3665 results about "Resource utilization" patented technology

The invention relates to the technical field of dispatching automation, and discloses a dispatching automation system application state management method which comprises the following steps: dynamic task state management: monitoring dynamic state indexes of various computing tasks in real time; according to different characteristics of task types, making differentiated priority management strategies; the task priority is dynamically adjusted, and it is ensured that key tasks are preferentially and efficiently executed; dynamic resource state management: continuously monitoring dynamic state indexes of various computing resources, wherein the dynamic state indexes comprise utilization rates and load conditions; dynamically adjusting a resource allocation scheme according to the resource state; dynamically optimizing resource allocation to ensure that the overall resource utilization rate of the system is continuously improved; and optimizing a self-adaptive scheduling strategy: introducing a self-adaptive scheduling algorithm based on reinforcement learning. According to the application state management method of the dispatching automation system, dynamic state management is adopted, a traditional static dispatching mode is abandoned, and the method can better adapt to a complex and changeable computing environment.

The invention discloses an intelligent scheduling method for a comprehensive virtual power plant, and relates to the technical field of power plant intelligent scheduling, and the method comprises the steps: carrying out the redundancy elimination filtering and unified time sequence calibration of multi-node sensing data of a cold and hot energy station, energy storage equipment and the like through the deployment of an edge side high-concurrency collection module, generating a high-quality time sequence fusion sequence, and screening schedulable resources; on-line model correction is realized through an error dynamic evaluation mechanism, a multi-stage scheduling strategy is generated by adopting an improved particle swarm algorithm aiming at the targets of economy, reliability and environmental protection, and an optimal instruction is output in combination with an equipment constraint condition. Through priority control and real-time monitoring index evaluation deviation, a rolling correction and standby strategy is triggered, equipment-level fault risk quantification and self-healing control are realized, abnormity is rapidly isolated, and redundant resources are started. Through data-prediction-scheduling-execution closed-loop cooperation, the resource utilization rate, the scheduling precision and the system fault-tolerant capability are significantly improved, and the scheduling capability of source heterogeneous resources is enhanced.

The dynamic bandwidth allocation and resource scheduling method based on the 5G network comprises the steps that S1, a base station collects service requests, network loads and channel information of user equipment of the 5G network, and the services are divided into real-time services, big data services, key services and conventional services; s2, the real-time service calculates the bandwidth by using deep learning; the big data service is distributed according to the idle bandwidth and the data volume proportion; key service main and standby links distribute bandwidths; conventional businesses come first and then are subjected to service distribution; s3, the base station maps bandwidth resources by using a multi-carrier and dynamic subcarrier algorithm, and optimizes signal transmission in combination with a beam forming technology; s4, processing service data according to a priority scheduling queue, and verifying signal transmission according to a channel quality adjustment coding mode; and S5, updating the data and evaluating the effect according to the user feedback, and re-optimizing the scheduling strategy as required. Through intelligent dynamic bandwidth allocation and resource scheduling, efficient, flexible and reliable transmission of different types of services is realized, and the network resource utilization rate and the service quality are improved.

The invention discloses a charging pile power regulation and control method and system, and particularly relates to the technical field of charging pile management, data is collected from a multi-dimensional data source and summarized to form a preliminary information set, and a standard information set is obtained through format standardization processing; feature data of the charging pile are extracted based on the standard information set, and charging task analysis and resource occupation analysis are carried out respectively; inputting the analysis result into a pre-trained machine learning model to generate a charging priority coefficient; constructing a priority matrix, and dividing the charging piles into high priorities and low priorities according to the charging priority coefficients; and finally, dynamically adjusting a power distribution strategy according to the priority type, optimizing low-priority task power distribution while meeting high-priority task requirements, and improving the operation efficiency and the resource utilization rate of the charging station. According to the method, the real-time operation state and the charging task characteristics of the charging piles are combined, the power distribution strategy is dynamically adjusted, the priority division and power distribution proportion of the charging piles are adjusted in real time, and the self-adaptive capacity of operation of the charging station is improved.

The invention provides an intelligent logistics optimization method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-source fusion processing of the Internet of Things sensor data and historical business data of a multimodal transportation reloading node in a logistics process, and obtaining a spatial-temporal feature vector; performing operation adaptability evaluation on the reloading nodes by using a P median model according to the spatial-temporal feature vectors to obtain a comprehensive score reflecting the node operation condition; a Tabu search algorithm is adopted to carry out combinational optimization on operation parameters of the reloading nodes based on the spatial-temporal feature vectors and the comprehensive scores, and an initial reloading scheme is obtained; and according to the initial reloading scheme, performing multi-agent cooperative scheduling on the reloading nodes on the digital twin platform to obtain a logistics resource optimal allocation scheme. Through combination of multi-source fusion and an efficient optimization algorithm, dynamic operation analysis and collaborative scheduling of multimodal transport reloading nodes are realized, logistics timeliness and resource utilization rate are improved, and the method is suitable for a complex logistics network and a changeable market environment.

The invention discloses a distributed component dynamic resource allocation method based on multi-objective optimization, which is characterized in that a PPO algorithm is introduced into a distributed system, dynamic adjustment is carried out aiming at a plurality of optimization objectives to optimize the overall configuration of resources, and the system firstly collects the real-time state, the task demand and the resource use condition of a distributed component; and then training an intelligent agent by using a PPO algorithm to gradually optimize a resource allocation strategy according to environment feedback, and finally realizing long-term optimization of a resource scheduling process by continuously interacting with the environment and continuously adjusting the strategy through the PPO algorithm. According to the method, the PPO algorithm in reinforcement learning is combined, efficient resource allocation of the distributed components in the complex dynamic environment is achieved, different from an existing rule driving or static optimization method, the allocation strategy can be adjusted in a self-adaptive mode according to task requirements, resource use conditions and system loads which change in real time, the resource utilization rate is increased, and the resource utilization rate is increased. And the system burden is reduced, and efficient operation of the system under variable conditions is ensured.

The invention provides an automatic resource matching method and system based on trust degree dynamic grading, and relates to the technical field of data analysis and decision making, and the method comprises the steps: building a feature vector matrix through obtaining user historical behavior data, generating a trust degree grading model through the training of a deep learning model, and carrying out the trust degree grading of a target user; determining an accessible system resource pool based on the credibility level, collecting real-time service demand information, and generating a target resource configuration scheme through a resource demand prediction model; and the resource use efficiency is monitored in real time, the credibility level is dynamically updated, and resource redistribution is carried out. Accurate matching and dynamic adjustment of system resources are achieved, the resource utilization efficiency is improved, resource waste is reduced, and the safety and stability of system operation are guaranteed.

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention provides a dynamic resource scheduling method and device for multi-GPU cluster reasoning service, and belongs to the field of computing resource scheduling. Comprising the steps of obtaining load levels, request queue lengths and resource utilization rates of a multi-GPU cluster system at a current moment and a historical moment, and determining a resource allocation strategy of a GPU cluster by utilizing a reinforcement learning model based on the load levels, the request queue lengths and the resource utilization rates of the multi-GPU cluster system at the current moment and the historical moment; predicting a future load state by using a time sequence prediction model based on the load level, the request queue length and the resource utilization rate of the current moment and the historical moment, and adjusting a resource allocation strategy based on a prediction result; and after the resource allocation strategy is adjusted, updating the reinforcement learning model based on an online learning model according to a feedback result of actual operation of the GPU cluster. According to the method, the scheduling strategy is optimized through reinforcement learning, and the capacity expansion and shrinkage scheme is dynamically adjusted according to the system actual load, the resource utilization rate and the like.

The invention discloses a multi-agent non-cooperative game driven high-concurrency task reasoning method and system, and the method comprises the steps: decomposing a heterogeneous task into standardized task units, and carrying out the quantitative modeling of the calculation resource demands of the task; constructing a task auction mechanism of the multi-agent non-cooperative game model, performing task allocation by adopting an anti-strategic auction rule and a fragmentation asynchronous protocol, and constraining resource declaration behaviors of the agents through a credit pledge mechanism; the real-time resource utilization rate of the system is monitored, the task priority is dynamically adjusted in combination with the task preemption historical state, a resource soft preemption strategy is triggered, and a compensation queue is established; a self-adaptive model splitting strategy is adopted to distribute reasoning tasks to end side equipment and edge computing nodes, cooperative execution is achieved through data compression and transmission, and task rescheduling is conducted according to feedback of an execution result; according to the method, the task execution efficiency, the resource utilization rate and the system stability can be remarkably improved in a high-concurrency scene.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to a 5G network slice dynamic scheduling method and system based on multi-modal space-time perception and an event knowledge graph, and belongs to the technical field of mobile communication network resource management. According to the method, the change of a physical scene is sensed in real time by constructing a dynamically evolved event knowledge graph and designing a double-flow space-time cross network in combination with visual semantic analysis; dynamically adjusting the resource prediction model by adopting an event-scene dual-drive mechanism, dynamically adjusting parameters of the gated recurrent neural network through an elastic adjustment factor, and optimizing a multi-target resource allocation strategy based on a reinforcement learning algorithm; a two-stage resource scheduling mode is adopted, non-preemptive resource allocation of priority guarantee is implemented in an event triggering stage, and an optimization strategy of continuous adjustment is deployed in a steady-state stage. According to the method, the resource utilization efficiency and the service quality in a high-concurrency scene are remarkably improved, the method is compatible with an O-RAN standard interface, and the method is suitable for high-reliability and low-delay communication scenes such as smart cities and industrial internet.

The invention discloses a large model service platform for high-efficiency concurrent computing, particularly relates to the technical field of data analysis, and realizes optimal management of the large model service platform for high-efficiency concurrent computing through a task allocation module, a node health assessment module, a node division module, a dynamic adjustment module and a fault identification module. The task distribution module reasonably distributes computing resources according to task priorities and real-time monitoring data; the node health assessment module accurately assesses node health conditions through preprocessing and feature extraction; the node division module classifies the nodes according to the health assessment result; the dynamic adjustment module dynamically adjusts a resource allocation strategy by using fuzzy logic; the fault recognition module recognizes and repairs fault nodes in time by further analyzing the health conditions of the high-load nodes; the overall performance and the resource utilization rate of the system are improved, efficient and reliable execution of intelligent financial services is ensured, and the stability and the reliability of the system are enhanced.

The invention relates to the technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

The invention relates to the technical field of production line scheduling management, in particular to a bamboo chopstick production line data real-time intelligent scheduling management system which comprises a data acquisition module, an intelligent scheduling module, an anomaly detection module, an equipment collaboration and optimization module and a decision support module. The data acquisition module acquires data such as equipment operation state, production progress, raw material consumption and yield statistics; the intelligent scheduling module generates a production scheduling scheme by using a federated learning algorithm and integrating multiple factors; the anomaly detection module monitors anomaly by means of a depth auto-encoder; the equipment collaboration and optimization module optimizes equipment collaboration based on the two; and the decision support module generates a visual interface and an intelligent analysis report. Real-time intelligent management of the production process is achieved, production efficiency is effectively improved, resource utilization is optimized, product quality is guaranteed, management decision is assisted, intelligent development of the bamboo chopstick production industry is promoted, and enterprise competitiveness and sustainable development capacity are improved.

The invention discloses a multi-level storage control method based on access popularity, and relates to the technical field of information, the method comprises the following steps: building a storage resource monitoring module, and collecting data volume change, read-write frequency and capacity occupation proportion information of each node from a storage system in real time; the method comprises the following steps: collecting original data, carrying out preliminary cleaning and formatting processing on the collected original data to obtain a standardized resource state data set, comparing predicted data with operation parameters of a current storage system, and if a predicted demand exceeds a current capacity limit, automatically generating a capacity expansion scheduling task to obtain a final resource optimization configuration scheme; according to the multi-level storage control method and device based on the access popularity, intelligent monitoring, load balancing and capacity planning of storage resources are achieved, the resource utilization efficiency and expandability of a storage system are improved, and an effective solution is provided for stable operation and performance optimization of a large-scale storage system.

The invention discloses a computer system service optimization scheduling method for a photoelectric system, and particularly relates to the technical field of computer service optimization, which comprises the following steps of: sensing and extracting information such as task types and time constraints through task characteristics, and constructing a multi-dimensional resource model such as processing capability and idle degree by combining resource modeling; a weighting mechanism is used for evaluating task priorities and generating a scheduling queue, tasks are dynamically mapped to matched resources, a scheduling strategy is adjusted according to system loads and feedback, model parameters are optimized through service feedback, and efficient, self-adaptive and intelligent optimization of task scheduling of the photoelectric system is achieved; according to the method, a dynamic scheduling mechanism based on task characteristics and resource prediction is realized, and the response speed of the high-optimal task is improved; optimizing the resource utilization rate by adopting resource state modeling and prospective mapping; and reinforcement learning and a feedback closed loop are introduced, a sustainable optimization scheduling system is constructed, and the adaptability and stability of the system in a complex photoelectric task environment are enhanced.

The invention, which relates to the warehouse management field, discloses an intelligent WMS system with an intelligent scheduling function, comprising a cargo sensing module, a data processing and analysis module, a scheduling decision module, a decision optimization module, and an execution control module. And the multi-agent scheduling module is used for defining each independent operation task in the warehouse as an agent, modeling the warehouse environment, and setting a reward function for each agent for guiding the agent to make an optimal decision. According to the intelligent WMS system with the intelligent scheduling function, the task completion time, the resource utilization rate, the cost saving and the service quality can be comprehensively considered through the set reward function, an intelligent agent can be guided to make an optimal decision, the accuracy and efficiency of future decision making can be improved through learning, and the intelligent WMS system has good application prospects. Therefore, the intelligent agent can gradually master the optimal scheduling strategy in different environments, and maximization of the overall warehouse operation efficiency is realized.

The invention relates to the technical field of data processing, and discloses an intelligent supply chain optimization management method and system based on digital AI analysis. The method comprises the steps of collecting supply chain data to construct a resource library; analyzing a sales mode and K-means + + clustering customer features by using LSTM, and predicting demands; inventory parameters and supplier scores are calculated, and a collaborative data set is formed; commodity association is analyzed through a knowledge graph, and a commodity selection decision is generated; resource allocation is carried out by applying multi-objective optimization, and cross-channel inventory collaboration is realized; and an integral rule and a merchant enabling scheme are constructed based on the transaction data, and a digital asset closed-loop mechanism is formed. According to the intelligent supply chain optimization management method based on AI analysis, global visualization, dynamic scheduling and accurate allocation of inventory resources are realized, the technical problem of cross-channel inventory collaborative allocation is solved, and the operation efficiency and the resource utilization rate of the whole supply chain are remarkably improved.

The invention provides an SLA demand-oriented computing network multi-element fusion arrangement method, which comprises the following steps of: matching a demand timeliness priority queue with a directed acyclic graph of container arrangement, and searching an arrangement scheme with optimal resource utilization efficiency and most balanced load by applying a dynamic planning method to form an initial arrangement decision; in combination with a preset calculation network element arrangement strategy template, dynamic selection and adjustment are carried out according to actual conditions, including selection of an arrangement strategy with a preferential resource utilization rate in a service peak period and selection of an arrangement strategy with a preferential cost in a service valley period; and recording and analyzing various calculation network elements generated in a container arrangement process, including resource occupation, starting time, a dependency relationship and an arrangement decision, and optimizing a resource prediction and scheduling algorithm according to historical data.

The invention discloses an optical transmission network hidden danger detection method and device and a computer program product, which can realize dynamic adjustment of differentiated bandwidth requirements by acquiring optical network state information in real time, evaluating service priority and establishing an optical channel resource allocation model. And when a topology anomaly is detected, constructing a service quality evaluation model by using a long-short-term memory neural network, and quantitatively analyzing the influence of the anomaly on the service. When a fault occurs, distributed link fault detection and a fault diagnosis engine based on a decision tree are adopted, and fault hidden dangers are quickly positioned and classified. And then, through an elastic recovery mechanism oriented to service sensitivity, the service quality level of a standby optical path is adjusted, and an optical layer topology connection relationship is optimized. According to the method, the resource utilization efficiency, the fault recovery capability and the service quality of the optical network are remarkably improved, and an innovative solution is provided for intelligent optical network management.

The invention relates to a time delay optimized satellite network adaptive routing method and system, and belongs to the technical field of satellite communication. The method comprises the steps of multi-hop information age construction, multi-path routing, link state sensing, flow segmentation adaptive routing, dynamic routing decision and the like. A multi-hop information age model is adopted to consider the time-varying property of a link state, the real-time property of path information is accurately estimated, and the timeliness of data updating is ensured; a multi-path search algorithm predicts link occupation frequency according to ephemeris information to construct a plurality of disjoint transmission paths, so that the traffic congestion problem is relieved, and the link resource utilization rate is optimized; a flow segmentation adaptive routing algorithm adjusts the path flow distribution proportion, reduces the data packet loss rate and the information age, and guarantees the stability of time delay sensitive data transmission; according to the method, resource utilization is balanced in a high-dynamic satellite network, link congestion is effectively prevented, the overall transmission efficiency and reliability are improved, and a high-adaptability and efficient transmission scheme is provided for time delay sensitive services.

The invention relates to the technical field of project management, in particular to a BIM-based building project resource scheduling optimization method, which comprises the following steps: based on the current progress state of a construction task, collecting the consumption of constructors, mechanical equipment and materials, comparing the task plan progress with the real-time progress, screening out construction links with the backward task progress, and establishing a construction project resource scheduling optimization model; and judging a resource type, and establishing a task progress offset. According to the method, the construction progress is accurately monitored, real-time evaluation of resource consumption is achieved, waste and scheduling lag are avoided, the construction path is optimized by means of the BIM model, the key path monitoring precision is improved, progress management and control effectiveness is ensured, key path progress deviation is analyzed, the resource shortage risk is recognized, stable supply of construction resources is guaranteed, and on the basis of the influence of resource shortage, the construction efficiency is improved. The resource distribution sequence is optimized, the resource utilization efficiency is improved, the supply chain delivery cycle is matched, the resource scheduling is optimized, the influence of resource supply and demand imbalance on the construction progress is reduced, and the continuous and stable construction progress is ensured.

The invention relates to the technical field of computers and the field of data testing, in particular to a multivariable real-time measurement and state testing method oriented to a data center. The method comprises the following steps: acquiring original data from a sensor, equipment and an application program in a data center, and filtering, cleaning and formatting the original data to generate preprocessed data; storing the preprocessed data to a distributed file system, and constructing a reverse index and B-tree index structure to realize efficient retrieval and redundant backup; intelligent analysis is carried out on the stored data by introducing a machine learning model and an automatic operation and maintenance strategy, the abnormal state of the key performance index is identified, and the resource use condition is predicted; and based on an intelligent analysis result, executing dynamic scheduling and optimal allocation operation of the resources. Through multi-variable real-time monitoring and intelligent scheduling, the resource utilization efficiency and the operation stability of the data center are improved.

The invention discloses a resource allocation method and device of a server, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that firstly, hardware state data of the server, task queue data of a data processing task corresponding to the server and timestamp data of the data processing task are obtained; inputting the hardware state data, the task queue data and the timestamp data into a target resource prediction model, and predicting a resource utilization rate corresponding to a data processing task executed by the server within a preset time period; based on the resource utilization rate, generating a resource allocation strategy corresponding to the server; and executing real-time dynamic allocation of the computing resources of the server according to the resource allocation strategy. Compared with the prior art, the resource allocation can be flexibly adjusted according to the actual load change, the resource utilization rate is remarkably improved, the resource waste is reduced, the task execution efficiency and the service quality are guaranteed, and the intelligent management level and the operation efficiency of the system are improved.

The invention discloses an AGV scheduling system optimization method based on dynamic priority adjustment and real-time load balance, and the method comprises the steps: dynamically monitoring the emergency degree and arrival time of cargos, enabling a system to adjust the task priority in real time, guaranteeing the priority processing of emergency cargos, and giving consideration to the scheduling efficiency of common cargos; information such as current positions, loads and electric quantity of the AGVs is collected, weighted load values are calculated, weight factors are dynamically adjusted according to task types, load balance is achieved, and the situation that part of the AGVs are overloaded and part of the AGVs are insufficient in load is avoided; and finally, scheduling optimization is carried out by comprehensively considering factors such as the adaptation degree between the task and the AGV, the path distance, the predicted completion time and the load balance, and the overall performance of the AGV system is fully exerted. The flexibility, efficiency and resource utilization rate of the AGV scheduling system are effectively improved, and the AGV scheduling system is suitable for various scenes such as logistics storage and production and manufacturing.

The invention provides a distributed service intelligent master selection method and system based on double-engine cooperation, and relates to the technical field of intelligent master selection, and the method comprises the steps: collecting the processing capability index, the network throughput and the resource utilization rate of a service node to construct a performance data matrix, and generating an intelligent identification code based on a node scoring function and a physical clock value; nacos and Redis signals are monitored through a double-layer event monitor, the optimal incremental step length is determined according to the score sequence distribution trend, and a target score is generated to construct a dynamic priority sequence; and periodically selecting a main node and a preheating standby node, and when the main node fails, starting increment state synchronization based on Redis affairs after voting arbitration confirmation, so as to realize main and standby non-inductive switching. According to the method, the election accuracy of the main node and the switching reliability are improved, and the system interruption risk is reduced.

The invention discloses an intelligent storage resource dynamic allocation method and system based on deep reinforcement learning, and relates to the technical field of storage management. The intelligent storage resource dynamic allocation method based on deep reinforcement learning comprises the following steps: environment data judgment; inventory management evaluation; calculating complex evaluation; and effective evaluation. According to the method, whether inventory allocation is carried out or not is judged through the obtained environment data and the preset environment data, then the storage utilization evaluation value is obtained according to the obtained tobacco inventory management data, whether storage optimization is carried out or not is judged, then the calculation complexity evaluation value is obtained according to the obtained calculation resource data, and whether calculation complexity optimization is carried out or not is judged. And finally, obtaining an optimized effective evaluation value according to the storage utilization evaluation value and the calculated complex evaluation value, thereby achieving the effect of improving the storage resource utilization efficiency in the dynamic allocation process of the intelligent storage resources, and solving the problem of low storage resource utilization efficiency in the dynamic allocation process of the intelligent storage resources in the prior art.

The invention discloses a cloud computing resource allocation method based on dynamic optimization, a computer device and a storage medium, and the method comprises the steps: collecting resource use data of each virtual machine in a cloud computing environment when an update period is reached; calculating the activeness of the resource nodes, and generating a demand expected value and a corrected expected value of each resource according to the activeness; according to a resource state evaluation result, dynamically adjusting resources by using a first resource allocation strategy, and actually allocating the resources; monitoring resource use data, and carrying out dynamic migration and resource adjustment on the virtual machine; and monitoring the access popularity of each data set, and dynamically adjusting the resource allocation strategy according to the second resource allocation strategy. According to the invention, through a multi-level resource monitoring and optimization mechanism, the resource management level in a cloud computing environment is effectively improved, and the system performance and the resource utilization rate are improved through accurate resource allocation, adaptive resource adjustment, dynamic data set management and an optimized migration strategy.

The invention discloses a project progress and resource early warning method and system based on dynamic regulation and control, and the method comprises the steps: 1) collecting project progress and resource data in real time, and constructing a multi-source data fusion model; 2) based on the constructed multi-source data fusion model, judging whether the construction progress and the resource consumption are abnormal or not, and if so, executing the step 3); otherwise, continuing conventional construction progress tracking and resource management; 3) triggering a grading early warning mechanism, generating and executing an optimization regulation and control strategy, and adjusting the construction progress and resource configuration; and 4) evaluating the regulation and control effect, and returning to the step 2) after collecting feedback data. According to the method, the actual construction progress deviation and the resource supply and demand imbalance condition are comprehensively considered, the current overall operation condition of the construction project is analyzed, the construction management strategies are flexibly switched, the management parameters are flexibly adjusted, the construction progress can be rapidly and effectively guaranteed, resource utilization is optimized, and the construction efficiency is improved. And the intelligent level and the response efficiency of construction management are obviously improved.