1013 results about "Load balancing (computing)" patented technology

The invention provides a digital power grid power cloud resource real-time hierarchical distribution method, which comprises the steps of dividing power cloud resources into a plurality of sub-resource pools according to a physical architecture and a topological structure of a digital power grid, setting a special resource pool for key equipment and sensitive loads of the power grid, and dynamically adjusting network topology and a routing strategy. A low-delay and high-reliability network transmission channel is provided through a priority queue and a bandwidth guarantee mechanism; for each sub-resource pool, extracting periodicity, tendency and randomness characteristics of a load by analyzing historical load data and a current system state of power grid equipment, and updating a load prediction result in real time; after incremental resources are migrated, a power grid control request is routed to a corresponding computing node by adopting a consistent Hash algorithm in a sub-resource pool, load balancing is realized by dynamically adjusting the number and distribution of virtual nodes of a Hash ring, and weights of the virtual nodes are dynamically adjusted according to load optimization indexes of the nodes so as to adapt to power grid load changes.

The invention relates to the technical field of cloud platform monitoring analysis, in particular to a cloud platform computing power resource performance monitoring and real-time scheduling optimization method, which comprises the following steps of: acquiring real-time performance data of heterogeneous computing nodes; performing multi-dimensional index fusion on the real-time performance data to generate a fusion performance index set containing time correlation features; constructing a dynamic computing power topological graph based on the fusion performance index set; generating an elastic scaling strategy set according to the dynamic computing power topological graph; mapping a to-be-allocated task to a physical node meeting the topology constraint condition; and carrying out topology conflict detection on the scheduling result through a feedback verification module, and dynamically correcting the parameter weight of the elastic scaling strategy set. According to the method, resource conflicts are reduced, the utilization efficiency of computing power resources is improved, and the system bottleneck problem caused by topology conflicts is avoided, so that the stability and the load balancing capacity of the system are enhanced.

The invention relates to the technical field of cloud computing, and discloses a cluster load balancing processing method based on cloud computing, which comprises the following steps of: S1, initializing a cluster state: determining computing power, power consumption parameters and a task allocation model of each node in a cloud computing cluster; s2, acquiring a node operation state: providing dynamic input data for construction of a load distribution model; s3, constructing a load distribution model: defining a global optimization target; s4, generating a preliminary task allocation scheme: satisfying an optimization target; s5, aiming at the dynamic change of the task arrival rate and the node state; s6, task migration is executed, and the state is updated; and task migration between the nodes is implemented. Through a closed-loop process of node state acquisition, task allocation model construction, dynamic adjustment and feedback optimization, efficient matching of a task request and a computing node is realized. The method can adapt to dynamic changes in a cloud computing environment, the resource utilization rate is remarkably improved, and task processing delay is effectively reduced.

The invention relates to the technical field of real-time data processing, in particular to a distributed real-time data stream processing system and method, and the system comprises a state mapping modeling module, a load dynamic calculation module, a node task adjustment module and a resource scheduling execution module. According to the method, the dynamic identification and classification of the flow state are realized by analyzing the data flow change sequence and identifying the frequency and trend of state switching, the monitoring and understanding capability of real-time data is improved, the node resource pressure is accurately quantified, the predictability of load balancing is enhanced in the resource occupation and load range analysis, and the load balancing efficiency is improved. A scientific basis is provided for resource scheduling, task priority ranking and dynamic task adjustment are combined, the node computing resource utilization rate and the task processing response speed are optimized, flexible configuration of internal storage and computing resources is achieved, peak load pressure is relieved, and data processing efficiency and system stability are improved. And the adaptability and the reliability of processing large-scale data streams by the system are improved.

The invention relates to the technical field of server scheduling, and discloses a large model reasoning scheduling method based on an off-network computing power server, which comprises the following steps of: collecting hardware information of the off-network computing power server, and constructing a server resource vector; decomposing a large model reasoning task into a plurality of sub-tasks, and constructing task modeling; according to the hardware information of the off-network server and the task dependency relationship, performing task division by using an optimization method; performing dynamic task scheduling on the sub-tasks based on the current load state of the server; according to the real-time calculation load of the server, dynamically migrating part of tasks to other servers to ensure the load balance among the servers; and executing the distributed task, monitoring the running state of the server and the task completion condition, and dynamically adjusting a task distribution strategy according to feedback data. Through modeling task requirements and server resources, in combination with real-time monitoring and optimal scheduling, efficient calculation and resource utilization maximization of large model reasoning in an off-network environment are realized.

The invention relates to the technical field of computing power load balancing, and discloses a computing power node load balancing method and device, equipment and a storage medium. The computing power node load balancing method comprises the steps that a target computing power task is read, task feature information is extracted, and the computing power resource quantity needed by the task is calculated through a prediction model; calculating the load of each node in real time, and screening candidate nodes meeting the resource margin; and evaluating the matching degree of the candidate nodes and the tasks, and selecting an optimal node to perform task allocation. According to the method, the task resource demand is accurately predicted through the prediction model, the optimal node is dynamically screened in combination with the real-time load, the problems that a traditional strategy cannot sense the instantaneous load, the calculation complexity is high and cross-cloud adaptation is difficult are solved, the load balancing efficiency is improved, the system calculation overhead is reduced, and meanwhile the cross-cloud environment adaptability is enhanced.

The invention discloses a big data distributed storage and parallel processing collaboration method based on cloud computing. The method comprises the following steps: sensing data stream characteristics in real time through a self-adaptive dynamic partitioning engine, dynamically adjusting a partitioning strategy and generating a metadata label; constructing a node selection model through a comprehensive evaluation algorithm, selecting storage nodes to form an optimal storage cluster, and dynamically adjusting a resource matching weight coefficient based on a load state through a load optimization module; decomposing a data processing task into parallel subtask units, and constructing a dual-objective optimization model; triggering a dynamic rebalance mechanism through a distributed monitoring agent in combination with a hierarchical early warning strategy; and constructing a multi-level cache system to optimize a data access path, outputting a final result, pushing the final result to the user terminal, and updating the knowledge base. Through collaborative optimization of dynamic partitioning, multi-dimensional resource scheduling, elastic scaling and intelligent caching technologies, the resource utilization rate, the load balancing capacity and the stability in a high-concurrency scene of the system are remarkably improved.

The invention relates to the technical field of low-power-consumption AI processors, and discloses a load balancing method of a low-power-consumption AI processor, a chip and a storage medium, the method comprises the following steps: performing cache resource division on a plurality of neural mimicry cores in the low-power-consumption AI processor to obtain a double-layer cache structure; performing characteristic analysis on the target calculation task, and constructing a calculation task directed acyclic graph and a dependency intensity matrix; performing task segmentation to obtain a subtask division scheme; creating a task-core mapping table; organizing the neural mimicry core into a plurality of concentric ring layers based on the task-core mapping table to obtain a multi-layer ring cooperative computing network; and calculating a plurality of performance indexes of each neural mimicry core based on the multilayer annular cooperative computing network, and performing dynamic load optimization to obtain a dynamic load optimization strategy. According to the invention, the load balancing of the low-power AI processor is realized, and the calculation efficiency and the energy utilization rate are improved.

An AI orchestration system dynamically manages multiple artificial intelligence (AI) agents within a cloud computing environment to efficiently process user requests. A model orchestration subsystem determines whether a request is handled locally using a domain-specific database or by invoking one or more AI agents. The system maintains AI agents in active and inactive states, provisioning computing resources for inactive agents as needed. Real-time model metrics guide the selection of target AI agents, and if a degrading performance trend is detected, the system preemptively spins up additional AI instances. The system provisions processor cycles, memory, and network bandwidth through a cloud-based resource manager, instantiates containerized execution environments or virtual machines, and performs automated load balancing among AI instances.

The invention relates to a multi-modal large model dynamic compression and reasoning optimization method based on a MoE architecture. The method comprises the following steps: establishing an edge computing system conforming to medical equipment specifications, constructing a medical image analysis network based on an improved hybrid expert MoE architecture, and adopting a three-layer cascade structure of a feature coding layer, a dynamic routing layer and an expert execution layer; executing expert module dynamic loading and video memory optimization; executing knowledge graph compensation and domain knowledge injection; executing hardware instruction level optimization and calculation acceleration; executing multi-expert feature fusion and decision weighting; performing diagnosis result generation and confidence evaluation; performing real-time data return and model iterative optimization; executing multi-device cooperation and load balancing; executing system security monitoring and exception handling; and generating a structured diagnostic report. The problem that the precision loss of a multi-modal large model is difficult to meet actual requirements is solved, and medical feature adaptive dynamic compression, medical hardware collaborative energy efficiency optimization and cross-modal compensation of medical knowledge enhancement are realized.

The invention relates to the technical field of data processing, and discloses an automobile manufacturing industrial data distributed processing method and system based on digital twinning. The method comprises the following steps: monitoring and collecting production abnormal events, decision emergency degree parameters and simulation task types based on service states to generate a service priority evaluation data set, and performing priority ranking on a plurality of simulation tasks to form a dynamic priority queue and a resource demand matrix, the queue is input into a distributed simulation engine for CPU, memory and GPU load balancing processing to generate a node resource allocation scheme, and preemptive scheduling is executed to migrate low-priority tasks to idle nodes to form a task execution mapping table; and monitoring simulation progress and resource consumption through an adaptive scheduling mechanism to obtain scheduling performance feedback data, and updating the dynamic priority queue. According to the method and the device, the problem of lack of an intelligent scheduling mechanism during concurrent execution of multiple simulation tasks in the prior art is solved, and the response speed of the key simulation task and the utilization efficiency of computing resources are improved.

The invention discloses a self-adaptive load balancing ring main unit, relates to the technical field of power distribution network protection, and solves the problems that the load balancing strategy of the existing ring main unit is not flexible enough and the monitoring and early warning capabilities are insufficient. The system comprises a data acquisition module, a data processing and analysis module, a load balance control module, a monitoring and early warning module and an expansion and compatibility module. Load distribution is intelligently optimized through real-time data and edge calculation; pID control and fuzzy logic are integrated, parameters are dynamically adjusted, and efficient operation of a power system is ensured; a non-dominated sorting genetic algorithm and an ant colony optimization algorithm are applied to balance energy consumption and stability on the basis of meeting load balance, and an optimal load scheme is generated; safety is improved through closed-loop control and an early warning system, network resource management is enhanced through the software defined network technology, and it is ensured that the system is flexible and extensible; according to the invention, the intelligent load balancing capability of the existing ring main unit is greatly improved, and the operation efficiency and stability are enhanced.

The invention relates to the field of distributed machine learning, provides a big data-oriented distributed model training optimization method, and solves the problems of load imbalance, low resource utilization rate, large communication overhead, insufficient fault-tolerant efficiency and the like caused by data fragmentation staticization in the prior art. Load balancing is realized through intelligent clustering and overlapping control; the multi-dimensional heterogeneous resource evaluation model monitors calculation / storage / network indexes in real time, and realizes adaptive scheduling in combination with a task prediction and optimization algorithm; the hierarchical gradient synchronization mechanism adopts a tree-shaped parameter server and a dynamic compression technology, so that the communication traffic is reduced by 50%, and the precision loss is less than 0.8%; the incremental checkpoint system uses erasure code coding and parallel recovery to shorten the fault recovery time from 15 minutes to within 2 minutes, the resource utilization rate reaches 85% or above, the convergence speed is improved by 30%-40% in ResNet, BERT and other model training, and the large-scale training efficiency and the system stability are remarkably optimized.

The embodiment of the invention provides a kafka storage and calculation separated data processing method and device, and the method comprises the steps: constructing a multi-layer data processing architecture which comprises four layers: network communication, task distribution, processing scheduling and storage. Cold and hot data separation is realized through hierarchical design of a log pre-writing area and a main storage area, and intelligent scheduling of system resources is realized in combination with a copy manager, a coordinator and a load predictor. An adaptive compression algorithm and a verification mechanism are adopted to ensure the data transmission efficiency and integrity, a message cache region is created to store hotspot data, and flexible access of cold and hot data is realized based on message sites. According to the method, the defects of the traditional technology in storage and calculation coupling, storage efficiency, load balancing and the like are effectively overcome, and the performance and expandability of a message queue system are remarkably improved.

The invention discloses a heterogeneous computing node task scheduling method and system based on an intelligent algorithm, the core idea is that task load prediction, node state monitoring and an intelligent scheduling algorithm are combined to realize efficient matching and load balancing of tasks and computing nodes, and the method mainly comprises task load prediction, node resource evaluation and scheduling strategy optimization. The method aims at achieving efficient matching and load balancing of tasks and computing nodes through an intelligent algorithm (reinforcement learning and a genetic algorithm are combined), and particularly optimizing the utilization rate of computing resources and the task execution efficiency under the condition that a dynamic computing environment and resources are limited.

The invention discloses a virtual machine scheduling method in a distributed environment based on deep reinforcement learning, and belongs to the technical field of cloud computing resource scheduling. According to the method, the defects of a traditional method in multi-objective optimization and mixed action space collaborative decision-making are overcome by constructing a mixed action space joint decision-making mechanism. The method specifically comprises the following steps: establishing a mixed action space containing discrete node selection and continuous resource allocation, filtering invalid nodes by adopting a dynamic mask mechanism, and ensuring resource ratio constraint through projection gradient descent; designing a hierarchical reward function to realize multi-target dynamic balancing, and dynamically adjusting the priorities of energy consumption, load balancing and SLA guarantee based on an adaptive weight strategy; a multi-agent collaborative framework is provided, cross-node topological dependence is captured by using a graph attention network, and dynamic fusion of spatio-temporal characteristics is realized through cross attention in combination with LSTM coding time sequence load characteristics; a course learning strategy and a priority experience playback mechanism are introduced to improve training efficiency and strategy robustness.

The invention discloses a computing power resource collaborative scheduling method and device in a multi-cloud scene and a storage medium, and belongs to the field of computing power resource management. Constructing a virtual computing power resource pool based on the multi-cloud cluster; receiving a to-be-processed task, and obtaining a target server identifier and target computing power resource state information; performing task processing capability evaluation on the target cloud processing server, and judging whether the computing power resource requirement of the to-be-processed task is met or not; when the computing power resource requirement of the to-be-processed task is not met, dividing the to-be-processed task into a plurality of sub-tasks; and performing cross-cloud cooperative distribution on the plurality of sub-tasks, so that the loads of the plurality of cloud processing servers tend to be balanced, generating a target scheduling scheme, and executing cross-cloud cooperative processing of the plurality of sub-tasks. The technical problems of low computing power resource scheduling efficiency and unbalanced load in the multi-cloud environment in the prior art are solved, and the technical effects of improving the computing power resource utilization efficiency in the multi-cloud environment, realizing cross-cloud cooperative processing and optimizing load balancing are achieved.

The invention relates to the technical field of cloud computing storage, in particular to a cloud storage dynamic optimization method and system, and the method comprises the following steps: based on a cloud storage data access record, recognizing an access time interval sequence, analyzing the variation amplitude of adjacent time intervals, and calculating the standard deviation of the access time intervals; and determining the access period stability according to the standard deviation and the change amplitude, and classifying the data objects to obtain a data access period stability classification value. According to the method, task scheduling is more accurate by identifying a data access time interval sequence and changes thereof and refining data classification and storage resource allocation, a high-throughput and low-delay storage path is matched, the health condition of a storage node is evaluated, a fault node is identified in advance, data stability is guaranteed, and data copy distribution is adjusted; the storage reliability and stability are improved, the storage path for accessing the intensive data is optimized, the dynamic load balance of the storage resources is realized, and the storage efficiency is improved.

The invention relates to the technical field of industrial internet, in particular to a smart energy management method and system based on the industrial internet, and the method comprises the following steps: S1, data collection: collecting energy consumption data and environment data in a production process in real time; s2, data fusion: carrying out fusion processing on multi-source data by adopting a data fusion technology; s3, distributed energy optimization: optimizing the energy consumption of each node on the distributed nodes in real time by using an edge computing technology; s4, intelligent load balance optimization: dynamically adjusting the operation load of each industrial production device; s5, multi-level fault prediction: performing fault prediction on the industrial production equipment; and S6, control strategy generation: generating a self-adaptive control strategy in combination with the fault prediction result of the industrial production equipment. According to the method, the lowest energy consumption of the optimization result is ensured, the load is uniformly distributed, local overload and energy waste are avoided, and the overall energy utilization efficiency is improved.

The invention discloses a method and a system for realizing mine communication signal improvement based on edge computing, and relates to the technical field of mine communication signal improvement, and the method comprises the following steps: integrating a real-time monitoring system on each edge node, so as to continuously detect the fluctuation of communication flow and data request quantity, the real-time monitoring system identifies a potential communication peak period by collecting real-time data and generates an early warning signal to notify a network administrator or automatically trigger a load management program. According to the invention, through the real-time monitoring and early warning system on the edge node, active monitoring and peak prediction of mine communication are realized, the overload risk caused by traffic surge is reduced, and the stability of data transmission is ensured. And intelligent scheduling and dynamic resource allocation are combined, so that efficient execution of tasks under high load is ensured, and node overload is prevented. A self-adaptive load balancing and feedback mechanism enables the system to dynamically adjust distribution, quickly respond to a bottleneck and improve self-adaptability and stability.

The invention provides an edge node scheduling method and system based on a sinking distributed network. The method comprises the following steps: determining a scheduling strategy of an edge node based on a preset load balancing algorithm; receiving a service request from the terminal equipment; identifying a proper service node based on the data type of the service request; distributing the service node to process the service request; and adjusting the scheduling strategy based on the real-time monitored data feedback. Through the scheme of the embodiment of the invention, the problem of data consistency caused by insufficient data synchronization mechanism under the condition of high concurrent requests can be solved. And the problem of communication delay increase caused by lack of an adaptive routing protocol under the condition of dynamically changing network topology is solved. And the problem of computing resource waste caused by an unreasonable task allocation strategy under the condition of resource-limited edge nodes is solved. And under the condition of data intensive application, the problem of data processing bottleneck caused by an imperfect data caching mechanism is solved.

The invention relates to a competition management system and method based on cloud computing, and belongs to the technical field of resource allocation. The number of virtual machines, physical resources and competition levels are determined by dynamically acquiring competition information, resource priorities are automatically associated, stable allocation of high-level competition item resources is guaranteed, a scheduling model is constructed through a deep reinforcement learning algorithm, a virtual machine allocation probability matrix is generated, and long-term income is evaluated in combination with a reward function. Autonomous learning optimization of a scheduling strategy is realized; a multi-dimensional server state matrix is constructed, matching tasks and hardware characteristics are quantified, and the task interruption risk is reduced; a dynamic label system is generated in combination with static code analysis and runtime data, and key competition resource allocation is ensured; and staged competition items are staged, and resource demand vectors are defined, so that staged dynamic resource adjustment is realized. By remarkably improving the resource utilization rate and the load balancing capacity, the fairness of competition management, the system stability and the intelligent level are improved.

The present invention relates to the technical field of networks. Provided are a method and apparatus for adjusting the allocation of AI computing power hardware computing resources on the basis of the energy consumption of a base station. The method comprises: step S1, on the basis of a unit-time computing energy consumption function H and a computing time consumption function T of AI computing power hardware computing resources, obtaining training parameters during the training of an AI algorithm when the minimum value of computing energy consumption is taken, and setting the minimum value as predicted computing energy consumption of the AI computing power hardware computing resources during the training of the AI algorithm; S2, compiling statistics on energy consumption per unit time of each base station, and on the basis of the load balancing of each base station, estimating predicted reduced energy consumption of shutting down a base station; and S3, on the basis of the predicted computing energy consumption and the predicted reduced energy consumption, determining whether to perform the training of the AI algorithm or change the training parameters, wherein the training of the AI algorithm is used for computing a base station shutdown policy. By means of the method and apparatus in the present invention, the rational allocation and adjustment of hardware computing resources can be realized.

The invention discloses a performance test and verification method, system and device for a multi-core heterogeneous chip and a medium, and the method comprises the steps: configuring a test parameter set according to a target application scene, the test parameter set comprising a processor core type recognition parameter, a communication topology weight parameter and a load balancing threshold parameter; generating a test case covering the heterogeneous computing core cooperative working mode; the method comprises the following steps: acquiring characteristic data during testing through a reconfigurable monitoring sub-module, wherein the characteristic data comprises inter-core communication delay information, shared cache hit rate information and power consumption distribution thermodynamic information; and performing quantitative analysis on the feature data through a multi-dimensional evaluation model to obtain a performance deviation index and an architecture optimization suggestion parameter. By optimizing the chip performance verification process under the multi-core heterogeneous architecture, the verification precision is effectively improved, and the chip performance verification method can be widely applied to the technical field of chip design.

The present disclosure provides connection management techniques based on graph neural networks (GNN) and deep reinforcement learning (DRL) to optimize user association and load balancing. A graph structure of a communication network is considered for the GNN architecture and DRL is used to learn parameters of the GNN algorithm / model. Connection management is defined as a combinatorial graph optimization problem, and the DRL mechanism uses the underlying graph to learn weights of the GNN for an optimal user connections or associations. The connection management techniques can consider local network features to make better decisions to balance network traffic load while network throughput is also maximized. Implementations are provided based on edge computing frameworks include the Open RAN (O-RAN) architecture. Other embodiments may be described and / or claimed.

The invention relates to the technical field of digital computing and resource scheduling, in particular to a computing power resource intelligent scheduling and load balancing method. The method comprises the following steps: collecting performance indexes of a computing power node on different resource dimensions, and constructing a resource capability vector of the computing power node; extracting demand features of the calculation task on different resource dimensions, and constructing a task feature vector; matching the resource capability vector of the computing power node with the task feature vector to obtain an adaptation degree score; operation indexes of the computing power nodes are collected, comprehensive load scoring is carried out on the computing power nodes, and the current load states of the computing power nodes are obtained; and distributing calculation tasks based on the current load state of the computing power node and the adaptation degree score, and updating the current load state of the computing power node in real time. The problems that the resource utilization rate is low, the task response delay is high and the node load is unbalanced due to the fact that the computing power resources cannot be dynamically matched according to the task characteristics and the computing power node states in a traditional computing power resource scheduling method are solved.

The invention discloses an artificial intelligence large model training method in a heterogeneous multi-machine and multi-card environment, and belongs to the technical field of artificial intelligence large model training. Load balancing of heterogeneous equipment is realized by constructing a uniform interface, and the communication efficiency is optimized by adopting hierarchical pipeline aggregation and dynamic quantization compression; the node dynamic adjustment is realized in combination with the elastic topological structure, the problems of poor equipment compatibility, high communication delay and rigid topological structure in the prior art are effectively solved, and the method has the remarkable advantages of improving the utilization rate of heterogeneous computing resources, reducing the cross-node communication overhead and enhancing the fault-tolerant capability of the system.

The invention discloses a computing network resource scheduling optimization method based on multi-agent deep reinforcement learning, and the method comprises the steps: building a distributed service caching, task scheduling and computing network resource distribution model through comprehensively considering the multi-party computing power of a cloud side end, the system time delay, the system energy consumption and the load balance of heterogeneous nodes of an edge layer in a computing network scene; taking an edge server as an intelligent agent, considering the time dependence of a task request and the influence of the task request on service cache, and using an Attention-LSTM algorithm to make a service cache decision; based on a multi-agent depth deterministic policy gradient algorithm, constructing an MA-LDDPG algorithm to perform task scheduling and resource allocation decision; in consideration of the influence of service caching on task scheduling and resource allocation, a service caching strategy obtained by an intelligent agent is used as an additional observation state of task scheduling and resource allocation decision to participate in training, so that joint optimization of system time delay and edge layer heterogeneous node load balancing is achieved.

The invention discloses an Internet of Things operation management scheduling system based on cloud edge collaboration, relates to the technical field of task scheduling optimization, and aims to solve the problem of adaptability of edge computing and cloud edge collaboration architecture, the Internet of Things perception access technology is adapted and fused with heterogeneous equipment through an architecture adaptation module, various IOT data are stably acquired, and the cloud edge collaboration is realized. The task execution position is dynamically adjusted in combination with the task distribution unit; real-time stream calculation and an ETL processing mechanism are combined to fit a comprehensive scheduling index Zdzh, the utilization rate of calculation resources is increased, and task scheduling is optimized; internet of Things equipment data, internet data and government affair data are analyzed based on the data lake and warehouse architecture, a data access strategy is optimized, and the data fusion efficiency is improved; and calculating and evaluating a sudden anomaly prediction coefficient Tycs, triggering an anomaly early warning mechanism or a sudden anomaly emergency mechanism, optimizing task migration, calculating node load balancing and data traceability analysis, so as to improve the stability and intelligent level of an Internet of Things operation management scheduling system.

The invention belongs to the technical field of mobile communication, and particularly relates to a task unloading and resource allocation method for edge computing. According to the method, a three-layer network structure is established, a distributed decision framework is constructed through reinforcement learning, the task emergency degree is dynamically evaluated, and computing resources are distributed in a differentiated mode; and task unloading and resource allocation are optimized in combination with an edge-cloud collaborative architecture, so that calculation load balancing is realized. According to the method, aiming at a cloud edge-end collaborative edge calculation model, the total cost of a system is defined as a joint optimization problem of task unloading time delay and energy consumption, the problem model is converted into a Markov decision process, and multi-agent and multi-user oriented deep reinforcement learning algorithm agent near-end strategy optimization (MAPPO) is designed; and obtaining an optimal unloading decision through mutual learning among multiple agents. According to the method, the total cost of the system can be effectively reduced, the rationality of edge computing task unloading and resource allocation decision is realized, and meanwhile, the use experience of a user can be improved.