484 results about "Allocation method" patented technology

The invention relates to the technical field of cloud side end computing, and discloses a cloud side end cooperative computing resource dynamic allocation method based on task-aware migration. The method comprises the following steps: acquiring real-time load characteristics and resource demand characteristics of calculation tasks in a cloud side end system, and dividing task priority queues in combination with task type identifiers; extracting historical execution records of tasks at cloud, edges and terminal nodes, constructing a task execution feature library, and generating a resource demand prediction model in combination with real-time load features; analyzing network transmission time delay characteristics of a cloud end and edge nodes, measuring real-time calculation capability fluctuation data of terminal equipment, and establishing an inter-node resource collaboration degree evaluation matrix; generating an initial migration strategy according to the prediction model and the evaluation matrix, monitoring actual resource occupancy deviation of the task, forming a final decision in combination with a node resource state correction strategy, triggering cross-node migration, and synchronously updating the priority queue and the evaluation matrix.

The invention discloses a cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

The invention belongs to the technical field of computing power distribution, and particularly relates to a dynamic computing power distribution method and system based on reinforcement learning, and the method comprises the following specific steps: S1, covering cloud, edge and end full-node scenes, and collecting computing power resource states, task demand features and cross-domain network condition data in real time; s2, on the basis of standardized data output by a cross-domain computing power sensing module, by constructing a state space fusing computing power, tasks and a network, defining an action space of computing power scheduling direction and proportion, and designing a multi-target reward function for balancing the resource utilization rate, the task satisfaction rate and long-term conflict avoidance; and realizing self-learning and self-iteration scheduling strategy generation based on a reinforcement learning algorithm. According to the invention, the reinforcement learning agent autonomously learns the computing power demand of the emergency scene and the new type of task, the rule does not need to be manually preset and modified, and the method has the advantage of realizing dynamic adaptation of computing power distribution to complex and changeable scenes.

The invention relates to the technical field of equipment management, and discloses a multi-unmanned equipment task dynamic allocation method and system, and the method comprises the steps: collecting the initial state information of an unmanned equipment group and a to-be-allocated task set, analyzing the load balancing demands of equipment based on the initial state information, and extracting the task priority in the to-be-allocated task set, obtaining an initial task allocation scheme; monitoring the execution progress of the initial task allocation scheme to form execution progress data, and analyzing a task dependency relationship in the execution progress data to identify key task nodes; starting local negotiation between devices according to the priority of the key task node, processing device state information through a game coordination mechanism to form a coordination decision weight, and generating a dynamic adjustment instruction based on the weight; according to the method, the overall execution efficiency of the cooperative task of the multiple unmanned devices can be improved, meanwhile, reasonable configuration of device resources is achieved, the situation that the resources are idle or overloaded is avoided, and the stability and reliability of task execution are enhanced.

The embodiment of the invention provides an annotation task allocation method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a plurality of annotation tasks to be distributed; determining task portraits of the plurality of to-be-distributed annotation tasks according to the plurality of to-be-distributed annotation tasks; obtaining capability portraits of a plurality of annotation personnel and capability portraits of a plurality of data annotation models, wherein the data annotation models are used for data annotation; according to the task portrait, the ability portraits of the plurality of annotation personnel and the ability portraits of the plurality of data annotation models, constructing an optimization model used for determining an allocation relationship among the to-be-allocated annotation task, the annotation personnel and the data annotation models; and solving the optimization model by using a solving algorithm to generate a target task allocation scheme.

The invention relates to the technical field of computer storage, and particularly provides a large model training storage resource dynamic allocation method, device and system, and the method comprises the steps: collecting a workload index and a storage system state index of a large model training task; inputting the real-time monitoring data into a convolutional neural network (CNN) model, and outputting a feature identifier of a current training stage; inputting the real-time data and the stage identifier into a long short-term memory network LSTM model, and predicting future bandwidth, IOPS and storage space requirements; and generating a resource allocation scheme through multi-objective optimization according to a prediction result in combination with a system state, and executing load allocation, data layering and bandwidth reservation operations. According to the method, training stage perception and resource demand prediction are realized through cooperation of the CNN and the LSTM, and dynamic allocation and advanced scheduling of storage resources are realized, so that the resource utilization rate, the training efficiency and the system stability are improved.

The invention provides a container position distribution method based on a large model driven intelligent agent network. The container position distribution method based on the large model driven intelligent agent network comprises the following steps: step 101, carrying out multi-view hybrid coding on a container by adopting a double-flow attention mechanism to obtain overall feature representation of the container; step 102, adopting an autoregression decoder to generate a candidate container set according to the overall feature representation of the container and the current slot position; step 103, based on the candidate container set, using a deterministic physical model to carry out constraint check and index calculation; and 104, carrying out structured evaluation on the container by adopting the large model, and carrying out intelligent sorting. By the adoption of the method, the accurate and reasonable loading sequence and position of the containers can be ensured, and the loading efficiency of the containers is greatly improved.

The invention discloses an optical network dynamic resource allocation method fused with an AI large model, which relates to the technical field of real-time scheduling of network resources, and comprises the following steps: constructing a service prediction model based on terminal behavior characteristics, calculating bandwidth sensitivity Bs and burst probability Pt, generating priority weight Wp, and improving burst service scheduling response; the T-CONT allocation rate and period are adjusted in a self-adaptive mode through a resource allocation efficiency function Ef and a buffer area demand index Bh, and priority scheduling of key services is guaranteed; the scheduling pressure is judged by introducing double thresholds of a signaling overhead index So and a network congestion degree Oc, AI reestimation and resource reallocation are triggered when abnormity occurs, and meanwhile, a bandwidth recovery parameter Br is recorded to optimize a subsequent strategy, so that minute-level intelligent scheduling and resource utilization maximization are realized.

The invention provides a laser cutting production line task automatic allocation control method based on multi-device state coupling analysis, and belongs to the technical field of intelligent manufacturing and industrial automation. The method comprises the following steps: constructing a dynamic closed-loop control process through a central control scheduling system: receiving a task information packet of an MES; constructing an equipment state vector based on a real-time station state, and introducing a dynamic weight factor set to generate a weighted state vector; performing task triggering judgment through a coupling triggering judgment function in combination with the task dependency graph and historical task records; when the conditions are met, a task instruction is issued to the target station; and feeding back the state and updating the historical record after the task is completed. The invention further relates to AGV intelligent scheduling, visual positioning compensation, process parameter dynamic adjustment, predictive conflict detection, weight self-optimization and the like. According to the method, the production line cooperation efficiency is remarkably improved, manual intervention and system delay are reduced, and the method is suitable for an intelligent laser processing scene in which multiple devices run in parallel.

The invention discloses an execution time aware computing power network heterogeneous GPU cluster deep learning task node allocation method and system. The method comprises the following steps: firstly, based on a deep learning task, extracting and preprocessing task features and available node features; secondly, a sampler equally divides new tasks without historical data to available nodes, and each node performs mixed sampling on the tasks until all the tasks estimate execution time data; taking execution time data as a training set, taking the task features and the node features as a test set, and using a regression decision tree model to predict the execution time of the task on each node; performing task allocation on each node by using a cost search algorithm and a short job total JCT priority strategy; and finally, periodically monitoring node resources released in the cluster to obtain an optimal node allocation result. According to the method, task delay and total task JCT are remarkably reduced, cluster node resource changes are monitored in real time, and the resource utilization rate is increased.

The invention relates to the technical field of retail supply chain and store resource management, and discloses a service reservation data-based store resource dynamic allocation method, which comprises the following steps of: constructing a coupling association graph of a service item and commodity combination by adopting a graph optimization algorithm fused with sparse perception constraint; according to a core service item determined by the coupling correlation graph, obtaining a reservation fluctuation mode of the core service item by using a time sequence decomposition method of an embedded state feedback mechanism; constructing and training an attention convolutional neural network model combined with local feature self-correction, and predicting service resource demand distribution of the store by using the model; generating a store resource collaborative configuration strategy based on the service resource demand distribution and the associated commodity combination real-time inventory data; according to the invention, accurate coupling prediction and collaborative dynamic allocation of the service reservation demand and the commodity inventory demand are realized, so that the efficiency and accuracy of store resource allocation are remarkably improved.

The invention discloses a production line unit task allocation method based on reinforcement learning, which comprises the following steps: extracting a specific task instruction of each production line unit from a final task allocation matrix, verifying the feasibility of the instruction under the constraint of completion time through a simulation execution module, determining an instruction set passing verification, and performing task allocation on the instruction set; wherein the instruction set is obtained by fusing the solution of the multi-target conflict; according to the verified instruction set, production line feedback data such as actual execution time and energy consumption records are obtained, deviation is analyzed from the feedback data, if it is judged that the deviation is larger than a preset threshold value, a self-adaptive adjustment mechanism is triggered, and a corrected distribution strategy is obtained; and the optimized resource allocation indexes are extracted from the corrected allocation strategy, the indexes are pushed to the production line equipment through the real-time distribution system, the execution monitoring cycle after pushing is determined, and the monitoring cycle is obtained by continuously tracking the low-efficiency risk.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

The invention discloses a multi-head attention calculation task allocation method and related products, and the method is executed by a single processing core of an artificial intelligence processor, and is used for loading multi-head attention sequence interval calculation tasks allocated to the processing core. The sequence interval calculation task is obtained by dynamically splitting according to a comparison result of an actual calculation amount of a current processing core and a target calculation amount of each processing core, and the actual calculation amount of the current processing core is obtained by pre-estimating according to an input sequence of the multi-head attention calculation task and an action range of a causal mask; the target calculation amount refers to the average calculation amount of each processing core participating in the multi-head attention calculation task; and executing the sequence interval calculation task. According to the method, the calculation task is dynamically split according to the comparison result of the actual calculation amount of the current processing core and the target calculation amount of each processing core, so that overload of the processing cores is effectively prevented, and the problem that excessive tasks are allocated to any processing core to become a system bottleneck is avoided.

The invention discloses a node distribution method and system for a GPU (Graphics Processing Unit) use process in an NUMA (Non Uniform Memory Access) environment, and the method comprises the following steps: in a kernel mode, capturing an event that the process executes a new program and collecting meta-information of the process through an eBPF program mounted at a system call entry tracking point, and judging whether the process is a target GPU process or not based on the collected meta-information of the process; if the process is the target GPU process, transmitting meta-information of the process to a user mode, and if the process is not the target GPU process, ending and exiting; and for the meta-information transmitted to each target GPU process of the user mode, reading the meta-information of the target GPU process through the user mode daemon process, evaluating each node in the NUMA environment based on a preset node allocation rule, selecting an optimal NUMA node, and binding the target GPU process to a CPU of the optimal NUMA node for execution. The GPU computing efficiency and the resource utilization rate can be improved.

The invention provides a carbon satellite application cloud resource dynamic allocation method based on data volume prediction, and relates to the crossing field of cloud computing and remote sensing satellites. A fine-grained process monitoring module and an application feature labeling module are used for respectively acquiring a plurality of process performance data and a plurality of task feature tags of a plurality of data processing tasks in a carbon satellite application system; and performing cloud resource dynamic optimization and outputting an elastic cloud resource optimization strategy to dynamically adjust the resource ratio of the basic computing power resource pool to a plurality of data processing tasks for pre-allocation. The technical problems that the GPU / CPU utilization rate is in an extremely unbalanced state and a high-priority task is interrupted due to the fact that supply and demand of computing power resources are unbalanced in a carbon satellite service system in an existing resource allocation method in a cloud environment are solved. The technical effect of zero interruption of a high-priority carbon emission data analysis task is guaranteed while accurate matching of resource supply and demand is realized and the resource utilization rate is improved.

The invention relates to the technical field of computers, and discloses a multi-task processing-oriented Soc chip server computing power distribution method, which comprises the following steps of: acquiring execution primitive vectors of at least two tasks concurrently executed on an Soc chip; converting the execution primitive vector into an execution symbol sequence corresponding to each task; based on the execution symbol sequence, identifying an execution morpheme representing a task execution stage, and constructing a task grammar state machine, used for predicting a next morpheme, of each task; analyzing cross-task morpheme association between the execution morphemes of the concurrent tasks; and on the basis of the current morpheme of a certain task, the predicted next morpheme is associated with the cross-task morpheme, and a collaborative scheduling instruction is generated. By converting the execution primitive vector into the execution symbol sequence and identifying the execution morphemes based on the sequence to construct the task grammar state machine, the prediction of the next execution stage of the task is realized.

The invention discloses an overseas warehouse intelligent allocation method based on digital twinning, and the method comprises the steps: S1, collecting multi-source core operation data, and constructing an overseas warehouse digital twinning body; s2, preprocessing the multi-source core operation data to form a twin data set; s3, constructing a causal influence graph through an AGCN model based on Granger causality, and generating twinborn evolution data; s4, taking twinborn evolution data as a virtual environment, running an improved QMix algorithm, and outputting an optimal allocation strategy set through collaborative decision of a hierarchical reinforcement learning mechanism; s5, backtracking the verification strategy in the digital twinborn body, and generating a self-interpretation report and a standardized instruction; and S6, issuing an instruction to a physical platform for execution and displaying a result. According to the method, dynamic causal modeling and global collaborative optimization of the complex storage network are realized, the accuracy and economical efficiency of allocation decisions are improved, and the reliability and transparency of strategies are enhanced.

The invention provides a resource allocation method in task unloading, relates to the technical field of network and communication, and solves the technical problem of relatively high time consumption of joint optimization of channel resources, computing resources and power consumption control in a task unloading process. According to the technical scheme, the method comprises the following steps of S1, task unloading problem modeling; s2, realizing an unloading decision; and S3, designing and realizing a resource allocation algorithm. According to the allocation method, the search behavior can be dynamically adjusted according to the real-time performance of the particles, and exploration and development of resource allocation problem solving are dynamically balanced; according to the scheme, the time overhead performance of program calculation is reduced, and a high target utility function value can be maintained.

The invention belongs to the technical field of artificial intelligence education application and computing resource management, and discloses a course flexible construction and computing power elastic allocation method and system oriented to an education large model. According to the method, course contents are disassembled into knowledge unit plug-ins which can be independently deployed, and lightweight consistent deployment of course instances in different terminal environments is realized based on a dependency relationship between directed acyclic graph modeling plug-ins; by taking a course target as a constraint, analyzing a knowledge unit reasoning path on the dependency graph, only reserving components with weights higher than a threshold value, and cutting non-key sub-model components and a redundant prompt chain to generate a dynamically cut course instance; distributing an independent container operation environment for the course instance, and collecting an operation resource state through kernel-level monitoring; according to the method, tasks are divided into different types of task queues in combination with calculation characteristics of resource states and teaching tasks, resource demands are predicted based on historical resource use behaviors of users, and calculation resources are dynamically allocated or recycled.

The invention discloses a data-intensive workflow dynamic task allocation method and system oriented to cloud-edge cooperative computing, and relates to the technical field of cloud computing and edge computing collaboration.The method comprises the steps that workflow and cloud-edge resources are modeled in a unified mode, and a comprehensive cost function containing time, economy and energy consumption is defined; analyzing the workflow to obtain a conflict task set and a maximum data path set; in combination with resources and network states monitored in real time, candidate nodes are filtered according to data privacy constraints, edges or cloud nodes with the minimum comprehensive cost are selected for tasks for allocation, and sorting is performed according to priorities during multiple tasks; the system comprises a resource monitoring module, a workflow analysis and preprocessing module, a cost evaluation module and a dynamic scheduler module, the modules cooperate to realize dynamic allocation, the problems of resource heterogeneity, network dynamic and the like under cloud edge cooperation are solved, multi-objective optimization and privacy protection are balanced, and the workflow execution efficiency is improved.

The invention discloses a heterogeneous computer and system for dynamically optimizing cache resources and a cache allocation method. The heterogeneous computer comprises a hardware performance monitoring unit, a main controller, a plurality of heterogeneous computing units and a shared cache unit. The hardware performance monitoring unit detects the utility gradient of the heterogeneous computing units based on a perturbation and observation method, the utility index and the cache resource allocation amount of each heterogeneous computing unit are collected before and after the cache resource allocation amount is changed, and the utility gradient is the utility index variation corresponding to the unit cache resource allocation amount variation; if the utility gradient is not smaller than a preset first threshold value, the main controller increases the cache resource allocation quantity of the heterogeneous computing unit; and if the utility gradient is not greater than a preset second threshold value, the main controller maintains or reduces the cache resource allocation quantity of the heterogeneous computing unit. According to the method, dynamic adaptation, intelligent scheduling and efficient utilization of cache resources can be realized, the execution efficiency of high-priority tasks is guaranteed, and the performance loss caused by cache competition and conflict is reduced.

The invention discloses a model fragment distribution method and device for industrial equipment, equipment and a medium. The method comprises the following steps: dividing a pre-trained machine learning model into a plurality of model fragments according to a preset function type; obtaining the computing power performance level of each device in the target industrial scene and the task risk level of the corresponding to-be-executed task, and according to the computing power performance level and the task risk level, generating an adaptation model fragment subset of each device, so that each device executes the reasoning task by using the respective adaptation model fragment subset; running state data and to-be-executed task data of each device are continuously collected, so that a real-time comprehensive performance index and a real-time comprehensive risk index are calculated respectively, and if there are indexes which do not meet preset requirements, new adaptive model fragment subsets are generated for the corresponding devices. According to the invention, model fragmentation distribution can be carried out according to computing power and task requirements of different devices, and the adaptation degree of each hardware device and the intelligent reasoning model in an industrial scene is improved.

The application discloses a blockchain task allocation method and device based on DQN, equipment and medium, relates to the field of task allocation, comprising: determining the state vector of the DQN model, selecting the target action according to the state vector, and determining the predicted execution time and the actual execution time of the target action; the predicted execution time and the actual execution time are used as the input of the preset time length reward formula to obtain the time length reward; a plurality of experiences are randomly extracted, the target Q value of the DQN model is determined based on the plurality of experiences, and the DQN model is updated through the observation Q value and the target Q value of the DQN model; multiple rounds of updating are performed until the DQN model meets the preset requirements to obtain the target DQN model; the target blockchain task allocation model is constructed through the target DQN model, and after receiving the blockchain task allocation request, the target blockchain task allocation model is used for intelligent task allocation. Therefore, the task allocation efficiency can be improved, the dynamic environment change can be responded to, and the manual intervention can be reduced.

Embodiments of the present application disclose a HARQ process allocation method and device, a base station and a storage medium. The HARQ process allocation method can include: receiving first information reported by a user equipment, the first information being used to indicate whether the user equipment needs to enter an energy saving mode, the working mode of the user equipment including the energy saving mode and a normal mode, the power consumption of the user equipment in the energy saving mode being lower than the power consumption of the user equipment in the normal mode; determining a first target number of HARQ processes configured for the user equipment according to the first information; and issuing the first target number of HARQ processes to the user equipment. By implementing the method, the power consumption waste of the user equipment can be effectively reduced.

The application relates to the field of task scheduling allocation, and provides a work order allocation method, a work order allocation device and electronic equipment. The method comprises the following steps: determining a first target processing object corresponding to a target work order from a work order mapping table based on the type of the target work order; inputting the target work order into a target matching model in the case that the first target processing object is not determined, so as to obtain a second target processing object output by the target matching model; and determining a third target processing object in a target processing team in the case that the second target processing object is not determined. The work order allocation method provided in the application can combine the actual situation of a target work order to quickly and accurately allocate the target work order by using a multi-level allocation mechanism, and can improve the allocation accuracy and response processing efficiency of the target work order while ensuring that the target work order is quickly allocated.

The invention discloses a power RTOS system-oriented storage allocation method and related equipment, and belongs to the technical field of storage space management. The method comprises the following steps: identifying demand information of each task in a system; analyzing based on the demand information of each task in the system, and dividing a storage space of the system according to an analysis result to determine a plurality of task areas and an initial storage allocation strategy of each task area; and acquiring a running state and task scheduling information of the system, and adjusting the storage allocation strategy of each task area. According to the method, the system storage space is specifically partitioned according to system task requirements, different partitions correspond to different storage allocation strategies, and adaptive adjustment is performed, so that the storage space utilization rate of the power RTOS system is improved.

The control strategy allocation method and system for tunnel boring machine (TBM) control provided in this invention combines TBM state anomaly events with TBM state anomaly characteristics loaded during the TBM control process, schedules multiple corresponding prior TBM state anomaly events, and determines the TBM problem characteristics corresponding to the prior TBM state anomaly events. When the problem parameter value corresponding to the TBM problem characteristics is greater than a set value, the construction problem category characteristics corresponding to the multiple prior TBM state anomaly events are determined. Combining the construction problem category characteristics and the prior TBM state anomaly events, corresponding construction problem control nodes are scheduled to allocate construction problem control strategies for the prior TBM state anomaly events. In this way, construction problem control strategies can be allocated for TBM state anomaly events with TBM state anomaly characteristics.

The application discloses a memory allocation method, device, equipment, storage medium and computer program product, and belongs to the technical field of computers. The method comprises the following steps: receiving a dynamic memory application request sent by a target application program, wherein the dynamic memory application request carries the identification of the target application program, the size of a first memory block to be applied by the target application program, and the number of the first memory block; and allocating a memory block for the target application program from a plurality of reserved memory blocks based on the identification of the target application program, the size of the first memory block and the number of the first memory block. Before the plurality of application programs run, the static memory allocation mode is used to reserve the memory blocks necessary for the subsequent normal running of each application program. In the process of running the target application program, the dynamic memory allocation mode is used to allocate the memory blocks for the target application program in real time from the plurality of reserved memory blocks, so that the disastrous consequences caused by the failure of dynamic memory allocation can be avoided.

The application discloses a novel compact gas reservoir gas well reasonable production allocation method and system based on stable production period, and the reasonable production allocation method comprises the following steps: S1, obtaining the production-time data of a target gas well, and arranging the production-time data according to the production size in a power reduction manner; S2, calculating the matter balance time-production data of the production data arranged in the power reduction manner; obtaining the maximum matter balance time according to the matter balance time-production data; S3, judging the relationship between the stable production time and the maximum matter balance time, and then substituting the stable production time into the matter balance time data according to the matter balance time-production data to obtain the reasonable working system of the target gas well at the stable production time. The method depends on the static data and dynamic data in the gas well production data, effectively reduces the cost of a large number of experiments and dynamic monitoring required by other methods, can quickly determine a reasonable production allocation scheme, and realizes efficient and stable exploitation of the gas well.