24results about How to "Improve labeling accuracy" patented technology

The invention belongs to the technical field of data processing, and discloses an enterprise multi-type data labeling method and system based on feature engineering, and the method comprises the steps: collecting initial data of an enterprise internal information system, and dividing the initial data to obtain a text data set, a table data set and an image data set; feature vectors of different types of data are fused after features are extracted and screened, and a multi-dimensional feature space is constructed; constructing an annotation model according to the feature information of the feature space, constructing similar sample pairs as positive sample pairs and irrelevant sample pairs as negative sample pairs for unannotated new data, and comparing, learning and optimizing the similarity between the similar sample pairs; and carrying out label inference based on the feature vectors obtained by comparative learning, and generating a labeling result. According to the method, efficient and automatic labeling of various types of data can be achieved, the method is suitable for various heterogeneous data such as texts, tables and images, and high labeling precision and expansibility are achieved.

The invention discloses a wafer appearance detection method and device and computer equipment. The method comprises the following steps: labeling defect categories of defect samples of various wafer appearances to obtain a sample set with defect labeling categories of various wafer appearances, and training the sample set with defect labeling categories of various wafer appearances to generate a defect detection model of wafer appearances, and detecting wafer appearance defects according to the defect detection model of the wafer appearance. By means of the mode, the detection efficiency and accuracy of wafer appearance defect detection can be improved.

The invention provides a graph neural network construction method and device, electronic equipment and a storage medium, and relates to the technical field of biological information. The method comprises the following steps: encoding single cell data in a single cell data set into a low-dimensional submerged space to obtain a first feature vector which retains key features and / or type specificity information of the single cell data; obtaining a constructed gene regulatory network, and encoding the gene regulatory network into low-dimensional-factor nodes; under the condition that the global network structure of the gene regulation and control network is reserved, extracting local topological features of the low-dimensional factor nodes to obtain a second feature vector; the second feature vector is used for reflecting interaction among all genes in the single cell data set; constructing a graph neural network based on the first feature vector and the second feature vector; and the graph neural network is used for automatically labeling the single cell data. The problems that cell labeling is inaccurate, time and labor are wasted, and the cell labeling is limited by the batch effect in the prior art are solved.

The invention provides a data annotation system and method based on a large language model, a medium and a terminal, and the system comprises a data access layer which is used for collecting to-be-annotated data, and carrying out the preprocessing of the to-be-annotated data, so as to enable the to-be-annotated data to be converted into a unified data format; the intelligent annotation engine layer is used for retrieving context information of a vertical field for the received to-be-annotated data in the unified data format, splicing the retrieved context information and the to-be-annotated data and then inputting the spliced information and the to-be-annotated data into a plurality of pre-trained large language models; the large language model is used for generating a labeling result of the to-be-labeled data; and the quality control layer is used for monitoring the quality of the labeling results generated by the plurality of large language models and carrying out feedback analysis according to the monitored labeling quality problem. According to the method and the device, the automation of the whole process from the acquisition of the to-be-labeled data to the generation of the labeling result can be realized, the vertical domain knowledge is integrated, the labeling result is strictly controlled, and the process is continuously subjected to feedback optimization.

The embodiments of the present disclosure provide a method, device, electronic device and readable medium for labeling walls of a building model, the method comprising: determining a target wall object in the building model, the target wall object having a first direction, the building model having a target space range, the target space range including at least one room space; determining a target wall point in the target wall object; translating the target wall point in a second direction by a target translation amount to obtain a first translation point; translating the target wall point in a third direction by a target translation amount to obtain a second translation point, the second direction and the third direction being opposite; determining the labeling result of the target wall object according to the positional relationship between the first translation point and the second translation point in at least one room space within the target space range. The method, device, electronic device and readable medium for labeling walls of a building model provided by the embodiments of the present disclosure can automatically label wall objects with high labeling accuracy.

The application provides a model updating method and device for image labeling and electronic equipment. The method automatically labels original image data through a pre-labeling model to obtain a labeling data set. The pre-labeling model is a model to be updated. At least part of the labeling data in the labeling data set is subjected to a correction operation. Correction operation data corresponding to the at least part of the labeling data is determined. A correction difference pair is formed based on the at least part of the labeling data and the corresponding correction operation data. If the number of the correction difference pairs is greater than or equal to a preset number threshold, the pre-labeling model is fine-tuned according to the correction difference pairs to obtain an updated pre-labeling model. The original pre-labeling model is replaced by the updated pre-labeling model. The original pre-labeling model is replaced by the updated pre-labeling model to label image data, which can provide more accurate, more efficient and more stable image labeling, reduce the workload of manual auditing, and reduce the labor cost and time cost.

The present invention discloses a method and device for image data processing, and relates to the field of computer technology. A specific implementation of the method includes: annotating a data set to be processed using an image annotation model trained with an initial training sample set to obtain an annotation result data set; determining the similarity between the annotation result data set and the initial training sample set; and updating the initial training sample set according to the similarity. This implementation can efficiently update and correct wrong labels in the initial training sample set, improve the annotation quality of the initial training sample set, and thereby improve the performance of the trained image annotation model.

The utility model discloses an aluminum alloy cutting rapid marking assembly which comprises a sample plate, a marking assembly and a marking assembly. The marking piece is arranged in the guide rail and is configured to form a linear mark on the surface of the aluminum alloy; the flexible cable is connected with the marking piece; the guide rail is provided with two ports opposite in the extending direction, and the flexible cable is configured to drive the marking piece to move from one of the two ports to the other. According to the aluminum alloy cutting rapid marking assembly, the labor cost is saved, the working efficiency is improved, and the marking accuracy is improved.

The application discloses a kind of graphic annotation automation processing method and system, specifically related to image clustering and automatic annotation field, including the entropy value and gradient variance of the pixel region of input image are calculated to generate complexity index, according to complexity index adjustment region division scale, obtain division region;Color histogram, texture direction and edge curvature are calculated in division region, form region feature, and complexity index and region feature are combined to generate feature matrix;The application generates feature matrix by calculating region complexity index and extracting multi-dimensional features, inputs clustering model to identify boundary cluster and jump region, combined with weight map and segmented weighting generates multi-class and single-class annotation, to solve the problem of semantic confusion, unstable boundary and error accumulation.

The utility model relates to the technical field of carbon powder filling, in particular to a smoothing device for carbon powder filling and labeling. The utility model provides a position-corrected smoothing device for carbon powder filling and labeling. According to the technical scheme, the smoothing device for carbon powder filling and labeling comprises a labeling machine and a conveying table, the labeling machine is fixed to the conveying table, the smoothing device further comprises an opening guide plate and an adjusting assembly, the adjusting assembly is composed of fixing blocks, guide rods and limiting rods, the fixing blocks are symmetrically and fixedly connected to the conveying table, the guide rods are rotationally connected between the fixing blocks, and the limiting rods are connected between the guide rods. Symmetrical threads are formed in the guide rods, the opening guide plates are symmetrically arranged and connected to the guide rods through the threads, and the limiting rods fixedly connected to the conveying table are connected to the opening guide plates in a sliding and penetrating mode. The distance between the opening guide plates is adjusted through the guide rods, and the carbon powder tank is adjusted and guided by the opening guide plates after entering the conveying table, so that the carbon powder tank is accurately moved to a labeling position, and the carbon powder tank is effectively prevented from deviating.

This invention belongs to the field of data processing and artificial intelligence technology, specifically a method and system for automatic classification of medical device patents based on deep learning. The method includes the following steps: acquiring textual data of medical device patents and performing preprocessing; introducing medical device classification standards as an external knowledge base, and calibrating the labels of the preprocessed text data to form a standard dataset; for categories with insufficient sample numbers in the standard dataset, generating synthetic data based on the patent text using a generative model; constructing a deep learning classification model that integrates global and local semantic features; training the deep learning classification model using the enhanced dataset, and inputting the medical device patent text to be classified into the trained model for classification prediction, outputting its corresponding medical device classification standard category. This invention solves the problems of mismatch between existing patent classification systems and industry regulatory standards, and low classification accuracy.

The application discloses a model training method, a category prediction method, a device, a computing device and a computer readable storage medium, to solve the problem of low category prediction accuracy of the model. The training method comprises: training a category prediction first model by using an original sample object set; determining the matching degree between the label of the sample object and the predicted category of the sample object by using the trained first model; selecting sample objects meeting a preset condition from the original sample object set; and training a category prediction second model by using the selected sample objects. Since the sample objects used for training the category prediction second model are sample objects with relatively high label annotation accuracy, compared with the category prediction model trained directly based on the dirty data set, the category prediction model trained by the method provided in the application embodiment has higher accuracy.

The embodiment of the invention relates to a data annotation system. The system comprises an annotation scheduling module, a machine annotation module, an intelligent annotation model, an annotation knowledge base, an annotation interpretation module and a manual annotation feedback module, the labeling scheduling module is respectively connected with the machine labeling module and the manual labeling feedback module; the machine labeling module is respectively connected with the intelligent labeling model and the labeling interpretation module; the intelligent labeling model is connected with the labeling knowledge base; the annotation interpretation module is connected with the intelligent annotation model; the manual annotation feedback module is connected with the intelligent annotation model and the annotation knowledge base. According to the method and the device, the labeling accuracy of the model can be continuously improved while the labeling efficiency is ensured.

The embodiment of the invention provides an equipment knowledge graph data labeling method and device, and belongs to the technical field of industrial and mining equipment. The method comprises the following steps: collecting equipment characteristic data of a plurality of pieces of industrial and mining equipment, and performing data preprocessing on the equipment characteristic data to generate a data source which is de-noised and is uniform in data format; a labeling template is obtained, the labeling template and the data source are input into a pre-training large language model for preliminary labeling processing, preliminary labeling data corresponding to the multiple pieces of industrial and mining equipment are generated, and the preliminary labeling data comprise entities, entity types and entity relations corresponding to the multiple pieces of industrial and mining equipment; and performing semi-automatic evaluation processing on the entities, the entity types and the entity relationships corresponding to the plurality of pieces of industrial and mining equipment based on the data source, and generating final annotation data. The problems of high labor cost and time cost and low labeling accuracy caused by a labeling method in the prior art are solved.

The invention provides a diagenesis phase sample automatic labeling method and system fusing affinity propagation clustering and a graph convolutional neural network, and belongs to the field of diagenesis phase intelligent identification. The method aims to solve the problems of small sample size and low accuracy of diagenetic phase labeling work. The method comprises the following steps: preprocessing logging curve data, labeling a small amount of labels, and constructing an automatic labeling data set; establishing an incidence relation between logging curve depth nodes by utilizing an affinity propagation clustering structure map structure; node features are aggregated through a graph convolution layer, and rapid and accurate marking of lithogenous phases is achieved. Compared with a traditional method, the method has the advantages that complicated correlation and spatial distribution characteristics between lithogenous phases can be better processed, and the graph structure and the logging curve characteristics are combined, so that the marking accuracy and stability are improved.

The present invention discloses a long text data recognition method, device, electronic device and storage medium, and relates to the field of big data technology, wherein the method comprises: obtaining long text data, the long text data comprising: multiple topics; clustering the long text data based on a predetermined topic aggregation model to generate multiple topic module data; inputting the multiple topic module data into a pre-trained text recognition model respectively to generate annotation results for each topic module data, the annotation results are used to identify the topic of the data; identifying each topic data of the long text data according to the annotation results of each topic model data, and inputting each identified topic data into the corresponding text area part. Through the present invention, the annotation accuracy of the text recognition model can be improved, thereby improving the accuracy of long text data recognition.

The invention discloses a construction method and device of a cerebral apoplexy space knowledge graph database, equipment and a medium, and relates to the technical field of big data analysis. According to the application, the Tonyuqian question 2.1 model is utilized to deeply mine, analyze and publically obtain papers, identify and mark spatial distribution conditions of ischemic cerebral apoplexy genes, physiological regions and pathological regions, and the spatial distribution conditions are displayed in the form of a cerebral apoplexy spatial knowledge graph database. According to the method, genes and spatial distribution information thereof are automatically extracted from literatures, and a dynamically updated knowledge graph is constructed, so that the problems of low data integration efficiency, insufficient labeling accuracy, insufficient dynamic association and the like in cerebral apoplexy spatial heterogeneity research in the prior art are solved.

The invention discloses a case analysis auxiliary system based on lung cancer diagnosis and treatment, and the system comprises a case data preprocessing module which is used for cleaning, standardizing and enhancing medical image data so as to remove noise, correct distortion and fill missing values; the case data feature extraction module is used for collecting and numbering case analysis accuracy data, case expansion data and model generalization ability data; the case data verification module is connected with the case data feature extraction module through a network, and calculates an accuracy improvement value Xv, a data acquisition expansion rate Yv and a model generalization ability improvement index Rv; and the case intelligent decision module is connected with the case data verification module through a network to improve the case accuracy and expand the case data volume. According to the method, the marking accuracy of the pulmonary nodule samples can be improved, an infinite number of samples can be processed according to an automatic processing process, and the limitation of manual marking on quality and number is overcome.

The application provides a kind of semi-automatic labeling method and system of ultrasonic video, comprising: step S1, target marking is carried out to target frame image in ultrasonic video data, and first marking information is obtained;Step S2, according to first marking information, target identification marking is carried out to front and rear frame image, and then second marking information corresponding to all frame images is obtained;Step S3, when the same target exists different marking, all markings corresponding to the repeatedly marked target are arbitrated;Step S4, according to the arbitration result, update second marking information.Have beneficial effect: the application carries out target tracking marking to front and rear frame image by the marking information of the target of target frame image, can mark target more accurately, and can automatically update the marking of front and rear frame;At the same time, arbitration and update are carried out to repeated marking, determine the final marking result, can avoid labeling error and reduce labeling redundancy, can further improve labeling accuracy.

The application provides an extended training data set, and a method for classification using a classification model trained by the extended training data set. The method for extending the training data set includes generating merged data based on original data and auxiliary data, the auxiliary data being associated with an application scenario of the classification model and coming from a source different from a source of the original data; and updating a category of the merged data using the classification model to generate a first extended data set for training the classification model. The scheme of the application can expand the number of labeled data samples and improve the labeling accuracy of the data, so that the classification performance of the trained classification model is optimized.

The invention discloses a labeling method of any code snippets and a retrieval system thereof, and relates to the related field of machine learning, the method comprises the following steps: establishing a code snippet database, activating a multi-dimensional feature labeling model, executing code snippet labeling, and establishing a first labeling result; calling a self-adaptive annotation network, performing context backtracking analysis on the code snippets in the database by utilizing the self-adaptive annotation network, and establishing a second annotation result; performing static and dynamic fusion analysis on the database, establishing a joint function similarity, and establishing a third labeling result; code version change labeling is executed, and a fourth labeling result is established; and performing code labeling retrieval management according to the four labeling results. The technical problem that the management efficiency and the retrieval precision of the code snippets are low due to high complexity of the code snippets in multi-dimensional feature labeling is solved, and the technical effect that the labeling accuracy and the retrieval efficiency of the code snippets are improved in the mode that multi-dimensional labeling and self-adaptive backtracking analysis are combined is achieved.

The present application relates to an emotion data annotation method, device, computer equipment and storage medium. The method includes: obtaining a video stream; extracting the image data of each video frame of the video stream, annotating the emotion corresponding to the face image in each video frame image data, and obtaining the face emotion annotation result; extracting the audio data corresponding to each video frame image data, annotating the emotion corresponding to the audio data, and obtaining the semantic emotion annotation result; comparing the face emotion annotation result with the semantic emotion annotation result, and determining the emotion data annotation result corresponding to the video stream according to the comparison result. The method of the embodiment of the present application can effectively improve the efficiency of emotion data annotation, avoid the subjectivity of manual annotation, and effectively improve the accuracy of emotion data annotation.

This application discloses a method, apparatus, device, medium, and product for validating model training samples. The method includes responding to a class annotation event acting on product query term samples and product title samples in a candidate sample pool; obtaining the first labeled product category for the product query term samples and product title samples; calling the first sample class annotation model to annotate the second labeled product category corresponding to the product query term samples and product title samples with the first labeled product category; pushing product query term samples or product title samples with the same first labeled product category and second labeled product category to the training sample library; and updating the first sample class annotation model based on the latest converged second sample class annotation model trained on the training sample library. This application achieves automated annotation and validation of training samples, and forms efficient utilization of model resources, effectively improving the inference capability of the model to be deployed online.

The invention relates to the technical field of artificial intelligence, in particular to an automatic entity labeling method perpendicular to industry policies and regulations. The method comprises the following steps: analyzing a document format, judging whether the document is a text document, dynamically splitting the document into text blocks based on semantic integrity, vectorizing the text, identifying entities and attributes, vectorizing the entities, clustering based on vector similarity and the like. According to the method, through full-process automation of semantic-driven text partitioning, LLM entity recognition and disambiguation, knowledge network construction and graph fusion, the tedious links of reading-understanding-labeling-rechecking in traditional manual labeling are replaced, the requirements for professional labeling personnel and quality inspection personnel are reduced through the automatic process, and in addition, the automatic labeling efficiency is improved. Through cooperation of a dynamic partitioning strategy (reducing LLM single processing capacity), vector retrieval (quickly positioning similar entities) and map fusion (incremental updating knowledge), the labeling period is greatly shortened.