular to a metal composite material production process dynamic simulation method based on digital twinning. Comprising the following steps: constructing a digital twin model comprising a physical entity layer, a virtual model layer and a connection layer, and establishing a Fourier neural operator model embedded with a Navier Stokes equation and a heat conduction equation constraint; real-time molten pool monitoring data are mapped into model input through a connecting layer, and advanced time step deduction is executed by using a deep learning model to predict the state of a molten pool; based on the differentiable characteristic of a physical information neural operator, calculating the gradient of a prediction state and an ideal target by using an automatic differential algorithm, generating a process parameter field through reverse iteration updating, and discretizing the process parameter field into a galvanometer control instruction; and when the risk of aggregation of the reinforced phase particles is predicted to exist, automatically generating a swing scanning strategy to realize active suppression. The method gives consideration to simulation precision and real-time performance.

The invention relates to the technical field of data processing, and discloses a cable joint arc multi-physical field simulation method and system. The method comprises the following steps: establishing a defect-containing geometric model, judging according to a temperature gradient change rate and a current rising rate to obtain an adaptive time step length, and carrying out bidirectional coupling iteration on an arc temperature field and a material dielectric parameter based on the time step length to obtain an accumulated damage factor; arranging a virtual temperature detection point to convert the simulation temperature field into a virtual monitoring signal, extracting temperature characteristic parameters and ultrasonic spectrum characteristic parameters, and projecting the temperature characteristic parameters and the ultrasonic spectrum characteristic parameters to a two-dimensional characteristic space to obtain a fault mode fingerprint spectrum. According to the method, adaptive adjustment of the time step length is realized by establishing an arc evolution phase discrimination mechanism, and the problems of low calculation efficiency and insufficient transient characteristic capture caused by a fixed time step length are solved.

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

The invention discloses a glacier material balance simulation method and system based on feature interaction, and belongs to the technical field of glacier hydrology and climate change monitoring, and the method comprises the steps: sequentially carrying out the uniform format processing and slope feature enhancement of a multi-source data set of a glacier, and dividing the multi-source data set into a simulation data set and an observation data set; training a model based on a trans-attention Transform encoder framework by using the simulation data set to obtain a pre-training model; when one-glacier-leaving cross validation is carried out on the pre-trained model, multi-stage fine tuning training is carried out by using the observation data set to obtain a plurality of fine-tuned models; performing model integration based on performance index screening on the fine-tuned models, and constructing an integrated simulation model; and preprocessing the meteorological dynamic characteristics and the static topographic characteristics of the target glacier, inputting the preprocessed meteorological dynamic characteristics and static topographic characteristics into the integrated simulation model, and carrying out glacier material balance simulation to obtain a glacier material balance simulation result. According to the method, the simulation uncertainty is reduced, and the high-precision simulation of the glacier material balance is realized.

The invention discloses an aggregate random distribution concrete PFC (Power Factor Correction) simulation method, which comprises the following steps: a model selection step: determining a parallel bonding model as a mechanical model for concrete material simulation; a cement mortar matrix generation step of generating a cement mortar matrix by using particles in a set size range in a set space range based on the model, and setting pore density, particle density and contact model parameters; a coarse aggregate random distribution simulation step: randomly covering and grouping original particles in the matrix in a grouping and range mode; a concrete numerical model construction step: determining particle classification so as to distinguish cement mortar and coarse aggregate; and a model reliability verification step: comparing indoor test results through a uniaxial compression test. According to the method, the simulation accuracy is improved, the randomness of aggregate is reduced, the model reliability is ensured, the matrix simulation refinement degree is improved, and a method is provided for concrete modeling and performance research.