1621 results about "Allocation method" patented technology

The invention provides an oil depot tank field inspection robot task allocation method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. Constructing a multi-target collaborative scheduling model, extracting features by adopting a space-time attention mechanism, establishing a task priority evaluation and path planning sub-model, and generating an optimal task allocation strategy; and an inspection task instruction is issued, the execution condition is monitored in real time, and when abnormity is detected, the emergency module is started for optimization adjustment. According to the invention, intelligent and collaborative routing inspection task distribution is realized, and the routing inspection efficiency and safety are improved.

The invention relates to the technical field of crowdsourcing task allocation for multi-person cooperation of complex tasks, and discloses a crowdsourcing task allocation method for multi-person cooperation of complex tasks, which comprises the following steps: acquiring all persons in a talent pool and marking and numbering the persons in sequence, thereby facilitating subsequent management and information retrieval, acquiring a skill label of each person, and improving the efficiency of crowdsourcing task allocation. Multiple skills are considered, so that task allocation is more accurate, through skill label classification, the system can quickly match task requirements and personnel skills, obtain a historical task list to provide reference for task disassembly and estimation, and decompose tasks into small sub-tasks, and the system can accurately estimate task load and personnel allocation. According to the method, archives of the split tasks are recorded to provide data reference for follow-up, the average workload and the completion time of the historical tasks provide a basis for task allocation, uncertainty is reduced through data-driven estimation, and it is ensured that task load estimation is flexible and accurate through a similarity evaluation mechanism, so that the overall task execution quality and efficiency are improved.

The invention relates to the technical field of task allocation, in particular to a task allocation method for flexible employment. According to the technical scheme, the method comprises the following steps that a database of related information of tasks and employees is constructed, wherein the database comprises detailed information of the tasks and comprehensive information of the employees; respectively constructing a task model and an employee portrait based on the information in the database; and matching the task with the employee through an intelligent matching algorithm, wherein the intelligent matching algorithm comprises matching algorithm selection and optimization, real-time matching and dynamic adjustment, and multi-objective optimization and tradeoff. According to the method, multiple matching algorithms are combined for optimization, weights are selected and adjusted according to tasks and employee characteristics, accurate matching is achieved, a task model and employee portrait multi-dimension consideration adaptability is achieved, adaptive changes are matched in real time, tasks and multiple aspects of employees are optimized and balanced in a multi-objective mode, the development of rights and interests of the employees is considered, and the matching quality, the working enthusiasm and the stability are improved; flexible employment resources are managed more effectively, and the task allocation effect is improved.

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic signal edge computing resource dynamic allocation method and system.The method comprises the steps that firstly, real-time and historical traffic data of multiple intersections are obtained, a traffic prediction model is constructed, and future flow changes are pre-judged; thirdly, forming an edge computing resource demand matrix according to a prediction result and computing resource demands, and dynamically adjusting resource allocation to generate a resource slicing scheme; then, the flow gradient between intersections is analyzed, an edge node cooperative calculation model is established, and calculation task distribution and migration are optimized; and finally, in combination with system operation and traffic control effects, forming closed-loop feedback, continuously optimizing resource allocation and a cooperative strategy, and through traffic flow prediction driven resource demand modeling, realizing active pre-allocation of computing resources, converting resource allocation from passive response to active pre-adaptation, and improving the system resource utilization rate by 40%-60%.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The invention discloses an intelligent storage resource dynamic allocation method and system based on deep reinforcement learning, and relates to the technical field of storage management. The intelligent storage resource dynamic allocation method based on deep reinforcement learning comprises the following steps: environment data judgment; inventory management evaluation; calculating complex evaluation; and effective evaluation. According to the method, whether inventory allocation is carried out or not is judged through the obtained environment data and the preset environment data, then the storage utilization evaluation value is obtained according to the obtained tobacco inventory management data, whether storage optimization is carried out or not is judged, then the calculation complexity evaluation value is obtained according to the obtained calculation resource data, and whether calculation complexity optimization is carried out or not is judged. And finally, obtaining an optimized effective evaluation value according to the storage utilization evaluation value and the calculated complex evaluation value, thereby achieving the effect of improving the storage resource utilization efficiency in the dynamic allocation process of the intelligent storage resources, and solving the problem of low storage resource utilization efficiency in the dynamic allocation process of the intelligent storage resources in the prior art.

The invention discloses a request allocation method and system based on a heterogeneous computing system, equipment and a medium, relates to the technical field of computers, and determines reading time required for using key value cache when a plurality of heterogeneous computing power equipment accesses a memory under the condition of preferentially considering the use condition of a key value cache mechanism. Requests are reasonably distributed by considering the performance difference of the computing power information of the heterogeneous computing power equipment, the memory information corresponding to the memory extension and the characteristics of the reasoning task information. Furthermore, the reasonability of request allocation is improved according to a comparison relationship between the preset allocation request quantity and the concurrent request quantity and different strategies for judging whether allocation conditions are met or not. Therefore, the technical problem that more computing power resources are wasted due to the fact that the allocation balance of the reasoning request is poor when a separated memory or a local memory is called can be solved, and the technical effects that the reasoning task information is reasonably allocated to the heterogeneous computing power equipment, so that the allocation balance is improved, the computing power resources are saved, and the cost is reduced are achieved.

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 task package permission allocation method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. According to the method, firstly, a permission adjustment scheme is selected from a strategy library according to a security risk balance point, and task package permission is finely adjusted; meanwhile, the user operation behavior is monitored in real time, safety audit log analysis is triggered by using the abnormal behavior detection index, and the risk of the abnormal behavior of the user is evaluated. Furthermore, in combination with a risk assessment result and user behavior preferences, permission boundary setting is optimized by applying a genetic algorithm, and permission level boundaries are dynamically adjusted, so that the balance between system security and user experience is ensured. The mechanism effectively deals with authority conflicts and potential security threats, and realizes intelligence and dynamics of authority management. Through personalized authority distribution and real-time security monitoring, flexibility and high efficiency of authority management are realized, system security is guaranteed, and user experience is improved.

The invention discloses an intelligent load management and allocation method for an electric meter box, relates to the technical field of intelligent power grid management and energy optimization, and solves the problems that the adaptability of load management in an existing electric meter box is insufficient, the prediction accuracy is influenced by data prejudice, and the dynamic adjustment capability is limited. According to the method, diversified data are collected and analyzed through a real-time data interface and a gradient boosting tree technology, a power consumption prediction model is constructed in combination with a long-short term memory (LSTM) network, and meanwhile, prediction performance is optimized by adopting a data enhancement algorithm and an adaptive enhancement algorithm; fast response and dynamic adjustment are realized by using a sliding window prediction method based on Kalman filtering, a load distribution strategy is optimized through an adaptive depth deterministic strategy gradient algorithm, and collaborative distribution scheduling and management are performed on cross-regional electric meter boxes through a distributed collaborative scheduling system; according to the invention, the stability and efficiency of the power system are greatly improved, and the energy utilization efficiency and economic benefits are remarkably improved.

The invention discloses a calculation task allocation method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-executed calculation task, and extracting the features of the to-be-executed task to obtain a feature label; decomposing the to-be-executed calculation task based on the feature labels to obtain sub-tasks; constructing a directed acyclic graph based on the dependency matrix of each sub-task, and performing topological sorting on each sub-task to obtain a priority and an execution sequence; according to the method, a to-be-executed calculation task is decomposed into a plurality of sub-tasks through feature labeling, a directed acyclic graph is constructed according to the dependency relationship among the sub-tasks, and the sub-tasks are distributed according to the reference execution time, the priority and the execution sequence. The priority and the execution sequence of each sub-task are determined by utilizing topological sorting, and the sub-tasks are allocated to each GPU for execution in combination with the reference execution time of each sub-task on different GPUs, so that the calculation efficiency of the calculation task is effectively improved.

The invention discloses a heterogeneous computing resource-based dynamic task scheduling and allocation method, which comprises the following steps of: S1, creating a plurality of resource pools, and defining structure attributes of the resource pools; s2, classifying all the heterogeneous computing resources, and distributing the heterogeneous computing resources to corresponding resource pools; s3, classifying the target calculation task, scheduling the target calculation task to a resource pool matched with the target calculation task, decomposing the target calculation task into a plurality of sub-tasks, and scheduling the plurality of sub-tasks to a calculation unit in the resource pool; s4, judging whether the load data of the resource pool is greater than a preset threshold value or not, and if the load data is greater than the preset threshold value, migrating the target calculation task; and S5, continuously monitoring the load data of all heterogeneous computing resources, and dynamically adjusting a target computing task scheduling strategy by the scheduling module according to the load data. According to the method, the parallelism is improved through a fine-grained scheduling mode, overload of some resources is avoided, and the pressure of each computing unit is balanced.

The invention provides a task allocation method, device and equipment based on a distributed cluster and a computer readable storage medium. The method comprises the following steps: acquiring to-be-allocated tasks and determining priorities among the to-be-allocated tasks; selecting a to-be-allocated task node from the low-load nodes according to the task processing capability and the task processing type of the low-load nodes; according to the task number and the priority of the to-be-allocated tasks and the task processing capability and the task processing type of the to-be-allocated task nodes, estimating whether the load of the to-be-allocated task nodes after task allocation exceeds a preset load threshold value or not; if the load does not exceed the preset load threshold, establishing a distribution relationship between the to-be-distributed task and the to-be-distributed task node; and distributing the to-be-distributed task according to the distribution relation. According to the embodiment of the invention, tasks can be distributed more accurately, node overload and software crash are avoided, and load balancing is realized.

The invention relates to a large model workflow arrangement method, device and equipment and a storage medium, and the method comprises the following steps: carrying out the logic arrangement and configuration of a task decomposition tree structure through a preset LiteFlow, and obtaining a candidate execution scheme; performing performance evaluation on the candidate execution schemes to obtain a performance evaluation table; determining performance evaluation data based on the performance evaluation table, and judging whether the performance evaluation data is within a preset performance range, and if not, performing scheme defect analysis on the candidate execution scheme based on the performance evaluation table to obtain defect analysis data; and performing dynamic activity planning on the candidate execution scheme based on the defect analysis data through the LiteFlow to obtain a workflow arrangement scheme, thereby solving the problems that a conventional workflow arrangement method often adopts a static and fixed task allocation mode and cannot effectively adapt to dynamic change in a large model operation process, so that the system performance is reduced, and the system performance is influenced. And the resource utilization efficiency is low.

The invention discloses a dynamic network heterogeneous resource management and task allocation method, system and device. The method comprises the following steps: constructing a plurality of constraint conditions; constructing a first objective function for maximizing task execution utility and minimizing resource consumption and energy consumption; on the basis of the first objective function and the multiple constraint conditions, a static task allocation strategy is obtained through Lagrange relaxation solution; constructing a dynamic task allocation strategy when resource adjustment is performed on the static task allocation strategy, and based on the dynamic task allocation strategy, adjusting resources of each time step by adopting a Markov decision process to obtain a target resource adjustment amount of each time step; constructing a static task updating strategy according to the static task allocation strategy and the target resource adjustment amount; and based on the static task allocation strategy, the dynamic task allocation strategy and the static task updating strategy, adopting alternate optimization to obtain a target task allocation strategy. The overall resource utilization rate can be improved, and the task execution efficiency is improved.

The invention discloses a computing power distribution method based on a dynamic window and multi-version correction, and relates to the technical field of intelligent resource scheduling, the method comprises the following steps: constructing a scheduling state set by monitoring a to-be-scheduled task set and a platform resource state of a cloud platform resource scheduling system in real time; based on time window dynamic judgment, extracting a stable and effective task set; calculating a scheduling priority coefficient in combination with task resource intensity, urgency and historical completion performance, determining a priority scheduling set and executing a first version scheduling scheme; the difference between the current scheduling version and the historical optimal scheme is evaluated through the residual vector and the offset coefficient, and whether version correction needs to be carried out or not is judged; and based on resource use feedback in the scheduling execution process, calculating a feedback change rate and judging whether a scheduling execution state is stable or not, and if the scheduling execution state is unstable, dynamically adjusting scheduling parameters. According to the method, accurate allocation of computing power resources, adaptive scheduling optimization and abnormal risk control are realized.

The invention relates to the technical field of resource optimization, in particular to an unmanned aerial vehicle distribution task real-time optimization scheduling management system, which comprises a bat sound wave calculation module for performing echo positioning frequency conversion calculation on the distance between the current position of an unmanned aerial vehicle and a target distribution point, a time window and a carrying weight upper limit value through a bat algorithm, and obtaining a task sound wave waveform matrix. According to the invention, the application of the bat algorithm enables the unmanned aerial vehicle to accurately calculate the task search space when the unmanned aerial vehicle executes the distribution task, and avoids the resource waste caused by a static distribution mode. The task search vector is formed based on the comparison operation of the task waveform amplitude and the distribution distance, so that the unmanned aerial vehicle can quickly adjust the task priority in a dynamic environment, and the execution accuracy of the distribution task is improved. The task group is optimized through the subspace decomposition operation, so that the unmanned aerial vehicle can execute multiple tasks in a parallel mode, the load pressure of a single unmanned aerial vehicle is reduced, and the cooperation efficiency of the overall tasks is improved.

The invention discloses a task allocation method and system based on a hybrid game genetic algorithm, and belongs to the field of intelligent manufacturing, and the method comprises the following steps: obtaining task allocation data based on an industrial Internet of Things and an edge computing technology; constructing a task allocation-path planning-energy consumption control collaborative optimization model based on the task allocation data; the collaborative optimization model comprises a three-dimensional optimization objective function and a constraint system; the constraint system comprises an equipment health state constraint, an AGV dynamic motion constraint, a process connection space-time constraint and a multi-agent game equilibrium constraint; and solving the collaborative optimization model based on a hybrid game genetic algorithm to obtain a task allocation optimal solution. Through deep coupling of a multi-agent game and a dynamic evolutionary algorithm, the problem of decision dimension splitting of traditional single-target static scheduling is solved.

The invention provides a computing power resource management and distribution method and system and a medium. The method comprises the following steps: integrating various computing power resources in a private network, establishing a computing power resource information base, analyzing a computing power resource request, establishing a service demand model, optimizing and matching a computing power resource configuration template, obtaining a computing power adaptation template, and establishing a service demand model; optimal computing power resource adaptation is carried out according to dynamic information of target computing power resources matched with the computing power adaptation template, isolation processing is carried out at the same time, monitored performance index data are processed, a load trend is predicted, a corresponding computing power resource adjustment strategy is obtained, and the computing power resources are optimized and expanded according to big data analysis; therefore, efficient utilization and elastic expansion of the computing power resources of the private network are realized by constructing the dynamic computing power resource pool and the resource scheduling model and monitoring, adjusting and optimizing an expansion mechanism, and the processing efficiency and security of big data services are remarkably improved.

The invention discloses a power consumption distribution method and device, a storage medium and a program product, and relates to the technical field of servers, and the method comprises the steps: dividing a plurality of time slots, and in each time slot interval, according to the predicted load demand of each node and the task priority weight of a task executed by each node in the corresponding time slot, determining the task priority of each node; according to the method, the dynamic priority of each node is determined, and then the power consumption of each node is allocated based on the dynamic priority and the resource budget of each node in each time slot interval, so that the method can flexibly adapt to different load requirements and execution of tasks with different priorities in different time periods, and the tasks with high priorities can guarantee more resources; according to the method, resource waste is avoided, reliable decision support is provided for power consumption management of the server, system stability and task execution efficiency are effectively balanced, and double breakthrough of the data center in the aspects of energy conservation and performance optimization is promoted.

The invention relates to the field of artificial intelligence technology and financial science and technology, and discloses a computing resource dynamic allocation method, device and equipment and a storage medium, and the method comprises the steps: obtaining a pre-constructed resource scheduler cluster, and carrying out the initial environment modeling of the resource scheduler cluster, and obtaining an initial scheduler cluster; selecting a resource scheduling action in the initialized scheduler cluster by using a preset reinforcement learning model; performing model optimization on the reinforcement learning model according to the resource scheduling action to obtain a target scheduling model; and calculating a resource scheduling strategy of the resource scheduler cluster according to the target scheduling model. The method can effectively improve the utilization rate of computing resources and the completion efficiency of computing tasks.

To level work volumes among a plurality of target persons.SOLUTION: An information processing device 101, on the basis of a result of allocating work groups prior to work groups to be allocated for a plurality of target persons to which the work groups to be allocated will be allocated, selects a first target person to be an adjustment target of a work volume from the plurality of target persons. On the basis of skill information indicating skills possessed by the selected first target person, the information processing device 101 specifies a first work group that the first target person is to handle from the work groups to be allocated. If a total work volume of the specified first work group exceeds a predetermined allowable value, the information processing device 101 deletes at least some works from the first work group, thereby specifying a second work group corresponding to the first work group but the works deleted. The information processing device 101 fixes allocation of the specified second work group to the first target person, and performs allocation to the remaining target persons other than the first target person of the work groups other than the second work group among the work groups to be allocated.SELECTED DRAWING: Figure 1

The invention relates to the technical field of communication and automation control, and discloses an equipment address automatic allocation method, an address allocation circuit and communication equipment, the allocation method is applied to cascaded communication equipment, and the allocation method comprises the following steps: responding to an addressing trigger operation, reading an address input signal, and determining own equipment attribute according to the address input signal; determining an addressing mode and carrying out addressing according to own equipment attributes; after addressing is completed, a first addressing instruction carrying equipment addressing information is issued to the existing next-level communication equipment; the first addressing instruction is used for instructing the next-level communication equipment to perform addressing. According to the invention, the device address can be automatically addressed, and the correctness and reliability of address allocation are improved.

The invention discloses a task allocation method, device and equipment, a storage medium and a computer program product, and relates to the technical field of computers.The method comprises the steps that reasoning task information in a first preset time period and computing power information of all computing cards in a heterogeneous computing system are obtained; predicting a predicted number of the reasoning requests received in a second preset time period based on the reasoning task information; and according to the reasoning task information, the computing power information of each computing card and the predicted number, allocating a pre-filling task or a decoding task to each computing card, and determining the number of tasks allocated to each computing card, so that the time for the heterogeneous computing system to respond to the predicted number of reasoning requests is shortest. According to the method, the optimal matching of the heterogeneous resources and the reasoning tasks is realized, the utilization rate of the heterogeneous resources is improved, and the execution efficiency of the reasoning tasks is improved.

The invention discloses a workshop task allocation method and system based on multiple dimensions, and relates to the technical field of production control management. Comprising the steps of obtaining order data and then performing preprocessing; based on the preprocessed order data, an advanced machine learning algorithm is adopted to construct a decision tree model, and the processing complexity of each order is identified; determining a task allocation scheme by using an optimization algorithm according to the identified order processing complexity and the current state of the production line; monitoring the actual operation condition of each production line in real time, starting a dynamic adjustment mechanism when the work load of a certain production line is unbalanced, and re-evaluating and adjusting task distribution; through intelligent task allocation and resource optimization, the production efficiency and customer satisfaction are improved, and meanwhile, dynamic adjustment and intelligent management of the production process are realized.

The invention discloses a high-efficiency multi-core processor task allocation method, and relates to the technical field of computer multi-core processors, and the method comprises the following specific steps: S100, task analysis and classification: analyzing a to-be-allocated task, and according to task characteristics and system states, performing task allocation on the to-be-allocated task; classifying the tasks into task groups which can be executed in parallel, need to be executed in sequence and have a specific dependency relationship; according to the method, the migration cost evaluation model is accurately constructed, a plurality of key factors such as the data moving cost, the context switching overhead and the performance loss are comprehensively considered, accurate evaluation of the task migration cost is achieved, a scheme with the minimum migration cost can be preferentially selected in the task distribution and migration process, and the task migration cost evaluation efficiency is improved. Therefore, unnecessary resource consumption and performance loss are effectively reduced, in addition, through real-time monitoring and dynamic adjustment of task allocation and migration strategies, it can be ensured that the multi-core processor always keeps high overall performance during operation, and complex and variable task requirements are met.

The invention provides a distributed reasoning task allocation method for an edge computing large model, which belongs to the technical field of mobile edge computing and comprises the following steps of: considering a large language model distributed deployment scene, constructing a workflow for cooperatively finishing a reasoning task by a plurality of edge servers, and adopting accurate secondary reconstruction by considering the generation type characteristic of the reasoning task; the uncertainty of reasoning task resource occupancy is solved, mathematical model expression is constructed for the edge reasoning task allocation problem in the scene, the aim is to maximize the service provider income, the constructed mathematical model is reconstructed into a combination selection problem, an approximate solution with theoretical guarantee is given by adopting a dual method, and the problem of edge reasoning task allocation is solved. And the reasoning task distribution decision of the service provider is optimized, so that the income of the service provider is maximized.

The invention discloses a task allocation method and system, electronic equipment, a storage medium and a product, relates to the technical field of distributed resource allocation, is applied to a distributed system, the distributed system comprises all distributed equipment, and comprises the following steps: for any one distributed task in all distributed tasks on the distributed system, the distributed task is allocated to the electronic equipment; determining task requirements of the distributed tasks, and obtaining resource states of the distributed devices; based on the task demand, each resource state and a dynamic weighting algorithm, calculating a weighted score of each distributed device for executing the distributed task; and distributing the distributed task to the optimal equipment with the highest weighted score for execution. The technical problem that resource allocation is not intelligent during task scheduling is solved.

The invention provides a computing power resource allocation method of an intelligent computing center and a model training method and device.The allocation method comprises the steps that S1, operation data of all computing tasks running on the intelligent computing center at the current moment are obtained, and operation features are extracted from the operation data to obtain operation feature vectors; s2, inputting the operation feature vector into a computing power resource allocation large language model deployed in the intelligent computing center to obtain a computing power resource allocation strategy; the computing power resource allocation strategy represents a computing power resource type and a computing power resource quantity allocated to each computing task by the intelligent computing center; and S3, indicating the intelligent computing center to execute the computing power resource allocation strategy. According to the method, the computing power resource allocation strategy can be efficiently formulated, and the management efficiency of the computing power resources is improved.