67 results about "Multiple attribute" patented technology

The invention discloses a single-stage generative sequence recommendation method, which belongs to the field of personalized recommendation systems, and comprises the following steps: S1, item code representation: performing formalized representation on items according to attribute granularity, and generating code representation of the items through a large language model and a product quantization technology; s2, constructing a user interaction sequence: splitting the user interaction sequence into a plurality of attributes to form an interaction sequence consisting of project attributes; s3, generative sequence recommendation: improving an encoder architecture of Transform, including a sequence projection layer, a spatial aggregation layer and a conversion layer, so as to adapt to a sequence recommendation task; s4, recommendation item prediction: based on the user interaction sequence, learning probability distribution, and generating prediction of a next interaction item; according to the method, prediction of the next item possibly interacted by the user can be directly generated, similarity query does not need to be carried out in the candidate set, and the sequence recommendation effect is effectively improved.

Assets in a virtual environment are assigned to locations based on attributes of the locations, such as light level, temperature, etc. Attributes are determined based on simulating the virtual environment. The virtual environment can be simulated over a period of time, during which multiple attribute values can be recorded, resulting in an aggregated attribute value to better enable more immersive asset placement.

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.

In an implementation, a computer-implemented method includes receiving a plurality of inputs corresponding to a plurality of instances, where each input of the plurality of inputs representing multiple attributes of a respective instance. The computer-implemented method further includes training, using the plurality of inputs and as a trained supervised and multivariate machine learning model, a supervised and multivariate machine learning model. The computer-implemented method further includes determining multiple split point lists based on the trained supervised and multivariate machine learning model, where the multiple split point lists correspond to the multiple attributes, and where each of the multiple split point lists includes a number of split points and a number of split gains associated with a respective attribute.

The present application relates to an entity disambiguation method, apparatus, computer equipment and storage medium. The method comprises: obtaining profile data corresponding to a plurality of entities to be disambiguated; a plurality of the entities correspond to the same entity name; inputting the profile data into a pre-trained information extraction model, extracting key attribute information corresponding to the plurality of the entities through the information extraction model; calculating the similarity of two entities to be compared in a plurality of the entities on a plurality of attribute dimensions according to the key attribute information; for each group of two entities to be compared, weightedly summing the similarities of the two entities to be compared in the plurality of attribute dimensions, and obtaining the comprehensive similarity between the two entities to be compared in each group; disambiguating the plurality of entities to be disambiguated according to the comprehensive similarity. The method based on data analysis technology can improve the accuracy of disambiguating entities including unstructured data.

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.

Systems and methods for providing and executing a data processing tool are disclosed. The data processing tool may include an attribute processing agent which may be embedded in a database system. The attribute processing agent can receive input data and a custom-made attribute as an input and process the input data in accordance with the custom-made attribute. In some embodiments, there may be multiple attribute processing agents distributed in multiple computing nodes. Each attribute processing agent may be configured to process a portion of a data set based on the storage location of the portion of the data set.

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 present invention discloses a data screening method and device, and relates to the field of computer technology. A specific implementation of the method includes: obtaining a table to be filtered, determining the attributes to be filtered in the table to be filtered, wherein the table to be filtered is used to record multiple attributes of multiple items, each attribute includes multiple attribute values, the attribute to be filtered is one of the multiple attributes, and the attribute value of the attribute to be filtered is used as the first label; for each item, using the attribute values ​​of other attributes in the table to be filtered except the attribute to be filtered and a preset model, determine the first probability of the item under each first label; calculate the count matrix according to the first label and the first probability; determine the wrong data in the table to be filtered according to the first probability and the count matrix. This implementation can automatically find out the wrong data in the table, thereby improving the efficiency and accuracy of data screening.

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 discloses an attribute value interaction LPA * dynamic planning method for a disinfection robot, and particularly relates to an A * path planning method which modifies attribute values of grid points through an interactive method and combines dynamic search. The method comprises the following steps: firstly, processing environment data to construct a multi-attribute map; modifying attribute values of the grid points through an interactive method; and finally, considering the influence of multiple attribute values on the cost values of the nodes when searching the adjacent nodes, guiding the algorithm to search the nodes with low attribute values, and dynamically generating a path according to the change of the attribute values of the obstacles and the grid points. According to the method, an LPA * path planning method is improved, a multi-attribute map is combined on the basis of LPA *, compared with a multi-attribute A * path planning method, the attribute values of grid points can be dynamically adjusted, the limitation that the multi-attribute A * path planning method only depends on environment data to generate the attribute values is overcome, and the application range of the A * method is further expanded.

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.

Techniques for securely storing and processing data for training data generation are provided. In one technique, multiple encrypted records are retrieved from a first persistent storage. For each encrypted record, that record is decrypted in memory to generate a decrypted record that comprises multiple attribute values. Then, based on the attribute values and a definition of multiple features of a machine-learned model, multiple feature values are generated and stored, along with a label, in a training instance, which is then stored in a second persistent storage. One or more machine learning techniques are used to train the machine-learned model based on training data that includes the training instances that are stored in the second persistent storage.

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 an attribute-based weight distribution method and system and computer equipment, and the method comprises the steps: setting a plurality of attributes, and configuring weight scores for the attributes in a segmented manner; receiving request data sent by the client; analyzing the request data, and splitting all fields of the request data; matching the split fields with the attributes, and calculating a total score; evaluating a recommendation priority according to the total score; and sorting the recommendation priorities according to a sorting rule, and presenting a sorting result to the client. According to the method, the weight score of the attribute is configured, the weight score of the attribute can be self-defined according to actual requirements, the total score of each product is dynamically calculated in combination with a plurality of key indexes, the recommendation priority of the product is evaluated according to the total score, and the product with the high recommendation priority is presented to the client. The objectivity and the accuracy of weight distribution are remarkably improved, the flexibility and the user friendliness of the system are greatly enhanced, and the user can easily deal with complex and changeable application scenes.

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.