2796 results about "Cloud computing" patented technology

The invention discloses a cross-domain computing task processing method, a program product, equipment and a medium, and relates to the technical field of cloud computing. The method comprises the following steps: determining a computing node for executing a cross-domain computing task in a cross-domain collaborative scene, and generating an identity certificate bound with the computing node based on a hardware credible state of the node; collecting security policies of a plurality of management domains for conflict detection and decision, and generating a conflict-free session policy; scheduling the cross-domain computing task to a target computing node meeting a preset credible requirement, and issuing a revocable dynamic session token; and recording the operation information of the cross-domain calculation task in the whole life cycle as an audit event, and generating a chained audit log which is linked by the hash value and is digitally signed through a preset verifiable audit interface. By means of the technical scheme, it can be ensured that the whole life cycle of any computing task meets the closed-loop safety requirements of identity credibility, permission controllability, execution propriability and behavior traceability in the complex distributed computing environment.

An AI orchestration system dynamically manages multiple artificial intelligence (AI) agents within a cloud computing environment to efficiently process user requests. A model orchestration subsystem determines whether a request is handled locally using a domain-specific database or by invoking one or more AI agents. The system maintains AI agents in active and inactive states, provisioning computing resources for inactive agents as needed. Real-time model metrics guide the selection of target AI agents, and if a degrading performance trend is detected, the system preemptively spins up additional AI instances. The system provisions processor cycles, memory, and network bandwidth through a cloud-based resource manager, instantiates containerized execution environments or virtual machines, and performs automated load balancing among AI instances.

The invention discloses a cloud computing resource optimization method based on intelligent scheduling, and belongs to the technical field of cloud computing resource processing. The method comprises the steps of obtaining real-time operation data of target data in a data optimization detection range, collecting historical resource scheduling records and task execution logs, and constructing a multi-dimensional resource state data set; according to the method, multi-objective optimization, simulation verification and reinforcement learning feedback in the step S5 are carried out, a perception-prediction-scheduling-monitoring-optimization closed-loop mechanism is constructed, the resource utilization rate, the response time and the energy consumption cost of a multi-objective optimization function are balanced, and a particle swarm optimization algorithm is combined with simulation verification to generate a global optimal strategy; and reinforcement learning dynamically adjusts model parameters by taking the execution deviation as a reward signal, continuously updates a resource perception dimension and a prediction model, realizes continuous iterative upgrade of a resource optimization effect, and performs optimization processing on cloud computing resource optimization based on intelligent scheduling.

Cloud compliance monitoring for a cloud compute environment managed by a container orchestrator may be performed by a data platform. For example, the data platform may obtain cluster configuration data from a control plane of a cluster that is managed by a container orchestrator within a cloud compute environment, obtain node configuration data from one or more compute nodes associated with a data plane of the cluster that is managed by the container orchestrator, and generate a compliance statement based on the cluster configuration data and node configuration data. The compliance statement may indicate a configuration posture of the cloud compute environment according to a compliance benchmark. Corresponding methods, systems, and products are also disclosed.

The invention discloses an IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention relates to the technical field of safety management and risk prediction, in particular to an intelligent safety management and risk prediction method and system based on cloud computing. The method comprises the following steps: dynamically accessing multi-source heterogeneous data through a cloud platform, and forming unified event representation through time alignment and credibility labeling; constructing a hierarchical mixed probability safety twin model, updating dynamic parameters by adopting credibility weighted online variational Bayesian, and outputting a state interface by combining structural adaptation, cross-object graph regularization and physical constraint projection; mapping the twinborn state into a causal feature, constructing an intervening causal graph, generating causal embedding by using a credibility weighted attention network, simulating an intervention operation in an embedding space, and quantifying a risk probability; and generating a multi-candidate security policy, evaluating and sorting through a multi-objective utility function, executing an optimal policy, collecting feedback data, and updating the model and the policy. According to the method, credibility regulation and control, probability twinning and causal intervention are fused, and real-time intelligent decision making of an industrial safety scene is supported.

The invention discloses a natural resource engineering monitoring method and system based on cloud computing, and relates to the technical field of engineering monitoring. A cloud access layer is constructed, a standardized monitoring data set is formed, a monitoring quality abnormity label set is established, and an engineering state vector set is generated based on a space-time alignment monitoring data set; according to the method, standardization and unified management of multi-source monitoring data are achieved through cloud access, the data accuracy is guaranteed in combination with time synchronization, space mapping and a quality label mechanism, the method constructs an engineering state profile and generates consistent snapshots, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Historical comparison and tracing are supported, a risk event set and a risk evidence chain are generated through causal atlas inference, risk full-chain tracing is achieved, a scheduling instruction is further generated and combined with an execution receipt to form closed-loop management, differential evaluation and dynamic adjustment are supported, and therefore linkage and continuous optimization of monitoring, risk identification and engineering scheduling are achieved.

The invention discloses a building facility detection data intelligent analysis and report system and method, and relates to the technical field of building structure health monitoring, and the method comprises the steps: achieving the high-precision data capture in a strong light dust raising environment through a laser radar and a dual-spectrum camera of a multi-mode anti-interference collection module; a millimeter wave radar array and an IMU inertial unit of the high-risk area strengthening module penetrate through severe working conditions to monitor key structure displacement; the credibility management engine is provided with an NFC timestamp chip and a double-chain block chain, and data judicial-level credibility and operation traceability are ensured; through humidity response gel packaging and Peltier semiconductor refrigeration of the self-maintenance sensing network, node environment self-adaption and drift suppression are achieved; and the multi-stage analysis center fuses edge-cloud computing power based on federal learning, generates a dynamic risk map and automatically outputs a compliance report. Accurate and timely reference is provided for operation and maintenance decisions, and the safety management level of building facilities is effectively improved.

A method includes receiving, at a first instance of a resource reclamation service, a lifecycle event for a cloud computing tenancy, where the first instance is deployed at a first region of a plurality of regions of a cloud environment; identifying, at the first instance, a subset of the plurality of regions to which computing resources of the tenancy are deployed, the subset of the plurality of regions including the first region and a second region; transmitting, from the first instance to a second instance of the resource reclamation service, an indication of the lifecycle event, where the second instance is deployed at the second region; identifying, at the second instance, a first subset of the computing resources of the tenancy deployed at the second region; and executing, at the second instance, a first set of reclamation actions for the first subset of the computing resources of the tenancy.

A system and method for detecting a cybersecurity toxic combination prior to a virtual instance deployment is presented. The method includes: inspecting an entity in a cloud computing environment for a cybersecurity object, the cybersecurity object; detecting the cybersecurity object on the inspected entity; inspecting a code object utilized to deploy a virtual instance in the cloud computing environment prior to deployment of the virtual instance; detecting a toxic combination cybersecurity issue based on the cybersecurity object and the code object; and initiating a mitigation action on the code object.

The invention relates to a carbon emission checking system and method based on a block chain, and the system comprises a basic resource layer which collects enterprise energy consumption data in real time, employs cloud computing resources to provide data storage and computing capability, and employs a Hash algorithm to process the enterprise energy consumption data, and obtains a data abstract; the block chain network layer carries out distributed storage through an alliance chain architecture, carries out consensus evidence storage on the data abstract by adopting a PBFT consensus algorithm, and generates an encrypted data abstract; the intelligent contract layer receives the encrypted data abstract transmitted by the block chain network layer, and executes verification, calculation and supervision rules; and the application service layer calls the processing result of the intelligent contract layer, and respectively provides an enterprise carbon checking terminal service interface, an institution carbon checking terminal service interface and a competent department carbon supervision terminal service interface for the enterprise, a third-party checking institution and the competent department to check the carbon emission. According to the invention, high-efficiency check, high-credibility verification and whole-process traceable supervision of the carbon emission data are realized.

The embodiment of the invention discloses a resource scheduling method and device, equipment and a medium, and relates to the technical field of resource scheduling. The method comprises the following steps: acquiring a service quality constraint index of a data processing task, and resource states of a cloud center and an edge node; constructing a three-dimensional dynamic resource feature space according to the resource state fluctuation information of the edge node, the spatio-temporal information of the cloud computing and the edge node and the historical occurrence probability of the service quality constraint index; and performing particle swarm optimization based on the three-dimensional dynamic resource feature space to obtain an initial strategy set, scheduling computing resources in the cloud center and the edge node based on the initial strategy set, and processing the data processing task based on the scheduled computing resources. According to the technical scheme, scheduling is flexibly carried out according to the condition of the data processing task and the resource condition of the cloud center and the edge node, and the actual requirement of the data processing task is accurately met.

The invention belongs to the technical field of cloud computing and software testing, and particularly discloses an automatic fault injection and recovery testing method based on cloud native, which comprises the following steps: acquiring test basic data of a to-be-tested cloud native system; constructing a fault test execution model according to the test basic data; according to the fault test execution model, executing an automatic fault injection operation; generating an abnormal alarm log according to the system running state after the fault injection operation, and synchronously recording a fault injection operation log; according to the abnormal alarm log, executing automatic fault recovery operation; and generating a fault test report according to the fault injection operation log, the system monitoring data and the recovery operation process log. The invention aims to solve the problems of low automation degree, narrow scene coverage, weak monitoring evaluation and passive recovery in cloud native system fault test in the prior art, and provides a comprehensive, automatic, monitorable and evaluable fault injection and recovery test scheme.

The invention discloses an elastic resource beforehand early warning and scheduling method and system based on load prediction, and belongs to the technical field of cloud computing resource management and scheduling, and the method comprises the following steps: constructing a historical data collector, and collecting and preprocessing multi-dimensional time sequence load data of a business system in real time; constructing a prediction analysis engine, training a prediction model, accurately predicting future busy and idle time points and period trends of each service system, and generating a quantitative load prediction report; an elastic planner is constructed, and an elastic resource pre-expansion plan is automatically generated and executed before a business peak arrives based on a load prediction report and a preset strategy rule; constructing an intelligent scheduler, and executing an elastic capacity expansion action according to the elastic resource pre-capacity expansion plan; and outputting a complete elastic resource beforehand early warning and scheduling system based on load prediction. According to the invention, the response speed and service quality of the system are remarkably improved, and the method is suitable for resource management requirements in complex environments such as multi-cloud and mixed-cloud environments.

The embodiment of the invention relates to the technical field of unmanned vehicles, and discloses a multi-unmanned-vehicle cooperative path planning and scheduling method, device and equipment and a medium. According to the embodiment of the invention, on one hand, the joint search and global path reservation mechanism is introduced, so that the overlapping of the paths of multiple unmanned vehicles in time and space can be relieved fundamentally, the active collision prevention and congestion prevention of the global unmanned vehicles can be realized, and the system safety is improved. And on the other hand, the scheme of the embodiment of the invention can be moved up to the cloud, and the cloud control platform performs centralized decision making, so that the advantages of cloud computing can be inherited, the hardware cost of the single vehicle can be reduced, and the maximum total throughput and the shortest total task completion time of the system under multi-vehicle cooperation can be ensured (different from the target of realizing the shortest path of the single vehicle generally focused in the prior art); the global optimization value of cloud control is brought into play, and intensive and efficient utilization of computing power and road resources is realized.

The embodiment of the invention discloses a dynamic computing power scheduling method and device based on deep learning and a medium, belongs to the technical field of cloud computing, and solves the problem that cloud computing resources are seriously wasted due to the fact that a cloud computing scheduling method in the prior art is prone to unbalanced resource allocation. Comprising the following steps: preprocessing acquired cloud computing data to generate a structured feature vector; wherein the cloud computing data at least comprises cloud node state data, computing task attributes and environment data; through a multi-head attention mechanism, task resource space-time correlation features corresponding to the structured feature vectors are extracted; the task resource space-time correlation features are input into a weight dynamic prediction module and a resource matching degree scoring module at the same time so as to carry out parallel processing of weight prediction and matching degree scoring calculation; and solving a multi-objective optimization function corresponding to computing power scheduling based on a parallel processing result, and generating a computing power scheduling instruction based on a solving result.

Automated deployment of an anomaly detection framework, including: receiving data describing a deployment of an anomaly detection framework in a cloud computing environment; generating, based on the data, a bundle of configuration resources for deploying the anomaly detection framework in the cloud computing environment; and provide, in response to receiving the data, a reference to the bundle.

The invention provides a vehicle-mounted network security threat sensing system and method based on edge and cloud collaboration, and belongs to the technical field of vehicle network security. Comprising the following steps: S1, collecting vehicle multi-modal data through an edge layer; s2, preprocessing the multi-modal data of the vehicle; s3, performing feature extraction on the preprocessed vehicle multi-modal data to obtain a high-dimensional feature vector; s4, calculating a threat confidence score based on the high-dimensional feature vector; s5, performing hierarchical response decision based on the threat confidence score and a predefined response strategy library, and generating hierarchical response data; s6, extracting threat metadata based on the hierarchical response data; s7, performing global association analysis based on the threat metadata, and upgrading a defense system; and S8, based on the global threat intelligence and the upgraded defense system, carrying out edge layer updating, and then skipping to S1. The method is beneficial to eliminating response delay, relieving network bandwidth pressure and optimizing cloud computing resource load.

The invention relates to the technical field of surveying and mapping data processing, and discloses a surveying and mapping data processing method based on cloud computing, and the method comprises the steps: obtaining topographic elevation, topographic slope and environmental parameters, carrying out the multi-source fusion and feature extraction based on the data, obtaining a preliminary feature set, carrying out the dynamic correction and integrity verification of the preliminary feature set, and obtaining a final feature set; generating a correction feature set; the method comprises the following steps: obtaining an optimized feature set through residual analysis, dividing high-frequency feature cache, constructing a partition feature set, carrying out synchronization verification on partition data, obtaining a consistency feature set, executing multi-thread integration, forming a global integration feature set, completing abnormal region reconstruction and optimization, generating a high-precision feature set, and finally, carrying out parallel task decomposition and accelerated integration to obtain a high-precision feature set. And outputting high-precision surveying and mapping data. The method solves the problem that the scheduling precision is limited under multi-source complex data.

The invention provides a match alliance platform management system and method, and the system comprises a match authentication module constructed by a block chain, an AI-driven qualification auditing module, a process management module, a convolutional neural network, a big data analysis module, and an RBAC authority management module. Wherein the match authentication module dynamically analyzes and stores authenticated match rule data through an intelligent contract, and realizes rule version tracing by using an encrypted hash chain; the AI-driven qualification auditing module integrates a natural language processing engine to analyze the selection standard and construct a multi-dimensional evaluation model, and adopts a machine learning algorithm to analyze historical data of athletes to generate a visual qualification report; the process management module supported by cloud computing automatically generates an application process based on a rule engine, adopts a Monte Carlo algorithm to execute multi-constraint drawing, and constructs a directed graph model to dynamically generate a schedule topological structure containing a three-person rotation time sequence.

A system and method for cybersecurity threat detection using an activity baseline generated based on sensor-detected runtime execution data is presented. The method includes: receiving aggregated runtime data from a sensor deployed on a resource in a cloud computing environment; generating an event log based on the aggregated runtime data, each event in the event log generated by extracting data from the aggregated runtime data; generating an activity baseline for a process executed on the resource based on the event log; receiving a new event from the sensor; and determining that the new event is anomalous based on the generated activity baseline.

The invention discloses a task scheduling method and system based on big data analysis, and particularly relates to the technical field of big data analysis and intelligent scheduling, and the method comprises the steps: carrying out the modeling based on a graph structure and multi-scale time sequence features through a space-time prediction module, and precisely pre-judging resource demands and system dynamics; the man-machine cooperation module generates a scheduling strategy considering efficiency and constraint through dynamic weight balance AI calculation and artificial experience; the active scheduling module utilizes a health degree evaluation and fault propagation blocking mechanism to realize rapid isolation and resource migration of fragile nodes; the knowledge management module combines a knowledge graph and reinforcement learning, continuously optimizes a strategy library and digests rule conflicts. According to the method, resource prediction errors are remarkably reduced, task processing delay is shortened, fault recovery efficiency is improved, high reliability of key services is ensured, meanwhile, the method adapts to complex and changeable unforeseen scenes through self-learning ability, and the method can be widely applied to resource scheduling and fault-tolerant management in the fields of cloud computing, industrial Internet of Things and the like.

A system and method for inspecting multiple instances across cloud computing environments for a cybersecurity issue is configured to detect a code object in a configuration code file, the code object utilized to deploy a virtual instance in a cloud computing environment; generate in a security graph a code object node representing the code object; generate in the security graph a resource node representing a virtual instance deployed in a first cloud computing environment based on the code object, wherein the resource node is connected to the code object node; detect a cybersecurity issue on the virtual instance; and generate an instruction to inspect a second virtual instance deployed in a second cloud computing environment based on the code object, the second virtual instance represented by a second resource node connected to the code object node.

The invention relates to the technical field of forest fire management, in particular to an integrated forest fire intelligent analysis method and system. The system comprises a background service system, a central intelligent analysis system and a mobile intelligent analysis system. The background service system is deployed in a cloud computing server cluster and is used for uniformly storing personnel information, fire scene information and geographic information data and realizing multi-source data fusion and authority management through a standardized API (Application Program Interface); the central intelligent analysis system is deployed in a high-performance computer, integrates a forest fire danger auxiliary decision-making model, a forest fire spreading trend prediction model, a force distribution dynamic display module and a collaborative plotting module, and supports fire spreading prediction and real-time decision-making instruction issuing based on multi-dimensional data such as weather, terrain and vegetation; the mobile intelligent analysis system realizes fire spreading trend analysis, mobile plotting and instruction receiving in an offline environment, supports offline data caching and calling in an emergency scene, and effectively improves forest fire emergency response efficiency and resource scheduling accuracy.

The invention relates to the technical field of quantum cryptography, in particular to a dynamic key generation system and method based on multi-source QRNG and QKD, and the system comprises an interface registration method, a calling method, related equipment and a storage medium, is used for generating a high-security quantum key, and is a quantum key service system with a layered architecture. By abstracting, integrating and managing bottom-layer multi-source QRNG and QKD equipment and fully utilizing the flexible scheduling of quantum equipment, the security of key generation is ensured, so that the key service has higher performance, usability and security, and unified, dynamic and high-security quantum key generation and supply services are provided for various applications of the upper layer. The method is widely applied to the industries and fields of government, finance, energy, traffic, electric power and the like, meets the requirements of quantum key acquisition of various business applications in cloud computing and non-cloud environments, and ensures information security and business continuity.

The invention provides an irrigation district water resource scheduling optimization method based on cloud computing, and the method comprises the following steps: collecting irrigation district multi-source data in real time through a space-air-ground integrated sensing network, and uploading the data to a cloud platform after preprocessing; the method comprises the following steps: constructing digital twin bodies of an irrigation area in a cloud platform, dividing the irrigation area into a plurality of sub-areas according to water consumption attributes, and integrating hydraulics and hydrological agriculture models; real-time data and future weather information are fused, and a water demand prediction value is generated through a water demand prediction model by taking the sub-region as a unit; a scheduling scheme is generated by adopting a multi-objective optimization algorithm based on the predicted value, and the minimum economic benefit, water resource utilization rate and energy consumption are taken as optimization objectives; and performing simulation evaluation on the scheme by using the digital twinborn body, outputting a performance index aided decision, and finally determining an optimal scheduling scheme. According to the method, accurate prediction, dynamic optimization and intelligent scheduling of the water resources in the irrigation area are realized, and the utilization efficiency of the water resources and the irrigation management level are improved.

A system and method for inspecting multiple instances across cloud computing environments for a cybersecurity issue is presented. The system is configured to: detect a code object in a configuration code file, the code object utilized to deploy a virtual instance in a cloud computing environment; generate in a security graph a code object node representing the code object; generate in the security graph a resource node representing a virtual instance deployed in a first cloud computing environment based on the code object, wherein the resource node is connected to the code object node; detect a cybersecurity issue on the virtual instance; and generate an instruction to inspect a second virtual instance deployed in a second cloud computing environment based on the code object, the second virtual instance represented by a second resource node connected to the code object node.

The invention discloses a low-altitude security event automatic classification and response method based on a cloud computing platform, and the method comprises the following steps: S1, collecting monitoring data in a low-altitude region, and carrying out the preprocessing of the monitoring data; s2, constructing a heterogeneous airspace behavior map, and extracting an evolution path and a behavior sequence pattern between nodes; s3, inputting the feature embedding matrix into a double-branch classification network, and respectively executing event type identification and event level evaluation; s4, constructing an event index, and designing a multi-objective strategy function; s5, the response instruction set is issued to the multi-source processing terminal through the cloud computing platform, and sealing control, interference, tracking or alarm operation is executed; and S6, performing incremental training on the graph convolutional neural network and the double-branch classification network, continuously updating network parameters and optimizing a multi-objective strategy function. According to the invention, high-precision identification and rapid intelligent response of low-altitude security events are realized, and the automation level and emergency disposal efficiency of airspace management and control are remarkably improved.

The invention discloses a dynamic fruit and vegetable fresh-keeping system, and relates to the technical field of fruit and vegetable fresh-keeping. The system comprises an intelligent monitoring subsystem, the intelligent monitoring subsystem integrates various sensor modules to collect temperature, humidity and gas component data in the fresh-keeping equipment in real time, and the intelligent monitoring subsystem is connected with a cloud computing platform through a microcontroller and regulates and controls operation parameters of the fresh-keeping equipment according to platform instructions; the strategy making subsystem is combined with information collected by the intelligent monitoring subsystem and the visual identification subsystem to automatically adjust a personalized fresh-keeping strategy and environment parameters, a block chain tracing technology is adopted, and according to the change of the variety, maturity and storage time of fruits and vegetables, the fresh-keeping strategy and the environment parameters are automatically adjusted; according to the fruit and vegetable fresh-keeping management system, fresh-keeping strategies and storage environment parameters are automatically adjusted, precise, dynamic and informatization management of fruit and vegetable fresh-keeping is achieved, the fresh-keeping period is effectively prolonged, the quality is improved, food safety is guaranteed, and an all-round innovative solution is provided for the fruit and vegetable fresh-keeping industry.

A system and method for agentless detection of network exposure of a software appliance is presented. The method includes detecting a software appliance in a cloud computing environment, wherein the software appliance includes an immutable preconfigured, self-contained software application; inspecting the software appliance for a proxy-type application; detecting network traffic associated with the software appliance; generating a network path based on the network traffic and a network identifier of a component of the cloud computing environment based on detecting the proxy-type application and an identifier of the component in the detected network traffic; actively inspecting the generated network path through an external network, wherein the external network is external to the cloud computing environment; determining that the network path exposes the component based on a result of actively inspecting the generated network path; and initiating a remediation action based on the result of the active inspection.