165 results about "Resource constraints" patented technology

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention relates to the technical field of cloud computing, and discloses a resource scheduling method, device and equipment, and the method comprises the steps: generating a multi-dimensional data topological graph of cloud environment monitoring data, decomposing a to-be-scheduled job set into quantum computing unit sets which can be processed in parallel, and adding priority labels for the quantum computing unit sets through a quantum heuristic algorithm, according to priority labels and a multi-dimensional data topological graph, physical resources of a cloud environment are divided into non-uniform resource fragments matched with task requirements, based on the non-uniform resource fragments, resource scheduling is carried out through an improved hybrid particle swarm optimization algorithm, and weighting and fitness evaluation and resource constraint correction are introduced. According to the resource scheduling method and device, the multi-dimensional data topological graph is generated to comprehensively present the resource state and association, then the to-be-scheduled job set is decomposed into the quantum computing unit sets, the priority labels are added, and then the non-uniform resource fragments are divided for resource scheduling, so that the resource adaptation accuracy is improved, and the scheduling efficiency and the resource utilization rate are improved.

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 production scheduling method, a production scheduling system and electronic equipment, and belongs to the technical field of production management and scheduling. The production scheduling method comprises the steps that according to production requirements and a preset production correlation model, a fitness function and an initial production scheduling scheme set are determined, and the fitness function comprises at least two of the following production scheduling strategies: total delay time minimization, resource utilization rate maximization, production line load balancing and intermediate product minimization; at least two preset algorithms are fused, the initial production scheduling scheme set is optimized based on the fitness function, the optimal production scheduling scheme is obtained, and the preset algorithms comprise any one of a genetic algorithm, a simulated annealing algorithm and a tabu search algorithm. The method can solve the problems of insufficient processing of resource constraints, low production scheduling efficiency and incapability of effectively finding the optimal production scheduling scheme in related technologies.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

Techniques for managing resource constraints of a cloud environment are disclosed. A system receives a request to initiate a provisioning process for provisioning a first service in the cloud environment. The system determines a resource constraint associated with a resource that the first service utilizes. Based on the resource constraint, the system determines a set of candidate services that also utilize the resource as candidates for deprovisioning from the cloud environment. The system identifies respective service features of the set of candidate services and generates a ranking of the set of candidate services based on weighting metrics associated with the respective service features. Based on the ranking, the system selects a second service of the set of candidate services for deprovisioning from the cloud environment. The system deprovisions the second service to alleviate the resource constraint and then provisions the first service by executing the provisioning process.

The invention relates to the technical field of edge computing, in particular to an operator edge computing node optimization method and system, and the method comprises the steps: obtaining local load features and cross-node association features of edge nodes in real time, and constructing a dynamic load vector and an association vector respectively; based on a preset time sequence prediction model, taking the load vector and the association vector as input, and obtaining a predicted load trend in a future preset time period; generating an optimal service migration strategy through a preset reinforcement learning model according to the predicted load trend in combination with the dynamic load vector, the association vector and the task attribute; wherein the migration strategy comprises a migration target node and a migration resource allocation scheme; wherein the task attributes comprise task priorities; and on the basis of the migration strategy, a resource pre-allocation scheme of the target node is generated in combination with a preset resource constraint condition, and the method has the advantages of improving service response, resource utilization rate, task success rate and the like in a high dynamic scene.

The invention provides a service scheme generation method and system, and relates to the technical field of computer application, and the method comprises the steps: obtaining user demand information which comprises a service type, a service scene, a resource constraint condition and an expected effect description; semantic analysis is performed on the user demand information based on the comprehensive domain knowledge graph, core service elements are extracted, and a user demand model is constructed; according to the user demand model, candidate service resources are matched in a service resource library through a heuristic search algorithm, and candidate service schemes are generated; a multi-objective optimization algorithm is adopted, real-time resource state data and user feedback information are combined, the candidate service schemes are dynamically adjusted, and the final service scheme is output, so that user requirements can be accurately analyzed, service resources are efficiently matched, the generated service scheme is optimized, knowledge and resource iteration is realized, and the scheme generation efficiency and quality are improved.

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

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The invention discloses an industrial control software public test resource scheduling method based on a self-adaptive genetic algorithm. The method comprises the steps of task scheduling modeling, resource constraint condition definition, task dependency relationship construction and dynamic scheduling strategy making. By introducing a self-adaptive mechanism, the crossover rate and the mutation rate in the genetic algorithm are adjusted to adapt to the search requirements of different stages, so that the optimization efficiency and the diversity of solutions are improved. According to the method, a public test resource scheduling optimization process specially aiming at industrial control software characteristics is designed, and the complex scheduling problems of multiple tasks, multiple constraints and limited resources can be effectively solved. Compared with a traditional static scheduling strategy, the method has the advantages that the test period is remarkably shortened, the resource utilization rate and the task completion rate are increased, the method is particularly suitable for industrial control software public test scenes with large-scale and multi-skill requirements and complex task dependence, and the method has good application prospects and popularization value.

The invention discloses an electric power resource hierarchical scheduling method based on dual-time scale deep reinforcement learning, and relates to the technical field of electric power scheduling, and the method comprises the following steps: S1, dual-time scale definition and constraint setting: dividing a scheduling period into a macroscopic time period (delta T) and a microscopic time step (delta t), and satisfying delta T = M * delta t (M is an integer); setting a resource constraint relationship, wherein the resource constraint relationship is a resource budget upper limit of the region r; s2, unified optimization target modeling: constructing a small-time-scale instant reward function based on time division of S1: coupling the macroscopic resource budget of S1, and designing a large-time-scale target function: S3, large-time-scale strategy modeling: based on the target function of S2, defining a regional state vector, and being capable of considering long and short-term scheduling requirements.

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, in particular to a flexible job shop scheduling optimization method for solving and adjusting resource constraints. Comprising the steps of initializing parameters and randomly generating an initial population; sequentially using a crossover operator and a mutation operator to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population from small to large according to the maximum completion time to form an elite population, and updating the elite population through question specific local search; and judging whether an evolution condition is met or not, if so, executing CP-based mathematical evolution, and outputting a final solution when the running time reaches the total running time. The method has the positive effects of reducing the resource waiting time, improving the machine utilization rate and improving the resource utilization efficiency and the scheduling performance of the whole workshop production.

The invention discloses a multi-rotating-speed flexible job shop scheduling method applied to double-resource constraint, and the method comprises the steps: taking the maximum completion time and minimum total energy consumption of a minimum machine as target functions, and constructing a flexible job shop scheduling model considering the rotating speed energy consumption of the machine and the production demands of a fine process; a machine speed gear constraint, a fine process constraint, a process sequence constraint, a completion time constraint, a machine processing constraint and a worker operation constraint are established as constraint conditions of the model; the flexible job shop scheduling problem is solved by adopting an improved artificial bee colony algorithm, bee colony search guided by excellent genes is adopted in bee learning operation in the improved artificial bee colony algorithm, and nectar source optimization is carried out based on the searched excellent genes; the following bee operation adopts a neighborhood structure which considers machine speed change and balances the working time of workers to carry out dynamic neighborhood search so as to optimize a nectar source. The effectiveness of the improved strategy is verified through experiments, and the superiority is verified through comparison of different algorithms on expansion standard examples.

The invention is suitable for the technical field of intelligent manufacturing scheduling, and provides a discrete manufacturing workshop multi-stage dynamic scheduling method based on transportation state feedback, which comprises the following steps: determining a multi-stage structure of tasks in a workshop, and establishing a corresponding task flow chart and a resource dependence model; a scheduling modeling basis with resource constraint and physical connection is formed; based on the current state of the system, key state variables are extracted, and a scheduling action space is defined; a transportation index is introduced into the reinforcement learning structure, and a composite reward function is constructed; reinforcement learning is adopted for training, and scheduling strategy parameters are iteratively optimized based on interaction between the intelligent agent and the environment. According to the method, rapid restoration and resource continuity maintenance of a scheduling strategy in a dynamic disturbance environment are realized, the real-time response capability and scheduling adaptability of a scheduling system under complex resource constraints are remarkably improved, and the method is suitable for manufacturing scenes facing high-frequency task change and transportation bottleneck problems.

The embodiment of the invention provides a resource option determination method and a resource display method, and the resource option determination method comprises the steps: dividing sample users into at least two types of user groups based on the historical behavior information of the sample users; based on the attribute features of the users in the user groups, candidate resource option groups of the user groups in a set resource constraint mode are determined, and the set resource constraint mode is used for indicating a screening mode of candidate resource options; based on the candidate resource option groups and feedback information of the candidate resource option groups in the corresponding user groups, target resource option groups corresponding to the user groups are determined, and the target resource option groups refer to combinations of multiple target resource options and are used for displaying the combined target resource options to the corresponding user groups. According to the method, a mode of optimizing different user groups and resource option combinations is provided, the test period of the resource options is greatly shortened, and the operation configuration efficiency is improved.

The invention provides a resource constraint workshop scheduling method and system based on a multi-target graph neural network, and belongs to the technical field of workshop scheduling. Constructing a resource-constrained workshop scheduling model according to the obtained workpiece set and workshop scheduling resources; performing nodal representation on the workshop scheduling model by using a disjunction graph, and expressing the state, action and reward value of the workshop scheduling model after nodal representation by using a Markov decision-making method; based on a multi-target graph neural network, determining the optimal action of the next step from the action space shown by the disjunction graph according to the current state; and executing workshop scheduling according to the determined optimal action. According to the method, high efficiency and high precision of optimal action selection can be kept on the basis of comprehensively considering all constraint conditions, and then reasonable resource constraint workshop scheduling is achieved.

The invention discloses a resource-limited equipment data-free federation incremental learning method and system, and relates to the technical field of transfer learning, and the method comprises the steps: training a model based on an incremental data flow through a deviation correction mechanism, obtaining a trained local parameter, uploading the trained local parameter to a cloud, and carrying out the data-free federation incremental learning of the equipment; calculating a difference value of the parameters to obtain a gradient of each edge computing device, calculating a federal average updating direction, calculating an aggregation weight according to a deviation degree between the updating gradient and the federal average updating direction, and performing weighted fusion on the updating gradient to obtain an updated global model; a synthesis sample of the old task is generated with the purpose of minimizing diversity loss; and carrying out knowledge distillation based on the synthetic sample, migrating old task knowledge to the updated global model, obtaining a final global model, and issuing the final global model to an edge end. Through deviation correction training, deviation degree and information entropy dual-perception aggregation and attention-guided data-free synthesis and distillation, federal incremental learning of resource-constrained edge equipment is realized.

The invention belongs to the field of electronic system resource management, and particularly relates to a self-adaptive task scheduling method of a multi-channel interference system based on a deep Q network. Aiming at the characteristics of a multi-channel interference system, the multi-task simultaneous execution is realized by comprehensively considering task merging, DBF and aperture division modes, and in order to effectively solve the multi-interference task scheduling under the high-dimensional resource constraint, a deep Q network method is adopted, specific actions, states and reward functions are set, and network parameters are trained, so that the multi-interference task scheduling efficiency is improved. And task scheduling is implemented by using the trained network. Simulation results show that the method meets the real-time requirement while having high execution benefits, and is a real-time and effective adaptive task scheduling method for the multi-channel interference system.

The invention discloses a multi-agent-based satellite edge task unloading and resource allocation method. The method comprises the following steps: acquiring a task; constructing a system model, wherein the system model comprises user equipment, a satellite edge computing server and a cloud server; respectively calculating the delay and energy consumption of the user equipment, the satellite edge computing server and the cloud server for processing the task according to the system model; constructing a target function; constructing an unloading model based on multi-agent deep reinforcement learning; and according to the unloading model based on the multi-agent deep reinforcement learning and a preset constraint condition, optimizing the objective function, and obtaining an optimized task unloading and resource allocation strategy. According to the method, a master-slave multi-agent structure is adopted, communication and resource constraints of the star edge computing server are considered at the same time, and service timeout or resource waste is avoided.

The invention discloses a method for building an automatic process based on natural language capability of a multi-modal large model. The method comprises the following steps: S1, collecting multi-modal data; s2, analyzing and processing the demands; s3, an appeal process is planned; s4, automatic task planning; s5, function division; s6, unit coordination; s7, verifying the integrity of the process parameters; s8, generating an actuator; according to the method, the dynamic memory matrix is established to realize long-distance dependence modeling, and learning can be performed through a memory matrix attenuation factor to adapt to different scene information life cycles; matrix processing is carried out on the important features, the dependency strength between contexts is determined according to the relation probability, adaptive association demands can be achieved conveniently, and accurate automatic process demands are obtained; the process planning model generates a process framework based on a process topology generator of a graph neural network, a task dependency analyzer and a resource constraint optimization algorithm, and optimizes the process framework according to a preliminary process framework and a final process framework.

The invention belongs to the technical field of communication networks, and discloses an agent interaction-oriented 6G side-end collaborative AI inference model deployment and optimization method, which comprises the following steps: step 1, carrying out data acquisition through various multi-modal sensors to obtain multi-modal sensing data, and constructing a LoRAHub-based three-layer agent network system model; 2, model deployment and unloading constraints, resource constraints, performance index constraints and agent model deployment constraints are comprehensively considered, a resource scheduling problem is constructed, and the resource scheduling problem takes joint optimization of response time and agent model reasoning precision as a target; and step 3, constructing a long-short time scale resource allocation framework, and solving the scheduling problem in the step 2. According to the method, a dual-time-scale resource scheduling optimization framework is constructed, so that the task response speed and the reasoning precision are effectively improved.

Computerized systems and methods useful for resource-constraint scheduling using visual representations on a graphical user interface to build one or more schedules across a business network, where the schedules are dynamically reconfigured based off real-time changes to resource availability, constraints and requirements that must be satisfied, where interactions by the user with the schedule at the user interface cause the dynamic, real-time recalculation of possible appointments meeting the constraints, after which a schedule is reconfigured and displayed at the user interface.

A forecast for a future period of time is translated into resource requirements for each interval of time of each day provided in the future interval of time. Resource constraints and resource objectives of an enterprise are converted into mathematical values and statements and provided as input features to a constraint optimization model along with the resource requirements, the interval of time, and the future period of time. The model outputs a data structure representing an optimized resource-assigned schedule for each interval of each day over the future period of time. The data structure is translated into a format that represents a resource assigned schedule. The resource assigned schedule is rendered though an enterprise to a user. In an embodiment, the resource assigned schedule is translated into an additional format and uploaded to a scheduling system of the enterprise using an application programming interface.

The invention discloses an aviation composite material laying workshop man-machine scheduling method and system considering a collaborative learning effect, and the method comprises the steps: taking the minimum maximum completion time, the minimum maximum team task load, and the maximum worker proficiency degree improvement total amount as objective functions; a dual-resource constraint flexible job shop scheduling model considering a worker collaborative learning effect is constructed, and the scheduling model is solved through a multi-target whale optimization algorithm driven by domain knowledge; according to the method, the individual learning effect and the collaborative learning effect of workers are considered, a corresponding skill proficiency improvement model is constructed, and an optimal scheduling scheme is solved by designing a hybrid initialization method, a neighborhood search operator, an adaptive leader whale division strategy and an adaptive similarity threshold strategy and predation and search operators based on domain knowledge. And a scheduling scheme of worker and equipment resources can be generated more accurately, so that the processing efficiency is improved, and a resource allocation mechanism is optimized.

The invention provides an enterprise resource scheduling optimization method based on artificial intelligence and a large model algorithm, and the method comprises the steps: recalculating the earliest start time and the latest completion time of each operation node in an affected downstream project according to a project dependency graph and a resource constraint condition, and obtaining an optimized time window distribution scheme; combining the personnel candidate set and the time window distribution scheme to calculate a matching degree score of the personnel skill level and the engineering demand characteristics, and if the matching degree score is higher than a set standard value, generating a personnel allocation suggestion list; and according to the target personnel flow scheme, updating the personnel configuration table and the skill structure distribution of each project, recalculating a project progress predicted value and a resource utilization rate index, generating a dynamic personnel redistribution execution scheme, and updating system configuration parameters.

The invention relates to the technical field of resource optimization decision, in particular to a multi-stage decision model construction method based on dynamic programming, which comprises the following steps: modeling based on resource constraint and an objective function, dividing a multi-stage decision problem into sub-problems, extracting logic association between states, identifying key nodes, and detecting path redundancy or missing. And evaluating resource allocation mode requirements, optimizing connection consistency between states, adjusting a decision model structure, identifying new mode attributes, and obtaining an updated multi-stage decision model. According to the method, the problems of key node identification and redundant path detection are effectively solved by accurately dividing the multi-stage decision problem and combining dynamic planning and analysis of the state transition path, the flexibility and accuracy of the decision process are improved, it is ensured that the decision is efficiently matched with external fluctuation all the time, decision lagging and low efficiency are avoided, resource allocation is continuously optimized, and the efficiency is improved. Coordination and consistency of all links are ensured, and the problems of resource waste and lag caused by the fact that real-time adjustment cannot be achieved in a traditional method are solved.

The invention relates to a deep learning task resource allocation method and device, equipment and a medium. The method comprises the following steps: respectively analyzing a computational graph structure and a historical resource monitoring log corresponding to a deep learning task, and generating a tensor dependency graph and a resource use time sequence matrix; performing time-varying demand prediction on the basis of the matrix, generating a time-phased resource constraint table, performing memory allocation processing on the basis of a tensor dependency graph, and generating a tensor memory partitioning scheme and an inter-partition communication cost matrix; and performing static resource pre-allocation based on the time-phased resource constraint table and the tensor memory partitioning scheme, generating pre-allocated resource configuration, and performing resource scheduling and outputting real-time resource configuration through a deep reinforcement learning model according to the time-phased resource constraint table, the inter-partition communication cost matrix and the pre-allocated resource configuration. According to the method, by means of dynamic resource allocation, cross-partition communication cost optimization, reinforcement learning optimization and the like, the resource utilization rate and task execution efficiency of a deep learning task are remarkably improved.

The invention discloses a task scheduling method and device based on resource optimization, and the method comprises the steps: carrying out the task feature analysis of a PLC program, and obtaining feature information; according to the feature information, task fragmentation is carried out to obtain a plurality of sub-tasks, and each sub-task corresponds to execution of one time slice; according to the plurality of sub-tasks after fragmentation, resource demand allocation is carried out to obtain a computing resource demand and a bandwidth resource demand; performing resource matching score calculation according to the calculation resource demand and the bandwidth resource demand to obtain a resource matching degree: calculating a resource matching score and a bandwidth resource matching score; according to the resource matching degree, calculating the score of each sub-task based on a task scoring model; executing a scheduling algorithm and performing resource constraint check on the task to be scheduled based on the score of each sub-task, distributing the sub-tasks passing the check to an execution queue, and performing resource state updating; and re-entering the sub-tasks which do not pass the check into the ready queue. The overall performance and the resource utilization rate of the system are improved.

The invention provides an advertisement processing method, a related device and a medium. The method comprises the steps of determining predicted associated resource data of a target advertisement based on target associated resource data and predetermined resource constraint data of the target advertisement, and determining an initial bid of the target advertisement based on the predicted associated resource data; performing bid exploration based on the initial bid, and determining actual consumption resource data; if the actual consumption resource data meets the predetermined condition, determining expected consumption resource data based on the traffic consumption data and predetermined resource constraint data; and updating the initial bid based on comparison of the actual consumption resource data and the expected consumption resource data, and returning to the step of determining the expected consumption resource data of the target advertisement based on the flow consumption data and the predetermined resource constraint data until a predetermined exploration condition is reached, and determining a target bid of the target advertisement. The bidding accuracy of the advertisement can be improved. The method can be applied to various scenes such as big data, content promotion and cloud technology.