3318 results about "Resource assignment" patented technology

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention provides a heterogeneous GPU resource management scheduling method, and relates to the technical field of GPU resource allocation, heterogeneous equipment management and unified abstract modeling are carried out, GPU resources of different architectures are registered to a container arrangement platform, and a unified abstract layer is constructed to shield bottom layer hardware differences; gPU cluster optimization management based on a multi-dimensional real-time monitoring and intelligent scheduling strategy is carried out, GPU operation indexes are collected, priorities are dynamically calibrated for tasks, and task performance portraits are constructed; scheduling decision making is carried out through multi-strategy cooperation, and optimal GPU resources are distributed for tasks; carrying out fine-grained resource allocation, carrying out space or time segmentation on the GPU, and dynamically adjusting resource allocation according to a load state; aPI conversion of cross-architecture tasks is realized through a unified runtime library, and task execution data is collected to feed back an optimization scheduling model; automatic detection, isolation and task migration of GPU faults are carried out, and unified monitoring and alarm are provided.

The invention relates to the technical field of robot scheduling, and discloses a robot task management framework system based on asynchronous communication. The system comprises a task asynchronous analysis module for performing time sequence decoupling on an original task instruction stream of a heterogeneous robot terminal by means of a multi-channel buffer queue to separate out independent executable task units; the resource dynamic mapping module is used for establishing a variable granularity resource allocation mapping table according to cluster real-time load and task resource requirements, and recording various resource dynamic binding relationships; the task priority reconstruction module is used for constructing a directed acyclic graph scheduling topology based on task logic dependence and deadline constraint, and generating a weighted execution sequence through topological sorting; the exception isolation processing module captures exceptions during operation and triggers an isolation strategy; and the cross-node cooperation module guarantees the multi-robot cooperation task state consensus through a distributed consistency protocol. The system is suitable for heterogeneous robot cluster complex task scenes.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a vehicle path planning system based on multi-agent collaboration, and aims to solve the problems of path response lag, insufficient global optimization ability and conflict between individuals and group targets in a dynamic traffic environment. The system adopts a three-level collaborative architecture of a central coordination server, a regional scheduling agent and a vehicle-mounted agent, realizes macroscopic regulation and local resource allocation through a global guidance potential field and an improved auction algorithm, and improves the dynamic adaptive capacity by combining event-driven replanning and a robustness verification mechanism. Different scheduling and multi-time scale coupling operation of heterogeneous vehicles are supported, and the road network passing efficiency and the path reliability are effectively improved.

The invention discloses a resource allocation method and system for 5G communication in an underground coal mine, and relates to the technical field of communication information processing, and the method comprises the steps: obtaining data information of an underground coal mine roadway, building a digital twin communication model through combining a synchronous positioning and map building algorithm and a wireless fingerprint matching algorithm, generating a channel profile map according to real-time data, and transmitting the channel profile map to a server; extracting channel data of each space node; constructing a computing resource pool for the 5G base station and the edge computing node, and packaging different service types into a plurality of network slice containers; according to the channel data, carrying out initial allocation on each container through a resource composer, and carrying out dynamic task migration among different MEC nodes to obtain a resource allocation state; dividing scheduling time according to a channel profile and a resource allocation state, and dynamically adjusting a duration proportion and bandwidth occupation of each window according to a task migration state; according to the invention, through digital twinning and dynamic scheduling, the problems of 5G communication coverage and interference in the underground coal mine are solved.

Embodiments of the present disclosure relate to methods, devices and computer readable media of communication. A terminal device receives, from a network device, an indication of a set of transmissions to be transmitted or received by the terminal device in a set of time intervals, a transmission in the set of transmissions being indicated to be transmitted or received by the terminal device in a time interval in the set of time intervals. If the time interval comprises a time interval associated with sub-band full duplex mode, the terminal device performs an operation related to transmitting or receiving the transmission. In this way, a sub-band non-overlapping full duplex scheme with respect to resource allocation may be well supported.

The invention relates to an unmanned aerial vehicle group-oriented sensing communication integrated network multi-resource joint scheduling method, and belongs to the technical field of wireless communication, and the method comprises the steps: building a system model of multiple UAV-ISAC tasks, and defining a joint optimization problem; extracting spatio-temporal features from the dynamic heterogeneous graph in which the unmanned aerial vehicle, the user and the sensing target are abstracted as nodes and the relationship is abstracted as edges; taking the features as input, and adopting a layered multi-agent reinforcement learning architecture to solve the joint optimization problem on line; in the architecture, resource allocation and trajectory planning actions are generated through cooperation of a central Actor and all unmanned aerial vehicle Actors, and system performance is evaluated by a central Critic; constructing a multi-target weighted reward function, stabilizing a training process by combining experience playback and a Mini-batch sampling mechanism, and updating network parameters in parallel; and obtaining an optimal resource allocation and unmanned aerial vehicle trajectory strategy through training. The sensing performance is improved, the communication quality is guaranteed, and the defects in the aspect of dynamic resource scheduling in the prior art are overcome.

Embodiments of the present disclosure relate to devices, methods, apparatuses and computer readable storage media of resource allocation for time-division or frequency-division duplexing communication. The method comprising: receiving, at a first device and from a second device, first configuration information indicating a set of resources for time-division or frequency-division duplexing communication between the first device and the second device, the set of resources comprising a first subset of the resources for uplink transmission; and receiving, from the second device, second configuration information indicating at least one second subset of the resources for the uplink transmission, the at least one second subset of the resources comprising at least one resource other than the first subset of the resources.

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

The invention relates to an intelligent logistics scheduling and resource distribution system based on multi-source data fusion, and relates to the technical field of logistics management technologies, and the system comprises the steps: collecting vehicle operation state data, road condition data, logistics order dynamic data and warehouse inventory change data in real time based on a multi-source heterogeneous interaction platform; constructing a multi-modal traffic logistics time series data set; based on a maximum priority scheduling algorithm, giving priority weights to the logistics orders in combination with logistics order timeliness demands and cargo features; based on the logistics order priority weight, path optimization is carried out by using a space-time hierarchical planning algorithm, and an initial transportation path set is constructed; real-time monitoring of the transportation process is achieved based on the digital twinborn technology, the path condition is sensed dynamically, the optimal transportation path is obtained through continuous adjustment, and an intelligent logistics scheduling and resource allocation scheme based on multi-source data fusion is generated. The method has the advantages that the resource allocation efficiency is improved and the transportation cost is reduced by optimizing the priority path and continuously adjusting the transportation path.

The invention belongs to the technical field of artificial intelligence, and discloses an artificial intelligence model training resource adaptive distribution system. The method comprises the following steps: acquiring and calculating graph structure data and hardware resource state data in real time, and calculating a data reuse rate and generating a candidate operator fusion scheme by constructing an operator execution time sequence constraint matrix and identifying an operator cluster of data locality characteristics; a resource competition hotspot prediction mechanism is introduced, memory bandwidth occupation fluctuation characteristics are analyzed, a resource conflict probability is calculated for a fusion scheme, and a dynamic balance optimization model of fusion income and resource conflicts is constructed. An optimal operator fusion decision sequence and a resource allocation strategy are generated through iterative solution, and accurate dynamic adjustment of computing resources in the training process is achieved. The training efficiency and the resource utilization rate are improved, the energy consumption is reduced, and the system stability is enhanced.

The invention relates to the technical field of communication networks, in particular to a resource allocation system and method in a satellite-ground fusion network, and the system comprises a satellite-ground topology and channel evolution predictor which is used for obtaining satellite ephemeris, user GNSS positioning and moving vectors, ground base station topology and a real-time CQI / RSRP measurement report; generating future link state information; the business QoE impact and switching cost modeling device is used for calculating transient interruption time of a switching action; generating switching cost information; the continuity risk quantification engine is used for generating continuity risk information containing the link maintaining risk probability, the instantaneous impact collapse probability and the link switching risk probability; the risk evasion type resource decision-making device is used for calculating maintenance link comprehensive cost and switching link comprehensive cost; calculating a dynamic switching damping threshold value; generating risk avoidance decision information; according to the invention, the high-frequency ping-pong switching near the edge state point is effectively inhibited, and the signaling overhead caused by invalid switching is reduced.

The invention belongs to the technical field of blockchains, and discloses a blockchain-based node computing power resource allocation method and a storage medium, and the method comprises the steps: collecting the load data of each chain in real time through a resource propagation chain control point mechanism as a node computing power resource, screening idle resources through a smart contract, and injecting the idle resources into a hierarchical resource pool, generating a cross-chain shared pool and a metadata index; the smart contract receives each chain resource request, obtains a two-dimensional priority, generates a scheduling sequence, synchronously arbitrates resource competition, and generates a resource allocation instruction; dividing a high-computing-power verification node and a low-computing-power verification node according to node computing power, dynamically matching an encryption strategy, generating a decryption key fragment, and forming an encrypted transmission packet; a target chain consensus mechanism is adapted, a resource request format is converted, a corresponding verification strength is matched, resource transfer is executed, and a handover voucher is generated; and the intelligent contract monitors the resource use condition, rewards points on the active shared nodes, deducts contribution degrees from the hidden resource nodes, recycles resources, and updates the quota of the cross-chain shared pool.

The invention relates to the technical field of natural language processing, and discloses a workflow calling method based on a large language model. The method comprises the steps of receiving an initial task description text of a user, and decomposing the initial task description text into a discrete intention unit set through a semantic analysis engine; inputting a pre-trained large language model to carry out context association analysis, and generating a task node topological graph containing a hierarchical relationship; detecting a data transmission dependency relationship between nodes, and marking a strong association cluster with bidirectional data streams; according to cluster dynamic load parameters, automatically dividing parallel execution domains and distributing independent data transmission channels; and monitoring channel throughput fluctuation in real time, and triggering a channel switching protocol when the channel throughput is blocked. According to the method, the complex task execution logic and the data flow path are straightened out, the problems of dependency conflicts and resource allocation are solved, the orderliness and stability of workflow execution are guaranteed, and the automatic task scheduling requirements under various complex scenes are met.

The invention relates to the technical field of wireless communication, and particularly discloses a high-voltage transformer remote metering monitoring system based on 5G network slicing, which senses network performance in real time by constructing a multi-dimensional network state matrix, performs path rehearsal and resource allocation calculation by using a digital twinning technology, and realizes remote metering monitoring of a high-voltage transformer. Generating a candidate transmission path set with optimal time delay consistency and a corresponding wireless resource block allocation scheme; calculating a path resource allocation factor and combining with network load prediction to generate an optimal resource scheduling strategy; and finally, converting the strategy into a specific network control instruction, establishing a special transmission channel with deterministic service quality, and implementing real-time monitoring and maintenance.

A technique for resource allocation estimation for media content items is described. In accordance with the described techniques engagement by a set of user accounts with respective media content items of at least one media content service provider system is obtained. The media content service provider system and / or a payment service system generates historical streaming data for the respective media content items based on the engagement of the set of user accounts. An estimated streaming count of a media content item over a time period based on the historical streaming data for the respective media content items is determined. An estimated resource allocation for the artist is determined based on the estimated streaming count and an advance of funds is facilitated based on the estimated resource allocation to an account of the artist during the time period.

The invention relates to the technical field of network communication, in particular to a 5G-A network slice dynamic adjustment and resource allocation system oriented to low-altitude economy. The system comprises a mobility management feature integration module, a network slice service level protocol evaluation module, a mobility management demand prediction benchmark construction module, a multi-dimensional trend prediction module, a joint optimization decision generation module and a strategy verification and execution module, and is used for acquiring a mobility management feature data set of a wireless network; performing network slice service level protocol evaluation on the wireless network to obtain a network slice state evaluation result; constructing a mobility management demand prediction benchmark; performing multi-dimensional trend analysis on the network slice state evaluation result to obtain a prediction result; performing joint optimization decision on the standard network slice configuration strategy to obtain a network slice adjustment strategy; and executing the verified slice adjustment strategy to realize dynamic adjustment and resource allocation of the network slice. According to the invention, the efficiency of network slice dynamic adjustment and resource allocation can be improved.

The invention relates to an intelligent scheduling method and device for communication network resources, equipment and a medium. The method comprises the following steps: performing IP packet header analysis on a service data packet to obtain an initial service priority identifier, and performing adjustment in combination with a preset priority rule, an equipment alarm state and a network congestion rate to generate a dynamic service label; based on the dynamic service label, the real-time link state and the energy consumption coefficient, generating a resource allocation strategy and a path scoring weight parameter through a preset deep reinforcement learning network; and calculating a path score based on the resource allocation strategy and the path score weight parameter, generating a path mapping table, generating a port suppression instruction based on the dynamic service label and the resource allocation strategy, and extracting a base station power regulation instruction. According to the method, the dynamic adaptability, the resource utilization rate and the energy consumption management and control capability of communication network resource scheduling are improved by means of dynamically adjusting the service priority, comprehensively modeling a resource allocation strategy and the like, and the transmission guarantee level of high-priority services is enhanced.

The invention relates to a computing power resource dynamic scheduling method, device and equipment based on deep reinforcement learning and a medium thereof, and the method comprises the steps: constructing a joint state vector through real-time fusion of a network layer channel state and computing layer node load data, and driving a strategy network to generate transmission parameters and resource allocation actions of cooperative control; the code modulation parameters of the wireless transmission module and the computing resource proportion of the target node are synchronously configured in the execution layer, and dynamic task scheduling in the channel decay environment is achieved; a multi-target reward mechanism is designed to couple transmission bit error rate penalty, resource utilization efficiency and task timeliness evaluation indexes, and a reinforcement learning agent is guided to balance communication stability and computing power demand conflicts; according to the method, strategy network parameters are optimized through time difference errors, closed-loop feedback is formed in combination with channel state prediction and node load updating, the problems of network and calculation layer splitting decision, insufficient dynamic adaptability and multi-target optimization imbalance in the prior art are effectively solved, and the task scheduling success rate in the time-varying wireless environment is improved.

The invention discloses a multi-scene adaptive AGV scheduling management system and a path planning method. Comprising a multi-scene perception and environment modeling module, a multi-AGV task scheduling and resource allocation module, a dynamic path planning and optimization module, a scene adaptive decision module, a multi-sensor fusion obstacle avoidance module, a heterogeneous communication and data synchronization module and a whole-process monitoring and fault diagnosis module. According to the invention, full-process adaptive control from environment modeling to decision optimization is realized. An SVM classification algorithm and dynamic matching degree calculation are adopted, so that the system can accurately identify characteristics of heterogeneous scenes such as warehousing, workshops and logistics hubs, and an optimal scheduling strategy is automatically switched; and a multi-dimensional cost function including path length, obstacle risk, smoothness and congestion degree is constructed, and seamless connection of global path pre-planning and local dynamic adjustment is realized.

The embodiment of the invention provides a self-adaptive resource allocation method and device, and the method comprises the steps: collecting calculation node metadata and corresponding physical equipment information through deploying a daemon process at each node, associating the calculation node metadata with the corresponding physical equipment information, and constructing an allocable equipment pool; the real-time state of the node is sent to a cloud native scheduler, the cloud native scheduler generates a corresponding scheduling decision for the delay exceeding node obtained through judgment, the scheduling decision comprises an isolation decision and a migration decision, and in the migration decision, migration resource allocation is constrained based on an equipment pool, the scheduling decision of the cloud native scheduler is received, and node resources are scheduled. The method can improve the certainty of the hard real-time task and the overall efficiency of the system.

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 the technical field of data processing, and discloses an Internet of Things gateway data grading processing method and system based on edge computing, and the method comprises the steps: collecting original operation data and a data traffic value received by an Internet of Things gateway; performing feature extraction and clustering grouping according to the original operation data, calculating an average influence coefficient of samples in a group in combination with a preset service weight, and determining an importance level corresponding to the group; important data is screened out for distribution immediate evaluation; performing aging characteristic analysis according to the important data, and predicting a data processing priority; analyzing resource demand allocation according to the priority sequence and the data flow value, and constructing a resource allocation proportion; according to a resource allocation proportion and an allocation proportion lower limit, identifying nodes with insufficient proportions and optimizing the priority sequence to obtain a final scheduling scheme; according to the final scheduling scheme, the processing sequence is applied to the original operation data, and data grading output is obtained. According to the method, efficient and accurate data processing is realized.

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

The invention belongs to the field of mobile edge computing, and particularly relates to a resource pricing problem based on a Stackelberg game in mobile edge computing. The method comprises the following steps: constructing a mobile edge computing model, wherein the mobile edge computing model comprises a server base station, task equipment and cooperative equipment; in a task execution period, for a server base station profit maximization problem of resource pricing based on a Stackelberg game in mobile edge computing, delay brought by task unloading is fully considered in problem modeling, and the delay requirement of a task is met to the maximum extent; analyzing and establishing a dynamic punishment and redistribution mechanism for task unloading failure of the cooperative equipment, wherein the mechanism comprises a punishment cost calculation module, a task value reevaluation module and a game strategy updating module; and obtaining an unloading strategy of the task equipment, a task allocation strategy of the server base station and a resource pricing strategy after the server base station and the cooperative equipment carry out the Stackelberg game by utilizing a greedy strategy, thereby obtaining a final task allocation scheme for maximizing the profit of the server base station. According to the method, the optimal task execution and resource allocation strategy of the server base station is formulated, and the relationship among the task value, the resource cost and the additional resource purchase cost is balanced as required, so that more flexible and efficient task scheduling service can be provided.

The invention discloses an unmanned ship-oriented task priority scheduling and resource awareness management method, and belongs to the technical field of unmanned ship control. The method comprises the steps of obtaining a task request generated by an unmanned ship system, inputting the task request into a task classifier for task classification and priority allocation, performing real-time monitoring on resources of the unmanned ship system based on a resource perception and threshold grading model, judging an alarm interval where the resources of the current unmanned ship system are located, and sending the alarm interval to the unmanned ship system. Obtaining a target scheduling strategy; allocating each task in the task request to a corresponding thread pool for execution, dynamically adjusting the scheduling of each task according to a multi-thread pool cooperation and preemption mechanism, and dynamically optimizing the load and channel of the unmanned ship system based on a load balancing and channel selection mechanism; feedback information during execution of each task is collected, a target scheduling strategy is adjusted through an adaptive optimization mechanism, and scheduling and resource allocation of each task are updated in real time. The method improves the resource utilization rate of the unmanned ship system.

The embodiment of the invention provides a distributed model training method and device, a server and a storage medium, and relates to the technical field of distributed model training. Determining a target sub-model according to the training dependency relationship among the sub-models, and determining a target computing node according to a resource configuration parameter and a model training parameter of the target sub-model and the resource state of each computing node; creating a plurality of training tasks corresponding to the target sub-model according to the resource configuration parameters and the model training parameters of the target sub-model, allocating each training task to each target computing node, and controlling each target computing node to perform model training; and under the condition that the target sub-model is successfully trained, determining the next to-be-trained sub-model as a new target sub-model, and carrying out model training on the new target sub-model until all the sub-models are successfully trained, so that the flexibility and adaptability of resource allocation in the training process can be improved, and the training efficiency is improved. The problems of long resource occupation period, insufficient utilization and scheduling stiffness are solved.

The invention relates to the technical field of communication, and discloses a sea area wireless network coverage method and device and a computer readable storage medium, and the method comprises the steps: obtaining the historical link performance, real-time marine environment data and business load information of a target sea area; constructing a sea surface electromagnetic wave propagation attenuation dynamic prediction model based on a gated circulation unit; and in combination with the prediction result and the service demand, jointly optimizing the base station transmitting power, the antenna beam pointing angle and the subcarrier resource allocation, and issuing a regulation and control instruction. The system comprises a data acquisition module, a propagation modeling module, a resource scheduling module and an instruction execution module. According to the invention, through deep fusion of marine environment physical field perception and wireless communication system control, a dynamic coverage regulation and control mechanism driven by a sea condition is established; the provided sea surface electromagnetic wave propagation attenuation dynamic prediction model can pre-judge the signal attenuation trend under the complex sea condition in advance, so that network resource scheduling has perspectiveness.