1854 results about "Scheduling (computing)" patented technology

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

The invention provides a computing resource optimization method and system for analysis tasks, and belongs to the technical field of computers.The computing resource optimization method comprises the steps that the priority of each analysis task is determined according to feature information of each analysis task and current available computing resource state information of the system, and a priority queue is generated; obtaining a task load prediction value according to the historical task load data and the real-time system state data; according to the type of the analysis task, the data scale and the data source position, the analysis task with the real-time requirement higher than a preset standard is allocated to an edge node to be executed, and resource allocation of the edge node is dynamically adjusted according to a task load predicted value; and dynamically allocating available resources from the computing resource pool according to the priority queue and the task load prediction value so as to execute the plurality of parallel analysis tasks. According to the resource management method based on task priority dynamic adjustment, task load prediction and edge computing optimization scheduling, real-time prediction and dynamic self-adaptive scheduling of computing resources are achieved.

The invention provides a GPU computing power scheduling method based on a one-cloud multi-core heterogeneous computing power platform, and the method comprises the following steps: S1, carrying out heterogeneous resource registration and modeling, accessing hardware equipment containing multiple types of GPUs through a resource registration module, collecting the equipment model, the video memory capacity and performance index data, and carrying out heterogeneous resource modeling; constructing a resource feature database containing a topological relation, and supporting hybrid access of chips; s2, virtualized resource reconstruction: pooling a physical GPU into virtual GPU resources by adopting a hardware abstraction layer technology, realizing video memory isolation and calculation unit division through a containerization technology, and configuring each virtual GPU instance with an independent drive stack and a security sandbox; and S3, submitting a multi-modal task, receiving a CUDA / OpenCL calculation task submitted by a user, analyzing task demand parameters including a calculation core number, a video memory occupation amount and a data throughput threshold, and generating a task descriptor containing a priority label.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention discloses an intelligent large-scale task scheduling method and system based on computing power host business characteristics, and belongs to the technical field of cloud computing and edge computing, and the method comprises the following steps: data acquisition and preprocessing of tasks and computing power host nodes; performing multi-dimensional quantitative analysis on the task business characteristics; dynamically monitoring the performance state of the computing power host node; constructing and optimizing a dynamic computing power matching model; task scheduling is optimized in an auxiliary mode based on an intelligent prediction mechanism; and finally distributing and executing the task. The problems that when an existing computing power host processes large-scale concurrent tasks, due to the fact that a scheduling strategy lacks accurate recognition and adaptation on different service characteristics, resources are wasted, tasks are stacked, and response is delayed are solved. The utilization rate of computing power resources can be improved, task response delay is reduced, and user experience and system overall performance are improved.

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 discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The present application relates to the field of information collaborative processing. Provided are a task collaborative scheduling method and apparatus, a device and a medium. The task collaborative scheduling method is applied to a collaborative computing system comprising a plurality of nodes, and the method comprises: first, on the basis of original task description information of a target task, performing splitting and orchestration on the target task to obtain a plurality of sub-tasks and a task logic topological relationship between the sub-tasks; then allocating, on the basis of node information of the nodes, from the collaborative computing system a corresponding execution node for each sub-task, and generating sub-task description information; and finally, issuing the sub-task description information to the execution nodes, such that all the execution nodes can complete all the sub-tasks according to the orchestrated logic topological relationship, thereby obtaining an output result of the target task. The present application can cover diversified task collaborative scheduling scenarios and enables compatibility with access and scheduling of devices having different capabilities, thereby meeting the collaborative processing requirements for diverse service types and scales.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The embodiment of the invention provides a task scheduling method, system and device based on cloud computing and a storage medium, and belongs to the technical field of computers. According to the method, task priorities are initialized according to task resource requirements, task types and task timeliness requirements, task scheduling strategies are determined according to task metadata and computing resource information, the task metadata comprise the task priorities and the task resource requirements, and the task scheduling strategies are used for representing task scheduling sequences and task allocation objects; task scheduling is carried out according to the task scheduling strategy, load fluctuation information and task demand change information of each computing node are obtained, the task scheduling strategy is adjusted according to the load fluctuation information and the task demand change information, and the step of task scheduling according to the task scheduling strategy is repeatedly executed, so that the task scheduling efficiency is improved in the task execution process. Task scheduling and allocation are adaptively adjusted based on load changes and task demand changes, and the task execution efficiency and the system resource utilization rate are 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 relates to the technical field of computers, in particular to a heterogeneous computing power scheduling method and device, equipment, a storage medium and a product. The method comprises the following steps: generating an equipment topological graph based on equipment information in a node resource pool; performing partition management on the computing devices in the device topological graph; based on a partition management result, analyzing the to-be-calculated task through a preset flow gateway, and determining a task request data position, a task scale and a task feature; determining a to-be-calculated node according to the task request data position, the task scale and the task feature; and distributing the to-be-calculated task to the to-be-calculated node, and generating a target scheduling strategy based on the real-time performance index and the historical execution data of the to-be-calculated node. According to the method, the utilization efficiency of computing resources is improved by generating the equipment topological graph and performing partition management, the scheduling strategy is optimized by combining real-time performance data, it is ensured that scheduling optimization can be performed according to the real-time state, and the effect of heterogeneous computing power scheduling is improved.

The invention relates to the technical field of automatic deployment of cloud computing platforms, in particular to a self-adaptive deployment method and system oriented to a credential heterogeneous environment. Aiming at three technical bottlenecks of low multi-CPU architecture adaptation efficiency, frequent software dependence conflicts and complex security baseline configuration in a localization process, a heterogeneous computing resource intelligent scheduling engine and a dynamic security policy generation mechanism are innovatively provided. The method comprises the following steps: constructing a heterogeneous resource portrait through a hardware feature automatic identification technology, and realizing component installation sequence optimization based on a DAG dependency relationship analysis algorithm and Kahn topological sorting; and creating an adaptive network security policy, and sensing the firewall state of the target system in real time through a probe. Compared with a traditional deployment mode, the method supports cross-architecture compatibility, solves the problem of dependency conflicts, improves deployment efficiency, and guarantees consistency and safety of the system.

The invention provides a multi-level heterogeneous computing power network task scheduling and resource allocation method and system. In a computing power network, each hierarchical network is connected with an intelligent agent for task scheduling and resource allocation. In the training process, the intelligent agent obtains local observation information of the computing power node in the hierarchical network. And the intelligent agent aggregates and converts local observation information according to the relation feature map to generate a feature matrix and an adjacent matrix. Local feature embedding is calculated by using a graph convolutional network, and feature aggregation is carried out in combination with information of a cooperative agent to obtain a local embedding state; and the intelligent agent executes a joint action according to the local embedding state, wherein the joint action comprises task scheduling and resource allocation. And according to an execution result, the agent obtains a reward value and state information and stores the reward value and the state information into an experience playback buffer area. And sampling data from a buffer area, and updating a value function and strategy parameters by taking optimization of calculation task completion time and resource use efficiency as a target. And after a preset training round, obtaining a trained model. The resource overhead of information interaction can be reduced, and the scheduling efficiency is improved.

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 provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention provides a GPU heterogeneous computing resource allocation method and system based on task scheduling, and the method comprises the steps: carrying out the feature analysis of a to-be-scheduled task, so as to extract the calculation intensity, video memory demands, inter-task data dependence and historical execution data of the to-be-scheduled task, and obtaining a task feature vector; constructing a performance model of heterogeneous GPU resources to predict execution efficiency and resource consumption of the GPU under the target task based on hardware performance parameters and running states of the GPU; and matching the to-be-scheduled task with the GPU resources according to the task feature vector and the performance model of the heterogeneous GPU resources. Through task feature analysis and GPU resource performance modeling, the adaptive relation between tasks and GPU resources can be deeply mined, to-be-scheduled tasks are accurately allocated to the most appropriate GPU, the resource utilization rate is greatly increased, the task execution sequence and resource allocation are dynamically planned, and the task execution time is effectively shortened.

The invention discloses a heterogeneous computing multi-target adaptive task scheduling method based on deep reinforcement learning, and the method comprises the following steps: S1, constructing a multi-dimensional dynamic perception model of a heterogeneous computing environment, and collecting and computing node performance indexes, task feature parameters and network states in real time; s2, defining a reward function as a multi-target weighted combination, fusing task completion time, energy consumption, resource utilization rate and cost, and dynamically adjusting the weight by a fuzzy comprehensive evaluation algorithm; s3, establishing a dual-channel deep reinforcement learning network architecture based on an attention mechanism; s4, establishing an adaptive exploration mechanism, combining an epsilon-greedy strategy and entropy regularization, and balancing exploration and utilization; the method has the beneficial effects that dynamic balance of multiple indexes such as task completion time, energy consumption and resource utilization rate is realized through combination of deep reinforcement learning and multi-objective optimization, a dual-channel network and a cross attention mechanism are adopted, and a task time sequence characteristic and a topological dependency relationship are modeled at the same time, so that a scheduling strategy is more accurate.

The invention relates to the related technical field of distributed computing power resources, and discloses a distributed computing power resource dynamic fusion and cooperative computing system which comprises a resource sensing module, a task analysis module, a dynamic scheduling module, a virtualization adaptation layer, a cooperative computing engine, a global controller and a hierarchical communication bus. The resource sensing module collects and quantifies the hardware computing power of the heterogeneous computing nodes in real time. According to the invention, a complete closed-loop control system is formed through a five-layer dynamic fusion architecture (a sensing layer, an analysis layer, a scheduling layer, a virtualization layer and a collaboration layer), so that a distributed computing system can adapt to a scene that edge nodes are dynamically added and pushed out; meanwhile, through the purpose of developing virtualization middleware supporting cross-platform instruction real-time conversion and based on a multi-target dynamic weight adjustment mechanism of online learning, the heterogeneous task scheduling success rate can be greatly improved through the overall structure of the system, and the fault recovery time is shortened.

The invention discloses a task length prediction-based large language model reasoning calculation service energy consumption optimization scheduling method, which comprises the following steps of: firstly, reasoning by taking an Alpaca-52k instruction data set as an input source and a large language model (such as Llama3-8B), counting the number of output tokens of the large language model, and labeling each piece of input data; then, a Qwen2-1. 5B large language model is finely tuned by using an Alpaca-52k instruction data set and the response length of Llama3-8B reasoning, and computing resources of the prediction method are reduced on the premise that the prediction performance is guaranteed; then, the response length of the Llama3-8B reasoning task is predicted through the fine-tuned Qwen2-1. 5B model, task balanced sorting scheduling is carried out according to the response length so as to improve the large language model reasoning speed, and finally, a deep reinforcement learning power selection algorithm is used to reduce the calculation power as much as possible on the premise that the large language model reasoning task time delay is met so as to improve the large language model reasoning efficiency. Therefore, the energy consumption of large language model reasoning calculation is reduced.

The invention discloses a task scheduling method and device, equipment and a medium, and relates to the field of task processing.The task scheduling method comprises the steps that when a universal graphics processor writes a current calculation task into a current task queue, a normalized task priority and task execution estimated duration corresponding to the current calculation task are determined, and the task execution estimated duration is determined; the queue utilization rate of the current task queue is determined; determining a current load rate based on the task priority, the task execution estimated duration and the queue usage rate, and determining a target computing core from computing cores of the universal graphics processor according to the current load rate; each computing core comprises a tensor computing core and a computing core based on a unified computing device architecture; according to the method, the current computing task is scheduled to the target computing core, and the target computing core is used for executing the general matrix multiplication operation on the target matrix contained in the current computing task, so that each computing core in the general graphics processor is fully utilized, and hardware resources in the general graphics processor are utilized in a balanced and efficient manner.

The invention discloses an instruction execution method and device for an artificial intelligence chip, and relates to the technical field of artificial intelligence chips, and the method comprises the steps: carrying out the deep learning driven feature recognition and resource demand prediction of an input task, and generating a demand prediction report of the task for computing resources through the analysis of a computational graph and a data dependency relationship of the task; a computing unit and memory resources are intelligently scheduled, an optimal instruction execution path is dynamically selected, and meanwhile a caching strategy is optimized; automatically generating a micro instruction set corresponding to the task according to the computing resource demand, the computing characteristic and the intelligent scheduling result of the task; when multiple tasks are executed in parallel, the execution sequence of the multiple tasks is dynamically adjusted according to the calculation load and the resource sharing condition of the tasks, and resource allocation is optimized. According to the method, the computing resources and the memory bandwidth required by each task can be accurately predicted through the deep learning driving analysis of the task computing graph, so that the allocation of the computing resources is optimized.

The invention relates to the technical field of distributed learning, in particular to a large model pre-training system based on distributed parallel processing. In the system, a data distribution layer collects a node resource state through a fragmentation module and generates a dynamic scheduling strategy; a computing resource layer configuration model initialization module and a strategy switching module support flexible switching of multiple modes such as tensor parallelism, data parallelism and assembly line parallelism; the network communication management layer is combined with topology perception and gradient compression technologies, so that the communication efficiency is improved; and the model aggregation layer realizes global parameter updating and training tuning under privacy protection through a security aggregation and optimization control mechanism. All layers of the system operate cooperatively, the calculation efficiency, the communication performance and the data security of large model pre-training can be effectively improved, and the method is suitable for model development and deployment in a large-scale heterogeneous calculation environment.

The invention discloses a GPU (Graphics Processing Unit) and parallel IO (Input / Output) collaborative optimization method based on mode heterogeneous computing, and particularly relates to the field of heterogeneous computing optimizing.The method comprises the following steps: monitoring mode characteristics during execution of a target computing task in real time through an instruction analyzer, and synchronously collecting dynamic parameters of a heterogeneous computing environment to generate a multi-dimensional resource situation matrix; based on the mode and the resource situation matrix, collaborative optimization operation of the binding equipment dimension and the scheduling time dimension is executed; and constructing a three-level adaptive transmission mechanism and periodically implementing feedback optimization according to an execution state of the three-level adaptive transmission mechanism. According to the GPU and parallel IO collaborative optimization method based on mode heterogeneous computing, the data prefetching hit rate is increased by dynamically sensing the matching relation between a heterogeneous computing mode and an I / O access mode; end-to-end delay is reduced and task contention conflicts are reduced through a dynamic matching strategy of an equipment capability portrait and a task type; and the heterogeneous storage bandwidth peak value utilization rate and the I / O throughput are improved by differentially utilizing the hierarchical characteristics of the multi-stage storage.

The invention relates to a numerical control equipment cooperative scheduling method and system based on a heterogeneous computing architecture, and aims to solve the core problems of multi-brand numerical control equipment protocol isomerism, weak digital twin generalization ability, low multi-objective optimization efficiency, quantum era communication security and the like in the industrial internet. Real-time analysis and unified format conversion of 18 industrial protocols are realized through a PDSA-Chip hardware-level protocol conversion module; in combination with a transfer learning digital twinning technology, cross-brand equipment parameter self-calibration is completed within 72 hours; a quantum annealing and NSGA-III collaborative optimization algorithm is adopted, the machining parameter optimization period is shortened to 1 / 3 of that of a traditional method, energy consumption, precision and the service life of a tool are dynamically balanced, and the service life of the tool is prolonged by 37% in a high-load scene; an NTRU lattice encryption and chaos confusion fused anti-quantum security communication system is constructed, the G code confusion degree is larger than 98%, and SL3-level quantum attacks are defended.

The invention relates to a dynamic CPU scheduling method, system and device based on a CPU busy state and a medium. The method comprises the steps that busy degree indexes of all CPU cores are dynamically generated through monitoring; service logic to be executed is analyzed into a task set, and task attributes which cannot be changed are distributed to tasks; determining respective target CPU cores based on busy degree indexes of all current tasks, generating binding parameters, and writing the binding parameters into task attributes of the corresponding tasks; executing the allocation logic according to the task attribute of each task, and submitting the task to a running queue of the target CPU core to wait for execution; and when the single execution duration of the task in the target CPU core reaches a preset threshold value or is terminated by external interruption, resetting the corresponding task into the operation queue. According to the invention, breakthrough improvement is realized in three dimensions of hardware resource utilization rate, system real-time performance and scheduling security, and a solution with response speed and stability is provided for task scheduling in a computing environment.

The invention discloses a solid state disk management system and a data processing method, and relates to the technical field of solid state disk management. Flexible task scheduling and resource management are provided through a lightweight operating system module, and a computing task program defined by a user is supported to be dynamically loaded; edge computing or machine learning operators are efficiently executed in combination with a programmable hardware processing unit and a DMA channel of the computing acceleration engine module, the data preloading module is utilized to predict and preload data to DDR based on LBA access history, access delay is reduced, an NVMe protocol is expanded by means of the task unloading interface module, host task issuing and result returning are achieved, and the data processing efficiency is improved. And hardware-level memory protection is ensured through the security isolation module, so that the data calculation processing capacity of the solid state disk is remarkably improved, localized calculation tasks such as edge calculation and machine learning are supported, mass data transmission is effectively reduced, and bus and network loads are relieved.

A GPU cluster computing power optimization architecture oriented to large model training is characterized in that a computing power resource management module is used for monitoring and managing the computing power resource state of each GPU in a GPU cluster in real time, a task scheduling module is used for allocating GPU resources according to the requirements of training tasks, and a computing power optimization module is used for dynamically optimizing the computing power of the GPU cluster in the task execution process. And the result feedback module is used for collecting and analyzing the optimized computing power use condition so as to adjust a subsequent task scheduling strategy. According to the method, the computing power resource state of the GPU cluster is monitored and managed in real time, the GPU resources are dynamically allocated according to the demand of the training task, and the computing power of the GPU cluster is dynamically optimized in the task execution process, so that the computing power utilization efficiency of the GPU cluster is effectively improved, and the training cost is reduced.

The invention provides an interaction method based on artificial intelligence equipment, belongs to the technical field of artificial intelligence, and remarkably optimizes the efficiency of an AI equipment interaction system through distributed resource dynamic scheduling, multi-framework heterogeneous collaboration and full-link intelligent monitoring. Based on an elastic capacity expansion and contraction and priority scheduling strategy of Kubernetes, GPU resource islands are eliminated, and the computing power distribution efficiency is improved; a Flink checkpoint mechanism and Spark RDD blood relationship tracking are integrated, training task breakpoint continuous calculation and millisecond-level fault recovery are achieved, and repeated data processing is avoided; gPU node performance indexes are collected in real time through Prometheus Exporter, and in combination with Grafana visual early warning, the anomaly detection response speed is shortened to the second level; seamless access of frames such as TensorFlow / PyTorch is supported, heterogeneous device protocol conversion is achieved through a unified API gateway, and the cross-platform migration cost is reduced.