654 results about "Dynamic resource" patented technology

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The invention provides an AI-based laboratory equipment scheduling optimization method and system, and the method comprises the steps: firstly obtaining a state monitoring data set containing the characteristics of equipment operation power consumption, idle time length, environment interference factors and the like in real time, and then carrying out the multi-dimensional analysis of the state monitoring data set; generating an availability evaluation index set containing characteristics of equipment load fluctuation, maintenance period prediction, compatibility matching and the like, and an experiment task priority queue, performing cross decision analysis on the availability evaluation index set and the experiment task priority queue based on a preset dynamic resource allocation model, and obtaining a scheduling strategy set containing a task allocation path, a cooperative operation rule and a conflict resolution mechanism; scheduling strategy parameters are calibrated according to experimental task operation log data, an optimized execution instruction set is generated, instructions are fed back to an equipment control system to adjust the equipment state, a dynamic resource allocation model is iteratively updated according to execution feedback data, and efficient scheduling optimization of laboratory equipment is achieved.

The invention discloses a QoS guarantee method and system for a communication network, and relates to the technical field of communication networks, and the method comprises the steps: collecting and preprocessing network state data, and forming a standardized data set; dynamically classifying service types based on an improved random forest algorithm, and predicting a future QoS demand trend of each priority service in combination with an LSTM neural network; establishing a mapping model of QoS demands and resource parameters, converting predicted demands into allocable resource indexes, monitoring the resource utilization rate in real time, and setting an elastic reservation mechanism and conflict early warning; when early warning is triggered, selecting an optimal transmission link by adopting a multi-path collaborative algorithm, and implementing differentiated resource allocation according to service priorities; and a closed-loop feedback mechanism is triggered to dynamically adjust resource allocation by monitoring the deviation between the actual QoS and a predicted value in real time. The method has the advantages that through multi-dimensional perception, LSTM prediction, dynamic resource management and multi-path scheduling, QoS requirements of services with different priorities are accurately matched, and dynamic changes of the network are efficiently coped with.

The invention discloses an industrial personal computer and multi-graphics card collaborative parallel computation acceleration system, which relates to the technical field of industrial resource allocation and parallel computation, and comprises a resource monitoring and predicting module, a resource management module and a prediction type resource preparation module, the task splitting and collaborative execution module comprises a task splitting module, a cross-node collaborative module and a collaborative operation engine; the intelligent scheduling and dynamic resource allocation module comprises an intelligent scheduler, a dynamic resource allocation module and a conflict avoidance module. According to the method, the GPU video memory utilization rate, the core utilization rate, the temperature, the video memory fragment rate, the available video memory total amount, the CPU core total utilization rate, the load condition and the idle core number index are collected in real time through a resource monitoring module, and the video memory capacity, the GPU core occupancy rate and the CPU load requirement are predicted in advance before a task is submitted in combination with a gradient boosting decision tree and a neural network prediction model; and resources are reserved, so that the scheduling delay is remarkably reduced, and the scheduling hit rate is improved.

The invention discloses a resource awareness and task migration method and system for industrial edge nodes. The method comprises the following steps: constructing an edge node resource dynamic monitoring system, and sensing, calculating, storing and network resource states in real time; establishing a node health degree evaluation model, and predicting a potential fault risk; designing an intelligent task migration decision-making mechanism based on a resource state; the guarantee of data consistency and service continuity in the task migration process is realized; and constructing a distributed task scheduling optimization framework. The system comprises a resource monitoring module, a health assessment module, a migration decision module, a data synchronization module and a scheduling optimization module. According to the method, the problem of unstable task execution caused by dynamic resource change in an industrial edge computing environment is solved, and intelligent resource management and efficient task migration of the edge nodes are realized.

The invention discloses a parallel processor dynamic resource allocation system and method based on reconfigurable hardware, and relates to the technical field of computer chips, and the system comprises a workload monitoring module which is responsible for monitoring the workload type and the resource demand of a task processed by a parallel processor in real time, and determining the task type and the resource demand by identifying the task type and the resource demand; a monitoring result is fed back to the resource allocation control module; the resource allocation control module is responsible for generating a corresponding resource allocation control signal based on feedback information of the workload monitoring module and dynamically configuring the reconfigurable hardware module; the reconfigurable hardware module is responsible for realizing efficient adaptation to diversified tasks through a plurality of reconfigurable units formed by programmable logic devices according to different working load dynamic reconfiguration functions and connection modes; and the data caching and transmission module is responsible for cross-module data circulation and global data storage. Different task requirements can be accurately adapted, and the resource utilization rate is improved.

The embodiment of the invention discloses a resource scheduling method and device, equipment and a medium, and relates to the technical field of resource scheduling. The method comprises the following steps: acquiring a service quality constraint index of a data processing task, and resource states of a cloud center and an edge node; constructing a three-dimensional dynamic resource feature space according to the resource state fluctuation information of the edge node, the spatio-temporal information of the cloud computing and the edge node and the historical occurrence probability of the service quality constraint index; and performing particle swarm optimization based on the three-dimensional dynamic resource feature space to obtain an initial strategy set, scheduling computing resources in the cloud center and the edge node based on the initial strategy set, and processing the data processing task based on the scheduled computing resources. According to the technical scheme, scheduling is flexibly carried out according to the condition of the data processing task and the resource condition of the cloud center and the edge node, and the actual requirement of the data processing task is accurately met.

The invention relates to the technical field of communication networks, in particular to a dynamic resource allocation method and system for an industrial 5G network. The method comprises the following steps: acquiring industrial terminal data; identifying network congestion nodes based on industrial terminal data; detecting a link congestion fault according to the network congestion node, and determining a bandwidth utilization rate; allocating spectrum resources based on the bandwidth utilization rate; performing frequency spectrum slicing on the frequency spectrum resources, and determining a flow load amount; adjusting load balance according to the traffic load, and generating a load resource report; dynamically adjusting time slot allocation by using a load resource report, and determining a task priority; task scheduling simulation is executed according to the task priority, and if congestion occurs, a congestion snapshot is stored; reducing scheduling conflicts according to the congestion snapshots, releasing bandwidth resources, and determining available network slice capacity; power resources are dynamically adjusted based on available network slice capacity. The bandwidth utilization rate and the task scheduling efficiency of the industrial 5G network are improved based on the communication network technology.

The embodiment of the invention discloses a distributed computing power resource scheduling method and system, a storage medium and a program product. The method comprises the following steps: decomposing a composite computing task into a plurality of sub-tasks and constructing a dependency graph; obtaining demand resource information of each subtask and constructing a demand resource matrix according to the demand resource information; obtaining performance data of the global computing power node and constructing a global dynamic resource portrait according to the performance data; dividing global computing power nodes into different areas based on the global dynamic resource portrait; based on the demand resource matrix and the performance data, determining an adaptive area of each sub-task from all the areas, and then determining adaptive computing power nodes of the sub-tasks from the adaptive areas of the sub-tasks; and sequentially executing each subtask based on the dependency graph and the adaptive computing power node of each subtask. According to the method, the problems of low cross-data center resource utilization rate, high burst task response delay and poor heterogeneous environment scheduling efficiency can be solved, the overall computing power resource utilization rate is improved, and the task average delay is reduced.

The invention provides a computing power service dynamic resource allocation method and system applied to AI model training. The method comprises the following steps: firstly, collecting real-time computing power resource use data (including computing node load, memory occupation and data transmission delay) and model training state data (including training task stage identification, model parameter updating frequency and training data batch processing progress) in AI model training; generating a computing power resource demand association feature set, constructing a computing power resource dynamic allocation decision model including resource allocation priority judgment, adjustment amplitude calculation and scheduling opportunity selection units based on the set, and outputting a computing power resource allocation scheme (including computing node number, memory capacity and data transmission bandwidth adjustment instructions) through the model. Resources are scheduled according to the scheme, and new data are collected to update the feature set, so that dynamic and accurate allocation of computing power resources is realized, and the resource utilization rate and the training efficiency are improved.

The invention provides an internet of things equipment collaborative task scheduling optimization method and system oriented to edge computing, and relates to the technical field of the internet of things, and the method comprises the steps: collecting a task request from an internet of things terminal, and extracting task features; constructing a dynamic resource state table; generating a candidate node set; selecting a target computing node from the candidate node set, and deploying a task execution unit on the target computing node; generating a resource utilization rate index based on the real-time operation index of the node; and generating a task performance index based on task execution feedback, dynamically adjusting a task scheduling rule, or isolating an abnormal computing node from an abnormal task. Through the task scheduling method and device, the technical problems of low resource utilization rate and high response delay caused by lack of global collaboration of task scheduling in the prior art are solved, and efficient task allocation between edge devices is realized by extracting task features and establishing an optimization model, so that the overall scheduling efficiency and the system performance are improved.

The invention provides a marine oil spill emergency resource scheduling method and system based on an improved ant colony algorithm, and relates to the technical field of data processing.The method comprises the steps that on the basis of a modified path transition probability function, multi-target ant colony collaborative optimization is executed on a dynamic resource scheduling architecture, a population is initialized to construct a scheduling path solution, and the scheduling path solution is optimized; calculating a time consumption target and an ecological loss target, iteratively updating pheromone distribution until convergence, and generating a multi-agent non-dominated scheduling strategy; according to a multi-agent non-dominated scheduling strategy, calculating a resource scheduling failure condition probability and an ecological sensitive area damage expected value; and if the scheduling failure conditional probability exceeds a risk tolerance threshold value, an ecological damage expected value is taken as a nonlinear penalty weight, and an anti-disturbance rescheduling mechanism is triggered to generate a flexible scheduling strategy. According to the method, the timeliness and accuracy of emergency resource transportation are improved, and collaborative optimization of'emergency efficiency-ecological protection 'is realized.

The invention discloses a k8s heterogeneous resource scheduling method, system and device based on intelligent sensing and a medium, belongs to the technical field of cloud computing and resource scheduling, and aims to solve the technical problems that a traditional scheduler is weak in sensing capacity, extensive in scheduling decision, low in resource utilization rate and high in resource utilization rate in a heterogeneous environment. According to the technical scheme, the method comprises the steps that a multi-dimensional resource sensing layer is constructed, specifically, real-time collection and convergence of heterogeneous hardware dynamic performance indexes are achieved by deploying an expanded monitoring equipment plug-in, and fine-grained runtime data of hardware including a CPU, an FPGA and an AI acceleration card are abstracted into a standardized index data set in a unified mode; constructing a node dynamic resource portrait: constructing the dynamic resource portrait based on the standardized index data set through a feature fusion and modeling technology, and generating a quantitative capability evaluation vector for each computing node in the cluster; and intelligent scheduling decision making: through a decision engine based on reinforcement learning, obtaining an optimal scheduling target according to the resource demand characteristics of the Pod to be scheduled and the dynamic resource portraits of the nodes.

The invention discloses a multi-tenant data isolation and resource sharing method and device and a medium, and relates to the technical field of software architecture. The method comprises the steps of obtaining tenant data, and verifying the tenant data to allocate a unique tenant identifier corresponding to a tenant; based on the unique tenant identifier, the tenant permission and the resource allocation condition are initialized, and tenant data are classified, encrypted and stored in a public data layer, a tenant shared data layer and a tenant private data layer; based on a preset permission allocation model, allocating the initialized tenant permission, and performing data isolation on private data in the tenant data; and monitoring the resource use condition of each tenant in real time, and adjusting the resource allocation condition based on a load balancing algorithm. According to the method, the unique tenant identifier is allocated to each tenant, and the data is classified, encrypted and stored, so that logic isolation of the data is realized, and accurate allocation of resources is ensured through a dynamic resource adjustment mechanism.

The invention relates to a large model energy consumption optimization method and device, computer equipment, a computer readable storage medium and a computer program product. Comprising the steps of collecting hardware state data and task data of a large model; performing stage identification according to the task data, and determining a current stage; performing state prediction according to the hardware state data and the task data through a prediction model corresponding to the current stage to obtain target state information; generating optimization parameters of the current stage through a joint optimizer according to the target state information; and adjusting the resources of the large model according to the optimization parameters. In combination with stage perception and dynamic resource adjustment, a differentiated resource adjustment strategy based on different stages is realized, specific energy consumption pain points in an AI scene are solved, high power consumption of large model training, instantaneous fluctuation of reasoning requests and the like are reduced, sustainable and efficient operation of an AI system is ensured, the computing power demand and resource consumption of a large model are balanced, and the system performance is improved. And wide landing and development of a large model in various scenes are facilitated.

The invention relates to the technical field of big data management, in particular to a platform saturation management method and system based on multi-objective optimization, and the method comprises the steps: collecting platform, task, resource and path data in real time through a resource coupling modeling module; a resource conflict strength value and a path conflict strength value are calculated based on a warehouse operation resource task queue and a key passage path occupation state, a dynamic resource coupling weight matrix integrating three-dimensional conflicts is constructed, and the problem of conflict data splitting is solved; the resource mismatch response module is used for triggering a degradation task priority instruction and iteratively outputting a platform allocation scheme set of which the resource conflict intensity value reaches the standard when the resource conflict intensity value exceeds a preset threshold value by taking the platform occupation state as a physical basis and combining with an operation timeliness requirement; and the secondary congestion suppression module performs weighting on the path conflict intensity value by multiplexing the matrix, screens the lowest congestion risk final case in the scheme set, blocks a secondary congestion spreading chain, and realizes collaborative optimization of resource utilization and path smoothness.

The invention provides an AI model intelligent training and reasoning integration method and system, and the method comprises the following steps: receiving a model training instruction, and carrying out the preprocessing of original data, and obtaining a training data set; executing model training based on the training data set, monitoring task priorities and resource requirements through a dynamic resource scheduling algorithm, and dynamically adjusting training resource allocation according to a real-time monitoring result; when the model reaches a preset performance index, performing model pruning and quantification to generate an optimization model, and performing parameter fine tuning on the optimization model to obtain a final deployment model; generating reasoning service configuration according to calculation characteristics of the final deployment model, migrating the model and the configuration to a reasoning environment, and starting reasoning service; and dynamically adjusting the number of reasoning nodes according to the real-time network flow of the reasoning service. By implementing the technical scheme provided by the invention, through bidirectional dynamic resource scheduling and model deep optimization, the computing resource utilization rate and the model deployment efficiency are improved, and the high availability of the reasoning service is guaranteed.

The invention discloses a mobile storage and charging optimization method based on a graph neural network. The method comprises the following steps: collecting operation data of a mobile storage and charging network, and generating node information and edge weights by using the space-time graph neural network; according to the node information and the edge weight, constructing an order insertion sandbox which contains a newly added task association node and keeps network connectivity; executing scheduling optimization in the order insertion sandbox to generate a local scheduling instruction; generating an external resource list in combination with the local scheduling instruction and the boundary node position information, forming a cross-boundary resource candidate set, and completing cross-boundary resource allocation; combining a plurality of order insertion sandboxes meeting a distance threshold condition into a sandbox alliance, and executing joint scheduling to generate an alliance scheduling instruction; and adjusting boundary parameters based on operation record statistics, and applying the boundary parameters to order insertion sandbox generation and scheduling optimization. According to the method, global correlation modeling and dynamic resource scheduling are realized by using the graph neural network, and the cross-regional resource collaboration efficiency and the task completion rate are improved in a complex task environment.

The invention discloses a hierarchical priority task orderly scheduling method based on a virtual thread, which comprises the following steps: S10, receiving task parameters transmitted by a business layer, performing task packaging setting, outputting packaged task objects, and transmitting the packaged task objects to a multi-dimensional calibrator; s20, performing task expiration verification, task fusing verification and dependency verification in the multi-dimensional verifier, returning a passing or failing identifier and a failing reason, and if the passing or failing identifier does not pass, triggering a monitoring alarm to record a discard log; s30, the verified tasks enter a two-dimensional queue manager to be subjected to hierarchical enqueue processing and capacity dynamic adjustment, and the capacity dynamic adjustment uses a dynamic resource allocator to achieve elastic allocation of queue capacity and virtual threads based on load awareness; and S40, performing task execution and task retry management on the tasks in the two-dimensional queue.

The invention belongs to the technical field of mobile edge computing, and discloses a task scheduling method based on dynamic cloud edge collaboration. According to the invention, through link-level modeling and graph neural network feature extraction, adaptive expansion of different numbers of terminals and servers is realized; according to the method, the problems of instability and over-estimation in training are effectively avoided by combining cutting updating of a near-end strategy optimization algorithm and a generalized advantage estimation method. According to the method, the elastic computing power of the cloud and the edge low-delay characteristic can be fully utilized, the dynamically changing network scale and terminal requirements can be adapted, flexible unloading of tasks and dynamic resource allocation can be realized, task processing delay can be effectively reduced in a dynamic environment, the resource utilization rate is improved, and the resource utilization rate is increased. And good expansibility and stability are maintained under the condition that the number of terminals and the scale of the server fluctuate, the problems of computing resource bottleneck and task scheduling stiffness existing in the virtual reality video service of the existing mobile edge computing are solved, and the interaction experience of the user is remarkably improved.

The invention relates to the technical field of intelligent management, in particular to an intelligent management system and a resource cooperative scheduling method for an entrepreneurship space, and the system comprises a dynamic resource topological graph module which is used for collecting resource distribution data in real time through a space IoT positioning system, and generating a dynamic resource topological graph; when the system is used, a resource distribution diagram is drawn in real time through a space IoT positioning system, three-dimensional coordinates and coupling weight are marked for each resource node, and the node state is dynamically updated, so that the coupling relationship between the space path cost and the resources is fully considered; according to the method, the situation of cross-region movement of members is greatly reduced, the resource cooperative utilization rate and the resource average turnover rate are improved, conflict propagation prediction and resource redistribution are carried out through establishment of a resource dependency relationship matrix and dynamic time window splitting operation, delay caused by conflicts is shortened, the resource vacancy rate is reduced, and the emergency response speed is increased.

The invention discloses an optical network dynamic resource allocation method fused with an AI large model, which relates to the technical field of real-time scheduling of network resources, and comprises the following steps: constructing a service prediction model based on terminal behavior characteristics, calculating bandwidth sensitivity Bs and burst probability Pt, generating priority weight Wp, and improving burst service scheduling response; the T-CONT allocation rate and period are adjusted in a self-adaptive mode through a resource allocation efficiency function Ef and a buffer area demand index Bh, and priority scheduling of key services is guaranteed; the scheduling pressure is judged by introducing double thresholds of a signaling overhead index So and a network congestion degree Oc, AI reestimation and resource reallocation are triggered when abnormity occurs, and meanwhile, a bandwidth recovery parameter Br is recorded to optimize a subsequent strategy, so that minute-level intelligent scheduling and resource utilization maximization are realized.

An energy-conscious workflow planning system based on LLM with dynamic resource allocation, consisting of: a workflow input interface configured to receive workflow-directed acyclic graphs (DAGs), energy budget constraints, system performance constraints, and natural language requests from human operators; a large language model (LLM) logic module connected to the workflow input interface and configured to analyze the workflow specifications and system constraints in natural language, generate energy-conscious planning recommendations based on the analyzed workflow specifications, and provide explainable planning rationales in natural language; a reinforcement learning-based scheduling unit connected to the LLM reasoning module and configured to: receive scheduling recommendations from the LLM reasoning agent, fine-tune task-resource assignments by dynamically adapting to runtime variations, and perform online resource redistribution under runtime variability; an energy monitoring unit configured to: continuously monitor CPU and GPU utilization in heterogeneous clusters, track power consumption and thermal limits per node, and generate energy profiles for system components; a multi-objective optimization engine configured to: perform a Pareto-optimal scheduling analysis that balances energy consumption, lead time and reliability, apply statistical and AI-supported trade-off analyses and ensure optimal resource allocation based on Pareto frontier analysis; a dynamic resource allocation unit configured to: use predictive models that incorporate LLM inferences and feedback from reinforcement learning, reassign tasks between nodes and clusters while minimizing energy consumption and improving system throughput based on the predictive models; a performance optimization module configured to optimize scheduling decisions using multi-criteria optimization analysis; and a user interface that allows human operators to override and refine planning strategies in real time based on verifiable planning reasons.

The invention provides a multi-tenant visual large model reasoning resource dynamic allocation and isolation method. An integrated system of a multi-tenant environment, visual large model reasoning, dynamic resource allocation and isolation guarantee is constructed. The system controls resources such as model copy number, video memory quota, batch size, queue weight, bandwidth and the like at tenant level fine granularity, and adjusts priority and quota based on closed-loop feedback of real-time indexes (such as queuing length, delay and throughput). Interference suppression among tasks is realized through priority grading, a hard / soft isolation strategy and a tenant-model copy mapping mechanism in combination with GPU MIG, video memory partitioning, queue scheduling and other technologies. Aiming at the characteristics of high video memory, large input and the like of a visual large model, model loading, batch processing and video memory multiplexing strategies are optimized, and differential resource allocation of heterogeneous models is supported. According to the overall scheme, on the premise that the service quality and isolation are guaranteed, the GPU resource utilization rate is remarkably increased, and the operation cost is reduced.

The invention discloses an unmanned aerial vehicle real-time task scheduling and resource management system, which belongs to the technical field of unmanned aerial vehicle computing resource management, and comprises a task load prediction module, a priority dynamic evaluation module, a resource elastic distribution module and a closed-loop feedback optimization module, the task load prediction module adopts an LSTM-GRU hybrid neural network to predict a resource demand peak value in advance and triggers a pre-scheduling signal; the priority dynamic evaluation module dynamically adjusts the task priority based on the multi-dimensional evaluation matrix; the resource elastic allocation module realizes elastic allocation through a reserved resource pool and a multi-level preemption mechanism; the closed-loop feedback optimization module monitors performance deviation and reversely adjusts parameters of each module, the four modules are deeply coupled to form a closed-loop cooperation mechanism, and intelligent task scheduling and dynamic resource optimization are achieved.

The invention provides an efficient task scheduling strategy recommendation method and system for a distributed network surveying and mapping scene, and relates to the crossing field of network space surveying and mapping, network security, distributed scheduling and intelligent optimization recommendation algorithms. Analyzing the task request to extract a task type, a task target network address set, a regional distribution feature, a task execution timeliness requirement and calculation and bandwidth resources required by estimation, and forming a task feature vector; and performing state acquisition and capability evaluation on the distributed surveying and mapping nodes based on task resource requirements defined by the task feature vectors, and performing normalization processing to form node capability vectors. Through multi-source information vectorization and closed-loop optimization, adaptive matching of task scheduling and resource dynamics is realized.

The invention discloses a dynamic resource demand characterization method and system for a task life cycle, and belongs to the technical field of artificial intelligence computing power resource management.The method comprises the steps that when a task is started, a monitoring agent is deployed, the task life cycle is dynamically divided through a GPU instruction sudden increase inflection point, and a CPU instruction stream is synchronously monitored; task semantic features, CPU / GPU hardware indexes and interaction time delay data are collected, key features are extracted after space-time alignment, and collaborative efficiency indexes are calculated; constructing a cross-stage resource demand coupling matrix based on a historical task library, and quantifying CPU / GPU resource conduction coefficients in adjacent stages; constructing a double-flow prediction model, predicting a resource demand in combination with the coupling matrix, and generating a three-dimensional demand matrix; and encoding the demand matrix into a dynamic vector, introducing a stage transition resource change intensity enhancement vector, and finally outputting an enhancement vector sequence to trigger CPU / GPU cooperative scheduling.

The invention discloses an automatic task intelligent distribution method and system based on robot state perception, and the method comprises the steps: collecting the priority, execution time and type information of all to-be-executed tasks through a scheduling interface; on the basis of a real-time interface of a robot platform, data such as the online state, the load level, the concurrent capability and the capability support type of each robot are collected, and a unified structured input data model is established. After a preliminary mapping relation is established between a task and a robot, matching degree scoring is carried out by fusing task urgency, resource availability and an adaptive scoring function of a load factor, scheduling priority ranking in a dynamic resource environment is realized, and after ranking is completed, a system completes task binding by adopting a static scheduling decision-making mechanism. And a resource exclusive mechanism is designed to avoid repeated allocation of the robot and ensure stable and controllable allocation. According to the method, registration, state persistence and high-concurrency safety triggering of tasks are realized, and the problems of scheduling loss and repeated execution are avoided.

The invention discloses a 5G-TSN network resource scheduling method based on a near-end strategy optimization algorithm, which completes 5G-TSN network resource scheduling by establishing a 5G wireless channel model, constructing a Markov decision process model and realizing action value output and strategy optimization through the near-end strategy optimization algorithm. According to the network resource scheduling method provided by the invention, a dynamic resource allocation strategy is realized based on gating state sensing, ultralow delay services such as robot control instructions and the like can be preferentially scheduled when gating of a time-sensitive network is started, and non-real-time services such as resource transmission video monitoring and the like can be intelligently multiplexed when the gating of the time-sensitive network is closed; the wireless resource utilization rate is effectively improved, meanwhile, it is ensured that data with high real-time performance can be transmitted as soon as possible in a short time, reliable transmission of equipment data in an industrial scene is ensured, scheduling mechanisms of a 5G wireless network and a time-sensitive network are fused, resource scheduling is efficiently carried out, and the real-time performance, reliability and resource utilization rate of data transmission are balanced.

The invention discloses a dynamic resource optimal configuration method for an electric power communication network, and belongs to the technical field of electric power system communication. The problems of low communication resource utilization rate and poor real-time response in the existing electric power communication are solved, the communication resources of physical network equipment are acquired through a network management system, and network operation state data and traffic flow characteristics are acquired in real time by utilizing a network probe or NETCONF / YANG; the method comprises the following steps: identifying service flows flowing into a communication network by adopting a deep packet inspection technology, classifying the service flows according to security partitions of power services, and mapping different types of service flows to different service levels; a quantitative QoS demand template is established for each type of services; and on the basis of the network operation state data, the service flow characteristics and the QoS demand template, constructing a multi-objective optimization model which takes guarantee of the service quality of key services as a highest priority objective, and solving by adopting an optimization algorithm to obtain an optimal resource allocation strategy. The method is suitable for optimal configuration of dynamic resources of the power communication network.