74 results about "Multi resource" patented technology

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 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 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.

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 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.

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.

Embodiments of this application disclose a multi-resource-unit transmission indication method, and a related device. The method includes: A first device generates an EHT TB PPDU, where the EHT TB PPDU includes a resource unit indication field, the resource unit indication field is used to indicate a first resource unit used by the first device to transmit the EHT TB PPDU, and the first resource unit is included in a resource unit allocated to the first device; and the first device sends the EHT TB PPDU to a second device. In the embodiments of this application, the RU used by the first device to actually transmit the EHT TB PPDU can be indicated. The technical solution provided in this application may be applied to an 802.11ax Wi-Fi system, an 802.11be Wi-Fi system, or the like.

The invention discloses a computing power resource scheduling method and device, a storage medium and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-dimensional resource data of at least one resource region in response to a resource request of a user; determining a candidate resource region from the at least one resource region based on the multi-dimensional resource data, wherein the candidate resource region is a resource region meeting a resource constraint condition corresponding to the resource request; determining cost data of resource scheduling of the candidate resource area based on the multi-dimensional resource data; and determining a target resource area in the candidate resource areas according to the cost data, wherein the target resource area is used for resource scheduling based on the resource request. According to the method, the multi-dimensional resource data of the multi-resource region is acquired, the candidate resource regions conforming to the constraint are screened, and the target region is determined in combination with the cost data, so that the utilization rate and reasonability of the computing power resource are improved, a resource scheduling scheme is ensured to adapt to the actual demand, and the global resource configuration efficiency is improved.

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

This invention discloses a multi-resource collaborative phase-by-phase fault self-healing method across voltage levels, belonging to the field of power grid fault self-healing. The invention includes: constructing a power supply and load information database covering the entire power grid based on the power grid's overall structure; wherein the power grid's overall structure includes faulty networks and fault-free networks across voltage levels, with the three phases of the faulty and fault-free networks connected via phase-by-phase converters; evaluating each outage node in the faulty network based on the power supply and load information database, determining the fault self-healing strategy for the current outage node; establishing an optimization model for the phase-by-phase converter switching matrix; solving the optimization model for the phase-by-phase converter switching matrix, and outputting the optimal switching matrix for the phase-by-phase converter. This invention achieves phase-by-phase power mutual assistance across voltage levels, while reducing energy waste caused by wind and solar curtailment, and improving the economic efficiency of power grid operation.

The present application belongs to the field of computer simulation and optimization algorithm, and relates to a multi-task cooperative scheduling and resource optimization method and system based on slime algorithm. The method comprises: constructing a multi-task and multi-resource cooperative scheduling model, establishing the allocation relationship between tasks and resources, and introducing time window constraints, resource capacity constraints and task priority constraints; encoding the scheduling scheme to represent it, and mapping it into a searchable solution space; constructing a multi-objective optimization model containing task completion time, running cost and resource energy consumption, and realizing multi-objective coordinated optimization through dynamic weight; using slime algorithm for iterative solution, optimizing the scheduling scheme through fitness evaluation, weight updating and position adjustment, and improving the solution performance by combining adaptive parameter adjustment and random disturbance; and outputting the optimal or near-optimal scheduling scheme after meeting the termination condition. The present application can improve resource utilization efficiency and scheduling performance, and is suitable for complex system scheduling optimization.

The application discloses a kind of SDN multi-resource pool authentication method, device, equipment, medium and product, the method includes when detecting that target user selects target resource pool, with the authority check request of resource pool information of target resource pool is sent to authentication service;When receiving the authority check result that authentication service returns after receiving authority check request, if authority check result passes, then allow switching to target resource pool;With the operation request of resource pool information is sent to target service of target resource pool;When receiving the operation result that target service executes operation request after operation request is authenticated, operation result is presented to target user.The multi-resource pool is introduced, and resource pool information is carried in the request to be sent to the service in corresponding resource pool to complete authentication, can take the microservice in the range of multi-resource pool as the authentication object, realize resource pool extensible, increase the authority division of resource pool granularity, and enrich the level of authority management.

A resource unit combination indication method and a communication apparatus are provided. The resource unit combination indication method includes: A transmit end generates a physical layer protocol data unit (PPDU), and sends the PPDU. The PPDU includes a resource unit allocation subfield. The resource unit allocation subfield indicates a multi-resource unit (MRU) to which a 242-tone resource unit (RU) corresponding to the resource unit allocation subfield belongs, and further indicates a quantity of user fields corresponding to the resource unit allocation subfield. According to the application, one or more users may be indicated to use a plurality of contiguous or noncontiguous RUs for data transmission, to improve RU allocation flexibility of a system and spectrum utilization of the system.

The invention provides a cloud desktop resource adaptive allocation method considering network bandwidth constraints, and belongs to the technical field of cloud computing and virtual resource management. Aiming at optimization management of various types of resources in cloud computing and virtualization environments, intelligent and refined collaborative scheduling is realized. According to the method, the allocation priority of resources is dynamically judged by analyzing the actual service type and historical bandwidth use characteristics of a user in real time, and multi-resource joint optimization is carried out in combination with a bandwidth sensitivity model, so that self-adaptive efficient allocation of resources such as a CPU (Central Processing Unit), a memory and bandwidth is realized. The system has self-learning and real-time adjustment capabilities, and can effectively improve the resource utilization rate, reduce service delay, slow down network congestion, and improve the overall operation efficiency of the platform and the user experience. The method is suitable for the field of resource management and scheduling in a cloud platform, a big data center and a new-generation virtualization system.

Disclosed are a computer-implemented method and a computer-implemented system for orchestrating hybrid quantum-classical execution between high-performance computing (HPC) infrastructure and one or more quantum processors (QPU). An orchestration module breaks down a work flow into subtasks and, for each subtask, compiles multiresource HPC and QPU processing telemetrics, normalises same to a canonical vector and evaluates proposed plans under hard budget constraints (time, executions, cost and / or depth). The system automatically selects an execution resource (HPC or QPU) and, when executed in an QPU, applies tiered error mitigation and verifies the result using an acceptance gate. In the event of rejection, the system executes a deterministic recovery policy with retry, execution resource switching and / or backup routing to HPC. An auditable record of decisions and metrics is generated for traceability and feedback.

The invention provides a resource allocation method of a data center, electronic equipment, a storage medium and a product. The method comprises the following steps: acquiring delay time of an online application running in a data center and utilization rates of various resources corresponding to the online application in real time; determining a target online application needing resource adjustment based on the delay time and a first preset threshold value, determining a target resource based on the utilization rate of the resource corresponding to the target online application, and increasing the target resource based on a preset minimum resource unit; and circularly executing the steps until determining that the target online application does not exist based on the comparison result. Therefore, the resource quota of the resource with the maximum use demand is increased according to the priority of the resource demand, and the resource quota is finely adjusted step by step through the minimum step length, so that the resource can dynamically match the actual demand of the online application, and resource waste and offline application operation blocking caused by one-time excessive resource adjustment are avoided; and a resource allocation scheme which is more flexible and has better dynamic adaptability is provided.

This patent discloses a scheduling control method and system for assembly lines based on continuous state evolution, applicable to complex assembly lines under multi-process and multi-resource constraints. The method first continuously models the execution conditions of assembly processes, forming a state expression that reflects the executability of the processes. Based on this, a scheduling focus is constructed according to the current main constraints of the production line, guiding the overall direction of scheduling decisions. Simultaneously, the assembly quality state is introduced into the scheduling state system in the form of reliability decay, allowing quality factors to participate in state evolution during the scheduling process. During scheduling operation, the system forms an ordered execution sequence and generates scheduling instructions. After the scheduling instructions are issued to the execution layer, their execution feedback drives the continuous updating of relevant states, thus forming a closed-loop scheduling control process. Through the above methods, adaptive adjustment of scheduling decisions is achieved, improving the operational stability and scheduling efficiency of the assembly line under complex constraints.