88 results about "Label propagation" patented technology

The invention relates to a real-time power grid topology analysis method based on a graph neural network, and the method comprises the steps: firstly, carrying out the electrical topology analysis at a substation level through constructing an optimized power system physical connection model, carrying out the electrical topology analysis in combination with a label propagation algorithm (LPA), precisely recognizing a calculation region, optimizing the division of the calculation region through employing the graph neural network (GNN), and enhancing the topology adaptability, mistaken division is reduced; and the robustness of equipment state change is improved. Besides, according to the method, the real-time performance, the accuracy and the intelligent level of power grid topology analysis are further improved by predicting the influence of the state change of the circuit breaker and the disconnecting switch on the power grid topology and training a GNN model and reinforcement learning (RL) optimization region division strategy through historical data. And finally, bus-branch connection is optimized through depth-first search (DFS), the topology integrity is ensured, and the efficiency of electrical island analysis, power supply area division and fault recovery is remarkably improved. The real-time performance, the accuracy and the intelligent level of power grid topology analysis are effectively improved.

The invention discloses a key sensor short-time abnormal distribution drift detection method in a unit start-stop process, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: synchronously triggering multi-channel signal collection through a main clock, and achieving noise suppression and data pre-screening through the combination of first-order difference and threshold filtering; constructing a nonlinear weighted feature matrix, and fusing a time attenuation coefficient and a shafting acceleration factor to enhance the transient feature expression capability; generating a sensor association graph based on double-threshold determination of weighted Pearson's correlation coefficients and mutual information, and dividing stable subgroups by using an incremental label propagation algorithm; designing a double-layer Cluster-GCN model, aggregating subgroup internal characteristics in the first layer, introducing a fuel valve position-acceleration comparison gating mechanism in the second layer to correct a global edge weight, and generating a node embedding vector sensitive to working condition change; gaussian kernel density estimation and an instantaneous deviation index of embedding similarity are fused, and a historical sliding mean value and subgroup connectivity analysis are combined, so that sensor faults and working condition abrupt changes are distinguished.

The invention relates to the technical field of intelligent education management, in particular to a self-adaptive learning progress informatization teaching test system, which comprises the following contents: a knowledge node construction module, a learning state identification module, a test task generation module, a path scheduling adjustment module and a feedback data backtracking module. According to the method, refined numbering management of knowledge points is realized by constructing a directed edge numbering set, a dependency direction is set in combination with a label propagation algorithm, the semantic expression ability of a node relationship is enhanced, mastering weights and stability coefficients are matched based on answering behaviors, a state weight sequence is formed, and the stability of classification and recognition is improved; cognitive ability coding and time-consuming median value linkage state weight are introduced, questions are screened, a priority queue is constructed, task pushing precision is improved, numbering and sorting weight are fused to construct a path chain, dependency intensity is calculated to expand path branches, and individual suitability and task rhythm coordination ability of path scheduling are enhanced.

The invention relates to the technical field of data processing, and discloses a fault analysis method and system based on a label propagation algorithm. The method comprises the following steps: acquiring operation state data through a monitoring equipment network, extracting the data to obtain a system topology data set, performing label propagation fault analysis on the system topology data set to obtain node fault information, and performing fault influence weight analysis on each node based on the node fault information to obtain a maintenance priority, and inputting the influence weight data into the graph neural network for coping strategy analysis to obtain a target processing strategy. According to the invention, the efficiency of fault propagation analysis and the accuracy of decision making are improved.

The invention discloses a micro spring washer manufacturing process optimization method using a collaborative filtering algorithm, and the method comprises the following steps: S1, collecting and preprocessing historical manufacturing data, and constructing a tensor structure comprising manufacturing batches, process parameters and product quality indexes; s2, converting the tensor structure into a scoring matrix, and predicting an initial process parameter combination of a target batch by adopting a collaborative filtering algorithm; s3, constructing a weighted graph, introducing a label propagation network to complement scores, and determining a recommendation parameter combination; s4, constructing a multi-objective optimization model with size consistency, residual stress balance and unit energy consumption as objectives based on the recommended combination; s5, solving by adopting an improved cat swarm optimization algorithm, and outputting an optimal solution; and S6, applying the optimal solution to the manufacturing process, collecting quality result feedback, updating the scoring matrix and the graph model, and realizing a manufacturing optimization closed loop. According to the method, the tag propagation network and the improved cat swarm optimization algorithm are fused, and the manufacturing process of the miniature spring washer is optimized.

The invention discloses a small sample point cloud semantic segmentation method, computer equipment and a storage medium, and relates to the technical field of three-dimensional computer vision, the small sample point cloud semantic segmentation method mainly comprises the following steps: according to a support set point cloud and a query set point cloud, using an embedded network to obtain support set features and query set features, using a feature correlation matching module to obtain enhanced support set features and enhanced query set features; according to the enhanced support set features, obtaining a support prototype by using a similarity perception prototype number constraint method and a multi-prototype generation method; according to the enhanced query set features and the support prototype, utilizing a prototype correction module to obtain a corrected support prototype; and according to the enhanced query set features and the corrected support prototype, obtaining a prediction mask by using a KNN graph construction and label propagation method. By implementing the small sample point cloud semantic segmentation method, the computer equipment and the storage medium provided by the invention, the small sample point cloud semantic segmentation precision can be improved.

The invention discloses a bacterial colony image time sequence classification tracking method based on dynamic time warping and label propagation, and solves the problems of inconsistent classification and accurate segmentation of bacterial colonies with different sizes caused by the change of bacterial colony morphology along with time. The method comprises the following steps: acquiring a bacterial colony image feature sequence and constructing a bacterial colony feature distance matrix; calculating an accumulated distance matrix based on a dynamic time warping algorithm, and calculating accumulated distance values point by point through a recursion formula; through a path backtracking algorithm, finding a time point matching path which minimizes an accumulated distance value between bacterial colonies, and obtaining an optimal time alignment relationship between feature sequences of different bacterial colonies; constructing a similarity matrix, and calculating a similarity value between bacterial colonies; and a classification corresponding relation between time points is established by adopting a cross-time-point target association algorithm, and the bacterial colony classification label at the first time point is propagated to the subsequent time point, so that the consistency of the time sequence classification labels is ensured.

The invention provides an automatic user label construction method and system based on multi-source heterogeneous data, and relates to the technical field of data mining, and the method comprises the steps: obtaining multi-source behavior data, carrying out the feature fusion through a bidirectional attention mechanism, and carrying out the clustering center dynamic adjustment based on time sequence drift. And constructing a multi-level label structure tree by using a label propagation algorithm considering node time sequence evolution characteristics. According to the method, heterogeneous data source information can be effectively integrated, user behavior changes can be adaptively captured, the tag accuracy and timeliness are improved, and support is provided for precise marketing and personalized recommendation.

The invention provides a large-model intelligent label synthesis and data automatic labeling integration method and system, and belongs to the technical field of label synthesis and data labeling, and the method comprises the steps: carrying out the semantic embedding and robust clustering of text data, and obtaining a stable cluster set; when new data is introduced, semantic consistency alignment of cross-round clustering results is realized through a confusion matrix matching strategy, and label drift is inhibited; maintaining an editable hierarchical label directed acyclic graph to support label system evolution; driving a large language model to generate a high-quality and interpretable cluster-level semantic tag based on the representative sample; carrying out automatic annotation and confidence evaluation by using large model context learning for clustering non-attribution or low-confidence samples; and propagating the cluster-level labels to the instances, and combining the cluster-level labels with an automatic labeling result to construct a full-process traceable label management mechanism. According to the method, the efficiency, quality and consistency of text labeling are improved, and powerful support is provided for large model training and intelligent data management.

The invention provides a cross-individual human body behavior recognition method based on self-training and active inquiry, and the method comprises the steps: obtaining a source domain data set with a label and a target domain data set without a label; processing acceleration data in the source domain data set with the label and the target domain data set without the label; dividing the target domain data into a label-free training set and a test set in proportion; establishing a feature extractor based on a dual-channel convolutional network, and extracting features of time and space dimensions from the input two-dimensional acceleration data at the same time; a cross-individual adaptation algorithm based on a confidence threshold value, sparse query and label propagation is adopted, a model is trained on a non-label training set on a target domain, and the adaptation problem caused by cross-individual data distribution difference is relieved. According to the method, the behavior recognition accuracy of the model in a cross-individual scene can be improved, the development cost and the user burden are reduced, and a more intelligent and adaptive solution is provided for application of wearable equipment such as exoskeleton robots.

The invention discloses a knowledge graph fusion method, device and equipment based on label propagation. The method comprises the following steps: acquiring a label propagation command, wherein the command comprises a knowledge graph list and a result output path; respectively executing a label propagation algorithm on each knowledge graph in the knowledge graph list, and determining initial label value distribution of each knowledge graph; determining an equivalent entity set among different knowledge maps; based on an initial label value and a local topological structure of each equivalent entity in the equivalent entity set in the knowledge graph, fusing the initial label value and the topological structure to obtain a fused entity, a fused label value and a fused topological structure; and according to the fusion entity, the fusion label value and the fusion topological structure, fusing the at least two knowledge maps in the knowledge map list to obtain a fusion result, and storing the fusion result in a result output path. According to the embodiment of the invention, cross-graph complex semantic calculation and conjoint analysis are supported.

The application provides a local consistency guided sparse label enhancement method, which is suitable for road drivable area detection and belongs to the technical field of images. The method aims to solve the problem that the existing deep learning model depends on a large amount of pixel-level label data. Firstly, the input image is sparsely labeled, and context enhancement features are constructed according to local and global image representations to establish the similarity relationship between superpixel nodes. Then, a label propagation model is constructed based on a graph convolution network to propagate sparse labels to unlabeled areas to generate pseudo labels. In the application, a local consistency guided weak supervision training strategy is adopted, and a joint loss function is designed to cooperatively supervise the labeled areas and the unlabeled areas, thereby improving the reliability of the pseudo labels and the overall segmentation accuracy. Experimental results show that the application can be applied to various road drivable area detection tasks, and the obtained high-quality pixel-level pseudo labels can be used for subsequent full-supervised model training.

The application belongs to the technical field of brain image analysis, and discloses a brain image data classification model establishment method based on a near neighbor propagation bias label learning, which comprises the following steps: S1, acquiring a training data set and corresponding original labels, solving correlation coefficients between samples and degrees of centrality of the samples, and generating a typical sample set and a category center set according to the correlation coefficients and the degrees of centrality; S2, determining candidate labels of training samples, and constructing a candidate label set; S3, performing label propagation by using a K+N near neighbor sample graph through the training data set, and establishing a classification model. The application can improve the accuracy and precision of the classification model.

The invention relates to the technical field of power system stability control, and provides a new energy black-start partition optimization method considering unconventional risks, which comprises the following steps: simulating unconventional risks possibly encountered by a power system by adopting Monte Carlo sampling; extracting the output characteristics of wind power under the unconventional risk by adopting variational mode decomposition, and performing output prediction in combination with a long-short-term memory neural network; a power system recovery partition is divided based on an LPA algorithm, and the LPA algorithm is improved by considering a partition size balance principle, so that the partition area is more balanced, and the tag oscillation effect is reduced. According to the method, the black-start partition optimization model fusing new energy output uncertainty modeling and unconventional risk scene generation is designed, and the partition scheme is cooperatively solved by adopting the improved label propagation algorithm, so that the robustness and the recovery efficiency of the black-start partition are effectively improved; and rapid and stable power supply of a power system under complex disturbance can be realized.

The application relates to an individual brain mapping method and device based on a brain mapping fusion model. The method comprises the following steps: receiving at least two groups of subject data for training of a brain mapping fusion model, extracting data features of the subject data, and pre-processing the subject data to obtain an adjacency matrix; inputting the data features and the adjacency matrix into an initial brain mapping fusion model based on a graph convolution model and a label propagation model, obtaining brain mapping prediction values output by the initial brain mapping fusion model, training the initial brain mapping fusion model based on the graph convolution and the label propagation algorithm according to the brain mapping prediction values, and obtaining a trained brain mapping fusion model; and inputting to-be-detected data into the trained brain mapping fusion model to obtain an individual brain map. The method can combine the graph convolution algorithm and the label propagation algorithm to process the subject data, and improve the drawing speed and precision of the individual brain map.

This invention discloses a microblog group identification method based on community discovery, comprising the following steps: S1, data collection and cleaning; S2, feature extraction and representation; S3, establishing a classification model; S4, community tagging and influence analysis. In this invention, an optimized Dynamic Topic Model (DTM) is used to mine specific groups within the microblog community. Microblog posts from the past year are selected as the research object, and the similarity of topics in posts from different authors is used as the weight of links between authors, mapping the microblog network into a directed weighted network. Community discovery is performed using the Label Propagation Algorithm (LPA), identifying the inherent community structure within the social relationship network. This invention conducts in-depth analysis of user relationships within the microblog network, and based on identification methods for user-generated content characteristics, user association characteristics, and environmental characteristics, it mines potential topics to identify users with similar interests and active user groups in specific fields.

The application provides a large model intelligent label synthesis and data automatic labeling integrated method and system, and belongs to the technical field of label synthesis and data labeling. The method comprises the following steps: performing semantic embedding and robust clustering on text data to obtain a stable cluster set; when new data is introduced, the semantic consistency alignment of cross-round clustering results is realized through a confusion matrix matching strategy to suppress label drift; an editable hierarchical label directed acyclic graph is maintained to support label system evolution; a large language model is driven based on representative samples to generate high-quality and interpretable cluster-level semantic labels; for samples that are not attributed or have low confidence in clustering, automatic labeling and confidence evaluation are performed through context learning on the large model; the cluster-level labels are propagated to instances and combined with the automatic labeling results to construct a full-process traceable label management mechanism. The application improves the efficiency, quality and consistency of text labeling, and provides strong support for large model training and intelligent data management.

The present application is applicable to the field of point cloud semantic segmentation technology, and provides a small sample point cloud semantic segmentation method, network, storage medium and processor. The small sample point cloud semantic segmentation method uses label propagation to extract pseudo-prototype features that are adapted to the query set data, thereby obtaining prototype features that are adapted to the query set data, and performs feature calibration by extracting the relationship between the prototype and the query set data. Prototype expansion effectively utilizes the distribution information of the query set data and the prototype information of the support set. This further improves the adaptability of the prototype to the query set data. Therefore, the present invention can obtain prototype features that are adapted to the query set, achieve effective segmentation of the point cloud scene, and reduce the impact of misjudgment results on the expanded prototype.

The application discloses a semi-supervised high-resolution remote sensing image change detection method based on label expansion, comprising the following steps: setting an input data set; constructing an encoder-decoder change detection model; constraining the consistency of the prediction results between weak enhancement and strong enhancement in the construction of the encoder-decoder change detection model; expanding pseudo labels through a position interaction graph; optimizing data through supervised loss and unsupervised loss; extracting features of the input data set through an encoder to obtain differential features; and obtaining a change detection probability graph through decoding of the differential features by a decoder. The application proposes a consistency regularization framework of one weak and two strong, and constrains the consistency of the prediction results between weak enhancement and strong enhancement and between the two strong enhancements. The position interaction graph is introduced, the global-local relationship between pixels is utilized, and the internal consistency of pseudo labels is mined, so that the model precision is improved.

The invention discloses a data processing method, electronic equipment, a storage medium and a computer program product, and relates to the technical field of large model technology and data query. The method comprises the following steps: acquiring seed data; pre-labeling the seed data in a label propagation mode to obtain labeled data; performing data quality inspection on the labeled data to obtain training data; and training the initial intention classification model by adopting the training data to obtain a target intention classification model. The technical problems that the training cost of a model with a tool selection function is high, the efficiency is low, and the tool selection accuracy and the query accuracy of the model obtained through training are poor in the prior art are solved.

The invention discloses a power grid partitioning method based on partial differential morphology and label propagation, and the method comprises the steps: abstracting a power network into a weighted graph, and based on a selection strategy of electrical centrality, namely selecting k nodes with the maximum electrical centrality as seed nodes, obtaining a seed node set, and distributing a unique label for each seed; morphological diffusion and label propagation are combined in a power grid partition, and outward diffusion propagation is carried out from a seed node set; label propagation and morphological diffusion run in parallel to dynamically adjust label attribution of nodes, and each node is endowed with a final label after multi-round iteration when the cost of all the nodes does not change any more, so that division of the power grid is completed. According to the method, efficient partition control and management of the power grid can be effectively achieved, accurate identification and reasonable division of the topological structure of the power grid are achieved, and scientific support is provided for planning and management of a power system.

The invention discloses a label propagation text classification method and device generated by fusing weak supervision information, and relates to the technical field of text classification. The method comprises the steps of selecting an initial category word set, pre-training an initial multi-label text classification model, inputting an original text into an encoder layer to obtain deep potential features, and inputting the deep potential features into a prediction layer to obtain an initial classification prediction result; determining a pseudo label set; gradually updating the pseudo label set, and further training the pre-trained multi-label text classification model; an integrated pseudo label set is obtained; determining an adjacent matrix of the k-neighbor graph; determining a label correlation matrix; performing noise correction on the integrated pseudo label set, and performing final training on the multi-label text classification model; and according to the trained multi-label text classification model, obtaining a label corresponding to the to-be-classified text. According to the invention, the noise supervision information is corrected by using the text neighbor relation and the label correlation, so that the classification accuracy is improved.

The application discloses a hardware design security vulnerability qualitative analysis method and system, according to a mixed attribute label propagation logic element library and a mixed attribute model construction method based on discrete mapping, so that a corresponding mixed attribute model can be constructed for any HDL design in linear time; the label designed by the mixed attribute model integrates two types of attributes, so that the safety behavior related to the safety attribute and the clock attribute can be modeled simultaneously; the method provided by the application can realize effective discrimination of hardware Trojan horses and hardware time measurement channels by verifying the clock attribute and the safety attribute; the application is deployed in the design and verification stage of the EDA process, so that the HDL design security vulnerability can be detected early, and the design basis is provided for high-reliability hardware design.

The invention relates to the field of data security assessment, and discloses an automatic classification and grading method and system for sensitive data of oil-gas exploration and development, and the method comprises the steps: carrying out the data feature engineering processing of multi-source heterogeneous data stored in a data lake, extracting name features, business ranges, data features and initial security grading information, constructing a data feature vector; based on the labeled data samples, calculating feature similarity of data feature vectors, constructing a relation network among data items, performing label propagation and security level evaluation, transmitting known security level information to unlabeled data items, and realizing automatic grading; a rule model mixing mechanism is adopted, a data security level classification result and a user feedback iterative optimization security classification result are output through label diffusion model evaluation, feedback data are applied to carry out label diffusion model retraining, and a classification result is obtained. According to the method, the security level automatic evaluation of multiple types of data can be realized through the label propagation algorithm based on the data association relationship.

The application discloses a pattern element grouping method and device based on hypergraph learning, and the method comprises the following steps: performing a pretreatment operation on a pattern, and obtaining an image segmentation result of elements in the pattern; extracting gestalt rule features of the pattern elements, including similarity, proximity, continuity and mixed features; modeling the correlation between the pattern elements based on different gestalt rules by using a hypergraph; designing an adaptive hypergraph fusion method to obtain a hypergraph fused with multiple gestalt rules; and using a label propagation method based on hypergraph learning and user guidance to obtain a grouping result of the pattern. The application proposes a representation of gestalt grouping rules on pattern elements, models the connection relationship between the pattern elements according to different grouping rules by using a hypergraph, and uses the hypergraph for unified representation, so that the fusion of the gestalt rules is realized, the problem of gestalt rule conflicts is effectively solved, and personalized grouping of the pattern elements according to the grouping intention of a user is realized.

The invention relates to a multi-level community identification method with a priority core node and a similar structure in a population flow network, and belongs to the technical field of community detection and complex network processing. The method comprises the following steps: quantizing the significance of a flow edge weight through an exceeding probability, and repeatedly extracting core node pairs to combine and form a microscopic community; identifying a core node by adopting a C-index centrality measurement index, and performing label alignment on the microscopic community and the core node; based on the node destination travel distribution sequence, calculating structural similarity by using a dynamic programming algorithm; and carrying out label propagation by combining with structure similar neighbors to generate a final macroscopic community. According to the method, the diversity of flow directions, the relative importance of flow edges and the distribution characteristics of travel structures in a flow network are comprehensively considered. Compared with a traditional algorithm, the method can more accurately describe the central distribution and the multi-layer structure of the population flow network, and can provide a scientific basis for urban agglomeration evolution analysis, traffic planning and sustainable city development.

The application relates to a hierarchical incremental label propagation method and device based on a probability distribution. The method comprises the following steps: constructing a relationship network through relationships among a plurality of users; assigning user labels to a first part of users in the relationship network according to a label strategy; generating a label dimension set of a second part of users in the relationship network through a probability distribution of the user labels of the first part of users according to a hierarchical label propagation algorithm; and determining user labels of the second part of users according to the label dimension set. The hierarchical incremental label propagation method and device based on the probability distribution, the electronic equipment and the computer readable medium can quickly and accurately determine user labels of users without labels in actual application, meet the demand of calculation and analysis, and compared with a label assignment mode in the prior art, the mode in the application improves the calculation speed and reduces the occupation of content and calculation resources.

The application discloses a battery application management and control method, which dynamically constructs a time sequence sequence prediction model cluster of multiple attribute characteristics based on multiple battery attribute characteristics, selects attribute characteristics most valuable for safety risk prediction online, adapts to safety risk prediction needs of different manufacturers, different models and different working conditions of batteries, and expands safety risk sample data by using a label propagation algorithm, deeply mines and fully utilizes unlabeled data, and can more accurately and stably perform safety risk prediction on lithium batteries.

The invention relates to the technical field of voice keyword detection, in particular to an anti-noise detection method and device for voice keywords, equipment and a storage medium. Comprising the steps of performing acoustic feature extraction on an original audio signal to obtain an acoustic feature sequence; calculating an attention weight vector in combination with the target phoneme sequence and the acoustic feature sequence; performing weighted fusion on the acoustic feature sequence based on the attention weight vector to obtain a fused feature sequence; performing feature coding on the fused feature sequence to obtain a task feature sequence, and obtaining posterior probability distribution; updating a preset decoding graph according to the attention weight vector to obtain a dynamic decoding graph, and performing identification transmission decoding based on the dynamic decoding graph and the posterior probability distribution to generate at least one candidate path; and determining confidence scores of all candidate paths, and determining a keyword detection result corresponding to the original audio according to the confidence scores. According to the application, the reliability of the keyword wake-up technology in the application can be improved.

The embodiment of the present application provides a community discovery method, device, equipment and computer storage medium, and belongs to the field of network analysis. The embodiment of the present application obtains user nodes and edges in a community, the edges include behavior relationships between the user nodes, the edges include edge labels, calculates the influence of the edges by the ratio of the degree of the edges and the degree of the connecting edges of the user nodes connected with the edges, the degree of the edges includes the degree of the user nodes connected with the edges, and updates the edge label of the edges to the edge label with the highest appearance frequency in the edge labels of the connecting edges of the user nodes connected with the edges according to the order of the influence of the edges from large to small. The edge label of each edge is used as the label of the user nodes connected with the edge, and the user nodes with the same label are determined to be in the same community. The embodiment of the present application can avoid randomness in the label propagation process, and further avoid that the user nodes are divided into inappropriate communities, thereby improving the accuracy of community division.