12 results about "Multiple attribute" patented technology

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.

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.

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.

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.

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 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 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 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.

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.