111 results about "Cloud deployment" patented technology

Detecting anomalous behavior of nodes in a hierarchical cloud deployment, including: gathering data describing a cloud deployment as a hierarchy of a plurality of nodes; presenting a graph depicting behavior at a particular hierarchical level of at least a subset of the plurality of nodes; and determining whether behavior associated with a particular node deviates from normal behavior based on a hierarchical portion of plurality of nodes including the particular node.

Detecting package execution for threat assessments, including: receiving, from an agent on a host of a cloud deployment, data describing one or more active packages installed on the host, wherein each of the one or more active packages are identified by the agent from a plurality of packages in response to detecting a corresponding file open event; and generating a threat assessment for the host describing which of the one or more active packages have any known vulnerabilities.

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.

A cloud deployment automation system consisting of: a deployment automation device housed in a rack-mountable enclosure, the device comprising: a multi-core orchestration processor configured to execute deployment logic as compiled execution graphs; a storage module operatively coupled to the orchestration processor, the storage storing a set of deployment templates, real-time execution states, telemetry logs, and policy configurations; a secure credential management processing unit embedded in the device, configured to generate, store, and rotate cloud access tokens, API keys, and user-specific credentials, and to provide encrypted access to those credentials during deployment execution; an in-memory workflow execution engine executed by the orchestration processor, configured to analyze a user-defined deployment configuration that includes a declarative specification of infrastructure resources and compile that configuration into a directed acyclic graph (DAG) that represents the resource deployment order, dependency mapping, and rollback relationships, a cloud provider interface subsystem communicatively connected to multiple heterogeneous cloud platforms via appropriate API adapters, the subsystem enabling the orchestration processor to send provisioning requests and receive status events from the platforms; a customizable workflow compiler unit configured to convert graphical workflow definitions or domain-specific language (DSL) scripts into execution sequences that can be used by the workflow execution engine, where the workflow compiler unit supports conditional branching, asynchronous operations, and runtime variable resolution; and A policy enforcement control unit integrated into the deployment automation device, with the policy engine configured to apply organization-specific compliance rules, tagging conventions, security group configurations, and runtime resource limits to all deployment actions in a context-aware manner prior to execution.

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.

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.

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.

Anomaly detection in cloud-based systems involves predicting identity behavior using historical activity data. Historical activities and their timestamps are analyzed to determine future intervals when activity is expected. Predictions are generated using weighted historical data emphasizing recent activity, and an anomaly score quantifying risk is calculated for each future interval based on deviation from expected behavior. Inline monitoring may detect and alert administrators or trigger automated responses to unexpected identity behavior. The method includes confidence scoring based on historical validation, visualization via graphical user interfaces, and lightweight, scalable computations suitable for monitoring extensive cloud deployments, enhancing both precision and efficiency in detecting suspicious cloud activity.

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.

The invention discloses a large-model-based auxiliary vehicle-mounted test case compiling method and device, and relates to the field of vehicle-mounted testing, and the method comprises a preparation stage and a testing stage; the preparation stage comprises a data acquisition and preprocessing step and an installation and deployment step; in the test stage, the method comprises the steps of generating a test case, adjusting and confirming the test case, executing and feeding back, generating a report and adjusting and optimizing the report. Wherein in the installation and deployment step, the preparation for generating the test case is in a standby state; and test case generation: calling the model to generate a preliminary test case. Through the above scheme, based on cloud deployment or / and local deployment, the client side or / and the server side are arranged, and an automatic processing mode of the vehicle-mounted test is formed. A large model is used as an important data processing means, data is efficiently processed, and a reliable test case is formed.

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 invention relates to the technical field of computer system performance evaluation and fault prediction, and particularly discloses an internet-based performance prediction system for software development. The system comprises a data acquisition module, a feature extraction module, a performance prediction model module, a feedback optimization module and a visual analysis module. The system constructs a high-dimensional feature vector set fusing code complexity, a calling relation, an abnormal mode and the like by collecting source codes, logs and defect data, and outputs response time, a resource occupancy rate and a reliability index based on a multi-target prediction model. And further optimizing the model performance through an error feedback mechanism, and displaying a prediction result and a risk score in a graphic mode. The system supports cloud deployment, has cross-project sharing capability and data security guarantee, and is suitable for large software project performance bottleneck diagnosis and risk early warning.

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 relates to a cloud edge data interaction method and device of a power grid dispatching cloud platform, computer equipment, a storage medium and a computer program product. The method comprises the following steps: deploying a central region in a central cloud of a power grid dispatching cloud platform, and deploying a plurality of unit regions in an edge cloud of the power grid dispatching cloud platform; constructing private network communication between the local data center and the central region to obtain a first cross-domain communication channel of the power grid dispatching cloud platform; based on the first cross-domain communication channel, constructing private network communication between the central region and each unit region, and obtaining a second cross-domain communication channel of the power grid dispatching cloud platform; and acquiring the data interaction flow between the central region and each unit region through the second cross-domain communication channel. By adopting the method, the cross-domain data interaction efficiency of the power grid dispatching cloud platform can be improved.

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 discloses a key information extraction and analysis method suitable for foreign trade English negotiation voices, and the method comprises the steps: obtaining the historical voices of foreign trade English negotiation in multiple industries, extracting the voice features, carrying out the text transcription, and generating a first tag data set; training a round semantic clustering model to realize automatic classification of negotiation rounds and clustering of similar issues; then in combination with an intention-entity label labeled by the cross-language semantic understanding model, training an intention-entity bidirectional mapping model, and outputting associated data; training a rule-oriented conflict traceability model by combining a cross-border trade rule base, and identifying information conflict points and complementary points; constructing a key information priority evaluation model, and outputting a key information list sorted according to scores; and finally, generating a structured analysis report and dynamic negotiation strategy guidance. Through multi-model cooperation and foreign trade scene adaptation design, key information can be accurately extracted, intentions and entities can be associated, rule conflicts can be detected, negotiation information processing efficiency and risk management and control capability can be improved, local or cloud deployment can be flexibly adapted, and requirements of different foreign trade enterprises can be met.

The invention discloses a pathological image classification method and system for assisting pathological diagnosis, and aims to solve the problems that the traditional pathological diagnosis is low in efficiency and the accuracy depends on artificial experience. According to the method, a pathological image is collected, denoising, enhancement and other preprocessing operations are executed, a pre-trained deep learning model is utilized to automatically extract image features, and classification results of benign, malignant or specific disease types are output in combination with a classification algorithm, so that efficient and accurate automatic diagnosis is realized; the system integrates image acquisition, preprocessing, feature classification and result display modules, supports multi-user concurrent access and cloud deployment, is equipped with model updating and user interaction functions, and can continuously optimize model performance based on new data. In addition, remote pathological diagnosis is supported, and balanced distribution of medical resources is promoted through a digital system; the method can significantly reduce the workload of pathologists, reduces the risk of human misdiagnosis, and is suitable for rapid classification and diagnosis of various pathological images such as tissue slices, cell smears and the like.

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.

This invention provides a container cloud deployment method, apparatus, and device based on the DPC protocol. The method includes: creating a single master node cluster and an empty node pool through a container management platform, and then including bare metal nodes in the empty node pool; wherein the bare metal nodes have a storage access environment that supports the DPC protocol; sequentially installing a DPC client and a third-party container plugin on the bare metal nodes, and then loading the image of the third-party container plugin into the local repository of the container management platform; creating an instance based on an application template generated from the loaded image, and assigning a unique name to the instance; deploying a storage interface tool on the master node and configuring connection parameters, and then creating a storage class associated with the instance on the container management platform; creating a PVC based on the storage class to verify whether the storage link is available; if the verification is successful, the container cloud deployment is considered successful. This invention significantly improves the efficiency of container access to storage by introducing DPC technology into the container cloud deployment scheme.

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.

One or more software products / services may be installed on a cloud deployment. Product versions of such services can be upgraded (or rolled back) based on a deployment plan comprising tasks adapted to reach a target deployment state. A central upgrade server can forward tasks to an upgrade agent for execution, the tasks being based on a current operational state of the cloud deployment (obtained by the upgrade agent) and one or more constraints. In multiple node deployments, some nodes may be upgraded to a new product version, while other nodes are kept at a current product version until stability of the new product version is determined. Traffic across nodes can be shaped to ensure a deployment is healthy before upgrading other nodes / deployments. If the health of a node / deployment does not meet specified criteria, an upgrade can be stopped, an alert can be triggered, and the node / deployment can be rolled back.

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.