5476 results about "Labeled data" patented technology

A video localization system localizes actions in videos based on a classification model and an actionness model. The classification model is trained to make predictions of which segments of a video depict an action and to classify the actions in the segments. The actionness model predicts whether any action is occurring in each segment, rather than predicting a particular type of action. This reduces the likelihood that the video localization system over-relies on contextual information in localizing actions in video. Furthermore, the classification model and the actionness model are trained based on weakly-labeled data, thereby reducing the cost and time required to generate training data for the video localization system.

A video localization system localizes actions in videos based on a classification model and an actionness model. The classification model is trained to make predictions of which segments of a video depict an action and to classify the actions in the segments. The actionness model predicts whether any action is occurring in each segment, rather than predicting a particular type of action. This reduces the likelihood that the video localization system over-relies on contextual information in localizing actions in video. Furthermore, the classification model and the actionness model are trained based on weakly-labeled data, thereby reducing the cost and time required to generate training data for the video localization system.

The invention discloses a large-model-assisted self-lifting multi-modal industrial equipment knowledge graph construction method. The method comprises the following steps: collecting and preprocessing multi-modal data; obtaining structured knowledge data in a triple form based on a few-sample prompt; gradually self-optimizing the triple from three evaluation dimensions in combination with thinking chain skills to ensure normalization, integrity and reliability of knowledge expression; the problems of data redundancy and ambiguity in the knowledge graph are solved by adopting a triple-step entity alignment method and utilizing the self-lifting capability of a large model; based on a knowledge graph completion template and a retrieval enhancement mechanism, a relationship prediction and entity completion task is completed by applying a large model, so that the integrity and applicability of the knowledge graph are ensured. According to the method, a large amount of manually labeled data is not needed, the automation level and knowledge quality of knowledge graph construction are improved through the self-lifting thought of a large model and multi-time iterative optimization, and powerful support is provided for industrial equipment management, process optimization and intelligent diagnosis.

The invention discloses a multi-source data fusion method and system based on cloud computing, and particularly relates to the technical field of multi-source data fusion, and the method comprises the steps: obtaining the real-time information of multi-source data, generating a health score and a label through a health evaluation model, and marking a data state and an abnormality type; performing format conversion and semantic enhancement processing on the health data to generate structured data; based on a knowledge graph reasoning technology, extracting a logical relationship to generate a fusion rule, and calculating a rule weight and confidence in combination with a health label, a data source confidence and a semantic feature; a fusion result of conflict data is judged through a semantic consistency index, if the fusion result does not conform to expectation, historical data and rules are loaded according to a verification model, a verification score is calculated, an optimization process is triggered after the verification score is compared with a threshold value, health assessment model parameters, a semantic embedding model and fusion rule logic are optimized, and fusion accuracy and reliability are improved.

The invention discloses a cross-modal adaptive interaction-based few-sample industrial anomaly detection method, which comprises the following steps of: calculating total loss according to aligned final features, normal semantic projection, abnormal semantic projection, optimized visual features and optimized text features, and updating parameters of an industrial anomaly detection model by using the total loss. A trained industrial anomaly detection model is obtained; and inputting a to-be-detected industrial image into the trained industrial anomaly detection model to obtain the aligned final feature, the anomaly semantic projection, the optimized visual feature and the optimized text feature for judging the anomaly condition of the to-be-detected industrial image. According to the method, under the condition that only a small number of normal samples are needed, the distinguishing capacity of the model for normal and abnormal features can be remarkably enhanced, dependence on labeled data is reduced, and an industrial anomaly detection solution which is high in precision, low in cost and capable of being rapidly deployed is provided for intelligent manufacturing.

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention provides a marketing strategy optimization method based on big language model analysis and evaluation driving, and the method comprises the steps: obtaining historical data including user behavior data, label data and statistical data, carrying out the analysis, and constructing a basic marketing strategy; user content data are collected in real time through Kafka, deep analysis is conducted on user content through a large language model, and a personalized marketing scheme is matched from a knowledge base based on the deep analysis result of the large language model and basic rule matching; through a feedback circulation mechanism, feedback of the user to marketing activities is collected, and a feedback result is stored in a knowledge base and used for optimizing a marketing strategy and a large language model; meanwhile, the decision-making process is logged, textualized and vectorized and is stored in a vector library, and the decision-making process and the marketing scheme are optimized in combination with a continuously optimized large language model, so that dynamic adjustment and matching are realized. According to the invention, the accuracy and effect of the marketing strategy are improved by using the large language model.

The invention relates to the technical field of data security, in particular to a data security sharing method and system. The method comprises the following steps: acquiring original shared data, carrying out reverse mapping and block marking, generating block marking data, carrying out inter-block random arrangement on the block marking data, carrying out differential disturbance simulation based on a random sequence, forming disturbance block data, and encrypting the disturbance block data by applying a progressive encryption technology. The method comprises the following steps: carrying out fragmentation storage, generating storage shared data, carrying out access monitoring on the storage shared data, collecting shared access data, and constructing an evidence chain and generating an access traceability data chain according to the data, thereby realizing the security and traceability of data sharing. According to the invention, a safer and more credible data security sharing method is realized.

A system and method for extracting data from large language model (LLM) outputs, including: training a model using labeled data items to assign rankings to LLMs; selecting, by the trained model, one or more of the LLMs based on the rankings; sending an LLM prompt to selected models; and outputting, by the model, a refined response to the prompt based on responses to the prompt by the LLMs. Some LLM prompts according to some embodiments may include different sets input parameters of different types—such as, e.g., a set of block parameters and a set of editorial parameters. In some embodiments, a model or LLM may be updated or retrained using a reinforcement learning approach and based output items or refined responses generated by that model or LLM—which may for example be scored or ranked and used in combination with reward or cost functions to update model parameters.

The invention relates to the technical field of natural language processing, and discloses a question and answer task processing model training method, device and equipment and a storage medium, and the method comprises the steps: adjusting a question and answer task processing model according to an initial training data set and a first model training algorithm, and obtaining a first intermediate question and answer task processing model; according to the initial training data set and the first intermediate question and answer task processing model, obtaining a first reasoning step and first annotation information for processing the question and answer task, and generating a first target training data set; according to the initial training data set and a second model training algorithm, adjusting a question and answer task processing model to obtain a second intermediate question and answer task processing model; and adjusting the second intermediate question and answer task processing model according to the first target training data set to obtain a target model. The problems that when the model for processing the question and answer task is trained, the accuracy of the model is affected by the quality of the manually-labeled data due to the dependence on the manually-labeled data, and the training process is high in cost and low in efficiency are solved.

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a time series data processing method, device and equipment and a medium. Performing a data enhancement operation on the original time series data to generate enhanced time series data; training a feature encoder based on the enhanced time sequence data in a comparative learning mode to obtain a pre-trained feature encoder; connecting the pre-training feature encoder with a sparse attention mechanism module to construct a time sequence modeling network; target time series data is processed using the time series modeling network to generate a processing result. According to the method, the enhanced view is constructed on the unlabeled data and the contrast learning training feature encoder is introduced, so that the time sequence representation with generalization ability is obtained, effective modeling of the long dependency relationship is realized in combination with a sparse attention mechanism, and the accuracy of time sequence modeling is improved under the condition of not depending on a large amount of labeled data.

The invention discloses a passive unsupervised domain adaptive medical image segmentation method and device based on pseudo labels, and relates to the technical field of computers, and the method comprises a supervised source domain model training stage, an unsupervised adaptive target domain model training stage and a segmentation stage. The method specifically comprises the following steps of: performing pixel-level self-marking on a source domain medical image by applying pseudo-tag pixel-level uncertainty estimation to generate a reliable pseudo-tag; and on this basis, a strong and weak enhanced teacher-student target domain model is used, and a high-quality pseudo tag is fully utilized to train a target domain medical image, so that the network adapts to representation specific to the target domain medical image. According to the method, unsupervised segmentation is carried out on the target domain data graph on the premise that the source domain model and the target domain unmarked data are known, source domain data do not need to be accessed, the features of the source domain model can be reasonably utilized, and the problem existing in SFUDA can be well relieved; and the accurate segmentation capability on the target domain is still maintained under the condition that less data is available compared with the UDA method.

The invention relates to the technical field of image processing, and provides a training data set labeling method and device for AI visual identification, and the method comprises the steps: obtaining and processing original image data, extracting feature mapping, inputting a target detection model to obtain a preliminary labeling result, carrying out the analysis, and determining a clustering center and a candidate target set; the candidate target set is classified based on the clustering center, the confidence degree of the classification labeling result is calculated, labeling data with the confidence degree higher than a threshold value serve as high-confidence-degree labeling data, rechecking and consistency checking are conducted on data with the confidence degree lower than the threshold value, and consistency labeling data are generated; and the target detection model is optimized in combination with the high-confidence data, the low-confidence data is re-labeled by using the optimization model, and the high-confidence data and the re-labeled data are combined to generate a complete labeled training set to train the recognition model. Through the above scheme, the consistency and confidence of the data are ensured, and the problems of inconsistent labeling and missed labeling and false labeling in complex scenes and multi-target recognition are effectively improved.

The invention discloses a field specialized large model fine tuning and deployment method and system based on adaptive optimization, belongs to the technical field of artificial intelligence and machine learning, and aims to solve the technical problem of how to improve the rapid adaptation capability of a large model on small-scale field data. In order to meet the requirements of special fields of law, medical treatment, finance and the like on professional terms and complex contexts, the technical scheme adopted by the invention comprises the following steps of: data processing and sample generation: performing cleaning, feature extraction and small sample expansion on field data, and generating a high-quality training sample through a field feature guide mechanism; small-sample fine tuning and migration: realizing rapid field adaptation of a large model through a small-sample learning technology and dynamic Prompt optimization, and reducing dependence on large-scale annotation data in combination with cross-field migration learning; compressing and optimizing the model; performing automatic deployment and collaborative reasoning; and monitoring and adaptive optimization are carried out.

The invention discloses an intelligent substation communication link fault accurate positioning method and system, and the method comprises the steps: obtaining a configuration file and equipment state data, carrying out the processing of the configuration file and the equipment state data, and generating a standardized link feature vector and a marking data set; constructing a hybrid deep learning model, and optimizing parameter configuration of the hybrid deep learning model by adopting an optimization algorithm to obtain a parameter-optimized hybrid deep learning model; training by using a real fault sample in combination with a virtual fault sample generated by a generative adversarial network, optimizing a time sequence prediction capability through an echo state network, and generating a fault positioning model; in combination with the link state data, outputting a fault link positioning result and confidence evaluation through multi-stage confidence evaluation and topological correlation analysis; and carrying out virtual-real corresponding verification in combination with the configuration file, carrying out parameter optimization on the fault positioning model, and outputting a fault positioning system. The problems that the fault positioning precision is low, the response speed is low, and complex fault scenes cannot be processed are solved.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

The invention relates to a data hierarchical caching and intelligent continuous transmission method and device in an off-network environment, and the method comprises the steps: obtaining to-be-transmitted data, carrying out the hierarchical evaluation based on the importance degree and timeliness of the data, and obtaining a corresponding priority data group; monitoring a network bandwidth state in real time, recording an interruption moment and triggering a hierarchical cache mechanism when network interruption is detected, and setting a corresponding storage area; distributing the priority data group to a corresponding storage area for caching to obtain a final cache area; when network recovery is detected, intelligent continuous transmission is performed on the priority data set according to the cache region, and a continuous transmission report is generated; detecting a network bandwidth state in real time, performing dynamic continuous transmission adjustment on the priority data set, and marking the priority data set after continuous transmission is completed to obtain a marked data set; and performing integrity verification on the marked data group, and generating a transmission state report in combination with the continuous transmission report. The method can adapt to different network environments and data transmission requirements.

The invention discloses an unmanned aerial vehicle system testing method and system based on a model, and relates to the technical field of unmanned aerial vehicle system testing, and the method comprises the steps: collecting the operation data of an unmanned aerial vehicle, preprocessing the data, and carrying out the labeling classification of the preprocessed data; inputting the labeled data into a generative adversarial network model, training a generator, and generating a simulated flight scene data set; carrying out transfer learning model pre-training based on the simulated flight scene data set; deploying the unmanned aerial vehicle in an actual test, collecting incremental data from a real environment, and performing fine tuning on the pre-trained transfer learning model; a flight mission plan is generated, and simulation evaluation is completed in combination with the simulated flight scene data set. By generating the high-quality flight data and continuously updating the model, the test and task planning capability of the unmanned aerial vehicle system in a complex environment is effectively improved, the test cost is reduced, and the coping capability of the model in different flight scenes and the stability of the model in actual operation are enhanced.

The invention discloses a tool wear loss prediction method based on physical information self-supervised learning under the condition of few marks, and belongs to the technical field of numerical control machining center tool wear state prediction. Comprising the steps of collecting monitoring signals such as cutting force, vibration and acoustic emission in the machining process; carrying out preprocessing operation of interception and Z score standardization on each sample; a mixed residual convolutional neural network tool wear monitoring model is designed, and information fusion and high-dimensional representation extraction are carried out on multi-source sensor signals; pre-training the tool wear monitoring model by using the auxiliary task I, and guiding the model to extract the invariance feature of the signal; according to the method, under the condition of scarce marked data, high-precision prediction and excellent generalization ability of tool wear are realized, the fluctuation amplitude of prediction errors can be effectively reduced, and the stability of a prediction result is kept. According to the method, a small amount of mark data is effectively utilized in model training, the stability and reliability of tool wear monitoring are ensured, and the requirements for monitoring precision and adaptability in actual production are met.

The invention discloses a hyperspectral image classification method based on mask Transform and contrast learning, and the method comprises the steps: obtaining and preprocessing hyperspectral image data, obtaining a sample set, and obtaining a training set based on the sample set; constructing an initial hyperspectral image classification model and a model overall loss function; dividing the training set into a labeled data set and a label-free data set, pre-training the initial hyperspectral image classification model based on the label-free data set and a model overall loss function, simplifying and recombining, and performing supervised training on the simplified and recombined model based on the labeled data set and a cross entropy loss function, and performing hyperspectral image classification through the trained model. According to the method, multi-scale mask reconstruction and local contrast learning are combined, so that the attention of the model to the ground feature in the hyperspectral image is improved, the space and channel information of the feature map is enriched, the weight of the ground feature is enhanced, and the accuracy and robustness of spectral image classification are improved.

The invention provides a high-density RFID tag group scanning system based on a multi-antenna array and beam forming, and relates to the technical field of the Internet of Things. The system comprises a multi-antenna array, an RFID reader-writer, a beam forming unit, a signal processing unit, a feedback unit and a data management unit. Through a digital beam forming technology, the system dynamically adjusts the direction and width of a beam, and achieves the precise scanning of a target area. And in combination with real-time signal feedback, a density peak clustering algorithm and a gradient descent algorithm are adopted to optimize beam parameters, so that the recognition efficiency is improved. The system further utilizes an anti-collision algorithm to dynamically adjust frame parameters, and performs logic grouping based on spatial positions and type characteristics of tags, so that signal collision and missed reading rates are remarkably reduced. Meanwhile, the data management unit stores and outputs the label data in a classified mode, and intelligent management capacity is provided for multi-scene application.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention provides a semi-supervised medical image segmentation method based on progressive pseudo label enhancement. The method comprises the steps of dividing a labeled original medical image data set into a labeled data set and a non-labeled data set, constructing a PPE network model comprising a student model and a teacher model, performing iterative training on the PPE network model by using the labeled data set and the non-labeled data set, generating a self-adaptive threshold value by using an SAT mechanism in each iteration, and outputting the self-adaptive threshold value. Filtering out false labels lower than a certain confidence coefficient range in the student model and the teacher model to obtain a trained student model and a trained teacher model; and inputting the preprocessed medical image into the trained student model, and outputting an image category segmentation result of the medical image. According to the method, the SAT mechanism is used for generating the self-adaptive threshold value, the utilization rate of reliable pixels is improved, then the cooperation and consistency regularization method of the teacher model is combined, unreliable pixels are effectively utilized, and comprehensive utilization of label-free data is achieved progressively.

The invention discloses a semi-supervised medical image segmentation method based on uncertainty-driven dynamic correction and multi-scale consistency learning, and the method comprises the steps: carrying out the preprocessing of a medical image data set, and dividing the medical image data set into a training set and a test set; constructing a semi-supervised segmentation model of dynamic correction and multi-scale consistency learning based on uncertainty driving; inputting the training set into a semi-supervised segmentation model, and performing iterative training and parameter optimization to obtain a trained semi-supervised segmentation model; inputting the test set into the trained semi-supervised segmentation model to obtain a medical image segmentation result; wherein the semi-supervised segmentation model adopts a mean teacher model of a V-Net network, a prediction block is added behind each up-sampling block of a V-Net decoder, and a dropout layer is added; according to the method, the problems that the existing semi-supervised learning method is difficult to adapt to the complexity of annotated data and unannotated data distribution, so that effective information is lost; meanwhile, a traditional uncertainty estimation method needs multiple times of forward transmission, and the calculation cost is high.

The invention discloses a semi-supervised multi-temporal satellite image time-varying information extraction method, and belongs to the technical field of remote sensing image processing. The semantic change detection performance of the model and the detection precision of complex shape change ground objects are improved. The method comprises the following steps: constructing a semantic change detection model, carrying out full-supervised training on the semantic change detection model by using a binary change detection supervised loss function and a semantic segmentation supervised loss function by using a small amount of labeled dual-temporal remote sensing images to obtain an initial model, and obtaining a semantic change detection prediction result of each pair of images; using a pseudo label optimization strategy to optimize the semantic change detection prediction result of each pair of images; combining the obtained pseudo label data with high confidence and a small amount of labeled dual-temporal remote sensing images into a new training set, using the new training set to perform semi-supervised training on the initial model in a semantic change detection model, and using a consistency regularization combination loss function to perform supervised training to obtain a new model; and until a preset number of iterations is reached.

Various embodiments of the present disclosure provide automated encryption and data de-identification techniques for improving computer security. The techniques apply machine learning and encryption techniques to transform input data objects to tagged data objects that may be locally decrypted using encrypted element representation stored within the tagged data objects. The techniques may include determining a protected data element from an input data object based on privacy criteria and generating the tagged data object from the input data object by replacing the protected data element with an anonymized privacy tag that identifies a privacy type of the protected data element. The techniques may further include generating an encrypted element representation of the protected data element and inserting the encrypted element representation to a portion of the tagged data object to enable decryption of the tagged data object by authorized entities.

The invention provides a railway knowledge question answering method and device based on a natural language model, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining a railway business question; inputting the railway business question into a pre-trained railway natural language large model to obtain an answer corresponding to the railway business question; wherein the railway natural language large model is obtained by pre-training a natural language large model based on a railway natural language sample library and performing supervised fine tuning on the pre-trained natural language large model; the natural language sample library is formed by labeling data in a railway natural language knowledge base; the railway natural language knowledge base is formed by classifying and storing railway business data. Through the method provided by the invention, the railway natural language large model with professional knowledge question-answering ability is trained and generated, and the efficiency and accuracy of railway knowledge question-answering are improved; and related problems of text application scenes in the railway field are solved.

Training data can significantly impact the performance of machine learning models. Its impact may be more significant in transfer learning. Different data sources can be used to generate training data used in transfer learning. The training data originating from user interaction logs may be subject to presentation bias. The training data originating from model generated labeled data may have false positives. Poor quality training data may cause the machine learning model to perform poorly. To address some of these concerns, a checker having one or more models can check for false positives and for labeled data entries that may have been subject to presentation bias. Such entries may be removed or modified. In some cases, the checker can generate a test that can be used to test the machine learning model and penalize the machine learning model if the model generates an incorrect prediction.