145 results about "Resource allocation algorithm" patented technology

The invention provides a network slice resource allocation method oriented to an industrial internet and related equipment. Comprising the following steps: designing an SDN-based network slice resource allocation management and control architecture which comprises a control plane and a data plane; an intelligent agent is deployed for each switch node in the control plane, and each switch node is provided with an IT slice queue and an OT slice queue; designing a path planning and network slice resource allocation algorithm; modeling joint path planning and network slice resource allocation as a multi-agent Markov decision process; defining a state space and an action space; designing a reward function; constructing and training a multi-agent deep reinforcement learning model; and inputting a network global view collected in the SDN-based network slice resource allocation management and control architecture and demand information of each slice into the deep reinforcement learning agent obtained by training, and outputting a traffic forwarding path and a network slice resource allocation scheme. And the bearable flow quantity of the network can be improved to the maximum extent.

The invention discloses a low-orbit communication constellation distributed beam allocation method and system. The method comprises the following steps: S1, distributed satellite group task distribution; a planning task is forwarded through a distributed centerless satellite group architecture, and broadcasting of the task in a satellite group is realized by dynamically determining an on-duty satellite; s2, target area wave position division based on a sine space; after a single satellite receives a task, according to the geographic position coordinates of the satellite and the ground terminal, the beam position number of the ground terminal is obtained through calculation; s3, single-satellite beam resource allocation based on a hopping beam; time is divided into time slots based on a beam hopping mode, and distributed low-orbit communication constellation beam allocation is realized through a resource allocation algorithm. According to the method and the system, the robustness of the system can be improved, the calculation amount is reduced and the calculation speed is improved.

The invention discloses a computing resource management scheduling method and system. The method comprises the following steps: carrying out resource demand prediction based on a machine learning algorithm; a main node main scheduler performs an allocation task by using the resource demand prediction result and the resource demand information provided by the execution node in combination with multiple computing resource allocation algorithms to realize multi-resource collaborative scheduling; the main scheduler realizes distributed scheduling of task, node and cluster levels based on a self-adaptive distributed multi-level scheduling architecture; and the main scheduler realizes monitoring data return through a feedback loop and carries out real-time distribution strategy adjustment. Aiming at the bottleneck problem in the resource management process, the invention provides a brand-new and dynamically adaptive computing resource management scheduling method and system, and the computing resource management scheduling performance is remarkably improved.

The invention discloses an unloading strategy and resource scheduling combined method based on the Internet of Vehicles. The method comprises the following steps: constructing a multi-server and multi-user vehicle edge computing system; constructing a communication model according to a vehicle edge computing system; constructing a calculation model; establishing a joint optimization problem model by taking balance of time delay and energy consumption and maximization of task success rate as targets; and solving the joint optimization problem model by using a grey wolf optimization algorithm based on chaotic mapping and differential mutation and a resource allocation algorithm based on delay balance to obtain a task unloading strategy set and a resource allocation strategy set. According to the unloading strategy and resource scheduling combined method based on the Internet of Vehicles, the problems that in the prior art, the task success rate is low due to uneven resource allocation in a multi-server environment, and single target optimization in a dynamic environment is difficult to meet the real-time performance and energy efficiency requirements at the same time are solved.

The invention belongs to the technical field of vehicle-mounted edge computing (VEC), and particularly relates to an unloading and resource allocation method for a DAG task in a vehicle-mounted edge computing environment. The method comprises the following steps: firstly, constructing a VEC unloading system in a two-way lane scene, and collecting task and equipment information and establishing an optimization model in combination with a vehicle moving model, a communication model and a calculation model; and secondly, aiming at a task in a directed acyclic graph (DAG), a task priority scheduling method based on a DAG topological structure is designed, so that a task scheduling strategy is optimized. On the basis, the problem is modeled as a Markov decision process by taking minimization of task completion time delay and system energy consumption as optimization objectives. The invention relates to the field of resource allocation, in particular to a task-dependent computing unloading and resource allocation algorithm based on a depth deterministic policy gradient (DDPG), and further provides a task-dependent computing unloading and resource allocation algorithm based on the depth deterministic policy gradient (DDPG) so as to solve the optimization problem.

The invention discloses an Internet of Vehicles communication resource allocation method based on multi-agent deep reinforcement learning. In order to solve the problems of dynamic channel interference, low resource allocation efficiency and multi-target collaborative optimization when a V2V link multiplexes V2I orthogonal spectrum resources in a cellular Internet of Vehicles scene, the method realizes joint optimization of discrete spectrum selection and continuous power control through agent autonomous decision and global collaboration, and improves the efficiency of resource allocation. The problem of quantization errors caused by an existing resource allocation algorithm with discrete action space is solved, the low delay and reliability requirements of the V2V link and the high-capacity communication requirement of the V2I link are effectively balanced, and the V2I link capacity is improved as much as possible while the transmission reliability of the V2V link is met.

The invention discloses a logistics storage video monitoring optimization scheduling method and system in an Internet of Things environment. The method comprises the following steps: acquiring dynamic information of a logistics storage area; inputting the dynamic information into an enhanced image processing model to obtain high-quality image data, and generating a computing resource scheduling scheme; predicting a cargo flow trend and planning a monitoring strategy by using the computing resource scheduling scheme, and ensuring sufficient computing resource support in a high-flow area; when an abnormal event is detected, adjusting the working state of a camera or increasing the monitoring density of a specific area according to a preset task priority and an optimized resource allocation scheme rapid response; and collecting operation effect data as feedback, and continuously learning and optimizing an intelligent computing resource allocation algorithm. By implementing the method provided by the invention, the burden of the data center can be reduced, the real-time performance is improved, the automation and anomaly detection capabilities are improved, the system resource allocation is optimized, the storage cost is reduced, and the system efficiency is improved.

The wide application of the Internet of Things enables spectrum resources to be scarce, and the terminal equipment is urgent for the demand of energy and efficient communication. The invention designs a wireless energy transfer communication (SWIPT)-non-orthogonal multiple access (NOMA) system model based on downlink cooperation. The invention provides an energy efficiency maximization resource allocation algorithm based on cooperation SWIPTT-NOMA by considering a resource allocation problem under the constraints of throughput, energy collection and transmitting power of a user, so as to improve the energy efficiency of a system. Through a Dinkelbach method, variable replacement and an SCA method, a nonlinear non-convex optimization problem is converted into a convex optimization problem, and a globally optimal solution is solved by using a Lagrange duality theory. A simulation result shows that compared with an optimization scheme based on a fixed power allocation factor and a fixed time slot selection factor, the proposed strategy can improve the system energy efficiency on the premise of ensuring the user throughput, and a more efficient resource allocation scheme is provided for a wireless energy supply network.

The invention discloses a deep reinforcement learning resource allocation method for green mobile edge computing, which comprises the following steps of: establishing a communication network model, and initializing a communication environment, the number of base stations, the number of users and the number of subcarriers; determining an optimization target and a constraint condition; the optimization problem is converted into a Markov decision process, intelligent agents, a state space, an action space and a reward function are determined, a deep reinforcement learning algorithm is used for training, and an optimal strategy is distributed for each intelligent agent; the intelligent agent continuously interacts with the environment through PPO and D3QN algorithms, and network parameters are optimized and updated; an optimal resource allocation scheme is obtained; through a distributed multi-agent deep reinforcement learning-based resource allocation algorithm, the maximization of long-term average energy efficiency is realized, and strategy coordination among multiple agents is promoted, so that resource allocation is optimized, and an efficient and sustainable resource management solution is provided for the development of edge computing and communication networks in the future.

The invention relates to a privacy-protected distributed acceleration resource allocation method and system, and belongs to the technical field of information communication. According to the method and the system, a parameterized distributed momentum method and a push-pull distributed resource allocation algorithm are combined to realize accelerated resource allocation in a directed network; a privacy-related variable is decomposed into two variables through a state decomposition scheme, one variable is used for information interaction, and the other variable directly related to privacy is used for local updating, so that privacy is protected, and noise disturbance is introduced in communication by adopting a differential privacy strategy to further enhance privacy protection. The algorithm disclosed by the invention is specially designed for a complex network scene, is suitable for applications needing privacy protection and efficient resource allocation, such as a smart power grid and energy management, shows wide applicability and innovation in distributed optimization, and has a wide application prospect.

Disclosed in the present invention is a URLLC multi-connectivity transmission and resource allocation method for industrial automation. The method comprises the following steps: S1, establishing a multi-connectivity technology-based URLLC system model; S2, setting constraint conditions of end-to-end transmission on the basis of quality of service requirements for an industrial automation application; S3, reporting the length of an information byte required for transmission, and determining a bandwidth optimization problem of multi-connectivity data transmission; and S4, executing a resource allocation algorithm to calculate an optimal cross-layer resource allocation solution for the multi-connectivity data transmission. According to the present invention, a URLLC multi-connectivity transmission and resource allocation method for industrial automation is established, to avoid the problem of cellular link failure caused by dynamic changes of channels in a traditional transmission mode; and under the constraints of end-to-end delay, reliability and maximum code length, an optimal cross-layer resource allocation method is provided, to minimize a bandwidth required for multi-connectivity transmission, thereby achieving the objective of saving spectrum resources.

The invention discloses a space-air-ground integration-oriented full-decoupling network non-feedback transmission and resource dynamic allocation method. A solution is provided for space-air-ground multi-node cooperation resource management. The method comprises the steps that a channel state information prediction model based on deep learning replaces traditional feedback signals with user geographical location information, and multiple-input-multiple-output transmission is achieved; and a many-to-one matching model is designed and is used for realizing adaptive allocation of spectrum resource blocks among space-air-ground heterogeneous nodes, so that the matching stability is ensured, and the method has the characteristics of low complexity and rapid convergence. For cooperative transmission of multiple nodes and resource allocation among heterogeneous nodes, the spectrum efficiency of the system is improved through non-feedback channel state information prediction and a resource allocation mechanism. Compared with a single connection mode and polling-based resource scheduling in a traditional network, the flexible resource allocation algorithm disclosed by the invention remarkably improves the network capacity and improves the user communication quality.

The invention discloses a resource allocation system and method based on an MLOps platform, and the system comprises an access controller which obtains a currently executed calculation task, verifies the calculation task, and carries out the access of the calculation task; the scheduler is used for acquiring resource use conditions corresponding to the cluster nodes in multiple dimensions and determining resource load conditions of the cluster nodes according to the resource use conditions; according to the resource load condition, distributing a corresponding cluster node for the calculation task; the control manager is used for managing the execution process of the calculation task; and the command line interface interacts with the user. Through a scheduler, according to a resource load condition, a resource request and limitation of a calculation task (MLOps task) are adjusted, a dynamic resource allocation algorithm is realized, college and optimal utilization of resources are ensured, and full utilization or excessive allocation of resource niches is prevented.

The invention provides a content marketing effect evaluation and optimization method and system based on deep learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining historical data of content marketing; constructing a double-tower deep neural network model, processing the content data through a content tower to generate a content representation set, processing the scene data through a scene tower to generate a scene representation set, and calculating a matching score of the content representation set and the scene representation set through an interaction layer; the double-tower deep neural network model is trained through a comparative learning framework of hierarchical scene perception and difficult sample mining, and iterative optimization is carried out in combination with a content-scene cross attention collaborative characterization mechanism; and based on the matching score, designing a resource allocation algorithm of nonlinear mapping and progressive two-stage optimization, and generating a resource allocation optimization scheme.

The invention discloses an AI digital human real-time rendering method based on GPU acceleration, and relates to the technical field of digital human rendering, and the method comprises the steps: S1, constructing a multi-GPU hardware feature and load monitoring module, and collecting hardware feature parameters and current load states of each GPU participating in collaborative rendering in real time; according to the method, by constructing the multi-GPU hardware feature and load monitoring module and combining a dynamic task allocation algorithm, dynamic allocation of the rendering sub-tasks to the optimal GPU is achieved, the problems of GPU performance bottleneck and resource idleness caused by traditional fixed task allocation are solved, the multi-GPU collaborative rendering efficiency is improved, and the multi-GPU collaborative rendering efficiency is improved. A scene complexity analysis module and a self-adaptive resource allocation algorithm are built, the proportion of GPU resources between an AI driving module and a rendering module is dynamically adjusted according to scene complexity, the AI digital human interaction naturalness is improved in a simple scene, the rendering frame rate and the picture quality are guaranteed in a complex scene, and the method is suitable for being used in a large-scale scene. And finally, dual optimization of rendering efficiency and picture quality in AI digital human real-time rendering is realized.

The invention discloses a priority-based semantic communication optimization method, device and system, and the method comprises the steps: obtaining key semantic information through analyzing to-be-transmitted data, carrying out the calculation according to the information, obtaining an information priority, and merging a coding data packet; a first network resource allocation strategy is obtained through calculation according to the network state index and the collaborative optimization algorithm based on a network resource allocation algorithm, the allocation strategy is optimized according to the network state index and the collaborative optimization algorithm, and a data packet is obtained and transmitted according to a first collaborative optimization result. And meanwhile, collecting and adjusting a first collaborative optimization result based on a distribution algorithm according to a transmission performance index, and transmitting data according to an adjusted strategy. The integrity and transmission reliability of important semantic information in to-be-transmitted data during data transmission are ensured through information priority confirmation, and the data volume of data transmission is reduced by combining coded data packets; the resource allocation algorithm and the collaborative optimization algorithm improve the practicability of the resource allocation strategy, so that the strategy can effectively cope with the change of the network environment.

The invention relates to the field of software deployment scheme optimization, and discloses a quick application system deployment method based on a two-stage resource allocation mechanism, which comprises the following steps of: performing complete set service modeling, performing modeling on deployment resource requirements of an application system in a layered manner, and performing resource allocation; the method comprises the following steps: modeling and describing deployment requirements of four service components, namely a basic platform service component, a platform component service component, an application service component and an application component, according to an application system architecture layer; deploying a basic management service, wherein the basic management service comprises a management node module and an agent node module; the system generates a to-be-deployed background service list according to an application requirement of a user and a strategy; the system automatically plans and deploys basic platform services according to a first-level resource allocation method; the basic platform service automatically plans and deploys platform component service and application service according to a secondary resource allocation algorithm; and the user downloads and uses the application system according to requirements. The automation of the deployment process is enhanced, the deployment difficulty and working time of deployment personnel are reduced, and certain universality is achieved.

The invention discloses an edge intelligent system resource management method based on a block chain. The method comprises the steps that a system controller senses artificial intelligence task information of current user equipment and current wireless environment information of a system; meanwhile, the system controller senses computing resource information of the user equipment, the edge server and the cloud server; related information is uploaded to a system controller from corresponding equipment through wireless connection; inputting the current artificial intelligence task information and the current wireless environment information of the system into a trained optimization model deployed in a system controller, and calculating user equipment transmitting power control, artificial intelligence task data volume control and user equipment artificial intelligence task unloading decisions based on a current state; calculating resource allocation conditions required by the reasoning process of the artificial intelligence task and the block generation process are transmitted to each calculation entity for execution; according to the method, the system efficiency and the user experience are improved by utilizing a task unloading and computing resource allocation algorithm.

The invention relates to a cloud edge multi-unmanned aerial vehicle cooperative resource optimization method assisted by a large language model, and belongs to the technical field of unmanned aerial vehicle communication, and the method comprises the following steps: S1, constructing an edge-cloud unmanned aerial vehicle cooperative reasoning system; s2, establishing a joint optimization model for discriminating gain maximization; s3, deploying a large language model at a cloud node, and generating a global strategy through a planner, a memory bank and an reflection evaluator; s4, deploying a deep reinforcement learning model at each edge node, and executing real-time optimization according to a global strategy and local observation data; s5, a collaborative feedback mechanism is established, the edge node feeds back an execution result to the cloud node, the cloud node updates a global strategy according to the feedback result, and the edge node adjusts real-time optimization parameters according to the updated global strategy; and S6, adopting an actor-commentator resource allocation algorithm for dynamic knowledge flow collaborative optimization, and realizing collaborative optimization through a distributed sensing and centralized decision framework.

The hotspot access control method, device and communication terminal of the satellite communication terminal provided by the present invention include: real-time monitoring of the channel state information of the hotspot signal transmitted by the satellite communication terminal to obtain a channel state data set; analyzing the channel state data set to obtain a channel quality index; obtaining the connection state parameters of each user device connected to the hotspot of the satellite communication terminal, and constructing a user connection state matrix; clustering the user connection state matrix to identify the corresponding user groups; wherein different user groups correspond to different load states; and according to the channel quality index and the load state of the user group, hotspot resources are allocated by a preset resource allocation algorithm to realize hotspot access control of the satellite communication terminal. In the present invention, hotspot resources are dynamically allocated according to the connection state parameters of the user device, thereby overcoming the defect of unreasonable resource allocation during hotspot access at present.

The invention discloses an unmanned aerial vehicle hunting resource allocation method based on confrontation generation imitation learning in a pursuit scene, and aims to determine a pursuit target of each pursuit unmanned aerial vehicle in a many-to-many pursuit scene so as to solve the problem of hunting resource allocation. The method comprises the following steps: firstly, establishing a system model for a many-to-many-chasing unmanned aerial vehicle escape scene and carrying out symbol declaration; and then introducing the concept of an Apollonius circle, and establishing a potential game model for unmanned aerial vehicle hunting resource allocation. And then, providing an optimal surrounding resource allocation scheme solving algorithm based on linear programming. And finally, providing an unmanned aerial vehicle hunting resource allocation algorithm based on confrontation generation imitation learning. The algorithm designed by the invention is not influenced by the number and the initial state of the unmanned aerial vehicles of both pursuit and escape parties, Nash equilibrium under a potential game can be searched by adopting a linear programming algorithm, and an optimal hunting resource allocation scheme is generated based on adversarial generation imitation learning and adaptive learning; and finally, real-time dynamic allocation and scheduling of surrounding resources in a many-to-many unmanned aerial vehicle pursuit scene are realized.

The invention provides a resource allocation method in task unloading, relates to the technical field of network and communication, and solves the technical problem of relatively high time consumption of joint optimization of channel resources, computing resources and power consumption control in a task unloading process. According to the technical scheme, the method comprises the following steps of S1, task unloading problem modeling; s2, realizing an unloading decision; and S3, designing and realizing a resource allocation algorithm. According to the allocation method, the search behavior can be dynamically adjusted according to the real-time performance of the particles, and exploration and development of resource allocation problem solving are dynamically balanced; according to the scheme, the time overhead performance of program calculation is reduced, and a high target utility function value can be maintained.

A multi-dimensional data processing system is disclosed for the automated ingestion, normalization, and computational mapping of project data across spatial, temporal, and regulatory dimensions. The system operates by ingesting diverse datasets—including two-dimensional schematics, three-dimensional data models, cost structures, and workforce logistics—and normalizing them within a unified schema. A dimensional calculations model enables the system to computationally align normalized data with spatial zones, project phases, and operational constraints, structuring it for further computational processing. By integrating this mapped data within a coordinated digital environment, the system applies task sequencing and resource allocation algorithms, generating optimized project plans that support complex, multi-dimensional data interoperability. The structured data is further configured for display through an interactive visualization interface, allowing computational resources to facilitate real-time analysis and adaptive data-driven project management.

The invention discloses an information and energy cooperative transmission method, device and system, and belongs to the technical field of wireless communication, the information and energy cooperative transmission method dynamically adjusts the priority of information and energy transmission through a resource allocation algorithm combining deep learning with a priority queue, ensures that high-priority transactions are processed in time, and improves the efficiency of information and energy transmission. And the resource utilization and response speed of the system is optimized. The beam direction of the antenna array is dynamically adjusted, intelligent beam forming is carried out, and accurate coverage of multiple devices is achieved. According to the method, information data is sent in a Ti time period to realize communication transmission, radio frequency signals are sent in a Te time period to supply power, and the method provides supply electric energy for wireless terminal equipment while realizing communication transmission, so that the self-adaptive capability of the system is enhanced, and the transmission efficiency of information and energy is improved. On the whole, the energy efficiency of the system can be improved, resource allocation is optimized, and the information energy cooperative transmission requirement of the mobile intelligent terminal in a complex environment is met.

The invention belongs to the technical field of emergency processing, and discloses a reservoir dam system natural disaster damage emergency disposal decision-making method and system, and the method comprises the steps: carrying out the classification of preprocessed multi-source data through a clustering algorithm, and constructing a disaster information database based on the classification result; constructing a risk assessment model to assess the damage risk of natural disasters to the dam system, generating a plurality of emergency disposal strategies in combination with a multi-objective optimization algorithm, and screening out an optimal emergency disposal strategy by using a scenario simulation technology; a dynamic resource allocation algorithm is utilized to allocate resources, and a digital twinborn technology is utilized to simulate the dam system in real time to predict damage risks; and executing corresponding emergency disposal measures and recording emergency disposal effect data, evaluating the emergency disposal effect data by using a data envelope analysis algorithm, and optimizing the risk evaluation model based on an evaluation result. According to the invention, the data integration and processing capability is enhanced, and the accuracy and stability of the prediction result are improved.

The invention discloses an intelligent gateway control method based on edge computing, and the method comprises the steps: S1, collecting the operation state data of network nodes through a distributed sensor, carrying out the filtering, denoising and normalization processing, sorting according to a timestamp, and generating the preprocessing data containing network flow, node load and response time delay; s2, calculating the resource demand quantity of each node based on the preprocessed data, executing a dynamic resource allocation algorithm in combination with the available resource state of the node, and generating a resource allocation result containing a calculation resource quota and a task allocation relationship; s3, inputting a resource allocation result into a gateway control model, adjusting model parameters based on a real-time network state, and generating a control strategy; and S4, encoding the control strategy into an instruction sequence, encrypting the instruction sequence through a digital signature, transmitting the encrypted instruction sequence to an execution unit through a secure channel, and executing a control instruction after verifying integrity and signature validity. Therefore, dynamic resource allocation, real-time network state adjustment and security instruction transmission are realized.

The invention relates to a joint task unloading and resource optimization design method in a communication-sensing-calculation integrated internet of vehicles, and belongs to the technical field of wireless communication. In the scheme, a task vehicle carries a communication-sensing integrated system, and needs to cooperatively complete a large task and independently complete respective sensing tasks; large-scale tasks can be split into a plurality of sequence dependent medium-scale tasks, and task vehicles can select local processing or unloading to service nodes. According to the method, an efficient task unloading and resource allocation algorithm is jointly designed, and an unloading strategy X, a power allocation strategy P and a bandwidth allocation strategy B are optimized, so that the maximum sub-task splitting number constraint, the maximum bandwidth constraint, the maximum transmitting power constraint, the vehicle communication range constraint and the vehicle radar sensing threshold constraint are satisfied; and the time delay of a large task is minimized. Simulation results show that the sensing performance is considered, the processing time delay of the large chain task in the communication, sensing and calculation integrated Internet of Vehicles is effectively reduced, and the method has very high application value and practical significance.

The invention discloses a resource allocation algorithm for edge-end collaborative video transcoding based on link quality, belongs to the technical field of mobile communication technologies and computer networks, and is used for solving the problems of how to maximize the utilization rate of system resources, reduce video acquisition time delay and improve network throughput in an edge scene of the Internet of Vehicles. The method comprises the following steps: firstly, an RSU end evaluates the link quality with a vehicle in real time by collecting vehicle request information and network conditions; secondly, determining a transcoding strategy according to the link quality; when the link quality is general, the RSU directly transmits a low-quality video stream to the vehicle, and the vehicle transcodes the low-quality video stream into a high-quality video by using local computing power. When the link quality is good, the video stream is divided into two parts, one part is transmitted after being transcoded by the RSU, and the other part is directly transmitted to the vehicle and is transcoded locally by the vehicle. And thirdly, the RSU reasonably allocates system resources according to the cooperative transcoding decision, and finally, through cooperative transcoding of the RSU and the vehicle, the vehicle obtains a video meeting the request quality. The algorithm can improve the network resource utilization rate and reduce the video acquisition time delay, and meanwhile, the throughput is remarkably improved when the link quality is good.

The invention relates to the technical field of fund management, in particular to an abnormal transaction management system based on multi-source data collaborative analysis. Comprising an information acquisition module, a fund flow analysis tracking module and an information optimization module. According to the method, the risk level of a target account is adjusted, abnormal transaction behaviors are limited, the target account is associated with a historical account, the path weight of a new transaction path is calculated, and the distribution condition of the path weights and the risk levels of the historical account and the target account can be visually seen by constructing a risk matrix, so that a data basis is provided for risk strategy adjustment. In addition, monitoring key points on the transaction path are divided based on the risk level of the transaction path, and efficient resource allocation and accurate risk prevention and control are ensured by formulating differentiated monitoring key points, so that the condition of imbalance of a resource allocation algorithm is reduced, and the resource allocation efficiency is improved. The resources are preferentially allocated to the fund flow with a great relationship, the key point is accurately investigated, and the tracking difficulty is reduced.

The invention provides an intelligent resource dynamic sharing and task scheduling method based on a cloud desktop system. According to the method, a resource monitoring module is deployed on a local cloud desktop to monitor the utilization rate of key resources in real time, resources are requested from a cloud desktop system when the resources are insufficient, a cloud desktop resource management module selects a proper cloud desktop to provide the resources, and tasks are reasonably allocated by using a resource allocation algorithm. Meanwhile, efficient operation of the compute-intensive application in the cloud desktop system is realized through task scheduling and monitoring, data synchronization and a feedback adjustment mechanism. According to the method, the resource utilization rate is improved, the task execution time is shortened, the system stability is enhanced, the cost is reduced, and the problems that an existing cloud desktop system is unreasonable in resource allocation and low in task execution efficiency are effectively solved.