865 results about "Cloud resources" patented technology

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

The invention relates to the technical field of cloud computing, in particular to an intelligent capacity planning method and system in a hybrid cloud environment. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, heterogeneous resources in a hybrid cloud are modeled by using a declarative description language, a dependency relationship among the resources is identified, and a directed acyclic graph (DAG) is constructed; the resource operation tasks are divided in batches, and the tasks without the dependency relationship are executed in parallel; synchronizing the running state of each resource in real time, and performing state consistency verification and abnormity marking; triggering a retry mechanism when an exception occurs; and after the resource arrangement process is completed, summarizing data in the whole process, and performing performance evaluation and strategy optimization on resource operation. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, the use condition of resources can be monitored and analyzed in real time, the resource demand can be accurately predicted, a reasonable resource scheduling strategy can be generated, manual intervention is reduced, and elastic expansion and optimal configuration of cloud resources are realized.

The present application discloses a cloud resource prediction method and system based on a GC-LSTM neural network model, an electronic device, and a readable storage medium. The method comprises two stages: a training stage and a prediction stage. In the training stage, historical data of cloud resources is collected and normalized to obtain a training data set, input data and labels used for training a neural network are organized and made, and an established GC-LSTM neural network model is trained. In the prediction stage, by means of acquired real-time sampling data, the trained model is loaded to perform result prediction, and, on the basis of a prediction result, resources may be scheduled and distributed to respond in advance. The present application aims to predict customers' demands for resources in advance and optimize customers' cloud experience. The model provided by the present application is simple and has few parameters, can guarantee the real-time performance of the prediction result, is practical and feasible, is suitable for the prediction of cloud resource utilization, and provides a guarantee for instant scheduling of resources.

The invention discloses an AI server intelligent data analysis method based on edge-cloud collaboration, and relates to the technical field of edge-cloud computing collaboration, edge equipment is deployed at an edge end, original data of each task is collected in real time, the data is preprocessed, task difficulty features are extracted, and a task difficulty feature sequence is formed; and based on the task difficulty feature sequence and a pre-trained AI data analysis model, the task difficulty of the collected task data is evaluated in combination with the current resource condition, and the feasibility of task execution at the edge end is judged. According to the method, equipment is deployed at the edge end and data is preprocessed, so that part of computing tasks are effectively unloaded to the edge end, the computing burden of a cloud AI server is remarkably reduced, the edge end can independently complete simple tasks, complex tasks are uploaded to the cloud according to needs, excessive centralized use of cloud resources is avoided, and the computing efficiency is improved. Therefore, the energy consumption and the operation cost of the cloud server are reduced, and the energy efficiency ratio of the whole system is improved.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent design method and system for electric power engineering cloud resources based on distributed computing. Comprising the following steps: constructing an electric power engineering multi-dimensional resource modeling system, dividing calculation nodes into three types of heterogeneous resource units including a real-time processing core, a data analysis core and a disaster recovery backup core, and establishing a dynamic attribute matrix containing time delay sensitivity, an energy consumption coefficient and a risk assessment value; the real-time processing core is configured with a hardware acceleration instruction set and supports microsecond response; a self-adaptive dynamic clustering algorithm is adopted, elastic resource clusters facing task requirements are generated according to the spatial topological relation of resource units and the load change trend, a quantum genetic optimization mechanism is introduced in the clustering process to dynamically adjust the inter-cluster coupling degree, and a cross-cluster communication link based on credibility evaluation is established; according to the design, the problems of low resource utilization efficiency and service quality degradation caused by a static allocation mode can be fundamentally solved.

The invention discloses an automatic resource planning method, system and device for hybrid clouds and a medium, and the method specifically comprises the steps: determining a business change trend through employing a time sequence analysis algorithm, and obtaining a demand prediction model; obtaining dynamic resource demand parameters from the demand prediction model, judging a resource allocation proportion of the private cloud and the public cloud, and determining an initial configuration scheme of a cross-cloud resource pool; based on the initial configuration scheme, a reinforcement learning algorithm is adopted to optimize resource scheduling dynamics, and a real-time scheduling plan is obtained; specific parameters of resource allocation are extracted from the real-time scheduling plan, whether the requirements for safety and flexibility are met or not is judged, and a final scheduling instruction is determined; and based on the final scheduling instruction, generating a configuration script and an automatic deployment file for the cross-cloud environment by adopting a preset template engine. According to the method, the hybrid cloud resource delivery efficiency and the resource utilization rate are improved, and deepening and breakthrough of the hybrid cloud technology in enterprise application are promoted.

The invention discloses a dual-module cloud resource prediction method based on deep learning, and belongs to the field of cloud resource prediction. The method aims at solving the problem that in a complex cloud computing environment, an existing cloud resource prediction model is difficult to effectively capture short-term fluctuation and long-term trend at the same time, and consequently resource prediction precision is low. The method comprises the following steps of S1, reading a data set, performing preprocessing, eliminating abnormal values and filling missing values; s2, designing an embedding module to map the preprocessed original data to a high-dimensional vector space; and S3, designing a global module to extract global features in the historical data. And S4, designing a local module to extract local features in the historical data. And S5, designing a fusion module, fusing the outputs of the global module and the local module, and generating a final prediction result. According to the method, a global and local dual-module structure is adopted, and the accuracy and real-time performance of cloud resource prediction are improved through joint learning of global and local module information.

Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, provenance IDs are used to efficiently identify a starting point (e.g., a base snapshot) for a cross-region replication process, conserve cloud resources while reducing network and IO traffic.

Examples analyze effective permissions in a role-based access control system. A prompt is created for a large language model (LLM). The prompt includes a role definition for a role of a role-based access control system, and action definitions. The role definition for the role includes an action and effective permissions text describing a summary of permission limitations provided by the role. The action definitions are provided in a hierarchical format. Query text is added to the prompt. The query text includes a question about effective permissions associated with the role. The prompt is submitted to the LLM, thereby generating response text from the LLM. The response text is displayed to a user.

The invention provides a satellite resource dynamic allocation method under a cloud-native hybrid cloud architecture, and the method comprises the steps: packaging satellite load resources and cloud resources through a containerization technology, so as to form a unified virtual resource pool; constructing a deep learning model, predicting resource demands of each beam coverage area in a future time period based on satellite task historical data and real-time demands, and generating a resource matching scheme; a reinforcement learning algorithm is adopted to dynamically adjust the beam radius, channel allocation and time slot scheduling so as to match the real-time load change of cloud resources; according to the method, a satellite and cloud digital twin model is constructed, a scheduling strategy is simulated and predicted based on a resource matching scheme through the satellite and cloud digital twin model, a resource state is mirrored in real time, and a strategy library is updated according to simulation feedback data of the satellite and cloud digital twin model, so that multi-satellite task automatic recombination is realized. The invention further provides a satellite resource dynamic allocation system under the cloud and native hybrid cloud architecture and a computer storage medium. According to the method, three-dimensional cooperation of dynamic resource pooling, intelligent prediction and elastic scheduling is realized, and the problem that satellite task response time, resource utilization rate and safety cannot be achieved at the same time can be effectively solved.

The invention relates to the technical field of cloud computing, in particular to a resource arrangement and automatic execution method and system based on a hybrid cloud architecture. The resource arrangement and automatic execution method based on the hybrid cloud architecture comprises the steps of pulling or receiving monitoring data, performing streaming and batch cleaning, and generating input required by training and reasoning in a feature processing pipeline; the model is deployed, a prediction result is sent to a decision module in real time, and the decision module triggers automatic execution to carry out capacity expansion and shrinkage on resources; and finally, comparing an execution effect with a prediction result to realize continuous optimization. According to the resource orchestration and automatic execution method and system based on the hybrid cloud architecture, cross-cloud resource dependence analysis and task-driven orchestration execution are realized, strategy-driven dynamic deployment and elastic capacity expansion and contraction are also realized, intelligent execution optimization and exception handling can be performed in combination with artificial intelligence or a rule engine, and the resource orchestration and automatic execution method and system based on the hybrid cloud architecture are high in practicability. The resource utilization rate and the operation and maintenance efficiency of the hybrid cloud environment are improved, and the risk of manual operation is reduced.

The invention relates to the technical field of industrial operation, and discloses a multi-interface industrial control device based on an OpenHarmony industrial operating system and a control method thereof, and the method comprises the steps: scanning an interface resource list of an industrial control computer after the OpenHarmony industrial operating system is started; industrial equipment connected to each interface is automatically found according to the interface resource list, and an equipment connection table is obtained; performing protocol conversion based on the device connection table to obtain a device communication data stream, and extracting a feature data set from the device communication data stream; and calculating an interface coordination scheme based on the feature data set, issuing a control instruction to each industrial device, and collecting execution feedback to obtain unified management and control data. Unified control and remote cooperation of the industrial control equipment are realized through the OpenHarmony distributed soft bus, the conflict problem during multi-protocol concurrent access is effectively solved, and local intelligent decision and cloud resource scheduling of the industrial operation system are realized.

Systems and methods of the disclosure include: identifying, by a cloud resource management system, a cloud resource consumption model associated with one or more cloud resources; generating, by the cloud resource management system, a sequence of instructions implementing a smart contract based on the cloud resource consumption model; sending, to a distributed ledger network, the smart contract; receiving, by the cloud resource management system, cloud resource usage data associated with the one or more cloud resources; and causing, by transmitting a message reflecting the cloud resource usage data to the distributed ledger, the smart contract to be executed.

The invention discloses an enterprise-level cloud computing resource dynamic allocation and management system and an implementation method thereof, and relates to the technical field of computing resource management. According to the invention, cloud resource use data is collected and processed in real time through the resource state sensing and preprocessing module, and a load change rule is accurately extracted in combination with the load analysis and period detection module, so that intelligence and adaptive adjustment of resource allocation are realized, and the resource utilization efficiency and the service response speed are remarkably improved; meanwhile, by recording an operation log, generating a trust certificate and dynamically adjusting the access authority, the credibility and transparency of the resource allocation process are enhanced by using a block chain technology and a zero-knowledge proof protocol, and data tampering and trust crisis are effectively prevented.

Systems and methods are disclosed herein for mitigating a security risk. In an example system, a resource ownership mapping is obtained that contains ownership information for resource names. For instance, the resource ownership mapping maps a resource name to a history of ownerships of the resource name and an action associated with the resource name. In an example, the history of ownerships includes a first owner. From the resource ownership mapping, a first ownership change of the resource name relating to the first owner is determined. A reference to the resource name in a first computing environment associated with the first owner is identified. A preventative action is performed to reduce a risk of a security event occurring in the first computing environment, such as generating a notification to the first owner relating to the identification of the reference to the resource name.

The invention discloses an intelligent electromagnetic induction heating system and method based on edge calculation, and aims to realize efficient and accurate heating control through advanced technical means. According to the system, a multi-source data real-time acquisition module is integrated, and various key parameters in the heating process can be comprehensively obtained. Through the data preprocessing and feature extraction technology, the system can accurately identify the heating state, and a reliable basis is provided for subsequent control. The edge computing node serves as the core of the system, has strong real-time analysis capability, can quickly respond to changes in the heating process, and dynamically adjusts a control strategy. In addition, through an edge-cloud collaborative optimization technology, cloud resources are fully utilized, and the intelligent level of the system is further improved. The problems of feedback lag, high cloud dependence and the like in the prior art are effectively solved, the heating uniformity, the energy efficiency ratio and the abnormity recovery capability are remarkably improved, and a reliable solution is provided for the fields of industrial heating, household induction cookers and the like.

A system for provisioning a plurality of resources in connection with a service to be provided in an autonomous vehicle (AV) infrastructure environment is described and includes a cloud platform executing a platform as a service (PaaS) cluster; a service-specific file specifying the plurality of resources; a values file specifying configuration information for each of the plurality of resource specified in the service-specific file; and a service for deploying the plurality of resources comprising the service on the cloud services platform using the service-specific file and the values file.

The technology disclosed relates to a method, system, and non-transitory computer-readable media that detects malicious communication between a command and control (C2) cloud resource on a cloud application and malware on an infected host, using a network security system. The network security system reroutes the cloud traffic to the network security system. The incoming requests of the cloud traffic are directed to a cloud application in the plurality of cloud applications, and wherein the cloud application has a plurality of resources. The network security system analyzes the incoming requests, determines that the incoming requests are targeted at one or more malicious resources in the plurality of resources. Also, the network security system prevents transmission of the incoming requests to the malicious resources, by making the malicious resources unavailable for receiving future incoming requests, while keeping other resources in the plurality of resources available for receiving the future incoming requests.

A cloud misconfiguration remediation application (“remediation application”) has been created that generates a remediation action for a resource misconfiguration detected with a CSPM policy. The remediation application includes a conversation agent that interacts with the foundation model according to a chain of prompts / input sequences. The conversation agent constructs the chain of prompts based on a template, the CSPM policy, metadata about the CSPM policy and the misconfigured cloud resource, and responses from the foundation model. The foundation model is implemented with retrieval augmented generation (RAG) that uses an embedding database built with remediation documentation of the CSP. Prompts from the conversation agent are augmented based on the implemented RAG. The remediation application aggregates the responses into a remediation action that can either be automatically performed or presented for consideration by a user.

The invention discloses a container cloud elastic expansion and contraction method, and particularly relates to a container cloud elastic expansion and contraction method based on load prediction, which comprises the following steps: S1, data acquisition and storage; s2, data preprocessing and rule filtering; s3, training a load prediction model; s4, load prediction and trend analysis; s5, capacity expansion and shrinkage judgment and parameter calculation are carried out; and S6, executing a capacity expansion and contraction strategy and performing real-time adjustment. Compared with a traditional load prediction method, the method has the advantages that the deep learning model based on the PatchMixer is adopted, the time sequence characteristics of the cloud resource load can be more accurately captured, and the method has obvious advantages especially when nonlinear and non-stationary data are processed. Through high-precision load prediction, reliable input data can be better provided for elastic expansion and contraction of the container cloud, and resource adjustment timeliness and prediction accuracy are remarkably improved.

The invention discloses a cross-cloud task arrangement optimization method based on multi-cloud resource scheduling, and the method comprises the steps: constructing a task topological weighted graph, and recognizing a parallelizable node set; constructing a task node preliminary scheduling feasibility table according to task topological weighted hierarchy information; constructing a granularity feature vector, and updating the task structure matrix; identifying granularity collaborative attenuation abnormal nodes, and performing cross-cloud task path repair and structure update; generating a cross-cloud scheduling time matrix based on each cloud platform resource supply capability index; predicting resource competition conflicts in combination with task execution index historical data, and generating a conflict prediction table; identifying granularity strategy deviation, and determining a repair trigger point; and generating a cross-cloud task granularity adaptive arrangement configuration file based on the repair trigger point information. According to the method, high reliability, high flexibility and high adaptability of task scheduling can be realized in a complex multi-cloud heterogeneous environment, and the stability, the resource utilization rate and the overall cooperation efficiency of cross-cloud task execution are remarkably improved.

The invention discloses a resource creation method, device and equipment for model service and a medium, and relates to the technical field of computers, and the method comprises the steps: converting a model service deployment demand input by a user into a standardized service template; analyzing the standardized service template to obtain a model service resource scheduling instruction, the model service resource scheduling instruction being used for indicating a target resource pool; and determining a target driver corresponding to a target resource pool indicated by the model service resource scheduling instruction, so as to create cloud resources of the model service in the target resource pool by using the target driver, thereby realizing automatic resource creation of the model service, greatly shortening the deployment period through full-process automatic operation, reducing the labor cost, and improving the deployment efficiency. The heterogeneous resource management process is effectively integrated, the delivery efficiency is remarkably improved, and an enterprise can flexibly and efficiently deploy various AI model services on a private cloud platform and deal with quickly changing business requirements leisurely.

The invention relates to the field of offshore platform monitoring and early warning, and discloses a remote video inspection system and method for an offshore unmanned platform, and the method comprises the steps: a multi-modal data fusion network implementation module obtains multi-modal sensing data; an edge computing node of the edge-cloud co-processing implementation module is responsible for receiving and caching multi-modal sensing data; the self-adaptive task scheduling mechanism module is used for monitoring load states of edge nodes and cloud resources in real time; judging that the task execution position is an edge end, a cloud end or a collaborative mode of edge coarse screening and cloud end fine judgment; when the network bandwidth is limited, the transmission of key alarm data and model updating data is guaranteed preferentially; the intelligent analysis module adopts an improved YOLOv8 target detection model and a multi-modal data fusion network to realize accurate identification and dynamic tracking of equipment abnormity and security risks; and edge end lightweight reasoning and cloud deep reinforcement learning are fused to form a closed-loop inspection flow integrating data acquisition, intelligent analysis and decision feedback.

An illustrative method for performing a risk scenario assessment and remediation may include identifying, based on posture data associated with a compute environment, one or more compute resources deployed in the compute environment that are configured to be connected to a network, accessing runtime workload data associated with the one or more compute resources representative of network activity for the one or more compute resources, and performing, based on the posture data and the runtime workload data, a remediation operation associated with the one or more compute resources.

The invention relates to a cloud mobile phone acceleration system and method based on edge computing, a processing device and a storage medium, the system comprises an edge computing layer, a protocol interaction layer and a cloud collaboration layer, and the edge computing layer comprises edge nodes and a dynamic scheduling module; the edge nodes are used for receiving real-time tasks or non-real-time tasks and preprocessing the distributed real-time tasks; the dynamic scheduling module is used for performing task allocation according to the edge node load state and the cloud resource state; the protocol interaction layer is used for receiving the preprocessed real-time task, encrypting a header field and transmitting the header field to the cloud collaboration layer; transmitting the audio and video stream sent by the cloud collaboration layer to a user terminal; transmitting the non-real-time task to a cloud collaboration layer; the cloud collaboration layer is used for decrypting the encrypted real-time task; and processing the decrypted real-time tasks and the distributed non-real-time tasks to obtain audio and video streams. The method can be widely applied to the crossing field of edge computing and cloud mobile phone technologies.

The invention provides a cloud resource dynamic adjustment method and device, a cloud vehicle machine and a storage medium, and relates to the technical field of cloud vehicle machines. The method comprises the following steps: predicting a cloud resource use demand of a user according to obtained historical use information of cloud resources of the user; and dynamically adjusting the cloud resources allocated to the user according to the cloud resource use demand. The historical use information of the cloud resources of the user is analyzed to predict the future use demand of the cloud resources of the user so as to ensure that the prediction result conforms to the use habit of the user, and the accuracy and reliability of the prediction result are improved; the cloud resource allocation adjustment is performed based on the prediction result, so that the cloud resource allocation for the user can dynamically adapt to the actual use demand of the user, the influence on normal use due to too little or untimely resource allocation is avoided, and the increase of the use cost of the user due to too much resource allocation is avoided; and the use cost of the user is reduced while the cloud resource use experience of the user is improved.

The invention provides a lightweight intelligent monitoring end-side cloud resource collaborative scheduling method and system, and the method comprises the steps: obtaining an edge autonomous domain based on an end-side device and a side server, the edge autonomous domain comprising a resource monitoring agent and an initial LSTM load prediction model; updating the initial LSTM load prediction model into a final LSTM load prediction model based on the real-time resource state data and a central coordination layer to obtain predicted resource state data; acquiring a comprehensive load index of the edge autonomous domain; and when a certain edge autonomous domain triggers real-time execution of the task, obtaining a priority score of a historical execution task in the autonomous domain, and generating a task scheduling strategy based on the predicted resource state data, the comprehensive load index and the priority score. By constructing a three-level coordination mechanism of an edge autonomous domain, a central coordination layer and global optimization, pre-intelligent prediction is realized, and global state aggregation and dynamic cross-domain scheduling coordination are realized on the premise of ensuring local corresponding efficiency.

The invention provides a cloud resource configuration method and device, electronic equipment and a computer program product. Relates to the technical field of cloud management. In view of the problem of complexity of the current cloud resource configuration process, the cloud resource configuration method provided by the invention comprises the following steps: acquiring a blueprint description file generated based on a cloud resource blueprint; the blueprint description file comprises cloud resource description information of to-be-configured cloud resources; analyzing the blueprint description file, and generating an abstract syntax tree for representing cloud resource description information; the abstract syntax tree records cloud resource description information through node information; generating a resource configuration file according to the node information of the abstract syntax tree and the mapping relation between the node information and the resource configuration information; based on the resource configuration file, executing a configuration operation on the to-be-configured cloud resource; according to the embodiment of the invention, the complexity of cloud resource configuration can be reduced, and the flexibility and convenience of cloud resource configuration are improved.

The invention relates to the technical field of cloud computing, and discloses a cloud resource flexible scheduling optimization method based on reinforcement learning, and the method comprises the steps: obtaining the dynamic demand data of tasks in a cloud computing platform, including but not limited to the type, priority, historical resource consumption, current system load and other information of the tasks; carrying out modeling on the obtained task; carrying out cloud resource scheduling by adopting a reinforcement learning algorithm; and a scheduling strategy is optimized through a feedback mechanism, and multi-objective optimization scheduling is realized by updating a state-action value function. According to the method, the dynamic demand data of the tasks in the cloud computing platform are acquired, the state space and the action space of the tasks are defined, the cloud resource scheduling strategy is optimized through the reinforcement learning algorithm, and the task scheduling is dynamically adjusted through the multi-dimensional reward function, so that the task allocation can be intelligently optimized under the real-time task load and resource constraint, and the task scheduling efficiency is improved. The resource utilization efficiency is improved, the task response time is shortened, and the system energy consumption is reduced.

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.