483 results about "Sample graph" patented technology

The invention relates to the technical field of defect detection, and particularly discloses an electronic component defect detection method, system and device, and the method comprises the steps: collecting a multiband original image set based on multispectral imaging, inhibiting batch material difference and surface reflection through orthogonal complementary space projection, and obtaining a de-noised image set; performing feature extraction on the de-noised image set, and identifying and marking all fragmented defect areas; based on a graph neural network space attention mechanism, establishing an association relationship between fragmented defect areas to form a complete defect feature chain; gray frequency value sequences of the to-be-detected image and the standard sample image are constructed respectively, and a first fitting curve and a second fitting curve are obtained through fitting of a skewed distribution function; and finally, comprehensively judging whether the electronic component has defects or not by combining the defect characteristic chain and the difference characteristics of the two fitting curves. According to the method, interference can be effectively suppressed, fragmented defects can be accurately identified, and the detection accuracy and reliability are remarkably improved through multi-dimensional feature fusion.

A semantic segmentation model training method and apparatus, an electronic device and a storage medium are provided. The semantic segmentation model training method includes: acquiring a sample image, and extracting visual image features corresponding to the sample image by a semantic segmentation model to be trained; processing the sample image to obtain a text image feature corresponding to the sample image, the text image feature being an image feature generated from language description text for the sample image; fusing the visual image features with the text image feature to obtain multimodal features, and performing image segmentation prediction based on the multimodal features to obtain a target loss; and training the semantic segmentation model to be trained based on the target loss to obtain a target semantic segmentation model.

The invention relates to the technical field of image generation, and discloses a country style and appearance design image generation method and system based on a knowledge graph and a large model, and the method comprises the steps: constructing a core element covering country style and appearance design and a knowledge graph; constructing a multi-level label system based on the knowledge graph; pre-processed rural style and appearance sample images are obtained, the rural style and appearance sample images are labeled based on a multi-level label system, and a special training set in the field of rural style and appearance design is obtained; constructing a basic diffusion large model architecture, and training the basic diffusion large model architecture by using the special training set for the country style and appearance design field to obtain a trained country style and appearance design image generation model; and generating a rural style and appearance design image by using the trained rural style and appearance design image generation model. According to the method, the problems that a general large model is insufficient in domain knowledge support and insufficient in data labeling specialty in rural style and appearance design image generation are solved, and the rural style and appearance design image generation efficiency and quality are improved.

The embodiment of the invention provides a training method and device of an open vocabulary detection model and electronic equipment. The open vocabulary detection model training method comprises the steps of obtaining sample data; the sample data comprises a sample image, a sample text and label information, inputting the sample data into a neural network model to be trained, and extracting text feature information of the sample text and image feature information of the sample image; generating dynamic text prompt information according to the image feature information; performing fusion processing on the text feature information and the dynamic text prompt information to obtain semantic fusion feature information; predicting an object in the sample image according to the semantic fusion feature information; and performing iterative training on the neural network model according to the object prediction result, the label information, the text feature information, the dynamic text prompt information and the semantic fusion feature information to obtain an open vocabulary detection model. According to the invention, the context sensing, semantic generalization and fine-grained understanding capabilities of the open vocabulary detection model can be improved.

A semantic segmentation model training method and apparatus, an electronic device and a storage medium are provided. The semantic segmentation model training method includes: acquiring a teacher semantic segmentation model that is pre-trained, the teacher semantic segmentation model including a first teacher network and a second teacher network, the first teacher network having structural characteristics of low depth and high width, and the second teacher network having structural characteristics of high depth and low width; processing a sample image based on the teacher semantic segmentation model to obtain a first segmentation map and a second segmentation map; and training a student semantic segmentation model that is lightweight according to the sample image, the first segmentation map and the second segmentation map, so as to obtain a target semantic segmentation model.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

The invention is suitable for the technical field of artificial intelligence, and provides an abnormal sample image generation method, electronic equipment and a storage medium, and the method comprises the steps: setting a material parameter library and a scene parameter library, and constructing a paired industrial defect sample data set in combination with three-dimensional geometric models of a plurality of sample workpieces; constructing category text description, defect text description and material category text description of various workpieces, and constructing and training an abnormal sample image generation model in combination with the paired industrial defect sample data set; obtaining a target normal image, a candidate defect area mask image, a target category text description, a target defect text description and a target material category text description of a target category workpiece, and inputting the target normal image, the candidate defect area mask image, the target category text description, the target defect text description and the target material category text description into an abnormal sample image generation model for processing to obtain a target defect image and a target defect area mask image; the training precision and flexibility of the abnormal sample image generation model are improved, and then the efficiency and precision of abnormal sample generation are improved.

The invention provides a knowledge distillation-based model training method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a first sample data set; the first sample data set comprises a plurality of first labeled texts, and a first sample image corresponding to each first labeled text has different visual effect types and comprises a plurality of objects; calling a pre-trained teacher model, and generating a first generation image based on the first annotation text; generating a second generation image based on the first generation image and the first annotation text through a to-be-trained student model; determining knowledge distillation loss of the student model based on the first generated image and the second generated image; and performing model parameter updating on the student model based on the knowledge distillation loss to obtain a trained student model. According to the method and the device, the images which have different visual effects and contain a plurality of objects can be generated through the trained student model, and the generation quality of the images can be improved.

The invention relates to the technical field of image generation, and discloses a high-quality human body image generation method and device, and computer equipment. The method comprises the following steps: constructing a training data set, wherein the training data set comprises a sample image of a human body, a text description of a corresponding human body attribute, a human body analysis graph and a plurality of attribute tags; an image generation network is constructed, the image generation network comprises an encoder, a UNet and a decoder which are connected in sequence, the image generation network further comprises a time feature aggregation module and an attribute perception reward module, and the attribute perception reward module is used for predicting a target reward score and a prediction reward score; training an image generation network by minimizing the difference between the target reward score and the predicted reward score; and inputting the text description of the human body image to be processed and the human body analysis graph into the trained image generation network to obtain a target image. By adopting the method, the high-quality human body image with space alignment and consistent attributes can be generated.

The invention relates to the technical field of artificial intelligence, and provides a method and system for improving generalization of a small computer vision model based on a text graph model, and the method comprises the steps: S1, constructing and optimizing a large text graph model; s2, constructing positive and negative sample text descriptions according to a text graph large model on the basis of features and scene requirements of a target detection object; s3, inputting the positive and negative sample text descriptions into the optimized text graph large model to generate corresponding positive and negative sample images, labeling the positive and negative sample images, and carrying out preprocessing and data enhancement on the labeled positive and negative sample images; s4, loading a CV small model, and performing model training according to the preprocessed and data enhanced positive and negative sample images; s5, evaluating the trained CV small model, and if an evaluation result does not meet a preset condition, adjusting hyper-parameters and returning to the step S4 for re-training; if a preset condition is met, the trained CV small model is stored, and the problems that training data is insufficient and samples lack real scene diversity are solved.

The invention discloses a method for identifying traditional Chinese medicinal materials by combining infrared spectroscopy with clustering analysis, which comprises the following steps of: acquiring original infrared spectral data, averaging the original infrared spectral data to obtain single-sample original spectral data, constructing a sample graph and a wavelength graph based on standardized spectral characteristics, and fusing the sample graph and the wavelength graph to obtain the single-sample original spectral data. Obtaining fusion image data, inputting the fusion image data into a pre-trained image neural network model, extracting a low-dimensional feature vector of a target sample through forward propagation, and inputting the low-dimensional feature vector into a pre-trained dynamic cluster diffusion module to obtain a clustering label and distance data; and determining and outputting the quality grade of the rhizome traditional Chinese medicinal material sample based on the clustering label and the distance data. Therefore, interference can be effectively reduced, key features can be extracted, associated features of fused graph data are extracted in combination with a graph neural network, and the quality grade of traditional Chinese medicinal materials can be accurately determined in combination with clustering analysis of a dynamic cluster diffusion module.

The invention discloses a radar weak target detection and positioning method based on four-polarization time-frequency feature fusion. The method comprises the following steps: constructing an initial four-polarization time-frequency feature detector; the initial four-polarization time-frequency characteristic detector is constructed by introducing four parallel Backbones into a YOLOv11 network; one parallel Backbone correspondingly processes one polarization channel; training the initial four-polarization time-frequency feature detector by using a pre-constructed training label set to obtain a four-polarization time-frequency feature detector; calculating detection statistic distribution based on the confidence corresponding to each pure clutter sample map, and determining a detection threshold according to the detection statistic distribution; based on a to-be-detected time-frequency diagram, a radar weak target detection and positioning method which is sufficient in feature utilization, high in detection capability and stable is provided by using a four-polarization time-frequency feature detector and a detection threshold.

The invention relates to a graph model fine tuning method based on graph prompt learning, and aims to solve the problem that a graph model pre-training task is inconsistent with a downstream task target and improve the performance of tasks such as node classification and graph classification. The method comprises the following steps of: firstly, sampling graph data by restarting random walk, and learning a pre-training model through graph-level contrast to fully mine graph representation capability; secondly, aiming at a downstream node classification task, extracting a two-hop neighborhood construction induction graph for each target node, and selecting the most representative dominating node based on the minimum dominating set; further, in order to enhance hierarchy and discrimination of the graph structure, a corresponding sub-graph-level node is introduced for each dominating node, a global graph-level node is added, and a unified prompt graph is constructed by connecting the dominating nodes with the corresponding sub-graph-level nodes and connecting all the sub-graph-level nodes to the graph-level nodes. And then, in a representation generation stage, fusing information of different granularities through a multi-level graph embedding aggregation strategy, and weighting to obtain final graph-level embedding representation. And finally, performing similarity calculation on the graph representation and a predefined class prototype, and selecting a class with the highest similarity as a target node prediction result, thereby converting a node classification task into a graph classification task. According to the method, the performance is excellent when upstream and downstream task targets are unified, the pre-training encoder is frozen during fine adjustment, and only relevant parameters of the prompt weight are updated, so that the model performance, the adaptability and the practical value are remarkably improved.

A fingerprint information processing apparatus includes: an output unit that outputs a certainty factor that is an index indicating probability in which fingerprints indicated by a fingerprint image correspond to at least one of a plurality of pattern types, by using the fingerprint image and a learning model constructed by machine learning using learning data including a sample image indicating fingerprints; and a processing unit that performs processing based on the certainty factor.

The disclosure provides a graph neural network training method, a training system and an abnormal account identification method. The graph neural network training method comprises: obtaining initial graph structure data corresponding to a terminal device; the initial graph structure data obtained by a plurality of distributed training terminals respectively is derived from the same sample graph structure data; the following graph structure data processing stage and graph neural network training stage are executed cyclically until a target neural network meeting the training requirement is obtained: determining a processing time of the current execution graph structure data processing stage according to historical execution data of the historical execution graph structure data processing stage and the historical execution graph neural network training stage; performing graph structure data processing on the initial graph structure data in the graph structure data processing stage according to the processing time to generate target graph structure data; the graph structure data processing comprises data sampling processing and feature extraction processing; and training the target neural network based on the target graph structure data in the graph neural network training stage.

The invention provides an automatic task execution method of a mobile terminal, an intelligent agent and electronic equipment, and relates to the technical field of artificial intelligence, the method comprises the following steps: determining a to-be-processed task input by a user; determining a target application document from at least one application document based on the to-be-processed task, and determining a graphical user interface state corresponding to the to-be-processed task; inputting the target application document, the to-be-processed task and the graphical user interface state into a local script generation model to obtain a task execution script output by the local script generation model; wherein the local script generation model is obtained based on training of the application document, the sample task, the sample graphical user interface state and the sample task execution script; and running the task execution script, so that the automatic execution of the task can be safely and effectively realized at the mobile terminal.

The invention relates to an image interpretation method and device based on a visual language model. The method comprises the following steps: determining a sample reasoning instruction according to a visual label of a sample image and a target knowledge graph; generating image interpretation information of the sample image based on the sample image and the sample reasoning instruction through a question and answer engine; training a to-be-trained visual language model based on the sample image, the sample reasoning instruction and image interpretation information of the sample image, and determining a target visual language model; and determining image interpretation information of the target image according to the target image and the target reasoning instruction through the target visual language model. According to the scheme, the data format of the model training data set is unified, the data set construction efficiency is improved, the labor cost is saved, and meanwhile, the trained visual language model can be subjected to deep knowledge reasoning.

The invention provides a node category prediction method, device and equipment for a backdoor attack and a medium, and relates to the technical field of deep learning, and the method comprises the steps: obtaining a disturbance diagram of the backdoor attack; determining at least one sub-graph corresponding to the perturbation graph based on the adjacency matrix of the perturbation graph and the similarity of each node in the perturbation graph; inputting each sub-graph into an integrated model to obtain a prediction category corresponding to each node in a disturbance graph output by the integrated model; the integrated model is obtained by training a plurality of sub-sample graphs corresponding to the sample graph attacked by the backdoor. According to the method, the accuracy of predicting each node category in the disturbance graph of the backdoor attack by the integrated model is improved.

The invention relates to the technical field of intelligent operation and maintenance of an electric power system, in particular to an inspection sample image data enhancement method for an electric power artificial intelligence platform, which is used for solving the problems that in the prior art, history and equipment knowledge cannot be fused to construct a forbidden area, a co-occurrence rule and component association, cross-component defect positions cannot be effectively adjusted, and the detection accuracy is poor. Defect distribution is difficult to accurately control, and physical rationality and engineering credibility are reduced. According to the method, a forbidden area, a co-occurrence rule and component association are constructed by fusing history and equipment knowledge, masks are generated through kernel density estimation to suppress invalid defects, co-occurrence frequencies are counted based on feature vectors, co-occurrence relationships are determined by combining distances and similarities, and the masks, matrixes and graphs are embedded and coded into conditional vectors, so that the non-ineffective defects are suppressed. And zero setting is performed on a forbidden area in the generative network, illegal co-occurrence is filtered, and cross-component defect positions are adjusted, so that defect distribution is accurately controlled, and physical rationality and engineering credibility are enhanced.

The invention provides a pleurotus citrinopileatus growth prediction model training method, device and equipment, and relates to the field of data processing. The growth prediction model at least comprises a bidirectional long-short-term memory network and a graph convolutional network. The training method comprises the following steps: collecting multiple groups of sample form data, multiple groups of sample image data and multiple groups of sample growth environment data of multiple pleurotus citrinopileatus samples according to a preset time interval; according to the multiple groups of sample image data, obtaining a plurality of adjacent matrixes representing the similarity between pleurotus citrinopileatus samples; processing the plurality of adjacent matrixes, the plurality of groups of sample morphological data and the plurality of groups of sample image data through a graph convolutional network to obtain morphological feature vectors; processing the multiple groups of sample growth environment data through a bidirectional long-short-term memory network to obtain growth environment feature vectors; and training the growth prediction model according to the growth environment feature vector and the morphological feature vector. The growth prediction model obtained through training can accurately predict the growth state of pleurotus citrinopileatus.

A power system transient voltage stability evaluation method based on a gain-swin transformer comprises the following steps: step 1, collecting historical online data and analogue simulation data of a power system, and constructing an initial sample set; converting the multivariate time series data into a GAF transient state integrated image with reserved time characteristics; generating an unstable or stable label for each sample image, and randomly dividing a training set and a test set according to a certain proportion; 2, a feature extraction module of the Swin Transform model is improved, and multi-scale spatial-temporal features are extracted through a shift window attention mechanism and a cross-window attention mechanism; and in the offline training process, an optimal transient voltage stability evaluation model is obtained. And step 3, deploying the optimal model in an online evaluation system, monitoring transient voltage data in real time and performing stability evaluation. When the power system is unstable, the model can also position a key bus. Compared with a traditional method, the method can achieve efficient and accurate voltage stability evaluation in a large-scale power system.

The invention discloses an industrial image anomaly detection method based on a multi-agent arrangement heterogeneous algorithm. The method comprises the following steps: S1, constructing an algorithm component library for shielding data representation differences among different architecture algorithms; s2, constructing a multi-modal large model system based on a multi-agent architecture; the multi-agent architecture comprises the following steps: receiving an image by using a visual expert agent, and outputting structured physical metadata and an unstructured visual suggestion text; the planner agent receives the physical metadata and the visual suggestion text, and generates an algorithm configuration tree based on an algorithm component library; the optimizer agent optimizes the algorithm configuration tree in the closed-loop feedback stage; s3, using the algorithm configuration tree to construct a reference model representing good product distribution according to the normal training sample images; s4, performing anomaly detection on the to-be-detected sample image by using the algorithm configuration tree to generate an anomaly judgment result; and S5, optimizing the algorithm configuration tree.

The embodiment of the application provides an image enhancement joint optimization method and system based on downstream task performance, and relates to the technical field of image processing. The method first acquires a training data set and task evaluation data, inputs sample images in the training data set into an image enhancement model to obtain enhanced images, and inputs the enhanced images into a task model to obtain task output results. Then, a target training mode is determined according to the task evaluation data, and a parameter update strategy is set based on the target training mode. Then, according to the parameter update strategy, task metric information is calculated according to the task output results and task annotation information, and the model parameters of the trained model are updated according to the task metric information. The method can form an evaluation and closed-loop optimization mode based on the performance of the downstream task as the core constraint, and by introducing the task metric information under controllable conditions, a task-driven optimization strategy and a stabilization mechanism are used in the training stage, so that the robustness and stability of the image processing process are improved.

An image processing method, apparatus, and computer-readable storage medium for training image generation models through semantic-aware denoising. The method obtains sample data including a sample image, its category, and identifier set. Training representation information is extracted from the sample identifier to represent training semantics expressing sample features under the category. A noise image is generated by adding marked noise to the sample image encoding. Based on training representation information, noise in the noise image is predicted. Model parameters are updated using differences between marked and predicted noise and between training semantics of the sample identifier and category semantics. The trained model performs denoising on noise images using text description information including sample identifiers to generate diffusion images.

The invention discloses an ultrasonic pulmonary artery segmentation method, system and equipment based on anatomical perception and a medium, belongs to pulmonary artery segmentation in an artificial intelligence technology, and aims to solve the technical problems of low accuracy and poor segmentation integrity of pulmonary artery segmentation. Comprising the steps of sample image acquisition, classification network fine adjustment and feature extraction, initial response diagram generation, pseudo label generation, segmentation network construction and first training, dynamic anatomy constraint iteration training and pulmonary artery real-time segmentation. When a pseudo label is generated, constructing an anatomical perception pseudo label generation module, inputting the initial response graph into the anatomical perception pseudo label generation module, and outputting an initial pseudo label by the anatomical perception pseudo label generation module; and during iterative training, constructing a differentiable anatomical constraint generator, and performing iterative training on the segmentation network by using the differentiable anatomical constraint generator. A rough and sparse initial response graph is converted into a complete, continuous and reasonably dissected initial pseudo-tag through a pseudo-tag generation module for dissecting perception.

The invention provides a variety-independent plant counting method and system. The method comprises the following steps: S1, segmenting a to-be-detected image and a plant prompt sample image into image blocks with fixed sizes; extracting features from the image blocks of the to-be-detected image and the plant prompt sample image by using a visual converter encoder, and generating a main feature map; s2, based on the enhanced features, performing local counting at the token level by adopting a multi-scale dynamic counter; s3, normalizing the redundancy counting graph by adopting an overlapping region weighted average strategy, and eliminating context redundancy; and S4, combining the local counting graph with the attention matching graph to generate an attention guiding prompt graph, and performing fusion up-sampling on the normalized local counting graph and the attention guiding prompt graph to generate a final high-resolution visual counting result graph. The plant image input method and device can adapt to plant image input of different types and different growth stages, support a unified counting framework and output format, and have high variety independence, accuracy and interpretability.

The invention provides a data security risk assessment method and system. According to the method, a data access request is acquired, a target tracking identifier corresponding to the data access request is generated or received for the data access request based on a target entry service, and inter-service calling related to the data access request is tracked according to the target tracking identifier to acquire calling link data corresponding to the data access request. On the basis of pre-established sensitive field metadata, labeling a field access behavior of target business data involved in calling link data as a corresponding sensitive data access event, constructing a data stream sample graph according to an association relationship between the sensitive data access event and a target tracking identifier, performing risk analysis on the data stream sample graph, and obtaining a sensitive data stream; therefore, the data security risk assessment result corresponding to the single data access request can be output at the request level, and the full-link assessment of the data security risk under the micro-service architecture is realized.

The application provides a multimodal large model security protection method, device and equipment, which comprises the following steps: obtaining a sample image and a sample text, and performing feature fusion on the sample image and the sample text to obtain a multimodal feature; performing a first perturbation operation on the multimodal feature to obtain a first perturbation feature, inputting the first perturbation feature into an initial multimodal large model to obtain a first prediction label; determining a first loss value and a second loss value based on the first prediction label; adjusting the initial multimodal large model based on the first loss value to obtain an intermediate multimodal large model; performing a second perturbation operation on the multimodal feature to obtain a second perturbation feature, inputting the second perturbation feature into the intermediate multimodal large model to obtain a second prediction label, and determining a third loss value based on the second prediction label; and adjusting the initial multimodal large model based on the second loss value and the third loss value to obtain a target multimodal large model. Through the application scheme, the calculation resources are saved, and the training time is reduced.

The invention provides a defect detection method based on a feature propagation semi-supervised network, and the method comprises the steps: S1, obtaining a sample image, and constructing a semantic segmentation label data set and a non-label data set; s2, a DualSFDNet model is constructed; s3, based on a semi-supervised learning optimization strategy, performing optimization training on the DualSFDNet model by utilizing a semantic segmentation label data set and a label-free data set; s4, collecting a high-definition surface image of a to-be-detected chip in real time, obtaining three random cutting images of a main body area of the to-be-detected chip, and inputting the three random cutting images into the trained DualSFDNet model to obtain three defect semantic segmentation masks; and S5, according to the three defect semantic segmentation masks, quantifying and visualizing the defect information of the to-be-detected chip to obtain a defect detection result. The method effectively solves the problems that the insignificant defects are difficult to detect, the manual labeling efficiency is low, and noise is easy to introduce in modular training.

The invention discloses a xanthoceras sorbifolia bunge fruit counting method based on an unmanned aerial vehicle video, and the method comprises the steps: obtaining an optimized xanthoceras sorbifolia bunge fruit detection model, and achieving the positioning frame detection of xanthoceras sorbifolia bunge fruits according to the optimized xanthoceras sorbifolia bunge fruit detection model, so as to obtain a positioning frame detection score; based on a preset score threshold value, dividing the positioning frame detection score into a high score detection frame and a low score detection frame; based on a camera global motion compensation mechanism, a global affine transformation matrix used for describing camera motion parameters is obtained according to the video frame sample graph, and based on an improved adaptive Kalman filtering algorithm, trajectory tracking is performed on the xanthoceras sorbifolia bunge fruits according to the global affine transformation matrix, and a tracking detection frame is obtained; based on an IoU-ReID fusion mechanism, a matching strategy can be flexibly adjusted in different scenes so as to confirm the number of xanthoceras sorbifolia bunge fruits. According to the method, the problem that the accuracy and reliability of automatic counting are reduced due to errors caused by target shielding, motion blur or repeated detection in a traditional counting method is solved.