7 results about "Host model" patented technology

The present disclosure provides a host-side live broadcast interaction method, comprising: acquiring host motion capture data in real time; driving a virtual host model based on the data; generating host-side interaction information comprising the data; and sending the host-side interaction information to a server. The present disclosure also relates to a client-side live broadcast interaction method, comprising: receiving host-side interaction information; driving a virtual host model of the client based on host motion capture data; acquiring client virtual model driving information in real time; generating client-side interaction information; and sending the client-side interaction information to the server. The present disclosure also relates to a server-side live broadcast interaction method, comprising: receiving host-side interaction information and sending it to the client; receiving client-side interaction information from the client and sending it to the host side and / or a designated client. The present disclosure also relates to devices, computing equipment, computer-readable storage media and computer program products that can execute these live broadcast interaction methods.

A system for accessing computing elements of a cloud computing infrastructure includes a dynamic host interface associated with an identity-based access management system. The dynamic host interface is configured to receive host catalogs from components of the cloud computing infrastructure, each host catalog including one or more host sets, each of the one or more host sets including references to one or more hosts, where each of the one or more hosts represents an addressable computing element. The system further includes a plugin-based hosts model in a memory, and being configured to receive, from the dynamic host interface, the host catalogs for identity-based access to the one or more host sets by a user of a client computer to address the addressable computing element represented by the host.

The invention relates to the technical field of network security operation and maintenance, and discloses an SCCD-plus model-based network security equipment unified digital governance method, which comprises the following steps of: constructing information following IEC 61850 system standards, expanding asset ledgers, network topology and the like, redefining SCCD model files of network security equipment and a station control layer host model, and establishing an SCCD model file of the network security equipment and the station control layer host model; the method comprises the following steps: acquiring actual operation configuration values of network security equipment and a station level host, checking and merging with a configuration fixed value baseline in an SCCD model file, generating an SCCD-plus archived file, instantiating a model of target equipment, generating a CID-plus file, and applying the CID-plus file to complete automatic configuration. According to the invention, unified acquisition and management of incremental equipment and stock equipment are realized, automatic configuration, compliance check and security policy deployment of network security equipment are supported, the efficiency and accuracy of network security management are effectively improved, and the operation and maintenance cost is reduced.

This invention provides a temporal model representation method based on task arithmetic and weight alignment, comprising: acquiring meteorological monitoring time-series data; preprocessing the meteorological monitoring time-series data to obtain processed meteorological time-series data; inputting the processed meteorological time-series data into a target host model for parameter fusion for prediction processing to obtain meteorological prediction results; the target host model for parameter fusion is a network model after covering the embedding layer of the target host model based on an enhanced embedding module; the enhanced embedding module is a feature processor based on a weighted linear interpolation method, which weights and fuses the parameters of the source auxiliary model and the target host model within the embedding layer; both the target host model and the source auxiliary model are pre-trained temporal models. This significantly reduces the deployment and operational overhead of meteorological forecasting systems, achieving high efficiency, high accuracy, and strong generalization ability in accurate prediction of meteorological elements.

The present application discloses a host model for front-end verification of a hard disk controller, and relates to the field of storage technology. In the host model of the present application, on the one hand, by constructing a submission model, a completion model, and a management model, and the submission model includes a submission queue, the completion model includes a completion queue, and the management model includes a pending command array, therefore, based on the submission queue, the completion queue, and the pending command array, the communication between the host and the hard disk controller can be simulated to achieve front-end verification of the hard disk controller. On the other hand, the submission model, the completion model, and the management model provide methods that can be called, so that the front-end verification personnel can complete the front-end verification of the hard disk controller by calling these methods without directly operating the storage space of the host, thereby solving the problem that the front-end verification of the hard disk controller in some technologies is difficult to implement.

The invention relates to the field of data processing, and discloses a machine learning model management method and device based on MLOps, equipment and a storage medium, which are used for improving the management efficiency of a model. The MLOps-based machine learning model management method comprises the steps of obtaining target original data and model related information submitted by a user, registering a model to a trusteeship platform according to the model related information, and associating a code version with a data version to obtain a trusteeship model; dividing the target original data into a training data set and a test data set; triggering a corresponding training task based on the training data set, the test data set and model version information corresponding to the hosting model; executing a model training task in a specified environment, and after training is completed, evaluating the model to generate a structured training report; and when a preset iteration triggering condition is met, performing iteration processing and updating the hosting model.

Model server replicas are initialized on a set of first host machines. The model server replicas are each configured to execute an instance of a machine-learned model by obtaining first model image partitions. Each model image partition stores a separate portion of the model. Initializer nodes are executed on a set of second host machines that are selected based on a geographic location of the set of first host machines. Each of the initializer nodes comprises a local image registry mirror provisioned with the model image partitions. Each of the model server replicas are configured such that the model server replica pulls the model image partitions from the local image registry mirror of an initializer node.