4615 results about "Virtualization" patented technology

The invention provides a multi-domain computing resource aggregation method and system based on a virtualized user network, and the method comprises the steps: firstly collecting a real-time resource state data set of a computing resource node in a multi-domain environment, covering a computing power load rate and the like, and then calling a virtualized resource mapping model to carry out cross-domain resource feature extraction, the method comprises the following steps: generating a multi-dimensional resource feature vector containing dynamic load fluctuation and other features, performing resource association analysis on the feature vector based on an intelligent aggregation strategy network, determining a collaborative matching weight, aggregating dispersed nodes into a virtualized resource pool, and finally performing dynamic adaptation on aggregation resources in the virtualized resource pool. And generating a resource scheduling topological structure matched with the target business demand, and deploying the resource scheduling topological structure to a multi-domain computing environment to trigger a resource collaborative service, so that multi-domain computing resources can be effectively aggregated, and the resource utilization efficiency and the service matching degree are improved.

The invention provides a GPU computing power scheduling method based on a one-cloud multi-core heterogeneous computing power platform, and the method comprises the following steps: S1, carrying out heterogeneous resource registration and modeling, accessing hardware equipment containing multiple types of GPUs through a resource registration module, collecting the equipment model, the video memory capacity and performance index data, and carrying out heterogeneous resource modeling; constructing a resource feature database containing a topological relation, and supporting hybrid access of chips; s2, virtualized resource reconstruction: pooling a physical GPU into virtual GPU resources by adopting a hardware abstraction layer technology, realizing video memory isolation and calculation unit division through a containerization technology, and configuring each virtual GPU instance with an independent drive stack and a security sandbox; and S3, submitting a multi-modal task, receiving a CUDA / OpenCL calculation task submitted by a user, analyzing task demand parameters including a calculation core number, a video memory occupation amount and a data throughput threshold, and generating a task descriptor containing a priority label.

In some embodiments, a computer system displays user interfaces for enrolling one or more features of a user of a computer system. In some embodiments, a computer system displays visual effects associated with a virtual avatar in an XR environment. In some embodiments, a computer system displays objects having different visual characteristics in an XR environment. In some embodiments, a computer system switches between different presentation modes associated with a user represented in an XR environment. In some embodiments, a computer system displays a virtual avatar in an XR environment.

The present invention discloses a method and system for signal detection and remote early warning of wireless base stations, comprising the following steps: collecting environmental factors and signal quality data of multiple base stations in real time through a dynamic environment perception system, combining historical signal data, comprehensively evaluating the signal health status, and generating a multi-dimensional analysis report on the signal health status, using a fuzzy logic algorithm to accurately predict signal failures and attenuation trends, and dynamically adjusting the signal early warning threshold to avoid false alarms and missed alarms caused by fixed thresholds, realizing signal sharing and collaborative analysis between base stations through an intelligent collaborative detection mechanism, optimizing signal health management through cross-base station linkage, identifying potential signal failure areas in advance and optimizing response strategies, using a remote monitoring and repair platform, creating an intelligent operation and maintenance system through virtualization technology and intelligent algorithms, simulating fault repair scenarios and optimizing repair paths, and the system can adaptively learn and continuously optimize signal quality prediction and risk control capabilities.

Examples of file analytics systems are described that may obtain metadata data and events data from a virtualized file server. The file analytics systems may detect one or more events from the events data matching a criteria indicating malicious activity. The file analytics systems may validate the detection of malicious activity. The validation may be performed by comparing the file type, such as the MIME type, of sample files before and after the suspected malicious activity. The systems may recover a share of the distributed file server including the one or more affected files by replacing the one or more affected files with stored versions of the one or more affected files from a snapshot of the share taken prior to the detected malicious activity.

The invention relates to the technical field of Internet of Things, and discloses an OpenHarmony-based Internet of Things control method and platform, and the method comprises the steps: carrying out the distributed discovery and lightweight authentication of Internet of Things equipment, and constructing a dynamic equipment topological graph; generating a device capability description file according to the authentication device set, and creating a virtualization layer; acquiring multi-source sensor data from the virtualization layer and performing fusion processing to obtain a scene control strategy; acquiring network data packets at key nodes of the dynamic equipment topological graph and extracting features to obtain a security situation assessment result; performing deep packet detection based on a security situation assessment result and a scene control strategy, identifying an abnormal behavior, and generating a network optimization strategy; the cluster head nodes are selected according to the network optimization strategy to perform load-balanced data transmission, and the node working mode and the network topology structure are dynamically adjusted to realize global energy efficiency optimization, so that the global energy efficiency optimization is realized, and the overall operation time of the Internet of Things system is prolonged.

The invention relates to the field of computing power network services, and provides a computing power network ecological digital simulator based on multi-agent self-organizing interaction. Comprising a multi-agent self-organizing interaction module, a resource modeling and dynamic allocation module, a service scheduling and collaborative optimization module, a risk management and emergency response module and a strategy evaluation and optimization regulation module. The digital simulator constructs a refined and virtualized computing network model through multi-agent self-organization interaction, can dynamically reproduce resource interaction behaviors in a complex environment, assesses the effectiveness of a scheduling strategy, and optimizes the adaptive capacity of a system in a high-dynamic environment, thereby providing a scientific basis for intelligent decision-making of computing network ecological operation.

Techniques are described for monitoring and analyzing input / output (I / O) messages for patterns indicative of ransomware attacks affecting computer systems of a cloud provider, and for performing various remediation actions to mitigate data loss once a potential ransomware attack is detected. The monitoring of I / O activity for such patterns is performed at least in part by I / O proxy devices coupled to computer systems of a cloud provider network, where an I / O proxy device is interposed in the I / O path between guest operating systems running on a computer system and storage devices to which I / O messages are destined. An I / O proxy device can analyze I / O messages for patterns indicative of potential ransomware attacks by monitoring for anomalous I / O patterns which may, e.g., be indicative of a malicious process attempting to encrypt or otherwise render in accessible a significant portion of one or more storage volumes as part of a ransomware attack.

The invention discloses a network attack AI detection analysis method and system based on the smart Internet, and belongs to the technical field of network security protection, and the method comprises the steps: building an attack feature library through distributed edge nodes in a cooperative manner, generating feature parameters of each node based on a local attack event, and transmitting the feature parameters to a central server for dynamic fusion through encryption; constructing a multi-modal interaction graph, identifying a potential attack link based on association strength among graph nodes, and deducing an attack intention to generate a defense strategy; deploying a virtualized network environment, dynamically injecting induction characteristics, and adjusting an induction strategy in real time according to the interaction behavior of an attacker; and monitoring an abnormal mode of the user behavior sequence, triggering an AI interaction verification process and storing a defense strategy. According to the method, rapid collection and fusion of network attack features are realized, the detection delay of network attacks is reduced, the accuracy of attack prediction is improved, the flexibility and effectiveness of network attack confrontation are enhanced, and the defense intelligence and adaptive ability of the whole network are improved.

The invention relates to the technical field of resource scheduling control, and discloses a network resource hierarchical coordination method and system based on multiple agents. The method comprises the following steps: monitoring a network node load to obtain a resource state parameter; virtually constructing a resource pool and dividing a regulation domain; deploying an intelligent agent to construct a self-adaptive control system; extracting load characteristics to generate an analysis report; executing a double-layer adjustment strategy recording execution process; and analyzing the energy consumption data to optimize control strategy parameters. According to the application, the efficient utilization of network resources and energy consumption optimization can be realized while the performance of a computer network system is guaranteed.

The invention provides a multi-source information fused virtualized network fault rapid positioning method, and relates to the technical field of electric digital data processing, and the method comprises the steps: classifying and sorting historical fault events, constructing a historical dynamic evaluation model, correcting a calibration index, capturing equipment performance changes and abnormities, and enhancing the fault recognition capability. Similar faults are accurately classified and preferentially processed according to severity; in the real-time monitoring stage, real-time multi-source and abnormal data are combined, a real-time dynamic evaluation model and a correction calibration index are calculated, a ratio is applied to calculate a fine adjustment strategy, the real-time performance and accuracy of fault positioning are remarkably improved, and network dynamic changes are quickly responded. And carrying out difference analysis on the adjusted real-time model and the historical model, calibrating a preliminary fault positioning result, and improving the positioning precision and reliability.

A method includes creating, via a server, a plurality of virtualized human personalities associated with respective human users; receiving, via the server, a cyberattack message; determining, via the server, the cyberattack message targets a human user of the respective human users; selecting, via the server, a virtualized human personality of the plurality of virtualized human personalities based on the virtualized human personality being associated with the human user targeted by the cyberattack message; and responding, via the server, to the cyberattack message using the virtualized human personality selected from the plurality of virtualized human personalities.

A system and method for mitigating cyber-attacks against a target network comprising interconnected note that is implemented by Open Systems Interconnection (OSI) layers monitors the target network for detecting vulnerabilities across one or more OIS layers. a virtual network comprising a virtualized representation of the target network where the virtual network includes one or more virtual nodes that are annotated with identified vulnerabilities of one or more corresponding nods of the target network. A reference database can be configured to store records of known cyber-attacks and their corresponding mitigations where cyber-attacks on the virtual network are simulated based on records of known cyber-attacks and successful cyber-attacks. An AI engine can be configured to generate one or more mitigation actions based on simulation of the cyber-attacks before implementing the one or more mitigation actions to the target network.

The invention discloses a prefetch instruction conversion optimization method based on memory access mode virtualization, which comprises the following steps of: when an executable file is executed in a dynamic instruction conversion mode, acquiring an address memory access mode of a prefetch instruction of x86 architecture, and analyzing an operand and an offset calculation mode of the prefetch instruction; according to an address memory access mode, an operand and an offset calculation mode, an instruction or an instruction sequence of an ARM architecture capable of completing memory address calculation and a prefetching function is constructed, a prefetching instruction of an x86 architecture is replaced with a corresponding ARM instruction or instruction sequence to complete instruction dynamic conversion, and conversion of the prefetching instruction of a complex address mode under the x86 architecture is achieved. And the execution reliability of the executable file in the dynamic instruction conversion mode is improved.

The invention discloses an extension equipment function configuration method and device, computer equipment and a storage medium, and relates to the technical field of data processing. According to the method, a hardware and firmware collaborative dynamic configuration framework is constructed, and a receiving queue is adopted at an extension equipment end to trigger a central processing unit firmware processing mechanism; in combination with virtualization design of a shadow configuration space and a shadow register, dynamic reconstruction of configuration parameters and an address mapping strategy during operation is realized; a host end supports configuration space access, base address register remapping and memory data transmission interaction through a transaction generation and routing mechanism driven by an event type. Hardware solidification limitation of traditional extension equipment is broken through through software and hardware decoupling design, the problems that function extension capacity is insufficient, resource dynamic allocation is difficult and protocol updating and virtualization scenes cannot be adapted are solved, and the technical effects of improving configuration reconstruction efficiency and address space utilization rate and supporting virtual function creation are achieved. And the adaptability of the equipment in cloud computing and edge computing environments is enhanced.

The invention provides a virtualized computing resource scheduling method and system based on a power wireless local area network, and the method comprises the steps: firstly obtaining power equipment operation load data of access equipment in the coverage of the power wireless local area network, including real-time current fluctuation and other features, carrying out the load feature extraction of the power equipment operation load data, and carrying out the load feature extraction of the power equipment operation load data; performing dynamic resource demand prediction on the set based on a preset load prediction model to generate a virtual resource demand prediction result including computing resource allocation magnitude and the like; generating a virtualized resource scheduling strategy containing edge computing node resource allocation topology and the like according to a virtualized resource demand prediction result, and finally dynamically adjusting virtualized computing resources based on the virtualized resource scheduling strategy, triggering resource reallocation and updating a global resource state mapping table. Effective scheduling of virtualized computing resources of the power wireless local area network is realized.

Disclosed are various embodiments for rate proportional scheduling to reduce packet loss in virtualized network function chains. A congestion monitor executed by a first virtual machine executed by a host computing device can detect congestion in a receive queue associated with a first virtualized network function implemented by a first virtual machine. The congestion monitor can send a pause signal to a rate controller executed by a second virtual machine executed by the host computing device. The rate controller can receive the pause signal. In response, the rate controller can pause the processing of packets by a second virtualized network function implemented by the second virtual machine to reduce congestion in the receive queue of the first virtualized network function.

The present invention relates to adaptive multi-state encoding and processing in virtualized computing environments. The system includes a virtualized state selection module that dynamically transitions between binary, ternary, quaternary, and higher-order encoding states based on workload, bandwidth, security posture, and compliance requirements. A virtual encoding engine utilizes hardware-accelerated components, such as vFPGAs, vGPUs, and cTPUs, to enhance encoding throughput. A compliance-driven feedback controller continuously monitors encoding efficiency, threat levels, and adherence to mandates such as GDPR, HIPAA, and FIPS 140-3. Additional features include AI-based anomaly detection, federated model refinement, distributed ledger-backed audit trails, and quantum-resistant encoding techniques. By integrating intelligent encoding decisions with scalable compliance enforcement, the system enables high-performance, secure, and regulation-ready data processing across distributed cloud and edge environments, delivering measurable improvements in system responsiveness, data integrity, and operational trust.

The invention discloses a network security active defense method and system for detecting abnormal network behaviors. The method comprises the following steps: S1, constructing a virtualized isolation network environment through a software defined network technology; s2, deploying intelligent dynamic camouflage nodes in the virtualized isolation environment; s3, acquiring a disguised node attack behavior, and identifying a network abnormal behavior through a deep learning technology; s4, extracting network abnormal behavior features, and identifying potential threats through a deep learning technology; s5, a defense mechanism is automatically triggered, a honeynet is started, and abnormal behaviors and potential threats are isolated; and S6, tracing an attack path, and carrying out source positioning. The network security active defense system comprises a network construction module, a deployment module, an identification module, a honeynet, an isolation module and a tracing module. The disguised nodes are deployed through the deployment module to detect abnormal network behaviors, active defense of network security is realized through the honeynet, the isolation module and the tracing module, and the network security is improved.

The invention discloses an intelligent private cloud resource scheduling management system based on a hyper-converged architecture, particularly relates to the technical field of virtual resource scheduling management, and is used for solving the problem of overall utilization rate reduction caused by cascade resource oscillation and fragmentation due to fault recovery in the prior art. Candidate buffer nodes are screened based on the resource occupancy state, a migration priority queue is generated in combination with the node resource isomerism matching degree and the real-time load, and an independent buffer resource pool is reserved at a target node; key resources are directionally reserved through resource locking labels, task dependence periods are associated, and task conflict risks possibly triggered after the resources are released are dynamically predicted; adjusting a label effective period to cover a task execution window based on a conflict identification result; and removing the label in the preset time window, releasing the resources to the buffer pool, synchronously triggering global resource integration and candidate node list updating, and realizing efficient recovery and dynamic redistribution of fragmented resources, thereby inhibiting multi-node resource fluctuation.

The invention belongs to the field of artificial intelligence, and discloses a scheduling strategy selection large model training method based on reinforcement learning, and the method comprises the steps: constructing a virtualized cluster environment; generating training data; carrying out large model base pre-training; learning environment interaction is reinforced; performing priority experience playback training; performing near-end strategy optimization; carrying out multi-expert strategy distillation; policy security verification is carried out; carrying out progressive online deployment; and feedback driving continuous optimization is carried out. According to the method, the resource topology, the task portrait and the dynamic load feature are decomposed to the special agent, and structured semantic tags such as a high-bandwidth demand task and a critical overheat node are generated, so that the heterogeneous resource state recognition accuracy is improved by 40%, and the multi-dimensional feature fusion efficiency is improved by 35%.

The invention relates to the related technical field of distributed computing power resources, and discloses a distributed computing power resource dynamic fusion and cooperative computing system which comprises a resource sensing module, a task analysis module, a dynamic scheduling module, a virtualization adaptation layer, a cooperative computing engine, a global controller and a hierarchical communication bus. The resource sensing module collects and quantifies the hardware computing power of the heterogeneous computing nodes in real time. According to the invention, a complete closed-loop control system is formed through a five-layer dynamic fusion architecture (a sensing layer, an analysis layer, a scheduling layer, a virtualization layer and a collaboration layer), so that a distributed computing system can adapt to a scene that edge nodes are dynamically added and pushed out; meanwhile, through the purpose of developing virtualization middleware supporting cross-platform instruction real-time conversion and based on a multi-target dynamic weight adjustment mechanism of online learning, the heterogeneous task scheduling success rate can be greatly improved through the overall structure of the system, and the fault recovery time is shortened.

The invention belongs to the technical field of front-end development, and particularly relates to a React framework-based tree component optimization method fusing virtualization rendering and increment rendering. Comprising the steps of S1, data preprocessing; S2, dynamic virtualization rendering: dynamically calculating a node index range in a visible area according to a real-time scrolling position of a user viewport, only rendering nodes in the range, and realizing seamless lazy loading of data through scrolling event monitoring; s3, intelligent incremental updating: identifying changed nodes through a state change comparison algorithm, and executing local re-rendering only for the changed nodes based on a component life cycle control mechanism of a React framework; and S4, lazy loading cooperative control. The method aims at solving the problems in the aspects of performance, memory management, user experience and the like when a tree component in a React ecological system processes large-scale data, and is widely applied to various Web application program development scenes needing to display and operate a large-scale tree data structure.

The invention discloses a compatible operation method for an Android camera HAL in a container based on memory access virtualization, and the method comprises the steps: taking a DMA-Buf memory heap of a Linux kernel host system as a target memory heap, taking an ION memory heap of an Android container system as a source memory heap, building a process exclusive memory management context, an FD cache table and a synchronous fence pool, and carrying out the execution of the process exclusive memory management context, the FD cache table and the synchronous fence pool; kernel registration and node binding of virtual ION equipment, pre-allocation of a first memory pool and access hook registration of kernel layer equipment are completed, and when an ION file descriptor is obtained in an HAL process, legality of a container process is verified, and context binding is initialized to the file descriptor; intercepting a memory allocation request of the HAL process, analyzing and adapting parameters, and preferentially multiplexing first memory pool resources to obtain an ION handle; when a data sharing request is processed, matching an FD cache or generating a new FD through an ION handle; when the HAL process releases the memory, resources are recycled according to a memory source, and when the HAL process exits, the context is cached or destroyed, so that cross-system memory operation compatibility is realized.

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention relates to the technical field of software testing, in particular to a self-adaptive software testing environment configuration method and system.The method comprises the steps that before software testing is started, a sensing layer analyzes testing requirements through a prediction model, and a preliminary environment configuration scheme is generated by analyzing codes, configuration, requirement documents and historical data; the containerization and virtualization management module creates a virtual machine and deploys applications according to the scheme, the SDN control module sets network topology and strategies, and the automatic configuration module executes environment establishment; in the testing process, a dynamic monitoring module monitors resource use in real time and feeds back the resource use to a decision-making layer based on a data interaction protocol, a test demand perception prediction model in the decision-making layer generates a resource adjustment strategy and a network optimization strategy based on monitoring data, and a test strategy and a case execution sequence are dynamically adjusted. And the optimization feedback module evaluates the environment configuration effectiveness and transmits the environment configuration effectiveness to the decision-making layer for adjustment. According to the invention, the beneficial technical effect of a more efficient, accurate and reliable software testing process is realized.

The invention provides a software ecological migration method and system based on intelligent analysis. Wherein a compatibility evaluation database is deployed in a target architecture, an instruction set mapping rule is preset, and migration risk data is generated through semantic verification; dynamically allocating resources through a heterogeneous virtualization scheduling engine, calling a hardware acceleration unit translation defect instruction, and generating virtualization equipment simulation parameters based on the migration risk data; deploying protocol conversion middleware, dynamically analyzing and reconstructing a network protocol data packet, and combining instruction execution delay data to adjust analysis granularity; in cross-architecture transmission, instruction conversion defects are injected into control logic, timing sequence errors are corrected through clock offset compensation, and corrected data are transmitted back to update instruction mapping rules. According to the technical scheme provided by the invention, the data leakage risk is reduced, and zero shutdown in the migration process is ensured.

The invention relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention discloses a UE4-based digital twin model construction system and method, and relates to the technical field of computer vision, and the system comprises a geometric modeling unit which generates three-dimensional model data based on geometric modeling data of a target physical entity, and achieves dynamic LOD rendering level switching through virtualization processing; the real-time data interaction unit is used for analyzing the real-time sensor data flow and generating equipment state data in combination with a transmission operation and maintenance instruction; the simulation engine unit is used for executing rigid body motion and collision simulation based on the equipment state data and outputting a collision event sequence and state transition data by utilizing an event trigger; the machine learning unit is used for training an LSTM (Long Short Term Memory) model based on the collision event sequence and the state transition data, and optimizing state covariance parameters through adaptive Kalman filtering; and the visual interaction unit is used for realizing data synchronization of the command large screen and the mobile terminal through multi-screen collaboration. According to the method, the digital model capable of accurately reflecting physical entity behaviors and characteristics is constructed.

Provided in the present invention is a node-type edge computing system. The system comprises a micro-service module, a CI / CD module, an application management module, an observation module and a security module. The node-type edge computing module in the present invention has the characteristics of having a low network delay, supporting mass data access, having an elastic infrastructure, etc. Moreover, in addition to reducing the transmission delay, the advantages brought about by shortening a spatial distance further comprise the delay of various PLCs, gateways, routing-transfer, and network device processing in a complex network is also reduced. In addition, since the probability of network link contention is greatly reduced, the overall delay can be significantly reduced. By means of the node-type edge computing module, a distributed capability is added to a traditional cloud center, some service logic is deployed on an edge side, and related data processing is completed, such that the pressure of transmitting data back to a center cloud can be greatly relieved. The node-type edge computing module can also provide an elastic virtualization capability, such as calculation, networking and storage, based on an edge location, and can truly realize "cloud-edge collaboration".