78 results about "Simulated data" patented technology

The application discloses an urban waterlogging intelligent prediction method and system based on a physically constrained enhanced video generative adversarial network. The method comprises the following steps: constructing a meteorological geographic data set containing static terrain data and dynamic rainfall data; generating waterlogging simulation data based on a physical numerical simulation model, and matching the waterlogging simulation data with the meteorological geographic data set into a comprehensive data set; constructing a physically constrained enhanced video generative adversarial network model, wherein the model comprises a generator and a discriminator; constructing a multi-angle loss function system that fuses an adversarial loss, a time sequence consistency loss, a data supervision loss and a water quantity balance constraint physical loss; and alternately optimizing the generator and the discriminator until the model converges, and outputting a spatiotemporally continuous waterlogging inundation depth prediction result. The application explicitly embeds a physical hydrological law into a deep learning network, and eliminates the physical logic contradiction of a pure data-driven model in continuous spatiotemporal sequence prediction through water quantity balance constraint.

The application discloses an engineering simulation method and system based on a spectral weight sparsification neural operator. The method collects original working condition information of an engineering PDE, discretizes the grid by means of a high-fidelity numerical solver, and generates a sample data set. The sample data set is subjected to Gaussian standardization, and the grid points are spliced to obtain a structured data. An SSNO model with a multi-layer spectral weight sparse Fourier layer is built, a learnable frequency domain transform spectral weight is set for each layer, a joint loss function is constructed by fusing a relative L2 prediction loss and a spectral weight L1 penalty term, and model training is completed by iteration relying on the function. The trained model is connected to a simulation platform, new working condition parameters are input into the model after the same preprocessing, and the full-field solution is output. Newly measured and simulated data continuously supplement the data set, and the model is cyclically updated and optimized, forming a closed-loop simulation link. The application solves the problems of the prior art, such as no built-in frequency domain screening mechanism, frequency domain parameter redundancy, and inability to simultaneously realize model precision improvement and frequency domain weight autonomous sparse optimization.

This invention relates to the field of vehicle design technology and discloses a method and apparatus for determining the display area of ​​a combination instrument cluster. The method includes: acquiring first simulated data of the steering wheel, second simulated data of the user's eye ellipse, and third simulated data of the combination instrument cluster display surface; determining a central field of view region based on the first simulated data, the third simulated data, and simulated data of the central eye point; translating the central field of view region based on the simulated data of the central eye point, at least one simulated data of an extreme eye point, and the third simulated data to obtain at least one extreme field of view region corresponding to each of the at least one extreme eye point simulated data; and determining the display area of ​​the combination instrument cluster based on the central field of view region and the at least one extreme field of view region. Applying the technical solution of this invention can meet the vision requirements of users of different body types, making the design position of the combination instrument cluster universally applicable.

The application belongs to the technical field of cutting simulation, and particularly relates to a cutting deformation zone dislocation instability judgment method based on molecular dynamics simulation, atomic scale simulation data of a cutting process is obtained; dislocation line length parameters, defect atomic space distribution parameters and atomic potential energy parameters are extracted from the simulation data; a dislocation burst index is determined based on the dislocation line length parameters, a defect concentration coefficient is determined based on the defect atomic space distribution parameters, and a potential energy fluctuation coefficient is determined based on the atomic potential energy parameters; three types of indexes are respectively subjected to benchmark normalization to obtain corresponding normalized judgment values; a comprehensive instability index is determined according to the normalized judgment values; and whether the cutting deformation zone is in a dislocation instability state is judged according to the relationship between the comprehensive instability index and a preset instability critical value. The time-varying characteristics, space distribution characteristics and energy change characteristics of dislocation evolution are included in the unified judgment framework, and compared with a single index analysis method, the dislocation instability state of the cutting deformation zone can be more comprehensively reflected.

The present application relates to the technical field of engineering blasting optimization, in particular to a dynamic optimization design method based on engineering blasting parameters, comprising: collecting stratum geological parameters such as rock hardness profile and rock mass fracture distribution of blasting site and blasting vibration three-direction velocity time history monitoring data, carrying out time-frequency joint analysis on time history record to extract dispersion curve and energy attenuation envelope, and constructing a digital rock mass structure model capable of calculating blasting stress wave propagation path. The vibration characteristic data is used as boundary condition to carry out numerical simulation to obtain simulated vibration velocity field, according to the difference between simulated data and measured data, combining with rock mass wave impedance distribution, the intelligent optimization algorithm is used to iteratively optimize the charge structure parameters and initiation time sequence scheme, and the optimized blast hole charge quantity, section and initiation delay are output. The method can improve the accuracy of blasting vibration propagation simulation, reduce the deviation between simulated and measured vibration velocity fields, and make the blasting parameter design more suitable for the actual geological environment of the engineering site.

A system and methods are disclosed for determining a model of a subterranean region. The method includes obtaining an observed dataset and a current model for the subterranean region, simulating a dataset from the current model, and determining a data penalty function based on a difference between the observed and simulated datasets. The method further includes training a machine learning (ML) network to predict a model from the observed dataset and determining the predicted model using the trained ML network. The method further includes determining a first model penalty function based on the current model, a second model penalty function based on a difference between the current and the predicted models, and a composite penalty function based on a weighted sum of the data penalty function, the first and the second model penalty functions. Finally, the method includes determining the model based on an extremum of a composite penalty function.

The application discloses a method for accurately evaluating the contribution of meteorological bias based on multi-technology integration of atmospheric pollutants simulation bias, which solves the technical problems of insufficient correlation and quantification of meteorological bias and pollution simulation bias, insufficient depth of bias analysis, and missing spatiotemporal contribution rule representation in the prior art. The WRF-CMAQ coupling mode is used to obtain meteorological and pollutant concentration simulation data, and combined with the observation data of the national control site, a standardized bias feature variable data set containing meteorological bias and pollutant concentration bias is constructed. The VIF factor is used to screen and eliminate parameter collinearity, and an XGBoost bias model is constructed to quantify the nonlinear correlation between meteorological bias and pollutant concentration bias. The SHAP model is introduced to analyze the contribution weight of each meteorological bias parameter. Combined with the spatiotemporal information of the site, the Kriging interpolation is used to generate the global spatiotemporal distribution atlas of the contribution of each meteorological bias parameter. The application provides a precise quantitative basis for the targeted optimization of the WRF meteorological field and the improvement of the simulation accuracy of atmospheric pollution.

The application discloses a submarine landslide multi-source data simulation data preprocessing method, belongs to the technical field of marine geological disaster data processing, and comprises multi-source simulation data aggregation and format normalization. Different types and formats of submarine landslide simulation data are integrated into the same data set and converted into a unified data format. According to the application, multi-source data is subjected to space-time registration in a unified geographic reference framework and time reference. Through topographic data re-projection, time series data interpolation synchronization, and accurate matching of strata and sediment parameters to corresponding land elements, strict alignment of multi-dimensional simulation data in spatial position and time node is realized, different physical processes are ensured to be comparable, fusable and superimposable in the same space-time scale, and the collaborative expression capability of multi-source information in landslide evolution mechanism analysis is significantly enhanced.

The application discloses a TI medium azimuth angle gather missing trace supplement method and system, and the missing trace supplement method comprises the following steps: acquiring target layer three-dimensional reflection seismic travel time data from pre-stack time migration stacking data; under the constraint of the target layer three-dimensional reflection seismic travel time data, using well logging data to establish model parameter information of a target layer three-dimensional reflection seismic time domain; performing amplitude normalization processing on actual azimuth angle gathers, and extracting amplitude normalized statistical seismic wavelets from the actual azimuth angle gathers; synthesizing the model parameter information and the statistical seismic wavelets to obtain simulated azimuth angle gather data; collecting missing seismic traces, and replacing actual missing seismic traces with simulated seismic traces; obtaining the model parameter information based on the azimuth angle gather seismic data after the missing trace supplement; obtaining new azimuth angle gather simulation data, and replacing the actual missing seismic traces; and constructing an azimuth angle gather without missing traces.

The application discloses a pipeline liquid accumulation thickness prediction method, device, equipment and storage medium. The method comprises the following steps: constructing a pipeline physical model and carrying out grid division, acquiring a gas-liquid two-phase flow numerical calculation model and a boundary condition and completing parameter configuration, carrying out numerical simulation under multiple working conditions to obtain liquid accumulation thickness simulation data sets under different working conditions; significant factors affecting liquid accumulation thickness are screened out through orthogonal test design and range analysis, response surface fitting is carried out based on the significant factors and the simulation data sets, and a pipeline liquid accumulation thickness rapid prediction model is established; finally, the gas phase flow rate and the water phase volume fraction of the pipeline to be predicted are input, and the pipeline liquid accumulation thickness result can be obtained. The application considers the accuracy of numerical simulation and the rapidity of the response surface model, can efficiently and accurately predict the pipeline liquid accumulation thickness, and provides reliable technical support for corrosion risk assessment, liquid discharge pigging and operation and maintenance strategy optimization of the gathering pipeline.

The application provides a lithology identification method and system based on unified drilling characteristics of measured simulation, which comprises deriving physical characteristics of drilling parameters in pretreated field data sets and simulation data sets, and splicing derivative physical quantities and basic drilling parameters; performing two-stage feature extraction on the newly spliced field data sets and simulation data sets to obtain hidden features; inputting the hidden features into a label classifier and a domain discriminator to calculate classification loss and adversarial loss respectively; training and performance verifying the model by using back propagation and an optimizer based on the classification loss and the adversarial loss; performing unified drilling characteristics of measurement and simulation based on the model verified in performance to obtain common characteristics; and identifying the lithology of unexcavated sections by using drilling parameters. The application eliminates the distribution difference between simulation drilling data and measured drilling data, and can realize accurate identification of lithology under the condition of less field sample quantity.

Embodiments of the present application provide a kind of acoustic echo sounder layout method, system and storage medium of hydropower station, a kind of acoustic echo sounder layout method of hydropower station includes: obtaining initial layout information of digital twin model and hydrograph;According to the first acoustic echo sounder layout scheme after initial layout scheme is corrected according to hydrograph and historical monitoring data;According to the first simulation data after digital twin model is monitored and simulated according to silt accumulation to the first acoustic echo sounder layout scheme, hydrograph and environmental interference information;Acquire unmanned ship monitoring data;According to the second acoustic echo sounder layout scheme after first acoustic echo sounder layout scheme is corrected according to the first simulation data and unmanned ship monitoring data.Through hydrograph and historical monitoring data correction initial layout scheme;And according to the second acoustic echo sounder layout scheme of unmanned ship monitoring data and first simulation data correction, improve the accuracy of acoustic echo sounder sensor layout, in turn improve the accuracy of hydropower station silt accumulation monitoring.

The application relates to a solid-state spin nondestructive detection device which comprises a diamond block, a data acquisition module, a data processing module and a main control module; the diamond block contains an NV color center, two paired magnetic characteristic frequencies of the NV color center are set as two selectable data sampling points, the data acquisition module is used for sampling analog data and is configured to alternately sample the two data sampling points in turn; the data processing module is used for real-time calculation of the average value of the previous N sampling data of the current sampling data, and the average value is subtracted from the current sampling data to obtain a difference value as real-time output magnetic field noise reduction characteristic data. The application has the advantages that the two magnetic characteristic frequencies are alternately sampled, the algorithm of "current sampling data minus average value of previous N sampling data" is combined, temperature suppression and magnetic field measurement noise reduction are realized, the magnetic field sampling rate and the frequency sampling rate are kept consistent, and the magnetic field sampling rate and the detection speed are improved.

The embodiment of the application provides a loose accumulation body destruction process simulation method and system combined with multiple physical fields, relates to the technical field of geological engineering and rock and soil mechanics, and first captures the traces of stress transmission field, pore medium seepage field and heat exchange field on the loose accumulation body, obtains a multiple physical field dynamic trace set; reversely models the multiple physical field coupling relationship based on the multiple physical field dynamic trace set, and obtains a reverse coupling model; then, imports physical parameters such as particle composition, pore distribution and mechanical response of the accumulation body into the reverse coupling model to obtain coupling evolution data; then, constructs a destruction evolution layered path according to the coupling evolution data; and finally, generates destruction process simulation data containing multiple-dimensional evolution information based on the destruction evolution layered path. The application can accurately simulate the destruction process of the loose accumulation body.

The present disclosure provides a data processing link stress testing method and device, equipment and storage medium, which relates to the technical field of computer. The method comprises the following steps: obtaining a first real-time data stream in a data stream processing service and partition information of the first real-time data stream in the data stream processing service; storing the first real-time data stream in a database according to the partition information to obtain historical simulation data; reading the historical simulation data from the database and sending the historical simulation data to a first topic of the data stream processing service to obtain a stress testing simulation data source; connecting the stress testing simulation data source to a to-be-tested link for data processing and storage operation to obtain stress testing performance data; and analyzing the stress testing performance data. The method improves the simulation degree of the stress testing data source and improves the stress testing effect.

This invention discloses a method, equipment, and medium for monitoring regional catering markets based on multi-source heterogeneity, belonging to the fields of data science and computer science. It addresses the technical problems in existing regional catering market monitoring, such as fragmented data sources of real-time consumption, limited data analysis dimensions, and lagging decision support. The method includes: collecting and processing relevant catering data for the current region using an adaptive web crawler framework to obtain multi-source heterogeneous consumption data; cleaning and anomaly monitoring the multi-source heterogeneous consumption data to obtain preprocessed multi-source heterogeneous consumption data; performing multi-layer data analysis on the multi-source heterogeneous consumption data under relevant multi-dimensional catering models to obtain catering market analysis data; simulating policy subsidies using the catering market analysis data to obtain catering subsidy simulation data; and visualizing the catering market analysis data and catering subsidy simulation data to generate a regional catering visualization map.

The application discloses an interface processing method and device, which are applied to an interface processing scene. The method comprises the following steps: in response to an interface recording request of a user end, recording data corresponding to the interface recording request is acquired; the recording data is sent to a management platform end, so that the management platform end generates an interface set according to the recording data; an interface request is generated, and the interface request is sent to a proxy server, so that the proxy server calls the interface set generated by the management platform end to obtain target simulation data responding to the interface request; and the target simulation data sent by the proxy server is received. Through the automatic and centralized interface recording and simulation mechanism, the efficiency and accuracy of the business link simulation are improved, and the maintenance cost is reduced and the flexibility is improved.

The application relates to the technical field of intelligent driving, in particular to a driving simulation test method, system, program product, medium and electronic equipment. The method can obtain auxiliary driving simulation data, wherein the auxiliary driving simulation data comprises M data fields corresponding to M driving environment parameters, the M data fields have a preset arrangement order, the M data fields comprise a target data field, the target data field has a first value, M is an integer greater than or equal to 2. Then, the first value of the target data field is modified into a first abnormal value to obtain fault simulation data of the auxiliary driving simulation data, wherein the fault simulation data is used for testing of auxiliary driving software to be tested. In this way, any data field in the auxiliary driving simulation data can be modified into an abnormal value, fine-grained fault injection can be realized, and the comprehensiveness of the test can be ensured.

The application provides a wharf rock-embedded pile critical pile length calculation method and system, relates to the critical pile length calculation technical field, through the deep integration and data calibration of numerical simulation and physical test, a high-fidelity comprehensive parameter database is constructed, and a continuous mapping relationship of the critical length-diameter ratio about the base rock strength and the inclination is established. First, numerical analysis is carried out under multiple working conditions, then physical model test is carried out under representative working conditions, the test data are used to construct a correction model to calibrate all simulation results, so that the accuracy of the end resistance ratio prediction is significantly improved. The application effectively solves the dual limitations that the pure numerical method lacks entity verification and the pure theoretical method is difficult to simulate the complex pile-rock contact behavior, through systematically diffusing the discrete test information into the comprehensive simulation data, the stress mechanism of the inclined rock-embedded pile is accurately described, and it is ensured that the mapping relationship established subsequently can truly reflect the physical law under the multi-factor coupling.

This disclosure relates to the fields of hydrodynamics and ecological environmental protection technology, specifically to a method and system for simulating the entire process of heterogeneous particle migration and deposition in a designated water area. The method includes: constructing a simulation model of the entire migration and deposition process using horizontal velocity interpolation functions, water depth interpolation functions, and a diffusion coefficient field established for the designated water area; under the constraint of the criterion for determining the simulated motion state of heterogeneous particles, inputting simulation data from different locations provided by the horizontal velocity interpolation function, water depth interpolation function, and diffusion coefficient field into the simulation model for training; and using the trained simulation model as the overall simulation model for the migration and deposition process in the designated water area. This disclosure fully considers the transformation mechanism of the entire process of heterogeneous particle migration and deposition (floating, suspended, deposited, and resuspended), and can efficiently and reliably simulate the entire process of heterogeneous particle migration and deposition.

This invention relates to the field of quantitative analysis of mining-induced fracture evolution in coal and rock masses, and particularly to a method for quantitatively characterizing the distribution and evolution of mining-induced fractures in overburden in loess gully areas based on fractal theory. The method involves measuring data from the 6102 face of the Chuancaogedan Coal Mine using a measuring instrument, and then establishing a physical similarity simulation model based on the 6102 working face. Simulated excavation is performed, with the simulation data set as follows: 5# coal seam mining height 4.6m, excavation step distance 10m, model recovery length 220m; 6# coal seam mining height 12.9m, excavation step distance 10m, model recovery length 220m. Based on the results of the physical similarity simulation experiment and fractal theory, this invention systematically studies and quantitatively describes the distribution and evolution of mining-induced fractures in overburden under multiple influences in loess gully areas. This has significant theoretical and engineering implications for revealing the laws governing overburden movement and damage, achieving quantitative evaluation of the degree of overburden damage during mining, and preventing damage during working face mining.

The embodiment of the present disclosure provides a gene variable splicing isomer batch effect deduction method, which belongs to the technical field of data identification and specifically comprises the following steps: acquiring a GTEx transcript count matrix as a parameter estimation reference data source; estimating a negative binomial distribution parameter and sampling to generate a new count matrix; injecting a batch effect and a differential transcript to use a DTU signal to construct a simulation data set; performing an ILR space countermeasure batch process on the simulation data set, that is, constructing an intragenic composition vector with a gene as a unit and performing ILR transformation, learning a batch discrimination direction in the ILR space with a batch variable as a supervision signal and projecting to remove, and then recovering to a count matrix after batch removal through inverse ILR transformation; and running a differential analysis method before and after batch removal to obtain a result table and comparing and evaluating. Through the scheme of the present disclosure, false positives are effectively reduced, the accuracy and robustness of DTU detection are improved, and the scheme is suitable for transcriptome data analysis.

This disclosure provides a data-driven and scalable method to discover cause-and-effect relationships in data from natural systems that include sparse data sets. This technique can learn a causal graph from heterogenous data sources by combining embeddings from real data and embeddings from simulated data generated by process-based models. The causal graph is used for what-if analysis in out-of-distribution settings. One application is understanding the factors that affect soil carbon. A causal model created by these techniques can be used to discover cause-and-effect relationships that affect soil carbon. This model has applications such as forecasting soil carbon for a future time point to help inform farm practices. Farm practices, like tilling, may be modified in response to predictions provided by the model.

A computer-implemented method, system and computer program product for detecting feature traits from an imbalanced dataset. A first regression model is built based on a simulated dataset to compute contribution scores for the features to make a target a positive case. A variance in the features' original values for each feature of the first set of features (those features with contribution scores for positive cases that exceed a threshold value) between the positive and negative cases is determined. A second regression model is built to calculate a predictor importance value for a second set of features (features from the first set of features with a variance in their original values for both positive and negative cases that exceeds a threshold value). Feature traits are then extracted from a group of clustered positive cases with features of the second set of features containing a predictor importance value exceeding a threshold value.

This invention discloses a spatial downscaling method for weather forecast models based on a terrain classification super-resolution model. The method first obtains nested data at each layer through dynamic downscaling of the weather forecast model, then trains a super-resolution model using data at different resolutions of the target area. Following the above process, super-resolution models are established for different terrains, and a terrain classification model is trained using terrain data. The super-resolution model and the terrain classification model are combined to obtain a fused prediction result. This fused model is the SRBTC model, which considers spatial correlation. The model outputs the similarity probability between the area to be predicted and various different terrains. The prediction results are weighted and summed to obtain the fused prediction result of the super-resolution model. During model training, a specified value scaling method is proposed to consider the differences between high- and low-resolution meteorological simulation data. When applying the model, a splitting and merging method is proposed to consider the differences between multi-scale simulation data, and the applicability of the model is specified.

A temperature and airflow predicting system, comprises the following elements. A sensing device senses a temperature and an airflow of an operating field to generate a current-state temperature distribution data and a current-state airflow distribution data. A simulating device performs a computational fluid dynamics simulation for the temperature and airflow based on a boundary condition data to generate a simulated temperature distribution data and a simulated airflow distribution data. A database establishes a current-state data set and a simulated data set based on the current-state temperature distribution data, the current-state airflow distribution data, the simulated temperature distribution data and the simulated airflow distribution data. A predicting device performs a model operation based on the current-state data set, the simulated data set and a predicting load model, so as to predict the temperature and the airflow and thereby generating a predicted temperature distribution data and a predicted airflow distribution data.

This invention relates to a method and system for correcting abrasive particle data of tunnel boring machine (TBM) oil based on multi-source simulation data, belonging to the field of intelligent TBM technology. The method includes the following steps: Based on the geometry of the main pipeline of the TBM's main drive oil and the bypass where the abrasive particle monitoring device is located, the main drive pipeline and bypass flow domains are extracted and discretized to obtain a main drive pipeline-bypass flow domain grid; the momentum exchange process between the continuous phase of the oil and the discrete phase of the abrasive particles is calculated bidirectionally to construct a CFD-DPM numerical simulation model, and physicochemical parameters and boundary conditions are set to obtain the numerical simulation solution results; a prediction model is constructed using an optimal surrogate model set, and the initial abrasive particle proportion data is corrected based on the predicted abrasive particle distribution data of the main pipeline and combined with the simulated abrasive particle proportion data. This invention achieves accurate estimation of abrasive particle data of TBM oil by combining the CFD-DPM numerical simulation model with the surrogate model.

Embodiments of the present application disclose a kind of building multi-working condition batch simulation data generation method, equipment and medium. Among them, the method comprises: determining the target configuration file corresponding to target batch simulation task;Wherein, the target configuration file is used to describe at least two target parameter adjustment modes for reference source file;According to the at least two target parameter adjustment modes, the reference source file is adjusted to obtain at least two target source files;Based on target building simulation software, the at least two target source files are run to generate target batch simulation data.This technical solution can quickly adjust the source file based on the configuration file to quickly and conveniently generate building multi-working condition batch simulation data, which can ensure the effective coverage of simulation data for various scenarios, improve the applicability, flexibility and generation efficiency of simulation data.

This invention provides an intelligent data sharing method and system for food inspection and testing, relating to the field of data processing technology. The method includes: Step 2, based on a standardized inspection dataset, using a time-series feature learning model to simulate data change patterns under different conditions, constructing a covariance matrix of the inspection index data, calculating the eigenvalues ​​of the covariance matrix, sorting them in descending order by eigenvalue size, extracting the eigenvectors corresponding to the top few largest eigenvalues, linearly combining the original data and eigenvectors, calculating the projection values ​​of each sample along the principal component direction, forming a key feature matrix reflecting the main change patterns of the data, and simulating and expanding the multi-dimensional correlation features of the inspection indicators by combining linear transformation and spatial mapping to generate an extended inspection scenario dataset. This invention improves the efficiency and consistency of food inspection and testing data in standardized integration and secure sharing.

This invention relates to the field of digital technology in industrial production, and particularly to a method and system for constructing a twin of an insulation board production line based on cloud data-driven approaches. The method includes the following steps: acquiring continuous slab flow data; binding sensing segments to the continuous slab flow data to obtain sensing segment data; reconstructing multi-dimensional time-history footprints based on the sensing segment data to obtain time-history footprint data; performing cross-process heterogeneous coupling simulation on the time-history footprint data to obtain cross-process simulation data; reconstructing a global heterogeneous data field based on the cross-process simulation data to obtain overall field data for the continuous slab; and continuously expressing the twin based on the overall field data for the continuous slab to obtain slab twin data. This invention, by constructing a continuous slab twin based on segmented modeling and cross-process coupled simulation, achieves continuous tracking and unified expression of multi-dimensional states during the production process, improving the modeling accuracy of the production line.