596 results about "Cloud resources" patented technology

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 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.

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 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 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 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.

The invention relates to the field of equipment monitoring, in particular to a power equipment state monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring operation data of power equipment, and generating an original data set containing temperature, vibration and electrical parameters; performing three-stage collaborative verification processing on the data to obtain a characteristic spectrum marked with an abnormal frequency band; generating a data acquisition strategy matrix including sensor priority adjustment and edge-cloud resource allocation according to a dynamic mapping relation between the characteristic spectrum and the equipment health degree index; performing multi-modal data fusion based on the strategy matrix to generate an equipment state feature vector; and updating the health degree evaluation model according to the feature vector, and feeding back and adjusting a sampling strategy. The problems that in the prior art, data processing is not standard, the intelligent degree is low, and the equipment health state cannot be accurately evaluated are effectively solved, and the intelligent level and reliability of power equipment monitoring are improved.

The invention discloses a disaster recovery resource scheduling method and device based on a heterogeneous cloud environment, and the method comprises the steps: constructing a multi-dimensional resource portrait model and a cross-cloud network topological graph in the heterogeneous cloud environment, and generating a standardized resource vector set and a topological affinity scoring matrix; aggregating the local SLA default risk prediction model of each cloud node through federated learning, and performing fine tuning through transfer learning to obtain a global prediction model; calculating load fluctuation entropy based on the SLA constraint template and real-time load data, and inputting the load fluctuation entropy into a global model to predict and obtain an SLA default probability set; constructing and solving a dynamic weight multi-objective optimization function to obtain an optimal takeover cloud node list and a migration strategy identifier set; a target cloud node and a migration strategy are determined through SLA simulation verification, and an adaptive execution adapter is called to complete virtual machine incremental snapshot migration or cross-cloud arrangement deployment of containerized services. According to the invention, intelligent and automatic scheduling of disaster recovery resources is realized, and the fault takeover speed and the cross-cloud resource utilization rate are improved.

Disclosed is a computer-implemented method and system for training a subject-specific machine learning model to infer inherent subject features from recorded or live video data. The system preprocesses the visual and audio channels, converting audio to text, and employs multiple pre-trained extraction models to generate feature embeddings. Ground truth data is obtained to guide training, where weights are assigned to produce and combine predicted feature values. Model performance is optimized by minimizing error. The trained feature extraction models are deployed on an edge device, while the subject-specific model resides in the cloud. A lightweight edge model, derived via knowledge distillation and model compression, supports local inferencing with reduced reliance on cloud resources. Synchronization ensures iterative updates for sustained accuracy.

The present disclosure provides a method of facilitating intelligent budget governance for a cloud resource. Further, the method may include receiving a budget data. Further, the method may include receiving a usage data. Further, the method may include determining a group budget status based on the budget data and the usage data. Further, the method may include calculating a threshold variance representing a deviation of the group budget status from a predefined budget threshold. Further, the method may include generating a control intent data representing a scheduled action on the cloud resource based on the threshold variance and a time policy. Further, the method may include storing each of the budget data, the usage data, the group budget status, the threshold variance, and the control intent data. Further, the method may include transmitting the control intent data to the cloud account system.

The invention discloses a distributed simulation platform elastic scheduling method and system based on cloud computing, and relates to the technical field of computer systems. According to the distributed simulation platform elastic scheduling method and system based on cloud computing, a cross-domain dynamic dependency processing module, a real-time task top-speed scheduling module, a mixed cloud resource adaptation module and an elastic scheduling core engine are included; and the cross-domain dynamic dependency processing module is used for realizing dynamic processing of a dependency relationship in multi-domain collaborative simulation, cross-domain collaborative allocation of resources and breakpoint self-healing of a task chain. According to the distributed simulation platform elastic scheduling method and system based on cloud computing, dynamic analysis of a multi-domain dependency relationship, cross-domain collaborative allocation of resources and breakpoint self-healing of a task chain are achieved through the cross-domain dynamic dependency processing module, the problem that a cross-domain dependency processing mechanism is rigid in the prior art is solved, and the distributed simulation platform elastic scheduling method and system based on cloud computing are high in practicability. The cross-domain data synchronization delay is reduced, and the resource utilization rate and the stability of the task chain are improved.

The invention discloses a smart city-oriented cloud resource adaptive scheduling method and system, and particularly relates to the technical field of cloud resource adaptive scheduling, and the method comprises the steps: precisely recognizing a high-risk task through calculating a task mobility misjudgment coefficient, and marking the high-risk task for deep risk control; calculating a node resource pseudo available coefficient for candidate nodes to be migrated by the high-risk task; a strategy degradation risk analysis model is constructed based on the coefficients to quantify and output a global strategy degradation trend, a'task misplacement-resource misplacement 'closed-loop fault mode is effectively revealed, and a quantifiable basis is provided for risk early warning and strategy correction; and on the basis of the evaluated high degradation risk degree, constructing a scheduling objective function to realize real-time optimization of task-node matching.

The present application relates to a satellite / cloud computing resource allocation method and apparatus, and a computer device and a storage medium. The method comprises: acquiring a target computing task and a real-time resource status; inputting the target computing task and the real-time resource status into a resource allocation model; and by means of the resource allocation model, outputting a satellite / cloud resource allocation strategy, the resource allocation model comprising a resource estimation module and a resource optimization module, wherein the resource estimation module is used for determining pre-estimated resources to be occupied for the target computing task, and the resource optimization module is used for determining the satellite / cloud resource allocation strategy on the basis of the real-time resource status and the pre-estimated resources to be occupied. In the present application, inputting a target computing task and a real-time resource status into a resource allocation model, determining, by means of a resource estimation module, pre-estimated resources to be occupied, and in full consideration of a dynamic resource environment, outputting a satellite / cloud resource allocation strategy by means of a resource optimization module and on the basis of the real-time resource status and the pre-estimated resources to be occupied can effectively improve the utilization rate of computing resources.

The invention discloses a cloud resource dynamic scheduling method, system and device and a storage medium, relates to the technical field of cloud computing resource management, and provides basic data support for resource demand prediction by collecting multi-dimensional core resource real-time data. The input feature weight is dynamically adjusted by means of an LSTM neural network model fused with a dynamic feature selection mechanism, and the short-term fluctuation and long-term trend of the resource demand are respectively captured through an LSTM structure, so that the time sequence analysis better fits the multi-scale characteristics of the load, and the accuracy of resource demand prediction is improved; a prediction result and a current resource configuration state are integrated through a benefit function, overall consideration of economic constraints and service quality requirements is realized, iterative solution is carried out in combination with an optimization algorithm, and a resource scheduling scheme can balance multiple objectives; meanwhile, the model design naturally has adaptive capacity to complex load changes, the deviation between resource configuration and actual requirements is reduced, and accurate prediction and efficient dynamic configuration of cloud resource requirements are achieved.

The invention relates to the technical field of Internet of Things communication, and discloses a gateway management system and method based on multiple Internet of Things protocols, and the system comprises a processor architecture, a protocol conversion module, a collaborative management module, a unified security framework module and an edge computing module. The method is applied to the system. According to the application, efficient interconnection and dynamic collaborative management of multi-protocol equipment are realized through FPGA module hardware acceleration protocol conversion, a genetic algorithm and a Petr network hybrid task scheduling framework and a unified security mechanism irrelevant to a protocol, and various different mainstream Internet of Things protocols are compatible through a modular processor architecture and a dynamic expansion interface, so that the implementation is convenient, and the implementation is easy. According to the method, the hardware cost is reduced, rapid integration of a new protocol is supported, local and cloud resource allocation is further optimized through an intelligent switching mechanism of edge computing and cloud collaboration, the system stability and the data processing efficiency are improved, and the method is widely applied to scenes such as smart home, industrial Internet of Things and smart cities.

The invention belongs to the technical field of data processing, and particularly relates to a cloud resource security access control method based on big data processing, which comprises the following steps: S1, constructing a real-time behavior calculation engine; s2, establishing a multi-modal risk assessment model; s3, generating and optimizing a self-adaptive strategy; according to the cloud resource security access control method based on big data processing, millisecond-level behavior data acquisition is realized by adopting Apache Flink, and a user-resource-behavior sequential relation network is constructed in combination with a Neo4j graph database, so that the static role limitation of the traditional RBAC is broken through, for example, when it is detected that a certain account accesses non-common resources in non-working time, the user-resource-behavior sequential relation network is not influenced, and the user-resource-behavior sequential relation network is not influenced. The system automatically triggers secondary authentication, so that the safety is improved; secondly, constructing an access graph through a sliding window, and calculating a node centrality index, namely, when a certain user node is suddenly connected with a plurality of high-sensitivity resources, namely deviating from a baseline by more than 3 sigma, immediately marking as a potential transverse unauthorized attack, thereby improving the intelligent level of identification and avoiding data leakage.

The invention discloses a data hierarchical storage migration service system based on cloud resources, and relates to the technical field of data migration, and the system comprises a cost decision-making unit which is used for establishing a cost optimization migration decision-making mechanism for data blocks in different storage hierarchies according to charging rules and price data of a cloud resource platform, and determining a migration opportunity scheduling table; the batch planning unit is used for constructing a migration demand distribution matrix and analyzing a resource constraint influence coefficient based on the migration opportunity scheduling table and the real-time state of the cloud resource platform, and generating a migration planning batch; and the migration execution unit is used for executing migration operation of the data blocks in the different storage hierarchies according to the migration opportunity scheduling table and the migration planning batch. According to the method, the migration decision can be dynamically optimized according to the elastic charging characteristics of the cloud resources, and the problems that in the prior art, deep utilization of the elastic charging characteristics of the cloud resources is lacked, and intelligent planning of migration opportunities and batches cannot be achieved are solved.

Systems and methods for selectively updating permissions associated with a cloud resource deployment are disclosed. An example method includes receiving a first request to deploy first target cloud resources based on a first specified state defined in a configuration repository, selectively updating deployment permissions associated with the first specified state and deploying the first target cloud resources based at least in part on the first specified state and the updated deployment permissions.

The invention provides a cloud resource pricing method based on cost sharing, a medium and equipment, and belongs to the technical field of cloud computing. Comprising the steps of inputting all physical equipment information of a data center; calculating and generating a cloud resource total amount table according to the input physical equipment information; inputting each operation cost of a specified year; configuring a resource allocation proportion, and determining a bottleneck resource of the current pricing cycle in combination with a cloud resource gross table; calculating the total cost of the cloud product according to each operation cost of the specified year, and further calculating the unit price of the bottleneck resource charging item; and according to the unit price of the bottleneck resource charging item, automatically generating or updating a price strategy in the cloud management platform for automatically charging the cloud resource use of the user. According to the invention, the cost of a single instance can be derived more accurately, and a foundation is laid for a cost-related module of operation analysis.

A method for detecting an unaccounted tenancy within a cloud environment is disclosed. The method includes storing, within a first database, (i) identifiers of a set of active cloud resources, and (ii) for each active cloud resource, an identifier of a corresponding tenancy; and storing, within a second database, (i) identifiers of a set of tenancies, and (ii) for each tenancy within the set of tenancies, a corresponding tenancy status. Within the second database, each of a first subset of the set of tenancies has an active status, and each of a second subset of the set of tenancies has a terminated status. The method includes querying the first and second databases to identify a first active cloud resource within a first tenancy, such that the first tenancy has a terminated tenancy status; tagging the first tenancy with an unaccounted tag; and undertaking mitigating actions for the unaccounted first tenancy.

The invention relates to a method and system for directly storing audio and video streams to a cloud end, and the method comprises the steps: enabling an audio and video collection terminal to carry out the coding and packaging of a video stream according to a GOP as a unit, and generating a transmission stream segment; establishing a data channel with a cloud server based on a link parameter provided by the signaling server, and uploading the transport stream fragment to the cloud server; after an upload success response is received, deleting the local transport stream segments, and continuously transmitting the remaining transport stream segments until a termination signal is detected; in response to the termination signal, sending a termination request to the cloud server; and based on the termination request, the cloud server performs merging operation on all the transport stream fragments which are accumulatively uploaded at this time, and a complete transport stream file is generated and stored. Through application of the method and the device, the problem that the memory pressure of the audio and video acquisition terminal and the cloud management efficiency cannot be considered at the same time due to the fixed slice duration is solved, and reasonable optimization of the local memory and cloud resources is realized.

The invention discloses an event-based computing power resource aggregation bill algorithm, and belongs to the technical field of charging algorithms. According to the algorithm, a state change event of a cloud resource is used as a boundary for dividing a charging period, a resource use track is divided into a plurality of continuous and non-overlapped state intervals, and each interval corresponds to a fixed resource state and specification information. And when the resources are in a certain state interval, the system accumulates the generated cost to the non-charge amount according to unit time in real time, and when the state interval is ended or the balance is insufficient, a bill is generated and the fee is deducted. According to the invention, a large number of fine bills generated by traditional charging according to fixed time slices can be avoided, user bill details are simplified, reading and auditing efficiency is improved, meanwhile, the user can grasp the expense condition in real time, and accidental arrearage is avoided.

The invention provides a digital twin hydraulic engineering operation and maintenance monitoring system and method based on multi-modal data, and belongs to the technical field of hydraulic engineering and digital twin. The method comprises the following steps: determining a plurality of sub-unit systems of a target water conservancy project to be monitored, and constructing a corresponding digital twin sub-model for each sub-unit system to obtain a digital twin engineering model of the target water conservancy project; acquiring multi-modal historical monitoring data related to the target water conservancy project; based on the multi-modal data, determining a construction environment of a digital twinborn model; mapping each digital twinning sub-model to a construction environment, and running a digital twinning engineering model; and acquiring multi-modal real-time monitoring data related to the target water conservancy project in real time, inputting the multi-modal real-time monitoring data into the digital twin engineering model, and performing operation and maintenance monitoring on the target water conservancy project. According to the method, the rigid-flexible container adaptive model is adopted, so that the cloud resource calling cost can be reduced while the visual operation and maintenance of the water conservancy project are realized and the monitoring efficiency and accuracy are improved.

The invention provides a task processing method and device based on end-cloud collaboration, is applied to a multi-agent collaboration system based on end-cloud collaboration, and relates to the technical field of data processing. The method comprises the following steps: firstly, converting user input into a structured intention message, and decomposing the structured intention message into a directed acyclic graph task flow; then, based on task node requirements and agent state information, dynamically allocating agents on the end side or the cloud to execute corresponding tasks; in the execution process, maintaining a global context state tree and realizing incremental synchronization between end clouds; and finally, all task results are fused and fed back to the user. Dynamic decomposition and node-level fine-grained scheduling of the composite task are realized, and the task processing efficiency is improved; end cloud resource utilization is optimized, and invalid network requests are reduced; and through context increment synchronization, the accuracy of personalized services is improved, and the user experience is remarkably enhanced.

The invention discloses a message issuing speed control method and system based on an AI large model. The method comprises the steps of collecting multi-source data in real time and constructing a multi-dimensional fusion feature vector; based on the BERT-CNN distillation architecture, detecting abnormity and executing hierarchical fusing; based on the fusion feature vector, using a reinforcement learning model to predict message issuing rates of a plurality of time windows in the future and dynamically adjusting priorities; in the offline stage, a mixed integer programming model is adopted to generate minimum cloud resource cost, and in the online stage, a deep reinforcement learning model is adopted to carry out real-time resource fine tuning; based on the node health degree, calculating a traffic allocation weight through a smooth weighted polling algorithm so as to carry out load balancing; different business scenes are adapted through domain adversarial training and multi-task learning, and different languages are adapted through multi-language BERT coding. The message rate is accurately controlled to improve the system stability and the resource utilization rate; low-cost scheduling fine tuning of cloud resources is realized; various business scenes and languages can be quickly adapted, and the development cost and time are reduced.

The invention discloses a cloud service providing method and device based on a public cloud technology, and belongs to the technical field of cloud services. The method comprises the steps that a cloud management platform displays a performance range of each specification supported by a server and a corresponding selling price range; the cloud management platform obtains a virtual instance creation request of the tenant from the cloud resource configuration interface, wherein the virtual instance creation request carries a first specification selected by the tenant, a first performance level of the first specification and a corresponding first selling price; and the cloud management platform selects a first server capable of providing a first performance level of the first specification from the plurality of servers, creates a first virtual instance conforming to the first performance level of the first specification in the first server, and charges the first virtual instance based on the first selling price. According to the method, the selection difficulty when the tenant creates the virtual instance is reduced.