639 results about "Manual annotation" patented technology

The invention discloses a chip defect weak supervision semantic segmentation method based on YOLO and a diffusion model, and the method comprises the steps: collecting a chip defect weak supervision data set, carrying out the weak supervision manual marking, and constructing a chip defect image weak supervision data set; constructing and training a target detection model based on the chip defect image weak supervision data set; obtaining a to-be-detected chip defect image, inputting the to-be-detected chip defect image to the trained target detection model for defect position prediction, and outputting a chip defect area positioning image; inputting the chip defect area positioning image into a diffusion model for image reconstruction, and outputting a chip reconstruction image; and performing difference value comparison and thermodynamic diagram analysis on the to-be-detected chip defect image and the chip reconstruction image, and outputting a pixel-level precision chip defect semantic segmentation result. According to the method, the model is trained by using the low-precision weak supervision annotation data, so that high-precision semantic segmentation is realized, the training cost is reduced, and the practicability of the model is improved.

The invention discloses a semi-supervised identification method and system for a small target of a container, and belongs to the technical field of artificial intelligence and computer vision, and the method comprises the steps: carrying out the target detection of a container image through a pre-trained target detection model, intercepting a sub-image, and inputting the sub-image into an initial classification model, and obtaining a classification confidence coefficient; the uncertainty of the model on a sample classification result is quantified through a Monte Carlo Dropout method; a feature space distance filtering and dynamic threshold adjusting mechanism is combined, and samples with high confidence, low uncertainty and consistent feature space are screened out to serve as pseudo label data; pseudo label data and initial synthesis data are mixed, and the generalization ability of the model is gradually improved through semi-supervised iterative training. According to the method, the dependence on manual annotation can be remarkably reduced, meanwhile, the distribution difference between synthetic data and real scene data is gradually reduced, and finally, high-precision recognition and strong generalization ability of a classification model in a real scene are achieved.

The invention provides an optimization method and device for a retrieval enhancement generation system, equipment and a medium. Comprising the following steps: constructing model training data according to a document extracted from a knowledge base and text fragments obtained by segmenting the document; performing model parameter optimization on the vectorization model based on the model training data; processing the user question and the text fragment based on a vectorization model to obtain a retrieval fragment; according to the retrieval fragment, the user question and the session context, configuring a cue word; calling a generation model to process the user question and the cue word, and generating a question answer text; a supervised fine tuning and reward feedback training mode is adopted, and a generation model is optimized according to a question and answer data set formed by a standard answer text, a user question and a text retrieved according to the user question; and determining a target evaluation index according to the question answer text, the manual annotation data of the user question and the question and answer evaluation index set, so as to evaluate the performance of the retrieval enhancement generation system according to the target evaluation index.

The invention provides an intelligent detection system for forging defects of a forge piece product, and relates to the technical field of industrial intelligent detection.The intelligent detection system comprises the steps that multi-angle images of a to-be-detected workpiece are collected and then spliced, and a complete surface expansion view of the product is generated; obtaining defect types of the defect candidate regions through region coordinates and region sizes of the defect candidates; obtaining a confidence value of a corresponding defect type judgment result; a lightweight deep network recognition model is called for secondary judgment, new sample data are generated in a manner of supporting manual annotation, and an equipment end is connected with a programmable controller for intelligent defect detection of the workpiece to be detected. According to the invention, the problems of incomplete defect coverage, failure to realize high-precision automatic identification of multiple types of defects and influence on the detection accuracy and the production efficiency caused by diversified and complex forging surface defects and limited image acquisition angles in the prior art can be solved, comprehensive acquisition and splicing of multi-angle images are realized, and the detection accuracy and the production efficiency are improved. The technical effect of improving the defect detection accuracy of the forge piece product is achieved.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a roadbed settlement data identification method based on artificial intelligence, and the method specifically comprises the following steps: collecting time sequence monitoring data of a plurality of roadbed settlement sensors, and carrying out the manual marking; carrying out adaptive time domain segmentation normalization on the collected time sequence monitoring data, designing a conditional diffusion process, and generating enhanced data conforming to a soil mass mechanics constraint in a submerged space; constructing a roadbed settlement data classification model based on a one-dimensional convolutional neural network, and inputting the enhanced data into the model for training to obtain a trained roadbed settlement data classification model; and preprocessing new time sequence monitoring data collected by the roadbed settlement sensor, and inputting the preprocessed data into the roadbed settlement data classification model to obtain a settlement category classification result. According to the invention, the roadbed settlement data classification model based on the one-dimensional convolutional neural network is constructed and trained, so that the discrimination capability of the model can be enhanced, and the accuracy and reliability of model identification can be improved.

The invention discloses a multi-granularity visual reasoning model construction method and device based on reinforcement learning. The method comprises the following steps: constructing an'image-reasoning query-bounding box 'triple as a training data set; designing a composite reward function including positioning precision, target counting precision and format reward; training the multi-modal large language model by adopting a GRPO algorithm; the trained model can output a region-level bounding box, and a pixel-level mask is generated and a contour-level result is extracted in combination with the segmentation model. According to the method, the problems that in the prior art, a reasoning path depends on manual annotation, multi-granularity tasks cannot be expanded, and generalization is insufficient are solved, and the autonomous decision-making ability, task expansibility and generalization in a distribution offset scene of the model are improved.

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

The invention belongs to the technical field of automatic classification of pathological images, and particularly discloses an intelligent analysis method and system for automatic classification of pathological images, and the method comprises the following steps: S1, carrying out the preprocessing of a hyperspectral image and a pathological section image; s2, performing deep analysis on the preprocessed hyperspectral image and pathological section image; s3, performing global feature fusion in a channel dimension through a three-stage fusion strategy; s4, generating a pseudo label and optimizing a loss function; s5, dynamic optimization and model construction; and S6, performing multi-level interpretive analysis on the hyperspectral data by using an attention mechanism. According to the intelligent analysis method and system for automatic classification of the pathological images, through cooperation of multiple modules, the accuracy of pathological image classification is effectively improved, meanwhile, the generalization ability and adaptability of the model to complex pathological data are remarkably enhanced, dependence of manual annotation is reduced, and the accuracy of pathological image classification is improved. And the automation degree and the diagnosis efficiency of the system are improved.

The invention relates to the technical field of power grid intellectualization, in particular to a digital power grid asset classification and evolution monitoring method, system and device based on self-supervised comparative learning and a storage medium. The method comprises the following steps: acquiring network flow data in a digital power grid, extracting multi-dimensional features such as a time interval, a direction, a data packet length, a protocol type and an address port, and constructing an asset behavior feature matrix; then constructing a self-supervised contrast learning model, taking the communication behavior sequences of the same asset in different time periods as a positive sample pair, taking the communication behavior sequences of different assets as a negative sample pair, and training an asset embedding model through an InfoNCE contrast loss function; then, asset communication behaviors are mapped to a semantic embedding space, and asset classification and identification are carried out based on embedded vectors; and finally, carrying out evolution monitoring on the assets by adopting a sliding time window mechanism, generating an asset evolution trajectory sequence, and carrying out abnormity perception early warning. Autonomous learning without manual annotation, high-precision asset identification and real-time state evolution monitoring are realized.

The invention relates to a meteorological data set automatic construction method and system based on modal bridging, and aims to meet the multi-modal large model training requirement in the meteorological field, and the method and system realize automatic conversion from an original meteorological image to a structured expert reasoning text through deep fusion of image and text information. The method comprises five stages of data preprocessing, image-text semantic modeling, causal reasoning generation, consistency screening and parallel processing, key meteorological elements are extracted by using a multi-modal model, a chained thinking process is constructed through a language model with meteorological knowledge, chained reasoning annotation is realized, cross-modal semantic alignment and a multi-round reasoning mechanism are introduced, and a multi-modal semantic model is established. The method is advantaged in that high-quality samples are screened in combination with rules and models, automation, high consistency and good expansibility are realized, manual annotation cost is substantially reduced, and the method is suitable for large-scale meteorological reasoning multi-modal data set construction.

The invention discloses a grid coding-based remote sensing image general basic model construction and analysis method, which comprises the following steps of: pre-processing multi-modal data, eliminating cloud layer influence, aligning space coordinates and unifying data scales; generating a unified identification code with geographical meaning by using efficient space-time grid integrated coding; performing multi-scale feature extraction by means of a visual Transform and a convolutional neural network, and optimizing multi-source data fusion in combination with an anchoring feature extraction strategy; and finally, cross-task downstream application adaptation is realized on the basis of a multi-task Transform architecture. According to the method, the problems of high manual annotation cost, difficulty in multi-source data integration, low mass data processing efficiency and the like in traditional remote sensing image analysis are effectively solved. Experiments show that the multi-source data integration efficiency is improved, the model generalization ability is enhanced, the classification precision in high-resolution series data tests reaches 90% or above, the segmentation IoU exceeds 80%, and an efficient solution is provided for remote sensing image analysis.

The invention provides a distributed optical fiber temperature sensing logging data blind denoising method and system based on physical self-supervised learning. The method comprises the following steps: acquiring original noisy distributed temperature sensing (DTS) logging data, and generating a self-supervised training sample through a space-time alternating downsampling strategy by using the space-time coherence of the original noisy distributed temperature sensing (DTS) logging data; constructing a physical self-supervised blind denoising network model by using a simplified structure, and inputting a self-supervised training sample for training; in the training process, a multi-physical constraint loss function optimization model is introduced until a loss function converges, and a denoising model is obtained; and inputting to-be-processed complete DTS logging data into the denoising model to obtain denoised DTS logging data. According to the method, manual labeling and large-scale labeling of the data set are not needed, the inherent physical characteristics and structural information of the DTS data can be fully utilized, efficient and accurate blind denoising of the DTS logging data is achieved, and the problem that an existing deep learning denoising method needs to depend on a large amount of labeled data is solved.

The invention relates to a ship trajectory data mining and macro-micro fusion route prediction method and system, and the method comprises the steps: S1, extracting ship trajectory data near an inland waterway from the data of an automatic ship recognition system, and carrying out the cleaning, filtering, sorting and preliminary analysis; s2, performing clustering analysis by using the features of the ship trajectory data, preliminarily mining trajectory features in combination with manual annotation, and establishing a ship trajectory database; s3, on the basis of historical data, constructing a deep learning model aiming at the inland river driving key path nodes of the ship from a macroscopic level; s4, training a deep learning model combining a convolutional neural network and a long-short-term memory network based on historical data, and predicting a ship position track from a micro level; and S5, performing macro-micro fusion prediction on the new ship data to obtain a ship navigation path and trajectory prediction coupling result. According to the method, coupling of macro and micro prediction means is adopted, and a certain guarantee is provided for the precision of ship trajectory prediction.

The invention discloses a multi-modal six-dimensional classroom effect evaluation and analysis method using AI, and the method comprises the steps: obtaining six-dimensional historical classroom data based on multi-modal historical classroom data; constructing an influence factor-weight mapping table; training a multi-modal fusion neural network model, performing feature fusion on the six-bit data, and constructing a classroom effect evaluation model; obtaining an influence factor value based on the real-time classroom time, and obtaining a weight distribution scheme with the highest matching degree with the current influence factor value; acquiring a classroom effect score, and performing targeted improvement based on the difference between the score and the optimal classroom effect score; and based on correction feedback data of students and teachers on the evaluation result, calculating the difference degree between model output and manual annotation through a contrast learning algorithm, and triggering model increment training. The method has the advantages that the weight is automatically adjusted by fusing multi-modal data and influence factors, the classroom effect is accurately evaluated, and personalized optimization suggestions are provided.

The invention provides a model fine tuning method and system based on feedback and reinforcement, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an output text generated by a language model, and carrying out the embedded coding; identifying a structural semantic unit in the text, and generating structural mark information; constructing a low-layer capsule set based on the structure marking information, and executing dynamic routing to generate a high-layer semantic capsule set; constructing a structure expression matrix according to the mapping relation between the high-level semantic capsule set and the structure mark; and inputting the matrix into a reward scoring model to generate a reinforcement learning return value, and updating language model parameters according to the reinforcement learning return value. According to the method, closed-loop linkage of the language model structure perception capability and the strategy optimization path is realized, and the text generation structure and semantic consistency can be improved under the condition that manual annotation is not needed.

The invention provides a non-autoregressive end-to-end dialect recognition method for a power supply service telephone system. The method comprises the following steps of dual-track telephone recording corpus preprocessing, automatic pre-labeling and context construction, manual labeling and dialect word library construction, dialect sub-area division and classification modeling, and dialect recognition model training and optimization. Based on the ffmpeg audio processing tool, the standardization and automation level of voice data processing in a telephone system scene is improved, and the manual annotation efficiency is greatly improved. And meanwhile, aiming at the problems that dialects are various in type, significant in difference and the like, a unified dialect labeling rule system is constructed, a model training process is optimized in combination with a transfer learning strategy, and the robustness and generalization ability of the model in a complex telephone voice environment are enhanced while high accuracy is ensured.

The invention provides a clinical data intelligent labeling and quality control method and system, and relates to the technical field of data processing, and the method comprises the steps: constructing a medical knowledge graph, carrying out the vectorization expression of nodes, extracting the context correlation features of a clinical text through a multi-head attention mechanism, and calculating the semantic similarity to form a hierarchical semantic link network; knowledge reasoning is carried out based on reinforcement learning to obtain an implicit association path, and the implicit association path is converted into a labeling rule and optimized. According to the method, efficient and intelligent labeling of the clinical data is realized, the labeling accuracy and consistency are improved, and the manual labeling cost is reduced.

The application provides a method for generating dialogue data, a method for training a model, and a method for processing dialogues. The dialogue data generation method includes: obtaining a prompt template and a generation content dependency relationship corresponding to each of human-machine dialogue elements in a target scenario, wherein the human-machine dialogue elements at least include: user queries and response content; progressively generating generation content of the corresponding human-machine dialogue elements based on the prompt templates and a pre-trained large language model according to the generation content dependency relationships; generating multi-round dialogue data in the target scenario based on the generation content respectively corresponding to the user queries and the response content. This method enables the fully automatic generation of multi-round dialogue data in a target domain, eliminating the need for manual annotation, reducing the cost of acquiring multi-round dialogue data, and improving the efficiency of dialogue data acquisition.

The invention discloses an abnormal data monitoring method and device based on artificial intelligence, and the method comprises the steps: 1, dividing an original data stream through a sliding window, extracting statistics, time sequence and change rate features, and dynamically screening features adaptive to data distribution based on an SHAP value; 2, constructing a double-flow model, capturing a global isolated mode by adopting an improved isolated forest in a static flow, capturing time sequence dependence on the basis of LSTM-AE in a dynamic flow, and fusing two-flow scores through performance-driven dynamic weight distribution; 3, combining a density peak value algorithm with historical density attenuation weighting, and dynamically adjusting an abnormal threshold value; 4, realizing low-delay incremental learning through a double-trigger mechanism and experience playback; 5, multi-granularity interpretation is generated, manual annotation feedback is supported, feature engineering and model training are integrated, and a'detection-interpretation-feedback-optimization 'closed loop is formed; high-adaptability anomaly monitoring is realized through dynamic feature screening, double-flow fusion detection, threshold value self-adaption and man-machine collaborative optimization.

The invention discloses a gesture recognition method and system based on lightweight calculation and storage, and belongs to the technical field of gesture recognition, and the method comprises the steps: collecting millimeter-wave radar original data through a millimeter radar, and generating point cloud data; on the basis of the point cloud data, feature vectors are extracted, and a feature vector matrix is constructed to capture gesture dynamic changes; based on the feature vector matrix, millimeter-wave radar data of different gestures are collected, and manual labeling is carried out to obtain a millimeter-wave radar gesture data set; constructing a CNN-LSTM hybrid neural network model, and performing training based on the gesture data set to obtain a Keras model; the Keras model is converted into a lightweight neural network model suitable for an embedded terminal; and deploying the lightweight neural network model to a millimeter wave radar chip with an Arm Cortex-M core for gesture recognition. According to the method, the calculation complexity and the storage requirement are reduced, and the independence and the data security of the system are enhanced.

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 discloses a reward model optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining training data which comprises cue words and training replies; receiving an annotation result corresponding to the training data, and determining a first reward score of each piece of training data based on the annotation result; scoring and marking the training data by adopting the original reward model, and determining a second reward score of each training data; determining an optimization function value corresponding to the original reward model based on the first reward score and the second reward score of the plurality of training data corresponding to the same prompt word; when the optimization function value does not meet the convergence condition, optimizing model parameters of the original reward model; and when the optimization function value meets a convergence condition, taking the original reward model as a target reward model. According to the method, the evaluation effect of the reward model can be close to the evaluation effect of manual annotation, and the purpose of improving the evaluation performance of the original reward model is achieved.

The invention provides an unsupervised three-dimensional point cloud semantic segmentation method based on progressive expansion super voxels, and relates to the technical field of three-dimensional point cloud processing. A progressive expansion super voxel is constructed, and a pseudo label is provided for the model in combination with a clustering technology, and is used as a supervision signal to guide the model to learn. In the training process, the gradually expanded super voxels are adopted to guide the training of the semantic segmentation model, so that the semantic segmentation model can gradually learn more global semantic information from local features, and the problems of fuzzy boundaries and insufficient understanding of complex scenes caused by lack of semantic information in a small local point set can be effectively solved. According to the mode, the network can learn meaningful semantic categories under the unsupervised condition without any manual annotation or pre-training model, time can be greatly saved, the adaptability to a new scene can be improved, semantic information can be accurately and efficiently extracted from the three-dimensional point cloud, and the processing method is clear and has high operability.

The invention discloses a coevolution driver cognitive load personalized quantification method, which relates to the technical field of traffic safety, and comprises the following steps: collecting a multi-modal physiological signal and processing the multi-modal physiological signal into structured feature data; constructing a labeled sample set D1 and an unlabeled sample set Du based on the structured feature data to perform semi-supervised collaborative pseudo labeling training, generating pseudo labels for unlabeled samples, and forming a self-labeled data set S; and constructing a training set and an enhanced training set by using S and D1, carrying out supervised contrast learning feature extraction, and carrying out model tuning in combination with an MAML algorithm. And finally, inputting any sample into the optimized model to generate continuous cognitive load value prediction. Wherein only a small amount of manual labeling is needed, the label quality can be improved, and the personalized quantification efficiency of the cognitive load can be improved; multi-modal information is fused, so that the robustness and the accuracy are improved; a continuous and fine-grained quantification mode is provided, and the accurate automatic driving decision-making requirement is met.

The invention discloses an Alzheimer's disease early warning method based on white matter lesion omics characteristics, and relates to the field of wisdom medicines.The method comprises the steps that magnetic resonance imaging data of a historical subject in the period from the mild cognitive impairment period to the period before diagnosis of Alzheimer's disease are obtained, and manual labeling of white matter and white matter lesion areas is carried out; a manual annotation data set is obtained; training a deep learning model for white matter lesion recognition based on the manual annotation data set; inputting to-be-identified magnetic resonance imaging data into the deep learning model, and extracting lesion features of the white matter; performing standardization and feature alignment on the extracted lesion features, and inputting the lesion features into a deep clustering model to form clustering results for different white matter lesion feature types; and an early risk assessment model is constructed based on the clustering result and the Alzheimer's disease transformation risk tag corresponding to the clustering result, and the Alzheimer's disease transformation risk level of the subject is output, so that the problems that multiple lesion features are difficult to quantify and details are difficult to identify are solved.

The invention discloses a model adjustment method and device, a medium and a program product, and relates to the technical field of data processing, and the method comprises the steps: determining target reasoning data and at least one prefix data corresponding to each group of training data through a teacher model, and carrying out the fine adjustment of a student model based on the prefix data and the target reasoning data, no annotation needs to be added in the process, so that a large number of computing resources do not need to be consumed, the technical problem that related fine adjustment methods depend on manual annotation is solved, and the technical effect of improving the model fine adjustment efficiency is achieved.

The invention relates to the technical field of code development, and provides an AI code effective proportion statistical method and device, a medium and equipment. The method comprises the steps of obtaining related information of codes submitted by a user; according to a user name in the related information, searching log information of an AI code generated by a corresponding user through adoption of a code generation tool; under the condition that the file name of the code submitted by the user is matched with the file name in the log information, searching a corresponding submitted code segment from the code submitted by the user according to the mark information in the log information; and calculating the similarity between the AI code and the submitted code segment, and counting the effective proportion corresponding to the AI code according to the similarity. Therefore, the calculation of the effective proportion considers the quality of the AI code, so that the effective proportion can accurately reflect the real contribution of the AI code, and the calculation process is automatically realized, thereby avoiding the tedious, time-consuming and labor-consuming conditions of manual labeling.

The invention relates to the technical field of aviation fastener detection, solves the technical problems that in a traditional method, the segmentation difficulty of a skin and a fastener is large, the detection precision and efficiency are low, and manual marking is difficult, and particularly relates to a weak supervision optimization-based aviation fastener concave-convex amount detection method. Comprising the following steps: acquiring an original image and three-dimensional point cloud data of an aircraft skin fastener; performing segmentation by adopting an SAM network model to obtain a two-dimensional mask image containing fastener features; mapping the pixel region of the fastener feature into the three-dimensional point cloud data; using a NumPy vectorization method to calculate the average deviation of the concave-convex quantity; and carrying out visual output on the average deviation of the concave-convex quantity in a label form. According to the method, rapid and accurate measurement of the concave-convex quantity of the nail head of the aviation fastener can be realized, visual output of the concave-convex quantity detection result is realized by utilizing the two-dimensional image mask and the three-dimensional point cloud mapping, the efficiency and the precision of aviation fastener detection are greatly improved, and a visual basis is provided for assembly quality evaluation.

The invention provides a multi-modal fusion road topology reasoning method and system, and the method comprises the steps: converting an extracted lane mask into a semantic seed point in a historical point cloud, and carrying out the pseudo-label setting based on the semantic seed point. And performing model training based on the historical image with the pseudo tag and the historical point cloud to obtain a lane detection model. Obtaining lane line information from the real-time image and the real-time point cloud by using a lane detection model, mapping the lane line information to a bird's-eye view feature map, calculating a topological relation between lane lines based on the lane line information in the bird's-eye view feature map, obtaining a topological reasoning matrix, and obtaining a topological reasoning result; and obtaining a corresponding road topology reasoning result based on the graph neural network according to the topology reasoning matrix. According to the scheme, on the basis of a pseudo label generation mechanism, dependence on manual marking is reduced, data preparation cost is reduced, effective fusion of a lane line representation method based on bird's-eye view space unified mapping and lane topology reasoning is introduced, and a lane topology reasoning result with high precision and good topology consistency can be obtained.

The invention belongs to the technical field of data annotation, and particularly relates to a large-model-based auxiliary annotation system and method.The large-model-based auxiliary annotation system comprises a corpus module, an annotation instructor operation module, a large model pre-annotation module, a standardized document module, a large model self-error correction module and a common annotation operator operation module; the auxiliary labeling system based on the large model is used for data labeling in the fields of machine learning, artificial intelligence, model training and the like, the mode of combining large model pre-labeling and manual labeling is introduced, the data labeling process is optimized, labeling efficiency is improved, cost is reduced, meanwhile, high quality of labeled data is ensured, and the method is suitable for popularization and application. The system is suitable for data labeling tasks in the fields of machine learning, artificial intelligence and the like, and has a wide application prospect.