76 results about "Resource reallocation" patented technology

The invention provides a computing power resource scheduling method and system based on cloud network integration. Selecting a computing power task to be scheduled as a current scheduling task, and generating a node resource adaptation matrix based on the real-time load data, the cloud network topological relation between the nodes and the dynamic bandwidth data; then calculating the lowest scheduling cost of each node according to the matrix, and determining a dynamic adjustment factor when a resource allocation conflict occurs in the current scheduling task; based on the lowest scheduling cost, the computing power demand scale and the data transmission estimated overhead, calculating the final scheduling overhead for scheduling the current scheduling task to each node; and finally, allocating tasks to a target node according to the final scheduling overhead, updating a running task queue, if a conflict occurs, calling a dynamic adjustment factor to execute resource reallocation, and updating the queue after the reallocation succeeds. And circulating the process until all tasks are scheduled. According to the scheme, optimal scheduling of computing power resources in a cloud network convergence environment can be realized.

The invention discloses an LCD display screen production whole-process collaborative management method and system based on the Internet of Things, particularly relates to the technical field of production process collaborative management, and is used for solving the problems of circular waiting and resource stiffness caused by lack of global resource allocation dependency relationship analysis in a complex production scene in an existing collaborative management method. Equipment state data and material position data of each process are acquired in real time, a resource allocation relation graph with production equipment as resource nodes and production tasks as task nodes is constructed, a resource access sequence of each production task is analyzed to identify a circular waiting path, and a process association network and a production flow network are combined to evaluate a blockage propagation risk. And finally, on the basis of the relief urgency degree, the production tasks are selected to carry out resource reallocation so as to realize the relief of the circular waiting path, and the overall efficiency of the production line and the resource utilization rate are improved.

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 relates to the technical field of project management, and particularly discloses a project management method and system based on an AI large model technology, and the method comprises the steps: collecting multi-dimensional project execution data, carrying out the data cleaning and feature coupling analysis of the multi-dimensional project execution data, and generating a normalized project feature tensor; inputting the task delay risk into a pre-trained time sequence prediction large model, and outputting a task delay risk probability distribution matrix; carrying out risk level label mapping based on the task delay risk probability distribution matrix, and generating a multi-level risk early warning signal; and in response to the risk event exceeding the preset threshold in the multi-level risk early warning signal, a resource redistribution scheme is generated, and a project manager not only can monitor the project progress in real time, but also can quickly adjust resource configuration when the risk occurs, thereby realizing dynamic management. The intelligent project management mode based on data driving not only improves transparency and controllability of the project, but also provides powerful technical support for successful implementation of the project.

The invention discloses a block chain edge computing resource collaborative scheduling system of Internet of Things equipment, and relates to the technical field of Internet of Things, comprising an equipment edge networking module used for deploying edge nodes in an Internet of Things equipment dense area and accessing the edge nodes to a block chain network; the resource state initialization module is used for sending task information to the edge node layer after the Internet of Things equipment generates a task; the intelligent task distribution module is used for calculating an optimal task distribution scheme after the edge node receives the task, and distributing the task to the edge node for processing according to the optimal task distribution scheme; and the dynamic resource adjustment module is used for monitoring the resource load condition of the edge node in real time, and reallocating resources according to a dynamic resource self-adaptive adjustment strategy when the resource load is unbalanced, so that the stable operation of the system is realized. And efficient collaborative scheduling and safe and credible management of edge computing resources among the Internet of Things equipment are realized.

The invention relates to the technical field of port scheduling, in particular to a port loading and unloading equipment dynamic regulation and control method and system based on a working road, and the method comprises the following steps: obtaining the real-time state data of port loading and unloading equipment in the working road, and obtaining the cooperative state index of the working road; deducing a task operation chain of the operation path in combination with an operation environment factor to obtain a dynamic time flow; obtaining an efficiency prediction index of the job path, and generating a resource allocation regulation and control strategy by constructing a global resource reallocation model; converting the resource allocation regulation and control strategy into an equipment regulation and control instruction set for optimization to obtain a final regulation and control instruction sequence; and executing the final regulation and control instruction sequence, obtaining a regulation and control effect evaluation result, correcting the global resource redistribution model, and realizing dynamic regulation and control of the port handling equipment. The active regulation and control of the port loading and unloading equipment are realized, and the operation efficiency, the adaptability and the intelligent level of the equipment are effectively improved.

The invention discloses a resource allocation method, a computer program product and electronic equipment. The method comprises the following steps: receiving an application data operation request, wherein the application data operation request carries a namespace identifier matched with a first application for triggering operation; determining a node list of a node database occupied by the application data of the first application in the first storage database cluster based on the namespace identifier; obtaining a node demand resource load of a node database in the node list in a second processing period; and determining a first node database from the second storage database cluster and migrating the application data of the first application to the first node database under the condition that the first application is determined to meet the resource redistribution condition based on the node demand resource load. The technical problem of low resource utilization rate caused by neglecting the resource use demand change condition of the application is solved.

The invention discloses a digital twin-driven intelligent construction site resource dynamic scheduling system and method, and belongs to the technical field of building construction management, and the system comprises a four-dimensional resource topology construction module which constructs a four-dimensional topological graph of construction resources based on UWB and RFID technologies; the mixed engine scheduling module adopts a mixed integer programming engine to generate a reference scheduling scheme in an initial stage, and adopts a reinforcement learning optimization engine to perform dynamic adjustment in an execution stage; the conflict prediction network module is used for establishing a conflict prediction model based on historical data and triggering a resource redistribution mechanism; the virtual interaction visualization module displays a scheduling scheme in a virtual reality environment and supports manual intervention, depth relation modeling, double-engine collaborative optimization and active conflict prevention of construction resources are achieved, the resource utilization rate is increased by 35% or above, the construction period is shortened by 15% or above, and conflict events are reduced by 50% or above.

The present application provides a method and system for intelligent scheduling of heterogeneous resources of a large-model training and pushing all-in-one machine, which initializes the maintenance task list of each heterogeneous resource node in the training and pushing all-in-one machine, and generates a cost matrix for resource scheduling of maintenance tasks based on all maintenance task lists; the minimum cost characteristics of each heterogeneous resource node are screened out from the scheduling cost of the resource scheduling cost matrix, and then the slack variables of each heterogeneous resource node are determined when the target task has an allocation conflict; the scheduling load cost of the target task for each heterogeneous resource node is determined based on each minimum cost characteristic and the load characteristic of the target task; the target task is heterogeneously allocated resources using each scheduling load cost, and each slack variable is used to reallocate resources to the target task each time a task allocation conflict occurs in the heterogeneous resource allocation. Based on the above scheme, resource reallocation when a task allocation conflict occurs in resource scheduling can be achieved.

The invention belongs to the technical field of data processing, and particularly discloses a human resource allocation method and device for elevator maintenance, electronic equipment, a medium and a program product. Constructing and updating a task-personnel dependency graph comprising task nodes, personnel nodes, time sequence edges, cooperation edges and the like; spreading delay from an event source node to determine an affected task set, evaluating task impact weights based on parameters such as urgency and default cost, and extracting high-weight tasks; and taking an event source as a center, extracting a local optimization domain and freezing out-of-domain distribution in combination with a multi-dimensional threshold value, inputting intra-domain information into a large model to generate a candidate strategy, and finally screening an optimal scheme based on a constraint condition and a target function. According to the invention, efficient human resource reallocation under local minimum disturbance is realized, delay cost and default risk are reduced, and the utilization rate of human resources is improved.

The invention discloses a dynamic optimization method for research and development project resource allocation, which comprises the following steps of: acquiring task state data and resource state data in a project execution process, constructing multi-source data and transmitting the multi-source data to a data processing center in real time; performing real-time monitoring on the multi-source data to obtain key indexes and resource pool states of research and development projects; based on a preset triggering rule, judging whether a triggering condition of resource redistribution optimization is met or not, and when the triggering condition is met, constructing a multi-target dynamic resource optimization model; solving the optimization model, generating a resource allocation scheme, and performing multi-dimensional evaluation to determine a final scheme; and executing the final optimization scheme, updating the project resource allocation state, and continuously collecting execution effect data for optimization adjustment, the research and development project resource real-time monitoring and dynamic optimization are realized, and the resource utilization efficiency and the execution quality of the research and development project are improved.

The embodiment of the invention provides a memory resource allocation method, a network cloud platform, computing equipment, a computer readable storage medium and a computer program product. The memory resource allocation method comprises the following steps: acquiring a kernel loading address of a client operating system; determining a kernel version corresponding to the memory resource of the virtual machine based on the kernel loading address; querying target scanning ranges and target scanning parameters of a plurality of physical pages from a target database based on the kernel version; based on the target scanning range and the target scanning parameter, scanning the memory resource of the virtual machine, and determining a target physical page in an idle state in the plurality of physical pages; and based on the target physical page, memory resource reallocation is executed. According to the method and the device, intrusion to the interior of the user virtual machine is avoided, memory resource redistribution is completed under the condition that the user virtual machine is non-inductive, isolation and safety of the user virtual machine are guaranteed, and influence on the performance of the user virtual machine is avoided.

The application relates to a production scheduling and worker allocation joint optimization method for an aircraft pulsating assembly line, which comprises the following steps: acquiring assembly task data and resource data of the aircraft pulsating assembly line, wherein the resource is a worker; constructing a dictionary order double-target optimization integer programming model, taking minimization of a pulsating period and total resource consumption as a target function; solving the dictionary order double-target optimization integer programming model, realizing joint optimization of worker allocation and multi-mode assembly task in the aircraft pulsating assembly line under a resource exclusive policy; estimating a resource-work period mapping of each workstation, and determining an initial resource allocation according to the estimated mapping; searching a scheduling scheme by using genetic operation; improving the mapping by using a mapping updating strategy; after a predetermined evolution period, performing resource reallocation to adjust and optimize the resource allocation. Compared with the prior art, the application has the advantages of high production efficiency of scheduling results, saved calculation overhead and the like.

The application relates to the technical field of network communication, and discloses a network self-healing device based on node state data, which aims to improve the stability and self-healing ability of network communication. By monitoring the network state in real time, combining the extended Kalman filtering algorithm to perform state estimation and filtering, and combining the ant colony algorithm and the simulated annealing algorithm to perform fault prediction, path selection and resource allocation, the potential fault can be predicted and quickly recovered. The device comprises five modules of state monitoring, state estimation and filtering, fault prediction and prevention, fault recovery and resource reallocation, forms a closed-loop optimization mechanism, and is especially suitable for complex large network environments, such as data center networks.

The invention discloses a multi-disciplinary simulation task intelligent planning method based on a knowledge graph, and relates to the technical field of intelligent scheduling, and the method comprises the steps: constructing a preliminary knowledge graph comprising multi-disciplinary task description, resource limitation and execution dependence, analyzing historical task data through a pre-trained deep learning model, and obtaining a multi-disciplinary simulation task planning model; dynamically adjusting the preliminary knowledge graph through adaptive learning, and outputting an adaptive knowledge graph; carrying out resource redistribution optimization according to the updated task scheduling strategy, continuing to execute the task, starting monitoring, and collecting task progress and resource data as a new feedback data stream; the resource reallocation and the new feedback data stream are fed back to the adaptive knowledge graph, and multidisciplinary task description, resource limitation and execution dependency are updated through reasoning to generate a final optimization task execution sequence; the synchronous suppression of waiting time and resource contention is realized, the average utilization rate of resources and the time sequence stability are improved, and the adaptive adjustment capability of operation disturbance is formed.

The application provides a virtualized computing resource scheduling method and system based on a power wireless local area network. Firstly, power equipment operation load data of an access device in the power wireless local area network coverage is acquired, including real-time current fluctuation and other characteristics. Then, load characteristic extraction is performed on the power equipment operation load data to obtain a load characteristic set containing device power consumption fluctuation and other characteristics. Then, dynamic resource demand prediction is performed on the set based on a preset load prediction model to generate a virtualized resource demand prediction result containing a computing resource allocation level. A virtualized resource scheduling strategy containing an edge computing node resource allocation topology is generated according to the virtualized resource demand prediction result. Finally, virtualized computing resources are dynamically adjusted based on the virtualized resource scheduling strategy, resource reallocation is triggered, and a global resource state mapping table is updated to achieve effective scheduling of virtualized computing resources of the power wireless local area network.

The application relates to the computer technical field and provides a resource recommendation method, which comprises the following steps: selecting a first quantity of to-be-recommended resources; distributing the to-be-recommended resources to corresponding recommendation positions for recommendation and starting to calculate a first recommendation duration; determining to-be-recommended resources that have completed recommendation as target resources; determining a unit-time exposure income value corresponding to each target resource; sequencing each target resource based on the income value to obtain a first sequence; and in the case that the first recommendation duration reaches a first duration threshold, re-distributing the recommendation positions of each target resource based on the first sequence, starting to calculate the first recommendation duration again, and repeatedly executing the step of determining the target resources, so that the problem that the promotion effect is poor when information is put in based on user types can be solved; since the recommendation positions are distributed to the target resources based on the unit-time exposure income, the promotion effect can be improved. In addition, an electronic device and a storage medium are also provided.

The invention relates to the technical field of industrial internet-of-things automatic control, and discloses an actuator internet-of-things multi-protocol adaptation system and method.The method comprises the steps that a protocol module constructs a state machine model for each protocol adapter, cross-protocol state collaboration and abnormal event release are achieved through tense logic rule verification, and a resource module receives abnormal events and sends the abnormal events to a server; the method comprises the following steps: dynamically updating a global resource constraint graph, analyzing an optimal resource reallocation scheme and an atomic task sequence, deconstructing the task sequence into a pi-calculation concurrent process network by applying a reconstruction module, recombining the process network and mapping the recombined process network into a cross-protocol instruction set to drive an executor, and finally, executing the Pi-calculation concurrent process network. Full-link adaptation from protocol layer state consistency guarantee and resource layer dynamic optimization scheduling to application layer process flexible reconstruction is realized, and the reliability and response efficiency of a multi-protocol actuator system in a complex industrial environment are improved.

The embodiment of the invention provides a memory resource allocation method, a resource allocation platform, computing equipment and a computer readable storage medium, and the memory resource allocation method comprises the steps that virtual machine kernel information is obtained from virtual machine memory resources through analysis, the virtual machine memory resources comprise at least one pre-allocated physical page, and the physical page comprises at least one physical page; the virtual machine kernel information comprises a scanning range and scanning parameters of at least one physical page; based on the scanning range and the scanning parameters, scanning the memory resources of the virtual machine, and determining a target physical page in an idle state in the at least one physical page; and based on the target physical page, memory resource reallocation is executed. According to the method and the device, intrusion to the interior of the user virtual machine is avoided, memory resource redistribution is completed under the condition that the user virtual machine is non-inductive, isolation and safety of the user virtual machine are guaranteed, and influence on the performance of the user virtual machine is avoided.

Embodiments of the present application provide a method of distributed resource management, apparatus, and system, a device, and a non-transitory readable storage medium, which relate to the field of computer technologies. The method includes: in response to receiving a power-on instruction, controlling a switch, a target device, and a target computing unit to power on synchronously; in response to receiving a reset instruction, performing a reset operation on a to-be-reset device indicated by the reset instruction, wherein the to-be-reset device includes at least one of the target devices, the target computing units, and the switch; and performing resource scheduling on target resources in the plurality of resource pools based on a resource scheduling request, wherein the resource scheduling includes resource reset and resource allocation. In this way, the distributed resource management system may achieve resetting of the entire system or device, supports both resource reset and resource reallocation during the resource scheduling process, provides a more efficient and flexible resource management architecture, achieves lifecycle management of pooled resources, and improves the practicability and flexibility of resource management.

The invention relates to the technical field of big data processing, and discloses an intelligent data processing method and system based on distributed transactions, and the method comprises the steps: obtaining a dynamic change index, carrying out the clustering and frequency analysis according to the dynamic change index, determining a transaction coordinator candidate set, and carrying out the clustering and frequency analysis according to the transaction coordinator candidate set. Performing responsivity analysis and load correction to obtain a consistency risk level, performing weight correction and resource redistribution according to the consistency risk level to obtain a load balance distribution vector, performing high-frequency interaction node positioning according to the load balance distribution vector to obtain a mapping priority sequence, and performing high-frequency interaction node positioning according to the mapping priority sequence to obtain a high-frequency interaction node. And carrying out transaction coordinator optimization to obtain a final coordinator, and carrying out efficiency monitoring and dynamic index updating according to the final coordinator to obtain a data distribution rule. The method can dynamically adapt to data distribution changes under high concurrency.

This invention belongs to the field of artificial intelligence technology and discloses a resource allocation method, apparatus, device, and storage medium. The allocation method includes: invoking a target business rule set and a target machine learning model that match the description information; generating a first initial allocation scheme based on rules and a second initial allocation scheme based on the model for the items to be allocated based on the target business rule set and the target machine learning model; generating a root cause diagnosis report when the difference between the first initial allocation scheme and the second initial allocation scheme exceeds a preset threshold; constructing a rule parameter optimization space based on the root cause diagnosis report; and searching for target rule parameters within the rule parameter optimization space that give the preset objective function an optimal solution, generating a final allocation scheme for the items to be allocated corresponding to the rule parameter optimization suggestions. This invention can be applied to business management systems in fintech, healthcare, and other fields, solving the technical problem of inaccurate resource reallocation.

The application discloses a kind of blockchain edge computing resource collaborative scheduling systems of Internet of Things equipment, it is related to Internet of Things technical field, including equipment edge networking module, for deploying edge node in the dense area of Internet of Things equipment, and edge node is accessed blockchain network;Resource state initialization module, for after Internet of Things equipment generates task, task information is sent to edge node layer;Intelligent task allocation module, for after edge node receives task, root calculates optimal task allocation scheme, and according to optimal task allocation scheme, task is allocated to edge node and is handled;Dynamic resource adjustment module, for real-time monitoring the resource load condition of edge node, when resource load is not balanced, according to dynamic resource self-adapting adjustment strategy, resource is re-allocated, and system stable operation is realized. Realize the efficient collaborative scheduling and safe and reliable management of edge computing resource between Internet of Things equipment.

The present application belongs to the technical field of data processing, and specifically discloses a human resource allocation method and device for elevator maintenance, an electronic device, a medium and a program product. The method comprises the following steps: detecting a temporary redeployment event of personnel and recording key information, constructing and updating a task-personnel dependency graph containing task nodes, personnel nodes, time sequence edges, cooperation edges and the like; propagating delay from an event source node to determine an affected task set, evaluating a task impact weight based on urgency, default cost and other parameters, and extracting high-weight tasks; taking the event source as the center, combining multi-dimensional threshold to extract a local optimization domain and freeze the allocation outside the domain, inputting the information in the domain into a large model to generate a candidate strategy, and finally screening an optimal scheme based on constraint conditions and objective functions. The present application realizes efficient human resource reallocation under local minimum disturbance, reduces delay cost and default risk, and improves the utilization rate of human resources.

The application discloses a logistics cost management method and system based on shared storage resources, comprising the following steps: step one: collecting original actual operation data of each warehouse in the shared storage platform, and performing time alignment and correction processing; step two: establishing a shared storage resource network diagram; step three: obtaining multiple complete cost occurrence paths, calculating various logistics costs, and obtaining a logistics cost set; step four: inputting the shared storage resource network diagram and the logistics cost set into an improved Graphormer model, obtaining a storage cost time sequence attention result through a resource occupation coding module, a cost connection enhancement module and a storage time sequence attention module; and step five: extracting a high-cost conduction risk path according to the storage cost time sequence attention result, calculating a shared warehouse comprehensive cost, and obtaining a shared storage resource reallocation scheme. The improved Graphormer model is used to realize intelligent reallocation of shared storage.

This invention discloses a cockpit-driving fusion redundancy system and method based on dynamic resource scheduling. The system includes an intelligent driving domain controller, an intelligent cockpit domain controller, environmental perception sensors, an intelligent sensor hub, a shared memory pool, and a dynamic resource scheduling kernel. The intelligent sensor hub sends sensor data to the intelligent driving domain controller and the intelligent cockpit domain controller via a first data channel (raw data) and a second data channel (feature data), respectively, achieving perception redundancy. The intelligent driving domain controller writes the intermediate perception feature data generated during processing into the shared memory pool. The dynamic resource scheduling kernel virtualizes the computing resources of the two domain controllers into a unified resource pool and triggers resource reallocation when an anomaly is detected in the intelligent driving domain controller, dynamically scheduling computing power to the intelligent cockpit domain controller. This invention solves the problems of single perception dimension, high takeover latency, and low resource utilization in existing redundancy schemes, achieving seamless and secure takeover with high reliability, low latency, and high resource utilization.

The application discloses an LCD display screen production whole-process collaborative management method and system based on an Internet of Things, and particularly relates to the technical field of production process collaborative management, and is used for solving the problems of circular waiting and resource deadlock caused by the lack of global resource allocation dependency relationship analysis in the complex production scene of the existing collaborative management method; by acquiring the equipment state data and material position data of each process in real time, a resource allocation relationship graph taking production equipment as a resource node and production task as a task node is constructed, the resource access sequence of each production task is analyzed to identify a circular waiting path, the blocking propagation risk is evaluated in combination with a process correlation network and a production flow network, and the urgency is quantitatively released according to the task priority, and finally, the production task is selected based on the release urgency to realize the resource re-allocation of the circular waiting path, so that the overall efficiency and resource utilization of the production line are improved.

The present application relates to the technical field of marine information processing, and specifically relates to a heterogeneous computing resource dynamic allocation method for a marine supercomputing environment, comprising the following steps: S1: real-time monitoring of dynamic demand characteristics of marine data processing tasks, including task computing amount and task real-time level; S2: dynamically calibrating task priority; S3: screening out target resources that simultaneously meet the task computing amount requirement and the response speed and real-time level match; S4: allocating the task to the target resources; S5: if the load rate of the target resources exceeds the preset threshold, triggering resource reallocation; S6: outputting the final resource allocation scheme, and recording the completion time of the corresponding task and the utilization rate of each computing resource. Through the task priority driven dynamic scheduling and real-time load perception reallocation mechanism, the present application realizes the time-effect guarantee of key tasks and the efficient use of heterogeneous resources in the marine supercomputing environment.

The invention discloses an intelligent operation and maintenance method and system based on big data analysis, and the method comprises the steps: obtaining a historical operation and maintenance log which comprises the historical operation and maintenance log of equipment; determining P devices influencing operation and maintenance duration change according to the historical operation and maintenance log, and predicting device operation and maintenance duration prediction values of the P devices in a second preset time period according to device operation and maintenance duration sequence data of the P devices in the first preset time period in the historical operation and maintenance log; determining abnormal fluctuation characteristics according to the predicted equipment operation and maintenance duration predicted values of the P pieces of equipment in the second preset time period and real equipment operation and maintenance duration data of the P pieces of equipment in the second preset time period; and according to the abnormal fluctuation characteristics, carrying out resource redistribution on the inspection period and the spare part inventory of the P devices, and determining an operation and maintenance resource configuration scheme. By adopting the technical scheme, the problem that resource allocation is rigid due to an existing equipment operation and maintenance method is solved.

The invention relates to the technical field of wireless communication network resource management, in particular to an interactive resource allocation method for a virtual anatomical model in a wireless network, which comprises the following steps of: 1, acquiring virtual organization deformation data caused by user operation in real time, and dynamically dividing operation precision grades based on the deformation degree; 2, generating a dynamic space influence range according to the operation precision grade, detecting a space overlapping region of multi-user operation, and calculating a conflict risk value; 3, dynamically calculating the resource allocation quantity by combining the operation precision grade, the organization deformation data and the conflict risk value; and 4, triggering resource redistribution when tissue deformation suddenly changes or an operation tool is replaced. Through accurate operation precision division and dynamic adjustment of resource allocation, resource waste can be effectively avoided in a multi-user interaction environment, meanwhile, real-time response of key operation is ensured, and the misoperation rate caused by delay or resource insufficiency is greatly reduced.