319 results about "Source model" patented technology

The invention provides a geophysical gravity and magnetic anomaly source model construction and rapid forward and reverse modeling method, and relates to the technical field of intelligent three-dimensional geological modeling and simulation. According to geological information of a research area, source body modeling parameters of a gravity and magnetic anomalous field are set for the research area, and a three-dimensional model matrix M capable of describing a plurality of underground anomalous bodies is obtained; constructing a fast forward modeling network, establishing nonlinear mapping from the three-dimensional model matrix to the simulated abnormal data through the fast forward modeling network, and calculating forward modeling response of the three-dimensional model matrix; and constructing an inversion network based on a concurrent module CFTBlock and combining a CNN and a Transform, and establishing nonlinear mapping from the gravity and magnetic abnormal data to a three-dimensional model matrix. The method has the advantages of fast and accurate forward modeling, high-resolution inversion and the like, and is suitable for better interpretation of actually measured gravity and magnetic data.

The invention belongs to the technical field of electric power system information processing, and particularly relates to an electric power information analysis method based on big data, through semantic fusion of multi-source heterogeneous data and dynamic feature mining of a time sequence attention mechanism, in a load prediction scene, compared with a traditional single data source model, the electric power information analysis efficiency is improved. After the meteorological data, the user power consumption behavior data and the power grid operation data are fused, the prediction average error rate is reduced; in an equipment fault early warning scene, through multi-dimensional correlation analysis of vibration signals, oil temperature data and environmental factors, transformer latent faults can be early warned in advance, and the fault identification accuracy is improved; meanwhile, a self-adaptive modeling engine and a closed-loop feedback mechanism enable the system to have a self-evolution capability: when the power grid topology is adjusted or the new energy grid-connected proportion is changed, the model does not need to be manually retrained, and self-adaptive adaptation can be completed in two scheduling cycles through dynamic feature weight adjustment and meta-learner parameter optimization, so that the analysis performance is maintained to be stable.

The invention relates to the technical field of transformer simulation, in particular to a simulation system and method for a temperature rise field of a dry-type transformer. The method comprises the following steps: acquiring geometric parameters of an iron core lamination of the dry-type transformer to be simulated, spatial arrangement information of a winding and a positioning relation of an insulating component to form structural design data; the method comprises the following steps: constructing three-dimensional geometric modeling data based on structural design data, importing the three-dimensional geometric modeling data into a finite element electromagnetic simulation platform, setting a power-on condition of an excitation winding, and applying rated current or voltage, so as to obtain local magnetic flux density distribution data at each iron core lamination position in a whole operation period. According to the method, on the basis of three-dimensional structure data, three physical processes of an electromagnetic field, a thermal field and a fluid field are fused, a high-precision local heat source model and a dynamic temperature rise prediction model are constructed, and geometry-material-boundary all-parameter modeling is achieved from the whole structure of an iron core lamination, a winding and an insulation assembly.

The invention discloses an electric arc additive manufacturing temperature field and stress field real-time reconstruction method, which comprises the following steps of: 1, constructing a sequential coupling finite element model in which heat conduction analysis and thermoelastic-plastic mechanical analysis are combined on the basis of process parameters of an electric arc additive manufacturing process and three-dimensional geometry of parts, generating a multi-physical field coupling data set among heat flux density, temperature and stress; step 2, constructing and training a neural network model based on a multi-physics field coupling data set, and realizing mapping from heat flux density to temperature and stress; 3, dynamically optimizing heat source model parameters in combination with real-time monitoring data and process parameters in the electric arc additive manufacturing process; and 4, based on the optimized heat source model parameters and the trained neural network model, real-time reconstruction of a temperature field and a stress field in the electric arc additive manufacturing process is achieved. According to the invention, the requirements of rapid sensing and intelligent regulation and control of temperature and stress states on an additive manufacturing site are met.

Systems and methods for determining a target multi-source model of a radiation source corresponding to an energy spectrum is provided. The systems may obtain an initial multi-source model of the radiation source, which includes an initial phase space file that includes information of a plurality of simulated particles of a plurality of energy levels. The systems may estimate, based on the initial phase space file, a plurality of component PDD curves corresponding to the plurality of energy levels. The systems may obtain a measured PDD curve corresponding to radiation of the energy spectrum. For each energy level, the systems may determine, based on the plurality of component PDD curves and the measured PDD curve, a weight for the each energy level. The systems may further determine the target multi-source model of the radiation source based at least in part on the initial multi-source model and the weights.

The invention discloses an on-orbit spacecraft temperature prediction method and device based on a physical information neural network. The method comprises the following steps: S1, constructing an object model and an orbit heat source model according to a spacecraft to be predicted; s2, solving a space external heat flow of the orbit heat source model, and converting the space external heat flow into a volume heat flow of the object model; s3, constructing a physical information neural network; wherein a three-dimensional transient heat conduction equation is embedded in the physical information neural network; s4, designing a total loss function according to the volume heat flow and the physical information neural network; and S5, training the physical information neural network according to the total loss function to obtain a temperature prediction result of the spacecraft to be predicted. According to the method, the situation that model grids need to be divided in traditional finite element simulation is avoided, the calculation efficiency is improved, the calculation time is shortened, the temperature result can be predicted in real time, and the generalization ability and accuracy of the model are improved.

The invention provides a water environment model coupling data processing method and system, and relates to the technical field of hydrology and water quality monitoring. The method comprises the following steps: determining a soil permeability characteristic descending trend and estimating an uncertainty range of a runoff coefficient; obtaining source model data according to the uncertainty range of the runoff coefficient by using a regional pollution production and convergence model; using the one-dimensional river network model to obtain error information according to the source model data, and outputting prediction data; and determining a prediction deviation mode by comparing the prediction data with the real-time monitoring data, and correcting parameters of all related models in the whole model chain. The method aims at solving the technical problem that the permissible discharge amount set based on a three-model coupling system does not conform to the actual absorption capacity of a water body due to continuous nonlinear deviation of river network model prediction and actually measured water quality data in the three-model coupling system, and accurate analysis of the urban pollution load and water environment quality response relation is achieved. And real-time and self-adaptive adjustment of the permissible emission amount is carried out.

The invention discloses a whole-process application method for an indoor decoration engineering digital model, and relates to the technical field of building information models.The method comprises the steps that a decoration semantic mapping engine is deployed, multi-source model attributes are standardized into a unified attribute dictionary, and a neutral semantic package is generated; splitting the neutral semantic package into single component nodes, and storing the single component nodes in a tense graph to realize object-level version management; generating an incremental packet by changing the monitoring stream and comparing the fingerprint; automatically combining decisions or generating a to-be-confirmed list by utilizing a conflict analysis pipeline; synchronizing the update fragment to a scheduling system and a logistics interface through a service bus; and finally, the field terminal dynamically superposes the scanning data and the sensing reading according to the component fingerprint, and writes back and feeds back to correct the mapping rule. The technical characteristics of the method cover semantic management, data synchronization and field feedback. The semantic consistency and the data accuracy are improved, the information islands are effectively inhibited, and the full-period management efficiency of the interior decoration engineering is improved.

The invention relates to the field of tunnel engineering, in particular to a tunnel multi-physics field coupling simulation and support design method considering construction disturbance, which comprises the following steps of: modeling a disturbance source, collecting disturbance factor information introduced in a tunnel construction process or an external environment, establishing a disturbance source model to represent the spatial position, intensity and evolution characteristics of a disturbance source; constructing a three-dimensional disturbance propagation function; coupling the three-dimensional disturbance propagation function with a multi-physical field model, and introducing disturbance as a source item into a tunnel heat-water-force coupling model; according to the construction method customization mechanism, modeling parameters and solving strategies are adjusted according to the characteristics of different tunnel construction methods, and corresponding disturbance source model parameters, boundary conditions and constitutive relation adjustment coefficients are set according to the different construction methods. The problems that disturbance modeling is rough, a disturbance propagation mechanism is idealized, the disturbance propagation mechanism is disjointed with a multi-physical field model, and a construction method is not included in a disturbance response system are solved.

The invention provides a distributed rendering task scheduling method based on dynamic load balancing, which relates to the technical field of virtual reality simulation, and comprises the following steps: analyzing and preprocessing input physical simulation data through a multi-source model, and converting the format of the processed data into a manageable virtual set data format; loading the texture data to a video memory as required in a virtual mapping mode; dynamic memory loading and coloring are carried out based on the virtual set, and LOD generation is completed; dynamic task fragmentation and scheduling are carried out on the virtual set rendering task and the heterogeneous multi-source rendering task, real-time monitoring is carried out on GPU / CPU / memory loads of heterogeneous resources, and task scheduling is carried out according to a monitoring result; through a distributed cluster mode, a high-resolution rendering task is divided into a plurality of low-resolution rendering tasks, and the low-resolution rendering tasks are rendered on different nodes. According to the invention, high-fidelity, high-response and high-reliability three-dimensional scene simulation capability is provided for high-complexity scenes such as a hybrid system.

The invention discloses a stochastic finite fault source model establishment method, which comprises the following steps of S1, determining a geometrical shape of a seismic fault based on regional geological data and seismic monitoring data, and estimating a non-planar fault parameter and a fault plane average sliding amount in combination with historical data and an empirical formula; s2, estimating distribution and sliding strength of concave-convex bodies and obstacles on the fault surface according to historical seismic inversion data, and obtaining the sliding amount of each sub-fault by randomly disturbing the average sliding amount; s3, the discrete fault plane serves as a sub-fault unit, and after an initial fracture point and the average fracture speed are determined, random fracture initial time conforming to normal or power law distribution is generated based on a random number method; s4, calculating the seismic oscillation time history of each sub-fault by adopting a random point source method, superposing contributions, introducing the influence of a shallow velocity structure V30, and generating a three-dimensional seismic oscillation field; and S5, comparing actual strong earthquake record adjustment parameters, and outputting a multi-risk-level earthquake motion parameter evaluation result after iterative optimization. According to the method, by fusing randomness and empirical data, the simulation precision of the seismic source model on the uncertainty of the seismic process is improved, the influence of the potential maximum-magnitude earthquake possibly occurring in the active fault zone in the future can be effectively estimated, the seismic oscillation simulation precision and the calculation efficiency can be improved, and the method is suitable for engineering structure aseismic design and seismic disaster evaluation.

A method for constructing a photon source model function of a medical linear accelerator, for calculating the dose of rays in a radiation therapy scheme is disclosed. A source model of a therapeutic photon beam of the accelerator includes a primary ray photon source model and a scattered ray photon source model. Physical parameters in the two parts of source model functions include the position coordinates of an emission point of a particle, a projection value of a unit momentum vector in a three-dimensional orthogonal direction, and the energy of the particle. By utilizing the model functions, photon fluence information, energy spectrum information, and unit momentum direction information of photons on any phase space plane can be accurately calculated. The method and thought for constructing the source model are applicable to construction of source models of photon beams with various nominal energies of the accelerator used in a radiation therapy.

The invention relates to a silicon carbide femtosecond laser processing process simulation and morphology evolution prediction method, a medium and electronic equipment, and the method specifically comprises the steps: constructing a silicon carbide model, and carrying out discretization processing to obtain a silicon carbide grid model; fluid heat transfer parameters and fluid flow parameters are calculated; constructing a femtosecond laser heat source model; introducing the femtosecond laser heat source model into the double-temperature model and correcting the femtosecond laser heat source model; based on fluid heat transfer parameters and fluid flow parameters, coupling the corrected dual-temperature model with a Navier-Stokes model, and constructing a multi-physical field model; an ablation threshold value is set, and when laser energy reaches or exceeds the ablation threshold value, the grid deforms; calculating the grid deformation speed of the silicon carbide phase explosion; and simulating the removal process of the material according to whether the grid is deformed or not and the deformation speed thereof. According to the method, the multi-physical field model is constructed, so that the process of femtosecond laser processing of silicon carbide can be simulated more accurately, the actual processing condition is reflected, and the prediction accuracy of the processing process is improved.

The invention discloses a vegetation photosynthetically active radiation absorptivity inversion method based on multi-source data fusion. The vegetation photosynthetically active radiation absorptivity inversion method comprises the following steps: acquiring a modeling data set for inverting a photosynthetically active radiation absorptivity; a pre-trained long-short-term memory network inversion model PreTLSTM is constructed, the pre-trained long-short-term memory network inversion model PreTLSTM is obtained through training of a training set, and the training set is modeling data generated based on a soil-leaf-canopy coupling model SLC; taking the pre-trained long-short-term memory network inversion model PreTLSTM as a source model, constructing a migration training set through satellite data and measured data, training the source model by using the migration training set, and obtaining a migration training inversion model TLLSTM; and inputting the modeling data set into a migration training inversion model TLLSTM to obtain a vegetation photosynthetically active radiation absorptivity estimation result. According to the method, complex ecological and biophysical processes in the remote sensing data can be better captured.

The invention relates to the technical field of language model integration, in particular to a large language model integration method supporting semantic correction. The method comprises the following steps: S1, selecting several large language models, and sorting and preprocessing text data for training and testing; s2, training each source model and an optimization model by using an intelligent fusion language model technology; s3, fusing parameters of each source model by using an intelligent fusion language model technology, selecting model parameters for fusion according to the text data, and adjusting the fused model; s4, evaluating the semantic correction capability of the fused model, and adjusting a fusion strategy and regularization parameters according to an evaluation result; and S5, integrating the trained model into a target system. According to the large language model integration method supporting semantic correction, through an intelligent fusion language model technology, a context sensing and hybrid regularization technology is utilized to train and optimize a model and enhance adaptability, and a FuseLLM fusion technology is adopted to merge all source model parameters.

The invention provides a BIM (Building Information Modeling) full life cycle splitting and cross-model synchronization method and system. The method comprises the steps that BIM model components are coded based on a full life cycle coding rule, private attributes of all engineering stages and component codes of the engineering stages are bound and stored in component extension attributes, and a BIM master model containing multi-stage attribute information is generated; setting a BIM model splitting rule based on a rule engine; the code of each component in the BIM model is analyzed, the codes are compared with screening conditions in the splitting rules according to levels, and component data corresponding to the successfully matched codes are packaged and stored as sub-models; establishing association mapping between the models; and when the source model is modified, the synchronization engine executes synchronous updating of the target model according to the association mapping between the source model and the target model associated with the source model, so that the cross-stage multi-participant cooperation efficiency is improved, and a solid technical support is provided for digital transformation of the construction industry.

The invention discloses an ecological safety early warning evaluation system based on three-generation space coupling and multi-source model fusion. The system comprises an index framework construction module, a weight determination module, an ecological safety level evaluation module, a three-generation space coupling evaluation module and an ecological safety early warning evaluation module. The invention provides an ecological safety early warning evaluation system, which masters the possibility of occurrence of ecological environment problems and spatial and temporal distribution characteristics, and provides a scientific basis for management of governments and environmental protection departments in the region. According to the method, indexes are selected according to historical spatial data and various statistical yearbook, an ecological safety evaluation system is constructed based on a three-generation space theory, three-generation coupling coordination is calculated according to different land utilization functions, and finally an ecological safety early warning area is determined according to the space-time relationship between the two.

The invention provides a knowledge distillation passive domain unsupervised domain self-adaptive method, which realizes classification of a target domain without using source domain data by designing a teacher-student model and a competition mechanism. Firstly, a teacher network composed of a feature extractor, a classifier and a rotation classifier is constructed, source domain data is used for training, classifier parameters are fixed, a source model is finely adjusted through a self-supervised rotation sampling method, and a pseudo label of target domain data is generated through a clustering method. And then designing a student network composed of a feature extractor and a classifier, generating a pseudo tag only by using target domain data, and optimizing the discrimination and diversity of the tag by maximizing mutual information loss. And finally, screening the pseudo labels of the teacher-student network by using a competition module, and extracting the pseudo labels with higher confidence to update the student network, thereby completing knowledge migration. According to the method, source domain data is not needed, data storage and transmission cost is reduced, privacy and data security are improved, and unsupervised classification learning on unlabeled target domain data is supported.

An MIG welding parameter optimization method based on a composite heat source simulation model comprises the steps that firstly, a geometric model and a physical model for simulation are determined, then grid division work is carried out on the simulation model, reasonable boundary conditions are designed for the simulation model, and then a Gaussian heat source model and a cylinder heat source model are imported into the models; the method comprises the following steps of: setting a time step, starting a simulation process, monitoring, comparing a simulation result with an actual experiment result, verifying, and continuously and repeatedly optimizing the model until the simulation result and the actual experiment result have good consistency. A composite welding heat source model is embedded in a traditional heat source simulation model, so that real data such as the shape and the forming size of a welding pool under the welding wire filling condition can be more accurately simulated, the simulation precision is high, the data are reliable, and the size of the actual welding sample pool can be well fit.

The invention relates to the field of carbon emission prediction, and discloses a TimesNet multi-source migration prediction method for carbon emission, and the method comprises the following steps: S1, data collection and preprocessing; s2, pre-training a source domain model; s3, construction and fine tuning of a target domain multi-source model; s4, the trained multi-source model is migrated for prediction; and S5, outputting a final prediction result. According to the method, a TimesNet multi-source migration prediction model for the carbon emission is established, the time change of the carbon emission data is decomposed into two-dimensional tensors through the TimesNet model, and the time sequence characteristics of the carbon emission data are considered; a domain adaptive layer is added into the model to minimize inter-domain differences, so that the problems that the inter-domain differences in the later period of network learning are significant and the transferability is reduced are solved; by transforming the loss function and carrying out transfer learning, decomposition, integration and prediction of carbon emission can be completed without integrating a model, and the problem that the cost consumed by redeveloping a model for a carbon emission prediction task is large is solved.

The invention discloses a Xinanjiang model parameter automatic calibration method based on a dual-objective optimization genetic algorithm, relates to the technical field of genetic algorithms and Xinanjiang models, and aims to solve the problems that a Xinanjiang three-water-source model has many parameters, many influence factors and large solution space, and a traditional method cannot well solve the problems. M points are randomly selected from a search space of Xinanjiang model parameters, the m points are coded, and a population is initialized; establishing a target function based on the error index, and obtaining the fitness of population individuals through the target function; carrying out evolution operation on the population through selection, recombination and variation to obtain breeding offspring; and performing reinsertion operation on the obtained breeding offspring to complete automatic calibration of the parameters of the Xinanjiang model, and completing the automatic calibration of the parameters of the Xinanjiang model. Individuals most suitable for the environment are obtained, and flood forecasting and water resource management are carried out.

The invention provides a credible passive field adaptive mechanical equipment intelligent diagnosis method based on model calibration, and the method comprises the following steps: S1, installing a vibration acceleration sensor on the surface of mechanical equipment, and collecting the vibration monitoring data of different fault types of the mechanical equipment under different working conditions; s2, performing normalization preprocessing on the vibration monitoring data; S3, constructing a credible passive field adaptive mechanical equipment fault diagnosis model based on model calibration; comprising the steps of establishing an initial model, generating a source model, establishing an adaptive fault diagnosis model and calibrating the adaptive fault diagnosis model. And S4, using the adaptive fault diagnosis model to diagnose the fault type of the mechanical equipment. According to the invention, vibration monitoring data is directly used as input, and end-to-end cross-working-condition intelligent fault diagnosis is realized; according to the method, determinant-based mutual information is introduced to realize robust model cross-domain adaptation; the method also establishes a temperature scaling theory based on a target simulation data set, and improves the reliability of the prediction confidence.

The invention relates to the technical field of digital twinning, in particular to a digital twinning drive solid waste real-time matching optimization method and system. The method comprises the following steps: obtaining building solid waste source end data, and carrying out solid waste source end twinning modeling according to the building solid waste source end data to obtain a digital twinning source end model; obtaining building solid waste receiving end data, and carrying out receiving point twinborn modeling according to the building solid waste receiving end data to obtain a digital twinborn receiving end model; performing matching according to the digital twin source end model and the digital twin receiving end model to obtain a solid waste matching model; and performing task splitting fusion scheduling according to the solid waste matching model to obtain solid waste scheduling data. According to the method, the digital twinborn model of the building solid waste source end and the building solid waste receiving end is constructed, fine modeling and dynamic mapping of solid waste attributes, processing capacity and pollution loads are achieved, the environment diffusion risk in the transportation process is effectively reduced, and the feasibility of task splitting and fusion and the resource bearing efficiency are enhanced.

The invention discloses an additive manufacturing surface topography prediction method and system based on multi-source molten pool feature fusion. The method comprises the steps that a visible light image and temperature field data of a molten pool in the laser additive manufacturing process are collected; key molten pool features are extracted according to the collected visible light images and temperature field data; fusing the extracted key molten pool feature data through a grid mapping method to generate a feature matrix; scanning the surface of the formed part to obtain surface topography data, and processing the surface topography data through a grid interpolation segmentation method to generate a label matrix with the same size as the feature matrix; and using a deep learning network based on an adaptive multi-source model, taking the feature matrix as an input, taking the label matrix as an output, carrying out training, and constructing a surface topography prediction model. By means of the scheme, the surface appearance in the laser additive manufacturing process is accurately predicted in real time, and an effective monitoring and predicting means is provided for quality control of additive manufacturing.

Disclosed are example embodiments of systems and methods for virtual try-on of articles of clothing. An example method of virtual try-on of articles of clothing includes selecting a garment from a pre-existing database. The method also includes loading a photo of a source model wearing the selected garment. Additionally, the method includes generating a semantic segmentation of the model image. The method also includes extracting the selected garment from the photo of the model. Additionally, the method includes determining a correspondence between a target model and the source model by performing a feature point detection and description of the target model and the source model, and performing feature matching and correspondence validation. The method also includes performing garment warping and alignment of the extracted garment. Additionally, the method includes overlaying and rendering the garment.

The invention relates to the technical field of trusted artificial intelligence and computer vision, in particular to a deep neural network attack resisting method based on important neurons, which comprises the following steps: setting corresponding operation parameters for a source model, and when the number of iterations is matched with the operation parameters and the number of iterations reaches a preset operation parameter, executing the step of executing the step of executing the step of executing the step of executing the step of executing the step of executing the step of executing the step of executing the step of executing the step of executing the step. Calculating importance scores of all neurons in the source model, generating a binary mask, and obtaining a masked mask model; outputting an adversarial sample, respectively calculating cross entropy loss of the adversarial sample and the corresponding real label through the mask model and the source model, correspondingly obtaining a first gradient and a second gradient of the cross entropy loss about the adversarial sample, and weighting the first gradient and the second gradient to obtain a total gradient; sequentially updating the iterative adversarial samples according to the total gradient until iteration is finished, and generating a final-edition adversarial sample; important features of corresponding image categories can be better positioned, and the attack-resisting success rate of the attack-resisting sample for different attack target models is improved.

The invention discloses a data model field mapping method and device, a storage medium and a processor. According to the scheme, metadata of a source model and metadata of a target model are obtained; constructing a candidate field mapping set based on the metadata of the source model and the metadata of the target model; traversing each pair of candidate field mappings in the candidate field mapping set, calculating a comprehensive similarity value of each pair of candidate field mappings in the candidate field mapping set, and determining a mapping rule corresponding to each pair of candidate field mappings based on the comprehensive similarity value of each pair of candidate field mappings, and converting each pair of candidate field mapping from the source model to the target model based on the determined mapping rule. Compared with the problems of low efficiency, high error rate and the like of a manual maintenance mode in the prior art for a large number of fields and complex data structures, the method has obvious advantages.

The invention provides a complex data model non-intrusive field conversion platform, which comprises a determination unit used for determining a source model and a target model; the configuration unit is used for configuring at least one mapping rule scheme, and an effective condition of the current mapping rule scheme and at least one field mapping rule in the current mapping rule scheme are defined in each mapping rule scheme; the distribution unit is used for determining a mapping rule scheme as an effective rule scheme; the mapping selection unit is used for selecting a source field needing to be mapped and a target field needing to be mapped, and selecting a field mapping rule between the source field and the target field from the effective rule scheme; and the execution unit is used for mapping the source field to the target field based on the field mapping rule. According to the scheme, multiple sets of mapping rule schemes are configured on the platform according to the source model and the target model, and the appropriate mapping rule scheme is selected to flexibly perform field conversion on the complex data model.

The invention belongs to the technical field of laser in-situ welding, and discloses a SiC particle reinforced aluminum-based composite material laser in-situ welding and modeling method, and the method comprises the steps: building a laser heat source model considering the surface optical property difference of a particle melt; establishing a multiphase flow model based on magnetohydrodynamics; and a DEM-based particle migration-heat transfer model is developed. A particle contact force and deformation relation is described based on a Hertz contact theory, a contact heat conductivity coefficient of SiC particles is obtained by combining an experimental method, and dynamic tracking of contact and heat transfer is realized by adopting a discrete element DEM method; and molten pool-keyhole-particle multi-energy field multi-phase coupling integrated modeling is carried out. According to the method, the welding heat behavior description is more accurate, the difference of optical behaviors such as laser absorption, scattering and reflection on the surfaces of the fluid and the SiC particles is considered for the first time through the established numerical simulation model, and the characterization of the energy transmission behavior and the temperature evolution behavior in the welding process is more accurate.

The invention discloses a physical simulation method and device for seismic response certainty of an urban underground rail transit network, and the method comprises the steps: constructing a mixed seismic source model based on the active fault parameters and kinematic parameters of a target region, the seismic source model integrates a low-wavenumber deterministic slip component and a high-wavenumber random slip component on a fault fracture surface to generate a broadband seismic wave field source reflecting the physical process of a seismic source; the constructed seismic source model is used as input, a regional crustal velocity structure model is combined, propagation of seismic waves in a layered geological medium is simulated through a frequency-wavenumber domain method, and a seismic oscillation space-time field is obtained through calculation; and establishing a finite element model of the urban underground rail transit network, applying the seismic oscillation time-space field as non-consistent seismic oscillation input to the finite element model, and solving through dynamic time-history analysis to obtain the dynamic response of the whole underground rail transit network in the earthquake process. According to the invention, a complete physical chain from seismic source fracture to structural response can be reproduced.