1028 results about "Resource information" patented technology

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention relates to a TSN network configuration automatic generation method and system based on an anti-Agent. The system is composed of a network state perception and intention reflection module, a YANG model configuration parameter and structure decision module and a YANG model intelligent generation module. The network state sensing and intention reflecting module is responsible for structurally sensing the network state and resource information, the YANG model configuration parameter and structure decision module fuses multi-source knowledge and context information, intelligently generates deployable TSN scheduling parameters and a YANG configuration structure for service requirements, and sends the TSN scheduling parameters and the YANG configuration structure to the network state sensing and intention reflecting module. And the YANG model intelligent generation module automatically generates a corresponding YANG configuration model according to an optimized result, and issues the YANG configuration model to target equipment after completing grammar and semantic verification, so that automatic deployment of network configuration is realized. According to the method, the intellectualization level of TSN network configuration and the deployment automation capability are remarkably improved by introducing the reflection reasoning and feedback optimization technology.

The invention relates to the technical field of tourism resource information processing, in particular to a tourism hotel dynamic scoring and recommending method and system based on multi-factor fusion, and the method comprises the steps: constructing a dynamic game portrait containing a user space-time behavior mark and hotel multi-modal attributes, and carrying out the dynamic scoring and recommending of a hotel based on the dynamic game portrait; the method comprises the following steps: analyzing a recessive preference vector of a user and an adaptive strategy vector of a hotel by simulating an interactive game process of the user and hotel attributes, performing dynamic equalization matching on the recessive preference vector and the adaptive strategy vector to generate a hotel situation score, generating an anti-consensus recommendation sequence according to the hotel situation score, and performing recommendation on the hotel situation according to the anti-consensus recommendation sequence. The anti-consensus recommendation sequence is subjected to interpretable packaging, and a final recommendation result with a decision guide clue is output to the user, so that the user can obtain a personalized hotel recommendation list and can also understand logic and diversity values behind recommendation, and the user experience is improved. Therefore, the transparency, the user trust degree and the exploration satisfaction degree of the recommendation system are improved.

The invention is applicable to the technical field of cloud computing resource management and scheduling, and provides a resource scheduling method, device, equipment and medium based on instance specification awareness, and the method comprises the following steps: collecting multi-dimensional resource information and task load information of a plurality of computing nodes in a cloud computing platform in real time, and according to the multi-dimensional resource information and the task load information, scheduling the computing nodes according to the multi-dimensional resource information and the task load information; a target scheduling action meeting the instance specification requirement at the current moment is obtained through a pre-trained multi-dimensional resource specification perception scheduling model, the multi-dimensional resource specification perception scheduling model is obtained through population-based reinforcement learning training mechanism training, and task resource allocation is executed according to the target scheduling action. And the task execution result is monitored, and the multi-dimensional resource specification perception scheduling model is adaptively updated based on the feedback information obtained by monitoring, so that the cloud environment resource utilization efficiency, the task matching rate and the dynamic environment response speed are improved.

The invention relates to the field of artificial intelligence, in particular to a heterogeneous GPU-oriented large model reasoning platform adaptation method and system. The method comprises the following steps: continuously and dynamically collecting and maintaining all available GPU resources in a server cluster and attribute and state information of a deployed reasoning acceleration framework instance through proxy service deployed in each server node; providing a single entry point to receive a reasoning service request, and analyzing the request to determine a large model needing reasoning; according to the analyzed large model needing reasoning and the currently aggregated resource information, selecting a reasoning acceleration framework instance which is optimal in load state and compatible for the request through a decision algorithm, and routing the task to the reasoning acceleration framework instance; heterogeneous response formats returned by different inference acceleration framework instances are converted and packaged into a unified standardized format, and then the unified standardized format is returned to a requester. Scattered and diversified computing resources are effectively utilized, and dependence on a single hardware manufacturer is reduced.

The invention provides an electric power computing network multi-dimensional resource scheduling method, device and platform and a storage medium, and belongs to the technical field of electric power computing power networks, and the method comprises the steps: obtaining the request information of a current service terminal and the computing power resource information of a cloud side computing node; determining an end-to-end time delay constraint, a cloud edge computing power resource constraint and a cloud edge end network bandwidth constraint of the intelligent server; according to the cloud edge computing power unit price and the network bandwidth unit price, determining an objective function of computing network multi-dimensional resource scheduling optimization by taking minimization of the network bandwidth and the cloud edge computing power overhead as an optimization objective; solving the plurality of scheduling constraint models to obtain an optimal solution; and outputting a computing network multi-dimensional resource scheduling strategy according to the optimal solution. According to the method, under the condition that different computing network resource service quality requirements of multiple services are considered, by dynamically deploying multi-dimensional resources such as network bandwidth, cloud computing power and edge computing power, the low response delay requirements of key services such as environmental risks and equipment faults in power grid operation are effectively met, and meanwhile it is ensured that the computing network resource consumption economical efficiency is optimal.

The invention relates to the technical field of campus resource management, and discloses a campus resource collaborative management method and system based on big data. The method comprises the following steps: acquiring basic use information of campus resources, and generating a preliminary resource allocation strategy in combination with resource attribute details; monitoring resource real-time state information, evaluating whether a preliminary strategy needs to be modified or not, collecting detailed resource use records of each monitoring partition after the modification is confirmed, obtaining resource distribution characteristic information through characteristic analysis, and further calculating a resource distribution characteristic index of each monitoring partition; matching the indexes with the previous records one by one, correcting the preliminary strategy according to a matching conclusion to obtain an optimization strategy, if the indexes are not completely consistent with the previous records, calculating the approximation degree of the indexes and the previous records, and correcting the preliminary strategy according to the approximation degree. Resource information is integrated through a big data technology, real-time monitoring and historical data matching are performed, dynamic optimization of resource allocation is realized, the management requirements of various resources in a campus are adapted, and the accuracy and adaptability of resource collaborative management are improved.

The invention relates to a k8s-based fine-grained monitoring method and system for a shared GPU container, and belongs to the technical field of computers.The method comprises the steps that configuration information of a monitoring module is set and transmitted into a service container in an environment variable form; the method comprises the following steps: deploying a monitoring module on each k8s cluster machine to obtain GPU process information; acquiring local process information of a machine by using a monitoring module to obtain a system-level index; obtaining a k8s container UID through a process identifier PID in the GPU process information, associating the k8s container UID to a k8s container name, calculating occupation information of a plurality of GPU containers, and obtaining a process-level index; and exposing the system-level index and the process-level index to a prometheus system alarm tool and carrying out verification on the system-level index and the process-level index. According to the method and the system provided by the invention, the granularity of GPU resource collection can be improved through two modes of obtaining the GPU resource information in the machine and obtaining the GPU information in the container.

The invention discloses an intelligent workflow generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining multi-modal demand information inputted by a user, carrying out the semantic understanding of the multi-modal demand information, and obtaining a semantic tag set and a workflow node link; according to the semantic tag set and the workflow node link, screening a basic template matched with the multi-modal demand information from a plurality of workflow generation templates, and expanding nodes in the basic template to obtain a target template; and acquiring hardware resource information of the user equipment, adjusting the node configuration information in the target template, and generating a target workflow file according to the adjusted target template. According to the technical scheme provided by the embodiment of the invention, the technical difficulty of a workflow generation scheme can be reduced, the generation efficiency of the workflow and the quality of a workflow generation result are improved, the problem that the template cannot normally operate due to the fact that the hardware of the user equipment is not supported or the plug-in is missing is avoided, and the workflow can smoothly fall to the ground.

The present invention relates to the technical field of artificial intelligence, and in particular to a deep-learning-based method and system for root cause localization in cloud platform alert scenarios. The method comprises the following steps: acquiring cloud platform alert information, alert resource information, resource log information, and alert processing historical information. The beneficial effects are as follows: in the deep-learning-based method and system for root cause localization in cloud platform alert scenarios provided in the present invention, on the basis of collected cloud platform alert information, resource information, log information, etc., different alert scenarios are formed by means of alert clustering; for each alert scenario, the range for log analysis is determined, an association with resources and an association with logs are deeply analyzed, a final fault cause is identified; and for different alert scenarios, descriptions of the fault causes, key alert information, etc., are provided. In this way, scenario-based management is performed on alert, and more accurate problem localization is provided, thereby effectively shortening the period of time for operation and maintenance personnel to locate problems, improving the efficiency, and better ensuring the normal operation of a cloud platform.

The invention discloses a user input information processing method and system for a model reasoning stage, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining a plurality of complete prompts inputted by a user; periodically acquiring hardware resource information and current task information; determining hardware resource allocation information corresponding to the complete prompt; pre-executing parallel computing operation on the complete prompt according to the hardware resource allocation information in the pre-filling stage; according to the hardware resource allocation information, calling the first type of computing equipment with the computing power reaching a preset computing power threshold value in the feed-forward network stage to read the intermediate characteristics from the preset resource pool for decoding and storage operation, wherein the computing power of the first type of computing equipment reaches the preset computing power threshold value; and calling the second type of computing equipment with the memory access performance meeting the preset memory access performance condition in the attention stage to read data from the preset resource pool to perform attention computing storage operation, obtaining a model reasoning result and outputting the model reasoning result, thereby solving the technical problem of hardware resource waste, and achieving the technical effects of improving the resource utilization rate and reasoning efficiency.

The invention relates to the technical field of computer software, and discloses a low-code-driven end-side cloud collaborative intelligent development and operation system and method, and the system comprises a low-code modeling module, an end-side cloud intelligent operator library, a resource sensing module, a monitoring module, an optimization module, an intelligent scheduling module and a deployment module. The low-code modeling module converts the operation information into a task description file; the intelligent scheduling module determines an initial execution position of each target operator in the task description file based on the task description file; calling an initial target execution operator corresponding to each target operator; the optimization module determines the final operation position of each target operator based on the computing resource information of the current terminal, the edge end and the cloud end; the deployment module calls a final target execution operator corresponding to each target operator; and the final target execution operator is allocated to the corresponding final operation position, so that migration and optimization of the end, the edge and the cloud can be dynamically performed, and a cross-layer application can be quickly constructed.

The invention discloses a reasoning request task processing method, device and equipment, a medium, a product and a heterogeneous system, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring reasoning task information corresponding to each reasoning request task and computing power resource information of a heterogeneous computing system; according to the reasoning task information and the computing power resource information, determining target computing equipment for executing a pre-filling stage and a decoding stage of each reasoning request task and corresponding task starting time on the basis of a condition that the processing cost of each reasoning request task is lowest under the condition that each task processing requirement information is met; and sending the task allocation result as a task allocation request to the heterogeneous computing system, and executing a corresponding task by each target computing device according to the task starting time of the task allocation request. The problem that task processing requests and low execution cost are difficult to consider at the same time in the prior art can be solved, and the computing devices are allocated to the reasoning tasks as low as possible under the condition that the task processing requirements are met.

The invention discloses a centralized management system operation and maintenance server, which is deployed in a data center or a cloud platform and is used for performing centralized operation and maintenance management on target hosts in a plurality of service systems. The system operation and maintenance server comprises a resource registration module, a monitoring acquisition module, a configuration management module, a task arrangement module, an alarm and self-healing module, an operation and maintenance auditing module, an interface management module and an identity authentication and authority control module. The resource registration module is used for receiving information of a target host and a business system to which the target host belongs, generating a unique resource identifier and establishing a resource information base; the monitoring acquisition module issues a monitoring acquisition strategy based on the resource identifier and acquires monitoring index data and an operation log; the configuration management module performs difference comparison on the current configuration and the configuration baseline, and issues a configuration template and performs configuration version management after confirmation; and the task arrangement module arranges the atomic operation and maintenance operation into a task execution plan containing an execution sequence and a rollback strategy.

The invention discloses a construction data management system and method for intelligent building mechanical and electrical installation, and relates to the technical field of intelligent construction, and the method comprises the steps: collecting the multi-source data of a construction site, carrying out the real-time analysis and matching, recognizing a disturbance event, and associating to a corresponding component of a BIM model and a construction plan target process; extracting resource information, calculating the influence of disturbance on subsequent processes, and generating a coupled digital twinborn model; progress and resource deviation analysis is carried out, the construction scheme change influence is simulated, and a comprehensive deviation report is output; calling a knowledge base historical scheme, generating a correction scheme in combination with a current resource state, simulating an implementation effect, and executing after examination and approval; and monitoring the execution of the correction scheme in real time, comparing the actual progress, updating the knowledge base and the BIM model, and forming reusable experience entries.

A method for beam adjustment in a base station disclosed herein may comprise the steps of allocating a first resource for transmitting candidate beams determined on the basis of the number of sweeps and the number of subcarriers of which beams are squinted in an angle search space; transmitting, to a terminal, first resource information indicating the allocated first resource; mapping beam indexes to each of the candidate beams; transmitting, to the terminal, information about the beam indexes mapped to each of the candidate beams; transmitting a reference signal by using the first resource; receiving reception quality information for the reference signal from the terminal; and determining a transmission beam on the basis of the reception quality information.

The invention relates to the technical field of intelligent network connection, in particular to a driving task arrangement generation method and device, a vehicle, a medium and a program product, and the method comprises the steps: obtaining task information and resource information of at least two tasks in driving task arrangement; determining at least one optimization target of task arrangement based on the task information and the resource information; and inputting the task information and the resource information into a pre-trained large language model to output initial task scheduling data meeting at least one optimization target, and verifying the initial task scheduling data until task scheduling data meeting a preset verification condition is obtained. Therefore, the problems that in the related technology, manual analysis is prone to errors, the management efficiency is low, and the actual use scene cannot be adapted are solved; model processing depends on a fixed rule, is lack of dynamic scheduling capability, increases response delay of a system, causes task timeout, and cannot guarantee reliability and safety of a driving task.

The application provides a method and device for switching network slices, applied to the field of network slices. The method can include: a slice selection network element receiving resource information of an original network slice from a network slice management function network element, wherein the resource information of the original network slice includes one or more of the following: performance indicators of the original network slice, alarm information of the original network slice, and an analysis report for the original network slice; then the slice selection network element determines whether to switch a terminal device on the original network slice to a target network slice according to the resource information of the original network slice and a network slice switching strategy, wherein the network slice switching strategy includes a trigger condition for switching network slices, an identifier of the target network slice, an identifier of the original network slice, and an identifier and / or application identifier of the terminal device on the original network slice. Through the application, the service experience after network slice switching can be improved.

The invention provides a computer system starting method and device, electronic equipment and a storage medium, and can be applied to the technical field of computers. The method comprises the steps of executing a first initialization task under the condition that power-on of a computer system is completed; the first initialization task comprises executing an initialization configuration operation on first equipment of the computer system; under the condition that the execution of the first initialization task is completed, based on the global resource information of the equipment resources, allocating the equipment resources to the plurality of processors, and determining local resource information of each of the plurality of processors; executing the plurality of second initialization tasks in parallel by utilizing the plurality of processors, and determining respective initial resource allocation results of the plurality of processors; under the condition that execution of the multiple second initialization tasks is completed, resource unification adjustment is executed according to the multiple initial resource allocation results, and a target resource allocation result is obtained; and in response to resource adjustment completion, loading an operating system of the computer system to start the computer system.

A relay node may receive a pattern index indicating a beam pattern. Further, the relay node may receive resource information indicating a beam type. Also, the relay node may receive one or more first beam indexes associated with the indicated beam pattern. On a condition that the indicated beam type is narrow and a beam index of the one or more first beam indexes corresponds to a wide beam type, the relay node may determine second beam indexes for a set of narrow beams associated with the beam index of the one or more first beam indexes corresponding to the wide beam type. Moreover, the relay node may determine a beam and time-domain resources for each of the determined second beam indexes based on the pattern index and resource information. Accordingly, the relay node may transmit data using the determined beam and at least one of the determined time-domain resources.

The invention discloses a storage device and method for an artificial intelligence operation and maintenance robot, which are deployed in a data center or a cloud platform and are used for carrying out centralized operation and maintenance management on target hosts in a plurality of service systems. The system operation and maintenance server comprises a resource registration module, a monitoring acquisition module, a configuration management module, a task arrangement module, an alarm and self-healing module, an operation and maintenance auditing module, an interface management module and an identity authentication and authority control module. The resource registration module is used for receiving information of a target host and a business system to which the target host belongs, generating a unique resource identifier and establishing a resource information base; the monitoring acquisition module issues a monitoring acquisition strategy based on the resource identifier and acquires monitoring index data and an operation log; the configuration management module performs difference comparison on the current configuration and the configuration baseline, and issues a configuration template and performs configuration version management after confirmation; and the task arrangement module arranges the atomic operation and maintenance operation into a task execution plan containing an execution sequence and a rollback strategy.

The invention provides a task processing method, device and equipment for a server cluster, a computer program product and a computer readable storage medium. The method comprises the steps that a to-be-scheduled target calculation task is received, and the target calculation task comprises the resource demand quantity; performing resource matching on the resource demand quantity and the resource information of each server, and determining a plurality of candidate servers from the plurality of servers according to a resource matching result; determining a target server from a plurality of candidate servers according to the historical scheduling record of each candidate server; a task scheduling instruction is sent to a target server, and the task scheduling instruction indicates that the target calculation task is scheduled to the target server. By means of the method and device, efficient scheduling of server cluster tasks and reasonable resource allocation can be achieved, and the overall processing efficiency is improved.

The invention provides a resource information determination method and device, equipment and a storage medium, relates to the field of human resources, and is used for determining reserve resource information of a main body object of an institution and providing reliable data support for the institution to formulate long-term and accurate resource planning. The specific scheme is as follows; the method comprises the following steps: quantifying historical business processing data of a subject object to obtain an efficiency index, and fitting based on the quantified efficiency index to obtain a first growth rate of efficiency index growth of the subject object; a target senescence factor coefficient is introduced to correct the first growth rate to obtain a second growth rate which reflects the law of efficiency growth slowing of the subject object after the target time window; combining the first growth rate, the second growth rate and the target time window to generate an efficiency growth curve, and representing the change of the efficiency of the main object along with time; and finally, determining resource information of the mechanism in the target time window based on the efficiency growth curve of each main body object.

The invention discloses a water conservancy project construction progress and resource allocation intelligent optimization system, and relates to the technical field of water conservancy projects, and the system comprises a resource identification generation module which employs a Hash algorithm to generate unique identification codes for each batch of construction resources used in a water conservancy project construction process, calculating according to the resource attribute information, the construction task code and the batch identification associated data to obtain a non-repeatable digital resource code; when the same batch of resource information is detected, grouping identification is carried out according to a time sequence, a unified coding system covering the whole process of purchasing, transportation, entering, warehousing and construction is formed, and space coordinate basic data corresponding to each resource is generated; according to the intelligent optimization system for the construction progress and resource allocation of the water conservancy project, accurate control of the construction progress of the water conservancy project, intelligent optimization of resource allocation and comprehensive visualization of operation of a construction site can be realized, and the management efficiency, the resource utilization rate and the overall construction quality of the construction of the water conservancy project are greatly improved.

The invention discloses a power industry construction unit safety management and control dynamic evaluation method and system, and the method comprises the steps: constructing a full-scene safety standard knowledge base covering power transmission and transformation and power distribution scenes, and building cross-scene semantic association through a knowledge graph; real-time resource information of a construction unit is obtained, and a basic element parameter model is constructed and dynamically updated through a multi-granularity strategy; based on a project scene matching standard security element set, evaluating the security risk of the whole process through a deep Q network algorithm fusing transfer learning and reinforcement learning, and generating a quantitative index and a resource configuration scheme; and generating project-level, process-level and operation-level three-level management and control instructions and pushing the instructions to the terminal. According to the method, scene self-adaption, resource state real-time mapping and risk disposal closed-loop feedback of safety assessment are realized, and the risk identification timeliness, the resource configuration accuracy and the field instruction executable performance are improved.

Disclosed in the embodiments of the present application are a resource determination method and apparatus, and a program product and a storage medium. The method in the embodiments of the present application comprises: receiving an inference request; determining a first computing resource required for when a large model performs inference on the inference request; acquiring first resource information of a heterogeneous cluster; and on the basis of the first computing resource and the first resource information, determining a first inference resource from the heterogeneous cluster, wherein the first inference resource is used by the large model to perform inference on the inference request. In this way, for an inference request received in real time, a computing resource required for performing inference on the inference request is determined in real time, which enables dynamic determination of computing resources corresponding to inference requests of different lengths. Thus, an inference resource corresponding to the inference request can be dynamically determined on the basis of the computing resource, and the corresponding inference resource can be more accurately determined from the heterogeneous cluster, so that inference is performed on the inference request on the basis of the inference resource, such that an inference process of the large model can avoid being affected.

The invention relates to the technical field of multi-modal data processing and intelligent decision making, in particular to a complex-task-oriented multi-modal data alignment and cross-modal reasoning method and system for an agent with a body, and the method comprises the following steps: multi-skill task and resource information collection, original data preprocessing, three-level multi-modal data alignment, cross-modal reasoning, multi-skill task and resource information collection, three-level multi-modal reasoning and cross-modal reasoning. Cross-modal reasoning and decision generation, reasoning result verification and iterative optimization; the method has the beneficial effects that through a three-stage framework of'space-time calibration-semantic mapping-constraint adaptation ', the problems of space-time asynchronization and semantic segmentation of multi-modal data are effectively solved, the alignment accuracy is greatly improved compared with that of a traditional method, and a high-quality data basis is provided for subsequent reasoning.

The invention discloses a design method of an SCPI-based instrument automatic identification and unified control system, and the method comprises the steps: constructing a multi-interface instrument resource unified search framework through integrating a VISA library function and a multi-interface adaptive logic; and designing a multi-feature fusion instrument automatic identification algorithm, and reading instrument identification information through an SCPI standard command to realize accurate classification of general instruments. A layered resource management framework and a unified control interface are established, instrument core resource information is stored in a centralized mode, SCPI instruction differences of different manufacturers and different instruments are shielded, and recognition-management-control whole-process closed-loop control is achieved. Through deep fusion of a multi-interface instrument resource search framework, an instrument automatic identification and classification system, layered resource management and a unified control interface, automatic search and automatic identification of multi-model instruments and unified management and flexible switching of instrument resources are realized, and the compatibility and test efficiency of the test system to the multi-model instruments are improved.

The invention provides a resource scheduling method and device for model service, equipment and a program product. The method comprises the following steps: receiving a creation request of a target model service, wherein the creation request comprises the number of service instances required by the target model service and a scheduling strategy of the service instances; and obtaining computing power resource information of the plurality of service clusters, wherein the computing power resource information is used for representing available computing power resources of the service clusters. And deploying all service instances required by the target model service to at least one target service cluster supported by available computing power resources according to the scheduling strategy based on the computing power resource information of the plurality of service clusters. And allocating traffic resources for accessing locally deployed service instances to each target service cluster.