526 results about "First class" patented technology

Techniques are described for a hierarchical caching mechanism enabling efficient cross-region replications. In some embodiments, replication-related information (e.g., key-value pairs) is stored in a particular layout in a binary tree (B-tree) of a file system for replication processing. A hierarchy of caches storing a first type of information (e.g., crypto keys associated with iNodes) may be arranged to match the particular layout in the B-tree to enable efficient parallel processing of a second type of information (e.g., files, file data, or symbolic links), where the replication-related information in the B-tree is partitioned into multiple key ranges for parallel processing. In some embodiments, the caches in different hierarchies may be shared by different parallel-processing key ranges and replication jobs in a file system.

One or more embodiments of the invention provide an information issuing method and system based on an intelligent door lock, at least one intelligent door lock is added to the same multicast group, a routing device corresponding to the multicast group maintains the state of each interface, and the method is applied to the routing device. Comprising the following steps: receiving to-be-published information sent by a door lock management platform according to a communication address of a multicast group; determining a first-class interface in a full-effective state, and sending the to-be-published information to an intelligent door lock corresponding to the first-class interface for display through the first-class interface; and determining a second type of interfaces in a semi-effective state, and storing a corresponding relationship between the to-be-published information and the second type of interfaces, so as to send the to-be-published information to an intelligent door lock corresponding to a target second type of interface for display through the target second type of interface when it is detected that the state of any target second type of interface is updated to a full-effective state. According to the invention, convenient information release can be carried out through the intelligent door lock.

The invention provides a passive field adaptive target detection model generation method based on class prototype alignment and a target detection method, and the method comprises the steps: carrying out the further extraction of target features from the features extracted from a student model and a teacher model through a target detection frame in a training process employing the teacher model and the student model; iteratively updating a first type prototype and a second type prototype which belong to the same target type on the basis of the extracted features of the target detection frame and target type prediction results output by the student model and the teacher model on the basis of the features of the target detection frame, and constructing the alignment loss of the prototypes by using the type prototypes; according to the method, feature extraction results and prediction results of the student model and the teacher model are aligned according to the feature extraction results and prediction results of the student model and the teacher model, so that historical knowledge is brought into the training process through iterative updating of the class prototype, the problem of model training collapse caused by incorrect supervision signals brought by false labels is avoided, the performance of the target detection model obtained through training is improved, and the training efficiency is improved. Therefore, the accuracy of the target detection result output by the target detection model is improved.

A device may receive first data associated with a first class and second data associated with a second class that is different than the first class, and may process the first data, with a generative adversarial network model, to generate synthetic data. The device may train a variational autoencoder (VAE) model using the second data, to generate a trained VAE model, and may utilize the first data, the second data, and the synthetic data with the trained VAE model to generate anomaly scores. The device may combine the anomaly scores with the first data, the second data, and the synthetic data to generate final data, and may train a machine learning model with the final data to generate a trained machine learning model. The device may perform one or more actions based on the trained machine learning model.

The embodiment of the invention provides a data processing method based on a three-dimensional hierarchical data model, a platform and a medium. The method comprises the following steps: firstly, acquiring multi-source hydrogeological data; classifying the data to obtain a first type of data and a second type of data; then, according to the three-dimensional hydrogeological hierarchical model, extracting a hierarchical association relationship of the two types of data; the first class data and the second class data are used for representing the incidence relation of the first class data and the second class data at different levels of geological observation angles; storing the first type of data in a relational database, and storing the second type of data in a non-relational database; and finally, according to the hierarchical association relationship, establishing a hydrogeological knowledge graph and storing the hydrogeological knowledge graph in an interactive coordination system. According to the method, after the multi-source hydrogeological data are classified, the incidence relations at different levels of geological observation angles are extracted and stored in the relational database and the non-relational database respectively, mapping between the databases is established by adopting the knowledge graph, the multi-source hydrogeological data are effectively integrated, and meanwhile query is facilitated.

A computer acquires training data including first text, first class information indicating a class mapped to a single word contained in the first text, first position information indicating a position of the single word in the first text, and first range information indicating a range of a first named entity that includes the single word in the first text. The computer executes, based on the training data, machine learning of a machine learning model which is used to estimate, from text, class information, and position information, range information of a named entity included in the text.

The invention discloses a multi-modal multi-target tracking method and a related device, and relates to the technical field of multi-target tracking, and the method comprises the steps: carrying out the fusion processing of a multi-target-containing image detection result and a multi-target-containing point cloud detection result corresponding to a detection region at a current moment, and obtaining a first-class target set and a second-class target set, according to the track corresponding to the detection area at the previous moment, predicting to obtain a three-dimensional track state estimation value and a two-dimensional track state estimation value corresponding to the detection area at the current moment, and matching and associating the targets in the first type of target set with the three-dimensional track state estimation value, and matching and associating the targets in the second type of target set with the two-dimensional trajectory state estimation value, and finally determining the trajectory corresponding to the detection area at the current moment. According to the invention, multi-target tracking based on multi-modal data fusion can be realized, and the tracking effect is improved.

A time series data analysis method, includes: generating a plurality of first feature vectors including feature amounts of a plurality of feature waveforms, based on distances from a plurality of first time series data sequences to the plurality of feature waveforms, the first time series data sequences belonging to a first class; generating a plurality of second feature vectors including feature amounts of the plurality of feature waveforms, based on distances from a plurality of second time series data sequences to the plurality of feature waveforms, the plurality of second time series data sequences belonging to a second class; and updating the plurality of feature waveforms, based on the plurality of first feature vectors, the plurality of second feature vectors, a performance indicator parameter related to a performance indicator for a classification model and a model parameter including weights on the plurality of feature waveforms.

The invention discloses a storage resource allocation method, a storage medium and electronic equipment, and relates to the technical field of resource allocation, and the method comprises the steps: determining a group of candidate nodes from a second type node set under the condition that a first type node corresponding to a storage request does not exist in a first type node set, the first type of nodes corresponding to the storage request are bound service types corresponding to the service data, and the first type of nodes are in an available state, according to resource utilization parameters of the candidate nodes in the group of candidate nodes, the allocation probability of the candidate nodes in the group of candidate nodes is calculated, and the allocation probability of the candidate nodes in the group of candidate nodes is calculated; and storing the service data into the candidate node with the maximum distribution probability in the group of candidate nodes, thereby solving the technical problem of load imbalance, and achieving the technical effect of improving the performance of the storage system.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for evaluating samples. The method includes receiving, at an edge device, a classification model from a cloud server. The method further includes acquiring a sample distribution corresponding to each class in a plurality of classes of the classification model. The method further includes acquiring an input sample which is marked as a first class in the plurality of classes. The method further includes determining whether the input sample conforms to a first sample distribution corresponding to the first class. The method further includes identifying, in response to the input sample conforming to the first sample distribution, the input sample as a trusted sample. The trusted sample indicates that the input sample is correctly marked. By the method, a noise sample with a wrong label can be recognized, thus avoiding model degradation during update.

System, method, and various embodiments for a request processing system are described herein. An embodiment operates by determining a first sampling rate for each of multiple different types of requests being processed by a first computing landscape, and detecting a change in a performance of a first type of request. The first sampling rate for the first type of request is increased, and a second sampling rate for the first type of request for a second computing landscape responsive is also increased. Results of the tracing corresponding to the increased sampling rates are aggregated, and one or more differences between the first computing landscape and the second computing landscape are identified. An alert to reduce processing of the first type of request is generated based on the aggregated results and the identified one or more differences.

The embodiment of the invention provides a processor core, a processor and a method for the processor. The processor core includes a processing pipeline configured to rename and execute a first instruction including at least one of a first architectural register and a second architectural register, and includes: a first type of physical register configured to be mapped by the first architectural register and configured to store a first type of data; the first type of physical register is configured to be mapped by the first architecture register and configured to store data of a first type, the second type of physical register is configured to be mapped by the second architecture register and configured to store data of a second type, the first architecture register comprises a vector architecture register, the data of the first type stored by the first type of physical register comprises vector data, and the second architecture register comprises a mask architecture register; the second type of data stored in the second type of physical register comprises mask data. The processor core may mitigate processing pipeline stagnation and increase less area.

Systems for improving security of a computer-implemented artificial intelligence by monitoring one or more transactions received by the machine learning decision model; receiving a first score generated by the machine learning decision model in association with a first transaction; identifying the first transaction as belonging to a first class, in response to the first score being lower than a certain score threshold and the first transaction having a low occurrence likelihood; receiving a second score in association with the first transaction based on one or more adversarial latent features associated with the first transaction as detectable by an adversary detection model; and determining at least one adversarial latent transaction feature being exploited by the first transaction, in response to determining that the second score falls above the certain score threshold.

The invention relates to the technical field of data processing, in particular to a space intelligent reasoning method and system, and the method comprises the steps: generating a plurality of initial space reasoning tasks corresponding to a to-be-executed task instruction of a robot according to a pre-constructed space intelligent machine, and determining a first type of reasoning tasks and a second type of reasoning tasks from the initial space reasoning tasks, obtaining target space environment information corresponding to the first type of reasoning tasks and the second type of reasoning tasks, and obtaining structured data corresponding to the first type of reasoning tasks and the second type of reasoning tasks through a deep learning model, performing calculation according to the structured data corresponding to each first type of reasoning task by using a target calculation program corresponding to each first type of reasoning task in the sandbox simulator to obtain an accurate space prediction result; through the combination of the deep learning model and the sandbox simulator, the calculation accuracy can be improved, and the accuracy and reliability of overall space intelligent reasoning are enhanced.

The invention provides a fault injection method, device and equipment, and the method comprises the steps: obtaining a fault injection command which comprises a command word and fault injection data; if the command word is the command word corresponding to the first type of network card fault, writing the command word and the fault injection data into a target parameter structural body in a specified storage medium; if an IO request sent by the RDMA application to the RDMA network card is received, determining a target command word corresponding to the IO request; and if the target parameter structural body comprises target fault injection data corresponding to the target command word, sending the target fault injection data to the RDMA application so as to analyze the response condition of the target fault injection data based on the RDMA application. Through the technical scheme of the invention, the capability of the RDMA application to cope with the fault of the RDMA network card can be obtained, and the system stability and the data security are improved.

The invention provides a keyword generation model training method, a keyword generation method and a related device, and relates to the technical field of artificial intelligence, in particular to the technical field of large models, keyword generation and the like. The specific implementation scheme is as follows: constructing a first fine tuning instruction based on a first type of samples; the first type of samples comprise a first search sample and a first type of word set comprising at least one first keyword; wherein under the condition that the resources are searched based on the first search sample, the resources expressed by the first keyword at least meet partial search requirements of the first search sample; constructing a second fine tuning instruction based on the second type of samples; the second-class sample comprises a second search sample and a second-class word set comprising at least one second keyword, and the second search sample and the second keyword are constructed based on a preset association relationship; and performing cross training on the initial large model based on the first fine tuning instruction and the second fine tuning instruction to obtain a keyword generation model.

The invention provides a clustering method and system based on complex entropy and a complex information hierarchy construction method, and the method comprises the steps: initializing the features of elements in a first category, calculating the centroid of the category based on the initialized elements, and calculating the similarity based on the centroid of the first category to obtain a first combined category, if the number of the first merging categories is larger than or equal to a first preset value, a first entropy value is calculated through complex entropy, the first entropy value is obtained through similarity difference calculation, clustering results are generated based on the first entropy value, the clustering results at least comprise a first clustering result and a second clustering result, and the clustering results are of a tree structure. According to the method, a tree-shaped clustering result is generated according to complex entropy calculation, and similar questions are aggregated, so that the problem of low accuracy of answering complex questions is solved.

The invention discloses a data processing method and a related device, and relates to the technical field of message queues, a target client requests to carry out target operation on business data of a target theme, a server verifies whether a permission mapping relation matched with a permission authentication request exists in first-class permission data, and if yes, the first-class permission data is sent to the server; the server searches target message queue cluster information matched with the target theme in second-class authority data and sends the target message queue cluster information to the target client, the second-class authority data comprises a plurality of routing relationships, and the routing relationships comprise corresponding relationships between the theme and each message queue cluster in the message queue system; and the target client side executes the target operation in the target message queue cluster according to the target message queue cluster information, so that the flexibility of accessing multiple clusters by each client side is improved through centralized maintenance of authority data by the server side.

The method may include generating, by a vector embedding model, a vector embedding of multiple terms in an input document to obtain multiple term encodings. The method may also include generating, by a cascading classifier model, a classification of the input document. Generating the classification includes iteratively: traversing a directed acyclic graph ordering multiple class groups, and while traversing the directed acyclic graph, classifying the input document into a first class of a current class group of the class groups using the term encodings, where classifying the input document into the first class uses at least one second class of at least one parent class group of the class groups, and where the classification includes the first class and the at least one second class. The method may furthermore include obtaining a set of target fields corresponding to the classification. The method may in addition include extracting a set of values from the input document matching the set of target fields.

The invention provides an intelligent file merging method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence and cloud computing, in particular to the technical direction of big data. According to the specific implementation scheme, first-class files and second-class files are recognized from a to-be-processed file set, the file size of the first-class files is smaller than a target size, and the file size of the second-class files is larger than that of the first-class files; splitting the second class file into a plurality of sub-files; determining a to-be-merged file set based on the plurality of sub-files and the first type of files; and carrying out merging operation on the to-be-merged file set.

The invention provides a project review method, and the method comprises the steps: carrying out the matching of review key points and review guidance information corresponding to a to-be-reviewed project according to the content of the to-be-reviewed project; based on the project review key point information and the review guidance information, a target review instruction set of the to-be-reviewed project is generated through the multiple first-class intelligent agents, and the target review instruction set comprises at least one target review instruction; based on the target review instruction set and the large language model, constructing at least one second-class agent, each second-class agent corresponding to at least one target review instruction; and based on the at least one second-class agent, reviewing the to-be-reviewed item to obtain a reviewing result of the to-be-reviewed item. According to the invention, the to-be-reviewed item can be reviewed automatically without depending on manual review, and the accuracy of the review result can be improved.

The invention relates to the technical field of building information model optimization, in particular to a large-volume BIM model optimization system based on intelligent loading, which comprises a model analysis unit, a function identification unit, a simplified execution unit and a model recombination unit, and is characterized in that the model analysis unit reads an IFC file and separates component data; the function identification unit outputs a decoration component (first class) set, a non-bearing structure (second class) set and a bearing component (third class) set through geometric and semantic dual-channel analysis, the simplified execution unit performs edge folding on the first class, performs vertex clustering on the second class and skips simplification on the third class, and the model recombination unit binds metadata and constructs a dynamic octree index. The problems that a traditional simplification strategy is single and core information is lost are solved, the model loading efficiency and application precision are improved, and the method is suitable for complex building scenes.

This specification provides a detection method, device, and computer equipment for drilling risks based on all samples, which can be used in the field of oil and gas drilling. Based on this method, before specific implementation, according to the preset training rules, in the model construction stage, by effectively using the sample logging data of all sample wells, an initial joint risk prediction model is constructed, which at least integrates an intermediate first-class detection model, an intermediate second-class detection model, and an intermediate third-class detection model; in the model application stage, by effectively using the logging data of the target well where the model prediction fails during the drilling process of the target well for negative sample learning, a target joint risk prediction model that meets the requirements is trained. During specific implementation, the target joint risk prediction model is used to detect whether there is a drilling risk in the target well by processing the logging data of the target well. Thus, it can efficiently and accurately detect and judge whether there is a drilling risk during the drilling process of the target well, and effectively protect the safety of drilling construction.

A control section is a control section that controls combined printing of a plurality of documents and includes: a first setting processing section configured to set, in regard to a first setting item, which is a setting item included in a first category, from among a plurality of setting items defining content of processing to be performed on the documents, a setting value of the first setting item of any one document from among the plurality of documents for the first setting item of each of the plurality of documents; and a second setting processing section configured to set, in regard to a second setting item, which is a setting item included in a second category that is different from the first category, from among the plurality of setting items, a default value of the second setting item for the second setting item of each of the plurality of documents.

A method for training a large language model, a method for generating a large language model, an electronic device and a storage medium are provided, relating to the fields of large language model, model training, text processing and other technologies. The method for training a large language model includes: training an initial model according to first training data to obtain a first model; wherein the first training data comprises a first type of text; training the initial model according to second training data to obtain a second model; wherein the second training data comprises the first type of text and a second type of text; and performing parameter fusion according to the first model and the second model to obtain the trained large language model.

The invention discloses a version adaptation method and device, electronic equipment and a storage medium, and relates to the technical field of computers.The method comprises the steps that a starting class of a current application program is loaded through a preset first class loader, and if it is determined that the type of the current application program is a web application program in the starting class, the current application program is started; obtaining a to-be-adapted jar dependency file in a dependency library directory of the current application program; unpacking the to-be-adapted jar dependent file, and selecting a target byte code file related to class loading; in the search class method of the first class loader, determining a calling position for calling the target bytecode file, and inserting a preset version adaptation method behind the calling position; according to a version adaptation method, performing adaptation processing on the target bytecode file to obtain an adapted bytecode file; and replacing the target byte code file by using the adapted byte code file. By adopting the method and the device, when the adapted byte code file is loaded, the currently running JDK version can be effectively compatible.

A device may process data associated with a first entity and a second entity, using one or more machine learning techniques, to identify a set of trends associated with a set of documents. The device may receive new documents associated with the first entity and the second entity. The device may generate, based on the set of trends, a first set of exceptions indicating that a new document is associated with a first type of error. The device may generate, using a similarity analysis technique, a second set of exceptions indicating that a new document is associated with a second type of error. The device may communicate with one or more systems to perform one or more actions associated with correction or prevention of processing errors relating to the new document based a claim relating to the first set of exceptions or the second set of exceptions.

A stripe-based command pertaining to a set of host data items at management units (MUs) of a memory sub-system configured to support non-stripe based commands is received. A set of operations to be executed at the MUs based on the stripe-based command is determined. The set of operations include one or more first operations associated with the set of host data items, the one or more first operations having a first type, and one or more second operations associated with the set of host data items, the one or more second operations having a second type. A first set of commands corresponding to the one or more first operations and a second set of commands corresponding to the one or more second operations is executed.

The invention provides an exception handling method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of performing first fault injection on a first module of a database system based on a plurality of first scripts, wherein the first scripts are in one-to-one correspondence with first types of database system faults; performing a first test on the first module subjected to the first fault injection to obtain a first test result of the database system corresponding to each first type; determining an alarm triggering condition of each first type based on the first test result of each corresponding database system; and if the performance index of the database system meets the alarm triggering condition of the first type, determining a fault processing strategy of the database system corresponding to the first type according to the first type. According to the invention, the equipment fault can be automatically identified and repaired.