53 results about "Resource combination" patented technology

The invention provides a resource dynamic scheduling method and system, and the method comprises the steps: obtaining the real-time output, gradeability, start-stop records, health states and other operation information of various adjustable resources, and constructing an operation information sequence according to a unified time reference; performing quality verification and completion on the operation information to generate an operation information basic database; extracting a multi-dimensional capability index based on the operation information, and constructing a resource elastic portrait through normalization, time correlation modeling and feature fusion; generating a dynamic adjustable capacity index according to the elastic portrait, and reflecting the real-time adjustment capability of each resource along with the change of the running state; determining an output adjustment interval of the resources according to the capacity index, and forming a priority scheduling sequence; according to a power gap obtained by load prediction and wind and light output prediction, generating a resource combination scheme meeting an adjustment demand and issuing a scheduling instruction; and monitoring the actual output of the resources, updating the elastic portrait and the capacity index when the deviation exceeds the limit, and correcting the scheduling scheme in a rolling manner.

The invention discloses a cloud data server mixed training method and system, which are applied to a cloud data server cluster comprising a plurality of computing resources. According to the method, a model structure, a data set, a time delay constraint and an isolation constraint of a training task are analyzed to generate a task portrait, resource configuration, an operation state and an isolation capability of a calculation node are collected to generate a resource portrait, and a co-located interference degree index table of a task and resource combination is constructed based on historical monitoring data. During scheduling, a heterogeneous computing power utilization rate, an estimated training time delay deviation and a co-located interference degree are taken as indexes, a candidate resource allocation scheme is subjected to weighted evaluation to generate a mixed training scheduling strategy, and resource isolation is implemented through container and accelerator multi-instance division. In the operation process, the training time delay and the actual co-located interference degree are continuously monitored, the scheduling weight and the co-located interference degree index are dynamically adjusted according to the deviation, closed-loop optimization of multi-task mixed training is achieved, the heterogeneous resource utilization rate is increased, and time delay default and co-located interference are reduced.

The invention relates to a network resource flexible scheduling method, system and device and a medium. The method is applied to a backbone transmission network and data network intelligent management and control system. The method comprises the following steps: disassembling network resources into a plurality of atomic power units in a virtualization layer, marking dynamic attribute tags for the atomic power units to obtain an atomic power library, and generating a trigger event when a real-time performance index exceeds a preset threshold value; according to the trigger event, the atomic power library and the alarm knowledge graph, carrying out combination optimization on the atomic power units to generate a resource combination scheme, and calling a three-dimensional topology model to carry out pre-verification on the resource combination scheme; and according to the execution scheme, carrying out capacity expansion and shrinkage operation on the atomic power unit through the virtualization layer, adjusting a load balancing weight based on real-time flow distribution, and generating a resource allocation result. According to the method, dynamic cooperative scheduling of network resources is realized, and the service quality and the operation efficiency of a backbone transmission network and a data network are improved.

The invention discloses a computing power resource determination method, apparatus and device, a medium and a product. The method comprises the steps of obtaining real-time computing power resource data of a natural language demand and a resource library; performing intention analysis on the natural language demand based on the historical performance database, and determining a computing power task instruction; and on the basis of the real-time computing power resource data, performing dual-algorithm collaborative matching on the computing power task instruction, and determining a target resource combination meeting requirements from a resource library. According to the method, intention analysis is carried out on natural language requirements in combination with a historical performance database, fuzziness of intention analysis is eliminated from the source, cooperative matching is carried out through a bi-pass algorithm so as to reduce matching deviation of computing power resources, accurate analysis of the requirements is achieved, and then accurate matching of the computing power resources is achieved.

The invention discloses an education resource sharing method and system based on an education cloud platform, and relates to the technical field of cloud platform education resource sharing, and the system can effectively eliminate semantic redundant resources, reduce the repetition rate of a resource library and improve the retrieval efficiency through a resource collection and purification module; the multi-modal resource combination and adaptation module supports the accurate generation of a resource packet according to the teacher demand and the teaching progress, and enhances the teaching fitness of the resource content; the resource recommendation evaluation module constructs recommendation indexes based on user behavior data, dynamically adjusts resource priorities, and realizes priority sharing of high-quality resources; and the verification and authorization module ensures that the structural integrity and teaching adaptability verification is completed before the resources are shared, disqualified content propagation is avoided, and the teaching experience is improved. The whole system realizes a closed-loop process from resource collection, redundancy removal, combination and adaptation to intelligent recommendation and sharing verification, and effectively supports intelligent sharing and accurate pushing of educational resources.

The invention discloses an enterprise service demand prediction and resource optimization configuration system based on artificial intelligence, and relates to the technical field of artificial intelligence. According to the method, the multi-source heterogeneous data is integrated through the multi-modal data fusion algorithm, the demand change rule is accurately captured through the time sequence prediction algorithm, the demand prediction precision is remarkably improved, and the problem that the demand perception ability is limited due to insufficient data utilization in a traditional method is solved; the resource optimization allocation algorithm is combined with the comprehensive utility function of the resource utilization rate and the customer satisfaction, the resource combination is dynamically adjusted, the resource allocation efficiency is greatly improved, the defect that a traditional resource allocation mode is static and lack of flexibility is overcome, and the user experience is improved by continuously monitoring customer interaction and service feedback data. Model parameters are dynamically updated, a resource allocation algorithm is optimized, continuous optimization of resource allocation is achieved, the problem that an existing method lacks an effective feedback mechanism is solved, long-term efficient operation of a system is ensured, and more competitive operation support is provided for enterprises.

This invention proposes a demand response resource combination optimization method that considers response reliability and main transformer load balancing. By constructing a demand response resource combination optimization model, the reliability of resource response and the load balancing of multiple main transformers are incorporated into the evaluation. A heuristic optimization algorithm is used to solve the problem, resulting in a demand response resource combination. The demand response resource combination optimization model includes: an objective function that minimizes the overall network demand response resource response cost; response reliability constraints of participating resources; load balancing constraints after the demand response resource is connected to the main transformer; and logical constraints representing whether the demand response resource is included. The heuristic optimization algorithm, combined with the objective function that minimizes the demand response cost, sequentially judges the satisfaction of response reliability constraints and main transformer load balancing constraints after a certain demand response resource is included in the response, and adjusts the combination of demand response resources to provide a feasible solution to the problem.

The application discloses a cloud data server hybrid training method and system, which is applied to a cloud data server cluster containing various computing resources. The method analyzes the model structure, dataset, time delay constraint and isolation constraint of the training task to generate a task image, collects the computing node resource configuration, running state and isolation capability to generate a resource image, and constructs a co-location interference degree index table of the task and resource combination based on historical monitoring data. During scheduling, the heterogeneous computing power utilization rate, estimated training time delay deviation and co-location interference degree are taken as indexes to perform weighted evaluation on the candidate resource allocation scheme to generate a hybrid training scheduling strategy, and resource isolation is implemented through container and accelerator multi-instance division. During running, the training time delay and actual co-location interference degree are continuously monitored, the scheduling weight and co-location interference degree index are dynamically adjusted according to the deviation, closed-loop optimization of multi-task hybrid training is realized, the heterogeneous resource utilization rate is improved, and time delay violation and co-location interference are reduced.

The invention provides a GPU computing power resource scheduling method, system and device and a medium. According to the method, task information of at least one to-be-scheduled task is obtained, a corresponding task portrait is constructed based on the task information, resource state information of each computing node in a GPU cluster is obtained, a corresponding node state is constructed based on the resource state information, and then the scheduling priority of the to-be-scheduled task is determined according to the task portrait and the node state. And then, according to the task portrait, the node state and the scheduling priority, determining a target GPU resource combination used for executing the to-be-scheduled task, so that the task scheduling efficiency is improved under the conditions of large-scale operation of the GPU cluster, various task types and dynamic change of resource requirements. And selecting a target GPU resource combination which can improve the overall resource utilization rate, reduce inter-task interference and shorten task completion time while ensuring that the high-priority tasks are executed in time.

The invention discloses a multi-resource combination constrained plan-oriented two-point automatic path-finding method and system, belongs to the technical field of path planning, and aims to solve the technical problem that the prior art is disjointed from engineering practical application. Comprising the following steps: acquiring geographic coordinates, construction plan indexes and resource state data as input data; forming a multi-resource combination graph model comprising a node network, edges, a resource composition scene and constraint parameters; calculating the comprehensive weight of each resource combination scene of each edge through a plan-oriented cost function; constructing an improved Dijkstra algorithm introducing constraint screening, screening the edges and the corresponding resource combination scenes in the multi-resource combination graph model through the improved Dijkstra algorithm based on the comprehensive weight of each resource combination scene of each edge, and selecting an initial path scheme which meets a constraint condition and has the minimum comprehensive weight to form an initial path scheme; and the optimal path is solved again by improving the Dijkstra algorithm.

The invention discloses an Internet laundry order resource accounting method and system based on LSTM, and relates to the technical field of intelligent decision making, and the method comprises the steps: constructing an LSTM model, and outputting the real-time fatigue index of each physical resource; receiving and analyzing an internet clothes washing order, and generating a process link; screening out available resources of which the current fatigue index is lower than a safety threshold from all physical resources of the physical resource type to form a candidate resource set; selecting a target physical resource combination for the process link based on the resource combination optimization logic; for a high mechanical load link, selecting a physical resource with the lowest current fatigue index; for a high-heat-load or high-precision requirement link, selecting a physical resource of which the current fatigue index has the largest difference from the average fatigue index of the type of resources; and according to the target physical resource combination and the fatigue index, calculating estimated time consumption and energy consumption of each process link.

A user equipment (UE) is described. The UE may comprise a receiver and a transmitter; wherein the transmitter is configured to perform a Type 2 channel access procedure for sensing a channel in the one or more time resources indicated by the Resource combinations field included in the SCI format 2-C.

This invention relates to the field of teaching resource scheduling technology, and discloses a dynamic scheduling system and method for teaching resources based on multi-agent collaboration. The system includes: a basic modeling module for constructing teaching chain units, teaching chain graphs, resource information tables, and matching rules; a constraint generation module for generating a set of constraint information; a disturbance identification module for identifying affected units, affected ranges, and non-scheduling domain states; a candidate generation module for generating a set of effective composite resource candidates; a collaborative evaluation module for generating evaluation results for candidate combinations; a scheduling execution module for determining the optimal composite resource combination and completing local rescheduling; and a state update module for updating execution state information. This invention realizes the dynamic adjustment of teaching resources and the updating of execution state during the teaching execution process.

A satellite resource pooling management system based on satellite-ground network integration comprises a resource access and standardization module, a resource state monitoring and global resource view module, a resource pooling scheduling engine, a satellite-ground network integration control module and a service adaptation and feedback module. The resource access and standardization module realizes multi-resource access and resource standardization processing; the resource state monitoring and global resource view module realizes resource state real-time monitoring and global resource view construction; the resource pooling scheduling engine screens an optimal resource combination through a scheduling strategy and a dynamic adjustment mechanism based on service requirements and global resource states; the satellite-ground network integrated control module realizes user terminal cross-satellite / ground station switching and network link optimization; and the service adaptation and feedback module realizes service classification adaptation and closed-loop feedback. According to the invention, pooling integration and integrated management and control of satellite, ground station and network link resources are realized, and the resource utilization rate, the service continuity and the system adaptability are improved.

The invention discloses a hardware port reflection method and system for a real-time simulation test resource combination system, and the method comprises the steps: carrying out the port conversion of hardware resources in an interface processing and simulation test resource control module of the real-time simulation test resource combination system; when the real-time simulation platform receives data or detection signals, bus data or signals are obtained from the electrical interface through the physical layer, then the received data / signals are analyzed through the application protocol layer, variable data needed by simulation testing are obtained, and the variable data are stored in the DMA; the real-time simulation platform directly obtains variable data in a form of accessing a memory address; when the real-time simulation platform sends data or outputs signals, the simulation test execution program directly writes variable data generated by output into the DMA and then sends or outputs the variable data. The system is used for executing the method. The method has the advantages of being simple in principle, wide in application range, capable of improving the real-time performance of simulation work and the like.

Disclosed methods and systems for implementing serverless management of platform resources maintain a function-as-a-service (FaaS) mapping database associating a resource and management function combination with a locator identifying a container image for implementing management interactions required to perform the management function. When a FaaS request indicating a function-resource combination is detected, the applicable locator is retrieved from the mapping database and the corresponding container image is deployed. Each resource may correspond to a specific version of a specific make or model, e.g., a server model and a firmware version. API, protocol, and messaging specifics for performing the function on the resource are embedded in each container image. Thus, the container image for an inventory operation on a server with a first version of firmware may execute SOAP-based messaging while the image for the same operation on the same server model provisioned with a second firmware version may invoke a RESTful API.

A system and method for a using a genetic algorithm with an extra bit in the genome to obtain a best-fit of resource combinations for a particular task to be scheduled. The system can generate a genome from a plurality of jobs and then add an extra bit that can be used to find the best resource combination in the event that multiple resource combinations are equally suitable as an initial fit.

This disclosure provides a resource scheduling method and apparatus, electronic equipment, and computer programs. The resource scheduling method includes the steps of: obtaining a plurality of candidate resource combinations corresponding to a target task in response to a resource scheduling request for a target task, each candidate resource combination including at least one type of target resource and the number of target resources required for the target task; for each candidate resource combination, searching a multilevel resource pool in order from low level to high level based on at least one type of target resource and the number of target resources to determine a seed resource pool corresponding to the candidate resource combination; and determining a target resource pool from a plurality of seed resource pools to satisfy the resource scheduling request based on the level of the seed resource pool corresponding to each candidate resource combination. This disclosure is used in the technical field of networking. Embodiments of this disclosure can improve the efficiency and flexibility of resource scheduling.

The invention provides an equipment first-aid repair task planning method, and belongs to the technical field of equipment first-aid repair, and the method comprises the steps: collecting basic parameters, faults, positions and task association information of to-be-repaired equipment, carrying out the standardization processing, and constructing an equipment first-aid repair information set; performing quantitative scoring on each task in the equipment first-aid repair information set, and generating a first-aid repair task priority ranking table; establishing a first-aid repair resource dynamic database, updating resource state information in real time, and matching an optimal first-aid repair resource combination for each first-aid repair task by adopting a priority priority and resource adaptation principle according to the priority ranking table; and in combination with time constraint, space constraint and resource constraint conditions of each first-aid repair task, performing overall scheduling on the matched tasks and resources to generate an initial first-aid repair task planning scheme. According to the equipment first-aid repair task planning method provided by the invention, the first-aid repair response time is shortened, the resource utilization efficiency is also remarkably improved, and the requirements of rapid first-aid repair of equipment in emergency rescue and other scenes are effectively met.

The invention provides a distributed system resource allocation method which can be applied to the technical field of artificial intelligence. The method comprises the steps of performing structured processing on unstructured data in resource related data based on a preset optical character recognition algorithm to obtain an entity association relationship of resources, and performing special analysis on the entity association relationship through preset functional differentiation analysis agents to obtain special analysis data; carrying out dynamic weighted summation on the special analysis data by utilizing a decision-making agent to obtain fusion analysis data, and generating an execution instruction corresponding to the fusion analysis data in response to that the fusion analysis data passes compliance verification; and calling a regulation and modification program interface according to the execution instruction, and carrying out configuration regulation and modification on the distributed subsystem based on the regulation and modification program interface according to resource category allocation in the resource combination optimization scheme. The invention further provides a distributed system resource configuration device and equipment, a medium and a product.

The invention discloses an intention-based heterogeneous computing power resource declaration and automatic configuration system and method, and belongs to the technical field of computing power distribution. The intention-based heterogeneous computing power resource declaration and automatic configuration system aims at solving the problems that in the prior art, heterogeneous computing power resource configuration is complex, depends on artificial experience, is tightly coupled with applications and resources, and is difficult to automatically optimize configuration directly according to an application performance target. According to the method, the system can automatically deduce and configure the appropriate heterogeneous computing power resource combination only by declaring the performance intention of an application developer, so that the configuration process is simplified, the resource utilization rate is increased, and the application performance is guaranteed.

The application provides a kind of quantity super intelligence cross-domain computing power unified scheduling method and system, the method generates corresponding cross-domain collaborative object in response to receiving mixed job request, and then reads resource state information from each target domain involved in the mixed job, determines target resource combination and initiates resource reservation, and then determines single-domain readiness, cross-domain start alignment between target domains and rollback cost of canceling resource reservation through resource state information, and then determines whether the target resource combination meets the joint start condition, obtains consistency permission judgment result, binds the job and submits it to each target domain, or maintains the current reservation, or performs rollback processing, realizes cross-domain resource unified scheduling, which can reduce the problem of long-time idling, waiting or occupation of intelligence computing nodes, supercomputing nodes or quantum devices caused by inconsistent start time, and can avoid long-term maintenance of invalid reservation in non-recoverable or low-benefit situations.

The invention relates to a work generation method and device, equipment and a storage medium, and relates to the technical field of multimedia. According to the method, for a played first work, a user can check various creation elements and various creation tools used by the first work, and the user can select at least part of the creation elements and at least part of the creation tools to obtain a resource combination; furthermore, a new work is generated by one key based on the creation elements and the creation tools in the resource combination, so that a user can add a plurality of creation elements in the first work and use a plurality of creation tools in the first work in the new work by one key; the creation elements do not need to be searched through multiple times of interface skipping, the creation elements do not need to be manually added in the works one by one, the creation tools do not need to be searched through multiple times of interface skipping, the creation tools do not need to be used in the works one by one, operation steps are reduced, the interaction path is shortened, and the interaction efficiency is improved. And the man-machine interaction efficiency is improved.

The invention discloses a general-calculation integrated dynamic resource scheduling method and system, a medium and a product. The method comprises the following steps: acquiring business portrait information corresponding to each business in real time; inputting the portrait information of each service into a preset service perception model, and obtaining a resource allocation strategy corresponding to each service in combination with an adaptive resource allocation mechanism; inputting the resource allocation strategy into a preset time-varying graph neural network to obtain computing power demand prediction information corresponding to each service; and when the first service triggers the first resource demand, triggering a resource scheduling operation through the block chain and the smart contract, determining an optimal resource combination in the multi-dimensional resource pool according to the computing power demand prediction information corresponding to the first service and the first resource demand, and performing resource allocation for the first service according to the optimal resource combination. The invention provides a general-calculation integrated dynamic resource scheduling method and system, and aims to solve the technical problems of low scheduling efficiency, response lagging and insufficient robustness of an existing resource scheduling method.

The invention relates to the technical field of intelligent Internet of Things, in particular to a 5G-based edge-side intelligent Internet of Things access rescue cabin system, which comprises a task driving module used for receiving a task request, determining a resource combination by using a preset optimization function, setting a plurality of task nodes, planning an optimal path according to the task nodes and road condition real-time factors, and sending the optimal path to a server; task objects are generated and distributed through broadcast; the multi-terminal interaction module is used for dynamically constructing an interaction topological graph according to all interactive terminals related to the task object at the current task node, and matching and establishing a corresponding communication session link according to an interaction demand and the interaction topological graph; and the resource dynamic scheduling management module is used for determining an optimal resource allocation matrix by utilizing a resource selection function according to the task object and the interactive topological graph, calling hardware resources, personnel resources and communication resources, and forming a resource calling record. According to the invention, intelligent coordinated configuration of resources can be realized, and scheduling precision and response efficiency are significantly improved.

The embodiment of the invention provides a task allocation method and device, electronic equipment and a storage medium, and belongs to the technical field of logistics. The method comprises the following steps: acquiring a target order feature of a to-be-processed order, a target task feature of a current order task, a target device feature of a candidate execution device in a target warehouse, and a target path feature of a candidate transportation path corresponding to the current order task; calling a feature extraction network to perform feature extraction on the target order features, the target task features, the target device features and the target path features; performing attention fusion according to the target order embedding, the target task embedding, the target device embedding and the target path embedding to obtain a target fusion feature; and calling a scoring network to score the target fusion feature to obtain a target matching score of the candidate resource combination corresponding to the target fusion feature, and determining a target resource combination from the plurality of candidate resource combinations according to the target matching score. According to the embodiment of the invention, the task allocation accuracy can be improved.

The invention provides a communication method and a communication device. The method may comprise: receiving indication information, the indication information being used for determining a resource of a reference signal, the resource of the reference signal being composed of a first resource and a second resource, the first resource being determined based on a first matrix, and the second resource being determined based on the first matrix and an offset matrix of the first matrix; and transmitting or receiving the reference signal based on the resource of the reference signal. Based on this, the incidence relation between the resource of the reference signal and the matrix is established, so that the reference signal with sparse position can be designed based on the matrix, and the resource overhead occupied by the reference signal is reduced. Moreover, the resources of the reference signal can be the two-stage resource combination, namely the first resource and the second resource, and when the resources of the reference signal need to be indicated or updated, the resources of the reference signal in partial stages can be indicated or updated, so that the signaling overhead caused by indicating the resources of the reference signal can be greatly reduced.

This application provides a GPU computing resource scheduling method, system, device, and medium. The method acquires task information for at least one task to be scheduled and constructs a corresponding task profile based on the task information. It also acquires resource status information for each computing node in the GPU cluster and constructs a corresponding node status based on the resource status information. Then, based on the task profile and node status, it determines the scheduling priority of the task to be scheduled. Finally, based on the task profile, node status, and scheduling priority, it determines a target GPU resource combination for executing the task to be scheduled. Thus, under conditions of large-scale GPU cluster operation, diverse task types, and dynamically changing resource requirements, it selects a target GPU resource combination that can improve overall resource utilization, reduce inter-task interference, and shorten task completion time while ensuring timely execution of high-priority tasks.

The invention discloses an electric power resource scheduling method of a virtual power plant, an electronic device and a storage medium, and the method comprises the steps: obtaining a subset of an electric power resource combination of the virtual power plant and a data set of frequency modulation performance indexes corresponding to the subset of the electric power resource combination, and establishing a mapping relationship between the power resource combination and the frequency modulation performance index based on the data set, selecting the power resource combination with the highest frequency modulation availability score based on the mapping relationship and the state of the power resource in the virtual power plant, and scheduling the power resource according to the selected power resource combination to perform frequency modulation on the power system.

The invention provides a work order intelligent scheduling and resource optimal configuration method and system oriented to the energy industry, and relates to the technical field of energy intelligent management, and the method comprises the steps: analyzing a work order text, and generating a work order type feature vector and a work order element set; constructing a dynamic relation graph based on the energy resource knowledge graph; recommending an optimal work order scheduling scheme; calculating the degree of adaptation, and outputting an optimal resource combination; generating a complete work order configuration scheme; the execution scheme is optimized in real time through dynamic monitoring and feedback, and a final scheduling execution result is output; the system comprises a work order analysis and feature extraction module, a dynamic relation graph construction module, a scheduling scheme recommendation module, a resource adaptation optimization module, a scheduling plan generation module and an execution monitoring and feedback optimization module. According to the invention, intelligence and resource optimization configuration of the whole work order scheduling process are realized, the plan achievement rate is obviously improved, the man-hour deviation rate is reduced, the emergency maintenance response time is shortened, and the resource utilization rate is improved.