95 results about "Multiple classification" patented technology

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

Methods and systems are provided for improving substrate defect classification in semiconductor manufacturing by using more than one machine learning model to classify substrate defect data. The method comprises a defect inspection module that captures substrate defect image data and a defect classification part that processes the data using more than one machine learning model. The output from each model is used to produce the final classified data. The defect score is calculated based on the classification results and this defect score is used to identify the misclassified substrate defect. The model can be updated after each inspection run cycle leading to increased accuracy and a lower escape rate.

The invention discloses an order dispatching method and device, a nonvolatile storage medium and electronic equipment. The method comprises the steps that a to-be-dispatched fault work order is acquired, and complaint information received in a preset time period is recorded in the to-be-dispatched fault work order; the work order classification model is called to perform content analysis processing on the to-be-dispatched fault work orders to obtain a classification result output by the work order classification model, the classification result is used for indicating a dispatch result corresponding to each to-be-dispatched fault work order, and the work order classification model is trained by adopting historical complaint information and historical dispatch results as training data; feedback results of the multiple classification results are obtained, and the feedback results comprise verification results and correction results of the classification results; and according to a feedback result, carrying out order sending operation. According to the invention, the technical problem of low order sending accuracy caused by manual order sending is solved.

The invention relates to a method and a device for generating a report under the assistance of a label, and the method comprises the following steps: 1) extracting a structured label set from a text report of a sample based on a large language model, wherein the structured label set comprises a multi-classification group consisting of dichotomous labels and mutual exclusion options; 2) aggregating the labels in batches, after a threshold value is reached, merging and de-weighting, performing specification and mutual exclusion group merging on synonymous, near-synonymous and redundant labels, and converging into a unified label library; 3) based on the text report and the tag library, outputting a tag subset of each sample through a large language model; 4) multi-modal multi-label classification model training: extracting each visual modal feature, performing weighted aggregation and splicing, and outputting each label group logits through a classification head to perform weighted group loss optimization; (5) carrying out joint training on the multi-modal large language model by using samples of'only images-reports' and'images + labels-reports', and (6) carrying out label prediction and screening on the images by using the classification model, and inputting'images + prediction labels' into the multi-modal large language model to obtain a final report.

The present invention discloses a test-time adaptation method for a calibration-free brain-computer interface, the method comprising the following steps: obtaining labeled training data from multiple source domain users to establish classification models for multiple categories; establishing the classification model, performing Euclidean alignment on the training data of each user, then merging all source domains, training multiple classification models using traditional cross-entropy loss, and obtaining multiple trained source domain models; obtaining the next test sample on the target domain data stream of the current user, performing incremental Euclidean alignment, and inputting the data into the trained multiple source domain models to obtain predicted probability values based on the multiple source domain models on the target domain data; using a spectral meta-learner method to perform integrated prediction of multiple models to obtain the predicted value of the current test sample. Based on the predicted probability value, each model is optimized separately by minimizing conditional entropy and using adaptive marginal distribution constraints within the batch to obtain optimized models adapted to the target domain; the present invention takes into account the real-time application of the cross-user brain-computer interface system, and adaptively adjusts the model without adding a calibration link.

The application provides a model training method, an audio classification method, a device, a medium and a program product, and mainly relates to machine learning technology in the field of artificial intelligence. The training method comprises the following steps: obtaining a first audio, actual classification results and actual position encoding results of the first audio in multiple classification dimensions; inputting the first audio into a target neural network model to obtain predicted classification results and predicted position encoding results of the first audio in the multiple classification dimensions; obtaining a classification loss according to the actual classification results and the predicted classification results; fusing the actual position encoding results of the first audio in the multiple classification dimensions to obtain actual fusion results, and fusing the predicted position encoding results of the first audio in the multiple classification dimensions to obtain predicted fusion results; obtaining a position encoding loss according to the actual fusion results and the predicted fusion results; and training the target neural network model according to the classification loss and the position encoding loss, so that the classification accuracy can be improved.

The present disclosure relates to the field of energy technology, and provides a carbon emission measurement method and device based on a smart park operation and management scenario. The method includes: determining the scenario category of the operation and management scenario; collecting a feature data set related to the carbon emission behavior corresponding to the scenario category; classifying the feature data set to obtain multiple classification data subsets, each classification data subset including at least one classification data; assigning a carbon emission conversion coefficient to each classification data in each classification data subset; if it is determined that a preset carbon emission measurement model library contains a target carbon emission measurement model that matches the carbon emission behavior category and carbon emission conversion coefficient of multiple classification data subsets, then the target carbon emission measurement model is retrieved; according to the target carbon emission measurement model, the total carbon emissions under the operation and management scenario are calculated, which can effectively and flexibly calculate carbon emission measurement under different daily management and operation scenarios.

The application is suitable for the technical field of data processing, and provides a software type detection method and device, terminal equipment and storage medium, the method comprises the following steps: obtaining software data of a to-be-detected software; extracting software features of the to-be-detected software from the software data, wherein the software features comprise operation code features, text features, permission features and image features; classifying the to-be-detected software according to the software features to obtain multiple classification results of the to-be-detected software; determining composite software features of the to-be-detected software according to the multiple classification results; and determining the software type of the to-be-detected software according to the composite software features. The application can solve the defect that the use of a single data type leads to low detection accuracy in the related art, help to improve the accuracy and robustness of detection, and does not need to update a feature library, thus helping to reduce the maintenance cost.

This invention discloses a product classification and recognition method and system based on AI technology, belonging to the field of AI recognition technology. By constructing a semantic alignment master vector Edis, the system achieves structured extraction and ambiguity resolution of the overall semantic representation of the product. On this basis, the system introduces the construction of a product classification label semantic vector set Gvec and a semantic similarity score set Sset, enabling the system to perform fine-grained matching of multiple classification labels based on contextual semantic features. This constructs a confidence score Conf reflecting the classification credibility, which is then compared with a preset classification judgment threshold Thrs to ensure that product labels are output only when the classification judgment credibility is sufficient. Through the connection of the above processes and the linkage of output results, this method not only significantly reduces the probability of misjudgment caused by expression ambiguity and word order reversal, but also provides a reliable rejection mechanism when accurate classification is not possible, effectively improving the overall robustness and practical value of the classification system.

The invention relates to a fund multi-dimensional quantitative ranking method and system, and the method comprises the steps: collecting the real-time position data of a target fund, and dividing the fund types based on all ranking framework dimensions; calculating each quantitative index value of the target fund in each time window, and obtaining the similar ranking, ranking level and similar mean value of the target fund according to the quantitative index values; obtaining structured input data of the target fund based on the structured cue word, and submitting the structured input data to the large language model to generate a natural language report, the natural language report including fund analysis, risk cue and adaptation suggestion; and repeating the steps based on each ranking framework dimension, generating a natural language report of the same target fund under different fund categories, and constructing a multi-dimensional ranking system of the target fund. A fund multi-dimensional quantitative ranking system is constructed by covering a plurality of classification dimensions, a plurality of time periods and a plurality of indexes and giving appropriate comments to each index data.

The application discloses a kind of material handling control method and system with classified goods allocation function, it is related to the process control technical field based on big data analysis, based on high priority attribute subset, fusion path distance calculation generates optimization path sequence;On the basis of extracting path node conflict, temperature cross influence and time window overlap and other mutual restraint relations, iteratively adjust storage occupancy and handling order, get balanced order list;Finally, combined with material scheduling demand parameters and cargo classification dimension determines target handling instruction set, effectively eliminates the core contradiction of emergency material delay, temperature-sensitive material deterioration and low handling efficiency, realizes when receiving a batch of goods allocation instruction containing multiple attribute constraints, according to current specific circumstances automatically determine the relative importance of each dimension, and generate a set of reasonable handling instruction that meets multiple classification requirements simultaneously.

The invention relates to the technical field of intelligent classification garbage cans, in particular to an intelligent classification garbage can. Comprising a garbage can main frame, a plurality of classification boxes arranged in the garbage can main frame, a garbage temporary storage module, an identification module, a mechanical pick-and-place module and a visual control module for controlling the garbage temporary storage module and the mechanical pick-and-place module based on the identification module. According to the method, the classification accuracy can be improved, different types of garbage can be accurately distinguished, the garbage classification accuracy can be improved, the garbage can be quickly classified and treated, compared with manual classification, the treatment speed is higher, a large amount of garbage can be treated in a short time, various resources can be better recycled through accurate classification, and the method is suitable for popularization and application. The recycling rate of resources is improved, various valuable resources are contained in different types of garbage, the resources can be effectively separated out and reprocessed and reused, resource waste is reduced, and harmful garbage can be accurately separated from other garbage.

The present invention relates to a pattern recognition-based engine fault warning method, device, and storage medium. The method comprises: obtaining and preprocessing raw engine data; performing feature selection on the data using a random forest model as training data; selecting multiple classification models and connecting the selected classification models based on weights to form a fault recognition model, and using the training data to perform hyperparameter tuning; performing a performance evaluation on the trained fault recognition model; reselecting a classification model or re-adjusting hyperparameters if the model does not meet the requirements; and executing the next step if the model meets the requirements; establishing an engine fault warning model based on the trained fault recognition model; and establishing model evaluation indicators to evaluate the performance of the engine fault warning model; adjusting the hyperparameters of the engine fault warning model if the model does not meet the requirements; and performing a fault warning using the trained engine fault warning model if the model meets the requirements. Compared with existing technologies, the present invention has the advantages of high prediction accuracy.

The invention discloses a hyperspectral dry pea thermal damage identification method fused with multilayer wavelet decomposition characteristics. According to the method, spectral information of dry peas within the range of 400-1000 nm is obtained through a hyperspectral imaging technology, seven-layer decomposition is carried out on an original spectrum by adopting a db6 wavelet function, and multi-scale detail features are extracted. And an optimal wavelet layer is screened in combination with correlation analysis, and a multi-scale fusion feature set is constructed, so that the discrimination capability of the model on the heat loss grade is improved. And carrying out modeling analysis on the features by adopting a plurality of classification models such as PLS, SVM, LR and RF, and comparing the recognition performance of the features under different feature combinations. According to the method, redundant information interference is effectively reduced, the expression ability of the spectrum to the thermal damage characteristics is enhanced, and the accuracy and stability of the model are remarkably improved. The method has the advantages of non-contact, high throughput and intelligence, and is suitable for heat damage identification and quality grading of agricultural products such as dry peas.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

The application provides a ship type classification prediction method and system based on K-means and XG-Boost, acquires ship data and carries out pretreatment, then adopts a K-means clustering algorithm to respectively cluster each kind of data in the pretreated ship data to obtain multiple clusters, calculates error sum of squares of all data in each cluster, calculates the classification number of each kind of data according to the error sum of squares, selects a certain classification number by using an elbow method, marks the ship type of all clustered ships according to the classification number, then takes the ships with marked ship types as training set samples, adopts an XG-Boost classification algorithm to train the training set samples to obtain multiple classification prediction models, verifies the multiple classification prediction models to obtain an optimal classification prediction model, and predicts the ship types of all ships in the world according to the optimal classification prediction model and marks the ship types. The application can accurately classify all ships in the world, and can avoid overfitting and underfitting of the model while ensuring the accuracy.

The utility model provides a marine organism collecting and classifying device, and mainly relates to the field of organism collecting and classifying equipment. A marine organism collecting and classifying device comprises a collecting barrel. The fish collecting device has the advantages that fishes of different sizes are classified according to the sizes by arranging the collecting barrels separated by the multiple stages of classifying plates, the rotating structures and the baffles are further arranged so that all the partitions can rotate, and influences between the fishes of different sizes are prevented through separation of the baffles; furthermore, a water inlet and outlet structure is arranged in the device, so that seawater flow can be controlled from the upper part to flow into the classification device to provide a living environment for marine fishes and discharge the internal water flow, and a multi-stage classification plate is arranged to be of a spiral structure, so that the multi-stage classification plate can be matched with a baffle to rotate to drive the fishes to be moved out step by step in a multi-stage manner; mutual influence caused by simultaneous discharge of fishes with different sizes is avoided.

The utility model discloses a kind of waste storage box for cell culture, belong to cell culture technical field, including outer frame, the inside of the outer frame is provided with storage box, the side of storage box is provided with square groove, the inside of square groove is provided with classification mechanism, the side of outer frame side and classification mechanism is adjacent and is fixedly connected with pull rod, the top of outer frame is provided with top cover, the bottom of outer frame is provided with support mechanism, the side of outer frame bottom close to support mechanism is provided with universal wheel, the top of storage box is provided with discharge chute, the utility model is provided with classification mechanism, and storage box can be inserted multiple classification boxes by square groove, hold classification box, simultaneously, the slider of classification box bottom is inserted into sliding slot, multiple classification boxes can be fixed below discharge chute, each classification box corresponds to a kind of waste, push the plug into jack, and fixed in fixed groove, classification box can be closed, prevent waste overflow, the classification storage of waste through classification box can improve the rigor of experiment.

This invention discloses a method, apparatus, device, and storage medium for managing digital assets. The method includes: real-time traffic monitoring of a target database; hierarchical parsing of detected network traffic data to obtain target chain classification results matching each network traffic data; the chain classification results including multiple classification categories arranged in a progressive order; obtaining target data levels matching each target chain classification result based on the mapping relationship between the chain classification results and data levels; and classifying and grading the digital assets in the target database according to the target chain classification results and target data levels for each network traffic data. This invention solves the problem of managing tens of thousands of digital assets and achieves secure monitoring of data information on network traffic.

The embodiment of the invention provides a model training method, an image classification method, equipment, a medium and a program product, and relates to the technical field of computers, and the method comprises the steps: carrying out the classification processing of a sample image through a preset classification model, and obtaining a prediction classification result of a plurality of classification levels of the sample image; the classification model comprises a plurality of classification components, and any classification component is used for outputting a prediction classification result of one classification level of the sample image; constructing a loss function of each classification level according to the prediction classification result of the plurality of classification levels and the actual annotation classification result of the sample image in the plurality of classification levels, and determining a gradient corresponding to the loss function; adjusting the gradient corresponding to each classification hierarchy based on the similarity between the gradients corresponding to each classification hierarchy to obtain the adjusted gradient corresponding to each classification hierarchy; and updating model parameters of the classification model by using the adjusted gradient corresponding to each classification level to obtain a trained classification model. According to the method, the model performance can be improved.

The present application discloses a method, training method, apparatus, device, and storage medium for processing command text, belonging to the field of artificial intelligence technology. The method comprises: obtaining a command text in natural language form, wherein the command text is used to command a non-player character in a virtual scene, wherein the non-player character has multiple behavioral capabilities in the virtual scene, and wherein the multiple behavioral capabilities correspond to multiple classification labels; calling at least one hierarchical prediction network to perform intent recognition on the command text to obtain a classification label for the command text intended to command the non-player character; wherein the hierarchical prediction network includes at least two layers of sub-networks, and the at least two layers of sub-networks are constructed based on a tree structure of the multiple classification labels.

The application discloses a regional lithology interpretation method, system, device and medium based on multiple physical characteristics, and the method comprises the following steps: acquiring regional logging data and geophysical data; preprocessing the regional logging data to obtain a target regional logging data set; establishing multiple classification models associated with the input of multiple physical characteristics; training and verifying the multiple classification models by using the target regional logging data set to determine the best classification model; performing multidimensional inversion on the geophysical data to obtain an inversion result; and inputting the inversion result into the best classification model for processing to obtain a lithology interpretation result. Therefore, by establishing multiple classification models associated with the input of multiple physical characteristics, and determining the model with the optimal classification effect, i.e. the best classification model, from the multiple classification models, the best classification model can be applied to the lithology interpretation of the geophysical inversion result, so that the lithology interpretation result with relatively better accuracy, efficiency and intelligence can be obtained.

A computer-implemented method of generating interpretable, composite marker indexes that are discriminative and noise-robust is provided. The method comprises storing remotely collected multimodal digital markers from a first cohort and a second cohort. The method further comprises grouping multicollinear features in the multimodal digital markers into clusters, and then selecting representative features for the clusters for multiple classification tasks that require discrimination between the first cohort and the second cohort. The method further comprises linearly combining the representative features into an interpretable, composite marker index such that relative contributions of each of the representative features to the interpretable, composite marker index are known.

This invention discloses a classification method for expanding training samples based on multi-classifier recognition results, comprising: selecting multiple classification methods; selecting initial training samples including each category from the dataset to be classified to form an initial training sample set; classifying and recognizing the dataset data using each classification method and the training sample set to obtain the classification results of the dataset to be classified by each classification method; calculating the classification result acceptance rate for each data based on the classification results of the dataset to be classified; comparing the classification result acceptance rate with its preset threshold to obtain new training samples and expand the training sample set; determining and executing the next iteration classification based on the expanded training sample set; and taking the maximum acceptance classification result of the last iteration as the final data classification result of the dataset to be classified. This invention improves classification accuracy by iteratively verifying the results of multiple classifiers and gradually expanding the training samples.

This application relates to a data management method, apparatus, equipment, and medium for an air-based energy storage power station information platform. The method includes: acquiring a newly added dataset and current data filtering rules corresponding to the current dataset; partitioning the newly added dataset based on the current data filtering rules to obtain multiple partition data; processing each partition data independently based on a distributed computing framework to obtain classification evaluation results corresponding to each partition data; if any of the multiple classification evaluation results contain unqualified results, then modifying the current data filtering rules based on the unqualified classification evaluation results and the multiple partition data to obtain modified data filtering rules, thereby providing the user with first-target data based on the modified data filtering rules. This application dynamically modifies the current data filtering rules according to the classification evaluation results, enabling the data filtering rules to adaptively adjust and ensure the convenience, accuracy, and relevance of the filtering results.

The invention discloses a core image classification method based on multi-modal information fusion and related equipment. The method comprises the following steps: acquiring a core image and general text description; wherein the general text description comprises a plurality of classification categories and text description of each classification category; inputting the core image and the general text description into a pre-trained visual language model to generate a preliminary classification result; wherein the preliminary classification result comprises the classification probability of each classification category; matching from an expert text description pool based on the preliminary classification result to obtain expert text description; and inputting the core image and the expert text description into a visual language model to generate a final classification result. By fusing image and text multi-modal information, the limitation of purely depending on visual information is reduced, the classification accuracy is improved, the pre-trained visual language model can automatically process the image and the text, the complex process of traditional manual feature extraction is avoided, the overall efficiency is improved, and the method can be widely applied to the technical field of rock core classification.

The invention discloses a positioning method and electronic equipment based on multi-source information fusion, and the method comprises the steps: obtaining a plurality of classification regions and the number of classification types according to channel frequency response, received signal strength and angle time delay response; obtaining a target feature according to the channel frequency response, the angle time delay response, the first feature extractor and the second feature extractor; and carrying out regression operation on the target features according to the plurality of classification regions and the number of divided categories to obtain positioning information, so that the positioning precision can be improved.

The present invention discloses an entity classification method, device, storage medium, processor and electronic device. The method comprises: obtaining an entity to be predicted; constructing multiple instances using the entity to be predicted and multiple relationship types, wherein each instance in the multiple instances comprises: a text portion, a question portion and an answer portion; classifying each instance in the multiple instances to obtain multiple classification labels; and determining the category of the entity to be predicted based on the multiple classification labels. The present invention solves the technical problems of low efficiency and low accuracy of entity risk review caused by the variable risk types in the entity risk classification process and the complex and difficult basis for judging the entity risk type, thereby accurately identifying the risk types of different knowledge entities in the knowledge graph knowledge automatic production process and effectively improving the efficiency and accuracy of entity risk classification.

The present application discloses a method for identifying a target item and its apparatus, device, medium, and product. The method comprises: obtaining an item image to be identified as containing a target item; performing encoding and decoding on the item image at multiple scales to obtain multiple image feature information capturing the item's contour features; extracting an item segmentation map from the item image based on the multiple image feature information; classifying each image feature information to obtain a classification probability that the image feature information contains the target item, and calculating the average probability of the multiple classification probabilities obtained by classifying all image feature information; performing image recognition on the item segmentation map to obtain a recognition probability that the item segmentation map contains the target item; fusing the average probability with the recognition probability to obtain a fused probability for result judgment; and determining that the item image contains the target item when the fused probability is greater than a preset threshold. The present application can more accurately identify target items from item images.

This application discloses a wafer defect classification method and related apparatus. By introducing a multi-branch classification tree, decision nodes can have at least three branching scenarios, resulting in multiple classification results corresponding to multiple leaf nodes. The target classification result is then determined based on these multiple results. Since the multi-branch classification tree allows multiple leaf nodes to point to the same classification result, the same defect can obtain the same classification result through different decision paths. This allows the classification result to be verified through multi-path logic, thereby improving the fault tolerance of the defect classification process, reducing the probability of misclassification due to errors in single feature judgments, and improving the accuracy and stability of wafer defect classification results.