2405 results about "Labeled data" patented technology

The invention discloses an intelligent substation communication link fault accurate positioning method and system, and the method comprises the steps: obtaining a configuration file and equipment state data, carrying out the processing of the configuration file and the equipment state data, and generating a standardized link feature vector and a marking data set; constructing a hybrid deep learning model, and optimizing parameter configuration of the hybrid deep learning model by adopting an optimization algorithm to obtain a parameter-optimized hybrid deep learning model; training by using a real fault sample in combination with a virtual fault sample generated by a generative adversarial network, optimizing a time sequence prediction capability through an echo state network, and generating a fault positioning model; in combination with the link state data, outputting a fault link positioning result and confidence evaluation through multi-stage confidence evaluation and topological correlation analysis; and carrying out virtual-real corresponding verification in combination with the configuration file, carrying out parameter optimization on the fault positioning model, and outputting a fault positioning system. The problems that the fault positioning precision is low, the response speed is low, and complex fault scenes cannot be processed are solved.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention discloses a multi-source heterogeneous medical data fusion and intelligent diagnosis method, and relates to the technical field of medical data processing and intelligent diagnosis, and the method comprises the specific steps: firstly, synchronously collecting medical images and clinical text data of a patient, and carrying out the correlation and integration to form a heterogeneous diagnosis data set; performing standardized feature extraction to obtain a feature set in a unified format; then constructing a parallel model, fusing features by using a cross-modal attention alignment technology, and guiding correction by means of a knowledge graph; and finally, the cross-modal diagnosis features are input into the reference model, automatic focus positioning is realized through processing, and a visual marker graph is output. Heterogeneous data of medical images and clinical texts are synchronously integrated, and the diagnosis feature reliability is improved through standardization processing, feature fusion and the like; a focus sensing mask is generated through comparison with a normal model, a multi-scale feature fusion technology is combined to realize automatic and accurate positioning of the focus, a large amount of labeled data is not needed, the process is simplified, and the diagnosis efficiency and accuracy are improved.

The invention relates to the technical field of data processing, and discloses a knowledge graph incremental updating and consistency checking method and system. The method and the device are used for solving the problem of low incremental updating and consistency checking efficiency of a large-scale knowledge graph. The method comprises the following steps: collecting a to-be-updated data source, sorting and separately storing data, and marking priority labels; performing change detection on the marked data source, identifying change items by comparing entities and relationships, and generating a log; incremental updating is executed based on the log, and nodes and edges are processed in a hierarchical fusion mode; preliminary consistency verification is carried out, and attribute uniqueness and relation directivity are checked; expanding a verification range, traversing an association path through cascade check, and recording problems; and optimizing storage according to the record, merging the update area, updating the index and cleaning the log. The method solves the problem of low efficiency of incremental updating and consistency verification of the large-scale knowledge graph, improves the response speed and the data accuracy of the system, and is suitable for a high-frequency dynamic data environment.

Provided is a method for perceiving and eliminating an abnormal state of active distribution network based on data enhancement, including: acquiring, by synchrophasor measurement device, data of each node of active distribution network in target domain in real-time and transmitting to processor; inputting the acquired data into a classification model, and outputting abnormal detection and classification results in real time; and analyzing the abnormal detection and classification results, and transmitting an abnormal state eliminating instruction to a distribution terminal to eliminate the abnormal state. Wherein, hidden distribution features in node data of active distribution network are mined through dynamic clustering, a large amount of unlabeled data are clustered, a data label is generated through self-coding and label correction rule, training samples with balanced category distribution is generated through data enhancement and is used to train the classification model based on dynamic graph attention network by domain adaption method.

The invention discloses an intelligent analysis system for ultrasonic phased array detection data of a welding seam, and relates to the technical field of nondestructive testing, and the intelligent analysis system is characterized in that a four-dimensional wave field tensor is constructed by collecting full-waveform ultrasonic data under multi-channel, multi-path and multi-angle conditions; extracting reflected signals which keep time coherence in a discontinuous path, and generating a high-dimensional coherence feature matrix; inputting the features into a self-supervised contrast learning model to obtain a defect semantic embedding vector; recognizing a suspected weak defect area based on the distribution density and the boundary change trend, and performing reverse beam focusing in combination with original data to obtain a defect three-dimensional positioning map; the shape bifurcation index and the boundary stability are calculated through topological analysis, and intelligent discrimination of artifacts and microcrack defects is achieved; the method does not need label data, has high automation and robustness, and is suitable for weak defect identification of complex structure welding seams.

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 GIS interactive visualization processing method and system for geographic coordinate data, and relates to the field of GIS and the technical field of computers. The method comprises the following steps: collecting multi-source geographic coordinate data, preprocessing the multi-source geographic coordinate data, and standardizing the preprocessed multi-source geographic coordinate data to obtain target multi-source geographic coordinate data; loading the target multi-source geographic coordinate data to a GIS map, and displaying the target multi-source geographic coordinate data through different layers; automatically detecting and marking data quality based on a display interface corresponding to the GIS map; and correcting the detected problem data based on a GIS map by adopting an interactive data correction mode. According to the method and the device, the technical problem of low data processing efficiency caused by difficulty in intuitively reflecting the spatial distribution characteristics and the association relationship of the data because the current geographic coordinate data treatment method mostly depends on the traditional data processing means and presents the data in the forms of texts and tables is solved.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention relates to an intelligent inspection data processing method and system. The intelligent inspection data processing method comprises the following steps: analyzing an infrared image in an inspection scene to obtain an image resolution, a temperature matrix and a pseudo-color image of the infrared image; synchronizing annotation information of the infrared image and the visible light image in the inspection scene to obtain multi-modal annotation data; based on preset reference object information, performing defect quantitative analysis on the multi-modal labeling data, and generating a real physical size quantitative result of the defect; and carrying out associative storage on the image resolution, the temperature matrix, the pseudo-color image and the quantification result. According to the invention, the whole-process optimization of the inspection data can be realized, and the processing stability, the defect detection accuracy and the system practicability are effectively improved.

Broadly speaking, the present techniques provide an automatic way of classifying data items within an environment (e.g. a business, workplace, organisation, etc.). This is advantageous over existing techniques which require manual classification of data items, which is time consuming in environments where hundreds of new data items may be generated in a day or week. The present techniques use an embedding machine learning, ML, model and an LLM to automatically determine the relevant classification label(s) for an unlabelled data item.

The invention relates to the technical field of artificial intelligence and data analysis, in particular to an unsupervised anomaly detection method and system based on comparative potential fusion. The method aims at solving the problems that in the prior art, an unsupervised anomaly detection method is limited in feature expression ability, sensitive in noise, insufficient in potential feature discrimination and lack of statistical interpretability in detection results. According to the method, the global potential features generated by comparison learning and the self-encoder reconstruction residual error are fused, the statistical model is combined for self-adaptive threshold judgment, the problems of insufficient feature expression and high noise sensitivity in multi-source heterogeneous time series data anomaly detection are effectively solved, and the method has the advantages that the detection precision and robustness are improved, and the dependence on labeled data is reduced.

The invention relates to the technical field of multi-peak spectrum demodulation, and particularly provides a fiber bragg grating multi-peak spectrum demodulation method and system. The method comprises the following steps: extracting local spectral features based on an experimental reference spectrum to form initial atoms, and performing translation offset and normalization processing to obtain an over-complete spectral atom dictionary; performing global offset preliminary estimation based on the dictionary, and obtaining preliminary estimation values of peak sites of the measurement spectrum and the reference spectrum through cross-correlation calculation; executing constraint orthogonal matching pursuit sparse recovery based on the estimated value, and recovering atomic displacement from the measurement spectrum by using block sparsity, translation consistency and non-negative constraint; and performing wind speed inversion and calibration based on the atomic displacement, and converting the wind speed into a wind speed estimated value through a nonlinear calibration model to obtain a final result. According to the method, a dictionary based on experimental data is constructed, dependence on large-scale labeled data is reduced, a physical mechanism and sparsity prior are fused, and the problems that a traditional method is insufficient in precision and weak in generalization ability in a complex environment are solved.

The invention discloses an incomplete multi-view multi-label data classification method based on semantic enhancement and pseudo-label uncertainty perception, and the method comprises the steps: employing a dual-channel feature extraction and decoupling module to obtain the shared semantic representation and specific representation of each view in each sample for a constructed incomplete multi-view multi-label data classification network model; performing cross-view fusion on the shared semantic characterization and the specific characterization, obtaining a unified shared characterization and a unified specific characterization corresponding to each sample, performing feature fusion, obtaining a fusion characterization of each sample, inputting the fusion characterization of the sample output by the dual-channel feature extraction and decoupling module into a classifier for multi-label prediction, and performing multi-label prediction on the fusion characterization of the sample. Therefore, a multi-label classification prediction result is obtained, and model training is carried out based on a total contrast learning loss function and a joint supervision classification loss function. According to the method, the classification performance and the model robustness on incomplete multi-view multi-label data are remarkably improved through training learning under the guidance of semantic enhancement and uncertainty.

The invention discloses a smart city planning three-dimensional scene reconstruction optimization system combined with semantic segmentation, and belongs to the technical field of three-dimensional scene reconstruction optimization. The system comprises a data sensing module which collects aerial images and laser point clouds, and complements a sheltered area to obtain city modeling data; the preprocessing module performs denoising, data registration and data fusion on the city modeling data to generate texture point cloud data; the semantic understanding module realizes multi-modal semantic segmentation of the texture point cloud data through a fine-tuned SAM network and an improved RandLA-Net, and semantic tag data is obtained through restoration and optimization; the reconstruction optimization module constructs an initial three-dimensional grid model based on the texture point cloud data, and optimizes a ground feature boundary and a missing region of the model in combination with a semantic tag; and the output application module converts the optimized model into a standard format and outputs the model. Through deep coupling of semantic segmentation and reconstruction optimization, the precision of the three-dimensional scene model is improved, and reliable support is provided for smart city planning.

The invention relates to the technical field of data intelligent traceability, and discloses a whole-process business data intelligent traceability method and system. The method comprises the following steps: collecting first data of a preset business link and creating a semantic tag to form a semantic tag data set; the semantic tag data set is stored in a Merkle tree structure, and a service block chain is constructed; extracting second data of each business link from the business block chain, and generating a fusion data set based on the second data; and establishing a relation graph according to the fused data set, after receiving a traceability query request, starting to search a traceability path from a starting service point in the relation graph, and outputting a traceability result, thereby ensuring the reliability of the traceability result, and solving the problems of serious data island, missing association relationship and low traceability precision of the traditional traceability technology.

A flow tagging technique includes tagging a data flow at a plurality of points in the data flow. For example, the data flow can be tagged at a socket and at a proxy manager API. By tagging the data flow at multiple points, it becomes possible to map network service usage activities to the appropriate initiating applications.

The invention relates to a DAS noise modeling method based on a conditional diffusion model, and belongs to the technical field of distributed optical fiber sensing data processing. DAS noise data is acquired and analyzed, a DAS-conditional diffusion model is constructed, and model training and data generation are carried out. According to the method, the statistical characteristics and conditional constraints of the DAS noise are fused in the diffusion process, so that the physical consistency and diversity of the generated noise are effectively improved, and the problem of lack of high-quality mark data in the DAS system is solved. Experiments show that the generated noise and the real noise are highly similar in time-frequency domain and statistical characteristics, the performance of the denoising network based on the extended training set is remarkably improved, high-quality data support is provided for tasks such as DAS data denoising and signal enhancement, and the application capability of the DAS technology in a complex environment is enhanced.

The invention provides a collaborative classification method and system fusing advantages of large and small models, and the method comprises the steps: inputting to-be-classified data into a trained zero-sample classification small model, and outputting candidate label domain screening information and an initial classification result; inputting the to-be-classified data, the candidate label domain screening information and the initial classification result into the trained large model, and outputting a final classification result; the training process of the small model comprises the following steps: inputting training data into the zero sample classification small model to obtain a preliminary prediction result; screening and obtaining pseudo label data based on an active learning strategy; checking and re-marking the pseudo-label data by using the large model to obtain a modified pseudo-label data set; and carrying out iterative training on the zero sample classification small model by utilizing the modified pseudo label data set. The method has the advantages that a small model has classification performance close to that of a large model while keeping lightweight calculation characteristics; the calculation burden of a large model is reduced, and the accuracy of classification decision is improved.

The invention provides an electric power line defect detection method and system based on visual identification, and relates to the technical field of line detection.The method comprises the steps that firstly, a visible light and infrared image dual-light registration and differential operation technology is adopted, and a fusion feature map capable of reflecting component thermal anomaly and material difference at the same time is generated; secondly, a black box type target detection model is abandoned in a part positioning link, but line segment screening and reconstruction are carried out by combining probability Hough transform with specific prior geometric knowledge of a power line, so that dependence on a large amount of labeled data is reduced, interpretability of a positioning process is enhanced, and the positioning accuracy is improved; according to the method, accurate areas of key components such as wire insulators can be extracted in a complex background, the concept of a probability saliency map is introduced in a defect identification core link, so that a defect area is enhanced and highlighted, and then a complete defect contour is determined by adopting an adaptive threshold segmentation and area growing algorithm; accurate mapping from pixel-level features to object-level defects is realized.

The invention discloses a semi-supervised medical image segmentation method based on causal uncertainty decomposition, and belongs to the field of medical image processing and artificial intelligence. According to the method, a segmentation model of teacher and student architectures is constructed, and a causal uncertainty decomposition module, a self-adaptive consistency learning module and a topology perception consistency loss module are integrated. The total uncertainty is decomposed into cognitive uncertainty and random uncertainty, so that targeted processing is realized; a dual-path weight fusion and differential modulation strategy is adopted to realize pixel-level adaptive learning; and a Betti number is introduced to calculate a topological distance, so that the integrity of an anatomical structure is kept. According to the method, under the condition that only 5%-20% of annotation data is used, the Dice coefficient on multiple medical image data sets is increased by 3.2%-4.8%, the segmentation precision and the boundary positioning accuracy are remarkably improved, the segmentation problem under the condition that medical image annotation is scarce is effectively solved, and the method has important clinical application value.

The invention discloses a weak supervision pathological image tissue segmentation method based on text prompt learning. The method comprises the steps of feature extraction and initial class activation graph generation; using an MCRM module to optimize the initial class activation graph to obtain a refined class activation graph; and aggregating the plurality of refined class activation graphs to form a fused pseudo mask, taking the fused pseudo mask as a supervision signal, training a segmentation model, and after the training is completed, segmenting the new pathological image tissue by using the segmentation model. According to the method, a text prompt learning mechanism is utilized to focus the model on learning high-discrimination features, so that the influence of tissue co-occurrence is reduced. An initial class activation graph is optimized through a multi-mode class activation graph refining module, and the integrity of boundary segmentation is enhanced. Meanwhile, pseudo masks from different network layers are fused to train a segmentation model, and semantic segmentation of the pathological image is realized. According to the method, high-annotation data dependence is effectively relieved, and the generalization ability of the model is improved, so that application in the field of artificial intelligence-assisted medical treatment is promoted.

The invention belongs to the technical field of computer vision, and discloses a target identification tracking method based on a self-supervision mechanism, and the method comprises the steps: enhancing a self-supervision pre-training module through causality, constructing a causal sample pair through unlabeled video data, learning universal features through combining with comparison loss, and achieving the high-precision tracking without large-scale manual labeling. A multi-modal feature fusion and dynamic calibration mechanism further reduces dependence on annotated data, is especially suitable for industrial inspection, field monitoring and other scenes where data acquisition is difficult, significantly reduces time and labor costs in a data preparation stage, and broadens the application range of the technology in resource limited scenes; a causal reasoning and physical constraint mechanism is introduced, a dynamic relation between targets is modeled through a space-time causal graph, unreasonable tracks are filtered in combination with a physical rule, and complex conditions such as shielding, rapid movement and extreme weather are effectively dealt with; the dynamic feature calibration module corrects feature drift in real time, and ensures stable model performance in long-term tracking.

The invention discloses a motor fault diagnosis method and system based on voiceprint analysis, and relates to the related field of motor fault diagnosis technology, and the method comprises the steps: collecting sound signals, vibration data and working condition parameters during the operation of a motor, carrying out the preprocessing, separating the voiceprint features of the motor through a harmonic vector analysis method, and removing the irrelevant sound source interference; obtaining a pre-training comparison learning model through a small amount of motor fault data in combination with data enhancement, fault feature analysis and similarity calculation; constructing and training a motor fault diagnosis model, taking the motor voiceprint features, the vibration data and the working condition parameters as input, embedding a pre-training comparison learning model to learn fault information in the motor voiceprint features, extracting fault features through a time delay neural network, inputting motor operation data which are collected and preprocessed in real time into the trained model, and performing motor fault diagnosis. And outputting a judgment result of the motor fault type. The problem that an existing motor fault diagnosis model excessively depends on labeled data is solved, and model generalization is improved.

The invention discloses a semi-supervised LPI radar signal modulation identification system and method based on entropy perception pseudo labels, and relates to the technical field of radar signal processing and mode identification. The system comprises a preprocessing module, a multi-scale reconstruction enhancer, a classification backbone network and a semi-supervised training module. The multi-scale reconstruction intensifier is used for reconstructing dual-channel separation through high-frequency detail enhancement and a low-frequency structure and enhancing discriminative characteristics in a noise environment; the classification backbone network introduces an adaptive contraction unit to realize channel-level noise suppression; and the semi-supervised training module dynamically evaluates the uncertainty of the unlabeled samples by adopting an entropy sensing mechanism, and generates weighted pseudo labels to carry out consistency regularization training. The method realizes signal modulation identification based on the system. According to the method, the problem of feature shielding under the condition of low signal-to-noise ratio is solved, the dependence of the model on labeled data is reduced through a reliable pseudo label generation mechanism, and stable and efficient modulation identification can still be realized in a severe channel environment with scarce labeled data.

The invention provides a multi-document key phrase extraction method based on graph structure node influence, and relates to the technical field of natural language processing and text mining. Firstly, a candidate phrase set is generated through noun phrase extraction and standardization; secondly, a semantic relation between phrases is captured through local subgraph construction and a sliding window mechanism, and the semantic relation is integrated into a global phrase co-occurrence graph; thirdly, dynamically dividing theme communities based on two-dimensional structure entropy minimization and a potential game model, and identifying phrase groups with high semantic aggregation; then, cross-topic nodes are processed through a structure entropy heuristic function, and flexibility of topic division is enhanced; and finally, in combination with node influence sorting, extracting key phrases with theme representativeness and propagation capability. The method does not need to label data, is suitable for multiple fields of academic literatures, news texts and the like, has high efficiency, accuracy and universality, and provides an innovative solution for multi-document key phrase extraction.

The invention provides an SAR ship wake detection method based on multidirectional perception convolution and frequency-space fusion attention, and the method comprises the following steps: S1, collecting an SAR image with a ship wake target, arranging the SAR image into a data set, and marking the ship target in the data set; s2, converting the format of the data set labeled in S1 into a YOLO format, and dividing the data set into a training set, a verification set and a test set; s3, on the basis of YOLOv8, constructing an SAR ship wake initial detection model based on multi-direction perception convolution and frequency-space fusion attention; s4, training and verifying the initial detection model by using the training set and the verification set to obtain a final detection model; and S5, detecting the wake target by using the final detection model generated in the step S4, and outputting a detection result. The method has excellent detection precision and robustness for the ship wake target in the SAR image.

Systems and methods of the present disclosure provide systems and methods related to using foundational model(s) for wellbore applications. The foundational model(s) may be constructed using a deep learning model with high capacity to train using data at scale. Additionally, the foundational model(s) may be constructed from such well logs containing unlabeled data and may be constructed using self-supervised approaches. The foundational model is generalized and suitable for performing multiple downstream tasks / applications using the foundational model.

The invention relates to the cross technical field of artificial intelligence and medical image processing, in particular to a medical image segmentation system and method based on a wavelet bridge diffusion model and an efficient conditional random field. A WBDM-ECRF framework is constructed and comprises a discrete wavelet transform module, a BDM-T module, a BDM-S module and an ECRF module; decomposing the image through discrete wavelet transform, extracting a low-frequency sub-band, and enhancing the contrast ratio of a focus and normal tissues; the BDM-T takes U-Net as a backbone, integrates a FlashAttention mechanism, and optimizes a variance formula to realize efficient training; the BDM-S adopts a leapfrog sampling strategy, so that the reasoning time is greatly shortened; the ECRF introduces a multivariate potential function of a structural similarity index and smooth operation through edge expansion, and accurately optimizes edge segmentation. According to the method, the dependence of marked data is reduced, the training and reasoning efficiency is improved, the problem of fuzzy edge segmentation is solved, the Dice coefficient and intersection-union ratio performance on the ISIC data set is excellent, and reliable quantitative support is provided for disease diagnosis and treatment.

The invention discloses a meteorological data processing and storage method and system, and relates to the technical field of intelligent meteorological data processing, and the method comprises the steps: collecting multi-source meteorological observation data, radar data and numerical forecasting data, carrying out the access judgment, obtaining an access data set, carrying out the standardization and elevation correction, and obtaining a data set; calculating a comprehensive quality score to obtain a quality mark data set; screening samples based on the mass label data set to obtain an alignment data set, obtaining grid fusion data through the alignment data set, and performing smooth processing to obtain a fusion data set; obtaining a sealing partition based on the fused data set, and revising by using a late sample to obtain a versioned sealing partition; and obtaining a fact table and a dimension table according to the versioned sealing partition, establishing a topic domain model and a multi-version query interface, and calculating a prediction deviation and a hit rate. According to the method, unit standardization, spatial elevation correction and comprehensive quality scoring are performed on the accessed data set, so that dynamic evaluation and reliable marking of the meteorological data quality are realized.