68 results about "Cloud deployment" patented technology

Providing generative artificial intelligence (AI)-enabled notebook interfaces for a security framework, including: receiving a request to generate a notebook interface for a security framework monitoring a cloud deployment; generating, in response to the request, the notebook interface, wherein the notebook interface comprises one or more notebook cells for interacting with the security framework, wherein the one or more notebook cells comprise a natural language input cell for querying a generative artificial intelligence (AI) model; and presenting the notebook interface.

Using generative artificial intelligence to interface with a knowledge graph, including: providing, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment; and providing a response to the request based on the knowledge graph and output from the generative AI model.

The invention provides a low-voltage power distribution network load change trend intelligent prediction and management method, belongs to the field of low-voltage power distribution network load prediction, and is used for solving the problems of large load fluctuation, insufficient prediction precision and high cloud deployment delay of a hybrid industry transformer area in related technologies. The method is deployed at an edge node of a transformer area, high-quality data is output through multi-modal data anomaly detection and scene completion, a four-dimensional dynamic load portrait is constructed based on the high-quality data, model super-parameter self-adaptive parameter adjustment is realized by combining transfer learning and Bayesian optimization, and accurate load data is output through three-dimensional linkage resource scheduling and dynamic fusion residual error correction. The method improves the load prediction precision and efficiency, reduces the response delay, and can effectively support the real-time scheduling of the power distribution network.

The invention provides a fault diagnosis method for a conveying belt. The fault diagnosis method comprises the steps that vibration data, pressure data and temperature data in the belt running process are collected through a sensor module; carrying out denoising processing on the collected vibration data, pressure data and temperature data, and removing abnormal values; feature parameters are extracted from the data subjected to data preprocessing; the extracted feature parameters are uploaded to a private cloud server through an Internet of Things module, and a lightweight algorithm is adopted to analyze the feature parameters; according to the analysis result of the lightweight algorithm, a preset fault threshold value is matched, and the fault type of the belt is output. According to the method, multi-source data fusion and targeted feature extraction are adopted, the fault false alarm rate is reduced to 10% or below, industrial data leakage is avoided through private cloud deployment, and the deployment cost is reduced by 60% compared with public cloud; based on a sensor and an open source tool, deployment can be completed within 30 days without professional teams, and belt conveyors in different scenes such as mine underground and logistics sorting can be adapted.

The invention provides an online interactive programming education system based on a micro-service architecture, and the system comprises an infrastructure layer which serves as a bottom layer computing power and provides an elastic computing power through a cross-cloud deployment technology and a dynamic resource scheduling technology; the platform core layer constructs a stateless micro-service cluster through a service registration discovery module, a load balancing module and a fusing mechanism module depending on elastic resources of the infrastructure layer, and the infrastructure layer provides physical resource guarantee for operation of the micro-service cluster of the platform core layer; the platform support layer is used for integrating an authority management component, a workflow engine component and a general service component based on a stateless micro-service cluster constructed on the platform core layer, and providing resource isolation and high-concurrency task scheduling support in a multi-tenant environment for the programming education system; the platform core layer is combined with distributed transaction management to provide stable service scheduling and communication capability for an authority management component, a workflow engine component and a general service component of the platform support layer; and the platform application layer combines service scheduling and communication capabilities, realizes teaching functions such as multi-language development, code debugging visualization, AI personalized suggestion and collaborative learning, and directly serves programming learning of students and teaching management requirements of teachers.

The invention relates to the technical field of cloud computing, and discloses a multi-cloud certificate deployment intelligent arrangement engine system, which comprises a unified abstraction layer used for providing a standardized certificate deployment interface; the intelligent arrangement engine is used for analyzing a deployment task dependency relationship and generating an optimal deployment path; the distributed transaction management module is used for coordinating deployment transactions of the cross-cloud platform and ensuring the consistency of operation; and the strategy optimization and learning module is used for optimizing the deployment strategy based on the historical deployment data. According to the method, the cloud platform difference is shielded through the unified abstraction layer, the consistent deployment experience is provided, and the adaptation workload in the multi-cloud environment can be remarkably reduced; the deployment path is optimized through the intelligent arrangement engine, so that the deployment time can be remarkably shortened; through distributed transaction management, transaction consistency of cross-cloud deployment is ensured, and strong fault recovery capability is provided. Through multi-objective optimization and online learning, the system can automatically balance the cost, the performance and the reliability, so that the resource utilization rate is greatly improved.

A computing platform may train, using historical node performance information and historical application parameter information, a node selection model, which may configure the model to select nodes for application cloud deployment. The computing platform may receive a request to deploy an application to a cloud network. The computing platform may select a node, of the plurality of nodes of the cloud network, to which the application should be deployed. The computing platform may queue, along with other applications scheduled for deployment to the plurality of nodes, the application for deployment to the node. After identifying that the application is first in the queue, the computing platform may deploy the application to the node of the cloud network, which may create, at the node, a container corresponding to the application.

The application discloses a cloud multimodal consistency constraint key abnormal event identification method and system. For the multimodal time series data generated by cameras, environmental sensors and other devices in smart city, industrial internet of things and other scenes, a completely unsupervised event-level abnormal understanding framework is proposed: through a multimodal consistency constraint mechanism, a cooperative abnormal period is accurately positioned; the abnormal period is clustered online to form an event cluster; based on Z-score significance analysis, the most abnormal feature dimension in each cluster is extracted and mapped into a human-readable semantic description; combined with the three indexes of the number of affected devices, duration and state deviation, the keyness of the event is quantified, and a ranked list is output. The application does not need to predefine event types or label data, can discover, name and sort known and unknown abnormal events at the same time, significantly improves the discovery and response efficiency of key abnormal events, and is suitable for lightweight cloud deployment of large-scale sensing networks.

Predicting attack paths using code analysis, including: detecting a vulnerability in code by performing a static code analysis of the code; identifying an attack surface for the vulnerability in a cloud deployment; and generating an alert for the vulnerability by assigning a priority to the alert based on the attack surface.

The invention discloses a fault self-check recovery method and system based on a cloud deployment system, and the method comprises the steps: carrying out the multi-dimensional deep detection of the operation states of an operation system layer, a database layer and an application layer of a cloud platform through constructing an all-dimensional real-time monitoring system, and automatically triggering an adaptive recovery mechanism once an abnormality is found, and continuous and stable operation of the system is ensured. The matching tool integrates core modules of state acquisition, anomaly analysis, recovery execution, log recording and the like, collaboratively realizes real-time acquisition of operation data, accurate anomaly identification through an intelligent algorithm, rapid matching and implementation of a recovery scheme, and records logs in the whole process for troubleshooting and system optimization. According to the tool, server nodes, database states, service accessibility and webpage availability are checked in sequence, emergency processing is started immediately when abnormity is found, the stability and reliability of a cloud platform are remarkably improved, and the manual intervention cost is reduced.

The invention belongs to the field of artificial intelligence and machine learning, aims to solve the problems of high manual generation cost, poor authenticity, low negative sample quality and insufficient scene coverage of multi-modal tool calling data, and comprises four core modules: a tool knowledge base module stores API metadata through PostgreSQL, and generates various user intentions and coherent calling sequences in combination with pre-training LLM; the multi-agent interaction module generates conversation and calling data containing multi-modal input through cooperation of a user, an assistant and an environment agent; the adversarial environment simulation module generates high-quality negative samples in batches by means of three adversarial strategies and automatically marks the high-quality negative samples; and the human feedback iteration module determines convergence by a performance improvement rate according to a model failure case optimization strategy. The system supports local / cloud deployment, adapts to LLM and Agent model training, and is suitable for training and robustness evaluation of dialogue type AI assistants and vertical field intelligent Agents.

The application discloses a petrochemical field precise space-time information processing and service system based on Beidou. The application is based on a petrochemical Beidou ground enhancement network of the Beidou navigation satellite system, relies on a petrochemical special network to collect satellite observation data, deploys a set of precise space-time information processing and service system of China Petrochemical in a petrochemical intelligent cloud, and provides independent controllable real-time centimeter-level positioning navigation and geographic information collection services in a plurality of high-precision positioning and navigation work scene applications such as long-distance pipeline inspection, dangerous chemical vehicle monitoring, industrial control system time service, oil and gas pipeline safety monitoring, geographic information data collection and geological disaster prevention monitoring.

The invention provides a remote vehicle control test method and system, and belongs to the technical field of automobiles, and the method comprises the steps: obtaining a test case and a signal mapping file; generating a control instruction corresponding to a remote vehicle control test process based on the test case and a signal mapping file; the simulation equipment generates a test signal based on the control instruction and sends the test signal to the tested equipment; and feeding back a test result, and comparing the test result with a predicted result of the test case. A flexible and easy-to-use remote vehicle control test platform is constructed through stepwise module separation and configuration uploading by adopting a cloud deployment method, and test cases and signal mapping files can be automatically generated based on a generation tool, so that different vehicle types or hardware can be quickly replaced and adapted, and the test efficiency is improved. The overall reconstruction cost caused by equipment replacement is reduced, the manual writing workload can be omitted, and rapid iteration is supported to adapt to a new process.

Analyzing the security posture of a storage system using generative artificial intelligence (AI), including: accessing a policy implemented in a cloud deployment and data describing one or more requirements for the cloud deployment; providing the policy and the data as input to a generative AI model; and generating, by the generative AI model, based on the data and the policy, an analysis of the policy.

Methods, apparatuses, and products for automating remediation actions in a cloud environment, including: detecting an alert associated with a cloud deployment; selecting, based on the alert, a remediation type; and providing, to a user, one or more resources to perform a remediation corresponding to the remediation type.

Security policy generation using generative AI, including: accessing data describing one or more requirements for a cloud deployment; providing the data as input to a generative AI model; and generating, by the generative AI model and based on the data, a policy for the cloud deployment.

Methods, systems, and products for leveraging user feedback for alert generation in an anomaly detection framework, including: receiving, from a user, user feedback for an alert of a cloud deployment; and initiating, based on the user feedback, a workflow for modifying one or more parameters for generating the alert.

A system and method for optimized generation of cloud deployment configurations for an application is provided. The invention provides for receiving parameters associated with application deployment configuration generation and deployment of application on a target cloud platform. The parameters are processed for generating an application source code template. Cloud deployment configuration is generated based on generated application source code template. Package deployment associated with application deployment is automated by integrating with Application Programing Interfaces (API) for abstracting packaging applications to be deployed on target cloud platform. Pipeline inputs associated with target cloud platform are processed for determining cloud deployment configuration constraints associated with application source code. Service mesh configuration files are generated for deployment of application to target cloud platform by processing cloud deployment configuration constraints associated with application source code. A cloud deployment template is generated for deploying application associated with application source code to target cloud platform.

A method comprises receiving a request for cloud deployment of at least one microservice, wherein the request includes one or more features of the at least one microservice. The one or more features are analyzed using one or more machine learning algorithms. The method further comprises predicting, based at least in part on the analyzing: (i) a cloud platform of a plurality of cloud platforms to deploy the at least one microservice; and (ii) a cloud instance in which the at least one microservice is to be executed, and interfacing with the cloud platform to enable deployment of the at least one microservice.

The application discloses a kind of distributed monitoring method and system for distribution switch, it is related to power equipment monitoring technical field, to improve distribution switch monitoring efficiency.The application is deployed at each distribution switch to be monitored and obtains operating data by monitoring component containing local, cloud deployment module;Local deployment module is handled into time sequence slice structure data as operating data, creates corresponding data window and constructs window association chain;Cloud deployment module filters the data window under non-execution time sequence, corresponding data window is stored in cloud environment after reintegration window association chain;Distribution switch determines the data window of current operation process as execution time sequence in real time, when abnormal, establish guiding chain operation window association chain, update the data window in cloud environment, and then determine the equipment state of each distribution switch, realize the hierarchical collaborative processing of distribution switch operating data, and can quickly and accurately locate equipment anomaly.

The invention provides an intelligent construction site management system. The intelligent construction site management system comprises cloud deployment, edge deployment and end deployment. The cloud deployment comprises a data receiving and processing module, a service fusion and rule making module and a data storage and management module, and is responsible for data receiving, service fusion, rule making and storage and management of mass data. And the edge deployment comprises a data acquisition and primary processing module, a self-adaptive algorithm application module and an edge calculation module, and is responsible for data cleaning, screening, threshold rule dynamic adjustment and field data processing and analysis. The end deployment comprises a data acquisition and uploading module, an anomaly detection and alarm module and a feedback mechanism module, and is responsible for real-time data acquisition, anomaly detection and alarm and operation strategy adjustment. According to the system, through cooperative work of cloud, edge and end deployment, intelligentization and high efficiency of construction site management are realized, the management efficiency, the safety and the resource utilization rate are remarkably improved, and green construction and sustainable development are promoted.

The disclosure provides a cloud service deployment method, device and platform, relates to the field of cloud computing, in particular to the field of hybrid cloud deployment, and is suitable for network storage application scenarios.A specific implementation scheme is as follows: a request for deploying a service of a target application program is acquired, wherein the request comprises a deployment package and resource management information of the target application program; an image of a container is constructed based on the deployment package, and the image is pushed into an image warehouse; a Pod is selected based on the resource management information, and the service is deployed in the selected Pod; and a business process of the container is started through an agent process.This implementation only needs to maintain a set of codes, and can be used and run in different environments, thereby saving the maintenance cost of R&D personnel.The product can be quickly deployed and landed in different environments.

A communication system, method, and apparatus are provided. The communication system includes a first edge cloud deploying first group of user service nodes (USNs) that form a hash ring corresponding to the first edge cloud and a second edge cloud deploying a second group of USNs that form a hash ring corresponding to the second edge cloud. A USN is configured to provide a service for a terminal device corresponding to the USN. The communication system may further include a mapping system including one or more distributed mapping nodes that store information about the USNs on the first and second edge cloud. The information about a USN includes identification information of the USN and identification information of the terminal device corresponding to the USN. When the mapping system includes a plurality of distributed mapping nodes, the mapping nodes form one or more hash rings corresponding to the mapping system.

A holistic approach protects serverless applications in various cloud and / or non-cloud data center computing environments. An illustrative data storage management system deploys, or causes to be deployed, a discovery tracker function in a customer's cloud service account. The discovery tracker identifies and tracks what applications are executing therein, whether they are already known to the illustrative system or not. This aspect ensures that the system is up-to-date with a customer's cloud deployments, so that it may timely back up active applications, and conversely, so that it may dispose properly of copies of applications that are no longer deployed. The discovery tracker discovers application assets, relationships, and interoperability dependencies. The illustrative system creates a corresponding “application entity” that references the various assets. An orchestration function coordinates storage management operations to generate a set of copies of the application's discovered assets. The set of copies forms a point-in-time copy of the application.

The present application relates to a memory-enhanced deep personalized input method system based on end-side large language model, and belongs to the technical field of artificial intelligence. The present application solves the problems of the prior art, such as the loose coupling of the model and the input core process, the shallow personalization mechanism, the delay and privacy risk caused by cloud deployment, and the insufficient efficiency of the end-side operation that cannot meet the real-time input requirements. The system includes a front-end real-time interaction module, a stylized large model post-training module, a hierarchical memory mechanism module, and an end-side reasoning optimization module. The present application can realize low-delay real-time text generation under the condition of highly limited mobile terminal resources, and at the same time, through continuous modeling of user input behavior, it can complete deep personalization adaptation and ensure the end-side retention and privacy security of user input data throughout the process.

A software architecture within a public cloud network may include units of: (i) a plurality of computational instances respectively related to managed networks, (ii) a plurality of servers configurable as load simulators, (iii) administrative components configured to deploy and update the software architecture, and (iv) shared infrastructure services, wherein the units of the software architecture are implemented on virtual machines of the public cloud network and are connected to but logically isolated from one another by way of different access controls or policies. A provider network, coupled to the software architecture by way of network gateways within the shared infrastructure services, may be configured to deliver the configuration, software packages, and database schema to the infrastructure-as-code platform.

A system and method are introduced for a virtual application infrastructure (VAI) that executes apps remotely for users to interact with, preferably within a cloud-based container, while focusing on the user interactivity with these remote apps. A core innovation of the VAI lies in a novel method called ‘raining,’ designed to enable real-time interaction with remote applications, with a name that differentiates it from streaming. Unlike traditional streaming media, ‘raining’ an app requires only the discrete data of the current image of the app displayed in the cloud, eliminating the need for a history of previous data, such as required for audio and video. This innovative approach enables users to interact with remote apps efficiently, even when network bandwidth fluctuates. Additionally, the VAI may be offered as a service (VAI SaaS), which extends the advantages of traditional cloud deployments. It integrates both the cloud infrastructure and endpoints in the cloud, simplifying deployments, enhancing redundancy, reducing costs, and allowing service providers to focus on delivering services while mitigating security, regulation, and rollout risks.

A system and method for optimizing the deployment of a cloud computing environment is presented. The method includes: inspecting the cloud computing environment; generating a representation of the cloud computing environment based on a result of inspecting the cloud computing environment; determining a resource cost associated with each entity of a plurality of entities represented in the generated representation of the cloud computing environment; generating an optimization action for an entity of the plurality of entities, wherein the optimization action reduces the determined resource cost; and initiating the optimization action in the cloud computing environment.

This application provides a method, system, and medium for calling a software parameterized Web service based on reverse polling and local computation. The method includes: starting a local modeling node and registering it with the central service layer; obtaining the CAD software type and version information supported by the format; receiving parameterized modeling task requests; storing them in a task queue; and initializing the task status to be in the queue; extracting tasks to be processed at preset time intervals; parsing the modeling parameters of the tasks to be processed; calling the corresponding CAD software to perform parameterized modeling; generating model files and engineering drawing files; and uploading them to the file storage area specified by the central service layer; and updating the task status to result upload and completion. This application significantly reduces deployment and operating costs, avoids data security risks, eliminates the need to deploy CAD software on a cloud server, processes core modeling data on a local computer without uploading it to the cloud, and completely solves the data security risks of existing cloud deployment solutions.

A method and system are directed to enabling tenant-side design checks for cloud field-programmable gate array (FPGA) deployment. A pseudo-trusted execution environment is created at a tenant device to tie a hash of a compiled bitstream to a hash of design rule or virus scan results, which is sent along with an encrypted version of a bitstream to a cloud provider device that recovers the hash of the design and forwards the encrypted bitstream to a root-of-trust (RoT) on a FPGA to program. The RoT checks the hash provided by the cloud provider device against a supplied bitstream from the tenant device to confirm they match before programming.