2983 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.

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.

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.

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 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.

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 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 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 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.

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors analyze user prompts using one or more natural language understanding techniques. One or more processors then enrich the user prompts by integrating contextual data from user interaction history and adapt the enriched user prompts to align with characteristics of Large Language Models (LLMs) and Application Programming Interfaces (API) requirements of the LLMs.

In some embodiments, a computer system receives data representing a pose of at least a first portion of a user and causes presentation of an avatar that includes a respective avatar feature corresponding to the first portion of the user and presented having a variable display characteristic that is indicative of a certainty of the pose of the first portion of the user. In some embodiments, a computer system receives data indicating current activity of one or more users is activity of a first type and, in response, updates a representation of a user having a first appearance based on a first appearance template. The system receives second data indicating current activity of the one or more users and, in response, updates the appearance of the representation of the first user based on the current activity of the one or more users using the first or a second appearance template.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

The invention discloses an equipment access control method and system based on a security sandbox, which adopts an advanced virtualization technology, constructs a highly isolated operation environment, combines technologies such as deep packet inspection, system call interception and process operation logic analysis, comprehensively monitors access behaviors of processes in the sandbox to virtual equipment, and improves the access performance of the virtual equipment. And the security and credibility of the equipment are evaluated, and illegal processes are prevented from running on the access terminal, so that the security access control of the equipment is realized. The monitoring comprises data packet content, protocol filtering, threat detection, system call interception, process creation, execution and termination behavior monitoring and the like. In combination with a trust evaluation algorithm, behavior data features are collected and extracted, trust scoring is performed by applying a machine learning model, and the equipment trust level and the access strategy are dynamically adjusted according to the score. According to the invention, the access control strategy is managed in a unified manner and deployed quickly based on the security sandbox management program and the authentication server. After identity authentication of the equipment, real-time monitoring of process dynamic behaviors in the running process of the equipment is added, and the safety of the equipment in the data exchange process is further improved.

The invention discloses an artificial intelligence assisted makeup and makeup robot collaborative makeup method, and belongs to the technical field of artificial intelligence and robots. The method comprises the following steps: collecting user face data through a multi-modal sensor, extracting multi-scale features based on improved DenseNet and HRNet networks, and generating a three-dimensional face model; a high-fidelity virtual makeup effect is generated by using a conditional generative adversarial network in combination with user requirements, and real-time comparison and display are performed through an augmented reality interface; the makeup difference is quantified through multi-dimensional feature point mapping, multi-color space analysis and texture comparison, and correction suggestions are dynamically generated; operation is executed through the seven-degree-of-freedom redundant mechanical arm, a hybrid force control strategy and a visual monitoring system are integrated, and safe and accurate execution is ensured. The method solves the problems of uncontrollable traditional makeup effect, low execution precision and lack of dynamic feedback, has the characteristics of high adaptability, real-time response and zero-damage safety, and can be widely applied to the fields of family beauty, film and television makeup and special crowd nursing.

Methods and apparatus for application aware memory patrol scrubbing techniques. The method may be performed on a computing system including one or more memory devices and running multiple applications with associated processes. The computer system may be implemented in a multi-tenant environment, where virtual instances of physical resources provided by the system are allocated to separate tenants, such as through virtualization schemes employing virtual machines or containers. Quality of Service (QoS) scrubbing logic and novel interfaces are provided to enable memory scrubbing QoS policies to be applied at the tenant, application, and / or process level. This QoS policies may include memory ranges for which specific policies are applied, as well as bandwidth allocations for performing scrubbing operations. A pattern generator is also provided for generating scrubbing patterns based on observed or predicted memory access patterns and / or predefined patterns.

The invention relates to the technical field of virtualization management, in particular to a virtual machine operation management system based on RISC-V. The system comprises an instruction set mapping module, a resource scheduling optimization module, a virtual machine monitoring integration module, an operation tuning update module and an operation configuration distribution module. According to the method, by analyzing the operation instruction of the virtual machine and combining the RISC-V underlying characteristics, accurate instruction mapping is achieved, the processing efficiency and accuracy are remarkably improved, the performance loss and conversion delay caused by too high abstraction level of a traditional virtual machine on the instruction level are overcome, the optimal allocation scheme is screened, the resource utilization rate and the system response speed are effectively improved, and the system performance is improved. The operation state and performance indexes of the virtual machine are integrated, anomaly detection and resource recovery are embedded, system stability and effective resource reutilization are ensured, basic configuration is continuously and incrementally updated, operation configuration refinement and automatic management and updating are achieved, the overall operation efficiency and management flexibility are improved, and the manual intervention cost is reduced.

The invention discloses a GPU computing power resource scheduling method and device based on load awareness and a medium, and relates to the technical field of computing power scheduling. The method comprises the steps that according to the video memory capacity and the number of calculation cores of the GPU, the video memory capacity is divided into a plurality of continuous fragments, the calculation cores are divided into a plurality of logic calculation groups, and sub-resource units are obtained; recording occupation states and load indexes of the sub-resource units in real time to obtain a resource pool; analyzing a job submitted by a user, and collecting an execution period of a kernel function, a video memory access mode and an instruction pipeline blocking rate when the job runs; and on the basis of a reinforcement learning algorithm, predicting a resource demand inflection point of the job according to the load fingerprint model, performing hierarchical scheduling on the sub-resource units based on virtualization isolation and multi-dimensional resource quantitative evaluation, and generating a preemptive allocation strategy of the sub-resource units. According to the method, efficient utilization and secure sharing of GPU resources are realized through collaborative design of dynamic granularity segmentation, load aware scheduling and hardware-level security isolation.

The invention discloses a virtual power plant optimization system and method, and the system comprises a virtualization platform module which is used for integrating distributed energy, energy storage equipment, an electric vehicle, a flexible load and other resources, and achieves the virtualization and centralized management of the resources; the dynamic resource aggregation module is used for dynamically matching resource characteristics with power system requirements; the market transaction module is used for optimizing the earnings of the virtual power plant; and the core algorithm module is used for optimizing the load prediction precision. The market transaction module implements two-stage market bidding based on a multi-agent game model, analyzes the uncertainty of quotation of competitors through a multi-scene simulation technology, optimizes a dynamic transaction strategy, and achieves the maximization of spot market benefits and the controllability of risks. Through cooperative work of a virtualization platform module, a dynamic resource aggregation module, a market transaction module, a core algorithm module, a security management module and the like, efficient management of resources and maximization of market benefits are realized.

The invention relates to a GPU resource space-time slicing and dynamic reconstruction virtualization method and system based on AI training, and the method comprises the steps: receiving an AI training task, obtaining the task feature data of the AI training task, and transmitting the task feature data to a task prediction model; the task prediction model outputs a training demand portrait of the current AI training task based on the task feature data, and the training demand portrait comprises an estimated computing power demand, a peak video memory demand, an expected running time length and a task priority; based on the training demand portrait, a preset AI scheduler generates a vGPU slicing strategy corresponding to the AI training task according to the current GPU resource distribution condition; operation information of each vGPU is obtained in real time and updated to the GPU resource pool, and the computing power or the video memory size of the vGPU in operation is adjusted in real time according to the training demand portrait. The method has the advantages that the dynamic allocation capacity of the GPU load resources is improved, and therefore the task training efficiency of the GPU is improved.

The invention discloses an unmanned aerial vehicle tunnel inspection method based on three-dimensional modeling, and the method comprises the steps: obtaining multi-modal data through a mobile collection device, constructing a high-precision three-dimensional model, extracting spatial attitude information, analyzing a deformation trend, recognizing structural defects through combining deep learning, and fusing multi-source data to generate a dynamic risk thermodynamic diagram. And finally, constructing a digital twinborn model to realize virtualized inspection. According to the routing inspection method, comprehensive perception, accurate analysis and visual display of the tunnel structure state are achieved, the intelligent level of tunnel health monitoring and the accuracy of risk assessment are effectively improved, a reliable basis is provided for tunnel operation and maintenance decisions, and the routing inspection method is of great significance in guaranteeing tunnel structure safety and prolonging the service life.

Described herein are techniques for implementing a virtualized physical controller. The techniques may include receiving user input(s) on a touchscreen display of a user device, including a first touch from which can define a primary input region and one or more secondary touches offset from the first which can define or more secondary input regions arranged adjacent to the primary input region. The input regions are associated with respective input mechanisms or commands in the configuration data. While the configuration data is implemented, the touchscreen display can monitor for touches, detect touch input in the configured regions, and generate activation data for each input mechanism or command in response to touch. The positions and shapes of the input regions may be iteratively redefined as touch input is monitored on the touchscreen display.

The invention discloses a cloud server physical GPU acceleration capability release method and system, and belongs to the technical field of computer GPU capability release, and the method specifically comprises the steps: collecting GPU operation parameters in real time, carrying out the preprocessing, storing the GPU operation parameters in a shared state database, constructing a lightweight virtualization abstraction layer on a physical GPU, dividing the physical GPU into a plurality of logic units, and storing the logic units in a shared state database; the method comprises the following steps: establishing a physical GPU, constructing an isolation layer, utilizing a three-dimensional resource segmentation strategy, adopting a priority queue, a jump mechanism and a delay tolerance mechanism in a scheduling process, dynamically allocating logic unit resources of the physical GPU, accelerating release of GPU resources, and realizing unification and real-time updating of physical GPU state information among nodes of a cloud server through a distributed state synchronization mechanism; through dynamic task scheduling, a jumping mechanism, a delay tolerance mechanism and distributed state synchronization, efficient GPU resource management is realized, and GPU capability release is accelerated.

The invention discloses a reservoir management optimization method and system based on digital twinning. The method comprises the steps of multi-source data acquisition and virtualization mapping, digital twinning modeling, reservoir risk early warning and reservoir management decision optimization. The invention relates to the technical field of reservoir intelligent management, in particular to a reservoir management optimization method and system based on digital twinning, and the method comprises the steps: constructing a virtualization data field through multi-Bezi wavelets and fractal interpolation, and carrying out the self-learning of dynamic weight fusion through a time sequence convolution network; constructing a macroscopic twinborn body and a microcosmic twinborn body, and coupling through an asynchronous message bus; constructing multi-source risk vectors of water pressure gradient, over-overflow water level, dam body stress and seepage flow, adopting clustering and spatial diffusion, generating a dynamic risk thermodynamic diagram, and performing early warning; by taking flood control safety, water supply guarantee and ecological environment as multiple targets, a multi-target particle swarm optimization model and flow constraints are established, an optimal scheduling scheme is solved and executed in real time, and high-precision simulation, precise early warning and intelligent scheduling are realized.

The invention discloses a virtual machine computing resource dynamic allocation method and system based on a PCIe card and a storage medium, and belongs to the field of virtualization technology and hardware resource management.The method comprises the steps that the computing unit resource utilization rate of the PCIe card is monitored in real time through a hardware abstraction layer; dynamically generating a resource allocation scheme based on an improved auction algorithm, wherein the algorithm optimizes a resource quota through a time attenuation factor and a resource reuse penalty term; utilizing a programmable register remapping technology to distribute independent VF equipment for the virtual machine and establish a direct access channel; realizing interruption isolation between virtual functions through an interruption redirection controller; and during resource redistribution, atomic migration of a calculation task is completed by adopting a check point migration mechanism, and the atomic migration comprises context preloading and PCIe link layer retraining. According to the method, the problems of hardware resource distribution rigidness, insufficient isolation and migration service interruption in the prior art are solved, and fine-grained control and service lossless switching of the heterogeneous computing units are realized.