177 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 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.

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 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.

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.

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 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 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 discloses a supervision and control-based punctual scheduling method and system for wafer combination equipment, and mainly solves the problems of high probability of blockage, path conflict and low scheduling efficiency during multi-resource parallel operation of the existing wafer combination equipment. According to the implementation scheme, the method comprises the following steps: respectively constructing corresponding automaton models for process units in equipment, and carrying out synchronous splicing according to a real process flow to form an overall model capable of reflecting dynamic behaviors of a system; a corresponding constraint model is established by combining factors such as process sequence constraint, equipment capacity limitation, carrying time constraint and residence time limitation; and the supervision controller continuously judges whether the event to be executed can cause blockage or not, gradually eliminates the blockage by disabling the controllable event, triggers the optimal event according to the priority of the event, and guides the scheduling path to propel and output the optimal scheduling scheme along the optimal direction. According to the method, the overall model capable of being dynamically adjusted is constructed, the event priority and multi-class constraint collaborative optimization scheduling is utilized, the wafer residence time can be remarkably shortened, the overall scheduling efficiency is improved, and the method can be used for operation inspection, evaluation and optimization of an equipment system.

The invention discloses a ship pipe fitting workshop scheduling method, system, equipment and medium based on a large model assisted evolutionary algorithm, and relates to the technical field of ship pipe fitting workshop production operation, and the specific steps are as follows: establishing a hybrid flow workshop scheduling model, adopting a multi-dimensional coding and decoding structure to code a workshop scheduling scheme, generating an initial population, and establishing a hybrid flow workshop scheduling model; and guiding the individuals to carry out iterative evolution by using a search operator sequence generated by the large model based on a historical evolution effect or a randomly generated search operator sequence, and outputting an optimal scheduling scheme with the shortest completion time. According to the method, modeling and solving of a multi-resource collaborative complex scheduling problem are realized, and the problem of insufficient scheduling scheme quality caused by neglect of transportation processing coupling or low algorithm search efficiency in an existing method is solved. By introducing a large-model intelligent guiding mechanism, the evolutionary process has adaptive optimization ability, and the feasibility of a scheduling scheme is ensured through a complete coding and decoding framework.

The invention discloses a county isolated network frequency correction control system supported by multiple resources cooperatively, which relates to the technical field of intelligent power grids and comprises a control center, and the control center is connected with a resource acquisition module, a cooperative processing module, a support analysis module and an intelligent correction module. Constructing a power grid comprehensive layout map, collecting power grid composite data, extracting an isolated grid regional frequency to construct a regional frequency dynamic graph, and performing frequency tense judgment on the regional frequency dynamic graph to obtain an isolated grid frequency judgment result; performing frequency measurement updating on the power grid comprehensive layout diagram to obtain an abnormal power grid marking diagram; performing anomaly grading on the abnormal power grid mark graph to obtain a node frequency deviation degree, and performing frequency supplementary rescue on the isolated network block according to the node frequency deviation degree to obtain a dynamic correction scheme; the frequency control capability is improved and the power system stability is enhanced.

The invention discloses a multi-constraint genetic optimization heterogeneous resource collaborative scheduling method, and belongs to the technical field of intelligent optimization algorithms, and the method comprises the steps: determining platform basic information and task target attribute parameters, taking the distribution condition of platform resources and task targets as a genome, and randomly selecting a large number of chromosomes as an initial population; based on task target benefit evaluation, resource consumption, platform priority, task response speed and a multivariate evaluation model in a task window set in a special scene, calculating a fitness score condition of each generation of individuals; a dynamic penalty function is adopted to process resource capacity and time constraints, and a non-inferior solution is reserved to participate in genetic iteration. The invention aims to solve the problem of allocation of limited resources considering time window constraints in a multi-task scene, ensures achievement of a task target and maximization of a resource utilization rate by optimizing a matching relationship between the resources and the tasks, provides technical support for automatic decision making and intelligent scheduling in a complex scene, and improves the resource utilization rate. The method is suitable for scenes requiring multi-resource joint optimization.

The invention belongs to the technical field of resource scheduling, and discloses a dynamically constrained multi-resource scheduling method and system based on a decision threshold, and the method comprises the steps: obtaining a real-time resource state and a current environment constraint state, and calculating a value function under the current resource state and the current environment constraint state; constructing a multi-source dynamic resource coupling system model, establishing a cross-state optimization equation set based on the model and the value function, and solving the cross-state optimization equation set to obtain a decision threshold; constructing a two-state environment constraint indicator, judging an executable state of an environment constraint state based on the indicator, calculating a resource proportion according to a real-time resource state, and obtaining a relative state index; and based on the decision threshold and the executable state, constructing a resource scheduling dynamic decision engine, and performing real-time decision on multi-resource scheduling. According to the method, the dynamic decision engine is constructed to process the resource state and the constraint state in real time, a scientific threshold type decision signal is output, and the decision efficiency of resource scheduling with constraints is remarkably improved.

The invention discloses a productivity calculation method and system oriented to process multi-resource priority allocation. The method comprises the following steps: acquiring process route information of a target product, extracting selectable resources corresponding to each process, priorities of the resources and unit capacity occupation parameters, and acquiring capacity calendar information of the resources under a preset time granularity; calculating an allocable capacity upper limit of each resource in each time period based on the capacity calendar and the unit capacity occupation parameter; allocating the requirements of each process according to the resource priorities, preferentially allocating to high-priority resources, and sequentially allocating to secondary-priority resources when the remaining requirements are not met; identifying productivity bottleneck resources based on the preliminary allocation result, and performing reallocation on the bottleneck related allocation until a preset stop condition is reached on the premise of meeting the priority and allocatable upper limit constraints; and outputting a final distribution result. According to the method, a convergent allocation result can be obtained in each resource allocatable upper limit constraint, so that the production satisfaction rate is integrally improved.

The invention provides a power distribution network multi-resource cooperative voltage stability control method and system and a storage medium, and belongs to the technical field of power distribution network voltage regulation and control. The method comprises the following steps: S1, configuring a to-be-regulated and controlled resource set of the power distribution network, acquiring resource parameters of the power distribution network according to the to-be-regulated and controlled resource set of the power distribution network, and generating a regulation resource pool; s2, constructing an LSTM-Transform hybrid model, and generating a photovoltaic output spatial-temporal distribution prediction curve of the power distribution network in a future preset time in a rolling manner based on the LSTM-Transform hybrid model and the adjustment resource pool; s3, constructing a voltage immune control constraint condition according to a Lyapunov stability condition; and S4, generating a multi-resource cooperative scheduling instruction based on the photovoltaic output space-time distribution prediction curve of the power distribution network and the voltage immunity control constraint condition, and executing the multi-resource cooperative scheduling instruction. According to the invention, by executing the multi-resource cooperative scheduling instruction generated based on the voltage immunity control constraint condition and the photovoltaic output prediction result, the voltage fluctuation suppression effect of the power distribution network can be effectively improved.

The invention discloses a power grid multi-resource joint collaborative optimization peak-load regulation scheduling method and a power grid multi-resource joint collaborative optimization peak-load regulation scheduling system. The method comprises the following steps: S1, collecting power grid data, and analyzing a support mechanism and a mutual coupling influence mechanism of source network load storage multi-resources on power grid peak regulation and supply guarantee according to the power grid data to obtain a resource analysis result; s2, establishing a peak regulation effect evaluation index system according to a resource analysis result in S1; s3, according to the resource analysis result in the S1 and the evaluation index system in the S2, establishing a power grid multi-resource joint collaborative optimization peak regulation scheduling model; and S4, substituting the resource analysis result in the S1 into the power grid multi-resource joint collaborative optimization peak regulation scheduling model in the S3, and solving to obtain an optimal scheduling plan of the power grid under the condition of difficult peak regulation and supply guarantee. According to the method, the scheduling result can be evaluated from multiple aspects of safety, adequacy, economy and high efficiency, and a scheduling scheme optimization reference is provided for scheduling personnel.

The application discloses a kind of distributed reservation systems and methods for multi-resource cooperation, comprising: transaction manager, at least two resource managers, lock manager, transaction log module and workflow controller;Transaction manager coordinates each resource manager through two-phase commit 2PC protocol, realizes the atomicity execution of cross-node shadow and summary table update;Lock manager adopts read-write lock separation mechanism to resolve concurrent conflict, and handles deadlock problem through timeout detection and active unlocking strategy;Transaction log module is based on prewrite log mechanism Complete record shadow and summary table linkage operation details, provide reliable support for fault recovery and transaction rollback;Workflow controller encapsulates distributed call details, realizes service survival real-time detection and automatic reconnection function.The application has high expansibility, strong consistency, high concurrent adaptation capability and perfect exception fault-tolerant mechanism, and can be widely applied to travel service scene of multi-node deployment.

The invention relates to the technical field of cloud computing and containerization, and discloses a containerization operation and maintenance orchestration engine and a resource dynamic scheduling method in a multi-cloud environment. The method comprises the steps of collecting and integrating multi-cloud environment information, collecting service characteristics of each cloud platform, carrying out analysis and modeling of containerized application, constructing an application model according to an analysis result, formulating a resource dynamic scheduling strategy, and executing the scheduling strategy by utilizing a containerized operation and maintenance orchestration engine. The multi-resource dynamic scheduling method solves the problems that in a traditional resource dynamic scheduling prediction method, resource demand and supply prediction is not accurate, resource dynamic scheduling lacks flexibility and intelligence, and complexity of a multi-cloud environment cannot be effectively handled, and a complex change mode of resource demand is accurately captured; dynamic changes of cloud platform resource supply are effectively coped with, data are collected in real time, a prediction model is optimized through a machine learning algorithm, an accurate basis is provided for a resource dynamic scheduling strategy, and efficient and intelligent resource scheduling is achieved.

The present invention belongs to the technical field of power grid system flexibility optimization, and specifically discloses a multi-resource optimization configuration method that takes into account both supply and consumption under a high-proportion new energy power grid, including: quantifying the flexibility demand of the power grid system, obtaining the flexibility demand interval and the flexibility supply interval, and then calculating the flexibility supply and demand balance interval of the power grid system; calculating the supply and consumption flexibility evaluation index based on the flexibility supply and demand balance interval; based on the supply and consumption flexibility evaluation index, taking the economic cost and flexibility risk penalty items as optimization goals, constructing a multi-type flexibility resource optimization configuration model; using the MILP method to solve the multi-type flexibility resource optimization configuration model, when the difference between the supply flexibility index and the consumption flexibility index drops to a preset threshold, outputting the optimal configuration plan for the multi-type flexibility resources. The present invention solves the contradiction between the high penetration rate of new energy and the weak grid adaptability, and takes into account the improvement of the system load supply capacity and the new energy consumption rate.

The invention provides a provincial and regional cooperative scheduling method and system for a novel source-load-storage multi-resource power system. According to the method, under the background that source load storage and new energy are widely connected to a power grid, operation requirements such as power supply guaranteeing, new energy absorption guaranteeing and key section out-of-limit adjustment occur in provincial dispatching, main transformer and line equipment overload and reverse transmission conditions occur in local dispatching, and a provincial and local coordinated dispatching method considering source load storage multi-resource is provided; according to the method, the adjustable capacity of various resources of provincial dispatching and local dispatching source load storage is fully considered, provincial dispatching business requirements and local dispatching business requirements are processed through provincial and local interaction, and meanwhile, the verification process of safety verification of related resource equipment within the range of provincial dispatching and local dispatching application for adjusting provincial dispatching management resources is considered. And the new energy consumption capability and the system operation reliability in the power system can be effectively improved.

The invention discloses a digital circuit matrix peak point detection method with low resource occupation. The method comprises the following steps: firstly, creating a 3 * 3 data comparison matrix; then, sequentially comparing the sizes of central point data and peripheral data of the 3 * 3 data comparison matrix to judge a peak point; then, the position of the 3 * 3 data comparison matrix is moved, and whether data in the original 3 * 3 data comparison matrix is a peak point or not is judged in a traversal mode, the method greatly saves resource occupation of a digital circuit in a chip achieved through an algorithm, the whole row of matrix data does not need to be cached, hardware resources of a multi-path data selector do not need to be consumed, and the method is easy to implement. Compared with a traditional index cache data method, the cache area proportion of a digital circuit is 3 / C (C is the number of matrix columns), namely the larger the number of the matrix columns is, the larger the resource income is.