125 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 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 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 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 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 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 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 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.

To provide a resource allocation method capable of setting an accurate resource unit (RU) / multi-resource unit (MRU) index to a station.SOLUTION: In a wireless local area network (WLAN), a resource allocation method determines, by a transmitting end, a bandwidth indication and an RU allocation indication based on RUs / MRUs allocated to a physical layer protocol data unit (PPDU). The band indication indicates an index of a band of the RU / MRU, an index range corresponding to the band indication in the 320MHz bandwidth is reused for an index range of the band indication, the RU allocation indication indicates an RU / MRU index corresponding to the RU / MRU on the band, and if the bandwidth of the PPDU is less than or equal to the 80MHz, the index range corresponding to the RU allocation indication in the 80MHz bandwidth is reused for an index range of the RU allocation indication.SELECTED DRAWING: Figure 12

Logic to generate a 320 megahertz (MHz) extremely high throughput (EHT) physical layer protocol data unit (PPDU) comprising a multi-resource unit (MRU). Logic to generation of a non-orthogonal frequency division multiple access (non-OFDMA) 320 MHz EHT PPDU, wherein the MRU comprises a 240 MHz multi-MRU, the 240 MHz MRU comprising at least two contiguous 996 tone resource units (RUs). Logic to generation of an OFDMA 320 MHz EHT PPDU, wherein the MRU comprises at least one 996 tone RU and one 484 tone RU. Logic to cause the transmission of the 320 MHz EHT PPDU. And logic to receive and decode the 320 MHz EHT PPDU.

The invention relates to the technical field of logistics storage intelligent scheduling, and discloses a logistics storage intelligent scheduling method and system. The method comprises the following steps: constructing a virtual storage model and injecting a real-time operation data stream to form a storage dynamic twinborn body; implanting a self-adaptive scheduling logic core in the twin body, and performing multi-dimensional task analysis through the core to generate a task decomposition atlas; resource matching operation is carried out in the logic partition based on the atlas, and a resource matching scheme is output; generating a control instruction sequence containing time sequence constraint according to the scheme and issuing the control instruction sequence to a physical execution terminal; and collecting a feedback data stream to correct the twinborn state. Through real-time data flow driving, synchronous sensing and instantaneous response of the storage state are realized, and the scheduling deviation caused by data updating delay of a traditional system is solved. Through the task decomposition atlas, deep structured analysis and multi-resource collaborative matching of complex tasks are realized, and the overall utilization efficiency of resources and the task execution fluency are improved.

The application discloses a multi-resource task planning method and system based on reinforcement learning and performance evaluation, and the target task is subjected to resource calculation and cooperative strategy calculation based on a target behavior threat library, a task resource library and a cooperative strategy library which have been constructed to obtain a cooperative planning strategy; different means task resource data are accessed through a network, and the data are collected and analyzed through middleware to form a multi-means task resource library; and the application forms a heterogeneous task resource cooperative behavior strategy according to existing task resource occupation conditions and target behavior threat analysis calculation, and supports multiple task resources to cooperatively complete the same target task.

The present invention provides a multi-timescale, multi-resource collaborative scheduling method for low-voltage interconnected distribution substations, comprising the following steps: S1, constructing a low-voltage interconnected distribution substation architecture and a loss model for key equipment; S2, constructing a multi-timescale, multi-resource collaborative scheduling model for low-voltage interconnected distribution substations with the objectives of optimizing system operation economy, three-phase imbalance, and overall voltage deviation; S3, collecting long- and short-timescale distribution network source-load data; S4, inputting the long-timescale distribution network source-load data into the collaborative scheduling model to obtain OLTC and CB action schemes; S5, inputting the short-timescale distribution network source-load data and OLTC and CB action schemes into the collaborative scheduling model to obtain F-SOP and commutator action schemes; and S6, integrating the OLTC, CB, F-SOP, and commutator action schemes to obtain the multi-timescale, multi-resource collaborative scheduling result.

The application discloses a kind of ground-based radar multi-resource adaptive joint scheduling methods for high-speed target, first in noise and clutter background, ground-based radar system receives and carries out signal processing to the echo in detection airspace, obtains the plot information of detection airspace, then the plot velocity of each wave position is analyzed, then the wave position that appears cross-range cell walk phenomenon calculates the cross-cell walk energy loss coefficient of high-speed target and constructs multi-resource optimization function, finally the adaptive scheduling result of space-time resource and false alarm probability resource is obtained by using CVX toolbox solution, until the overall target detection process is completed.The method of the application considers the energy loss caused by high-speed target range migration, and combines ground-based radar multi-resource cooperative scheduling, realizes the reasonable use of resources while improving the detection performance, the detection performance of high-speed target in clutter and noise environment is significantly improved, and the adaptive optimization of multi-resource cooperative scheduling is realized.

This invention discloses an optimized scheduling method for virtual power plants with multiple resource types, belonging to the field of virtual power plant technology. This method aims to solve the technical challenges of delayed scheduling response due to the strong volatility of distributed resource output, insufficient adaptability to multiple scenarios, and weak distribution network resilience under renewable energy access. The method establishes a multi-resource power adjustable range model based on real-time data, uses linear scaling and displacement algorithms to generate aggregated power domain boundaries, and adjusts the weights according to the different target types of the virtual power plant. It combines ridge regression conditional expectation correction and sub-Bruker optimization to construct a dynamic risk budget, optimizing generation plans and reserve capacity in the day-ahead phase. In the real-time phase, it responds in stages according to power deviation levels, triggering rapid energy storage compensation or emergency interruption of controllable loads. The model parameters are iteratively corrected through a closed-loop feedback mechanism. This method significantly improves the response accuracy and reliability of virtual power plants in frequency regulation, peak shaving, and self-balancing scenarios.

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.

Aspects described herein relate to receiving control information related to scheduling multiple resources for wireless communications, including one of semi-persistent scheduling (SPS) for multiple downlink resources for receiving downlink communications or configured grant (CG) scheduling for multiple uplink resources for transmitting uplink communications, and transmitting, for a resource of the multiple resources, in an uplink resource, and based on receiving the control information, a predicted channel report indicating predicted channel measurements for the resource before receiving a downlink communication or transmitting an uplink communication over the resource. Other aspects relate to transmitting the control information and receiving the predicted channel report.

The invention provides an atomization multi-resource data transmission optimization method. Metadata and a dependency relationship of the atomized resources are obtained by scanning project source codes, a resource dependency graph is constructed, and logic association and a hierarchical structure among the resources are accurately described; intelligent clustering and grouping are carried out based on the atlas in combination with resource use frequencies, and high-frequency co-occurrence and functional coupling resources are divided into the same resource group, so that the subjectivity and redundancy problems are avoided; the resource group is packaged into the set package file, and the list file is generated, so that aggregation packaging of the atomized resources is realized, and the integrity of resource delivery is ensured; in the application stage, client requests are intercepted, and corresponding set packages are loaded based on list file matching, so that the actual network request times are reduced, and the head overhead and the head-of-queue blocking risk are reduced. Therefore, not only is the performance bottleneck caused by a large number of small file requests solved, but also the on-demand loading and caching efficiency of the resources is guaranteed through a dynamic grouping mechanism, and the flexibility and execution efficiency of front-end resource transmission are fundamentally improved.

The invention belongs to the field of cloud computing, and provides a computing power network work load resource mode prediction method, device, equipment and medium, and the method comprises the steps: determining a shape set according to a work load set, carrying out the processing through a membership function, obtaining the membership degree of the work load to a mode fragment with the combined multi-resource utilization rate, and obtaining the mode fragment with the combined multi-resource utilization rate; further determining time and space coupled workload distribution of the computing power network; determining a first-order dynamic neighbor load set according to the working load, and determining a non-stationary fuzzy set according to the first-order dynamic neighbor load set and the fuzzy set; determining fuzzy representation of competition of a working load resource mode in the target computing power network by adopting an r-hop space tuple and a link loss factor; and predicting the mode of the workload resource of the target computing power network in the next time slot by adopting a multi-order adaptive cross entropy loss function. The method has the beneficial effect that the prediction accuracy of the working load resource mode of the computing power network is improved.