344 results about "Dynamic load balancing" patented technology

The invention belongs to the technical field of server cluster scheduling, and particularly relates to a dynamic load balancing-based server cluster scheduling method, which comprises the following steps of: acquiring load data of each server in a server cluster in real time; performing quantitative evaluation on the acquired load data through a preset load evaluation model to obtain a real-time load value and a load stability score of each server; receiving an external task to be processed, and analyzing resource demand parameters and task type characteristics of the task; determining a target server of the task based on the server state level, the load stability score, the task resource demand parameter and the task type feature; and updating the load evaluation model and the scheduling strategy in real time based on the historical scheduling data, the task operation feedback data and the industry scene characteristic parameters. According to the method, through multi-dimensional load evaluation, accurate matching of tasks and servers and dynamic strategy optimization, the resource utilization rate and task processing efficiency of the server cluster are effectively improved, and the requirements of different industry scenes are met.

The invention relates to the technical field of distributed computing, and discloses a distributed computing power scheduling method and system based on dynamic load balancing. The method comprises the following steps: acquiring real-time resource state data of a plurality of nodes in the distributed system and demand characteristics of a task to be allocated, generating a dynamic task allocation strategy, allocating the task to a target node for execution, monitoring a resource state of the target node in real time, and triggering task reallocation when the resource state deviates from a preset threshold value. The system comprises a monitoring acquisition module, a task analysis module, a strategy generation module, a scheduling execution module and a resource abstraction module. According to the method, tasks and node resources can be accurately matched, the resource utilization rate is improved, the real-time performance and reliability of the tasks are guaranteed, the overall performance of a distributed system is optimized through heterogeneous resource unified scheduling and a resource reserving and recycling mechanism, and the method is suitable for various distributed computing scenes.

The invention relates to the technical field of data processing, and discloses a heterogeneous computing acceleration method and system based on a deep learning framework network. The method comprises the following steps: acquiring performance parameters of heterogeneous computing equipment to obtain an equipment characteristic data set; receiving a calculation task and analyzing the calculation task into an operation sequence; the operation sequence is coded into a gene sequence, task decomposition is optimized through a genetic recombination algorithm, and a subtask set marked with acceleration characteristics is obtained; performing matching analysis on the sub-task set and the equipment characteristic data set to obtain a task allocation scheme; deploying the subtasks to corresponding equipment according to the allocation scheme to obtain a distributed execution framework; and monitoring the running state of the framework in real time, and dynamically adjusting resource allocation to obtain a calculation result of speed-up ratio improvement. According to the method, a self-adaptive task decomposition and resource allocation and dynamic load balancing mechanism can be realized, and the execution efficiency and the resource utilization rate of the deep learning task are improved.

The invention discloses a charging pile system management method based on dynamic load balancing, and particularly relates to the technical field of charging pile management, and the method comprises the steps: collecting time domain scheduling data of a charging pile side, a power grid side and a distributed renewable energy output device side, carrying out the fusion filtering, and obtaining a station-level state vector; generating a station-level prediction sequence of the charging pile in the next control period based on the station-level state vector and a rolling prediction model; an inner-layer controller reads the station-level state vector and the station-level prediction sequence, establishes a power prediction optimization model and solves the model, and outputs an expected station-level power trajectory and station-level power redundancy; a result output by the inner-layer controller is received, and a station-level power envelope curve and an energy storage charging and discharging set value are comprehensively solved and transmitted; the slope upper limit of the station-level power envelope curve is contracted based on the oscillation amplitude, the new slope upper limit is substituted into the next solution, the subsequent power change rate is limited, and the problem of unstable power exchange between the charging station and the superior power grid is effectively solved.

The invention relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention discloses an edge computing node dynamic load balancing method based on a multi-agent system, and relates to the technical field of node load balancing. According to the edge computing node dynamic load balancing method based on the multi-agent system, the current computing power communication data and the operation environment data of each edge computing node are collected, the operation load index of each node is analyzed, and whether an overload node exists or not is judged; if an overload node exists, a plurality of nodes adjacent to the overload node are obtained, a task migration candidate set is screened out, finally, a target node is determined from the candidate set, and task migration operation is executed; the operation load index of the edge computing node is dynamically quantified by collecting and comprehensively analyzing multiple parameters such as the CPU utilization rate, the memory occupancy rate, the communication delay, the temperature rise rate, the I / O blocking rate, the power consumption fluctuation rate, the cache write-in waiting time and the vibration disturbance amplitude in real time. Compared with a traditional single index or fixed threshold mode, the method can identify the node overload state more accurately and timely.

The invention discloses a charging pile dynamic load balancing scheduling system based on reinforcement learning, and the system comprises a data collection module which comprises 5G communication units installed at the charging piles, and is used for obtaining the power of the charging piles, vehicle battery parameters and power grid load data in real time; the reinforcement learning decision module maps the data in the data acquisition module into a state vector through a feature extraction network, and generates a power distribution strategy by adopting a PPO + DQN hybrid algorithm; the communication protocol module realizes state synchronization and control instruction transmission between the charging piles based on an OCPP2.0 standard; the execution control module converts the control instruction into a PWM control signal, and adjusts the output power of each charging pile in real time; through the reinforcement learning decision module, the system can analyze the charging pile power, the vehicle battery parameters and the power grid load data in real time, generate an accurate power distribution strategy, ensure accurate matching of the charging pile power and the electric vehicle demand power, and thus significantly improve the charging efficiency.

The invention provides an all-weather autonomous inspection method and system based on cluster task dynamic load balancing, and relates to the technical field of inspection monitoring, and the method comprises the steps: collecting the remaining electric quantity, the positioning position, the task queue length and the sensor health state of an unmanned aerial vehicle cluster in real time through an airborne terminal; fusing the meteorological data and the multi-source environment sensing data, and constructing a dynamic obstacle map and a meteorological influence model; dividing an inspection area into a plurality of sub-areas based on an electronic fence, dynamically adjusting the inspection priority of each sub-area, setting task weights of a water taking head and a raw water pipeline facility, and distributing inspection tasks according to the priorities; based on the remaining power of the unmanned aerial vehicle, the task priority, the current load and the meteorological data, a bipartite graph matching model of the unmanned aerial vehicle and the task is constructed, the matching weight is dynamically calculated, the optimal matching distribution of the unmanned aerial vehicle and the inspection task is completed, and the efficient inspection operation of the unmanned aerial vehicle cluster in the complex environment is realized.

The invention relates to the technical field of wireless communication and edge computing fusion, in particular to a wireless network resource allocation method based on edge intelligence, which comprises the following steps: S1, collecting a multi-dimensional parameter set of network edge nodes in real time; s2, converting node position coordinates into a network topological structure map, and mapping service type labels and bandwidth requirements into service requirement feature vectors; s3, generating a domain division matrix based on weight distribution of the network topological structure atlas; s4, outputting a resource block configuration tensor according to the time-frequency feature of the business demand feature vector; s5, generating a resource allocation mapping relation table; and S6, generating and executing a node-level resource scheduling instruction set. According to the invention, by constructing a resource scheduling mechanism fusing a topological structure and service features, precise resource allocation and dynamic load balancing of a cooperative domain level and a node level are realized, and the efficiency and the resource utilization rate of multi-task concurrent processing in an edge computing environment are improved.

The invention relates to the technical field of computer architecture and parallel computing, and discloses a parallel task scheduling algorithm for a heterogeneous multi-core processor, which comprises the steps of task modeling, resource mapping, dynamic load balancing, communication optimization, task scheduling decision and execution monitoring. Task allocation is adjusted in real time through dynamic load balancing, cross-core communication delay is reduced in combination with communication optimization, and an efficient task allocation sequence is generated by using an improved genetic algorithm. According to the method, the resource utilization rate and the task execution efficiency of the heterogeneous multi-core processor in a high-performance computing scene can be improved, meanwhile, the robustness and adaptability of an algorithm are enhanced, and the task allocation problem in a complex computing scene is effectively solved.

The invention discloses an intelligent management and control system for machine room equipment, and relates to the field of machine room equipment management and control, the system comprises an energy consumption management and control module, a predictive maintenance module and a security situation awareness module, the energy consumption management and control module is based on the synergistic effect of a dynamic load balancing module, a phase change heat dissipation control module and a block chain energy consumption account book module; and the energy consumption cost of machine room equipment is reduced. According to the intelligent management and control system for the machine room equipment, power supply strategies can be switched according to the actual state of the machine room equipment through the dynamic load balancing module, so that the energy consumption of the machine room equipment can be effectively reduced; according to the invention, corresponding measures can be taken at different stages when the equipment has faults, the influence of the equipment faults on services is reduced to the greatest extent, the personnel movement track, the operation instruction and the environmental parameters can be fused through the arranged security situation awareness module, and the illegal intrusion detection accuracy is improved.

The invention relates to the technical field of power distribution management, in particular to an intelligent UPS power distribution management method and system based on dynamic load balance, and the method comprises the steps: monitoring the energy consumption parameters of each load in real time, and generating a multi-dimensional energy consumption data set based on the energy consumption parameters; inputting the multi-dimensional energy consumption data set into a preset power prediction model, and predicting a power demand prediction result of each load in future time; dynamically generating a load priority queue based on the power demand prediction result of each load and the real-time capacity state of the UPS, and adjusting the power distribution strategy of the UPS to each load in real time; and acquiring working data of the UPS system under the current power distribution strategy, acquiring abnormal data based on the working data of the UPS system, and updating the load power distribution strategy according to the abnormal data. According to the invention, the energy efficiency of the UPS power distribution system is improved, the stability of the system is enhanced, and the output power distribution of the UPS is dynamically adjusted.

The invention relates to the technical field of cloud edge collaborative computing power scheduling, and discloses a heterogeneous computing power scheduling optimization method based on a cloud edge collaborative architecture. The method comprises the following steps: acquiring real-time computing power state data of all available computing nodes in the cloud edge collaborative architecture; performing heterogeneous type division on the computing nodes according to the real-time computing power state data to generate a three-layer computing power resource pool containing cloud computing nodes, edge computing nodes and terminal computing nodes; extracting task calculation features for the current to-be-scheduled task set, wherein the features comprise calculation intensity, data dependence and real-time requirements; constructing an initial task allocation scheme based on the matching relationship between the task calculation features and the three-layer computing power resource pool; iteratively optimizing the initial scheme by adopting a dynamic load balancing strategy to generate a final task scheduling instruction; the instructions are distributed to the corresponding computing nodes to be executed, and computing power state changes in the task execution process are continuously monitored.

The invention relates to the technical field of AGV intelligent scheduling, and discloses an intelligent scheduling and resource allocation method and system for a flexible AGV, and the method comprises the steps: carrying out the grid processing of an AGV operation region, and obtaining a dynamic scheduling network; modeling an AGV state space in the dynamic scheduling network to obtain a multi-agent scheduling model; calculating a task priority score of the AGV based on a multi-agent scheduling model, discretizing an operation time axis into time slices, and obtaining a time slice resource allocation result; according to the time slice resource allocation result, the AGV scheduling decision is optimized to obtain a distributed scheduling strategy; dividing the distributed scheduling strategy to obtain a master control AGV and a slave AGV, and generating a cluster collaborative decision result; and resource allocation and dynamic load balancing are carried out based on a cluster collaborative decision result to obtain a scheduling instruction of the AGV cluster, collaborative decision of the AGV cluster is realized, and consistency of global task decomposition and local resource allocation is ensured.

In a GPU design, “launching a worker” is de-coupled from “assigning a work item” in a work distributor, and new handshake mechanisms between a worker and the work-distributor is provided for work assignment, in order to provide persistent kernel functionality. In example embodiments, software specifies the work that has to be done, hardware selects a variable number of workers based on available resources, and a hardware scheduler handshaking with the executing workers assigns more work as previously assigned work is completed and / or more resources become available.

The invention provides a charging pile power distribution method and system based on dynamic load balancing, and the method comprises the following steps: obtaining the local state information of each charging pile, the state information at least comprises the charging queuing length, the current vehicle charge state, the current load power, the battery health state, the predicted residence time, the electricity price information and the power grid load state; based on the historical charging behavior, the electricity price fluctuation and the traffic flow data, the vehicle access frequency, the electricity price trend and the power demand of each charging pile in the future time period are predicted through a time sequence neural network; a prediction model is constructed; according to the method, forward-looking power planning is realized by entering a multi-dimensional prediction mechanism; the power distribution efficiency is improved by adopting a local collaborative game; the self-adaption and generalization ability is realized through reinforcement learning, and the robustness of a scheduling strategy in multiple scenes is enhanced; through a multi-target scheduling index, the electricity price, the battery life, the waiting time and the power grid load are optimized at the same time.

The invention provides a distributed rendering task scheduling method based on dynamic load balancing, which relates to the technical field of virtual reality simulation, and comprises the following steps: analyzing and preprocessing input physical simulation data through a multi-source model, and converting the format of the processed data into a manageable virtual set data format; loading the texture data to a video memory as required in a virtual mapping mode; dynamic memory loading and coloring are carried out based on the virtual set, and LOD generation is completed; dynamic task fragmentation and scheduling are carried out on the virtual set rendering task and the heterogeneous multi-source rendering task, real-time monitoring is carried out on GPU / CPU / memory loads of heterogeneous resources, and task scheduling is carried out according to a monitoring result; through a distributed cluster mode, a high-resolution rendering task is divided into a plurality of low-resolution rendering tasks, and the low-resolution rendering tasks are rendered on different nodes. According to the invention, high-fidelity, high-response and high-reliability three-dimensional scene simulation capability is provided for high-complexity scenes such as a hybrid system.

The invention provides a geological environment monitoring method and system, and the method comprises the steps: constructing an air-space-earth-depth four-dimensional cooperative monitoring network, collecting multi-modal geological environment data, carrying out the preprocessing of the data, and obtaining multi-modal feature data and abnormal data points after dynamic load balance distribution of calculation resources; fusing the data to generate a comprehensive geological environment feature map containing space-time correlation features, abnormal hot spot distribution and a geologic body three-dimensional reconstruction model; and based on the map, performing geological risk prediction by using a geological risk prediction model to obtain a prediction probability. A monitoring network is constructed to integrate multi-modal geological data, resource allocation is optimized by combining edge calculation dynamic load balancing, geological risk prediction is realized by using a prediction model, the problems of single data, poor dynamic adaptability and weak model generalization ability of a traditional method are solved, the prediction precision is improved, the response time is shortened, and the prediction efficiency is improved. And a high-risk area is visually displayed through a three-dimensional risk thermodynamic diagram.

The invention relates to the technical field of virtual reality interaction, and discloses a virtual reality multi-person interaction method, which comprises the following steps: realizing environment synchronous initialization based on space anchor point calibration and dynamic object loading, and configuring value physical rules and interaction logic; executing a data synchronization mechanism by establishing connection and session management; performing real-time action synchronization and interaction logic processing according to user input processing and action capture; user data privacy and security are protected, and content security and user behaviors are supervised. According to the virtual reality multi-user interaction method and system, by adopting a distributed server architecture and dynamically distributing the nearest edge node according to the geographic position of the user, dynamic load balancing can be realized, QoS priority division is started, low-delay transmission is realized by adopting a UDP + RUDP protocol, communication data is encrypted by adopting an AES-256-GCM, and a key is dynamically updated by adopting a dual-ratchet protocol; and the biological characteristics are converted into irreversible hash values, so that the security of privacy data of the user can be effectively improved.

The invention discloses a distributed server load balancing system based on multi-modal data fusion. The distributed server load balancing system comprises a multi-modal sensing layer, a fusion analysis layer, an intelligent decision-making layer and an elastic execution layer, the multi-modal sensing layer collects multi-source heterogeneous data; the fusion analysis layer is used for mining an association relationship between data of different dimensions; meanwhile, converting multi-source data into a unified feature vector, and outputting a dynamic load balancing strategy; the intelligent decision-making layer generates an executable load balancing strategy according to the fusion analysis result; and the elastic execution layer converts the strategy generated by the intelligent decision-making layer into an actual action, and deals with load change through an elastic mechanism. According to the method, server performance, network topology and request semantic data are fused, a unified feature tensor is generated by using a cross-modal attention mechanism, and load association is accurately quantified; on the basis of cooperation of a global mixed integer programming model and a local lightweight DQN agent, load migration is completed in a very short time, and the response speed is increased.

The invention belongs to the technical field of computers, and particularly relates to a data center server resource allocation method and system based on dynamic load balancing, and the method comprises the steps: collecting a physical resource state, instance constraint and network flow through a multi-dimensional probe, constructing a dynamic weighted load scoring model, and introducing a migration penalty term to correct node load evaluation; solving maximum weight matching of the bipartite graph with constraint by combining a Hungary algorithm, and realizing global optimal mapping of the migration instance and a target node; a memory, a CPU and network resources are pre-configured before migration, a delay recovery mechanism is triggered after migration, and the service quality is guaranteed. The system comprises a resource acquisition module, a score generation module, a matching solution module, a pre-configuration module, a self-adaptive period adjustment module and the like. The cluster resource utilization rate is obviously improved by 28%, hotspots are reduced by 76%, the SLA default rate is reduced by 92%, meanwhile, energy is saved by 15%, and collaborative optimization of efficiency and service quality is achieved.

The invention relates to the technical field of dynamic load balancing processing and communication, and discloses a dynamic load balancing transmission method based on synchronous adjustment, which comprises the following steps: acquiring a synchronous parameter set of each communication node in real time; calculating an initial load weight of each communication node through a dynamic feedback algorithm; a priority coefficient is introduced, and the initial load weight is dynamically corrected according to the real-time level of the transmission request; a final load weight is generated through normalization processing, the sum of the load weights of all the nodes is made to be 1, and a dynamic load distribution strategy is generated according to the final load weight; and dynamically distributing the transmission request of the terminal equipment to each communication node according to the load weight proportion of each node by adopting a weighted polling algorithm according to a dynamic load distribution strategy, thereby completing dynamic load balancing of the transmission request. According to the method, the synchronous parameter set of each communication node is acquired in real time, and the load weight of the node is accurately calculated and dynamically corrected, so that an efficient and accurate dynamic load balanced distribution strategy is realized.

The invention provides a CAN bus message period adjusting method and system based on dynamic load balancing, and the system measures and calculates a network load rate and an error state through a network load real-time detection module, carries out the priority grade division and parameter configuration for different messages, carries out the load partition definition according to the load rate, and carries out the adjustment of the CAN bus message period. And dynamically adjusting a message sending period according to a measurement result, adding a feedback mechanism to realize closed-loop control, and finally realizing load balancing of the system.

The invention relates to a resource allocation method and system for dynamic load balancing of a data center elastic optical network, and belongs to the technical field of communication. Comprising the following steps: initializing data center elastic optical network parameters, and generating a connection request; judging whether the residual computing power resources of the source node and the destination node meet the request requirement or not, if so, calculating K working paths, selecting the shortest path, calculating the number of required spectrum slots, and judging whether the paths have residual spectrum resources or not; if yes, layered traffic grooming evaluation parameters are calculated, and the optimal spectrum layer is selected for grooming traffic; if not, selecting the highest-price modulation format; judging whether bandwidth and optical channel resources on the path are enough, if so, allocating spectrum resources, otherwise, returning to select the next shortest path; judging whether the spectrum resources meet constraint conditions or not, if yes, allocating computing power resources, and requesting successfully; if not, returning to reselect the path; and after success, updating the network transmission path weight. According to the invention, the network resource waste and blocking rate are effectively reduced, and the optical network energy consumption is reduced.

The invention discloses an underwater acoustic processing CPU-GPU dynamic load balancing method based on a task flow model, and belongs to the technical field of underwater acoustic processing. The method comprises the following steps: deconstructing an underwater acoustic processing application into a task flow model represented by a directed acyclic graph; establishing a feature portrait including calculation complexity, parallelism and data throughput for each task node, and constructing a cost prediction model; the CPU / GPU utilization rate and the data transmission performance are monitored in real time; a processor is distributed to each task node by using a dynamic programming algorithm in combination with a cost prediction model and a real-time system state with the goal of minimizing the total task flow completion time; and dynamically scheduling tasks through a central scheduler according to a decision result, and periodically updating a strategy. According to the method, the defects that static task division lacks adaptability and neglects task dependence and communication overhead are overcome, dynamic and efficient utilization of CPU and GPU resources is achieved, and the efficiency and real-time performance of underwater sound processing are remarkably improved.

The invention discloses a dynamic load balancing method and device for an intelligent charging pile management system, and the method comprises the steps: obtaining the operation parameters of a target area transformer corresponding to the intelligent charging pile management system in a current control period, so as to obtain the real-time total load of the target area transformer in the current control period; dynamically calculating the real-time available capacity of the transformer based on the rated power and the real-time total load of the transformer in the target station area; calculating the predicted available capacity of the transformer in the next control period through a load prediction model based on the historical power data and the external environment variables; and according to the real-time available capacity and the predicted available capacity, dynamically adjusting the operation power of the plurality of accessed charging piles, and expanding the number of the accessible charging piles in a load trough period. By fusing the real-time power grid state and the multivariable predicted future load demand, the dynamic fine adjustment of the power of the charging pile and the prospective expansion of the off-peak access amount are realized, the safety of the power grid is ensured, and the capacity utilization efficiency of the transformer is remarkably improved.

The invention discloses an automobile charging pile charging allocation method based on dynamic load balancing, and the method comprises the following steps: S1, collecting the real-time operation data of a charging pile, and storing the real-time operation data in a charging pile load database; s2, constructing a charging scheduling optimization model, and setting an optimization target and a constraint condition; s3, a charging power distribution scheme is initialized, population individuals are generated by adopting a doliolaria group algorithm, and a fitness value is calculated; s4, running the dogvessel squirt group algorithm for optimization, adjusting the power by the navigator according to the optimal solution, and updating the power distribution scheme by the follower; s5, population diversity is calculated, and if the proportion of low-diversity individuals is lower than a threshold value, differential evolution algorithm optimization is triggered; s6, the fitness values are compared, and an optimal charging power distribution scheme is selected; and S7, scheduling the charging power in real time and monitoring the state by applying the optimal charging power distribution scheme. According to the invention, charging power balanced distribution is realized through an intelligent optimization algorithm, user waiting time and power grid load fluctuation are reduced, and charging station operation efficiency is improved.

The invention discloses a GNSS network RTK base station distributed data processing method and device, belongs to the technical field of satellite navigation and positioning, and is used for solving key problems of base station networking, baseline solution, error correction calculation, model establishment and the like in a large GNSS network RTK system. The method is especially suitable for efficient calculation, task allocation and dynamic load balancing processing of a large-scale base station network. The method comprises the following steps: 1) dynamically networking a core server, determining a baseline to be solved, generating a baseline solving task and establishing an error model; (2) a plurality of data processing servers solve all baselines in parallel, and baseline error correction numbers are extracted; (3) each base station calculates relative corrections of other stations relative to the base station according to the related baseline corrections, and establishes an error model, and (4) dynamically allocates a baseline resolving task and an error modeling task. Through multi-thread and distributed parallel computing, the task processing efficiency and the resource utilization rate of a large-scale base station network are remarkably improved.

The invention discloses a multi-core scheduling system and method based on interrupt affinity and a storage medium. The technical problem that data locality guarantee and load balancing are difficult to consider at the same time is solved by constructing a self-adaptive optimization closed loop of perception-decision-execution-feedback, and the method comprises the steps that a routing interruption configuration mechanism provides a basis for dynamic adjustment; an interrupt guide task placement mechanism ensures that an interrupt service program and an associated task are executed in the same CPU core, and the cache hit rate is increased; the execution overhead of an interrupt service program is brought into core load statistics through task context switching and a'task + ISR 'two-dimensional precise load evaluation mechanism triggered by ISR inlet / outlet double nodes, and real load awareness is achieved; a load-driven dynamic interrupt routing mechanism is combined with dual control of a hysteresis threshold and cooling time, so that a ping-pong effect is avoided. According to the method, dynamic load balancing is realized on the premise of keeping data locality, and the real-time response performance and throughput of the multi-core system are remarkably improved.

The invention relates to the technical field of server management and cloud computing, in particular to a dynamic load-oriented intelligent identification heterogeneous server resource expansion method. The method aims at optimizing the resource management efficiency, the hardware expansion flexibility and the overall operation stability of the system in a data center and a cloud computing environment by integrating an advanced intelligent identification technology, a real-time data analysis capability and a self-adaptive resource scheduling mechanism. The method is particularly suitable for modern data center scenes requiring high concurrent processing capability, dynamic load adaptability and high reliability, such as a large-scale cloud computing service platform, an enterprise-level distributed storage system and an artificial intelligence computing cluster.