28 results about "Category attribute" patented technology

For at least a selected class attribute of the multiple class attributes, one or more bias metrics are determined that estimate a degree to which a particular workflow (having a set of processing stages) is biased in association with the class attribute. Each user of a set of users is associated with a set of user data to be processed by the particular workflow. At least one of the set of processing stages includes executing a machine-learning model. It can be detected that a bias-mitigation option corresponding to a specific class attribute has been selected. For each of at least two of the set of processing stages: a de-biasing technique is selected; and the processing stage is modified by applying the de-biasing technique. A modified version of the particular workflow (which includes the modified processing stages) is applied to each of a set of input data sets.

The invention discloses an object recognition method and device, a storage medium and electronic equipment. The method comprises the following steps: determining target category attribute description information of a target object from a first object interaction platform; obtaining candidate category attribute description information of candidate objects collected from a second object interaction platform according to the target category attribute description information; performing description conversion on the candidate category attribute description information according to a first category attribute description tree to obtain converted reference category attribute description information; comparing a reference category attribute feature generated based on the reference category attribute description information with a target category attribute feature generated based on the target category attribute description information to obtain a feature similarity; and identifying the object of which the feature similarity indicated in the comparison result meets a threshold condition as a similar object of the target object. According to the object recognition method and device, the technical problem that the accuracy of an object recognition result is low in an object recognition method provided by the related technology is solved.

Embodiments of the present application disclose a decision tree model generation method and a data recommendation method based on the decision tree model. The method comprises: obtaining a training data set formed by feature information of a plurality of training samples, the training samples having known category attributes; in a process of generating a decision tree model according to the training data set, iteratively calculating information gain of each feature attribute under each node, and dividing a data set contained by a current node according to a feature attribute corresponding to maximum information gain until a category attribute can be determined according to the data set contained by the node; if information gains of a plurality of feature attributes under the current node are equal and are maximum information gain, then calculating respective correction information gains of the plurality of feature attributes, and determining a feature attribute for dividing the data set contained by the current node according to the calculated correction information gains; and outputting the decision tree model formed according to the training data set. The decision tree model generated by the present application has more accurate classification effect.

This disclosure provides a data processing method and training method, a data processing device, a training device, an electronic device, and a computer-readable medium. The data processing method includes: acquiring input features of input data corresponding to a target network layer of a target neural network; inputting the input features corresponding to the target network layer into a prediction model to predict the category attribute corresponding to the target network layer, wherein the category attribute characterizes whether the target network layer is suitable for quantization processing of the input data. According to the technical solution of this disclosure, the quantization scheme of the network layer of the target neural network can be determined efficiently and accurately, which is beneficial for the quantized target neural network to ensure that it simultaneously possesses fast inference speed and high inference accuracy during inference operations.

The application discloses an agent-based sensitive data grading method and device, equipment and a storage medium, and relates to the technical field of information security. The method comprises the following steps: performing quality filtering on original data to be graded to obtain candidate sensitive data; pre-training and fine-tuning an original large language model through an industry corpus dataset to obtain a data security large model comprising a data classification agent and a data grading agent; calling the data classification agent to classify the candidate sensitive data based on a classification grading standard to obtain data category attributes; and calling the data grading agent to grade the candidate sensitive data according to the data category attributes and the classification grading standard to generate a classification grading list. The original large language model is pre-trained and fine-tuned through the industry corpus dataset, which provides the agents with the ability of natural language understanding and generation, and can adapt to sensitive data recognition in different scenarios, thereby improving the recognition accuracy.

The invention provides an association rule analysis method and device for multi-valued attribute data, and is applied to the technical field of operation and maintenance systems.The method comprises the steps that original log data is obtained; based on the original log data, attribute types are identified, and the attribute types comprise numeric attributes, interval attributes and category attributes; based on the identification result of the attribute type, performing conversion processing on the numeric type attribute, the interval type attribute and the category type attribute to obtain a numeric type feature vector, an interval type feature vector and a category type feature vector; performing feature fusion based on the numerical feature vector, the interval feature vector and the category feature vector to obtain a unified feature matrix; performing rule extraction and pruning based on the unified feature matrix to obtain an association rule; and on the basis of the association rules, a rule knowledge base is constructed, and the rule knowledge base adopts a hierarchical storage architecture. According to the invention, the data processing efficiency can be improved while the resource consumption is reduced.

The application discloses a debiasing method based on local search, an electronic device and a medium, and the method comprises the following steps: acquiring original data, marking a category attribute in the original data, and obtaining a marked data set, which is recorded as a data set X; performing local search-based clustering analysis in the data set X selected in step (1), using P to represent a point set of clustering, and for a point x in the point set P with a size of n, r(x) is a radius, so that at least n / k points from P are in a ball with x as the center and r(x) as the radius. According to the method, sample points in different cluster groups in the data set are as different as possible, that is, the distance in the class is as small as possible, and the distance between classes is as large as possible. The distribution of sensitive attributes of each cluster group is as uniform as possible, and the fairness of the data set is improved.

The invention discloses a picture classification method and device and a storage medium, and relates to the technical field of image processing. According to the method, the picture text is extracted through the OCR and the sentences are divided, the sentences are converted into the vectors by utilizing the pre-training vector generation model, the core semantic information associated with the category attributes in the text is fully mined by virtue of the semantic capture capability of the pre-training model, and the sentence text weight is determined by calculating the word frequency and the inverse document frequency after word segmentation. According to the method, the sentences corresponding to the important semantics are made to obtain higher weights, then the weights and the vector sequence are subjected to weighted average to generate the document vectors, the document vectors are made to accurately focus on the core semantics, the effectiveness of text representation is improved, and finally accurate classification is achieved through the classification model.

The invention discloses a commodity recommendation method, system and device of an aspect category enhancement architecture and a medium, and relates to the field of commodity recommendation. The method comprises the following steps: constructing an aspect graph based on a user comment data set, and according to explicit features and implicit preference features of the aspect graph, constructing an aspect category enhancement architecture; extracting a user aspect term subset and a commodity aspect term subset from the user comment data set; based on a Transform architecture and a factorization machine, combining category attributes of aspect term nodes in the aspect graph to construct an aspect category enhanced recommendation model; using the user-commodity pair set as a label, and inputting the user aspect term subset and the commodity aspect term subset into an aspect category enhanced recommendation model for training to obtain a user commodity recommendation model; and the user commodity recommendation module is used for outputting corresponding recommended commodities when the user is received. According to the invention, more accurate commodity recommendation is realized.

The disclosure provides a text recognition method and a model training method and device, relating to image processing, deep learning and natural language understanding in the artificial intelligence technology. The specific implementation scheme is: performing optical character recognition on the obtained to-be-recognized image to obtain initial text of the to-be-recognized image, performing analysis and processing on the initial text to obtain a category attribute of the initial text, if the category attribute of the initial text represents that the initial text is an incorrect text, performing error correction processing on the incorrect text to obtain correct text for correcting the incorrect text, and generating text content of the to-be-recognized image according to the initial text and the correct text, thereby avoiding the drawbacks of text errors caused by the OCR recognition technology, and improving the accuracy and reliability of text recognition.

The invention relates to the technical field of remote sensing image intelligent processing and analysis, and discloses a zero-sample hyperspectral remote sensing image classification method based on knowledge graph semantic representation, and the method comprises the steps: firstly constructing a remote sensing domain knowledge graph which comprises the types, attributes and relationships of ground features, and obtaining structured semantic features through knowledge graph representation learning; meanwhile, visual features of the hyperspectral image are extracted by using a convolutional neural network. And performing cross-modal distribution alignment on the visual features and the semantic features in the shared hidden layer space through a dual variational auto-encoder. And finally, taking the hidden layer semantic features corresponding to the unknown category in the knowledge graph as category prototypes, generating enhanced features and training an unknown category classifier, and realizing effective identification of the unknown ground feature category under the condition that only known category labeling samples exist. According to the method, the dependence on a large amount of labeled data is remarkably reduced, and the classification precision and generalization ability in a zero sample scene are improved.

This invention provides a zero-shot method for identifying space targets using attribute vectors. The method involves acquiring ISAR images of the target from radar equipment; generating a category attribute vector for space targets belonging to the same class; using a trained attribute model network to identify the target in the ISAR image, obtaining a predicted attribute vector; and comparing the predicted attribute vector with the category attribute vector to determine the category of the space target in the ISAR image. Because this invention utilizes attribute knowledge from training data and sample data information, it can predict the attributes of the target in the ISAR image without requiring an ISAR image sequence, thus having wide applicability. Furthermore, this invention leverages the attribute knowledge of space target categories to identify space target categories that lack training samples, overcoming the problem of existing network-based space target identification methods requiring a large number of training samples for each target class.

The present application is suitable for the field of geographic information processing and spatial data mining technology, and provides a kind of geographic feature causal mining method considering topological neighborhood, comprising the following steps: obtaining the data of multiple types of geographic features in the target area, and pre-processing, obtaining the geographic feature set containing spatial position coordinates and category attributes;Based on the geographic feature set, the target area is divided into research units, and a plurality of basic research units are obtained, and based on the category attribute of the geographic feature, the spatial distribution of different category geographic features in each basic research unit is counted, and the distribution characteristic value is obtained.The quantitative identification of the causal action direction and the causal action intensity between geographic features can be realized;The structural connection between geographic features can be effectively described, the influence of pseudo correlation is reduced, and the accuracy and stability of causal relationship identification are improved.

The invention discloses an intelligent detection method for vehicles in a tunnel, and specifically belongs to the technical field of intelligent traffic safety monitoring. The method comprises the following steps: performing target detection and multi-target tracking on a real-time video stream collected by a camera in a tunnel based on an ROI region, and determining a target category attribute list; based on the ROI region, carrying out screening and coordinate conversion on the target category attribute list to obtain an ROI category plane list; performing parking state identification on a target category in the ROI category plane list, determining target vehicles in a parking state, obtaining a first parking list, and calculating a first parking occupancy rate of each lane; normal parking vehicles are removed, and a second parking list is determined; and carrying out accident parking identification on the target category in the second parking list, determining a vehicle in an accident state, and carrying out accident alarm. According to the method, the slight traffic accident can be accurately defined, misjudgment is reduced, the calculation complexity is low, and vehicle accident alarming is carried out in time.

The embodiment of the invention discloses an intelligent braking method, device and equipment applied to a vehicle and a medium. The method comprises the following steps: collecting target data in a visual field range; inputting the target data into a pre-trained target classification model, so that the target classification model outputs a preset category attribute corresponding to each category and an output result confidence coefficient; determining a sample matching attribute of the target data and each sample image in a sample library; according to the category attribute, the output result confidence coefficient and the sample matching attribute, determining whether the target data comprises a target object or not; and if the target object exists in the target data, the vehicle is braked. Through the technical scheme of the embodiment of the invention, whether the target object exists in the field of view in the vehicle driving process or not can be accurately and conveniently identified, vehicle braking processing is effectively carried out on the basis of accurately identifying that the target object exists, and the identification precision of the target object and the accuracy of vehicle braking are improved.

The invention belongs to the technical field of image information processing, and discloses a cross-domain few-sample remote sensing target classification method based on discriminant text prompt learning. Firstly, a support set and a query set are sent into an image encoder to obtain an encoded feature sum; through a semantic prompt generation module and a support set prototype feature guide prompt generator, a discriminative semantic prompt S adaptive to a few-sample task is generated, and a fixed template is replaced to accurately represent category attributes; then processing a discriminative semantic prompt S through a text encoder, performing intra-class contrast regularization, constructing a same-class multi-view prompt pair and a different-class negative sample pair, and adopting temperature modulation contrast loss to suppress instance noise and improve cross-domain robustness; and finally, bimodal decision hybrid prediction is provided, and the defect that domain invariance and detail discrimination under few samples cannot be considered in a conventional method is overcome through vision-text semantic similarity and vision-vision feature matching degree.

The invention discloses a geographic entity construction method based on a framing topographic map. The method comprises the following steps: performing quality inspection on original topographic map data, removing data with unqualified data quality, unifying a data space coordinate system and an elevation reference, executing a storage operation, and integrating multiple pieces of standard framing stored topographic map data into an SQLite database; designing all data layers and attribute fields of the geographic entity, establishing a conversion rule comparison table from the original topographic map elements to the target geographic entity, and performing data category conversion; analyzing attribute information of geographic entity categories, extracting data from the SQLite database according to an entity attribute similarity principle, and obtaining a data set of line elements and surface elements with the same basic attributes; aiming at the line element set and the surface element data set, carrying out spatial data fusion by setting tolerance parameters, and combining lines or surfaces with similar attributes and adjacent spaces into a complete geographic entity; and performing element integrity, logic consistency, topology error check, spatial precision and attribute check on the constructed geographic entity data. According to the method, the overlapping and gap problems among the vector data are intelligently processed based on the attribute consistency and the spatial proximity, the geographic entity production efficiency is improved, and the labor cost is reduced.

The invention discloses a disease control big data analysis method and system, and the method comprises the following steps: obtaining a multi-source heterogeneous disease control data set, carrying out the preprocessing of the multi-source heterogeneous disease control data set, carrying out the independent optimal sequence search of each class attribute in a cluster to which the class attribute belongs, obtaining the optimal sequence of the class attribute, and obtaining the optimal sequence of the class attribute; determining a distance structure suitable for disease control big data analysis based on the optimal sequence; according to the obtained optimal sequence, distance measurement between the samples and the clusters is calculated, and a clustering model is constructed; alternately updating a sample attribution matrix and a category attribute sequence by minimizing a target function, and dynamically adjusting a clustering result; and outputting a case group, a propagation chain identification result and a risk assessment report based on a clustering result. According to the method, the clustering result is adaptively adjusted through an attribute sequence learning and sample clustering optimization conjoint analysis method, and the problem that an epidemic prevention and control decision is seriously affected due to the fact that the clustering result in the traditional technology cannot reflect business logic such as a real propagation chain and risk population layering is solved.

The present application relates to the technical field of smart grid, and discloses a large-scale power grid high-order tensor network construction method, device, equipment and medium, comprising: acquiring a plurality of category attribute sets of heterogeneous nodes in a large-scale power grid; performing feature extraction on the plurality of category attribute sets to obtain attribute features of the heterogeneous nodes; establishing multiple feature subspaces according to the attribute features of the heterogeneous nodes by using a deep hash mapping model; aligning the multiple feature subspaces to a unified feature dimension by using a breadth learning strategy; calculating distance measurement values between the heterogeneous nodes according to attribute feature vectors of the unified feature dimension by using a distance measurement method; determining a base tensor for representing an interaction relationship of the heterogeneous nodes according to the attribute feature vectors of the unified feature dimension and a weight value of the interaction relationship of the heterogeneous nodes; and constructing a large-scale power grid high-order tensor network according to the base tensor and the weight value of the interaction relationship of the heterogeneous nodes by using a tensor multiplication operation rule. The problem of lacking a model for efficiently representing a large-scale power grid is solved.

The application belongs to the technical field of image information processing, and discloses a cross-domain few-shot remote sensing target classification method based on discriminative text prompt learning. First, the support set and the query set are sent into an image encoder to obtain encoded features. Through a semantic prompt generation module, a support set prototype feature guides a prompt generator to generate discriminative semantic prompts S that adapt to the few-shot task, and replace the fixed template to accurately represent the category attributes. Then, the text encoder processes the discriminative semantic prompts S, the intra-class contrast regularization is used to construct the same-class multi-view prompt pair and the different-class negative sample pair, the temperature modulation contrast loss is used to suppress instance noise and improve cross-domain robustness. Finally, the application proposes a bimodal decision hybrid prediction, which overcomes the problem that the previous method cannot balance the domain invariance and the detail discrimination under few-shot.

The application relates to the field of data processing, in particular to a data processing method and device, electronic equipment and storage medium, which are used for accurately obtaining associated data of objects in a delivery platform. The method comprises the following steps: clustering a plurality of to-be-analyzed objects based on an object category attribute and a preset number of a to-be-analyzed object, and obtaining at least one object cluster; respectively sending a corresponding acquisition request to the delivery platform for each object cluster, wherein each acquisition request is used for acquiring associated data of each to-be-analyzed object in the corresponding object cluster; receiving each feedback message returned by the delivery platform, wherein, when one feedback message representing acquisition failure is received, re-clustering the corresponding object cluster based on a comparison object number carried in the received feedback message, obtaining an updated object cluster, and sending an acquisition request to the delivery platform again based on the updated object cluster until the corresponding associated data is obtained. In this way, the associated data of the objects in the delivery platform can be accurately obtained.

This invention provides a visual language model image classification method based on attribute anchor cue word fine-tuning, relating to the fields of computer vision, deep learning, and visual language model technology. The invention generates attribute text based on the image dataset to be identified and constructs a training sample set; it constructs hybrid cue words, inputs the hybrid cue words and the image dataset to be identified into a visual language model, and identifies the category to which each image in the image dataset belongs. Under the condition of freezing the pre-trained visual language model, this invention utilizes an efficient parameter fine-tuning method for category attribute-enhanced text cueing to solve the problems of unclear semantics and weak generalization ability in existing cue learning, ensuring that attribute information is not overwhelmed by category names and improving image recognition performance on both base and new classes in multi-dataset scenarios.

The invention discloses a service load resource deployment method and device, electronic equipment and a storage medium, and relates to the field of information security or other related fields, and the method comprises the steps: obtaining the current deployment information and operation information of a service load in a financial system, the method comprises the following steps: acquiring first labeling information used for describing a category attribute and a performance attribute of a resource domain from a resource database of a resource pool, and acquiring resource demand data used for indicating a service load and second labeling information of an evictable attribute value, and generating a target deployment scheme based on the current deployment information, the operation information, the first annotation information and the second annotation information, and performing resource deployment again according to the target deployment scheme. According to the invention, the technical problem that the in-transit transaction is affected and the system performance is lost after the business load is expelled due to the fact that a conventional resource hybrid deployment mode cannot match a proper resource domain for the business load in the prior art is solved.

The application relates to the index technology field of database structure, and particularly discloses an integrated index method for a geothermal system three-dimensional numerical simulation database, which comprises the following steps: reading geothermal system three-dimensional numerical simulation data and analyzing data fields, the data covering two data structures of scattered point data and body element data, sequentially performing attribute coding, space coding and time coding, and generating a unique identification suffix, splicing the unique identification suffix into a single row key in a fixed order, enabling the single row key to bear the multidimensional constraints of attributes, space and time, and completing the construction and writing of unified indexing; receiving a query request, performing range scanning in the unified index table, obtaining an index row key set, and then performing table query to obtain complete business data, and performing secondary precision screening to output results; the application considers the coding of category attributes, scalar attributes and vector attributes, converts multidimensional query into prefix matching and range scanning, and solves the problems of index dimension isolation, low query efficiency and memory overflow in the prior art.

A classification system includes classification training data-specific classification data correct attribute setting configured to set not only the correct classes of pieces of classification training data but also the correct attributes of the pieces of classification training data and classification / attribute training configured to train classification by comparing the class / attribute estimation result outputs with the correct classes set by correct class setting and comparing the attribute estimation result outputs with the correct attributes set by the classification training data-specific classification training data correct attribute setting. Pieces of classification target data from classification target data acquisition are newly inputted to the trained classification means. Classification / attribute result output outputs both the classification results and attribute results of the pieces of classification target data so that validity of the classification results can be considered.

The invention discloses a typical frame-type strander part drawing design query method and system, and belongs to the technical field of industrial part information management. The method comprises the steps that a CAD file directory is scanned through a DOS batch processing command to generate a file list, and the file list is imported into Excel to establish an initial database; utilizing a VBA program to automatically extract graph numbers, names, materials, categories and process coding attributes, and using a small amount of manual input attributes as assistance; generating category attributes based on a predefined classification rule and automatically classifying; and generating a sorted hyperlink list and a related attribute detail list in the classification form to realize rapid positioning and opening of the drawing. According to the method, the problem that the target drawing is quickly and accurately queried from a large number of drawings under the condition that the drawing number is unknown is solved, the traditional query time of tens of minutes is shortened to several seconds, and the design efficiency and accuracy are remarkably improved.

The invention relates to a method for automatically positioning picture elements based on human-computer interaction. The method comprises the following steps: acquiring an interface picture operated by a user; positioning the element according to whether the mouse click position and the release position of the user are in the same element or not: if not, positioning the element according to the relative relationship between the mouse click position and the release position; otherwise, determining whether the element has a unique identifier; for the element with the unique identifier, positioning the element based on the unique identifier; for the element without the unique identifier, determining whether the element has a name attribute or not; for the elements with the name attributes, positioning the elements based on the name attributes; for the elements without the name attributes, determining whether the elements have category attributes or not; for the elements with the category attributes, positioning based on the category attributes; and for the elements without the category attributes, positioning the elements through absolute paths of the elements. According to the method, the optimal positioning path is generated through relative position positioning of the mouse state event and multiple attributes of the elements.

The invention relates to a commodity page generation method and device, computer equipment and a readable storage medium, and relates to the technical field of computers. The method comprises the steps of determining a target commodity category to which a target commodity of a to-be-generated commodity page belongs in response to selection of any one commodity category in a plurality of commodity categories; obtaining a target category attribute template corresponding to the target commodity category from a plurality of preset category attribute templates, and displaying the target category attribute template; in response to an information input operation on the target category attribute template, obtaining attribute information of the target commodity under each attribute in the target category attribute template; and obtaining a target commodity page template corresponding to the target category attribute template, and generating a commodity page of the target commodity according to the attribute information of the target commodity under each attribute in the target category attribute template and the target position of each attribute in the target category attribute template in the target commodity page template. By adopting the method, the building efficiency of the commodity page can be improved.