112 results about "Model sample" patented technology

The invention relates to the technical field of data management, in particular to a pearlescent material production data management system and method based on data integration analysis. The method comprises the following steps: obtaining a process original data set of a pearlescent material production line, and carrying out standardization processing to obtain a standardized process data set; obtaining a quality detection data set of the pearlescent material; performing time interval division on the standardized process data set and the quality detection data set, and performing batch number marking to obtain a process-quality batch data pair; and based on the process-quality batch data pair, designing a modeling sample data set, and constructing a quality process coupling model. According to the invention, the intelligent, data-driven and high-quality stable control of the pearlescent material production process is comprehensively realized by constructing a closed-loop system integrating data integration, modeling prediction and regulation and control optimization.

The invention discloses an image editing method and device and a storage medium. The method comprises the steps that diffusion processing is conducted on an original image through a diffusion model; determining the total number of noise reduction iterations by using a first model trained in advance; and in each noise reduction processing process, sampling a network module of the noise prediction network in the diffusion model by using a pre-trained second model, and carrying out noise reduction processing by using the network module selected by sampling to obtain an edited target image. By applying the scheme of the embodiment of the invention, the first model determines the total number of iterations of noise reduction, and the second model samples the network module of the noise prediction network, so that the proper number of iterations and the proper network module can be adaptively selected for different images, and the noise reduction efficiency is improved on the basis of ensuring the image quality. The complexity of a long iteration process and a network structure is greatly avoided, and the generation efficiency of image editing is effectively improved.

The present disclosure provides a method and an apparatus for evaluating the quality of model samples, a storage medium and a computer device. The method includes: inputting sample data into an AI-generated content detection model to obtain a hit probability of the sample data; matching a content evaluation system based on the attribute information of the sample data; processing the sample data based on the evaluation rule in the content evaluation system to determine a test value of the sample data relative to at least one preset evaluation index; and performing a weighted calculation on the hit probability and the test value based on a target weight corresponding to the hit probability and the preset evaluation criterion, to obtain a quality score of the sample data. This method is capable of filtering data that may mislead model training, and also realizing a high-precision evaluation of the training data.

The invention relates to the technical field of artificial intelligence and hydraulic engineering, in particular to a gate dam safety monitoring multi-element collaborative prediction and early warning method based on deep learning. The method comprises the following steps: synchronously collecting monitoring data such as displacement, osmotic pressure and temperature of a gate dam through an automatic safety monitoring facility, carrying out collaborative correction processing on the monitoring data, and converting the monitoring data into a standardized sequence sample; intercepting the standardized sequence sample into a source sequence and a target sequence, constructing a model sample library, and dividing the model sample library into a training set and a verification set; training a pre-constructed gate dam safety monitoring multi-element collaborative prediction and early warning model by using the training set, evaluating the precision of the model by using the verification set, and automatically calibrating model parameters; and utilizing the determined model to execute prediction so as to prompt early warning. According to the method, the problems of lack of fusion of global association and local dependence, short model prediction period and the like of traditional safety monitoring single-station modeling can be solved, the limitation of single-element threshold alarm is broken through, and the multi-element cooperative monitoring and early warning precision and efficiency are improved.

The invention discloses a medical image clustering method based on correlation entropy and an adaptive bipartite graph, and the method comprises the steps: collecting image data of medical imaging equipment, and forming a sample data matrix; selecting anchor point samples from the sample data matrix, and establishing an original sample low-dimensional representation-anchor point low-dimensional representation graph for capturing a local geometric structure of an embedded space; introducing an orthogonal basis matrix; carrying out joint modeling on the sample reconstruction error, the structure retentivity, the adaptive graph constraint and the discriminative features by adopting a joint objective function; iteratively updating the joint objective function; and inputting the finally obtained low-dimensional representation of the original sample as an embedded representation into a clustering device of K-means, and completing final clustering label distribution. According to the method, more stable, efficient and interpretable unsupervised clustering is realized, the clustering precision and generalization ability are improved, and the method is particularly suitable for medical image data analysis tasks of high-dimensional and complex structures.

The invention relates to the technical field of livestock measurement, in particular to a beef cattle body size intelligent measurement device and method, in the beef cattle body size intelligent measurement device and method, hoof joint point pixel coordinates are converted and angle differences are calculated in combination with a lexicographical sequence block reordering algorithm, identity binding codes are accurately generated, uniqueness and stability of beef cattle individual recognition are ensured, and the accuracy of beef cattle body size measurement is improved. The method effectively reduces the identification confusion risk when multiple beef cattle are measured at the same time, generates a simulation sample through local pixel block rendering and generative adversarial network, screens a sample according with a threshold according to authenticity scores, improves the sample richness, optimizes the coverage of a model training set, extracts body size fields and position coding values, and calculates correlation coefficients between fields. Low-correlation field pairs are removed, only high-correlation fields are reserved for cross combination, a modeling sample pool is constructed, the representativeness of samples and variable combination diversity are enhanced, and the fitting precision and popularization capability of a body size estimation model are improved.

The invention provides a laying hen feed raw material sample screening method and system based on multi-source variability, and relates to the technical field of data processing analysis, and the method comprises the steps: obtaining multi-source attribute data and conventional component content data of raw materials, mapping the attribute data into tensor modal dimensions through multi-source variability tensor construction processing, and obtaining a multi-source variation tensor model; the component data is used as a characteristic component, and a multi-source variability tensor is obtained through decoupling and compression. Variability spectrum decomposition processing is carried out, local rank spectrum decomposition is carried out along producing areas, time and component dimensions, and a variability spectrum vector set is obtained; and identifying a candidate modeling sample set through multi-scale extremum and sparsity screening. And evaluating the contribution degree and sensitivity of the sample to a standard ileum amino acid digestibility prediction equation through a leave-one-out method and sensitivity analysis, and screening out an optimal modeling sample. According to the method, the multi-source variation information of the raw materials can be integrated, the variation spectrum is comprehensively covered with the minimum sample size, and the precision and generalization ability of the prediction model are remarkably improved.

The embodiment of the invention provides a user interface defect image generation method, a model training method, a user interface defect detection method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of image processing. The method comprises the steps that segmentation information, element information and copywriting information of a screenshot of a user interface are obtained, the segmentation information is used for indicating a segmentation area corresponding to each pixel in the screenshot, different segmentation areas represent different control elements, and the element information indicates a reference area, in the screenshot, of each control element in the user interface; the copywriting information indicates a copywriting area of each copywriting in the screenshot; and respectively processing the screenshots through the construction mode of the at least one user interface defect image and the target information required by the corresponding construction mode to obtain the user interface defect image of the at least one defect type. User interface defect images of various defect types can be generated without limiting the number, and the problem that the number of model samples is small is solved.

The application provides a training method and device of an intent recognition model, and an intent recognition method and device. The method comprises: obtaining a training sample set; a sample in the training sample set comprises a question sentence labeled with an intent label; a pre-training model is trained using the sample in the training sample set; an initial output vector corresponding to the sample is obtained; an initial back propagation gradient of the pre-training model is determined according to the initial output vector; a preset number of disturbances are added to the initial output vector based on the initial back propagation gradient to obtain a target back propagation gradient; and a model parameter of the pre-training model is updated according to the target back propagation gradient to obtain an intent recognition model. The application uses the gradient of the back propagation in the pre-training model to perform adversarial disturbance on the output vector corresponding to the model sample, i.e. the word embedding layer vector. This adversarial training method can improve the robustness of the model.

The invention discloses a numerical control system-oriented defect visual classification detection method and system, and the method comprises the steps: obtaining an original defect sample covering a numerical control system part, carrying out the multi-modal image collection, carrying out the weighted fusion of visible light, near-infrared light and ultraviolet light images in an original sample library through a multispectral fusion algorithm based on the original defect sample, and carrying out the detection of the defect visual classification. The method comprises the following steps: generating an original defect sample, generating an enhanced sample, acquiring three-dimensional geometric information of the surface of a part by utilizing a 3D laser scanning device, transmitting and receiving ultrasonic waves through an ultrasonic detection module, detecting defects in the part, generating a virtual sample based on the original defect sample, the enhanced sample and a 3D model sample, and combining the original defect sample, the enhanced sample, the 3D model sample and the virtual sample to obtain a three-dimensional image. And training a defect classification model based on the mixed sample library. Defect evolution under different working conditions can be simulated, virtual defect samples conforming to real physical laws are generated, the defect state coverage range is wide, and defect data which are difficult to obtain by real samples are supplemented.

The invention provides an agent training method and device, electronic equipment and a storage medium. The method comprises the steps that a plurality of opponent learning models are generated according to pre-labeled sample data, the sample data comprise game data of a plurality of roles at different grades, each opponent learning model comprises at least one opponent strategy, and each opponent strategy corresponds to one grade; a to-be-learned course is generated according to the grade corresponding to each opponent strategy, the to-be-learned course comprises a plurality of course sets with the grades arranged in sequence, and each course set comprises a plurality of opponent strategies with the same grade; and training based on the to-be-learned course to obtain a target agent. According to the method, the intensities of different opponent strategies can be effectively distinguished, meanwhile, different roles of different grades can be considered, the diversity of the opponent strategies is guaranteed, the confrontation with human players is improved, and the actual game environment of the players is better met.

The invention relates to a soil organic carbon content estimation method based on multi-feature coupling ensemble learning. The method comprises the following steps: screening a soil hyperspectral image of a target area by using a spectral index; establishing an SOC content feature library, calculating feature importance by using an LGBM model, and selecting input features based on the feature importance to construct a training data set; an SOC estimation framework model is constructed, a first layer comprises a random forest model, a multi-layer perceptron model and a K neighbor model, and a second layer adopts CatBoost as a meta learning device; and inputting the training data set into a first layer of the SOC estimation framework model, respectively generating predicted values of the SOC content, performing five-fold cross validation on each model sample to obtain a group of predicted superposed values, and training a meta-learner of a second layer to obtain a final SOC content prediction result. Compared with the prior art, the method has the advantages of high applicability, high prediction precision, excellent interpretability and the like under different space-time conditions.

The invention discloses an uncertainty guidance collaborative learning method for text-to-image pedestrian re-identification, and relates to the technical field of text-to-image pedestrian re-identification. The invention provides an accurate corresponding relation identification module, which combines global features and local features to realize more accurate data set classification, effectively reduces the risk of wrong supervision, reduces mismatching and misidentification, realizes more accurate data classification through a multi-level sample division strategy, and improves the accuracy of data classification. According to the method, the error supervision risk is effectively reduced, mismatching and misrecognition are reduced, an uncertainty guiding alignment module is provided, modeling is conducted on sample uncertainty, uncertainty is estimated, positive sample pair contribution is enhanced, negative influences of mismatching pairs are restrained, a processing mechanism of unreliable matching pairs is optimized by estimating the sample uncertainty, and the mismatching and misrecognition accuracy is improved. And the negative influence of the mismatching pair is inhibited while the contribution of the positive sample pair is enhanced.

The invention relates to a drug effect evaluation method for fusing target molecules and time-concentration dependent cell phenotypes, which comprises the following steps: S1, preparing samples including a modeling sample and a to-be-detected sample; s2, carrying out FRET imaging and cell fluorescence imaging; s3, carrying out FRET image processing and FRET efficiency calculation; s4, cell fluorescence image processing and feature extraction; s5, calculating a phenotype characterization value; s6, calculating an FRET characterization value; s7, basic drug response value calculation; and S8, comprehensive drug effect evaluation. Target molecule information and time-concentration dependent cell phenotypic response are fused, a comprehensive drug effect evaluation model is constructed, the effect of drugs on whole cells is reflected, and whether the drugs target specified molecules or not is specifically indicated.

The invention discloses a network training method, a modal prediction method, related equipment and a medium, the network training method is used for training a geometric deep learning network, and the network training method comprises the following steps: obtaining first sample data of a target component, the first sample data comprises a sample finite element model of the target part, a sample non-visual parameter and a corresponding sample modal real result; performing prediction based on the sample finite element model and the sample non-visual parameters by using a geometric deep learning network to obtain a sample modal prediction result of the target component; and adjusting network parameters of the geometric deep learning network by using the difference between the sample modal prediction result and the sample modal real result. By training the geometric deep learning network in this way, the accuracy of predicting the modality of the target component by the geometric deep learning network can be improved.

The application provides a product modeling design method and system, comprising the following steps: obtaining a research object, establishing a product perceptual vocabulary library and a modeling sample library; performing preliminary screening on the product perceptual vocabulary library based on a word vector; scoring product modeling samples by using a semantic difference method; screening an advantage perception intention vocabulary by using a factor analysis method; obtaining product modeling characteristic morphological elements and coding by using a morphological analysis method; establishing a mapping relationship between the characteristic morphological elements and the product perceptual intention modeling elements based on a three-layer BP neural network model, and performing finite element analysis based on the screened modeling elements; performing topological optimization on force distribution characteristics of product modeling, coupling a topologically optimized structure model and product perceptual intention modeling elements; outputting a product modeling scheme; evaluating the product modeling scheme based on an eye tracking experiment, and generating a product modeling design scheme when the evaluation is passed.

The invention relates to a smart power grid deep learning model classification method and device based on model response fingerprints and computer equipment. The method comprises the steps of obtaining a model sample set according to smart grid data, performing disturbance processing on the model sample set to obtain a plurality of disturbance sample sets, inputting the disturbance sample sets to a to-be-verified model, obtaining disturbance response vectors corresponding to the disturbance sample sets, determining a target sample set from the disturbance sample sets according to all the disturbance response vectors, and verifying the target sample set according to the target sample set. And based on the disturbance response vector corresponding to the target sample set, constructing a fingerprint sample set of the to-be-verified model, and inputting the fingerprint sample set into a pre-trained classifier to obtain a classification result of the to-be-verified model. The method can accurately identify the attribution of the power grid model.

The invention discloses a method for constructing a biaxial compression strength prediction model of a double-hole model sample, which comprises the following steps of: correcting a Mohr-Coulomb strength criterion by combining intermediate principal stress to obtain a Mohr-Coulomb strength formula corresponding to the triaxial compression strength of a complete rock; introducing a hole reduction coefficient, and obtaining a reduction model corresponding to the hole influence; a Mohr-Coulomb strength formula corresponding to the triaxial compression strength of the complete rock is reduced, and a triaxial compression strength prediction model of the double-hole model sample is obtained; and obtaining a biaxial compression strength prediction model of the double-hole model sample. By introducing the hole reduction coefficient, the coupling influence of coexistence of the two holes and geometric configuration of the two holes on the macroscopic strength of the rock mass is clearly quantified, the technical defect that an existing rock mass strength prediction model cannot accurately reflect the strength weakening effect caused by interaction of the two holes is overcome, and compared with a complex full-three-dimensional numerical simulation method, the method has the advantages that the method is easy to implement. The model calculation process is simpler, more convenient and more efficient.

The invention relates to the field of aerospace craft overall design and the like, and discloses a solid rocket performance-cost analysis method based on structured subspace learning, which comprises the following steps: constructing a model sample data set, constructing a structured sparse subspace feature selection model in combination with a similarity graph matrix, and solving to obtain a model sample data set; obtaining an optimized row sparse orthogonal projection matrix, wherein a characteristic parameter corresponding to a row index of a non-zero row in a model sample is a screened cost driving factor; constructing a training sample data set of the agent model, calculating a corresponding specific delivery cost as an output response based on a training sample, and training the agent model; and for each cost driving factor, a prediction sample data set is constructed through a random sampling method, optimization is carried out in the prediction sample data set by adopting a sequential quadratic programming method, and a prediction sample with the minimum specific delivery cost is determined as a final solid rocket design scheme.

The invention discloses an optimization scheme determination method and device for a main combination valve, a medium and a product, and relates to the technical field of electrical equipment maintenance, and the method comprises the steps: constructing a three-dimensional model sample piece of a target main combination valve according to a preset part design scheme and device parameters of the target main combination valve; performing simulation test on the three-dimensional model sample piece by utilizing the test requirement of the target main joint valve to obtain a simulation pull-out force test result; a standby three-dimensional model sample piece is determined from the three-dimensional model sample pieces based on the simulation pulling-out force test result, and a test sample piece of the target main combination valve is constructed based on sample piece parameters of the standby three-dimensional model sample piece; and performance testing is conducted on the test sample piece according to the test requirement of the target main combined valve, a test pulling-out force test result is obtained, and an optimization scheme of the target main combined valve is determined based on the test pulling-out force test result. According to the method, the optimization scheme of the target main combined valve can be quickly determined at low cost, and the replacement efficiency of the main combined valve is improved.

This invention discloses a method, apparatus, and storage medium for predicting user behavior in a metaverse space. The method includes: acquiring a set of behavioral data of users in the metaverse space during a preset time period; processing the behavioral data set into model sample data for multiple time steps; retrieving a pre-trained behavior prediction model, and inputting the corresponding model sample data into the behavior prediction model according to the time steps to obtain a final behavioral feature data; and acquiring the typical behavior corresponding to the final behavioral feature data as the prediction result. Using the embodiments disclosed in this invention, it is possible to predict the actions and behaviors that users in the metaverse space will perform based on recorded data of individual behavior in the metaverse space along the time dimension. This is beneficial for monitoring user behavior in the metaverse space to maintain order in the metaverse space, and can provide data for obtaining user profiles for various commercial applications applied to the metaverse.

The application discloses a market supervision internet information monitoring and analysis system, which comprises a CNN model construction module, an internet information collection module, an information preprocessing module and an information mining module; the application uses a CNN model algorithm to model sample data marked by artificial marking, and uses a model to determine illegal behaviors; through continuously accumulating sample data, repeatedly training and achieving the purpose of machine learning, the determination accuracy is gradually improved.

The invention relates to the technical field of resource prediction, in particular to a mineral resource prediction method and system using a digital twinborn technology. A mineral resource prediction system using a digital twinborn technology comprises a mineral digital twinborn building module, an analog data filling module, a mineral resource prediction module and an updating module. A large number of geological model samples are generated through Monte Carlo modeling, a backtracking and forward geological evolution mechanism is introduced, an optimal target sample is screened from samples conforming to geological process constraints, and unsurveyed voxels are filled with the optimal target sample; through the method, the digital twinborn body not only has the static three-dimensional attribute mapping capability, but also has the state deduction capability adjusted along with the geological evolution process, so that the expression precision and stability of a subsequent AI prediction model on the ore body occurrence probability are remarkably enhanced; and the generalization ability of a mineral resource prediction system in sparse control, complex construction and high-uncertainty scenes is effectively improved.

This invention discloses a near-infrared model maintenance method, comprising: acquiring the pure spectral signals of each modeling sample of the near-infrared model to be maintained and the pure spectral signals of each test sample in the test sample set; performing principal component analysis on the pure spectral signals of each modeling sample to determine the principal component score and principal component model of each modeling sample; projecting the pure spectral signals of each test sample onto the principal component model to obtain the feature score of each test sample; determining each maintenance sample based on the principal component score and the feature score of each test sample in the test sample set; and updating the near-infrared model to be maintained based on each maintenance sample. This method can achieve rapid and accurate correction of the original model, not only expanding the coverage of its sample spectral principal component space but also expanding the applicability of the model, thereby making the model more adaptable to the prediction of new samples, and the prediction ability of the corrected model is greatly improved.

The application discloses a wind turbine component simulation model generation method and device, and relates to the technical field of simulation. The method is as follows: based on a pre-established basic three-dimensional model, a design variable is determined; according to the design variable, a target sample space is obtained; based on each sample point in the target sample space, a corresponding model sample is generated; based on the finite element analysis result of the model sample, a finite element graph carrying a stress value label is determined; based on the finite element graph, a corresponding training sample is generated; and the training sample is used to train a graph neural network model to obtain a target simulation model. The method uses the design variable to generate the training sample, ensures that the training data required in the model training process is reliable and sufficient, generates the training sample in combination with the finite element graph, makes the target simulation model different from a traditional neural network model, realizes direct prediction of a stress field simulation result from geometric input, and makes the target simulation model meet the simulation requirements of wind turbine components.

The invention provides a product evaluation method and system and a storage medium. The method comprises the following steps: constructing a score matrix according to a product sample image and an expert score; determining key emotion vocabularies in the emotion sample vocabularies according to the scoring matrix, and constructing model training data according to the key emotion vocabularies and the model sample images; performing model training on the product evaluation model according to the model training data until the product evaluation model converges; and inputting a to-be-evaluated product image into the converged product evaluation model for emotion evaluation to obtain an emotion score, and generating a product evaluation result according to the emotion score. According to the embodiment of the invention, model training is carried out on the product evaluation model through the model training data, so that the converged product evaluation model can accurately capture emotion preferences of the user on different product images, emotion evaluation can be effectively carried out on the input to-be-evaluated product image, an emotion score is obtained, and the user experience is improved. And a product evaluation result can be automatically generated based on the emotion score.

The invention provides a digital twinborn modeling method and system for a distribution network tower. The method comprises the following steps: carrying out classified statistical analysis on geometric structure characteristic data of a target tower to determine a parameter distribution proportion of each component; modeling samples are selected from the tower point cloud database to construct a three-dimensional modeling sample set, and it is ensured that component parameter distribution in the sample set is consistent with the statistical proportion; and generating a model construction instruction based on the sample set, driving a digital twin modeling engine to perform tower reconstruction, and collecting performance data in a modeling process to generate a quality evaluation index. According to the method, accurate and efficient reconstruction of the tower model is realized through parameterized sample selection and standardized instruction generation, and a quantitative evaluation basis is provided for modeling quality. According to the method, the construction precision and efficiency of the digital twinborn model of the distribution network tower can be improved, and modeling evaluation and optimization are supported.

The invention belongs to the technical field of AI training data annotation, and particularly relates to an AI active learning-based annotation quality dynamic verification and authority management and control method, which comprises the following steps of: initializing a core module, a rule base and an authority matrix; the AI active learning model samples and distributes samples based on data uncertainty and annotator ability matching; the dynamic verification module executes multi-dimensional verification and feeds back abnormal features; the authority management and control module dynamically adjusts the authority according to the verification result; and iteratively optimizing the model and the rule base based on feedback, and circulating until the annotated data meets a quality threshold value. According to the method, autonomous optimization of labeling quality, resource dynamic configuration and advanced avoidance of abnormal labeling are realized, the abnormal labeling recognition accuracy and the resource configuration efficiency are remarkably improved, the model iteration speed is increased, and excessive dependence on manual intervention is not needed.

The invention relates to the technical field of data processing, in particular to a fan blade vibration response prediction method and system, and the method comprises the steps: determining the uncertainty parameters of a fan blade; on the basis of the uncertainty parameters, the structural attribute of the fan blade is determined; generating a model sample for vibration response analysis based on the structure attribute; based on the model sample and a physical information neural network model trained by the model sample, determining a vibration analysis result of the fan blade; and based on the vibration analysis result, performing response prediction on the fan blade to obtain a target prediction result of the fan blade. According to the method, physical information is combined with a neural network model, so that the prediction result not only considers statistical characteristics of data, but also fuses influences of physical laws and system uncertainty, and the precision of vibration response prediction is further improved.

The present application relates to the technical field of machine learning, in particular to a seismic wave propagation feature analysis and prediction system, the system comprising: a feature decoupling coding module, a conditional model generation module, a forward propagation prediction module, and a feature quantization analysis module.In the present application, the intrinsic response features representing the medium are separated from the actual multi-channel seismic data channel set, and based on the features, a series of possible velocity model samples are probabilistically constructed using a conditional generation model, and then all the velocity model samples are iteratively simulated for forward propagation, thereby obtaining a wave field state snapshot sequence set containing multiple possibilities.Through statistical calculation of the set, the expected propagation amplitude can be obtained and the predicted uncertainty can be quantified, and finally a comprehensive atlas of expected wave field propagation and uncertainty analysis is generated.