47 results about "Multiple attribute" patented technology

The embodiment of the invention provides an evaluation data set construction method and device, computer equipment and a readable storage medium. The method comprises the following steps: receiving a data set acquisition request sent by a target client, and reading a candidate data set according to the data set acquisition request; labeling each piece of original data according to a preset attribute labeling rule to obtain candidate labeling data, each candidate labeling data being associated with a plurality of attribute labels; on the basis of the multiple attribute tags, the multiple candidate annotation data are subjected to duplicate removal processing, and multiple target annotation data are obtained; constructing at least one piece of problem statement data for each piece of target annotation data, and performing question element updating on each piece of target annotation data based on the at least one piece of problem statement data to obtain updated annotation data; and constructing an evaluation data set according to the plurality of updated annotation data, and sending the evaluation data set to the target client. In this way, the applicability of the reconstructed evaluation data set can be improved while redundant data in the data set can be effectively removed.

According to one aspect of the present disclosure, a method for decoding a point cloud that is represented in a one-dimension (ID) array that includes a set of points is provided. The method may include parsing, by at least one processor, a bitstream to obtain a first syntax element indicative of an enablement of multiple attribute parameter sets for the point cloud. The method may include determining, by the at least one processor, whether the first syntax element indicates that multiple attribute parameter sets are enabled for the point cloud. In response to determining that the multiple attribute parameter sets are enabled for the point cloud, the method may include decompressing, by the at least one processor, the point cloud based on the multiple attribute parameter sets.

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.

The embodiments of this specification disclose a test data construction method, device and electronic device. The test data construction method includes: obtaining attribute feature information of business data in a production environment, wherein the attribute feature information includes feature values corresponding to multiple attribute features of the business data; performing clustering training based on the feature values corresponding to a single attribute feature of the business data to obtain categories corresponding to the business data under multiple attribute features; inputting the attribute feature information of the business data into a pre-established scene recognition model to obtain the business scene to which the business data belongs, wherein the scene recognition model is trained based on the attribute feature information of the sample business data and the corresponding scene label, and the scene label is used to indicate the business scene to which the sample business data belongs; constructing test data corresponding to the business scene to which the business data belongs based on the business data and the categories corresponding to the business data under the multiple attribute features.

The embodiments of the present application disclose an information recommendation method, an information recommendation device, an electronic device, and a storage medium. The method comprises: obtaining multiple recommendation features, the multiple recommendation features including target object features, virtual object features corresponding to multiple virtual objects to be recommended, and attribute sequence pattern features associated with the attribute sequence of the target object, wherein the attribute sequence of the target object includes the attribute sequence of the target object for different virtual objects, the attribute sequence includes multiple attribute identifiers arranged in chronological order, and the attribute identifiers are used to characterize the historical operation attribute types of the target object for the corresponding virtual objects; fusing the multiple recommendation features to obtain a fused feature, and determining the recommendation probability of the virtual object to the target object based on the fused feature; and recommending to the target object virtual objects whose recommendation probability is greater than a preset threshold. The technical solution of the embodiments of the present application can improve the accuracy of information recommendation.

The invention discloses a list matching method and device, electronic equipment and a computer program product. The method comprises the steps that data to be matched are obtained, and the data to be matched comprise attribute values to be matched corresponding to a plurality of attribute items respectively; the to-be-matched attribute values are matched layer by layer in multiple matching layers of a preset list Hash tree to obtain a matching result, the to-be-matched nodes are attribute nodes used for being matched with the to-be-matched attribute values in the preset list Hash tree, and the matching result is used for representing whether a path exists in the preset list Hash tree or not; under the condition that the matching result shows that the access exists in the preset list hash tree, it is determined that the to-be-matched data conforms to rules recorded in a preset list, the preset list is used for establishing the preset list hash tree, the preset list comprises multiple rules, and each rule comprises preset attribute values corresponding to multiple attribute items. The technical problem that the matching efficiency of the multi-attribute black list and the multi-attribute white list is poor is solved.

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.

This invention proposes a sequential recommendation method based on multifaceted item features and absolute temporal relationships, including: S1, obtaining the target user's historical interaction sequence, embedding the multifaceted item features into the user's historical interaction sequence through an embedding layer, modeling the user's preferences for multiple attributes, and simultaneously enhancing the input historical sequence to obtain an enhanced input sequence embedded with the multifaceted item features. Furthermore, the absolute temporal relationship between each interaction is derived through an absolute temporal relationship layer; S2, inputting the absolute temporal relationship and interaction sequence into a multi-layer self-attention network sequential recommendation model based on absolute temporal relationships for recommendation learning, thereby obtaining a representation of the user's multifaceted preferences; and S3, using this representation and item combination embedding to predict the user's score for each item. This method not only considers the relative position of items in the sequence but also the absolute temporal relationship between any two items, thereby improving the accuracy of the model's predictions.

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 relates to an industrial time series data anomaly detection method based on Skip-Range attention, and the method comprises the steps: S1, obtaining multi-attribute time series data, and obtaining a first multi-attribute time series data sequence; s2, converting the first multi-attribute time sequence data sequence into a two-dimensional oscillogram; s3, extracting image features based on the two-dimensional oscillogram; s4, the first multi-attribute time sequence data sequence is subjected to Skip-Range transformation, and then the sequence serves as a time sequence feature; s5, inputting the image features and the time sequence features into a trained fusion decoder to obtain a second multi-attribute time sequence data sequence; and S6, calculating a square error of the front and rear sequences, judging whether the square error is greater than a pre-configured error threshold value, and if so, judging that the sequence is abnormal. Compared with the prior art, the method has the advantages that the understanding of the model on the mutual relationship among multiple attributes is enhanced from the visual perspective, and the global features and the local features are combined, so that the dependency relationship among different attribute data is mined, and the accuracy of anomaly detection is improved.

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 invention relates to an attribute value adjusting method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a key value pair set of a to-be-processed attribute name; the key value pair set comprises key value pairs corresponding to the to-be-processed attribute name under a plurality of target entities; the key value pair comprises the to-be-processed attribute name and the attribute value; inputting the plurality of attribute values into a preset large language model to obtain an attribute value merging result of the plurality of attribute values; the attribute value merging result comprises a merged attribute value corresponding to each attribute value; and adjusting the attribute value of each target entity based on the attribute value merging result. By adopting the method, the product data analysis accuracy can be improved.

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.

The invention discloses an icon attribute value determination method and device and electronic equipment. The method comprises the following steps: receiving an attribute value determination request; in response to the attribute value determination request, determining a plurality of attribute matching vectors according to the to-be-determined attribute item and the plurality of attribute identifiers; determining matching index values respectively corresponding to the attribute matching vectors according to vector positions respectively corresponding to the hit attribute identifier under the attribute matching vectors; determining a first matching vector from the plurality of attribute matching vectors according to the matching index values corresponding to the plurality of attribute matching vectors; and determining a target attribute value corresponding to the to-be-determined attribute item according to the first matching vector. According to the method and the device, the technical problems of high workload and low determination efficiency in the process of determining the icon attribute value in related technologies are solved.

The present application relates to a device testing method, system, computer device and storage medium. The method includes: determining interactive objects with multiple attributes set for the target device to be tested; interactive objects are objects that will interact with the target device during the use of the target device; there are differences in the interaction results between interactive objects with different attributes and the target device; when testing the target device, the interactive objects with multiple attributes are switched; for each interactive object switched to, the target device is controlled to move, and during the movement of the target device, the target device is controlled to interact with the interactive object to obtain the interaction result; the relative position of the target device and the interactive object changes during the movement; the performance test result of the target device is determined based on the interaction results of the target device for each interactive object switched to. The use of this method can improve the test efficiency.

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 provides a semantic communication method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the technical field of semantic communication. The method is applied to a receiving end, and the specific implementation scheme is as follows: target information source data sent by a sending end is received, and the target information source data is obtained by extracting multiple pieces of attribute information from original information source data and then encoding the multiple pieces of attribute information; decoding the target information source data to obtain decoded attribute information; and reconstructing the original information source data through the generative receiver by using the decoded attribute information to obtain reconstructed information source data. According to the invention, only multiple pieces of attribute information of the original information source data are transmitted and processed, so that errors generated in encoding and decoding processes are reduced, efficient compression and reconstruction of data transmission are realized, bandwidth occupation is reduced, and communication reliability and efficiency are improved.

The embodiment of the invention provides a prediction method, a training method and a system. In the prediction method, a prediction system generates a plurality of core descriptors and a plurality of non-core descriptors for describing a target material based on proportion information corresponding to each element in the target material and attribute values of a plurality of attributes, and inputting at least part of the descriptors into the target network to obtain a predicted value of the amorphous critical cooling rate of the target material. The target network is a network obtained by training a plurality of sample materials by a training system, and comprises a plurality of sub-networks which have the same structure and are arranged according to levels, each sub-network comprises N input nodes and one output node, the output node of the i-th level sub-network is used as the input node of the (i + 1)-th level sub-network, and the output node of the (i + 1)-th level sub-network is used as the input node of the (i + 1)-th level sub-network; n core descriptors input into each sub-network in the first hierarchy are the same, N-N1 non-core descriptors are not completely the same, both N and N1 are greater than 1, and i is greater than or equal to 1.

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.

The embodiment of the invention provides an information interaction method and device, equipment and a storage medium. The method for information interaction comprises the steps that in response to triggering of a first interface of an object, content indicated by a first interaction component is obtained, and the content indicated by the first interaction component comprises voting interaction information of a user group executing interaction on the object on multiple attribute values of the object; the first interface comprises object display information of the object; and presenting the first interaction component based on the voting interaction information in a first interface of the object.

The embodiment of the invention relates to a knowledge graph completion method, device and equipment and a computer program product. The method includes obtaining a manufacturing knowledge graph. The method further comprises the steps of obtaining a plurality of entity embedding, a plurality of attribute embedding and a plurality of relation embedding of the manufacturing knowledge graph, wherein the plurality of entity inserts, the plurality of attribute inserts, and the plurality of relationship inserts are generated by the ontology map data, the manufacturing knowledge map data, and a mapping relationship between the ontology map data and the manufacturing knowledge map data. In addition, the method further comprises the step of complementing the manufacturing knowledge graph based on the multiple entity embedding, the multiple attribute embedding and the multiple relation embedding. According to the graph completion scheme provided by the embodiment of the invention, not only can the information of the manufacturing knowledge graph be utilized, but also the information of the ontology graph can be utilized, so that the relationship between entities can be fully mined to realize more detailed graph completion, and the comprehensiveness and accuracy of graph completion are improved.

The invention discloses a multi-modal data conversion method and device and computer equipment. The method comprises the steps that multi-modal data to be converted are acquired, and the category of the multi-modal data comprises at least one of pictures, videos, audios and texts; performing multi-level attribute extraction on the multi-modal data to obtain a plurality of attributes of the multi-modal data; and determining structured data corresponding to the multi-modal data according to the multiple attributes. The multi-modal data processing method and device at least solve the technical problem that the multi-modal data processing efficiency is low in the prior art.

Embodiments of the present disclosure provide a data processing method and an electronic device, which relate to the computer field. The method includes: acquiring a time-series dataset, the time-series dataset comprising multiple time-series data items each comprising a time and multiple attribute parameters corresponding thereto; acquiring a target attribute parameter being at least one of the multiple attribute parameters; determining, based on a time-series model, at least one influence factor of the target attribute parameter, the at least one influence factor indicating at least one attribute parameter influencing the target attribute parameter and at least one time corresponding to the at least one attribute parameter; and outputting the at least one influence factor. As such, according to the embodiments of the present disclosure, for a target attribute parameter, at least one influence factor can be determined based on a time-series dataset and a time-series model. Therefore, an accurate reference can be provided for a decision on the target attribute parameter, and the decision made in this way is more accurate.

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.