303 results about "Multi resource" patented technology

The invention belongs to the technical field of resource scheduling, and particularly discloses a resource scheduling method and system based on reinforcement learning, and the method comprises the steps: describing the dependency and conflict relation between tasks through constructing a task causal relation graph which can be dynamically updated; constructing a state space and an action space based on a current task state and a causal relationship, training an intelligent agent by adopting a reinforcement learning model in combination with a multi-target reward function, and generating a scheduling and resource allocation strategy; linear programming and heuristic joint resource allocation are carried out under strategy guidance, and task priorities and causal relationships are dynamically adjusted in combination with task states; by collecting execution data and analyzing strategy deviation, a model structure and parameters are further adjusted, and a complete closed-loop optimization process is formed. According to the method, task priority dynamic adjustment, resource allocation strategy self-adaptive updating and scheduling process closed-loop optimization are realized, and the method is suitable for complex project management scenes under multi-task and multi-resource constraints.

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The invention discloses a multi-resource collaborative power system short-time dynamic supply and demand balance evaluation method and device, and the method comprises the steps: constructing a target function based on the power generation cost of an adjustable generator set, the starting and stopping cost of the adjustable generator set, the wind and light abandoning penalty cost, the load shedding cost and the load transfer cost, and determining a corresponding initial constraint condition; determining a prediction error constraint based on meteorological elements, a frequency support constraint of an adjustable generator set and a set fault constraint, carrying out optimization replacement on an initial constraint condition, and giving an optimization constraint condition; based on the objective function and the initial constraint condition, determining a unit commitment start-stop decision; and performing short-time dynamic supply and demand balance evaluation on the power system based on a unit combination start-stop decision in combination with an objective function and optimization constraint adjustment. According to the invention, more accurate and timely information support can be provided for scheduling and operation of a power system, and the short-time supply and demand balance demand and capability in a high-proportion new energy access scene can be effectively evaluated.

The invention relates to an unmanned aerial vehicle group-oriented sensing communication integrated network multi-resource joint scheduling method, and belongs to the technical field of wireless communication, and the method comprises the steps: building a system model of multiple UAV-ISAC tasks, and defining a joint optimization problem; extracting spatio-temporal features from the dynamic heterogeneous graph in which the unmanned aerial vehicle, the user and the sensing target are abstracted as nodes and the relationship is abstracted as edges; taking the features as input, and adopting a layered multi-agent reinforcement learning architecture to solve the joint optimization problem on line; in the architecture, resource allocation and trajectory planning actions are generated through cooperation of a central Actor and all unmanned aerial vehicle Actors, and system performance is evaluated by a central Critic; constructing a multi-target weighted reward function, stabilizing a training process by combining experience playback and a Mini-batch sampling mechanism, and updating network parameters in parallel; and obtaining an optimal resource allocation and unmanned aerial vehicle trajectory strategy through training. The sensing performance is improved, the communication quality is guaranteed, and the defects in the aspect of dynamic resource scheduling in the prior art are overcome.

According to some aspects of the techniques disclosed herein, a UE adapted to transmit on different antenna subsets transmits an indication that the UE can transmit a number of distinct RS resources, where each of the RS resources comprises at least one RS port. The UE transmits capability information indicating that the UE is capable of transmitting simultaneously on multiple RS resources and / or receives first and second RS configurations, where the first RS configuration is a first list of SRS resources that at least correspond to RS resource indications used for PUSCH transmission, and the second RS configuration is a second list of RS resources that may be used for SRS transmission. The UE receives an indication of at least one RS resource and transmits a physical channel on antennas of the UE associated with the indicated RS resources.

The invention belongs to the technical field of cloud computing container platforms, and particularly relates to an elastic capacity expansion and contraction scheduling method based on mixed scaling under multi-resource constraint. Comprising the following steps: monitoring index information of a k8s cluster based on a Kubernetes container management platform; the method comprises the following steps: constructing a vertical-horizontal collaborative elastic expansion framework, aiming at the problems of low utilization rate of horizontal expansion and contraction resources and upper limit of vertical expansion and contraction Pod resources in a single scaling mode, carrying out resource state division of multi-level thresholds based on hybrid control vertical elastic expansion and contraction scheduling according to a cost-benefit-based energy efficiency model under multi-resource constraint, and carrying out resource state division on the multi-level thresholds based on the hybrid control vertical elastic expansion and contraction scheduling; enabling the system to switch strategies in different pressure scenes; by dynamically balancing single instance resource allocation and the number of cluster instances, the resource cost is minimized on the premise of guaranteeing the service quality. According to the method, resource utilization can be optimized to the maximum extent, cost waste is reduced, and the efficiency and reliability of the whole system are improved.

The invention relates to an electric energy quality comprehensive optimization method based on multi-resource cooperative control, and belongs to the field of electric energy quality comprehensive optimization. According to the method, operation of two kinds of differential regulation and control characteristic equipment of the SVG and the multifunctional grid-connected inverter is coordinated, and comprehensive treatment of three kinds of typical electric energy quality problems of harmonic distortion, voltage deviation and three-phase imbalance is considered. Wherein the SVG device is used as a common reactive compensation device and only participates in compensation of voltage / reactive power problems, and the multifunctional grid-connected inverter needs to meet active power output and also considers regulation and control of harmonic wave and reactive power problems. According to the invention, the problems of insufficient cooperative control and poor comprehensive treatment effect in the current electric energy quality regulation and control method can be solved.

The invention discloses a virtual power plant system integrating multiple resources and a flexible scheduling method of the virtual power plant system, and relates to the technical field of virtual power plant management. Analyzing photovoltaic fluctuation characteristics, energy storage response capability indexes and stability indexes of the load power data corresponding to various types of data, and performing normalization processing; establishing a resource interaction tensor based on the collected data, fusing the power grid frequency deviation and the line load, and constructing a collaborative optimization model containing time delay compensation; updating the power grid frequency and the line load rate with a fixed time window, dynamically solving the collaborative optimization model, and outputting a load regulation quantity and an energy storage target scheduling instruction; and obtaining the power grid impedance characteristics and load distribution parameters of the area to be loaded with the control instruction, analyzing the scene deviation degree of the area, and correcting the scheduling value based on the scene deviation degree. The scheduling precision is improved, and the complex power grid compatibility is improved.

The invention discloses a multi-resource coordination control method and system in a new energy high-proportion power grid, and the method comprises the steps: collecting power grid data which comprises new energy output data, meteorological data, power grid load data, power grid operation state data and energy storage system operation parameters; constructing a new energy output prediction model based on the power grid data; calculating quantitative cost based on the power grid data and the new energy output prediction model, wherein the quantitative cost comprises energy storage system cost, power grid operation cost and power grid stability cost; constructing an energy storage charging and discharging strategy model based on the quantitative cost; obtaining an optimal energy storage charging and discharging strategy based on the energy storage charging and discharging strategy model; and constructing a multi-resource cooperative scheduling model based on the optimal energy storage charging and discharging strategy, and outputting a scheduling plan. According to the method, the energy storage depreciation, the power grid loss and the stability risk are quantified into specific cost indexes by calculating the quantitative cost, hidden loss can be avoided, and the operation cost of the system is minimized.

The invention discloses a dynamic allocation optimization method for cloud computing resources, belongs to the technical field of cloud computing resource management, and is used for solving the problems that part of node resources are idle and part of node resources are deficient due to static allocation of traditional cloud computing resources. Comprising an acquisition module, a data analysis module, a central database, a resource allocation library and a resource allocation module, the acquisition module acquires resource use data and allocation conditions of each computing node of cloud computing regularly, and ensures data integrity. The central database stores sorted data, the data analysis module calculates a resource use comprehensive evaluation value according to historical data and determines high and low load state points and resource-intensive and multi-resource composite time periods, the resource allocation library schedules resources according to different time periods, the resource allocation module sorts task priorities according to factors such as task urgency, and the resource allocation module performs task scheduling according to the task urgency. It is ensured that high-priority tasks are preferentially allocated with resources and executed, dynamic allocation optimization of cloud computing resources is achieved, the resource utilization rate is increased, and the system performance is improved.

Methods, systems, and computer-readable storage media for executing, within the container orchestration system, the application using one or more resource units, each resource unit including an application container and an ancillary container, the ancillary container executing a set of GPU metric exporters, receiving, from the application and for each resource unit, a GPU metric including a GPU utilization associated with a respective resource unit, determining, for each resource unit, a custom GPU metric based on a respective GPU metric, the custom GPU metric aggregating values of the respective GPU metric over a time period, determining, by an autoscaler, an average GPU metric based on one or more custom GPU metrics, and selectively scaling, by the autoscaler, the application within the container orchestration system based on the average GPU metric by adjusting a number of resource units executing the application.

A cloud management platform obtains service configuration information configured by a tenant on the cloud management platform, where the service configuration information includes one or more of the following: a network identifier, a terminal node identifier, and a terminal node type. The network identifier indicates identifiers of networks including at least two resource pools that establish a network connection, each terminal node corresponds to one resource pool, the resource pools correspond to a plurality of service providers, each resource pool includes a plurality of computing nodes, the plurality of computing nodes are used to run a business of the tenant, and the terminal node type indicates a type of a resource pool corresponding to the terminal node. The cloud management platform creates corresponding terminal nodes for the at least two resource pools based on the terminal node type.

The invention relates to the technical field of power system regulation and control, in particular to a park multi-resource intelligent flexible control method and device considering energy and carbon consumption collaboration, and the method comprises the steps: obtaining historical energy consumption data and historical carbon emission data in a target park; training the historical energy consumption data and the historical carbon emission data based on a feature-level constraint preference optimization algorithm to obtain a park multi-energy prediction model based on feature-level constraint preference optimization so as to predict energy demands in a future time period; constructing a park multi-resource optimization scheduling model according to the energy demand in the future time period; and based on a preset energy storage equipment charging and discharging strategy and a preset flexible load control strategy of the target park, generating a multi-resource energy-carbon cooperative flexible control strategy of the target park according to the park multi-resource optimization scheduling model. Therefore, the problem that an existing cooperative control method cannot meet the comprehensive requirements of the modern park in the aspects of energy conservation, carbon reduction, cost optimization and system stability is solved.

The invention relates to the technical field of field programmable gate array physical optimization, and discloses a field programmable gate array multi-resource division method, device and equipment, the method comprises the following steps: generating a hypergraph comprising vertexes and hyperedges based on a netlist, the hypergraph comprising one or more types of resources; carrying out first partition division on the hypergraph, and carrying out multi-level vertex combination; partitioning the hypergraph of the maximum merging hierarchy, dividing vertexes in the maximum merging hierarchy to each partition, and carrying out the following processing on the hypergraph of each hierarchy from the maximum merging hierarchy to the minimum merging hierarchy: moving a first vertex between the partitions in the target merging hierarchy, and meeting the multi-resource constraint of the partitions in the moving process; splitting the vertexes in the target merging hierarchy into the vertexes of the previous hierarchy of the target merging hierarchy; until a first hypergraph is obtained; moving a first vertex in the first hypergraph to obtain a first partitioning result; and carrying out partition division again until the position of each functional module is determined.

The invention relates to the technical field of power system automation, in particular to a source-grid-load-storage multi-resource inertia quantitative calculation method, device and equipment and a medium, and the method comprises the steps: constructing a power supply side inertia support model based on the output characteristics of a conventional synchronous generator, wind power generation and photovoltaic power generation, and calculating the equivalent inertia of a power supply side; inertia support models of a power grid side, a load side and an energy storage side are respectively constructed by combining inertia characteristics of a direct-current power transmission system, a motor, flywheel energy storage and electrochemical energy storage; deducing a unified inertia calculation model of source-network-load-storage multi-resource collaborative support according to the inertia support model of each side and the commissioning capacity proportion; the inertia of each resource side of the power grid is integrated and calculated by deriving the performance of the internal source-grid-load-storage resources of the power grid in different commissioning ratios, and a source-grid-load-storage multi-resource inertia quantitative calculation method is obtained, so that inertia quantification of multi-resource cooperative support is realized, and the system frequency stability is improved.

An electronic device (such as an access point) is described. This electronic device transmits an orthogonal frequency division multiple access (OFDMA) frame to a recipient electronic device (such as a client or a station). The OFDMA frame includes multiple predefined resource units (RUs) allocated to the recipient electronic device in a set of predefined RUs having associated frequency bandwidths. Moreover, the multiple predefined RUs include two or more first predefined RUs having a first number of tones less than a predefined amount, or two or more second predefined RUs having a second number of tones greater than or equal to the predefined amount. For example, the predefined amount may include 242 tones. Note that the multiple predefined RUs may have the same or different numbers of tones. Moreover, the electronic may receive an acknowledgment or a block acknowledgment from the recipient electronic device.

The invention discloses a feeder load and multi-type resource cooperative control power distribution network multi-target peak regulation method. The method comprises the following steps: establishing a feeder load power control-based power distribution network multi-target peak regulation optimization model and constraint conditions; a peak regulation optimization model based on feeder line load and multi-resource cooperative control is established on the generated photovoltaic feature scene uncertainty set; and model solving is carried out based on an improved multi-target particle swarm optimization algorithm in combination with an approximate ideal solution sorting method based on information entropy weight establishment, and a power distribution network multi-target peak regulation optimization strategy is obtained. On the premise that a peak regulation model based on feeder load power control is efficiently and accurately solved, collaborative optimization is carried out on feeder loads and multi-type distributed voltage regulation equipment, and peak clipping and valley filling are achieved on a power distribution network while it is ensured that economic benefits are optimized and the voltage does not exceed the limit.

The invention belongs to the technical field of power grid system flexibility optimization, and particularly discloses a multi-resource optimal configuration method giving consideration to supply guarantee absorption under a high-proportion new energy power grid, and the method comprises the steps: quantifying the flexibility demand of a power grid system, obtaining a flexibility demand interval and a flexibility supply interval, and calculating a flexibility supply and demand balance interval of the power grid system; calculating supply insurance and consumption flexibility evaluation indexes according to the flexible supply and demand balance interval; constructing a multi-type flexibility resource optimization configuration model by taking economic cost and flexibility risk penalty terms as optimization targets based on supply insurance and consumption flexibility evaluation indexes; and solving the multi-type flexibility resource optimal configuration model by using an MILP method, and outputting a multi-type flexibility resource optimal configuration scheme when a difference value between the supply guarantee flexibility index and the consumption flexibility index is reduced to a preset threshold value. According to the method, the problem of contradiction between high permeability of new energy and weak net rack adaptability is solved, and the system load supply capacity and the new energy consumption rate are both improved.

The invention belongs to the technical field of electrical engineering, and discloses a power distribution network source-network-storage multi-resource collaborative optimization method and system, and the method comprises the steps: segmenting the estimated fault repair time, obtaining an SOP control mode and the number of uncharged islands in each time period, and carrying out the optimization of the SOP control mode and the number of uncharged islands; and constructing a controllable power supply-new energy coordinated micro-grid partition model taking recovery of total load power as an optimization target and taking various resource constraints as conditions, and solving to obtain SOP opening and closing states in each time period as a fault recovery scheme. According to the method, various flexible resources such as distributed new energy, a controllable power supply and stored energy in a future power distribution network are fully considered, and optimization and coordination of the resources in fault recovery are realized through segment processing and model construction. By adopting the method, the problem that the existing fault recovery means cannot comprehensively consider various resources is effectively solved, various flexible power generation resources can be more reasonably integrated, the recovery effect of the power distribution network after the fault is effectively improved, and the influence caused by power failure is reduced.

The invention provides a power transmission section transmission capacity analysis method and system considering a multi-resource coupling relationship, and the method comprises the steps: carrying out the three-permanent fault testing of a transmission line in a full wiring state and a maintenance state, and dividing a transmission section cut set based on the voltage stability and power angle stability sensitivity; in a normal operation state, carrying out on-off operation on the restricted fault line, and checking and obtaining a power transmission limit of a sending-out section in a static and stable state; in a normal operation state and a maintenance state, through adjusting a coupling relation among a load, new energy and starting-up, checking and obtaining a sending-out section power transmission limit of a restricted fault line under transient stability; and according to the power transmission limits of the sending-out section under static stability and transient stability, an operation rule is formulated. According to the method, the coupling relation of multiple resources is adjusted, and the power transmission limit which restricts the fault line to be transmitted out of the section under static and transient stability is checked and obtained, so that the accuracy and reliability of analyzing the transmission capacity of the power transmission section are improved.

The invention discloses a source network load storage oriented multi-resource hierarchical regulation and control method and system, and relates to the technical field of source network load storage hierarchical regulation and control. The multi-resource hierarchical regulation and control method oriented to the source network load storage comprises the following steps of source-network transmission matching analysis, network-load scheduling analysis and load-storage collaborative analysis. According to the invention, in the source network transmission stage, transmission matching analysis is carried out according to source network double-side data to determine whether transmission matching optimization regulation is carried out, then in the power distribution stage, load scheduling analysis is carried out to determine whether load scheduling optimization regulation is carried out, and finally in the load-storage linkage stage, load scheduling optimization regulation is carried out. Load-storage collaborative analysis is carried out according to the load-storage bilateral data to judge whether load-storage collaborative optimization regulation is carried out or not, so that timely and efficient source-network-load-storage multi-resource layered regulation is realized; the problem that in the prior art, when renewable energy sources are accessed, corresponding multi-resource layered regulation and control are not timely, and consequently, the'source-network-load-storage 'cooperation stability is not high is solved.

The invention discloses a virtual power plant-oriented multi-resource aggregation participation power grid regulation potential evaluation method, and relates to the technical field of virtual power plant management, and the method comprises the steps: carrying out the resource classification of a virtual power plant, and analyzing the resource characteristics; performing fine evaluation on the resource scheduling potential of the virtual power plant; and calculating a virtual power plant resource scheduling potential value, and issuing a regulation and control instruction. According to the virtual power plant-oriented multi-resource aggregation participation power grid regulation potential evaluation method provided by the invention, aggregation and classification management of distributed photovoltaic, energy storage systems, controllable loads, electric vehicles and other dispersed resources and characteristic analysis of flexible loads, distributed power generation resources and source loads are carried out; the virtual power plant can provide more flexible power grid regulation services, such as frequency regulation and peak reduction, and can optimize resource configuration, more reasonably dispatch the resources and improve the use efficiency of the resources at the same time.

The invention discloses a service request processing method and device, a medium, equipment and a program product, and relates to the technical field of smart home / smart home. According to the method, a first service request is received, the first service request comprises at least one target resource, a lock identifier is generated for the first service request, the lock identifier comprises a serial number used for identifying the receiving sequence of the first service request, and the lock identifier of the first service request is added into distributed queues corresponding to the at least one target resource. Under the condition that the lock identifier of the first service request is arranged at the head in the distributed queue corresponding to the at least one target resource, requesting to allocate a respective resource lock of the at least one target resource for the first service request; according to the method, the uniform lock identifier containing the serial number is generated for the service request, and resource lock coordination is performed based on the distributed queue, so that the consistency of a multi-resource locking sequence is ensured, deadlock is effectively avoided, and the stability and reliability of service processing in a high-concurrency scene are improved.

The invention discloses a multi-resource collaborative power system flexibility evaluation method and device, terminal equipment and a storage medium, and belongs to the field of power systems. The method comprises the following steps: acquiring historical output data, power transaction cost, resource operation data and power grid power flow data of a to-be-evaluated power system; constructing a power system time sequence model and model operation constraints according to typical daily source load output data, power transaction cost, resource operation data and power grid power flow data obtained according to historical output data; solving the power system time sequence model under the model operation constraint to obtain the output of each resource at each moment and the charging and discharging state of the energy storage equipment; and solving the constructed flexibility aggregation evaluation model according to the output of each resource at each moment and the charging and discharging state of the energy storage equipment to obtain geometrical characteristic parameters corresponding to the multi-resource coupling feasible region, and evaluating the to-be-evaluated power system to obtain a flexibility evaluation result. By implementing the method, the problem of low flexibility evaluation accuracy in the prior art can be solved.

The invention relates to the technical field of power system scheduling, and provides a microgrid group scheduling method and system based on multiple resources, and the method comprises the steps: constructing a centralized task scheduling center based on a microgrid group; basic resource parameters of the micro-grid group are collected, and resource models are constructed for different resources of the micro-grid group; constructing a resource scheduling model based on the centralized task scheduling center and the resource model; solving the resource scheduling model in the resource scheduling model by taking minimization of the operation cost of the micro-grid group and maximization of the profit of the centralized task scheduling center as targets to obtain a micro-grid group scheduling scheme, and scheduling the micro-grid group through the centralized task scheduling center based on the micro-grid group scheduling scheme. According to the method, the coordination and the utilization efficiency of different resources in the micro-grid group are remarkably improved.

The invention provides a source network load storage oriented multi-resource aggregation regulation and control method and system, and relates to the technical field of electric power intelligent scheduling, and the method comprises the steps: obtaining the real-time electric power operation data of each electric power decision point to be subjected to optimal scheduling in a power grid system; constructing power operation time sequence data for each power decision point, and inputting the power operation time sequence data into a preset power operation prediction model to determine corresponding estimated power operation data; constructing a target graph network based on the estimated power operation data corresponding to each power decision point; and inputting the target graph network into a preset reinforcement learning model, so that a corresponding target management scheduling scheme is determined for each node in the target graph network based on the reinforcement learning model. Therefore, by performing fusion analysis on the data of various power decision points of the source network load storage in the power grid system, multi-resource aggregation regulation and control oriented to the source network load storage are realized.

The invention discloses an energy storage and multi-resource optimization scheduling method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining the historical operation data of a power system, and constructing an energy storage and multi-resource optimization scheduling model; defining an energy storage and multi-resource optimization scheduling model as a Markov decision process, and giving a state space, an action space and a reward function; packaging the defined state space, action space and reward function of the Markov decision process into a reinforcement learning environment; based on historical operation data of the power system, interactive learning is carried out through a reinforcement learning environment and a reinforcement learning algorithm, agent actions are executed, an environment state is updated, a reward value is calculated, and an agent is trained; and inputting the trained intelligent agent into the energy storage and multi-resource optimization scheduling model to obtain an optimal scheduling scheme. According to the method, the adaptive capacity to the uncertainty of new energy can be improved, and the optimal scheduling scheme can be quickly given online.

The invention discloses a parallel machine batch flow scheduling method considering multi-resource constraints. The method comprises the following steps: constructing a mixed integer linear programming model considering cutter resources and worker differences; determining constraint conditions of the mathematical model; coding and decoding based on the improved grey wolf-whale swarm algorithm are set, and then workpiece batches, procedures and machines are coded; initializing the population and selecting a guiding individual for each grey wolf; selecting corresponding interaction modes for workpiece batching, machine distribution and sub-batch sorting to generate a next generation population; performing non-dominated sorting on the new-generation population to select offspring; judging whether a termination condition is met or not, and if not, returning to the step of interactively generating a new generation of population; and according to a decoding mode based on the improved grey wolf-whale swarm algorithm, decoding is carried out, and then a corresponding machine-process Gantt chart and a corresponding worker-process Gantt chart are output. According to the method, the parallel machine production considering the multi-resource constraint can be efficiently scheduled.