25 results about "Multiple attribute" patented technology

In various example embodiments, systems and methods for data grid platforms are proposed. User-associated attribute data is accessed from multiple attribute sources. Analysis of the attribute data is performed. Based on this analysis, actions corresponding to the user are executed.

Embodiments of this disclosure relate to methods, electronic devices, and computer program products for managing storage systems. The method includes determining whether the storage device failure will no longer occur based on reference values ​​of attribute parameters in a first set of the attribute parameters and current values ​​of attribute parameters in a second set of the attribute parameters, if it is determined that the storage device will fail based on current values ​​of multiple attribute parameters in the storage system. The method further includes determining the cause of the failure based on the attribute parameters in the first set if it is determined that the storage device failure will no longer occur. Embodiments of this disclosure can analyze the cause of storage device failure, thereby avoiding the erroneous identification of internally faulty storage devices as internally faulty ones.

Obtain responses that reproduce the variability in responses from multiple respondents. [Solution] The information processing device includes: an acquisition unit that acquires multiple attribute values ​​for each of multiple attributes of a person's profile, and a probability distribution of the multiple attribute values ​​for each of the multiple attributes; a determination unit that determines the attribute values ​​for each of the multiple attributes for each person's profile of a plurality of respondents based on the probability distribution; a generation unit that generates a prompt instructing each of the plurality of respondents to answer a question as a person's profile having the determined attribute values ​​for each of the multiple attributes; and a processing unit that inputs the generated prompt to a generating AI model for each of the plurality of respondents and acquires the respondent's answer.

The invention relates to a digital twinborn model reconstruction method, in particular to a fractured well site digital twinborn model reconfigurable method, which comprises the steps of designing a fractured well site digital twinborn model multivariate attribute module and designing a fractured well site digital twinborn reconfigurable module. The multivariate attribute module comprises an equipment model size, a safety boundary, a current coordinate, a last coordinate, a well site model size and the like, and reconfigurable definition of various digital twin models of a fracturing well site is realized; and designing a digital twinning reconfigurable module of the fracturing well site, and supporting to manually or automatically construct a digital twinning fracturing scene consistent with an actual operation scene according to variable operation requirements through the established reconfigurable constraint conditions and different operation requirements. According to the method, the fracturing well site is reconstructed through the digital twinborn model, the planning and layout efficiency of the well site is improved, the problem of repeated design caused by a fixed mode is avoided, and the method is suitable for reconstruction of the digital twinborn model of the fracturing well site.

The invention provides a query method, device and equipment based on scene corpora and a storage medium, and relates to the technical field of computers.The method comprises the steps that entity attributes in a knowledge base are classified, multiple attribute types are determined, a cue word template is generated for each attribute type, and based on data in the knowledge base, the cue word template is generated; instantiating each prompt word template, and constructing a corpus based on a plurality of obtained scene corpora; determining a corpus result matched with the query information from a corpus; and constructing text input based on the query information and the corpus result. The entity attributes are classified, the cue word template is set, instantiation is carried out in combination with data in the knowledge base, and corpora wide in coverage degree and high in quality can be automatically generated in batches. When the user query is responded, the corpus result related to the user intention can be accurately matched from the constructed high-quality corpus, and the accuracy of the answer content of the large language model is improved.

In a computer network monitored for security threats, security incidents corresponding to groups of mutually related security alerts may be ranked based on values of a diversity metric computed for each incident from attribute values of an attribute, or multiple attributes, associated with the security alerts. In some embodiments, values of attribute-specific sub-metrics are determined for each incident and combined, e.g., upon conversion to p-values, into respective values of the overall diversity metric. Based on the ranking, an output may be generated. For example, a ranked list of the security incidents (or a subset thereof) may be communicated to a security administrator, and / or may trigger an automated mitigating action.

The embodiment of the invention provides a strategy searching method and device, and the method comprises the steps: receiving a data message from another device, and obtaining an attribute value set of a first attribute of an endpoint of the data message; determining a search key set based on the attribute value set; searching a strategy matched with the first search key from the first storage unit based on the first search key, searching a strategy matched with the second search key from the second storage unit based on the second search key to obtain a strategy set corresponding to the attribute value set, and executing the search step based on the first search key and the search step based on the second search key in parallel; and processing the data message based on the strategy set. And a parallel searching function is utilized, so that a plurality of strategies corresponding to a plurality of attribute values can be searched as short as possible.

This invention discloses a method, device, storage medium, and processor for predicting user churn, comprising: acquiring attribute information of multiple user attributes, wherein the multiple attributes are attributes included in an attribute set; and predicting user churn based on the attribute information using a classification model trained by machine learning, wherein the classification model includes a first classifier model and a second classifier model. The attribute set includes a first subset and a second subset, and the attributes included in the first subset and the second subset are mutually exclusive. The first classifier model is trained based on the attributes in the attribute set and performs classification operations based on the attributes in the first subset. The second classifier model is trained based on the attribute set after adding new attributes. The new attributes are formed by combining at least a portion of the attributes in the second subset. This invention solves the technical problem of inaccurate prediction in existing technologies.

This invention belongs to the field of multidimensional identifier communication network technology and discloses a method for constructing an addressing matrix based on multidimensional identifiers. The specific process is as follows: a commander enters the instruction to be executed into the client of the multidimensional identifier network; after receiving a complete instruction, the client extracts the addressing attribute information text from the instruction entered by the commander; then it determines whether the addressing is multi-attribute addressing or single-attribute addressing; based on the addressing attributes, a target addressing matrix is ​​constructed; and the constructed target addressing matrix is ​​sent to the multidimensional identifier network server for addressing. This application establishes relationships between attributes, matrixes the multidimensional identifier attributes of communication entities, constructs an addressing matrix supporting multi-attribute addressing, and realizes the process of using multiple attributes to search in the multidimensional identifier database and return the multidimensional identifier of the communication peer that meets the conditions.

The invention discloses a coding and decoding method, a code stream, a coder, a decoder and a storage medium. Attribute header information is decoded, and a value of a first syntax element of a current attribute type is determined; if it is determined that the attribute code of the current attribute type exists in the code stream according to the value of the first syntax element, decoding the value of a second syntax element of the current attribute type; if the first number is greater than 1, decoding the attribute header information according to the first number, and determining a grouping parameter of each attribute data set of the current attribute type; and decoding the attribute data sets of the current attribute type in groups according to the grouping parameters of each attribute data set of the current attribute type. Therefore, the encoding and decoding scheme of the point cloud attribute header information is optimized so as to be suitable for encoding and decoding of a single-attribute data set and encoding and decoding of a multi-attribute data set, and the attribute encoding and decoding efficiency is improved.

The embodiment of the invention relates to a user bucket dividing method and device, the method comprises multiple rounds of iteration, and any round of iteration comprises the following steps: obtaining respective current segmentation points of multiple attribute dimensions of a user and corresponding current segmentation interval combinations; the plurality of attribute dimensions are selected from one or more of the following attributes: a frequency class attribute, an activeness class attribute, a consumption class attribute and an interaction class attribute; for a first segmentation point in the current segmentation points of any first attribute dimension, searching candidate points in a target range of the first segmentation point; if the first candidate point meeting the optimization condition is searched, updating the first segmentation point as the first candidate point; the optimization condition comprises that the value of the target function is reduced after the target user group is subjected to bucket separation operation again according to the updated segmentation interval combination determined based on the first candidate point; the objective function is used for measuring the dispersion degree of the number of users among the multiple buckets.

The invention provides a data processing method, device and equipment, a computer readable storage medium and a computer program product. The method comprises the steps that in response to a calling request of a first application service for a second application service, object data constructed through a first programming language is received, and the object data comprises multiple attributes of an object and the attribute value of each attribute; obtaining a first data type of each attribute value in the object data, and determining a second data type corresponding to each attribute value in a second programming language based on the first data type of each attribute value; based on the object data and the first data type and the second data type of each attribute value, determining a target structural body constructed by using a second programming language; and calling the second application service based on the target structure body. Through the application, the object in the first programming language can be converted into the target structure body in the second programming language, so that the application development efficiency is improved.

This application provides a data processing method, electronic device, storage medium, and product. The method includes acquiring multiple attribute data corresponding to data to be processed; calculating the comprehensive fuzzy proximity degree corresponding to each attribute data based on the multiple attribute data of the data to be processed; and calculating the attribute fusion result corresponding to the data to be processed based on the comprehensive fuzzy proximity degree. The method obtains the comprehensive fuzzy proximity degree of multiple attribute data corresponding to any position of the data to be processed in the data matrix through a membership function, then calculates the weight value of each attribute data based on the comprehensive fuzzy proximity degree, and finally performs data fusion to obtain a comprehensive attribute fusion result. Although the attribute fusion result loses the physical meaning inherent in the original attribute data, it retains the commonalities and advantages of each attribute data, enabling a more accurate, comprehensive, and reliable description of the same target. Therefore, it improves the accuracy of describing and characterizing underground geological structures.

The invention discloses a fairness optimization method and device for a machine learning model and a storage medium, and the method comprises the steps: obtaining sensitive attributes of training samples in an initial training set of the machine learning model; according to the sensitive attributes, performing depolarization adjustment on the training samples in the initial training set to obtain a target training set; wherein the plurality of selectable categories corresponding to the sensitive attributes are uniformly distributed in each attribute training sample combination, the categories of the target attributes of the training samples in the same attribute training sample combination are the same, and the categories of the target attributes of the training samples in different attribute training sample combinations are different; and retraining the machine learning model according to the target training set to obtain the machine learning model after fairness optimization. Therefore, a plurality of sensitive attributes under multiple attributes can be debiased, the fairness problem of a machine learning model under a multi-attribute interleaving situation is effectively solved, and finally the prediction effectiveness of the model is improved.

This invention proposes a general rule-based filtering method, apparatus, computing device, and readable storage medium. The method includes the following steps: defining meta-rules for filtering, wherein the meta-rules include at least one attribute category among attribute name, attribute storage location, attribute value type, attribute filtering operation set, and attribute selectable value range; extracting multiple attribute values ​​from the original data and importing them into the attribute categories of the meta-rules for storage; selecting at least one meta-rule and defining filtering logic for the meta-rule to form a filtering dimension; performing set operations on multiple filtering dimensions to form a filtering unit; performing multiple set operations on multiple filtering units to form a filtering model with a tree structure; and batch executing multiple filtering models to obtain a target set. This invention can improve the execution efficiency of general filtering model algorithms required for refined operations and alleviate the problem of increasingly slow target user extraction.

The application provides an attribute-based weight distribution method, system and computer device, comprising setting multiple attributes, and configuring weight scores for the attributes in a segmented manner; receiving request data sent by a client; parsing the request data and splitting all fields of the request data; matching the split fields with the attributes and calculating total scores; evaluating recommendation priorities according to the total scores; sorting the recommendation priorities according to a sorting rule, and presenting the sorting result to the client. By configuring weight scores for the attributes, the application can customize the weight scores of the attributes according to actual needs, dynamically calculate total scores of various products in combination with multiple key indicators, evaluate the recommendation priorities of the products according to the total scores, and present the products with high recommendation priorities to the client. The application significantly improves the objectivity and accuracy of weight distribution, greatly enhances the flexibility and user friendliness of the system, and enables users to easily cope with complex and changeable application scenarios.

Techniques are provided for using machine learning techniques to learn embeddings for content items. In one technique, training data is used to learn embeddings for each attribute value of multiple attribute values of multiple content items, embeddings for each attribute value of multiple attribute values of multiple entities, and weights for a set of contextual features. In response to receiving a content request, a content item that is associated with one or more targeting criteria that are satisfied based on the content request is identified. A first set of embeddings for the content item are identified, a requesting entity that initiated the content request is identified along with a second set of embeddings for the requesting entity, and a set of feature values for the set of contextual features is identified. The content item is selected based on the sets of embeddings, the set of feature values, and the weights.

The application discloses a kind of multi-authority attribute-based encryption protocol implementation method and system, method as follows: one, each attribute management authority center executes main key generation according to the attribute set managed by itself;Two, client obtains the key corresponding to attribute set from multiple attribute management authority centers;Three, server selects random number, encrypts model parameter according to access structure, generates ciphertext component containing policy information;Four, client reconstructs coefficient using the private key component held locally, calculates shared secret locally and decrypts model parameter if attribute satisfies access structure;If attribute does not satisfy access structure, ciphertext does not need re-encryption through policy token to realize policy update.The protocol system proposed in the application significantly improves the flexibility, verifiability and system stability of permission management in large-scale collaboration, long-term task and high supervision requirement application scenarios.

The invention relates to a method, a device and a computer program product for anomaly detection of a storage system. The method includes obtaining a plurality of attribute values for a plurality of volume data block entries. The method further includes clustering a first portion of the plurality of volume data block entries into a congeneric pool based on the plurality of attribute values of the plurality of volume data block entries. In addition, the method also includes determining a second portion of the plurality of volume data block entries that is not clustered to the similar pool as an abnormal entry. By means of the mode, the storage system can automatically screen out the volume data block entries possibly having exceptions, so that the fault diagnosis time can be saved, the system fault solving efficiency is improved, and the service quality is improved.

The application provides a robot simulation data generation method, device, equipment, medium and product, relates to the field of data processing, and the method comprises the following steps: acquiring an attribute template of a robot and an article, wherein the attribute template comprises static attribute data and perturbable attribute data; copying multiple attribute templates, modifying the perturbable attribute data in the attribute template, and obtaining multiple robot instances and multiple article instances; assigning position information to the robot instances and the article instances in a task scene, and generating a robot simulation task; and inputting the robot simulation task into a simulation engine for simulation, and generating robot simulation data. The application can improve the generation efficiency of robot simulation data.

The application discloses a kind of training method, device and storage medium of multi-element attribute prediction model, it is related to image recognition technical field.The training method of multi-element attribute prediction model includes: training image is input into multi-element attribute prediction model, obtains the prediction value corresponding to the multiple attributes of object in training image respectively, wherein, the mark value of the training image includes the mark value corresponding to each attribute;According to the difference between the mark value and the prediction value of each attribute, determine the loss function weight corresponding to each attribute, wherein, the difference between the mark value and the prediction value of each attribute and corresponding loss function weight are in negative correlation relationship;According to the loss function weight corresponding to each attribute, and the difference between the mark value and the prediction value of each attribute, determine the total loss function value of the multi-element attribute prediction model;According to the total loss function value, the parameter of the multi-element attribute prediction model is adjusted.The application can improve the prediction accuracy of model.

The application discloses a kind of data augmentation methods for multi-attribute word extraction, it is related to data processing field, including: first, for the original training sample sentence containing multiple attribute words, attribute word labeling is carried out based on text fragment;Then, according to the labeling result, construct multi-attribute word label data set;Finally, each subset in the multi-attribute word label data set is combined with the original sample sentence to generate new training samples;The application reduces the cost of artificial data labeling, alleviates the problem of insufficient labeled data, designs a data augmentation method based on text fragment label in the multi-attribute extraction application scenario, synthesizes new training samples, and expands the training data;The augmented training data set enables the model to learn more features from the data, enhances the robustness of the model, prevents overfitting, and improves the generalization ability.

The application discloses a recommendation method, in particular to a recommendation method and device based on decoupling learning and target behavior-oriented learning. The method comprises the following steps: constructing a user-item interaction graph under a behavior based on the interaction conditions of users and items under multiple behaviors; performing decoupling attribute domain representation processing on the user-item interaction graph to obtain item decomposition embedding corresponding to item nodes and user decomposition embedding corresponding to user nodes in multiple decoupled attribute domains; calculating the attention score between a user node and its adjacent item node in an attribute domain based on the item decomposition embedding and the user decomposition embedding for multiple attribute domains corresponding to the user-item interaction graph; performing aggregation processing on the user node and the item node based on the attention score to obtain user target embedding and item target embedding under the behavior; and performing analysis and processing on the user target embedding and the item target embedding under multiple behaviors to obtain a recommendation result. The method can obtain more accurate recommendation results.

Data query methods, apparatuses and computer-readable media are described. In an example process of processing, by a third party, a federated data table formed based on data of a plurality of data parties, if related data need to be obtained based on sorting of multiple attribute columns of the data table, when the federated data table is sorted based on attribute values of the attribute columns, a row identifier is introduced for an out-of-order data table obtained by disordering the federated data table, and an index is created based on the row identifier. The row identifier is determined by the third party and exists in a form of ciphertext in an index table. A row identifier of a candidate row can be restored to a plaintext.