756 results about "Service resource" patented technology

The invention relates to the technical field of information management, in particular to an information scheduling scenarized service agent system. The system comprises an information scheduling acquisition and analysis module, a scene classification and service matching module, a service scheme instruction generation module and a service intelligent execution module, user behavior data, environment perception data and business system data corresponding to information scheduling can be obtained, and an original information set is generated; the original information set is preprocessed, an information scheduling scene classifier is constructed, the service matching degree is calculated in combination with the real-time service resource state, and a candidate service scheme set is generated; performing optimization sorting on the candidate service scheme set to generate an optimal service scheme, and disassembling the optimal service scheme into an executable service instruction sequence; receiving a service instruction sequence and generating a service evaluation report; and if the display does not reach the preset service quality threshold value, triggering a dynamic adjustment mechanism to realize self-iteration upgrading of the information scheduling scenarized service. According to the invention, intelligent scheduling and service of information in a complex scene can be realized.

The invention discloses a fault root cause positioning method and system for a server cluster, and relates to the technical field of network fault diagnosis. According to the method, nanosecond-level synchronous acquisition of micro-service call chains, container indexes, physical nodes and network data is realized through a precise time protocol, and a consistent data set is constructed through entity association and standardized processing; a service-resource topological graph is dynamically constructed, and an inter-service calling edge weight model is innovatively designed: a real-time load factor and a historical fault index attenuation sum processed by a Sigmoid function are fused, and the weight is periodically updated to accurately quantify the inter-node influence intensity; converting the topological graph into a Bayesian network; when a fault occurs, a three-level assembly line compression alarm is adopted, frequent item sets are mined through bitmap indexes and parallel FP-Growth, and strong causal association item sets are screened in combination with topological edge weights and KL divergence; strong causal alarm is taken as evidence, probabilistic root cause sorting is output through reverse random walk sampling, and high-precision positioning of complex distributed system faults is achieved.

The invention relates to the technical field of semantic analysis, can be applied to business scenes in the fields of medical health, financial science and technology, intelligent interactive service and the like, and discloses a business service processing method based on dialogue recognition, which comprises the following steps: performing semantic analysis on current dialogue content, determining a key problem type, and obtaining a business processing record of a dialogue subject; under the condition that the service processing problem type is identified, obtaining a target operation record in combination with problem occurrence time and a current time interval, constructing a user operation behavior track based on an interface interaction event in the target operation record, and identifying a deviation node in the track; and generating a business processing exception report, matching the target service item in the service resource library according to the service demand keyword and the business processing record, and outputting operation guidance information. By fusing dialogue semantic analysis and user behavior trajectory modeling, automatic identification and accurate service recommendation of customer problems are realized, and the business processing efficiency and the service intelligence level are improved.

The invention discloses a digital intelligent service system for enterprise services, which belongs to the field of enterprise digital services and comprises a user demand analysis module, an intelligent data adaptation module, a service resource matching module, a service efficiency evaluation module and a user-friendly interface. The user demand analysis module is used for semantic analysis and classification decision making; the intelligent data adaptation module identifies and analyzes the protocol type, and establishes an intelligent data transmission channel; the service resource matching module carries out resource pre-screening and accurate matching; the service efficiency evaluation module tracks an identifier and captures a four-dimensional index system in real time, and the dynamic evaluation unit realizes service state grading early warning through a sliding window algorithm; according to the invention, accurate demand identification is realized, the response speed and the data transmission efficiency are improved, the user satisfaction is improved, the service matching accuracy is enhanced, the resource utilization efficiency is optimized, the service capability and the customer experience are improved, and maximization of economic benefits and social values is realized.

The embodiment of the invention provides a local life service integration method and device and terminal equipment. The method comprises the following steps: integrating a plurality of heterogeneous local life service providers into a gas station service platform to obtain a local life service platform, and collecting real-time service data in real time; non-oil sales fluctuation of the gas station is predicted based on the real-time service data, and the order quantity and the delivery cycle of upstream and downstream suppliers are dynamically adjusted according to a prediction result; constructing a multi-dimensional data analysis model, predicting user traffic and various service demands in different time periods every day, and configuring a corresponding local life service time window based on a prediction result; dynamically updating callable local life service resources and dynamically deploying gas station human resources based on the order quantity, the delivery cycle and the local life service resource configuration; and generating a personalized service list of the user by using a collaborative filtering algorithm and a reinforcement learning algorithm, interacting with the user in an interaction interface, and providing corresponding local life service resources for the user.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The invention discloses a self-adaptive micro-service resource management method and device, and the method is applied to a resource-limited micro-service system, and comprises the steps: receiving a task, carrying out the modeling of the time sequence data and spatial dependency of a micro-service based on a space-time diagram neural network, and predicting the resource demands of a future time step, including a CPU, a memory and a network bandwidth; classifying the micro-services into resource redundancy, resource insufficiency and resource self-sufficiency according to the predicted resource demand, the current resource allocation and the resource guarantee threshold; according to the resource state and the business importance, the dynamic priority of the micro-services is calculated, the micro-services with redundant resources are sorted in an ascending order, and the micro-services with insufficient resources are sorted in a descending order; and recovering redundant resources from the low-priority micro-services, and allocating the redundant resources to the high-priority micro-services until available resources are exhausted or resource requirements are met, so that the resource utilization rate is optimized, and a service level protocol is guaranteed. According to the method, when resources are limited, the adaptability of the micro-service in different load environments can be improved.

The invention provides an intelligent business resource scheduling method and system based on big data, and relates to the technical field of data processing, and the method comprises the steps: 1, capturing equipment layer sensor data, business layer order data and logistics layer vehicle track data in real time through distributed data collection nodes, and carrying out the real-time acquisition of the data; a dynamic time window division algorithm is adopted to carry out time sequence alignment processing on the heterogeneous data flow, a ternary state vector matrix with a unified timestamp is generated, the row dimension of the matrix represents a collection time sequence, and the column dimension of the matrix corresponds to equipment layer operation parameters, a service layer task queue state and logistics layer transportation node coordinates respectively. According to the invention, through multi-source data fusion, three-dimensional space analysis and an intelligent algorithm, resource gap accurate identification and cross-level scheduling optimization are realized, and the business processing efficiency and the resource utilization rate are improved.

The invention discloses a service node deployment method, system and device, equipment and a storage medium, and can improve the deployment and debugging efficiency of service nodes. The method comprises the following steps: acquiring MCP Server configuration information and resource configuration information of service resources; generating deployment parameters used for operating the MCP Server and the service resources; sending the deployment parameters to a target server, so that the target server deploys an operation environment corresponding to the MCP Server based on the deployment parameters; in response to a debugging instruction for the service resources, performing debugging operation based on the running environment to obtain a debugging result; under the condition that the debugging result indicates that the service resource meets the preset operation condition, a service node construction request is sent to a target server, so that the target server constructs an MCP Server node in a to-be-called state, and node metadata of the MCP Server node is generated; and receiving the node metadata sent by the target server, and registering the node metadata in the MCP Server management platform.

The invention relates to the technical field of supply chain collaboration, in particular to a multi-level supply chain service ecological digital twin network collaborative optimization system which comprises a system and is used for building, optimizing and executing a supply chain digital twin network. The data acquisition module is used for acquiring physical entity data and service state data of a whole link of a supply chain in real time through an Internet of Things device and an information system interface; the data fusion and twinning construction layer is in communication connection with the physical perception layer and is used for carrying out fusion and standardization processing on the collected multi-source heterogeneous data and is based on a unified supply chain knowledge graph; according to the method, supply chain whole-process accurate mapping and real-time visualization are realized by constructing a multi-level digital twinborn body, automatic optimization is carried out among multiple targets such as cost aging carbon emission by applying an intelligent optimization algorithm, third-party service resources are dynamically scheduled based on a multi-attribute utility theory, cross-organization credible collaboration is realized by means of a block chain intelligent contract, and the service efficiency is improved. And a complete perception decision execution optimization intelligent closed loop is formed.

The invention provides a service scheme generation method and system, and relates to the technical field of computer application, and the method comprises the steps: obtaining user demand information which comprises a service type, a service scene, a resource constraint condition and an expected effect description; semantic analysis is performed on the user demand information based on the comprehensive domain knowledge graph, core service elements are extracted, and a user demand model is constructed; according to the user demand model, candidate service resources are matched in a service resource library through a heuristic search algorithm, and candidate service schemes are generated; a multi-objective optimization algorithm is adopted, real-time resource state data and user feedback information are combined, the candidate service schemes are dynamically adjusted, and the final service scheme is output, so that user requirements can be accurately analyzed, service resources are efficiently matched, the generated service scheme is optimized, knowledge and resource iteration is realized, and the scheme generation efficiency and quality are improved.

The invention relates to the field of edge computing, in particular to a task migration method and device based on an edge computing system, equipment and a medium. By acquiring the current service resources of each service node of the edge computing system, available resources of the edge nodes and the cloud can be mastered in real time, resource finiteness consideration is met, and a complex and high-consumption algorithm is avoided. Task execution data of a target user terminal is accurately captured, and an initial task migration strategy and initial global time delay are comprehensively analyzed in combination with service resources, which means that a decision can be flexibly made according to the actual situation of a task and the current state of a node. When edge node performance fluctuates and a queuing task changes, a strategy is dynamically generated based on real-time data, a switching decision is enabled to accord with a network working condition, and the problems of lack of dynamic adaptability and mismatching of a node selection algorithm and edge equipment in the prior art are solved.

The invention discloses a resource creation method, device and equipment for model service and a medium, and relates to the technical field of computers, and the method comprises the steps: converting a model service deployment demand input by a user into a standardized service template; analyzing the standardized service template to obtain a model service resource scheduling instruction, the model service resource scheduling instruction being used for indicating a target resource pool; and determining a target driver corresponding to a target resource pool indicated by the model service resource scheduling instruction, so as to create cloud resources of the model service in the target resource pool by using the target driver, thereby realizing automatic resource creation of the model service, greatly shortening the deployment period through full-process automatic operation, reducing the labor cost, and improving the deployment efficiency. The heterogeneous resource management process is effectively integrated, the delivery efficiency is remarkably improved, and an enterprise can flexibly and efficiently deploy various AI model services on a private cloud platform and deal with quickly changing business requirements leisurely.

The invention discloses an inference service resource allocation method, electronic equipment and a readable storage medium, and relates to the technical field of computers, the inference service resource allocation method comprises the following steps: obtaining a model file of an inference service, and analyzing the model file to determine a minimum storage resource demand required for running the inference service; determining a resource specification meeting the minimum storage resource requirement, wherein the resource specification is used for representing nodes required for running the reasoning service and storage resource configuration corresponding to the nodes; and according to the storage resource state of the node in the reasoning cluster, determining the resource configuration strategy matched with the resource specification, and configuring the storage resource in the reasoning cluster according to the resource configuration strategy to operate the reasoning service, thereby solving the technical problems of low automation degree and poor utilization rate of the storage resource when resource configuration is performed on the reasoning service, and improving the resource configuration efficiency. The error risk and cost of manual operation are reduced, and the utilization rate of storage resources is also improved.

The invention discloses an electricity customer credit rating management method and system based on a differentiated service strategy, particularly relates to the field of energy service, and solves the problem of cold start user credit evaluation deviation and power service resource dynamic adaptation optimization. Seamless migration of credit scoring parameters and smooth transition of user portraits are realized by introducing multi-dimensional feature similarity analysis of virtual data and real data and a time-driven weight attenuation strategy, and continuity and adaptive optimization capability of an evaluation system are ensured; meanwhile, an intelligent service matching network is constructed based on the credit rating updated in real time, and a closed-loop service management and control system driven by data feedback is formed by dynamically adjusting a prepayment mode threshold value and an electric charge overdraft elastic interval strategy, so that the credit value of a user is deeply associated with the power resource configuration efficiency, and the user experience is improved. The operation risk and the service cost of a power grid enterprise are reduced, and finally collaborative optimization ecology considering energy safety management and user behavior guidance is established.

The invention discloses a service resource automatic migration method, system and device and a medium, and relates to the technical field of service resource migration, and the method comprises the steps: carrying out the evaluation of service resources of different platforms in a company through a resource evaluation model, determining the use conditions corresponding to different types of service resources, determining whether migration is carried out or not based on the use condition corresponding to any type of service resources in any platform; and when a determination result is yes, determining an optimal migration platform based on the use condition of the same type of service resources of other platforms except the platform, and completing migration processing based on the optimal migration platform. According to the method, all types of service resources in all platforms are evaluated, so that the service resource use condition in a company can be accurately and comprehensively controlled, and the service resources can be distributed more reasonably by carrying out subsequent service resource migration processing on the basis of the service resource use condition.

The invention relates to the technical field of computers, and particularly provides an application upgrading method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: dividing an application program into at least two service function modules based on a service function which can be realized by the application program; for each service function module, obtaining a domain name for accessing the service function module; when an upgrading event for the application program is detected, determining a target service function module needing to be upgraded from divided service function modules of the application program, and obtaining upgrading resources corresponding to the target service function module; and based on the resource access path corresponding to the target service function module, updating the service resources stored in the resource access path corresponding to the target service function module by using the upgrade resources. Therefore, the service resource is updated by taking the service function module as the minimum upgrading unit, a tedious file comparison process is not needed, and the time consumed by upgrading is reduced.

The invention discloses a service-oriented distributed resource automatic extraction and packaging method and a server, and relates to the technical field of micro-services, and the method comprises the following steps: querying to obtain a service packaging task state according to a service identifier; determining whether a packing task being executed exists or not; if not, performing dynamic dependency analysis on the target service corresponding to the service identifier during operation, and constructing a service dependency graph; detecting cyclic dependency based on the service dependency graph, and if the cyclic dependency exists, determining a resource acquisition range; obtaining multi-dimensional service resources from the plurality of distributed data sources; carrying out resource aggregation processing on the multi-dimensional service resources to generate a service basic information file, and packaging the multi-dimensional service resources and the service basic information file to generate a service resource package; and uploading the service resource packet to a distributed service management platform for deployment, and creating an asynchronous task. According to the method, the dependency relationship dynamically generated during operation can be effectively captured, the error rate is reduced, and the efficiency is improved.

The invention belongs to the technical field of wireless network communication, and discloses a dual-time-scale optimization method for multi-unmanned aerial vehicle cooperative auxiliary task unloading and caching, which comprises the following steps: setting an updating time slot of an unmanned aerial vehicle service caching decision as a first time slot, updating time slots of the unmanned aerial vehicle flight path decision, the computing resource allocation decision and the user unloading decision are second time slots; wherein the first time slot is composed of a plurality of second time slots; constructing a first optimization objective function and a second optimization objective function; in each first time slot, obtaining requirements of a user task for various service resources, and updating a service cache decision of the unmanned aerial vehicle according to the first optimization objective function; and in each second time slot, obtaining an unmanned aerial vehicle position, a user task and an unmanned aerial vehicle service cache, and updating an unmanned aerial vehicle flight path decision, a computing resource allocation decision and a user unloading decision according to the second optimization objective function.

The invention belongs to the technical field of hotel operation virtual simulation analysis, and particularly discloses a hotel operation virtual simulation analysis method and system and a storage medium, and the method comprises the steps: integrating construction data, historical operation logs and other multi-source data, and carrying out the multi-dimensional correlation analysis through the simulation initial data; the problem of decision deviation caused by the fact that a traditional method depends on artificial experience is solved, and operation bottleneck problems such as a service efficiency threshold value and resource allocation conflicts can be accurately recognized through a three-dimensional simulation model, so that high-precision data support is provided for optimization decision. Competition pricing data are collected in real time through a web crawler tool, competition influence factors are monitored, the check-in waiting loss probability is analyzed, in combination with environment monitoring data, competition opponent strategy change and market change data can be synchronized in real time, dynamic pricing weight parameters are loaded in a three-dimensional simulation model, the customer waiting duration and the loss rate are reduced, and the user experience is improved. And the technical efficiency of service resource scheduling is improved.

The invention discloses an industry and trade enterprise public health service resource optimization decision support system, and relates to the technical field of public health management, and the system comprises a data perception and access layer, a semantic fusion and modeling layer, a knowledge graph and risk calculation layer, an intelligent early warning and decision layer, and a feedback optimization closed loop. Multi-source heterogeneous data are collected through a protocol adapter, a unified data view is constructed through industry and trade public health field ontology semantic fusion, a space-time knowledge graph is dynamically generated, risks are deduced in combination with an improved SEIR space-time propagation model and a Monte Carlo method, visual early warning and interactive decision making are achieved based on digital twinborn bodies, and the method is applied to the field of industry and trade public health. According to the method, data islands can be broken, accurate advanced early warning of public health risks is realized, resource allocation is optimized, and the method is suitable for industry and trade enterprises with dense personnel such as machine manufacturing and electronic processing.

The invention provides a kernel function trajectory-based deep learning inference service performance analysis method, which comprises the following steps of: based on service indexes and hardware theoretical computing power acquired from a production cluster, defining floating point operation times per request (FPR) index to quantify service resource efficiency, and identifying high FPR hotspot services; positioning a reasoning iteration candidate boundary based on a GPU kernel function trajectory, verifying iteration integrity through fingerprint matching and chi-square test, and calculating a second reasoning iteration number IIPS and a model reasoning efficiency MIE; aiming at calculation-intensive operators on the key path, combining a dynamic Roofline model to estimate an operator theoretical performance upper limit, and based on actual execution time, calculating efficiency and a BottleScore index to identify a key bottleneck operator; and outputting targeted optimization suggestions according to analysis results of service efficiency analysis, model efficiency analysis and operator efficiency analysis. According to the method, the inference behavior pattern can be automatically identified from massive kernel trajectories, and the efficiency loss of each level is quantified.

The invention relates to a resource processing method and device of a cloud service platform and computer equipment. The method comprises the following steps: acquiring historical resource use data corresponding to a cloud service platform, wherein the historical resource use data is used for representing the use condition of cloud service resources in the cloud service platform in a historical time period; simulation resource use data corresponding to a plurality of demand scenes of the cloud service platform are determined, and the simulation resource use data corresponding to the demand scenes are data obtained by simulating resource use conditions of the demand scenes; predicting target resource demand data of the cloud service platform in a future preset time period in combination with the historical resource use data and the resource demand information corresponding to the plurality of demand scenes; wherein the target resource demand data is used for managing cloud service resources of the cloud service platform in a preset time period. By adopting the method, the resource processing of the cloud service platform can be better realized.

The invention relates to the field of power supply work order service management, in particular to a power supply service management method and system based on multi-source data. The method comprises the following steps: collecting a current fault repair work order based on a power supply service platform, carrying out deep work order text semantic analysis and standard fault instance modeling, and constructing a global fault instance map; recognizing a client real-time repair call audio stream, carrying out time window voice analysis one by one, and carrying out intelligent fault work order filling, thereby obtaining a real-time audio intelligent fault work order; performing fault demand decomposition on the real-time audio intelligent fault work order and the global fault instance map, performing power supply service resource dynamic configuration regulation and control, and constructing a service resource regulation and control engine; and performing global fault work order real-time processing based on the service resource regulation and control engine, performing overdue work order early warning, and generating a service timeliness early warning strategy. Through dynamic resource allocation and preventive work order creation, the power supply service efficiency, the power grid reliability and the intelligent level are improved.

The invention discloses a service-based killer chain analysis construction method, which belongs to the technical field of cooperative combat, and comprises the following steps: constructing a service resource pool, and setting a service discovery mechanism; on the basis of the fusion situation set, a situation switching event is recognized so as to perform local updating, and a three-level situation view is constructed; constructing a directed hyperedge, generating a directed hypergraph in combination with function category constraints, generating a dimensionality reduction state space based on multi-dimensional constraints, and generating an initial combat candidate set; configuring a management node, sending task invitation information to the initial combat candidate set in combination with the subtasks, generating a response scheme, performing adaptive screening, generating an initial killing chain, and performing failure verification; constructing a servitization twinborn body, deducing the initial killing chain, and executing delay at a calculation end so as to perform elimination recombination or delay optimization; a service combination template is constructed, an efficiency feedback mechanism is established, the superposition influence of frequent situation switching and state space catastrophe is cooperatively solved, and the real-time performance of generation of the killing chain is remarkably improved.

The invention relates to a resource allocation method and device, computer equipment, a storage medium and a program product. Obtaining target feature data, related to the cloud service resources, of the cloud service system in the first historical time period; determining target resource demand data for the cloud service resources in a future preset time period according to the target feature data; according to the target resource demand data and the current available resource quantity of the cloud service resources in the cloud service system, increasing or reducing the cloud service resources of the cloud service system; and distributing cloud service resources for each future service request according to the service attribute information of each future service request in the future time period. According to the scheme, the cloud service resources are dynamically regulated and controlled, and the cloud service resources are allocated to the future service requests according to the service attribute information, so that the system performance is ensured, the resource waste is avoided, the cloud service resources are pre-allocated to the future service requests, and the response speed of the future service requests can be improved.

The invention discloses a service-oriented dynamic resource management method and system. According to the method, indexes such as a CPU and a memory on a physical node are directly collected, monitoring errors of a virtualization layer are avoided, it is ensured that resource data truly reflects the hardware occupancy condition, physical resource occupancy is attributed to specific services through service labels, abstract conversion from hardware resources to service requirements is achieved, and a foundation is laid for differentiated management; the threshold value of the resource utilization rate of each service is calculated hierarchically according to the type of the service, the core service is guaranteed preferentially, if the resource utilization rate exceeds the corresponding threshold value, manual intervention is not needed, a standardized control interface is automatically called to execute the resource expansion operation, and the accuracy, high efficiency and adaptability of resource allocation are achieved.

The invention relates to the technical field of retail supply chain and store resource management, and discloses a service reservation data-based store resource dynamic allocation method, which comprises the following steps of: constructing a coupling association graph of a service item and commodity combination by adopting a graph optimization algorithm fused with sparse perception constraint; according to a core service item determined by the coupling correlation graph, obtaining a reservation fluctuation mode of the core service item by using a time sequence decomposition method of an embedded state feedback mechanism; constructing and training an attention convolutional neural network model combined with local feature self-correction, and predicting service resource demand distribution of the store by using the model; generating a store resource collaborative configuration strategy based on the service resource demand distribution and the associated commodity combination real-time inventory data; according to the invention, accurate coupling prediction and collaborative dynamic allocation of the service reservation demand and the commodity inventory demand are realized, so that the efficiency and accuracy of store resource allocation are remarkably improved.

The embodiment of the invention discloses a micro-service intrusion tolerance scheduling method and system for a cloud edge collaborative network. A specific embodiment of the method comprises the following steps: constructing a Markov decision process model of a resource and security joint state space according to collected operation information, and analyzing a scheduling demand and a security risk of a micro-service instance; calculating a resource allocation strategy of each node according to the total resource amount, the minimum demand of the micro-service resource in the cloud edge collaborative network and the bargaining weight so as to perform resource allocation; a multi-agent reinforcement learning algorithm is adopted, and redundant deployment, migration paths and cleaning strategies of the micro-service instances are dynamically adjusted; issuing the optimized scheduling scheme to a cloud data center and an edge node; and monitoring the running state and the safety condition of the micro-service instance in real time, and dynamically adjusting the scheduling strategy. According to the embodiment, the security of the micro-service in the cloud edge collaborative network can be effectively improved, the risk that the system is attacked is reduced, and the resource utilization efficiency and the service performance can be optimized.

The present invention relates to a cloud software service resource allocation method based on QoS model self-correction, the method comprising: step S1: performing online self-learning to obtain a QoS prediction model; step S2: collecting runtime data under a certain workload, and improving the accuracy of the QoS prediction model under a current workload through self-correction control; step S3: constructing a fitness function in combination with the quality of service (QoS) and the cloud resource cost (Cost), and searching for a target resource allocation scheme by using an improved particle swarm optimization algorithm; step S4: comparing the current resource allocation situation with the searched target resource allocation scheme to obtain a difference therebetween, and then adjusting resources according to a certain proportion; and step S5: repeating steps S2 to S4 until the current resource allocation situation is the same as the target resource allocation scheme which means that resource adjustment is completed. The present invention can realize a best resource allocation when the QoS prediction model is inaccurate.