653 results about "Computing center" patented technology

The invention provides a supercomputing center computing task dynamic scheduling method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a current to-be-processed computing task request data set of a supercomputing center, and extracting a task feature set of each computing task; calling a pre-trained task scheduling strategy decision model to analyze the task feature set, generating a real-time scheduling priority parameter and a resource allocation constraint condition set of the calculation task, and calculating the task feature set based on the real-time scheduling priority parameter and the resource allocation constraint condition set of the calculation task; according to the method, the dynamic resource allocation instruction set of the computing nodes of the super-computing center is generated, the computing nodes are controlled to execute the task scheduling operation, intelligent and efficient scheduling of the computing tasks of the super-computing center is achieved by updating the resource occupancy state of the computing nodes and the task queue execution progress in real time, and the resource utilization rate and the task execution efficiency are remarkably improved.

The invention discloses a satellite collision avoidance system and method based on a satellite edge computing network. The system comprises a space monitoring network, a space traffic management subsystem and the satellite edge computing network. The space monitoring network is used for observing the running state of a space target to obtain multi-source observation data; the space traffic management subsystem is used for integrating the multi-source observation data to generate space target orbit data; the satellite edge computing network further comprises the steps that a GEO satellite broadcasts space target orbit data to an LEO satellite constellation and forwards collision risk screening information to a ground cloud computing center; the LEO satellite constellation calculates collision risk screening information according to the space target orbit data and formulates a collision avoidance control strategy; the LEO / GEO earth station is used for data transceiving and protocol conversion among the GEO satellite, the LEO satellite constellation and the cloud computing center; and the ground cloud computing center stores constellation orbit data through protocol conversion and satellite communication. According to the invention, the real-time performance of satellite collision avoidance is improved.

The invention discloses a communication infrastructure intelligent operation and maintenance management system based on cloud computing. The system collects multi-source operation and maintenance data through the data acquisition module, and the multi-source operation and maintenance data is transmitted to the cloud computing center module after being processed by the data transmission and preprocessing module. The cloud computing center module performs deep analysis on data by using a plurality of intelligent algorithm-based sub-modules such as equipment state evaluation, fault prediction, resource optimization and allocation, operation and maintenance decision suggestion and the like to generate an operation and maintenance strategy, the operation and maintenance strategy is implemented by the operation and maintenance operation execution module, and the operation and maintenance condition is visually presented by means of the visual display and monitoring module. According to the method, the powerful computing power and storage capacity of cloud computing are utilized, and multiple algorithm advantages such as machine learning and time sequence analysis are combined, so that intelligence and automation of operation and maintenance management of the communication infrastructure are realized, the operation and maintenance efficiency and quality are effectively improved, and stable and reliable operation of communication service is guaranteed.

The invention provides a method for realizing agent development MCP service through computing power of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, determining interface information of a target application programming interface API, the target API being an interface of a target platform; s2, generating a first coding task based on the interface information through an intelligent agent of the intelligent computing center; s3, generating a first MCP service component corresponding to the first coding task through the intelligent agent; and S4, registering the first MCP service component to the intelligent computing center. According to the invention, the development efficiency of the MCP service of the intelligent computing center can be greatly improved.

The invention provides a method and device for an intelligent computing center to provide computing power resources through computing power packages, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the steps: S1, determining a computing power package corresponding to a to-be-processed computing power task under the condition that the computing power resources need to be called, the computing power task is a task needing to call computing power resource processing, and the computing power package is used for metering the computing power of the intelligent computing center according to the calculated amount; s2, computing power resources are called based on an application program interface corresponding to the computing power package, the application program interface comprises a code program corresponding to operation for executing the computing power task, and the computing power resources called by the application program interface are matched with the operation environment of the computing power task. According to the method, the scheduling efficiency of the computing power resources can be improved, the computing power is metered according to the calculated amount through the computing power package, and wide application of the general computing power is achieved.

The invention relates to a bridge and tunnel disease detection method and system based on unmanned aerial vehicle-edge computing collaboration, and solves the problem that the detection efficiency is limited due to the lack of systematic design of a collaboration mechanism of an unmanned aerial vehicle and edge computing. The method comprises the following steps that: a distributed edge computing node fuses multi-source monitoring data to obtain a health index, compares the health index with a multi-level threshold value, generates a message containing space coordinates, levels and characteristics when the health index is abnormal, and transmits the message to an edge computing center; the center screens adaptive unmanned aerial vehicles, plans an optimal path, dispatches collected data, and preliminarily screens diseases through a parallel model; determining disease complexity and types in combination with abnormal features, and establishing a collaborative detection unit to specially collect multi-source data; centimeter-level positioning is realized through BIM registration and SLAM, and an accurate detection report is generated. The method has the following effects: accurate scheduling, real-time processing and centimeter-level positioning of disease detection are realized, an intelligent detection closed loop is constructed, and the accuracy and efficiency of bridge and tunnel operation and maintenance are greatly improved.

The invention discloses a data storage system of a digital country based on cloud computing, which comprises a client, a data transmission module and a cloud computing center, relates to the technical field of data storage, stores hot data on a high-performance hot data storage layer, ensures that frequently accessed data can be quickly responded, improves the system performance, and at the same time, stores the hot data on a high-performance hot data storage layer. The cold data is stored in a low-performance cold data storage layer, the storage cost is reduced, effective balance of performance and cost is achieved, the data is automatically migrated among different storage layers along with change of data access frequency, so that the performance and the cost are balanced, the frequently accessed data is migrated into a cache, the access speed of the data is increased, and the data access efficiency is improved. And establishing a relationship between a time interval and a data type of historical data, predicting data which are possibly accessed in a given time interval, adding the data into the cache, dynamically adjusting the cache, and ensuring the validity of the cache.

The invention relates to the technical field of intelligent traffic, and discloses a vehicle and road cloud data processing method and system based on an edge computing center, and the method comprises the steps: deploying a sensing module, collecting vehicle, road and environment data, and carrying out the standardization and alignment; constructing a three-dimensional tensor, and calculating a local gradient to reflect data aggregation; evaluating a node collaboration demand, and constructing a graph structure and a collaboration weight based on a gradient; constructing an optimization objective function, and balancing delay, resources and task importance scheduling; when the gradient exceeds a threshold value, nodes are selected, and state saving and task migration are executed; when communication is abnormal, autonomous reasoning processing is completed based on a local model and features; the system comprises a sensing module, a tensor modeling module, a resource collaborative evaluation module, a task scheduling module, a task migration module and an autonomous reasoning module. According to the method, the three-dimensional tensor structure is introduced to express the multi-dimensional information of the vehicle, the roadside equipment and the environment sensing terminal, the multi-dimensional information is fused and modeled, and the purpose of automatically extracting the area aggregation situation is achieved.

The invention provides a satellite-borne computing center system, in particular to a satellite-borne computing center system with high performance and high computing power, which comprises a data interaction module, a task management module, an artificial intelligence AI module M1 and an artificial intelligence AI module M2. The data interaction module is an input and output interface for high-speed data processing; the task management module serves as a task scheduling center and distributes calculation tasks to the AI module; the artificial intelligence AI module M1 and the artificial intelligence AI module M2 adopt domestic and imported AI chips, and the artificial intelligence AI module can work in an RC mode and an EP mode, provides computing power guarantee for satellite-borne high-computing-power computing tasks, and has the characteristics of flexible expansion and agile deployment. All the modules are interconnected through a serial port management plane, a gigabit network control plane, a PCIE data plane and a power supply plane, and control and data interaction between processors are achieved.

The invention provides a smart power grid data communication optimization method based on edge calculation, and relates to the technical field of smart power grids, and the method comprises the steps: obtaining an inspection topology network of a target power grid; performing multi-angle communication analysis to obtain K power transmission equipment communication feature tuple sets and L inspection site communication feature tuples; carrying out edge step identification to obtain M edge topology sub-networks; performing balanced configuration of sub-network service resources to obtain M edge computing servers; performing routing inspection by using a routing inspection unmanned aerial vehicle and a routing inspection robot to obtain routing inspection video data of L sites; and distributing the data to M edge computing servers for data preprocessing, and uploading the data to a cloud computing center for routing inspection data analysis to obtain a routing inspection analysis result. According to the invention, the technical problem of low data processing efficiency caused by data transmission delay and overweight network load due to the fact that power grid monitoring generally depends on centralized data processing in the prior art is solved.

The invention provides a computing power intelligent scheduling method and device oriented to a common computing power intelligent computing center, and relates to the technical field of computing power infrastructure, and the method comprises the steps: S1, obtaining first sensing data which is used for calculating the load condition of each computing resource in a computing power resource pool in a target time period, the computing power resource pool comprises a reasoning resource pool, a training resource pool and an elastic resource pool; s2, inputting the first perception data into a pre-trained calculation model, and determining a first target proportion in a target time period; and S3, scheduling the computing resources corresponding to the resource pools based on the first target proportion. In this way, idle or overload of the reasoning task and the training task caused by static allocation of the computing resources is reduced, and the utilization rate of the computing resources of the intelligent computing center is increased; according to the method, redundant computing resources reserved for coping with peak values are reduced, the cost of model development is greatly reduced by dynamically adjusting hardware resource allocation, and wide application of the cost benefit is achieved.

The invention relates to the technical field of collaborative construction, in particular to a collaborative construction method and system based on BIM, and the method comprises the following steps: obtaining component information and a construction path, calculating the center distance, the number of intersection lines and the blocking ratio, evaluating the density and the staggering degree, generating an urgent distribution set and a task list, extracting spatial parameters, and calculating the overlapping level. And judging the compression feasibility of the time window, and generating a non-parallel task list. According to the method, through quantitative evaluation of blocking risks between construction nodes, a dynamic sorting list generated based on urgency degree values and construction plan binding tasks, conflict levels between component pairs are divided through an operation space overlapping degree index, compression judgment is performed on an adjustable time window, and a minimum interference construction time period is allocated; strong interference task pairs are calibrated to avoid parallel construction, the sorting rationality of construction tasks and the space decoupling efficiency of job scheduling are effectively improved, and fine matching and efficient cooperation of component scheduling sorting and resource allocation in a dynamic scene are achieved.

The invention provides a super-computing center battery charging and discharging strategy optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining a real-time operation data set of a super-computing center battery system in real time, and carrying out the load feature extraction of the real-time operation data set; the method comprises the steps of generating a load feature set of a battery system, inputting the load feature set into a pre-trained reinforcement learning strategy network, performing charge and discharge strategy priority matching processing on the load feature set, generating a priority ranking result of a target charge and discharge strategy, adjusting a current charge and discharge strategy set according to the priority ranking result, generating an optimized charge and discharge strategy set, and outputting the optimized charge and discharge strategy set. And performing charging and discharging operation based on the optimized charging and discharging strategy set, and collecting battery performance feedback data after the charging and discharging operation so as to iteratively update strategy matching parameters of the reinforcement learning strategy network. According to the method, the problems of over-charging and over-discharging and power oscillation caused by lagging response of environmental parameters in a traditional fixed strategy are solved, and autonomous decision-making of multi-target optimization can be realized without depending on artificial experience rules.

The invention belongs to the technical field of railway inspection, and particularly relates to a space-air-ground integrated railway power transformation and distribution intelligent inspection method and system which comprises a sensing layer, a platform layer, a data and intelligent center, an application layer and a user and presentation layer. The sensing layer is composed of an aerial inspection layer, a ground inspection layer and a fixed monitoring layer; the platform layer is based on a cloud computing center and edge computing node collaborative architecture, combines data and an intelligent center, integrates multi-source heterogeneous data through a big data platform, carries an AI algorithm engine to realize automatic identification of equipment defects, analyzes images, synchronously predicts the images, and realizes automatic identification of the equipment defects. And the application layer is combined to complete the functions of automatic generation of an inspection report, automatic distribution of a maintenance work order, equipment health state evaluation and the like. According to the method, functions of railway power place inspection, power supply line inspection, power supply and distribution facility operation and maintenance emergency and the like can be realized, deep fusion analysis of multi-dimensional data is realized, and meanwhile, the problems of insufficient training samples, sparse defect types and the like of an existing AI model are solved.

The invention provides a computing power management method of an intelligent computing center and a related device, and the method comprises the steps: S1, carrying out the standardized conversion of heterogeneous computing resources of the intelligent computing center, and obtaining heterogeneous computing power resources represented in a standardized manner; the heterogeneous computing power resources represented in a standardized manner comprise a resource type, a total capacity and a current usage amount; s2, determining a computing power resource demand of a multi-modal computing task on the intelligent computing center; and S3, allocating the heterogeneous computing power resources to the multi-modal computing task according to a matching corresponding relationship between the resource types and modal data of the multi-modal computing task, the total capacity and the current usage amount of the heterogeneous computing power resources of each resource type, and the computing power resource demand. According to the method, the resource utilization rate of the heterogeneous computing power resources of the intelligent computing center in the multi-modal computing task can be improved.

The invention relates to the technical field of computing power management, and discloses an AI-based computing power resource intelligent scheduling system and method, and the system comprises a resource monitoring module, a multi-dimensional matching module and a computing power scheduling module which are in communication connection in sequence. The method corresponds to the system. According to the application, the resource monitoring module is utilized to provide a data basis by monitoring and predicting the use condition of the computing power resource, so that the system can timely deal with the dynamic change of the resource; the multi-dimensional matching module analyzes task multi-dimensional information and matches the most suitable computing center based on a computing power rule and a hardware rule, so that the defects of a traditional method in the aspect of task resource matching are effectively overcome; the computing power scheduling module updates actual scheduling according to a simulation result by means of simulation scheduling and an optimization mechanism, so that the scheduling accuracy and the resource utilization efficiency are greatly improved, the problems of imbalance of multi-task scheduling, insufficient system expansibility and the like are successfully solved, the stability and the overall efficiency of the system are remarkably enhanced, and the service life of the system is prolonged. And the improvement of the intelligence level of computing power scheduling is powerfully promoted.

The invention relates to a set communication method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring network state information of each intelligent calculation center in an intelligent calculation network; performing decision processing on the network state information through a pre-trained reinforcement learning model to obtain a set communication strategy for each intelligent calculation center; controlling data interaction among the intelligent calculation centers according to the set communication strategy; wherein the pre-trained reinforcement learning model is obtained by taking network state information as a state space and defining training performance of model training of the intelligent computing network as reward function training. By adopting the method, the set communication efficiency can be improved, the network congestion can be reduced, and the training rate of cross-computing-center model training can be further improved.

The invention provides a method for debugging an intelligent agent through computing power of an intelligent computing center, which comprises the following steps: S1, a debugging intelligent agent receives task information provided by a user through an interactive interface, and the task information is used for indicating to debug a debugged object; the debugged object is a debugged intelligent agent or a function module or a tool function of the debugged intelligent agent; s2, the debugging agent determines a debugging requirement according to the task information; s3, the debugging agent determines a debugging method according to the debugging requirement, deploys a debugging environment according to the debugging method, generates a running instruction and debugs the debugged object; s4, the debugging agent obtains the operation information of the debugged object and analyzes the operation information, and whether the debugging requirement is met or not is evaluated according to the analysis result; s5, if not, the debugging agent checks the code of the debugged object, modifies the code according to the check result, and returns to the step S3 until the condition is met; and S6, the debugging agent feeds back a debugging result to the user through the interactive interface.

The invention discloses a method and a device for supporting elastic expansion capability by computing power resources of an intelligent computing center. The method comprises the following steps: monitoring a QPS index of a Kubernetes cluster service in real time, and dynamically dividing service state grades after smoothing data through a sliding window; dynamically adjusting a QPS threshold value based on the historical data and the cluster load; the HF service dynamically expands and shrinks the capacity according to the QPS, the MF service keeps a single instance, and the LF service releases the instance and pre-caches resources to the SSD node; the service life cycle is managed through queue state transition, and cache is optimized in combination with an LRU-LFU mixed elimination strategy and a disk pressure trigger mechanism; the device comprises a monitor, a scheduler, a router, a three-level cache system, a fault-tolerant processing unit, a processor and a memory. And a three-level cache system is adopted to accelerate cold start, so that the fault recovery time is effectively shortened, and the problems of high loading delay, low resource utilization rate and cross-node scheduling of the AI large model are solved.

The invention relates to the technical field of spacecraft orbit control and space safety, and discloses a space debris orbit prediction and avoidance method, which comprises the following steps that: a ground computing center screens candidate targets and generates enhanced data packets for uploading; the spaceborne computer combines real-time navigation data to carry out geometric feature screening, and an instant high-risk target list is generated; controlling the star sensor to execute optical observation, and resolving an optical measurement relative orbit state by utilizing displacement superposition and Kalman filtering; selecting a high confidence coefficient or a conservative probability threshold according to the optical measurement state acquisition condition, and generating an avoidance instruction when the collision probability exceeds the limit; and the propulsion system responds to the instruction to execute avoidance, verifies the effect by confirming the semi-major axis variation and executes track recovery by selecting an aircraft. According to the method, the ground-air collaborative screening and shift superposition enhancement technology is adopted, the problems that the low-precision ephemeris false alarm rate is high and the detection capability of a satellite-borne sensor on a dark and weak target is insufficient are solved, and the accuracy of evasion decision making and the execution reliability are improved.

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.

The invention discloses a multi-chain collaborative center node switching method for ensuring authentication efficiency and data reliability, which is applied to a system comprising an identity chain, an authentication chain, an edge computing center, a key generation center, a plurality of unmanned aerial vehicle clusters and at least two aerial directors, and is characterized in that the identity chain is used for identity management of all the unmanned aerial vehicle clusters; the authentication chain is jointly maintained by all the unmanned aerial vehicle clusters and all the air directors. According to the multi-chain collaborative central node switching method for ensuring the authentication efficiency and the data reliability provided by the invention, the secure communication between the clusters can be quickly established in the authentication process of the unmanned aerial vehicle clusters, and the switching of the central nodes can be quickly carried out when the central nodes are threatened and attacked in a multi-chain manner; the new central node continues to execute the flight task, and the forward and backward security is ensured in the switching process, so that the problems of low authentication efficiency of the unmanned aerial vehicle cluster, strong dependency of the central node and insufficient security in the switching process of the central node are solved.

The invention provides a computing power routing addressing method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, and the method comprises the steps: S1, obtaining the configuration information of the computing power service of a user; s2, generating computing power routing information based on the configuration information; and S3, based on the computing power routing information, distributing the flow corresponding to the computing power service to at least one computing power service instance corresponding to the computing power service. According to the invention, the efficiency of computing power routing addressing can be improved.

The invention provides a computing power metering method and device of an intelligent computing center cloud platform based on a multi-dimensional performance index, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a first task type of a first computing power operation task; s2, determining a plurality of resource indexes corresponding to the first task type and a weight coefficient corresponding to each resource index; s3, collecting a parameter value corresponding to each resource index in each of the N periods; s4, calculating a computing power value corresponding to the first computing power operation task based on the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods; and S5, generating a first verification data packet based on the first task identifier of the first computing power operation task, the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods. The computing power credibility of the intelligent computing center cloud platform can be greatly improved.

The invention relates to an agricultural data interaction method and platform based on multi-source information, and belongs to the technical field of agricultural data processing. Comprising the following steps: processing collected data through a corresponding edge calculation module to generate a dynamic label; analyzing and processing the agricultural information data to obtain a supervision label and corresponding supervision data; all the tags and the corresponding data are imported into a geographic information system, matching is carried out according to the corresponding areas and the respective tags, and meanwhile, the monitoring tags are recognized and updated through a cloud platform computing center. The real-time data is fused and sent to the cloud computing platform through the edge computing module, the interaction prompt is generated through association with the supervision data, so that the real-time performance and the high efficiency of data processing are improved, meanwhile, the monitoring data and the supervision data are associated through the interaction prompt, and the interactivity of visual display is improved.

The invention provides an intelligent computing center model development method and device oriented to the common computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures. The method comprises the steps that S1, a scheduling instruction is received, and the scheduling instruction comprises a model code; s2, computing power prediction is carried out based on the data size of the model code, and required computing power information is obtained; s3, acquiring a target virtual accelerator card based on the required computing power information, wherein the target virtual accelerator card is a virtual accelerator card created based on a part of GPU computing power resources of a GPU accelerator card of the first intelligent computing center; and S4, creating a first container based on the target virtual accelerator card, wherein the first container is used for executing the model code. According to the method, the computing power resource utilization rate of the intelligent computing center can be greatly improved, and the economic cost of model development is greatly reduced.

The invention provides an edge calculation optimization method for a seawater parameter monitoring Internet of Things system, and belongs to the technical field of edge calculation, and the method comprises the steps: constructing a seawater parameter monitoring sensor network, and calculating an energy consumption evaluation reference value based on a data change rate and a disorder degree; performing feature extraction on the seawater parameter data; deploying a double-model collaborative architecture comprising a terminal lightweight model and a center complete model; setting a dynamic energy consumption threshold judgment mechanism to allocate calculation tasks; executing lightweight edge calculation at the sensing nodes; transmitting the processed data through a low-power wide area network; the calculation center verifies and fuses the data; constructing a seawater parameter space-time distribution model; dynamically adjusting an energy consumption evaluation reference value by using a Pareto optimal solution set method; and periodically evaluating system energy consumption efficiency and updating model parameters. According to the method, dynamic balance of energy consumption and calculation efficiency of the seawater parameter monitoring system is realized, and the continuous operation capability and the monitoring reliability of the system are remarkably improved.

Embodiments of the present disclosure provide a method for dynamic computing resource allocation based on an IIoT data center, the method comprising: receiving, via an IIoT user platform 110, a business demand of an enterprise user, and sending the business demand via an IIoT service platform 120 to a data computing center of a IIoT management platform 130; monitoring, by a monitoring module, resource data of a business management sub-platform; determining, based on the business demand, a resource demand feature of the enterprise user; determining, based on the resource demand feature and the resource data of the business management sub-platform, a resource allocation parameter; and, generating, via the control center, a resource allocation instruction based on the resource allocation parameter.

The invention discloses a mixed edge cloud-oriented satellite-ground convergence network service function chain (SFC) arrangement method. The method comprises the following steps: firstly, establishing a mixed edge cloud-oriented satellite-ground network model and a service function chain model; secondly, designing a service cost model and a link reliability model for the scene; thirdly, establishing a service function chain arrangement optimization problem model; then, converting the problem model by using a Markov decision process, performing feature extraction on the current network and service state, and inputting the features into a strategy generation network to obtain a deployment strategy; carrying out link mapping by adopting a Dijkstra algorithm based on reliability; and finally, optimizing an arrangement strategy by using deep reinforcement learning to realize an optimization target. According to the invention, the arrangement of the service function chain is carried out under the assistance of the cloud computing center in the satellite-ground convergence network environment, and efficient resource allocation and management are realized by taking the maximization of the service acceptance rate and the minimization of the service cost as targets.

The invention provides a computing power management method of an intelligent computing center and a model training method and device.The computing power management method comprises the steps that S1, multi-modal operation data of a multi-modal computing task operated on the intelligent computing center at the current moment are obtained, extracting context features of each modal operation data from the multi-modal operation data, and combining all the context features to obtain feature vectors; the multi-modal operation data comprises modal operation data of at least one of the following modals: texts, monitoring indexes and visual data; s2, inputting the feature vector into a multi-modal resource allocation large language model deployed in an intelligent computing center to obtain a multi-modal resource allocation strategy for allocating computing power resources to a multi-modal computing task; and S3, indicating the intelligent computing center to execute the resource allocation strategy. According to the method, the multi-modal resource allocation strategy can be efficiently formulated for the multi-modal computing task, and the management efficiency of the computing power of the intelligent computing center is improved.