1555 results about "Resource pool" patented technology

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

The invention discloses a resource allocation method and system for 5G communication in an underground coal mine, and relates to the technical field of communication information processing, and the method comprises the steps: obtaining data information of an underground coal mine roadway, building a digital twin communication model through combining a synchronous positioning and map building algorithm and a wireless fingerprint matching algorithm, generating a channel profile map according to real-time data, and transmitting the channel profile map to a server; extracting channel data of each space node; constructing a computing resource pool for the 5G base station and the edge computing node, and packaging different service types into a plurality of network slice containers; according to the channel data, carrying out initial allocation on each container through a resource composer, and carrying out dynamic task migration among different MEC nodes to obtain a resource allocation state; dividing scheduling time according to a channel profile and a resource allocation state, and dynamically adjusting a duration proportion and bandwidth occupation of each window according to a task migration state; according to the invention, through digital twinning and dynamic scheduling, the problems of 5G communication coverage and interference in the underground coal mine are solved.

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 belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention discloses a heterogeneous computing power resource dynamic cooperative scheduling method and system and a computer device. The method comprises the steps of obtaining multi-dimensional feature information of a to-be-scheduled task, current state information of each node in a heterogeneous computing power resource pool and scheduling environment state information; training a time sequence model by adopting historical associated data, inputting the multi-dimensional feature information, the current state information and the scheduling environment state information into the trained model to obtain associated trend information in a future preset time window, and then combining the multi-dimensional feature information and the current state information of all the nodes to obtain the scheduling environment state information. A multi-objective optimization function fusing task delay, cost consumption and energy consumption is constructed, the function is optimized based on a preset strategy, a target decision strategy is obtained, a scheduling instruction set is generated after analysis, allocation information and target nodes are determined according to the scheduling instruction set, and tasks are issued. According to the method, dynamic collaborative scheduling of heterogeneous computing power resources can be realized, and the overall utilization efficiency of the resources is accurately, efficiently and effectively improved.

The invention relates to the field of artificial intelligence, in particular to an intelligent standard knowledge retrieval system based on an AI large model, which comprises a standard knowledge management database, a multi-source acquisition preprocessing module, a semantic understanding and indexing module, an intelligent retrieval engine module, a knowledge enhancement module, a user interaction feedback module, a security control module and a deployment expansion module. The standard knowledge management database is used for storing standard knowledge design data, real-time retrieval data and feedback data and constructing a dynamically updated standard knowledge resource pool; through the multi-source standard knowledge acquisition and preprocessing module, multi-channel objective standard data can be integrated and dynamically updated, and the problem of knowledge fragmentation is solved; and through the semantic understanding and indexing module, industry professional semantic accurate matching is realized, the limitation of traditional keyword retrieval is broken through, and the accuracy and efficiency of standard knowledge retrieval are remarkably improved.

The present application relates to the technical field of large-scale data processing, and particularly relates to a parallel computing method and system suitable for large-scale data processing. The system comprises a task management unit, a distributed load balancing module, an elastic expansion architecture, an intelligent communication optimization module, and a resource monitoring unit, wherein the task management unit divides large-scale data into a plurality of sub-tasks by means of a task decomposer, and distributes the sub-tasks to computing nodes by means of a task scheduler and a priority distributor; the distributed load balancing module achieves global load balancing by means of a load sensing unit, a dynamic adjustment unit and a balance optimization unit; the elastic expansion architecture dynamically adjusts system resources by means of a node manager, a resource pool controller and an expansion decision-making device; the intelligent communication optimization module optimizes inter-node communication by means of a communication path planning unit, a bandwidth distribution unit and a delay compensation unit; and the resource monitoring unit monitors the system performance in real time by means of a performance collector, a state analyzer and an anomaly detector.

The invention provides a dynamic wavelength allocation method based on DCI wavelength division II type equipment. The method comprises the following steps: acquiring an occupation state and a transmission quality index of each wavelength channel of a current network; establishing a wavelength channel quality evaluation model, and scoring each wavelength channel according to the transmission quality index of each wavelength channel of the network; according to the bandwidth requirement, the priority and the QoS requirement of the service request, generating a traffic characteristic matrix containing the delay sensitivity and the bandwidth requirement through a traffic prediction algorithm; based on a network state and the traffic characteristic matrix, through resource availability calculation and path quality evaluation, forming an allocable wavelength resource pool and a quality score thereof; for the allocatable wavelength resource pool, generating an optimal wavelength allocation scheme by adopting an improved heuristic algorithm and comprehensively considering the resource utilization rate, the signal quality and the service priority; and according to the performance feedback of the optimal wavelength allocation result and the network state change, continuous optimization of wavelength allocation is realized through a self-adaptive adjustment mechanism. The method can effectively deal with the dynamic flow change in the DCI network, improve the resource utilization rate, ensure the signal quality and meet the QoS requirement of the service, thereby providing an efficient and reliable wavelength allocation solution for the data center interconnection network.

The invention discloses an air-sea cross-domain network slice configuration and response method based on a CTC8180 chip, and belongs to the technical field of air-sea cross-domain communication and network slicing, and the method comprises the steps: S1, configuring and parallelly accessing NETCONF and CLI paths, and converting a configuration instruction into a CTC8180 chip call instruction; s2, creating a resource pool, a logic port, a binding interface and a VRF strategy based on a FlexE technology; s3, finishing forwarding and mapping configuration of a slicing strategy by combining SRv6 path programming, service level mapping and gport identification slice identification; s4, performance monitoring, data acquisition and fault detection are carried out through three protocols; s5, closed-loop control is formed through resource dynamic adjustment, path switching, strategy linkage and the like; and S6, multi-chip compatibility and stable adaptive deployment are realized through the steps of integrating S1 to S5. The invention provides a specific implementation method for constructing logic network slicing capability on high-performance forwarding hardware for air-sea cross-domain specific requirements, and provides reliable communication guarantee for high-dynamic heterogeneous services.

The invention discloses a method and device for determining a resource allocation strategy and electronic equipment. The method comprises the steps that service information of a calculation task is acquired, and the service information comprises a service type and a resource demand of the calculation task; determining a target historical task matched with the calculation task from a plurality of historical tasks matched with the service type; an initial resource allocation strategy of the calculation task is determined according to a historical resource allocation strategy corresponding to the target historical task, and the historical resource allocation strategy is at least used for indicating the priority of resource allocation; a target resource pool meeting the resource requirement is determined from a plurality of sub-resource pools, the initial resource allocation strategy is adjusted according to the target resource pool, a target resource allocation strategy is obtained, and the sub-resource pools are logic division units of computing resources. According to the method and the device, the technical problem that the system performance is influenced due to non-uniform resource allocation during reasoning task processing because a static or semi-static resource allocation strategy is mostly adopted in related technologies is solved.

The invention relates to the technical field of cloud edge collaborative computing power scheduling, and discloses a heterogeneous computing power scheduling optimization method based on a cloud edge collaborative architecture. The method comprises the following steps: acquiring real-time computing power state data of all available computing nodes in the cloud edge collaborative architecture; performing heterogeneous type division on the computing nodes according to the real-time computing power state data to generate a three-layer computing power resource pool containing cloud computing nodes, edge computing nodes and terminal computing nodes; extracting task calculation features for the current to-be-scheduled task set, wherein the features comprise calculation intensity, data dependence and real-time requirements; constructing an initial task allocation scheme based on the matching relationship between the task calculation features and the three-layer computing power resource pool; iteratively optimizing the initial scheme by adopting a dynamic load balancing strategy to generate a final task scheduling instruction; the instructions are distributed to the corresponding computing nodes to be executed, and computing power state changes in the task execution process are continuously monitored.

The invention discloses a deployment method and device of a large model agent, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: determining agent configuration information of a to-be-deployed large model agent after an agent deployment request is received, and determining a target large model and model metadata; determining an available resource condition of a preset computing resource pool, and performing query matching according to the available resource condition and the model metadata to determine a plurality of hardware-compatible candidate computing resource nodes; performing load prediction operation on the to-be-deployed large model agent after the large model agent is online to obtain a predicted load, and generating a target deployment decision scheme based on the predicted load, a preset scheduling strategy, the available resource condition and the candidate computing resource node; and adjusting the runtime parameters of the target large model according to the resource characteristics of the target computing resource node, and loading the adjusted target large model on the target computing resource node so as to deploy and start the agent instance of the to-be-deployed large model agent.

The invention provides a heterogeneous computing power resource pooling scheduling platform which comprises a task and resource tensor construction module, a scheduling decision module, a breakpoint state generation module and a rescheduling and recovery module. According to the platform, task requirements and node resources are modeled into a tensor structure in a unified mode, and accurate initial scheduling is achieved through a structured scoring function containing conditional penalty terms. In the task running process, interruption judgment is carried out based on the node dynamic state and the scheduling score, and a breakpoint state tensor containing an execution state is generated. When interruption occurs, a proper node is selected through compatibility screening, and task seamless migration and execution recovery are realized by utilizing the stored state. According to the method, the technical problems of non-uniform resource expression, lack of scene adaptability and incapability of realizing uninterrupted migration in a heterogeneous computing power environment are solved, and the resource utilization rate and the service continuity are remarkably improved.

The invention relates to a memory management method and device applied to a server-free data center, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: in response to a virtual memory application instruction, performing virtual memory allocation in a local computing resource pool to obtain a virtual address space; in response to the virtual address access instruction, acquiring cache information of the computing resource pool, and triggering missing page interruption under the condition that data corresponding to the virtual address is not hit in the cache information; the cache information comprises locally cached cache data and page table information; acquiring physical memory resources in a memory resource pool according to the virtual address and the page table information under the condition of triggering missing page interruption of the anonymous page; and updating the page table information and the cache information according to the physical memory resources. By adopting the method, the processing delay of the data center can be reduced while the decoupled memory management logic is used for improving the resource utilization rate.

The invention provides a heterogeneous computing resource scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-scheduled computing task, and extracting operator features of the to-be-scheduled computing task; splitting the to-be-scheduled calculation task into a first sub-task suitable for being executed by a graphics processor and a second sub-task suitable for being executed by a quantum calculation unit according to the operator characteristics; a first resource group is matched for the first subtask in a preset resource group set, a second resource group is matched for the second subtask, and the preset resource group set comprises the first resource group and the second resource group which are obtained by dividing a heterogeneous computing resource pool; the first resource group comprises at least one graphics processor computing unit, and the second resource group comprises at least one quantum computing unit; and scheduling the first sub-task in the first resource group, and scheduling the second sub-task in the second resource group.

The invention discloses a civil aviation airport ground guarantee resource scheduling method and device and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a real-time flight plan, and constructing a guarantee resource coupling network based on the real-time flight plan and a predefined guarantee flow rule base; performing conflict prediction and conflict influence analysis on the guarantee work of the future flight by traversing the guarantee resource coupling network, and generating a structured conflict early warning report; triggering an on-demand scheduling mechanism of a virtual resource pool for querying and allocating available resources on demand according to the structured conflict early warning report, querying available shared guarantee resources in the virtual resource pool, and generating a conflict resolution scheme which can be directly executed; and executing the conflict resolution scheme, monitoring an execution result through the Internet of Things data, feeding back the execution result to the guarantee resource coupling network, and calibrating a duration parameter in the guarantee resource coupling network. Through the constructed support resource coupling network, the prospective performance and the cooperative robustness of ground support scheduling are improved.

A user equipment (UE) is described. The UE may comprise reception circuitry configured to receive, a higher layer message including a configuration of sidelink positioning reference signal (SL-PRS) for a sidelink positioning and configured to receive the SL-PRS from a second CE based on the configuration of the SL-PRS; wherein the configuration of SL-PRS is a configuration per resource pool for SL-PRS transmission.

The invention discloses an open architecture system for an underwater acoustic signal processor, and belongs to the technical field of underwater acoustic signal processors. The system comprises a hardware resource pool, a standardized algorithm container group, a visual processing link construction module, a containerized deployment and scheduling platform and a data processing and communication plane. According to the system, special hardware resources are pooled, an underwater sound processing algorithm is packaged into a standardized container, a user is allowed to arrange a processing flow in a graphical mode, an intelligent scheduling platform dynamically deploys the algorithm container to matched hardware for execution according to real-time resource conditions, and meanwhile, an efficient and reliable data communication channel is provided. According to the method, the problems that a traditional system is closed, poor in expansibility and low in resource utilization rate are solved, elastic sharing of hardware resources, agile deployment of an algorithm and flexible construction of a flow are achieved, and openness, flexibility and overall efficiency of an underwater acoustic signal processing system are remarkably improved.

The invention provides a containerized resource elastic scheduling method and system in a cloud computing environment, and relates to the technical field of containerized resource elastic scheduling, and the method comprises the steps: obtaining various resource contention events, and generating data which records a resource contention relationship between tenants in detail; identifying a preset high-frequency conflict mode by deeply analyzing the data, and determining a specific tenant group with a strong resource mutual exclusion effect; dynamically estimating a probability level of resource conflict between the new container and the specific tenant group in combination with historical contention information and a real-time resource load state; generating a set of dynamic isolation scheduling strategies according to the conflict risk indicated by the probability level; an independent conflict buffer resource pool is configured and adjusted for a specified key tenant, a tenant resource conflict mode can be identified, dynamic isolation scheduling and buffer resource configuration can be implemented, and the resource utilization efficiency and the system stability of a multi-tenant container in a cloud computing environment are improved.

A method and an apparatus for performing communication in a wireless communication system are disclosed. A method for performing communication by a first terminal according to an embodiment of the present disclosure may comprise the steps of: receiving, from a base station, configuration information related to a plurality of sidelink (SL) resource pools and a plurality of resource block (RB) sets; detecting and triggering a consistent SL listen before talk (LBT) failure for at least one of a first SL resource pool and a first RB set among the plurality of SL resource pools and the plurality of RB sets; and triggering, on the basis that the triggered consistent SL LBT failure is not canceled, SL resource reselection for selecting a second SL resource pool or a second RB set from among the plurality of resource pools and the plurality of RB sets.

The invention relates to a cache data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method is applied to a distributed storage system adopting a three-layer architecture design, and the three-layer architecture comprises a service application layer, a data processing layer and a persistence layer. Comprising the following steps: acquiring access request monitoring information through a service application layer to obtain an access feature data set, and performing pattern classification identification on the access feature data set to obtain a predicted access pattern; generating a prefetching task list and prefetching data according to the predicted access mode, and storing the prefetching data to a data processing layer for caching processing; and executing each prefetching task according to the sequence of the prefetching task list, and reading the prefetching data from the mechanical hard disk and writing the prefetching data into a cache space in a solid state disk resource pool under the condition that the prefetching data does not exist in the solid state disk resource pool of the persistence layer. By adopting the method, the cache performance can be improved.

Wireless communications systems and methods are provided for validating a physical uplink shared channel (PUSCH) occasion defined for 2-step random access channel (RACH) in case of collisions. For example, the user equipment (UE) may determine whether a collision occurs between the PUSCH occasion for the 2-step RACH and any RACH occasion assigned for either 2-step RACH or 4-step RACH after receiving the RACH configuration or updates from the network. The UE may then exclude the PUSCH occasion from the resource pool for 2-step RACH when a collision occurs.

A method for processing faulty memory module and apparatus, and an electronic device and a non-transitory readable storage medium are provided. The method for processing faulty memory module includes: acquiring a first power-down command, the first power-down command being used for instructing a baseboard management controller of a memory resource pool to perform power-down processing on a faulty memory module in the memory resource pool; and in response to the first power-down command, when a first memory module in the memory resource pool is in a fault state and is allowed to be powered down, sending a second power-down command to a target memory expander controller to which the first memory module belongs, the second power-down command being used for instructing the target memory expander controller to perform power-down processing on the first memory module.

The invention relates to a heterogeneous AI computing virtualization architecture and slice enhancement method and system, and the method comprises the steps: evaluating the demands of an AI task for GPU resources through an analytic hierarchy process, and dividing the GPU resources into ultrafine-grained slice units according to an optimization model; predicting a resource demand through an ARIMA model, allocating slice resources through a minimum error sum of squares algorithm, and generating a resource allocation scheme; the method comprises the following steps: dividing a GPU into a plurality of independent hardware isolation units based on a resource allocation scheme and GPU hardware topology, and generating a hardware unit configuration table; establishing an isolation mechanism based on the identity information and the ACL, and establishing a software isolation environment; the method comprises the following steps: dividing network partitions, configuring a firewall rule to control network access authority, monitoring network traffic in real time, processing abnormal traffic, and generating a network partition mapping table; gPU resources are abstracted into a logic resource pool, a resource scheduling platform based on an intelligent scheduling algorithm is constructed, and resource scheduling of heterogeneous slices is carried out.

The invention belongs to the technical field of computing resource allocation, and discloses a task-driven computing resource dynamic allocation system, which is characterized in that task differentiation features are extracted through a task feature intelligent analysis module, and global optimal allocation, 'task fingerprint 'matching and opportunity cost evaluation mechanisms are realized in combination with a reinforcement learning scheduling decision module. Mismatching conditions such as allocation of lightweight tasks to a high-video-memory GPU are avoided, meanwhile, the elastic resource pool dynamically adjusts the capacity, reduces resource locking during task pause, remarkably reduces resource waste, improves the overall utilization rate of computing resources such as the GPU, can quickly expand the capacity through the elastic resource pool to cope with burst tasks, and a cross-node resource cooperation module constructs a global view and improves the overall utilization rate of the computing resources. Efficient calling of idle resources is realized; a'logic super node 'and zero-aware migration technology breaks node barriers, avoids global redundancy caused by local optimum, enables resources to flexibly flow along with task loads, and improves the adaptive capacity of a system to dynamic tasks.

The invention discloses a workshop dynamic scheduling method considering equipment occupation under emergency order insertion, which comprises the following steps: receiving a trigger event in a production system, and obtaining workshop production real-time state data according to the trigger event; according to the real-time state data, a dynamic scheduling decision engine is activated, and a global scheduling problem is decomposed into deterministic sub-problems at the current decision moment; according to the deterministic sub-problem, identifying the state of a work-in-process and generating an unfinished process set, and re-bringing the unfinished process set into a rearrangement resource pool; selecting a rearrangement strategy based on a preset influence threshold according to the unfinished process set; according to the rearrangement strategy, a planning model is constructed, and the planning model aims at minimizing the maximum completion time and minimizing the order insertion change degree; according to the planning model, an improved genetic simulated annealing algorithm is adopted for solving, and a dynamic scheduling scheme is output and issued to a production execution layer. According to the invention, the disturbance of order insertion on the original production system can be minimized.

The invention discloses an unmanned aerial vehicle real-time task scheduling and resource management system, which belongs to the technical field of unmanned aerial vehicle computing resource management, and comprises a task load prediction module, a priority dynamic evaluation module, a resource elastic distribution module and a closed-loop feedback optimization module, the task load prediction module adopts an LSTM-GRU hybrid neural network to predict a resource demand peak value in advance and triggers a pre-scheduling signal; the priority dynamic evaluation module dynamically adjusts the task priority based on the multi-dimensional evaluation matrix; the resource elastic allocation module realizes elastic allocation through a reserved resource pool and a multi-level preemption mechanism; the closed-loop feedback optimization module monitors performance deviation and reversely adjusts parameters of each module, the four modules are deeply coupled to form a closed-loop cooperation mechanism, and intelligent task scheduling and dynamic resource optimization are achieved.

This application provides a communication method and a related apparatus, to avoid a sending conflict between a plurality of terminals and implement beam training. The method includes: A first terminal sends N sidelink synchronization signal blocks S-SSBs to a second terminal on N first time-frequency resources, where the N first time-frequency resources are located within a resource pool, and N is an integer greater than 1. The second terminal sends feedback information from the second terminal to the first terminal on one or more of M second time-frequency resources, where the feedback information indicates measurement results of one or more S-SSBs in the N S-SSBs, the N second time-frequency resources are located in the resource pool, M is an integer greater than 1, and M is less than or equal to N.

A train operation intelligent dispatching system and method are provided, where the system includes: a data acquisition and control module, configured to collect multi-source data affecting train operation; a data processing module, configured to perform data aggregation on the multi-source data, and process the multi-source data to form multi-source data in a unified data format; a multi-disciplinary data resource pool, configured to perform real-time storage and updating of the multi-source data in the unified data format, and store historical multi-source data; an intelligent fault analysis module, having intelligent fault analysis models therein, configured to perform fault analysis based on the multi-source data to obtain fault analysis results of corresponding disciplinary systems, and output fault advance warnings of corresponding disciplinary systems according to the fault analysis results; and a process preplan module, configured to provide preplan handling processes corresponding one-to-one with external multi-disciplinary systems.

The invention provides an automatic report generation method and system based on multi-source data integration and a medium, and relates to the technical field of data processing, cross-service-end redundancy check integration of a plurality of standardized data streams of a plurality of source service ends is received through a data integration cloud end, a uniform resource pool is output, and the report generation efficiency is improved. Dynamically extracting matched data in the uniform resource pool according to a real-time association label combination output by a user, and generating a temporary data set; and converting the temporary data set into a permission filtering report by taking the consulting permission of the user as a field filtering strategy. The technical problems that in the prior art, the mixed data storage precision is low, data redundancy is caused by lack of a conflict resolution mechanism, and then cross-system data calling response is delayed and the report error rate is high in subsequent practical application are solved. The technical effects of improving the data storage precision of multi-source heterogeneous data mixed storage, and improving the data calling speed and report output accuracy are achieved.