740 results about "Sample Label" patented technology

The invention discloses an automatic optimization method for cue words of a large language model, and relates to the technical field of cue word optimization. Comprises: setting an initial cue word; constructing training data, gradient generation cue word templates, cue word editing templates, optimizers and task models; randomly sampling a small batch of data from the training data as a training set, and predicting a sample of the training set by the task model according to the initial cue word to obtain an answer; comparing the answer with a sample label of a training set to obtain an error example set; inputting the error example set and the initial cue word into a gradient generation cue word template, and generating a gradient analysis request; the optimizer model receives and analyzes the gradient analysis request to generate a second natural language gradient; and inputting the second natural language gradient and the initial cue word into a cue word editing template, and modifying the initial cue word by the optimizer model according to the cue word editing template to generate a second candidate cue word. The technical problem that the efficiency of manually writing prompt words is low is solved.

The invention relates to the technical field of battery health management, in particular to a battery fault unsupervised detection method based on diffusion Transform and confidence coefficient calibration, which comprises the following steps: acquiring multi-modal time sequence data in a battery operation process, and performing preprocessing, including data cleaning, normalization processing, alignment and sampling; a diffusion Transform self-supervised learning framework is constructed, and the framework comprises a diffusion process based on a cosine scheduling strategy, multi-scale Transform architecture coding and a cross-modal self-adaptive fusion mechanism. Through the framework, potential space representation is optimized, and the battery state confidence coefficient is calculated; the optimal detection threshold value is dynamically determined by adopting a Bayesian optimization framework, whether the battery state is normal or faulty is judged according to the comparison result of the battery state confidence coefficient and the optimal detection threshold value, dependence on a fault sample label is completely eliminated, and a high-performance fault detection model can be trained only by utilizing normal sample data.

Provided is a defect detection method and device, computer equipment and a storage medium. The method includes: acquiring an RGB image, a depth image and a sample label of a detection object sample; performing feature map extraction and feature map fusion on the RGB image and the depth image by a feature extraction network of the defect detection model, to obtain a fused feature map; performing defect detection based on the fused feature map by a feature reconstruction network of the defect detection model, to obtain a defect score map, wherein the defect score map being obtained by fusing a global defect score map which is generated based on a global defect detection network with a local defect score map which is generated by a local defect detection network; and updating parameters of the defect detection model based on the defect score map and the sample label.

The invention provides a training method of a document image tampering detection model and a tampering detection method and device.The training method of the document image tampering detection model comprises the steps that multi-scale visual domain features are extracted from a sample document image, and multi-scale frequency domain compressed sensing features are extracted from frequency domain information; acquiring tampered area edge mask data from the document image; fusing the multi-scale visual domain features and the multi-scale frequency domain compressed sensing features to obtain multi-modal fusion features; performing semi-supervised training on the multi-scale sensing network by taking the multi-scale visual domain feature as a sample feature of a first prediction head, taking the multi-modal fusion feature as a sample feature of a second prediction head, taking a real label or a pseudo label as a sample label and taking joint loss as a loss function to obtain a document image tampering detection model; according to the method provided by the invention, document image tampering pixel-level detection under low labeling cost is realized, and the detection precision of a document image tampering detection model is improved.

The invention discloses an intelligent manufacturing defect automatic detection and classification method based on machine vision, and particularly relates to the technical field of defect automatic detection and classification, by constructing a high-resolution multi-source sample data set and introducing image preprocessing operation, defect expressions under different manufacturing batches, surface states and illumination conditions are covered, and the defect detection and classification accuracy is improved. Generating a defect probability heat map through an image segmentation network, extracting a primary defect candidate region, calculating a pseudo defect high-frequency interference coefficient by combining a high-frequency pseudo defect feature tensor, and calculating a multi-class defect overlapping coupling coefficient based on multi-classification confidence distribution and semantic adjacency; pseudo defect interference intensity and multi-class defect boundary fuzzy degree in the defect candidate area are accurately described, a sample label pollution risk assessment model is constructed to realize automatic identification and screening of high pollution risk samples in training data, and interference of mistakenly labeled samples on deep model training is significantly reduced; and erosion of error feature-label mapping on the generalization ability of the model is effectively prevented.

The invention belongs to the technical field of aero-engine state monitoring and intelligent fault diagnosis, and discloses a multi-modal information fusion bearing fault diagnosis method based on self-supervised learning. The method comprises the following steps: firstly, through mask reconstruction self-supervision pre-training, extracting stable feature representation insensitive to mask disturbance from an unlabeled multi-modal signal, and dynamically updating each modal feature reference point by using an index moving average algorithm; in a downstream fault diagnosis task, a multi-modal joint decision model comprising a pre-training encoder, a single-modal classifier and a fusion classifier is constructed, and adaptive weighted fusion of multi-modal decision is realized through contribution degree calculation based on a cooperative game Shapley value in combination with a deviation degree of modal features and a reference point. According to the method, the dependence of the deep neural network on fault labeling data is effectively reduced, the accuracy and robustness of the diagnosis system in a multi-modal signal diagnosis scene are improved through a dynamic fusion mechanism, and the method is suitable for industrial scenes with limited sample label resources.

The invention discloses a medical image recognition system based on label noise robust learning, and provides a hard sample label refinement strategy based on a confidence perception weighted prototype and an effective noise sample joint correction method to process divided subsets in correction before training so as to obtain training data with higher quality; in progressive hard sample reinforcement learning, data are input according to the learning difficulty of samples for training, the ability of model learning discrimination feature representation is improved by integrating cross entropy loss, consistency loss and credible contrast loss, and the influence of medical tag noise is reduced. According to the method, by integrating label refinement and a progressive hard sample reinforcement learning technology, the accuracy and robustness of medical image recognition under the condition of noise label data are effectively improved, overfitting of the model to noise samples is relieved, and the method can be used for correctly building a disease auxiliary diagnosis model under the condition that the noise label samples exist in training data.

The invention discloses a data processing method, device and equipment and a readable storage medium, and the method comprises the steps: inputting business sample data into an initial large language model, predicting the business sample data through the initial large language model, and obtaining M lexical element prediction vectors; based on the M lexical element prediction vectors, generating a sample text result and a sample confidence coefficient corresponding to the sample text result; generating a correctness reward value based on a sample label corresponding to the business sample data and the sample text result, and generating a confidence coefficient calibration reward value based on the sample confidence coefficient, the sample label and the sample text result; and based on the confidence calibration reward value and the correctness reward value, performing reinforcement learning training on the initial large language model to obtain a target large language model. By adopting the method, the confidence of model output and the accuracy of a prediction result can be improved.

The invention discloses an online incremental learning method and system based on a self-adaptive B + tree index, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an extended self-adaptive B + tree index structure, inserting a new sample, and calculating a trajectory disturbance value; generating a disturbance score by combining the disturbance value and a node state, and adding a new sample and an associated node sample into an incremental learning scheduling queue when a condition is met; when a node splitting, merging or reconstructing event occurs, acquiring an affected node and updating a structural code of the affected node; monitoring sample label track change, adjusting a label confidence mark when a threshold value is exceeded, and limiting participation frequency according to a screening mechanism; and performing forgetting compression operation on the leaf nodes with low access frequency and small label fluctuation degree. By constructing an adaptive B + tree index and combining disturbance-driven scheduling, structure change response and forgetting compression strategies, sample management refinement, learning update high efficiency and structure maintenance controllability are realized, and the stability and resource utilization efficiency of online incremental learning are improved.

The present disclosure provides a fault prediction method, a fault prediction model training method, a computing device, a storage medium, and a computer program product. The fault prediction method comprises: obtaining abnormal log data and unit attribute information of a service processing unit; determining an abnormal event sequence on the basis of the abnormal log data; and inputting the unit attribute information and the abnormal event sequence into a fault prediction model to obtain a fault prediction result of the service processing unit, wherein the fault prediction model is obtained by performing training on the basis of a positive sample, a first sample label corresponding to the positive sample, a negative sample, and a second sample label corresponding to the negative sample, the positive sample comprises a positive sample abnormal event sequence and sample unit attribute information, and the negative sample comprises a negative sample abnormal event sequence and sample unit attribute information. The training data of the fault prediction model is richer, so that the accuracy of prediction results of the fault prediction model during applications is improved.

The invention relates to a tool wear compensation control method, device and equipment and a medium, and relates to the field of machining control, the method comprises the following steps: adopting a wear state label with a label to carry out supervised constraint, adopting a Baum-Welch algorithm with label correction to carry out iterative optimization, and adopting a Baum-Welch algorithm with label correction to carry out iterative optimization; enabling the wear detection model to output an optimized state transition probability matrix and an optimized observation probability matrix; the wear feature vectors of different time steps, the optimized state transition probability matrix and the optimized observation probability matrix serve as training samples, the tool compensation amount corresponding to the wear feature vectors serves as a sample label, and a wear compensation prediction model is trained to be in a convergence state; and inputting the wear feature vector of the current time step into a wear compensation prediction model to predict the tool compensation amount of the operation tool in the future time step, and inputting the tool compensation amount into the numerical control machine tool to carry out tool wear compensation. The wear compensation amount of the tool can be predicted in real time and compensated, so that the service life of the tool is prolonged.

The invention relates to the technical field of artificial intelligence and deep learning, in particular to a road ice coagulation phase change latent heat intelligent prediction method and system based on an LSTM neural network, and the method comprises the steps: building a three-dimensional monitoring network: taking the side surface of a road as an X axis, the length direction as a Z axis, and the height direction as a Y axis, building a three-dimensional rectangular coordinate system, constructing a fiber grating sensor distributed topology network and a temperature monitoring network; a data acquisition and processing step: acquiring multi-dimensional state information; establishing a phase change physical model, taking phase change layer melting depth information as a sample label, and extracting a training data set and a test data set from the space-time simulation data; inputting the training data set into an LSTM network for training, and establishing a phase change state prediction model by taking the melting depth of the phase change layer as an output target; verifying the prediction precision of the LSTM network by using the test data set; and high-precision prediction of the road icing state is realized.

The embodiment of the invention provides a model training method, a facial expression recognition method and device and electronic equipment, and relates to the technical field of video processing. The model training method comprises the following steps: acquiring a sample video and a first sample label; extracting a time feature and a space feature of the sample video by using a time-space feature extraction network in the facial expression recognition model of the initial structure; calculating an attention weight representing the correlation between the spatial feature and the time feature by using a mapping network; performing weighted aggregation on the time features by using the attention weight to obtain fused spatio-temporal features; inputting the fused spatial-temporal features into a classification network to obtain a first recognition result; and performing model training based on the difference between the first recognition result and the first sample label to obtain a trained facial expression recognition model with higher accuracy.

The invention relates to the technical field of data processing, in particular to the technical field of artificial intelligence, and provides a mining model training method and device, electronic equipment and a storage medium which are used for improving venation mining efficiency and quality. The method comprises the following steps: training an initial mining model based on a first sample set to obtain an intermediate mining model; the model corresponds to a plurality of tasks, and each task represents a sub-process of the event venation mining process; the first sample set comprises a first sample and a sample label associated with each task; inputting at least one second sample associated with the task into the intermediate mining model to obtain a candidate result set output for each second sample; screening each candidate result contained in each obtained candidate result set according to a preset evaluation standard to construct a second sample set; the second sample set comprises each second sample and the corresponding positive candidate result and negative candidate result; and training the intermediate mining model based on the second sample set to obtain a target mining model.

The invention relates to the technical field of GNSS deception jamming detection, in particular to a GNSS deception jamming detection method and system based on a random forest, a GNSS software receiver is adopted to process deception data sets of different scenes, parameters of different processing stages are extracted to serve as sample features, sample tags are added according to the deception occurrence moments of the scenes, and the deception jamming detection method and system based on the random forest are obtained. The parameter characteristics of different processing stages comprise signal processing stage characteristics, original observed quantity characteristics and PVT calculation result characteristics; gNSS deception jamming detection is used as a dichotomy problem of signals not subjected to deception jamming and signals mixed with deception jamming, a random forest classifier is constructed by utilizing extracted sample training, and whether the deception jamming signals exist in GNSS signals to be detected or not is judged by utilizing the classifier. According to the method, the GNSS deception jamming is detected by selecting the parameters of different processing stages as the features and constructing the random forest classifier, the importance of each feature in deception detection is deeply analyzed, and a reference is provided for deeply understanding deception behaviors and reasonably formulating an anti-deception strategy.

The invention discloses a front-end performance optimization method and device, equipment and a storage medium. A browser event initiated by a target user at a front end is monitored in real time; extracting user behavior characteristics corresponding to the browser event; processing the user behavior characteristics by using a pre-trained behavior prediction model to obtain a behavior prediction result for the target user; the behavior prediction model is obtained by taking a behavior feature sample set of multiple users as a training sample and taking a real behavior result of each user as a sample label for training; determining each main thread task from the behavior prediction result, and generating a preloading task queue by each main thread task; and performing scheduling optimization on each main thread task in the preloading task queue to realize performance optimization of the front end. According to the resource loading method and device, the accurate behavior prediction result can be obtained, then the pre-loading task queue is constructed according to the behavior prediction result, scheduling optimization is carried out, therefore, resource loading can be accurately carried out, and the user experience is improved.

The invention relates to a water supply network leakage identification method and device based on data enhancement and hybrid neural network architecture, equipment and a medium, and the method comprises the steps: employing a generative adversarial network to generate a simulation pipeline vibration audio signal, and combining the simulation pipeline vibration audio signal with a real pipeline vibration audio to construct a pipeline vibration audio signal data set; performing feature fusion on the Mel-frequency cepstral coefficient feature matrix and a first-order difference matrix of Mel-frequency cepstral coefficients to determine a feature fusion matrix of each pipeline vibration audio signal frame; by taking the feature fusion matrix as a training sample and the leakage state of the pipeline vibration audio signal as a sample label, training a CNN-Bi LSTM hybrid neural network model to determine a water supply network leakage detection model; and inputting the to-be-identified pipeline vibration audio signal into the water supply network leakage detection model to determine whether the to-be-identified pipeline vibration audio signal is in a leakage state or a non-leakage state. According to the invention, the precision, robustness and generalization ability of leakage detection are significantly improved.

The invention discloses a data enhancement and transfer learning method for chip power supply network voltage drop prediction in a small sample scene. The method comprises the following steps: acquiring a disclosed large-scale training data set and a small number of training samples of new process parameters or new chip design types; extracting a label of each sample in the source domain S and the target domain T, calculating a gradient G of each label, respectively calculating average gradients Gmean S and Gmean T of the sample labels in the source domain S and the target domain T, and calculating a scaling coefficient R; scaling the features and labels of the samples in the source domain S according to the scaling coefficient R; and repeating the above steps until a reinforcement learning data set used for subsequent transfer learning is made. The technical problem that the prediction precision is seriously reduced due to scarcity of training samples in the current chip power supply network voltage drop prediction field can be solved.

The invention discloses an agent training data set construction method and system combined with cross-modal learning, and relates to the technical field of agent training, and the method comprises the steps: obtaining multi-modal intelligence data from a multi-source database, extracting feature information, and projecting the feature information to a preset semantic space to obtain cross-modal alignment features; constructing an agent function call syntax tree based on cross-modal alignment features, analyzing the syntax tree into an instruction sequence, constructing an analysis reasoning chain, and generating a sample label and an interaction track; constructing a task dependency graph by using the sample labels and the interaction tracks, decomposing the task dependency graph into a plurality of parallel decision branches, and dynamically adjusting a processing strategy to form a training data set; mapping the training data set to an agent target space, optimizing and analyzing an inference chain through a training feedback channel, and outputting a standardized sample library; and finally inputting to-be-analyzed data into the trained agent, and generating an intelligence analysis report according to the analysis reasoning chain.

The invention relates to an intelligent agent continuous training and effect evaluation closed-loop method and system fusing work order feedback, and a medium, and relates to the technical field of deep learning. The agent continuous training and effect evaluation closed-loop method comprises the following steps: receiving and analyzing work order feedback information, and recording the number of error information and information storage duration in combination with a preset structure sample label; dynamically judging the number of error information and the information storage duration, triggering a retraining rule of an original agent model, distributing candidate information weights, and screening a target sample set; directionally training and optimizing the original agent model according to a retraining rule in combination with the target sample set, and generating a new agent model; constructing a historical work order problem set, comparing and verifying a new agent model in combination with an original agent model, generating an agent evaluation table, and correcting a candidate information weight; by accurately focusing the weak link, the continuous and efficient enhancement of the model capability is realized, so that the answer accuracy of the intelligent agent is remarkably improved, and the AI illusion is effectively inhibited.

The invention belongs to the technical field of production control, and provides a combined optimization method and system for yarn combined weaving parameters. The method comprises the following steps: acquiring sample data containing first parameters such as yarn twist and elasticity and corresponding interaction characteristics, and sample labels such as pattern definition and cloth cover flatness; and inputting the data into the initial neural network, and calculating a loss value by combining a loss function which contains a mean square error and reflects a physical interaction weighted item through feature transformation of an input layer and a hidden layer. And by using a stochastic gradient descent algorithm, when the loss value is greater than a threshold value, performing back propagation to update the parameter training model until the loss value of the model meets the requirement and is not over-fitted in the verification set. Physical constraints such as influence of yarn twist and elastic interaction on pattern definition and influence of fabric density and twist on flatness are embedded through the mechanism sensing layer, effective interaction characteristics are constructed, and accurate prediction of silk fabric indexes is achieved.

The invention discloses a green vision index estimation model construction and green vision index monitoring method and device, and belongs to the technical field of geographic information, and the method comprises the steps: obtaining a plurality of remote sensing slice images and a plurality of streetscape images corresponding to the remote sensing slice images; wherein each remote sensing slice image corresponds to a road network; determining a sample label corresponding to the remote sensing slice image according to the plurality of streetscape images; calculating a corresponding auxiliary feature map according to the remote sensing slice image; wherein the auxiliary feature map comprises a normalized vegetation index map, a depth map and a visual field perception intensity map; and according to each remote sensing slice image, each auxiliary feature map and each sample label, training and verifying a preset deep learning model, and obtaining a green vision index estimation model. Therefore, by implementing the method and the device, the problem of how to perform large-range and periodic monitoring on the street green vision index under the pedestrian view angle can be solved.

The invention discloses a class incremental learning method for edge scene image recognition and electronic equipment, and the method comprises the steps: collecting real-time data, and taking the real-time data as an input sample; performing feature dimension raising on an input sample through a random mapping module of width learning, and mapping features to feature representation with higher dimensions; judging whether the input sample belongs to a new class sample or an old class sample according to the Grubrum matrix, and if the input sample belongs to the new class sample, performing amplification processing on a sample label; feature fusion is carried out on the Gramer matrix of the new-class sample and the Gramer matrix of the previous sample; and performing fine adjustment on the weight of an output layer of the width learning model by using the feature-fused Gramer matrix to obtain an edge scene image recognition model after class incremental learning. According to the method, feature fusion of new and old class samples after width mapping is realized by introducing the Grubrum matrix operation, and the incremental learning performance of a model class can be greatly improved. The method can be widely applied to the technical field of edge scene image recognition.

Provided in the present application are a model training method and apparatus, a speech detection method and apparatus, and a device, a medium and a product. The model training method comprises: acquiring at least one positive example speech sample, at least one noise sample, and at least one negative example interference sample comprising speech, wherein a sample label of the positive example speech sample represents a positive example sample, and sample labels of the noise sample and the negative example interference sample represent negative example samples; obtaining sample detection results of a speech detection model performing speech detection on the at least one positive example speech sample, the at least one noise sample and the at least one negative example interference sample, wherein the sample detection result of a sample represents whether the sample belongs to the positive example sample; on the basis of the sample detection results and the sample labels, determining a total training loss, and on the basis of the total training loss, performing iterative training on the speech detection model, so as to obtain a trained speech detection model.

The invention relates to the technical field of pattern design intelligent optimization, in particular to an embroidery pattern optimization design method and system based on a machine learning model.The method comprises the following steps that a grey-scale map structure is obtained, edges are extracted to construct a direction node map, node intersection points are positioned to generate path vectors, boundaries are drawn according to the symmetrical relation to generate annotation map blocks, and the annotation map blocks are obtained. Establishing a pattern code generation structure label group, and extracting a repeated paragraph classification style to generate a style training sample label set; according to the method, a composition node map is constructed through the direction continuity of edge pixel points, path candidate intersection points are positioned through the intensity of node space distribution and are connected into skeleton path line segments, an edge structure closed area is extracted by using a path symmetric projection relation, and ordered association of a pattern structure and a pattern is realized. The pattern design process has the capabilities of being controllable in structure, adjustable in style and traceable in feature, and the intelligent level of composition processing and style induction is remarkably improved.

The invention provides an incremental learning method for open world target detection based on a large language model. The method comprises the steps that a first RGB image sample marked with a first type of target is acquired and trained to obtain a first open world target detection model; establishing a training set comprising a plurality of second RGB image samples, wherein each second RGB image sample is labeled with targets of a first category and a second category; processing the first category and the second category by utilizing a large language model, and generating an attribute feature in a text form of each category; processing the second RGB image sample and the text-form attribute features of each category by using a first open world target detection model to obtain a target frame prediction value, a target category prediction value and an unknown category target prediction value, thereby determining a total loss value; and updating parameters of the first open world target detection model based on the total loss value, thereby obtaining a second open world target detection model. The generalization ability and adaptability of the model in a new scene are enhanced.

The invention provides a semantic recall model training method and device and a computer storage medium. The method comprises the following steps: acquiring a training sample set, wherein the training sample set comprises a plurality of sample pairs and sample labels thereof; for each sample pair in the training sample set, respectively inputting the query text and the title text into a first semantic encoder and a second semantic encoder of a semantic recall model to obtain a first semantic vector corresponding to the query text and a second semantic vector corresponding to the title text; at least inputting a second semantic vector corresponding to the title text into a semantic decoder of a semantic recall model to obtain a predicted event text; extracting a third semantic vector of the prediction event text; determining a first loss based on the first semantic vector and the third semantic vector corresponding to each sample pair in the training sample set and the sample label; and iteratively updating the parameters of the semantic recall model until a preset condition is met. According to the method, the timeliness semantic recall effect can be effectively improved.

The invention relates to the technical field of crop remote sensing samples, and discloses a crop remote sensing image intelligent sample library construction method based on a knowledge graph. The method comprises the steps of collecting multi-source remote sensing image data, and generating an enhanced image data set through fusion and enhancement processing; and constructing a crop field knowledge graph integrating the crop growth model, the soil type and the climate condition. A knowledge graph is used as guidance, crop areas are recognized through semantic analysis, multi-scale features are extracted, and category attributes and associated contexts are deduced through a graph reasoning algorithm in combination with a relation path; and generating a sample label candidate set through deep learning of the graph attention network, screening and correcting the sample label candidate set by using an optimization algorithm constrained by the knowledge graph, and outputting high-quality sample labels. And integrating and storing labels and image data into a graph structure sample library, implementing a dynamic updating mechanism to adapt to new data, running a quality monitoring process driven by a knowledge graph, and adjusting a construction strategy according to result feedback to realize intelligent and efficient construction of the sample library.

The invention discloses a magnetic data boundary identification method and system based on a physical prior selection module, and relates to the technical field of geophysical exploration, and the method comprises the steps: building a magnetic anomaly data set through underground target magnetic anomaly data with different parameters; generating magnetic measurement data through a magnetic anomaly forward modeling calculation formula, and forming a magnetic measurement data set; constructing a boundary recognition network model based on UNet + +, and integrating the physical prior selection module to the boundary recognition network model; inputting the training set sample, the verification set sample and the test set sample into a physical prior selection module for operator transformation, and generating a corresponding transformation result graph; and calculating the structural similarity index of each transformation result graph and the corresponding sample label, and splicing the transformation result graph with the maximum structural similarity index and the corresponding sample according to the channel dimension to generate a corresponding multi-channel feature. According to the method, the boundary recognition network model is established and the physical prior selection module is added, so that the robustness of the boundary recognition network model to complex noise is improved.

A method for training a speech recognition model includes: performing, by the speech recognition model, feature extraction on a speech sample to obtain a speech sample feature; performing semantic extraction on the speech sample feature to obtain a semantic feature of the speech sample; determining a first loss value based on the semantic feature; performing, by the speech recognition model, speech recognition on the speech sample based on the speech sample feature to obtain a speech sample recognition result; determining a second loss value based on the speech sample recognition result and a speech sample label corresponding to the speech sample; and training the speech recognition model based on the first loss value and the second loss value to obtain a trained speech recognition model.