476 results about "Data Annotation" patented technology

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 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 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 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 invention relates to the technical field of natural language processing, in particular to an archive structured information extraction method and system based on a multi-modal large model and a medium, and the method comprises the following steps: S1, establishing a mapping table of fields to be extracted; s2, data annotation; s3, constructing a layout analysis model, an archive structured extraction model and an archive structured integration model; s4, screening key information pages based on the layout analysis model; s5, extracting single-page structured information based on an archive structured extraction model; and S6, based on the archive structured integration model, integrating single-page structured information results. Through application of the multi-modal large model, accurate layout analysis, strong structured extraction capability and efficient information integration are realized, automatic extraction and integration from archive image data to structured information are realized, manual intervention is reduced, and processing efficiency is improved.

The invention provides an expressway disease detection method capable of accurately positioning. The expressway disease detection method comprises the following steps: S1, image acquisition and data annotation; s2, constructing an instance segmentation model; s3, extracting a multi-scale feature map and fusing multi-scale features; s4, outputting a category label, a bounding box coordinate and a confidence coefficient score of the detection box; s5, performing multi-target trajectory tracking; s6, eliminating global motion influence; s7, establishing an association relationship between the prediction trajectory and the current detection frame; s8, dynamically maintaining a track list; and S9, outputting bounding box coordinates and ID tags in each target continuous frame, and storing the bounding box coordinates and the ID tags as structured data. According to the method, a high-performance target detection framework, an advanced instance segmentation tracking technology and a GPS positioning technology are combined, pixel-level identification and continuous tracking of diseases can be realized while high detection precision is ensured, and the positions of the diseases can be accurately positioned.

The invention discloses a text generation image diffusion model enhancement method based on multi-target preference optimization. The method comprises the following steps: firstly, determining a plurality of reward models, and constructing a sample pair training set comprising positive and negative samples; and then, generating a loss weight of each sample pair in the sample pair training set, performing fine tuning training on the text map diffusion model by using the sample pair training set, and in the fine tuning training process, calculating a loss function value of each sample pair in combination with the loss weight of each sample pair until the training is completed, thereby obtaining an aligned text map diffusion model. According to the method provided by the invention, manual data annotation is not needed, the problems of preference inconsistency and over-optimization in a multi-reward scene are effectively solved, and the image quality, the text alignment capability and the multi-target optimization performance of the text-to-image generation model are remarkably improved. The method is superior to an existing optimization method under single-reward and multi-reward setting, shows higher generation quality and robustness, and can be seamlessly applied to various picture generation models.

The invention relates to the technical field of medical image processing, in particular to a cross-modal image fusion detection system for endoscopic early cancer lesions, which comprises a data acquisition module for acquiring a white light endoscope and a narrow-band imaging image; the data annotation module carries out pixel-level annotation, and the data enhancement module amplifies a data set by using CycleGAN; the white light data module and the narrowband data module train feature extraction models respectively; the feature alignment and attention module solves the problem that feature scales and semantics between two modes are inconsistent, and efficient fusion is achieved. The edge enhancement module enhances feature extraction of small focuses through the generative adversarial network; the fusion model construction module fuses the features and the transition area data to generate a fusion model; and the output module processes the white light and the narrow-band imaging image and outputs a fused image. Through feature alignment, an attention mechanism and a CyclGAN data enhancement technology, the problem of insufficient medical image data is effectively solved, the model generalization ability is enhanced, and efficient and accurate early cancer focus detection is realized.

The invention is suitable for the technical field of image annotation and intelligent transportation, and provides an intelligent image data annotation method and system based on multi-modal semantic fusion, and the method comprises the following steps: obtaining image data, point cloud data, voice data and environment information of the same time and space; performing semantic segmentation on the image data to obtain image segmentation information; performing alignment processing on the point cloud data and the image data to generate a 3D point cloud aligned with image pixels; according to the 3D point cloud, deducing the geometric contour of the shielded target to obtain point cloud geometric information; fusing the image segmentation information and the point cloud geometric information in combination with environment information to obtain image fusion information; and in combination with the voice data, marking visible targets and shielded targets in the image data with behavior semantics according to the image fusion information. According to the method, multi-modal data such as images, voices and point clouds are fused, so that semantic annotation can be accurately carried out on the occluded target in a complex scene.

The invention provides a medical image data annotation generation method, system and device and a medium, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining a medical image, carrying out the standardization processing of the medical image, and obtaining a standardized medical image; performing target detection and image segmentation on the standardized medical image in sequence by using the trained medical image analysis model to obtain bounding box information and a segmentation mask; generating a binary mask of the standardized medical image according to the segmentation mask; and inputting the standardized medical image, the bounding box information and the binary mask into a trained data annotation generation model, and generating a medical image data annotation by the generation model. According to the method, the specific organ contour or the pathological region shape in the standardized medical image can be accurately marked, the influence of noise and a fuzzy region is effectively overcome, the marking precision is remarkably improved, more comprehensive medical image analysis information is provided, and the generalization ability is high.

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

The invention provides a gust front wind shear identification method based on artificial intelligence, and the method comprises the steps: carrying out the noise filtering, missing value supplementary measurement, data smoothing, wind shear value calculation and sample screening extraction of collected radial speed data, and obtaining a gust front wind shear sample; performing coordinate system conversion, sample set division and data annotation on the basis of gust and front wind shear samples to obtain an expanded data set; designing and training a Mask R-CNN model architecture to obtain a gust and front wind shear identification model; and inputting the expanded data set into the gust and front wind shear identification model to carry out gust and front wind shear detection. According to the method, dependence on reflectivity factor data can be reduced, an identification model is constructed based on gust front radial speed data, gust front wind shear can be accurately identified, pixel-level segmentation and positioning of a wind shear area can be realized, and identification efficiency is improved.

The invention discloses a graph isomorphic network model-based power distribution network wiring mode identification method, which comprises the following steps of: according to a wiring mode analysis requirement, defining a sub-graph generation method, a rapid feature rule and a node mode category, and constructing and training a GIN model to realize analysis and identification of a power distribution network wiring mode. The specific process comprises the following steps: acquiring power grid data and simplifying subgraph extraction; performing preliminary classification analysis based on simple rules; marking equipment nodes according to the wiring mode, and generating a training data set; constructing a GIN model and carrying out model training; and according to the training model, applying all the simplified subgraph node classification data of the power distribution network to generate a wiring mode identification result. According to the method, a traditional graph search algorithm and a GIN graph neural network model are integrated, the defect that a traditional method depends on a manual rule arrangement process and a complex method for rule implementation is overcome, and a data annotation and model training method is adopted to achieve the recognition target of the wiring mode in the power distribution network.

The invention relates to the technical field of data governance, and provides a data platform metadata annotation completion method, system and equipment, and the method comprises the steps: collecting metadata basic annotations in a multi-source heterogeneous database through a standard application program interface; collecting a logic model definition document and a data standard specification document, and constructing a local knowledge base through vectorization processing; when a user triggers annotation completion, calling a local knowledge base through a large language model, and generating metadata intelligent annotations; and returning the collected basic annotation of the metadata and the generated intelligent annotation of the metadata to the user as annotation completion results. According to the data platform metadata annotation completion method, system and equipment, metadata collection normalization, multi-source knowledge fusion, knowledge base construction and intelligent annotation completion can be achieved, cost reduction and efficiency improvement are achieved, the metadata annotation quality is remarkably improved, and the use threshold is lowered.

The invention provides a question and answer agent model training method and system based on data annotation collaboration.The method belongs to the technical field of model training and comprises the steps that a historical training set, outer loop optimization data and graded annotation data are subjected to data set arrangement; performing model parameter optimization on the to-be-trained question and answer agent model by using the incremental data set; performing model deployment and scene adaptation based on the optimization model parameters; testing and evaluating the model based on the model API endpoints; optimization parameters are extracted from the optimization suggestions, and model online and operation are carried out based on the evaluation scores; the optimization parameters and the hierarchical data packets are used for replacing the outer loop optimization data and the hierarchical annotation data to carry out a data set arrangement process until the target model is output, and the target model is applied to the question and answer agent, resource waste can be avoided, the model optimization efficiency can be improved, and therefore the real-time performance, effectiveness and accuracy of intelligent question and answer of government affair items are improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence data annotation and cue word automatic construction engine system, which comprises a data annotation module for firstly carrying out preliminary annotation on data based on a pre-training model, then automatically annotating a data sample through an active learning algorithm, and meanwhile, monitoring the quality of annotated data in real time; and the cue word automatic construction module generates cue words based on task analysis, optimizes the cue words by using a reinforcement learning technology, and performs classified storage and management on the generated cue words. By adopting the semi-automatic labeling and active learning labeling functions, not only can the workload be greatly reduced, but also the unnecessary labeling work can be reduced, so that the labeling efficiency is remarkably improved, the labeling time is shortened, the large-scale data labeling requirement is met, and the problem of efficiency bottleneck caused by slow manual labeling is solved.

The invention belongs to the technical field of pet data acquisition and analysis, and particularly relates to a pet full-life-cycle universal data set framework system which comprises an intelligent necklace, a camera, a microphone array, an intelligent system terminal and an acquisition control system integrated in the intelligent system terminal. Signal output ends of the intelligent necklace, the camera and the microphone array are connected with the intelligent system terminal; the acquisition control system comprises a data annotation system and a data analysis and processing system, and the output end of the data annotation system is connected with the data analysis and processing system; and the intelligent system terminal is used as core equipment for data acquisition and management. According to the scheme, the whole life cycle is fully covered, and comprehensive data support is provided for studying health management, behavior development and the like of the whole life cycle of the pet. Multi-modal data fusion meets the training requirements of the AI model in multi-task scenes such as behavior recognition, health monitoring and emotion recognition, and the accuracy and generalization ability of the model are improved.

The invention relates to the technical field of computer vision and automatic driving, in particular to a self-supervised aerial view perception method fusing Gaussian spattering and time sequence modeling, electronic equipment and a readable storage medium, and the method comprises the following steps: S1, constructing a BEV model; s2, feature grid mapping is carried out; s3, rendering a two-dimensional image; s4, performing self-supervised learning and optimization; s5, downstream task application; according to the method, BEV features are mapped into three-dimensional Gaussian parameters, end-to-end self-supervised learning is achieved through differential rendering, the method is applied to downstream tasks, and three-dimensional target detection, semantic segmentation or occupancy prediction are carried out; downstream tasks are trained and reasoned through self-supervision loss, no manual data annotation is needed, the data construction cost is remarkably reduced, and meanwhile the perception performance and generalization ability of the model in an automatic driving scene are improved.

The invention relates to a sperm cell analysis and diagnosis system based on a multi-modal large language model, which comprises a multi-modal data co-processing unit, a cross-modal semantic alignment module, a dynamic diagnosis decision engine and a self-adaptive evolution system, the four-dimensional data processing module is used for synchronously processing microscopic images, motion trail videos, biochemical detection data and four-dimensional input data of medical record texts and comprises a feature selector based on a gating attention mechanism. According to the sperm cell analysis and diagnosis system based on the multi-modal large language model, quantitative analysis of sperm movement chaos features is realized for the first time, a nonlinear dynamic evaluation standard is established, a cross-modal knowledge distillation and meta-learning migration framework is developed, a data annotation dependence bottleneck is broken through, and an interpretable clinical decision support system is constructed; dynamic updating and probabilistic suggestion of diagnosis rules are achieved, semantic analysis of single-cell multi-omics data is achieved, and molecular mechanism research results are converted into clinically available knowledge.

The invention discloses a target detection method and device, equipment, a storage medium and a product, and relates to the technical field of image recognition. And synthesizing the first front view image and the second front view image into a central eye image. Data labeling is performed on the multi-camera data and the central eye image, a labeling data set special for small target detection is constructed, and labeling efficiency and quality are remarkably improved. Performing aerial view coding on multi-scale features contained in the multi-camera data based on a cross-space cross attention mechanism of aerial view query position and image semantic joint modeling to obtain aerial view features; and on the basis of the annotation data set, aligning the aerial view features to the central eye image to obtain aerial view enhancement features, and realizing small target semantic enhancement in the aerial view features. And carrying out target identification on the aerial view enhanced features so as to determine a detection target contained in the multi-camera data. According to the target detection scheme provided by the invention, the detection accuracy and robustness of the small-size target are remarkably improved.

The invention discloses an unsupervised semantic segmentation method for separating branches and leaves from forest point cloud. The method comprises the following steps: S1, acquiring three-dimensional point cloud data of a forest scene; s2, performing feature extraction on the three-dimensional point cloud data based on an unsupervised semantic segmentation deep learning network to obtain comprehensive feature representation of the point cloud; s3, performing super-point generation and clustering based on the comprehensive feature representation to generate a pseudo tag, and realizing separation of branches and leaves; the unsupervised semantic segmentation deep learning network does not need to carry out priori training through a manually labeled point cloud with a semantic tag. According to the method, automatic separation of forest point cloud branches and leaves is realized through the unsupervised semantic segmentation deep learning network, manual data annotation is not needed, and the method has relatively high segmentation precision and robustness.

The invention discloses an object pose estimation method based on deep learning and synthetic data and related equipment, and belongs to the technical field of computer vision. The method comprises the following steps: generating an instance segmentation and pose estimation synthesis data set by utilizing simulation modeling software and an automatic script; a YOLO11-seg model is trained, and a data set used for training a domain adaptation model is constructed in an auxiliary mode; training a simulation-to-reality domain adaptation model based on the improved CycleGAN and using the model to enhance the fidelity of the RGB image in the pose estimation synthesis data set; and training a pose estimation model based on multi-modal feature fusion and an attention mechanism by using an enhanced synthetic data set, extracting real scene target foreground RoI data by using YOLO11-seg, inputting the pose estimation model, and outputting a 6D pose of an object. According to the method, the object pose in the disordered scene can be accurately estimated, the grabbing success rate of the robot is improved, the inter-domain difference between synthetic data and real data can be effectively reduced, the data labeling cost is reduced, and the method can be suitable for industrial scenes.

The invention relates to the technical field of ecological system monitoring, and discloses an improved YOLO11-based water hyacinth target rapid detection method, which comprises the following steps: S1, data preparation: collecting image data, screening, sorting and collecting images in a classified manner, carrying out data annotation, and constructing to obtain a data set; processing the data set, and dividing the data set into a training set, a verification set and a test set; s2, optimizing and training the model: optimizing a backbone network of the original YOLO11 model by using the OfficientViT; a Lite-BiFPN multi-scale feature fusion structure is introduced; replacing an original convolution module with an SEAMHead convolution module which introduces a channel and a space attention mechanism; training a WH-YOLO11 model by using the preprocessed data; and S3, detecting and evaluating. According to the invention, the feature extraction module of the OfficientViT is improved, the multi-scale feature fusion structure of the Lite-BiFPN and the detection head design of the SEAMHead attention mechanism are adopted, so that the feature extraction accuracy of the water hyacinth under a complex background is improved, and the detection capability under different sizes and scales is improved; the key feature response is improved, the interference of the water surface background is inhibited, and the precision and robustness are improved.

The embodiment of the invention discloses a data annotation method and device, equipment and a medium. According to the scheme, the method comprises the steps of obtaining to-be-annotated data; obtaining to-be-labeled labeling type information for the to-be-labeled data; the annotation type information represents the type of annotation information which can be annotated by the to-be-annotated data; determining an annotation processing module corresponding to the annotation type information according to the annotation type information; the annotation processing module has a data annotation function of annotating annotation information corresponding to the annotation type information; and carrying out annotation processing on the to-be-annotated data by utilizing the annotation processing module to obtain annotated data containing annotation information.

The invention discloses an automatic detection and counting method for damaged and missing areas of a light-weight road waveform guardrail. The method comprises the following steps: S1, constructing a special data set; s2, carrying out image preprocessing and data annotation; s3, a lightweight GSYOLO model is constructed; s4, performing model training; s5, performing model evaluation; s6, guardrail damage and deficiency detection and counting analysis are carried out; and S7, outputting and visualizing a result. According to the method, automatic identification, accurate duplicate removal and quantifiable statistics of damaged sections and missing sections are realized, a structured detection result is output, and reliable and traceable data support is provided for road maintenance decision making.

The invention discloses an SVM algorithm automatic driving road detection system based on quantum computing enhancement, and relates to the technical field of automatic driving road detection in which quantum computing, multi-sensor fusion and artificial intelligence technologies are combined and applied. By combining quantum calculation and an SVM algorithm, the adaptability of the system to complex road conditions and illumination changes is enhanced, and the accuracy and stability of road detection are improved; a large amount of data in a complex scene can be quickly processed by means of the powerful computing power of quantum computing, and the model robustness is good. And by utilizing the characteristic that the SVM algorithm has low requirements on annotation data, the cost and time of data annotation are reduced. Quantum calculation is introduced to efficiently optimize parameters of the SVM model, an optimal parameter combination can be found in a shorter time through quantum particle swarm optimization, and the generalization ability and performance of the model are improved. And the parallel processing capability of quantum calculation is utilized to accelerate the data processing and model reasoning process, so that the real-time performance of the system is improved, and the requirements of automatic driving on the real-time performance and high efficiency are met.

The invention provides a construction drawing review optimization method and system based on deep learning, and the method comprises the steps: 1, obtaining a plurality of construction drawing samples, and carrying out the data annotation of the construction drawing samples, so as to obtain annotation samples; 2, constructing a deep learning model, and inputting the labeled sample into the deep learning model for training so as to construct a deep learning optimization model; 3, obtaining a to-be-processed construction drawing, preprocessing the to-be-processed construction drawing to obtain preprocessed data, and identifying and analyzing the preprocessed data by using the deep learning optimization model to extract key information in the preprocessed data; and 4, performing matching verification on the key information and a construction drawing review specification, and generating a corresponding optimization strategy based on a matching verification result.

The invention belongs to the technical field of biological information, and relates to an acetylcholin esterase inhibitor prediction method based on Stacking ensemble learning and molecular feature fusion, which comprises the steps of data collection and preparation, data annotation and optimization, feature extraction and analysis, construction of a Stacking model, result verification and feedback and construction of a prediction platform. The molecular fingerprints and the property descriptors are used as features, and an acetylcholin esterase inhibitor classifier is successfully constructed by adopting a Stacking algorithm. According to the method, the problems that the efficiency of finding the acetylcholin esterase inhibitor by a traditional experimental method is low, and a common quantitative structure-function relationship method is high in complexity and poor in generalization ability can be solved, the new drug finding speed is increased, experimental candidates are accurately positioned, and resource waste is reduced.