202 results about "Cloud deployment" patented technology

Guiding query creation for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input comprising one or more entity identifiers; gathering information based on the one or more entity identifiers; providing, to a generative artificial intelligence (AI) model, a prompt based on the natural language input and comprising the gathered information; and receiving, from the generative AI model, a response to the prompt.

Architecture for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input; selecting, by a delegator generative artificial intelligence (AI) model and based on the natural language input, a particular generative AI model from a plurality of selectable generative AI models; prompting the particular generative AI model based on the natural language input; and providing, via the natural language interface and in response to the natural language input, a response from the particular generative AI model.

The embodiment of the invention provides a safety monitoring method based on mixing of a large model and a neural network algorithm, and the method comprises the steps: carrying out the real-time detection through deploying a lightweight convolutional neural network at an edge end, and screening a to-be-rechecked sample through multi-scale feature extraction and a confidence scoring mechanism; a deep neural network model is deployed at a cloud end to execute fine analysis, deep semantic features are extracted by using a residual structure and an attention mechanism, and a re-check decision is performed based on feature sensitivity and a region weight. Meanwhile, a double-cache structure is constructed to maintain a training sample pool, differential updating strategies are achieved according to sample importance, model performance is continuously optimized through knowledge distillation and comparative learning, edge computing resource constraints and detection precision requirements are effectively balanced, and intelligent upgrading of a safety monitoring system is achieved; according to the method, the detection accuracy and the real-time response capability of the system can be improved by constructing an intelligent framework of edge cloud collaboration and combining the advantages of a large model and a lightweight neural network.

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.

The invention belongs to the technical field of communication, and discloses a network transmission management method and system, and the method comprises the steps that a data collection module collects multi-dimensional data in real time; the decision planning module optimizes a network route; the decision planning module performs protocol and encryption switching; the execution module dynamically adjusts the transmission strategy; the safety protection module performs four-dimensional dynamic protection on the data; the hybrid cloud deployment module guarantees the confidentiality of core data; the multi-active data center module realizes data synchronization among a plurality of data centers; the cross-domain collaborative module realizes privacy protection collaborative training between edge nodes through federated learning; and the block chain auditing traceability module records the key operation log. According to the method, routing and flow prediction are optimized through a mixed quantum classical calculation engine, multi-protocol heterogeneous scheduling and neural mimicry congestion control are combined, the network transmission efficiency is greatly improved, the data transmission speed is improved, and the data leakage risk is reduced through application of a zero-trust security matrix and a quantum key distribution technology.

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.

In some embodiments, an automation optimizer is configured to determine whether a provisioning condition is satisfied, for instance according to a current length of a job queue, or according to a current workload of a selected RPA host platform executing a plurality of software robots. When to the provisioning condition is satisfied, some embodiments automatically provision additional VMs onto the respective RPA host platform, and automatically remove VMs when automation demand is low. Exemplary RPA hosts include cloud computing platforms and on-premises servers, among others.

Guided interactions with a natural language interface, including: gathering data describing one or more user interactions with a system configured for monitoring a cloud deployment; generating, based on the data, a prompt comprising one or more natural language inputs for a workflow, wherein each of the one or more natural language inputs corresponds to a query for information related to the cloud deployment; and providing a selected natural language input to a natural language interface of the anomaly detection framework.

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 (100) for automating financial consolidation and reporting in a cloud-based ERP environment, comprising: a data aggregation and integration module configured to collect and integrate financial data from multiple sources, including ERP, accounting software, and third-party applications; an AI-driven cross-company reconciliation module configured to use machine learning algorithms to identify and eliminate cross-company transactions, detect duplicate entries, missing records, and transaction discrepancies; a currency conversion and standardization module configured to enable real-time currency conversions and ensure compliance with international accounting standards such as IFRS and GAAP; a compliance and audit module configured to monitor compliance with financial regulations and tax laws, automate audit trails, and generate compliance reports; a financial analysis and anomaly detection module configured to analyze financial patterns, detect anomalies, and prevent fraudulent activities using artificial intelligence; a report creation and customization module configured to automate the creation of financial reports, balance sheets, and cash flow reports with customizable templates; a scalability and cloud deployment module configured to deploy the system (100) in a cloud-based ERP environment and enable real-time access to financial reports and dashboards; an automation and workflow optimization module configured to minimize manual intervention by automating data reconciliation, reporting, and compliance checks; a security and access control module configured to implement role-based access control, encryption, and authentication mechanisms to ensure data security; and an integration and API management module configured to enable seamless connectivity with external financial applications and enterprises via APIs.

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 discloses a multi-cloud deployment scheduling method and device, equipment, a medium and a program product. The method can be applied to the field of cloud computing, and comprises the following steps: responding to a scheduling request of a to-be-processed task, and extracting state features of multiple dimensions from environment state information of a current multi-cloud environment; and constructing a first scheduling strategy based on an accumulated reward obtained by evaluating the system performance index and the user preference index and the state characteristics of the multiple dimensions, and performing deployment and resource allocation on the to-be-processed task based on the first scheduling strategy. According to the scheme, the first scheduling strategy is constructed in combination with deep learning and reinforcement learning, so that the scheduling system can realize self-learning and adapt to a complex and changeable environment, and the intelligence of multi-cloud deployment scheduling is improved; in addition, the system performance index and the user preference matching degree serve as the optimization target of reinforcement learning, and the overall system efficiency is improved.

An example method may include generating a license key for a product that manages resources in a data center by encoding client product license data. The client product license data may include a first resource usage quota for an on-premises version of the product and a second resource usage quota for a cloud version of the product. Further, the method may include enabling activation of the on-premises version and the cloud version of the product using the licensed key. Furthermore, the method may include monitoring license usage data associated with the on-premises version and the cloud version. Further, the method may include analyzing the monitored license usage data to ensure compliance of the first resource usage quota and the second resource usage quota for the on-premises version and the cloud version, respectively.

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 aim of the invention is to define a method that allows the management of a particularly large MES / MOM project to be simplified, which may involve a plurality of manufacturing sites and a plurality of servers that require strict guidance regarding as low as possible to hold patches and update thresholds. According to the invention, this object is achieved by a method for operating a plurality of production plants by means of a manufacturing execution system, said method comprising the following steps: a) providing central computing hardware as a regional hub for running MES software; the MES software can manage a production process running through a plurality of production factories; b) configuring a first manufacturing execution system for a first single plant connected to the regional hub; c) creating a respective manufacturing execution system for each other plant by inheriting all configurations of the first manufacturing execution system to the other manufacturing execution systems; and d) configuring these additional plants with existing solutions in the regional hub over time without the need to reinstall the software of the manufacturing execution system and without the need to reapply some configurations that have been executed in the first plant and / or without the need to stop the system. Therefore, the method provides a better value for a multi-plant solution by reducing TCO. Indeed, the same number of servers required to hosting MES / MOM solutions serving a single manufacturing plant in the prior art can now be used to manage multiple plants (as presented in Figure 1). The set of servers constitutes a single MES / MONM solution distributed environment and is hosted by the same regional hub or at least a single "virtual data center" in the event of selecting a public cloud deployment according to the functionality provided by the cloud provider.

Provided is an architecture for facilitating creation and enforcement of policy templates. The architecture includes a policy template designer, a script generator, a policy enforcement engine, and a plurality of technology-specific interpreters. The policy template designer records, for creating a policy template, selection of a set of technologies. The script generator generates a policy template script indicative the set of technologies. The policy enforcement engine generates a set of provisioning scripts indicative of a set of resources to be provisioned by the set of technologies. The policy enforcement engine communicates the set of provisioning scripts to a set of technology-specific interpreters of the plurality of technology-specific interpreters. The set of technology-specific interpreters communicate with the set of technologies to provision the set of resources.

The invention discloses an arrival time prediction optimization system and method based on pre-training learning, and belongs to the technical field of logistics distribution, the arrival time prediction optimization system comprises a server, a database module, a cloud deployment platform and an express terminal system, the server generates a basic model and stores the basic model in a database, the cloud deployment platform comprises a plurality of sub-platforms, and the sub-platforms are connected with the cloud deployment platform. The basic model is deployed on a plurality of cloud deployment sub-platforms to form a plurality of basic model sub-files, each sub-platform is connected with one express terminal system, and the sub-platforms transmit the basic model sub-files to the express terminal systems to realize time and route prediction. By adopting the system and the method, efficient order delivery and time prediction are realized through cooperation, the overall service level of the logistics industry is improved, the basic model can be efficiently utilized, the logistics delivery efficiency is improved, accurate time prediction service is provided, and finally the customer experience is improved.

A method and system are directed to protecting a hardware design against confidentiality and integrity attacks, the method comprising providing a bootable image (BI) to a tenant computing entity; receiving a certificate chain originating from the tenant computing entity via the BI; validating the certificate chain with a certificate authority; providing a design rule data object to the tenant computing entity via the BI; receiving an secure design rule result data object and an encrypted bitstream; determining a match of the secure design rule result data object with the design rule data object; and programming a cloud programmable logic instance with a decryption of the encrypted bitstream based at least in part on the match.

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.

Methods, systems, and devices for data management are described. A first data source may be identified to move from a source cloud deployment (that operates in accordance with a first release cadence for updating database schema) to a destination cloud deployment (that operates in accordance with second, different release cadence for updating database schema). The first source database is then migrated from the source cloud deployment to an intermediate cloud deployment hosted on the source cloud deployments that allows the schema of the first data source to remain unchanged for a duration of time that the first data source is hosted on the intermediate cloud deployment. The database schema associated with the first data source is then updated to an updated (most recent) database schema in accordance with the second release cadence of the destination cloud deployment, and migrated from the intermediate cloud deployment to the destination cloud deployment.

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.

Aspects of the disclosure relate to using machine learning models to automatically deploy computing workloads. A computing system may retrieve resource data. The resource data may comprise deployment costs of computing workloads that are currently deployed, indications of computing workloads that are preauthorized for automatic deployment, and indications of computing workloads that are not preauthorized for automatic deployment. Cloud service provider data indicating cloud service provider costs may be retrieved, via an application programming interface (API) connector. Based on inputting the resource data and the cloud service provider data into machine learning models, cloud deployment data may be generated. The cloud deployment data may comprise predicted deployment costs of the cloud service providers. The computing workloads that are preauthorized for automatic deployment may be deployed. Indications of predicted deployment costs may be generated for each of the computing workloads that are not preauthorized for automatic deployment.

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 invention discloses a personal health evaluation method based on a lightweight large language model, and belongs to the technical field of health evaluation, the method comprises the following steps: processing original multi-modal data exported by a bracelet, and adopting a preset static cue word template to realize standardized switching from the original data to a natural language instruction; constructing a single-round dialogue pair by calling an interface of the closed source large language model; adopting a random multi-round-pair splicing method to form a training sample for simulating multi-round dialogues; injecting an LoRA matrix with a rank of 16 into an attention mechanism layer of the open source model LLaMA3.1 by using an open source large model fine tuning tool Unsloth; and constructing a confrontation test environment comprising three closed source models and six open source models, and performing competitive dynamic evaluation. According to the method, on the basis of keeping the lightweight characteristic of the open source model, the contradiction that the performance and the cost are irreconcilable in a traditional scheme is solved, and meanwhile, the data privacy risk of cloud deployment is avoided.