2835 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 provides a risk abnormal behavior event identification method based on multi-source risk abnormal behavior data fusion analysis. The method comprises the following steps: S1, obtaining abnormal behavior label data; s2, constructing a knowledge graph ontology structure; s3, extracting entities, attributes and relationships involved in the structured data of the abnormal behavior label data into the constructed knowledge graph ontology structure; s4, for the constructed knowledge graph ontology structure, encoding graph data to obtain corresponding modal features; aiming at the structured data of the knowledge graph ontology structure, coding each source by adopting a corresponding feature coding method to obtain a corresponding modal feature; s5, the obtained modal features are input and mapped to the same vector space for alignment fusion; s6, performing fine tuning training to obtain an abnormal risk behavior recognition model LLM; and S7, superposing the fused multi-modal features, inputting the superposed multi-modal features to the LLM, and guiding the LLM to generate a corresponding output or decision according to the prompt of the specified input.

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 discloses a multi-modal collaborative video sequence segmentation method. The method comprises the following steps: obtaining a multi-scale local feature matrix and a multi-scale global feature matrix of an image sequence; obtaining a multi-scale text feature matrix of the text sequence; obtaining a multi-scale local-global fusion feature matrix of the multi-scale local feature matrix and the multi-scale global feature matrix; obtaining a multi-modal fusion feature matrix of the multi-scale local-global fusion feature matrix and the multi-scale text feature matrix; and utilizing a decoder of the pre-trained large model to predict and generate a segmentation mask, and outputting a semantic segmentation map. The video sequence segmentation method is stable in performance when facing complex and changeable scenes, does not need to depend on a large amount of labeled data, reduces the training cost, and is suitable for various practical application fields including intelligent monitoring, automatic driving, medical image analysis and the like.

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.

A method for developing a chatbot for mental health using generative Artificial Intelligence (genAI) and a system for recognition and recommendation are disclosed. The method comprises: designing a conversation flow, creating a flowchart that outlines the logical steps; defining guiding questions and the Depression, Anxiety, and Stress Scale (DASS) examination; fine-tuning large language models with the technical prompt engineering; collecting and labeling data for each model mental health issues detection: ill-being detection model and keyword recognition model; building pipeline and training Artificial Intelligent (AI) model for ill-being detection model and keyword recognition model with a Bidirectional Encoder Representations from Transformers (BERT) model or a pre-train model; collecting and processing mental health support resources such as Cognitive Behavioral Therapy (CBT) exercises, informative articles, inspiring movies, and effective coping strategies, and psychologists; developing mental health support resources system; and developing and integrating speech-to-text and text-to-speech models into the system.

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 discloses an intelligent data question answering and visualization system and method for leader decision making, and relates to the technical field of artificial intelligence, and the system comprises a knowledge base unit, an intelligent question answering module, a dynamic visualization unit, a quality control unit and a performance optimization unit. According to the method, the customized knowledge base is constructed through the knowledge base unit, cross-modal alignment and layered long context processing are weakly supervised during data fusion, the defects that cross-modal data labeling cost is high and long context processing is redundant can be overcome, and an interaction platform of the knowledge base and a data engine in the knowledge base unit is constructed through the intelligent question and answer module; the model mixing module mixes an NLP model, constructs a combined architecture of an LLM + field fine tuning model, ensures understanding of natural languages and intention analysis precision, introduces an online expert error correction mechanism, and reduces misjudgment during question answering, so that the processing capacity of long texts or complex logic can be ensured, missing of key information in question answering is avoided, and then the answering quality is ensured.

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 relates to the technical field of multi-sensor fusion, in particular to an automatic calibration method and calibration device for laser radar-camera external parameters, and the method comprises the steps: synchronously collecting the point cloud data and image data of a road scene based on a vehicle-mounted laser radar and a camera; performing semantic segmentation on the image data to generate a semantic mask set of each image; based on the point cloud data, geometric attributes of a road scene are extracted from the vectorized map, a laser radar coordinate system of the vehicle-mounted laser radar and points in the vectorized map are projected into the image in combination with the semantic mask set, and a target consistency function is constructed; and adjusting the external parameter matrix based on the target consistency function until the target consistency function reaches a maximum value, and completing automatic calibration. Therefore, the problems that the calibration precision is insufficient and the high-precision perception requirement of automatic driving is difficult to meet due to the fact that related technologies depend on a large amount of labeled data or a specific environment, the generalization ability is poor, and a high-precision map and road prior information cannot be combined 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 discloses an unsupervised domain adaptive medical image segmentation method based on multi-view alignment and pseudo label optimization, and aims to solve the problems of insufficient segmentation precision and low pseudo label quality caused by domain offset. According to the technical scheme, firstly, image level alignment is executed through a frequency domain smooth fusion module, and a class target domain image is generated; pre-training a segmentation network by using the image and generating an initial pseudo tag; then, through a two-stage optimization process, the integrity and the structural rationality of the pseudo tag are improved through prototype-based potential foreground completion and SAM-based structural perception enhancement in the process; and finally, on the basis of the optimized high-quality pseudo tag, constructing a multi-view prototype contrast learning framework to carry out final feature level alignment training. According to the method, the segmentation precision of the model on the label-free target domain is improved, and an effective scheme is provided for solving the challenge of scarcity of annotation data in medical image segmentation.

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.

The invention relates to a toxic text collection method and system based on retrieval enhancement generation, and the method comprises the steps: firstly obtaining target text data through a crawling platform, manually constructing a small-scale initial data set through a cold start mode, and injecting the initial data set into a knowledge base as basic data; and performing semantic retrieval on each batch of target texts, obtaining the first k texts with semantic similarity from the knowledge base, reasoning by adopting a plurality of large language models through thinking chain reasoning in combination with the texts, generating a toxic label, and labeling the target texts to be labeled in the current batch. And injecting the labeled target text into a knowledge base, continuously collecting and labeling data in an iteration mode, and continuously optimizing the performance of a retriever through an incremental learning mechanism in the iteration process so as to realize toxic text collection. According to the method, large-scale, fine-grained and high-consistency toxic text tagging corpora can be efficiently accumulated, and the problems of high tagging cost, inconsistent quality and poor expansibility in traditional toxic text data collection are remarkably relieved.

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 discloses a lithium battery health estimation method based on a feature alignment unsupervised migration network. The method comprises the following steps: acquiring a source domain data set and a target domain data set; performing normalization and sliding window processing on the data set to obtain a source domain sample and a target domain sample; inputting a source domain sample and a target domain sample into a feature extraction network combining LDC and T-SHIFT, and jointly modeling a global trend and a local difference feature of a capacity degradation feature; in the training process, supervised regression is performed through mean square error loss, and improved maximum mean value difference loss is introduced to align feature distribution of a source domain and a target domain, so that model parameters are optimized; and estimating the health state of the lithium battery based on the trained network. The method can still accurately estimate the state of health of the battery in the absence of target domain labeling data, effectively improves the cross-domain generalization ability and prediction precision of the model, and is suitable for application scenarios such as battery health management and life prediction.

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 relates to a traffic scene vehicle and event identification method based on cooperation of an edge small model and a cloud large model, and belongs to the technical field of intelligent traffic. Aiming at the problems of low recognition precision, dependence on a large amount of labeled data, incapability of recognizing unknown categories and the like in a complex environment in the prior art, the method provides a multi-modal traffic visual perception coding system, a large model enhanced small sample cooperative training algorithm and a dynamic trigger type double-model reasoning framework. Small target feature expression is enhanced through semantic and visual joint coding, dependence of a small model on annotation data is reduced by using a large model pseudo tag and knowledge distillation, and a cloud large model is dynamically called according to confidence and scene complexity for secondary discrimination. According to the method, the recognition robustness of the system under severe conditions is effectively improved, and the balance between open vocabulary perception and low-resource efficient deployment is realized.

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 relates to the technical field of distribution network management, in particular to a distribution network management electricity meter state analysis method and system. According to the technical scheme, the distribution network management electricity meter state analysis method comprises the following steps that S1, multiple layers of edge computing nodes are deployed in a power distribution network, first-level nodes are deployed in a power distribution station, second-level nodes are deployed in an electricity meter concentrator, and computing resource distribution of the edge nodes is dynamically adjusted based on electricity meter distribution density and real-time loads; s2, through a collaborative acquisition mechanism of the first-level node and the second-level node, acquiring operation data of the electric meter in real time; and S3, transmitting the classified and marked data by adopting a multi-modal compression strategy: compressing the periodic monitoring data by using difference value coding, and extracting the sudden abnormal data by using a feature abstract. By constructing a multi-level edge collaborative acquisition mechanism and a 5G network slice transmission channel, the real-time bottleneck of traditional manual inspection and fixed-period acquisition is broken through.

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.