1049 results about "Data Annotation" patented technology

A method and system are introduced for recognizing cognitive engagement in classrooms by utilizing multimodal data. Associated modalities of cognitive engagement are learned from visual and audio data. This approach involves constructing a multidimensional representation model for cognitive engagement that includes behaviors, emotions, and speech. To identify engagement, three distinct deep learning models are employed: You Only Look Once version 8 (Yolov8) for analyzing body posture, Efficient Network (EfficientNet) for facial expressions, and Text Convolution Neural Network (TextCNN) for speech text. These models are trained and refined with the aid of student engagement surveys, leading to a decision-making process that integrates the results from the different modalities. Additionally, a dataset and a data annotation system are developed for engagement recognition. This innovative method aims to achieve detailed engagement recognition, addressing various perception needs in real-world applications.

The invention relates to the technical field of artificial intelligence, in particular to a knowledge graph construction method and system based on a large model technology, and the method comprises the following steps: carrying out large model enhanced data annotation; performing entity extraction, relationship extraction, event extraction and causal relationship extraction by utilizing the large model; performing entity type extraction, relation type extraction, event type extraction and knowledge system extraction by utilizing the large model; the knowledge graph data is complemented by using a large model, so that the knowledge complementing capability is improved; performing relation reasoning and event reasoning by using the large model; the knowledge graph construction method and system based on the large model technology have the beneficial effects that the large model masters strong semantic understanding and context reasoning capabilities through deep learning and wide corpus training. Therefore, the knowledge graph can more accurately analyze the complex semantic relationship in the text and identify important entities and relationships hidden in the text, so that the information is effectively classified and integrated.

The invention relates to an automatic data labeling method of an ISP image signal processing visual sensor. The method comprises the following steps: carrying out noise reduction and signal enhancement on an original image data stream acquired by an ISP image signal processing visual sensor to obtain a standardized image sequence; performing time sequence feature extraction to obtain a time sequence feature tensor; performing spatial feature extraction to obtain spatial-temporal feature representation; inputting a double-fold teacher-student network for semi-supervised training to obtain an initial image annotation model; performing quality evaluation based on the prediction probability distribution and the time sequence consistency to obtain a labeling result with a quality score; and storing a high-quality sample in the labeling result with the quality score into a sample buffer pool, carrying out sample screening based on entropy and distance measurement, and carrying out parameter updating on the initial image labeling model through an incremental learning strategy to obtain a target image labeling model. According to the invention, the reliability evaluation of the labeling result is realized, and the accuracy of automatic labeling is ensured.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention discloses a contract auditing method, apparatus and device, and a computer readable storage medium. The method comprises the steps of obtaining an image file corresponding to a contract file; inputting the image file into a multi-modal analysis model for model identification so as to obtain text information of each text area corresponding to the image file; performing screening operation on the text information based on a preset keyword to obtain target text information; and auditing the target text information by adopting a large language model based on an entity recognition rule to obtain an auditing result. According to the method, the text information is screened, the target text information is audited on the basis of the large language model, intelligent audit of the contract file is achieved, meaningless input of the large model and the data annotation process can be reduced as much as possible through keyword screening and entity recognition rule matching, and the user experience is improved. The efficiency and accuracy of contract auditing are improved.

A system for automatically self-labeling a digital dataset includes a first sensor for generating a first data stream, a second sensor for collecting information to generate a second data stream, and a causal model manager (CMM). The CMM is configured to determine a first causal event from a first data segment of the first data stream, and a causal relation between the first causal event and a second data segment selected from the second data stream. The system further includes (a) an interactive time model for determining an interaction time between the first and second data segments, and (b) a self-labeling subsystem configured to derive a label from the second data segment, associate the first data segment with the derived label, form a self-labeled data pair from the associated first data segment and the derived label, and automatically annotate the self-labeled data pair with the interaction time.

The invention discloses a data annotation method and system of a collaborative computing architecture based on quantum computing, and belongs to the field of data annotation. The method comprises the steps that S1, multi-modal data are input and preprocessed; s2, extracting features of each mode after preprocessing; s3, coding the features of each mode into a quantum state, and carrying out mode fusion; s4, performing label reasoning on the quantum state after modal fusion, and performing label constraint optimization by using a quantum approximate optimization algorithm; s5, based on a quantum Bayesian network or an approximate causal graph generation method, generating explanation according to a modal contribution causal path, and deducing marginal contribution of each modal to final label prediction by using a joint probability measurement result; and S6, outputting a labeling result. According to the method, a quantum-classical cooperative computing architecture is designed, the efficiency and accuracy of multi-modal data labeling are remarkably improved, the interpretability, the distributed processing capacity and the high-dimensional feature modeling capacity of the system are enhanced, and a brand new solution thought is provided for development of the multi-modal labeling technology.

The invention discloses a science and technology public text intelligent classification and service method and device based on deep learning. The method comprises the steps that multi-source science and technology public text data are acquired and preprocessed; extracting keywords by adopting a keyword extraction algorithm, and splicing the keywords with the public text title to form enhanced text features; constructing a multi-dimensional public text classification system and performing data annotation; performing feature extraction and fine adjustment by adopting a BERT pre-training model to obtain a classification model; automatically classifying the newly-added public texts and visually presenting the newly-added public texts; and generating a personalized recommendation result based on the user portrait and the public text feature index. The invention further relates to a technical scheme of multi-objective quality diversity optimization, heterogeneous resource allocation and fusion of the LPLC2 neural network and the BERT. The technical problems that a traditional method is limited in complex semantic understanding ability, single in classification dimension and lack of an integrated solution are solved, and the accuracy of science and technology public text classification and the intelligent level of service are improved.

The invention provides a multimodal reasoning method based on a Monte Carlo tree and dynamic retrieval. The method is applied to an artificial intelligence assistant-assisted teacher paper judgment scene. Specifically, the framework comprises a unified retrieval module, wherein the retrieval module comprises a mixed modal retrieval corpus and a multi-modal retrieval module; the multi-modal retrieval module comprises two retrieval modules, namely a text retrieval module and a cross-modal retrieval module, key problem solving insights are selected by adopting knowledge concept filtering, and progressive multi-modal reasoning annotations of multi-modal reasoning are automatically obtained by utilizing Monte Carlo tree search and an active retrieval mechanism for data annotation; and gradually aligning a process reward model through course process reward modeling in two stages by utilizing the annotation data so as to realize fine-grained verification of multi-modal large model reasoning. An MCTS algorithm and an active retrieval strategy are combined to improve multi-modal reasoning. The framework can automatically obtain high-quality step-by-step reasoning annotations, so that a process reward model is aligned step by step, and finally reliable automatic multi-modal reasoning verification is realized.

The invention discloses a medical image segmentation method and system based on basic model assisted semi-supervised learning. In a medical image segmentation task, by introducing an innovative method of combining a visual basic model (SAM) and a U-Net model, the labeling precision and the segmentation effect are remarkably improved. Compared with an existing semi-supervised learning method, the method has the advantages that the problem of low-quality false labels is solved by introducing a reliability scoring mechanism and a foreground conflict processing strategy, and unreliable images are effectively utilized through a region exchange strategy. Finally, the invention provides a new solution for solving the problems of low false label quality and high data annotation cost in medical image segmentation, greatly improves the performance of medical image segmentation, and shows an excellent segmentation effect on a plurality of reference data sets.

The invention relates to the technical field of computer vision and automatic driving, in particular to a natural driving accident scene key element extraction method based on a visual large model. Comprising the steps of data collection and preprocessing, accident data cue word design, ontology-based accident scene description and data annotation, pre-training model fine tuning and optimization, key element identification and post-processing and the like, the efficiency of natural driving accident analysis is remarkably improved, and the time and cost of manual examination are reduced. Through an ontology-based accident scene description method, dynamic feature extraction based on an optical flow method and more accurate cue word design, the recognition accuracy of the model on key elements is improved. Through a data enhancement technology and reasonable loss function design, the robustness and generalization ability of the model in different scenes are improved. The method is not only suitable for analysis of natural driving accidents, but also can be expanded to other scenes, and a high-efficiency scene understanding method is provided for management of intelligent traffic, smart cities and safe cities.

The invention discloses a low-cost cell segmentation method, system and device based on active learning. The method comprises the following steps: selecting an initial cell image for preprocessing and bounding box labeling; forming a training set and constructing a target detection model for training so as to predict a bounding box of pictures of the verification set; taking the bounding box as a prompt for segmenting a cut model (SAM), and outputting a corresponding cell mask; adopting a Monte Carlo discarding method to predict the uncertainty of the unlabeled pictures, and selecting the pictures with higher uncertainty to be labeled by experts; adding the marked image data into the training set of the last round for updating, and repeating the training and sample selection of the model; and outputting a model weight and an index record after the required performance is achieved. According to the method, the data annotation cost of a cell segmentation algorithm is greatly reduced on the premise of ensuring the model performance.

According to the OpenCSG large model intelligent data annotation system provided by the invention, by virtue of an automatic annotation technology driven by a large model, the requirement of manual annotation is greatly reduced, and the data annotation efficiency is remarkably improved. The system can generate a high-precision labeling result by using the intelligent labeling module and the automatic quality control module, and ensures the consistency and accuracy of the labeling result through an automatic auditing process. No matter sentiment analysis, entity recognition or complex text classification, the system can efficiently adapt to various types of labeling tasks, and different data processing requirements are met. Through modular design, the system supports flexible expansion, and can meet various data labeling requirements from small-scale test projects to large-scale commercial projects. According to the overall architecture, all the function modules are closely coordinated in the system, so that efficient operation and high-quality output are always kept in a large-scale data annotation project.

The invention provides an intelligent data labeling method and system based on multi-modal fusion and large model verification, belongs to the field of artificial intelligence and data processing, and innovatively fuses multi-modal information such as an OCR recognition result, a layout structure, original image visual features and deep semantic analysis of a large language model (LLM). And a precise automatic labeling result credibility evaluation mechanism is constructed. According to the method, various errors in automatic labeling can be accurately recognized and adaptively corrected, and the errors comprise conventional error correction based on hard coding rules and complex semantic error correction driven by LLM. Meanwhile, the system can continuously optimize the data labeling capability of the system through an efficient man-machine cooperation and closed-loop feedback learning mechanism, and automatically precipitate domain knowledge assets. The invention aims to solve the problems of recognition accuracy bottleneck, heavy manual proofreading burden, lack of intelligent judgment and error correction, knowledge accumulation lag and the like in traditional document data labeling, so that the efficiency, accuracy and automation level of document data labeling are remarkably improved.

The invention discloses an uncertainty and representativeness combined active learning method and system for medical image data annotation, and relates to the technical field of medical image data. The method comprises the following steps of: training a variational auto-encoder infoVAE on an image pool; m samples are randomly extracted from the image pool and labeled, an initial labeled image set is constructed, and a segmentation model is trained on the labeled image set; t rounds of active learning circulation are carried out, and in each round of t active learning circulation, the following steps are specifically executed: screening candidate samples based on a representative method; screening a final sample based on an uncertainty method; updating the labeled data set and the unlabeled data set; retraining the segmentation model on the updated labeled image set, and optimizing model parameters; and obtaining final model parameters. According to the method, by designing a sample selection strategy integrating uncertainty and representativeness, the global performance of the model and the difficult sample segmentation capability are improved.

The invention discloses an image segmentation method based on an improved SAM model, and relates to the technical field of image segmentation, and the method comprises the following steps: collecting remote sensing image data sets of various different scenes and categories, and carrying out the data annotation and data enhancement preprocessing operation of remote sensing images; training a preset target detection model by using the labeled remote sensing image data set; using the trained target detection model to predict a remote sensing image, identifying a target area and category in the image, and outputting a prediction result; extracting multi-scale features of each target area detected in the prediction result based on ViT, and performing image segmentation based on SAM to obtain a classification result of each target area; and taking the classification result of each target area as a returned detection result. Through the remote sensing image classification method based on the convolutional neural network, the rapid classification task of the remote sensing images is realized, and powerful technical support and data support are provided for the fields of environment monitoring, urban planning, agricultural management and the like.

The invention discloses an image data annotation method and system, and relates to the technical field of computer vision, and the method comprises the steps: collecting image and text data, carrying out the updating through kernel density estimation and a kinetic equation, carrying out the optimization through employing Euler discretization in combination with CLP propagation and EA, carrying out the conversion through amplitude coding, and carrying out the construction through a quantum state circuit. The method comprises the following steps: carrying out calculation by using partial traces, generating topological weighted entanglement entropy through weighted fusion, carrying out updating based on an attention mechanism, generating potential conflict pairs, calculating priorities, generating final conflict pairs through a CNN model, outputting region features through a NeRF model, carrying out extraction by using a CLIP model, and carrying out updating through a loss function. According to the method, VAE is combined with GLP propagation and EA for optimization, the precision and consistency of labeling are improved, and through joint verification of quantum entanglement entropy calculation and the NeBF model, the efficiency and accuracy of labeling are improved.

The invention discloses a large-span bridge structure health monitoring abnormity identification method based on image classification, and relates to the technical field of bridge structure health monitoring, and the method comprises the following steps: 1, generating abnormal samples through employing a generative adversarial network (GAN), and the abnormal samples comprise cracks, peeling, and corrosion; according to the large-span bridge structure health monitoring abnormity identification method based on image classification, abnormal samples are generated and optimized through the generative adversarial network, and the data scarcity condition is relieved; a dynamic generation-learning-optimization closed-loop system and a distributed cooperation platform reduce the labeling cost; by means of weak supervised learning, a knowledge graph and an automatic quality inspection tool, the labeling quality stability is improved; the utilization rate of label-free data is improved during multi-modal data fusion by utilizing a self-supervised learning technology, the problems in the aspects of data annotation and quality are solved comprehensively, and powerful support is provided for health monitoring of a bridge structure.

The invention discloses an intelligent precise farmland irrigation direction control system based on the Internet of Things, and relates to the technical field related to intelligent irrigation. Comprising a data acquisition module, a data processing and analyzing module, a data labeling and classifying unit, a three-dimensional coordinate model building module, a nozzle angle adjusting device, a nozzle water quantity adjusting unit and a control instruction execution unit. According to the invention, the distance between the nearest nozzle and the water-requiring position is calculated, so that irrigation water can reach the water-requiring area more efficiently, the distance is accurately calculated to ensure that water can be quickly and accurately conveyed to the required area, water loss and energy waste caused by long-distance water conveying are avoided, the water spraying direction is adjusted in combination with the wind direction and the wind speed, and the water spraying efficiency is improved. The device can effectively cope with the influence of natural wind on irrigation, and by monitoring wind direction and wind speed information in real time and combining the wind direction and wind speed information with water spraying parameters of the spray head, the system accurately calculates horizontal and vertical angle adjustment values of the spray head, so that sprayed water flow can accurately reach a water needing position.

The invention relates to the technical field of computer vision, and particularly discloses a weed root recognition method and device based on attitude estimation, and the method comprises the steps: obtaining field image data containing a weed plant, detecting a leaf key point through a pre-trained leaf attitude estimation model, fitting a leaf vein extension line, calculating an intersection point cluster, and carrying out the recognition of a weed root. And screening a high-density candidate point set through density clustering analysis, calculating a weighted center coordinate as a weed root positioning result according to density weight, and transmitting the weighted center coordinate to laser weeding equipment to execute precise weeding operation. According to the method, accurate weed root identification is realized, the positioning accuracy is higher, the robustness to a complex field environment is higher, the real-time performance can be improved by utilizing model lightweight, efficient deployment is realized, and the data labeling cost is reduced.

The invention discloses a melanoma lesion area segmentation method based on a CLIP multi-modal fusion network. The method comprises the following steps: 1, constructing an MA-CLIP model; 2, a BLIP language model is finely adjusted through a manually-labeled text-image pair, a large-scale multi-modal data set is constructed, a training set, a test set and a verification set are divided, and preprocessing is carried out; 3, training the MA-CLIP model; 4, evaluating the performance of the MA-CLIP model and optimizing parameters; and 5, inputting a to-be-segmented melanoma clinical image into the trained MA-CLIP model, and outputting a segmentation result. According to the method, the problem of insufficient traditional medical data annotation can be solved, accurate guidance of clinical semantics on image segmentation is realized, and the recognition precision and boundary segmentation capability of a focus in a complex form are improved.

The invention belongs to the technical field of new-generation information, and particularly relates to a power plant metal supervision entity relationship extraction method based on dual coding, which comprises the following steps: multi-modal data preprocessing: collecting and cleaning data, and carrying out data labeling; constructing a dual coding joint learning model: designing a network layer architecture, and training the dual coding joint learning model; constructing and querying a dynamic knowledge graph: extracting a model to generate a triple, and performing time sequence evolution analysis, causal reasoning interface and dynamic updating; and incremental knowledge updating and dynamic model optimization: an incremental learning and feedback module forms a bidirectional closed loop between the knowledge graph and the relationship extraction model, and continuous evolution of the system is ensured through dynamic knowledge updating and adaptive model optimization. According to the method, through bidirectional feature modeling of dual paths, collaborative representation optimization between entities and relationships is realized while context information is captured, so that the requirements of complicated data types and diversified semantic associations in an engineering scene are met.

The invention discloses an automatic data analysis system and method based on artificial intelligence, and relates to the technical field of data management, and the method comprises the following steps: S1, data collection and preparation; s2, data annotation and classification; s3, feature selection and extraction; s4, data standardization and normalization are carried out; s5, model selection; s6, training and optimizing the model; s7, performing model evaluation; s8, model interpretation and visualization are carried out; s9, generating an automatic report; and the system is constructed based on the steps. According to the automatic data analysis system and method based on artificial intelligence, by setting a classification and labeling model, a training optimization mechanism and a dynamic model evaluation method, the efficiency and precision of automatic data analysis are remarkably improved, and the quality of data preprocessing is improved; meanwhile, through self-adaptive hyper-parameter adjustment and optimization, the efficiency of the training process and the model performance are optimized; the dynamic evaluation mechanism ensures that the model is more accurate in performance under different data characteristics.

The invention relates to the technical field of intelligent driving, and discloses a user service recommendation method for a vehicle-mounted scene, and the method comprises the steps: collecting the vehicle-using behavior data of a user, carrying out the correlation marking of the intention of the user through a multi-dimensional marking system, carrying out the vectorization, and storing the vectorized intention into a vector database; capturing real-time behavior data of a user to generate a real-time behavior vector; based on the vector database, generating an intention context corresponding to a real-time behavior vector through a hybrid retrieval strategy; and generating a prompt project based on the intention context, inputting a large model output service recommendation result, and pushing the result to the user. By integrating mechanisms such as data annotation, vector retrieval and large model reasoning, the problem of long cold start period depending on long-term buried point data learning in the prior art is solved, potential intentions are quickly matched, the system effective time is shortened, and the service recommendation accuracy based on the user intentions is effectively improved. The invention further discloses a user service recommendation device for the vehicle-mounted scene, the vehicle and a computer readable storage medium.

The embodiment of the invention discloses a single cell transcriptome sequencing data annotation method. The method comprises the following steps: acquiring single cell transcriptome sequencing data associated with a target disease, and clustering a plurality of cells corresponding to the single cell transcriptome sequencing data to obtain a plurality of cell clusters; aiming at each marker gene corresponding to the target disease, according to single cell transcriptome sequencing data, obtaining the cell cluster gene expression quantity of the marker gene in each cell cluster; for each cell cluster and each cell type corresponding to the target disease, accumulating the cell cluster gene expression quantity of each marker gene corresponding to the cell type in all the marker genes in the cell cluster to obtain the total gene expression quantity; and aiming at each cell cluster, according to the total gene expression quantity corresponding to the cell cluster under each cell type, annotating a target cell type for the cell cluster from all the cell types. According to the technical scheme provided by the embodiment of the invention, the accuracy of cell type annotation can be ensured.

The invention provides a tea tree fresh leaf identification method and system based on YOLOv8 in the technical field of tea leaf detection, and the method comprises the following steps: S1, obtaining a large number of historical images of tea tree fresh leaves, carrying out the preprocessing of data annotation and data expansion of each historical image, and constructing a data set; s2, creating a tea tree fresh leaf recognition model based on a YOLOv8 network, and setting a loss function of the tea tree fresh leaf recognition model as an I nner-S I oU function; s3, dividing the data set into a training set, a verification set and a test set based on a preset proportion, and training, verifying and testing the tea tree fresh leaf recognition model through the training set, the verification set and the test set; and S4, deploying the tea tree fresh leaf recognition model passing the test, and performing tea tree fresh leaf recognition by using the deployed tea tree fresh leaf recognition model. The method has the advantage that the identification accuracy of the fresh tea leaves is greatly improved.

The invention discloses a water ecological environment decision response intelligent interaction system based on AI natural language processing, and the system carries out the data collection and preprocessing, and forms a training set. In combination with the training set, data annotation and feature engineering are carried out, and AI model training including data segmentation, a training method and transfer learning is further carried out; on the basis, continuously collecting monitoring data, and inputting the monitoring data into the trained AI model for real-time prediction; data analysis and decision support are carried out; and once an abnormal trend is detected, an early warning mechanism is triggered. Through an AI large model technology in the vertical field, big data and an artificial intelligence algorithm are utilized to simulate and improve intelligence, convenience and precision of water ecological environment supervision, through combination of an AI large model and an intelligent voice interaction technology, based on analysis and processing capability of the AI large model, deep analysis is performed on water ecological environment data, and a water ecological environment monitoring result is obtained. And managers are helped to quickly make scientific decisions.

According to the big data management-based data labeling method and system provided by the invention, efficient, accurate and intelligent data labeling is realized by integrating multi-virtual machine collaborative labeling, multi-model fusion labeling, adaptive data preprocessing and hierarchical cleaning and a multi-dimensional task scheduling and dynamic load balancing mechanism. The method comprises the steps that self-adaptive preprocessing and layered cleaning are conducted on original big data, noise is removed, missing values are filled up, and format conversion is completed; dividing the preprocessed data into a plurality of subtasks according to data characteristics; distributing the sub-tasks to different virtual machine instances for parallel processing by utilizing a multi-virtual machine technology; labeling the same data fragment on each virtual machine by adopting a multi-model labeling mechanism; adopting a weighted fusion or voting mechanism to generate a comprehensive annotation result for the output results of the multiple models in each virtual machine; and summarizing the labeling results of the virtual machines to form a global data labeling result.

The invention provides a cross-modal retrieval enhancement method based on a visual large language model. The method comprises the following steps: acquiring a preliminary cross-modal retrieval result of a text and an image; performing relevancy scoring on the preliminary cross-modal retrieval result by utilizing a visual big language model; screening the preliminary cross-modal retrieval results according to the relevancy score; and sorting the screening results, and displaying the most relevant result to the user. According to the method, the accuracy and reliability of cross-modal retrieval are remarkably improved, and high data annotation and training cost is effectively avoided. Meanwhile, as a universal retrieval enhancement scheme, the method can be seamlessly integrated into various existing cross-modal retrieval systems, and the application flexibility and economical efficiency of the method are greatly improved.

The invention discloses a sleep thermal comfort perception and indoor sleep environment regulation and control method and device, and belongs to the technical field of computer vision and heating ventilation air conditioning control, and the method comprises the steps: obtaining video data, physiological data and environment data, which are measured in advance, of a subject during sleep; performing data annotation, feature extraction and data fusion on the video data, the physiological data and the environmental data to make a data set; inputting the manufactured data set into a pre-constructed sleep thermal comfort perception model to obtain a sleep thermal comfort perception prediction result; regulating and controlling the indoor sleep environment according to the obtained sleep heat comfort perception prediction result; according to the method, by predicting the sleep thermal comfort of the person, the operation parameters of the heating ventilation air conditioning system can be correspondingly adjusted, the influence of contact type equipment on a sleeper is avoided, the thermal comfort of the sleeper can be ensured, the energy use efficiency can be remarkably improved, and therefore the total energy consumption of a building is reduced, and energy conservation and emission reduction are achieved.