153 results about "Model sample" patented technology

The invention provides a diesel engine misfire fault diagnosis method based on a GRU and a PINN. The diesel engine misfire fault diagnosis method comprises the following steps of diesel engine multi-source data acquisition, data preprocessing and model sample generation; designing a gating circulation unit, introducing a physical constraint embedding module, constructing a misfire fault diagnosis model, and fusing the misfire fault diagnosis model with a long-short term memory model and a physical neural network; performing joint training and optimization on the misfire fault diagnosis model in the step S2 by using the samples in the step S1; and deploying a misfire fault diagnosis model and performing real-time diagnosis. The method has the beneficial effects that the time sequence data-physical model dual-drive diagnosis of the misfire fault of the diesel engine is realized; the accuracy of misfire diagnosis is improved; and the false alarm rate of complex working conditions is effectively reduced.

The invention discloses a cable defect detection method of a binocular intelligent inspection robot, and relates to the field of image processing, and the method comprises the steps: firstly obtaining the state information of a cable, then carrying out the preprocessing and stereo matching of the image information of the cable, and then constructing a target detection and segmentation model; the target detection and segmentation model is adopted to detect and segment cable defects, and model sample data is updated in real time in an incremental learning mode; fusing the cable state information acquired by the infrared camera, the thermal imager and the wireless sensor network by using a multi-sensor data information fusion model; the cable is maintained and managed based on a detection result; according to the invention, the detection precision and accuracy can be improved, cables of different types and specifications can be handled, the adaptability and stability are ensured, and the real-time performance and accuracy of detection can be improved.

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 a test method for simulating rock hydraulic fracturing in a true triaxial stress state, and relates to the technical field of hydraulic fracturing, the test method comprises the following steps: S1, a sample preparation stage: processing a rock block into a standard square rock sample, and processing a plurality of hydraulic fracturing holes according to a test target size; s2, a sample mounting stage; s3, an equipment inspection stage; s4, a pre-load applying stage; s5, a hydraulic pressure applying stage of the hydraulic fracturing model: injecting hydraulic fracturing fluid with different flow rates into the hydraulic fracturing holes at different flow rates by utilizing a constant-flow constant-pressure pump through the hydraulic fracturing hydraulic pressure applying pipeline, and recording data and closing the constant-flow constant-pressure pump after the square rock sample is damaged; s6, an unloading stage; and S7, ending the test. According to the invention, a single hydraulic fracturing hole or a plurality of hydraulic fracturing holes are processed on a square rock sample to simulate single well and multi-well model samples, and different hydraulic pressurization tests are realized, so that the qualitative analysis of the reservoir rock crack evolution law under the true triaxial stress condition is completed.

The invention relates to a continuous flow chemical reaction yield soft measurement method and a continuous flow chemical reaction yield soft measurement system. According to the method, real-time online prediction of the continuous flow chemical reaction product yield is realized through a deep learning technology. Firstly, soft measurement modeling parameters are selected, and after data acquisition, a soft measurement modeling sample set is created through data alignment processing and dynamic time window feature construction. The soft measurement model is based on a multi-head attention mechanism enhanced LSTM, a multi-scale feature extraction and frequency domain analysis mechanism is fused, and local dynamic features under different time resolutions are extracted through a multi-scale convolution kernel; key time steps in a long sequence and a global dependency relationship of the key time steps are effectively captured through a multi-head attention mechanism enhanced LSTM module; and the periodic fluctuation characteristics in the time series data are extracted and identified through the frequency domain characteristics. The invention provides an economical, efficient, real-time and reliable intelligent solution for yield monitoring in the continuous flow chemical reaction process.

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 present invention relates to the technical field of industrial device detection, and discloses a device damage intelligent monitoring method and system based on mechanism and working condition big data. The monitoring method comprises: first, setting various damage modes of a device, constructing a mechanism model sample of each damage mode on the basis of a technical standard and an engineering case, and constructing a working condition big data sample of each damage mode on the basis of field data; then, using the mechanism model sample and the working condition big data sample as data sets for model training so as to respectively construct a mechanism model and a working condition model; and finally, inputting real-time text data to be predicted and real-time working condition data to be predicted into the mechanism model and the working condition model, respectively, calculating the possibility that the real-time text data belongs to various damage modes and the possibility that the real-time working condition data belongs to various damage modes, and generating a device damage monitoring result on the basis of the two possibilities. The present invention effectively improves the utilization rate of real-time information during damage monitoring of a pressure-bearing device, so that the monitoring result is more accurate.

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 discloses an activity identification method based on conditional antagonism data enhancement, which comprises three parts of a conditional generative adversarial network, a kinematic feature constraint module and a prototype sample generator, a system hardware structure is designed based on an embedded heterogeneous computing platform, a main processor adopts an ARM Cortex-A72 core operation condition generative adversarial network, and the model sample generator is designed based on an embedded heterogeneous computing platform. The coprocessor adopts NPU to accelerate kinematics parameter calculation, and the sensor interface module is connected with a six-axis IMU sensor through an SPI bus to collect data of an accelerometer and a gyroscope in real time. According to the activity recognition method based on conditional antagonism data enhancement, enhancement data with diversity, authenticity and semantic accuracy can be provided for a human body activity recognition model in a scene of insufficient training data, so that the overall recognition accuracy of the model on a UCI HAR data set is improved by 14.7%, the F1 value on the aspect of few sample categories is averagely improved by 21.3%, and the recognition accuracy of the model on the UCI HAR data set is greatly improved. And an effective technical solution is provided for data scarce scenes such as medical monitoring and motion analysis.

The invention relates to the technical field of data storage, and discloses a storage management method and system based on data encryption, and the method comprises the steps: obtaining a model sample set, building a feature thermodynamic diagram based on the model sample set, determining a key feature cluster based on the feature thermodynamic diagram, carrying out the decoupling of the key feature cluster according to an auto-encoder, determining a model data set, and storing the model data set in a database. To-be-stored classification data are determined based on a multi-head self-attention model, each piece of to-be-stored classification data is layered based on analytic hierarchy process, a judgment matrix is constructed, and corresponding encryption strategies are adopted for all the to-be-stored classification data based on a data comprehensive score to determine encrypted data. The temporarily stored encrypted data is moved to the target sub-memory according to the data verification result, the backup mode of the target sub-memory is determined based on the storage occupancy rate, whether the backup mode is adjusted or not is judged based on the data retrieval condition, and the stability and reliability of data storage management are ensured.

Embodiments of the present disclosure relate to a general image prior based on Langevin diffusion. An original representation (image, 3D model, audio) is optimized to resemble a data distribution learned by a trained diffusion model. The original representation may be incomplete and is completed by the optimization process. In an embodiment, the diffusion model may be trained to use an additional conditioning input, such as a text prompt. The diffusion model receives a noisy latent as input and generates a denoised output, such as an image. Generally, the diffusion model samples the learned data distribution to produce the output. The conditioning input provides additional constraint that causes the output to be “nudged” towards the learned data distribution. An example synthesis problem is to generate a panorama image that is much larger compared with images used to train the diffusion model.

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 embodiment of the invention relates to a model sample generation method and device, equipment and a storage medium. The method comprises the steps that based on input keywords, a plurality of question and answer pairs in a natural language are sequentially generated through a first machine learning model, and a second question and answer pair in the multiple question and answer pairs is generated at least based on replies in a first question and answer pair generated before the second question and answer pair; integrating the plurality of question and answer pairs by using a second machine learning model, and generating a multi-hop question aiming at the question and answer model and a reference answer aiming at the multi-hop question; and in response to determining that the multi-hop question and the reference reply meet the first quality requirement, generating a test sample and / or a training sample of the question and answer model.

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 provides an intelligent construction system for a mineral deposit model spatial data sample library, and the system comprises a mineral deposit model sample library management module which is used for constructing a source spatial database, issuing a task to a sample library construction workbench module, monitoring the issued task, and carrying out the reprocessing of a delivery task of the sample library construction workbench module, constructing an ore deposit model sample library; the sample library construction workbench module is used for processing the published task and delivering the completed task to the ore deposit model sample library management module; and the prospecting forceful area delineation module is used for obtaining and processing data in the ore deposit model sample library, delineating a prospecting favorable area and performing task guidance on the sample library construction workbench module. By performing standardized processing, structured storage and intelligent management on multi-dimensional geoscience data of a typical ore deposit, a standardized sample set which can be used for machine learning, deep learning or expert system training is formed, and data support is provided for delineating a favorable prospecting area of an unknown area.

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.