2614 results about "Data hub" patented technology

The invention relates to a computing power network resource scheduling method. The method comprises the following steps: acquiring floating point operation performance parameters and operation states of node processors and energy index data of data centers where the node processors are located, and calculating to generate a node list; constructing a global resource pool based on the list, and generating a resource distribution table containing the total calculation power of the region; obtaining calculation requirements and time delay constraints of the task queue, extracting feature vectors in combination with the resource distribution table, and generating a resource utilization rate table; obtaining network link flow data, predicting a link congestion probability through a long short-term memory network, and generating a flow control strategy table; and finally, updating resource pool network constraints according to the resource utilization rate table and the flow control strategy table, and remapping tasks by taking node effective computing power as a weight to generate a scheduling execution scheme. According to the method, accurate quantitative evaluation of the computing power resources is realized, the matching precision of tasks and the computing power resources is effectively improved, the resource utilization rate of the computing power network can be remarkably improved, and the overall scheduling efficiency and stability of the system are enhanced.

The invention discloses a cross-system heterogeneous metadata intelligent acquisition method and system based on LLM, and the method comprises the following steps: S1, collecting and preprocessing heterogeneous system data, and constructing a heterogeneous graph; s2, analyzing nodes by the large language model, and splicing structural features to form initial representation of the nodes; s3, constructing a heterogeneous graph Transform, executing multi-layer propagation and low-rank decomposition, and outputting final node representation; s4, generating a field alignment path, an interface mapping path and a dependency path, and planning an acquisition path and an acquisition sequence; and S5, executing an acquisition task, converting a structured format, writing into a metadata center, and performing closed-loop optimization. According to the method, intelligent acquisition, semantic analysis and automatic integration of cross-system heterogeneous metadata are realized, and the efficiency and intelligent level of metadata management in a multi-source data environment are greatly improved.

The invention discloses an access anomaly analysis method and system based on multi-dimensional features and user behaviors, and relates to the technical field of dynamic access anomaly detection, and the method comprises the steps: based on a dynamic hypergraph structure, extracting high-order correlation features of the user behaviors through a multilayer hypergraph convolutional network, and generating a high-order feature matrix; based on the high-order feature matrix, generating an authority approval threshold through a causal reinforcement learning framework, constructing a user behavior causal graph to generate strategy network parameters, and storing the strategy network parameters to distributed nodes of a regional data center; based on strategy network parameters stored by distributed nodes, security multi-party computing is adopted, cross-node collaborative optimization is carried out, and global defense strategy parameters are generated through a security aggregation algorithm. According to the method, security multi-party computing is adopted, cross-node collaborative optimization is performed, and the global defense strategy parameters are generated in combination with homomorphic encryption and a block chain fragmentation technology, so that the collaboration efficiency and strategy consistency among distributed nodes are improved on the premise of ensuring data privacy.

The invention relates to the technical field of software defined networks, in particular to an SRv6-based SDN (Software Defined Network) data center load balancing method and system, and the method comprises the steps: obtaining node real-time load and SRv6 routing available resource information through an SDN controller, constructing a load balancing model, and recognizing an overload node and a congestion link; generating a flow scheduling path in combination with segmented routing characteristics; in combination with network state information, monitoring the load condition in real time when the node is unbalanced or the link packet loss rate is gt; when 5%, a load balancing adjustment mechanism is triggered, unbalance types are distinguished, a related flow set is determined, optimization is performed by using a DQN algorithm, and a flow scheduling strategy is generated and dynamically adjusted by taking throughput maximization and unbalance degree minimization as targets; and issuing the scheduling strategy to the data center network equipment, updating the flow table through the Netconf protocol, and redistributing the flow. Therefore, the problems of low load balancing efficiency, insufficient dynamic adaptability, low resource utilization rate and the like in the prior art are solved.

The invention relates to the technical field of operation and maintenance of data centers, and discloses an intelligent operation and maintenance monitoring method and system of a data center, and the method comprises the steps: building a hierarchical monitoring frame, carrying out the hierarchical data collection and standardization processing of three layers, namely, physical equipment, virtual resources and application services, and constructing an interlayer data sharing model; performing anomaly scanning on the running state of each layer by using a preset fault detection algorithm, triggering an alarm once an anomaly is found, and determining a propagation path influenced by a fault through data backtracking; and then analyzing a causal relationship between anomaly and cross-layer data by using an association rule mining algorithm, performing multi-dimensional verification in combination with a decision support rule base, and finally generating a fault positioning conclusion and a repair priority. In addition, response scripts can be automatically called for parameter adjustment, rapid positioning and automatic repair of faults are achieved, and the operation and maintenance efficiency and reliability of the data center are effectively improved.

The invention discloses an intelligent switch fault monitoring system which comprises a multi-modal data acquisition module, an intelligent feature extraction module, a fault prediction root cause positioning module, an intelligent decision module, a digital twin operation and maintenance module, a security situation awareness module, a model optimization and self-learning module and an enhanced display visualization interaction module. The fault prediction root cause positioning module uses a lightweight hybrid encoder model to efficiently encode the fused features, learns spatial-temporal feature representation of the switch operation state, uses a gradient SHAP algorithm to calculate the contribution degree of each feature to fault occurrence, generates a fault root cause thermodynamic diagram, and sends the fault root cause thermodynamic diagram to the switch operation state; through deep integration of artificial intelligence, digital twinning and augmented reality technologies, a passive mode of traditional switch fault monitoring is thoroughly changed, an intelligent operation and maintenance system with self-sensing, self-decision and self-recovery capabilities is constructed, and all-around guarantee is provided for reliable operation of a data center and network infrastructures.

The invention provides an intelligent harmonic dynamic compensation method and system for a data center. According to the method, server heat source surface temperature field distribution is monitored in real time through a distributed optical fiber temperature measurement array, and a dynamic impedance spectrum is generated; when the temperature gradient exceeds the flash evaporation critical value of the fluorinated cooling liquid, an active filter is activated to carry out dynamic impedance matching compensation on high-frequency harmonic current in the liquid cooling tank cabinet, and a pulse width modulation control signal of a circulating pump is generated; the compensation power of the filter and the turbulence intensity of the cooling liquid are synchronously adjusted by analyzing the coupling relation between the flowing vortex intensity of the cooling liquid and the dielectric polarization loss; when phase mismatch of a temperature field is caused by heat dissipation of multiple heat sources, a multi-band compensation strategy is reconstructed by utilizing frequency domain characteristics of a pulse width modulation signal and a compensation power historical value, and a cooling liquid branch flow distribution ratio and a filter frequency response curve are cooperatively adjusted. According to the technical scheme, the thermal stability and the electrical safety of the data center cooling machine room are improved.

The invention discloses an elastic management and optimal scheduling method and system for cloud computing resources, and belongs to the technical field of cloud computing resource management.The method comprises the steps that a cluster composed of multiple types of intelligent agents is constructed, and the intelligent agents comprise a resource evaluation intelligent agent, an elastic telescopic intelligent agent, a load balancing intelligent agent and a fault recovery intelligent agent; the intelligent agents are distributed and deployed on different nodes of a cloud computing data center, and information interaction is realized through a communication interface based on RESTful API and a distributed message bus constructed by a gRPC protocol; a unified agent management platform is set up and is responsible for registration, monitoring, scheduling and dynamic adjustment of agents, and orderly operation and efficient cooperation of the whole multi-agent system are ensured. According to the method, the functions of efficient elastic allocation, intelligent load balancing, rapid fault recovery, continuous optimization evolution and the like of resources in the cloud computing data center can be realized, and the quality and competitiveness of cloud computing services are effectively improved.

The invention relates to the technical field of data center monitoring, and discloses a data center-oriented multi-dimensional data fusion monitoring and intelligent evaluation method. The method comprises the following steps: acquiring an environment temperature sequence, an equipment power consumption sequence and network flow data of a data center to form a multi-dimensional monitoring data set; performing time-space correlation processing on the data to generate a fusion state feature set comprising a thermal distribution index, an energy consumption efficiency coefficient and a performance fluctuation map; analyzing the fusion state feature set by applying an intelligent evaluation engine to obtain a health evaluation result and an abnormal region mark; and generating an optimization strategy set containing a thermal management adjustment scheme and a resource scheduling optimization scheme based on the abnormal region mark. According to the method, comprehensive monitoring, accurate evaluation and dynamic optimization of the operation state of the data center are realized through multi-dimensional data fusion and intelligent analysis, and improvement of the operation and maintenance efficiency and the operation quality of the data center is facilitated.

The invention provides a semantic-based migration and consistency verification method, system and device and a medium, and relates to the technical field of databases. The method comprises the following steps: performing metadata topology scanning analysis by obtaining metadata, including generating an abstract syntax tree and constructing a semantic graph; according to a metadata topology scanning analysis result, performing data mapping and conversion, including loading a YAML rule and generating corresponding type mapping and constraint conversion; data migration is carried out through primary key fragmentation parallel migration, batch writing optimization and real-time double-writing verification; through structure-data-business three-layer verification, simulation business SQL comparison and automatic difference repair, the integrity of migrated data and business compliance are ensured, the semantic gap problem in heterogeneous database migration is solved, high-precision and high-efficiency database migration is realized, the migration efficiency is improved, and the data migration efficiency is improved. The method is suitable for scenes with strict requirements on data consistency and migration efficiency, such as financial, government affair and enterprise-level data centers.

The invention discloses a mold and formwork design system based on virtual simulation, and particularly relates to the field of mold design, the mold and formwork design system comprises a multi-mode perception fusion module, a digital twin modeling module, a multi-physics field coupling simulation module, a hybrid intelligent optimization module, a digital twin closed loop verification module and a data center module, and each module forms a design closed loop through a data center. The multi-modal sensing fusion module adaptively collects and fuses multi-source signals to generate high-credibility data; the digital twin modeling module constructs and iteratively corrects a model based on the data; the multi-physics field coupling simulation module realizes multi-solver co-simulation under a dynamic boundary; the hybrid intelligent optimization module accelerates to generate an optimal solution through secondary optimization and an agent model; the digital twin closed-loop verification module detects defects and generates a correction instruction; the data center module is responsible for data management, cross-module scheduling and precision control; the system improves the design precision and efficiency of the mold frame, reduces the physical mold testing cost, and is suitable for a high-precision mold development scene.

The invention belongs to the technical field of power system optimization scheduling, and provides a source network load storage intelligent collaborative optimization method, which comprises the following steps of: deploying sensors at four ends of a source network load storage respectively, collecting in real time by utilizing a cloud data center, enabling data of the four ends to be consistent in time sequence through a PTP protocol, constructing a topological graph according to parameters and data, and establishing a source network load storage intelligent collaborative optimization system. Selecting a model in a digital twinning environment for simulation; dividing independent agents at four ends of a source network load storage, setting observation data, an execution space and excitation feedback, forming an excitation item by economy, stability and environmental protection, interactively circulating actual data, a prediction instruction and an excitation value, recording into a sequence, inputting the sequence into a strategy network, and calculating and outputting logarithmic probability gradient to update the parameters of the strategy network; and the intelligent agent completes interactive circulation according to the strategy network, generates a local scheduling instruction, aggregates the instruction to perform weighted calculation, generates a global scheduling scheme, issues the global scheduling scheme to execution equipment, updates parameters by using an average deviation calculated by a deviation vector, resolves the global scheduling scheme and issues the global scheduling scheme to form a closed-loop mechanism.

The invention provides a data center hybrid energy storage and renewable energy intelligent scheduling control method and system, and relates to the technical field of data energy storage and resource scheduling, and the method comprises the steps: carrying out the load prediction through a double-layer time sequence memory network in combination with a multi-head attention mechanism, building a hybrid energy storage optimization scheduling model based on a renewable energy generation power prediction result, and carrying out the optimization scheduling of the hybrid energy storage. And a double-layer model predictive control framework is adopted to output a corrected charging and discharging power instruction, so that optimal control of the hybrid energy storage system is realized, the energy utilization efficiency is improved, and the operation cost of a data center is reduced.

The invention provides a ship comprehensive energy efficiency improvement platform and a data management and application method thereof, and belongs to the technical field of ship comprehensive energy efficiency optimization and big data. The ship comprehensive energy efficiency improvement platform comprises a ship end, a cloud end and a shore end, wherein the ship end realizes equipment data acquisition, edge processing and scheduling through a data acquisition module, an edge calculation module and a data scheduling module; the cloud end completes data aggregation, isolation and distributed storage through the data aggregation module, the data isolation module and the distributed storage module; and the shore end realizes data standard management, algorithm version control and micro-service business application through the data center layer, the algorithm center layer and the business center layer. The ship energy efficiency comprehensive improvement platform provides comprehensive management and optimization for various energy-saving devices and various ship energy efficiency improvement software, and optimization and improvement of ship comprehensive energy efficiency are achieved.

The invention relates to the technical field of computing power resource sharing in a data center application environment, and discloses an enterprise computing power layout and intelligent decision mobile application system and an implementation method thereof. And real-time monitoring and dynamic scheduling of internal or cloud computing resources of an enterprise are realized. Historical data and real-time load are processed by using machine learning and an optimization algorithm, an optimal scheduling scheme is generated, and an alarm is automatically triggered when abnormality is detected, so that efficient utilization of enterprise resources is ensured. The method has the advantages of being high in real-time performance, intelligent, flexible in expansion, safe, reliable and the like, and the operation and maintenance efficiency and decision response speed of enterprises can be remarkably improved.

The invention discloses a hospital Internet of Things equipment intelligent monitoring and fault early warning system based on edge computing, relates to the technical field of equipment monitoring and early warning, and aims to solve the technical problem that fault discovery lags in a high-real-time scene of an existing intelligent monitoring and fault early warning system. The system is used for collecting various data of hospital Internet of Things equipment and sending the data to a preset storage position, and comprises a local database, a data processing module and an abnormal data judgment unit; the Internet of Things base station is used for being connected with Internet of Things equipment, collecting equipment data and achieving intelligent monitoring and fault early warning through an algorithm model, and the algorithm model is constructed based on a core algorithm and rules; and the hospital Internet of Things edge computing platform is used for carrying out edge computing management on the data sent by the Internet of Things base station, feeding back an analysis result and storing the data. The method has the advantage of improving the fault discovery speed of the Internet of Things equipment.

The invention discloses a multi-mode intelligent linkage 3D visual data center operation and maintenance system and method, and relates to the technical field of data center operation and maintenance. The system comprises a multi-modal data fusion module used for mapping physical sensor data and video monitoring and operation and maintenance logs to a unified three-dimensional coordinate system and constructing a multi-modal fusion feature tensor; the three-dimensional particle modeling module is used for constructing a particle space structure based on a Voronoi diagram and Delaunay triangulation and adaptively adjusting the particle resolution; the multi-modal score analysis module is used for calculating a particle multi-dimensional score and generating a semantic heat map to recognize an abnormal region; the structured noise prediction module is used for simulating future responses under different instructions based on a diffusion model; the instruction generation and regulation module is used for realizing causal analysis and instruction optimization in combination with a knowledge graph; and the three-dimensional visual interaction module supports real-time rendering and interaction operation at a client. According to the method, the intelligence, the real-time performance and the visualization level of data center operation and maintenance can be improved.

The application provides a pumped storage power station data fusion intelligent analysis platform, comprising: a data source comprising a plurality of original data; the data center is configured to collect original data and convert the original data to obtain unified data; the data application comprises an algorithm middle table module, and the algorithm middle table module is configured to construct an operator library, associate corresponding measurement attributes with measurement point identifiers of unified data and form a matching table; obtaining an algorithm construction instruction; according to the algorithm construction instruction, the operator library and the measurement attributes, performing algorithm modeling through a drag-and-drop operation process to obtain a power station data analysis algorithm; instantiating the power station data analysis algorithm to obtain an instantiated algorithm; and processing the corresponding unified data according to the instantiation algorithm and the matching table to generate an analysis result data set. The pumped storage power station data fusion intelligent analysis platform provided by the invention can realize multi-source heterogeneous data integration and analysis, and is high in efficiency, low in cost and good in real-time performance and accuracy.

A personal tactical system including a load-bearing garment, a pouch with one or more batteries enclosed in the pouch, at least one power distribution and data hub, and at least one camera. The camera is incorporated into or removably attachable to the load-bearing garment, the pouch is removably attachable to the load-bearing garment and the one or more batteries are operable to supply power to the at least one power distribution and data hub. The at least one power distribution and data hub is operable to supply power to at least one peripheral device. A plurality of personal tactical systems is operable to form an ad hoc network to share images and other information for determining object direction, location, and movement.

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention discloses a communication equipment fault intelligent diagnosis method and device, equipment and a medium. The method comprises the steps of collecting equipment operation logs in real time, and extracting an abnormal point set; clustering the abnormal points, and matching a hardware fault, a software error and a network congestion type in combination with a preset mode library; dimensionality reduction is carried out on temperature, voltage and current sensor data based on principal component analysis, and a time-synchronized fusion feature vector is constructed; analyzing features and abnormal modes, and outputting potential fault points; associating the classification model to generate a fault priority by extracting the dynamic characteristics of the network flow; and a dependency network is constructed in combination with Bayesian reasoning, so that accurate fault positioning is realized. The system supports incremental learning, updates an abnormal mode library and automatically expands a knowledge base. Through multi-dimensional data fusion and spatio-temporal feature joint modeling, the problems that a traditional method depends on manual rules and is high in false alarm rate are solved, and the method is suitable for complex scenes such as a 5G base station and a data center.

The invention relates to a data lake-based industry templated modeling and dynamic access control method, which comprises the following steps of: acquiring multi-source heterogeneous data of a data center in real time, and performing data verification, key field extraction and metadata extraction through an analyzer; according to the data classification and grading key points, grading protection is carried out on the currently obtained data matching industry grading and classification template, and the protection grade is dynamically adjusted according to the data dynamic access control rule, the sensitivity and the service importance; performing threat detection and risk prediction on the abnormal behavior data by using a machine learning algorithm; constructing a distributed intelligent data lake storage architecture, and storing the currently acquired data into a data lake in a layered manner according to a classification result; and displaying the security situation and the early warning information through a visual interface. The invention also relates to a corresponding system. By adopting the industry templated modeling and dynamic access control method and system based on the data lake, the limitation of traditional data lake modeling is effectively solved.

The invention discloses a power equipment fault diagnosis system and method based on edge cloud cooperation. The system comprises an edge end module and a cloud end module. The edge end module is deployed in a power equipment site, is embedded with a lightweight diagnosis model, collects power equipment operation state parameters in real time, executes localized preliminary fault identification and alarm judgment, extracts key characteristic quantities, and uploads a processing result to the cloud end module through a network communication protocol; the cloud module is deployed in a data center or a control platform, and performs complex model reasoning, cross-device historical data comparison, fault depth research and judgment and model updating and distribution according to the characteristic quantity data uploaded by the edge module; the edge end module and the cloud end module perform data interaction through an FTP, MQTT or 5G protocol, a model trained by the cloud end can be automatically distributed to an edge end to realize rapid deployment and switching, and equipment fault diagnosis is realized. According to the invention, through a task division cooperation and data interaction mechanism, an efficient and low-delay fault diagnosis process is realized.

The invention provides a dynamic wavelength allocation method based on DCI wavelength division II type equipment. The method comprises the following steps: acquiring an occupation state and a transmission quality index of each wavelength channel of a current network; establishing a wavelength channel quality evaluation model, and scoring each wavelength channel according to the transmission quality index of each wavelength channel of the network; according to the bandwidth requirement, the priority and the QoS requirement of the service request, generating a traffic characteristic matrix containing the delay sensitivity and the bandwidth requirement through a traffic prediction algorithm; based on a network state and the traffic characteristic matrix, through resource availability calculation and path quality evaluation, forming an allocable wavelength resource pool and a quality score thereof; for the allocatable wavelength resource pool, generating an optimal wavelength allocation scheme by adopting an improved heuristic algorithm and comprehensively considering the resource utilization rate, the signal quality and the service priority; and according to the performance feedback of the optimal wavelength allocation result and the network state change, continuous optimization of wavelength allocation is realized through a self-adaptive adjustment mechanism. The method can effectively deal with the dynamic flow change in the DCI network, improve the resource utilization rate, ensure the signal quality and meet the QoS requirement of the service, thereby providing an efficient and reliable wavelength allocation solution for the data center interconnection network.

The invention relates to the field of power distribution network planning, and discloses a power distribution network and data center collaborative planning method based on security domain analysis, and the method comprises the following steps: S1, constructing a power distribution network-data center system security domain model containing a distributed power supply; s2, based on the constructed power distribution network-data center system security domain model, constructing a power distribution network-data center system double-layer collaborative optimization model; s3, solving the double-layer collaborative optimization model by using an algorithm solver to obtain a collaborative planning result of the power distribution network and the data center; in the step S2, the power distribution network-data center system security domain model represents a set of all working points meeting normal operation N-0 constraint and N-1 security constraint of the system. By constructing the power distribution network-data center security domain model and proposing the joint quantitative index, the continuous representation of the system security margin is realized, the anti-disturbance capability and the operation stability of the system are improved, and the local overload risk is avoided.

The invention relates to the technical field of data center high availability, in particular to a cross-data center fault isolation and switching method and system in a multi-tenant environment. The resource mapping table is constructed by taking the tenants as the minimum control units, the affected tenants are accurately identified, logic isolation is executed, the problem of waste of full-tenant service migration resources caused by node-level or cluster-level switching in the prior art is avoided, and the fault influence range is minimized. The optimal data center is dynamically selected by combining the multi-factor objective function with the tenant SLA level, the defects that an existing scheduling strategy is opaque and tenant priorities cannot be distinguished can be overcome, and it is ensured that key tenant services are preferentially recovered. And finally, through combination of a gray takeover mechanism and health feedback confirmation and multi-dimensional recovery verification after migration, the condition that an existing recovery mechanism is extensive can be changed, the fault processing precision and the resource scheduling efficiency of the data center in a multi-tenant environment are remarkably enhanced, and the service continuity is guaranteed.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention discloses an energy-saving economic dispatching method for a data center energy system based on a prediction model. The method comprises the following steps: establishing a digital twinborn model of the data center energy system; dividing the data center into a plurality of energy consumption partitions, and installing a temperature acquisition device in each energy consumption partition; after a cooling capacity demand prediction model of the data center under each time period partition is established, target combination parameters and target energy efficiency indexes PUE of the chilled water temperature and the air supply temperature of a refrigeration subsystem under each time period are obtained through calculation; establishing a data center energy efficiency index PUE prediction model, and establishing an upper-layer refrigeration subsystem optimization scheduling model by taking the minimum deviation between an energy efficiency index PUE prediction value and a target energy efficiency index PUE, the minimum deviation between actual combined parameters of chilled water temperature and air supply temperature and target combined parameters and the minimum cold capacity supply and demand deviation of each partition as targets; and establishing a lower-layer data center optimization scheduling model by taking the minimum power utilization operation cost of the system and the minimum load migration cost of the IT equipment as a target.

The invention discloses a cloud data center full life cycle monitoring and early warning system based on digital twinning, and the system comprises a multi-source collection and preprocessing module which is used for collecting and preprocessing multi-source data; the twinborn modeling and simulation module is used for constructing a digital twinborn body and completing virtual-real mapping and mechanism simulation; the time sequence modeling module is used for constructing a state space-Kalman enhancement time sequence evolution twinborn model and carrying out continuous time sequence modeling; the virtual-real residual error self-correction closed loop module is used for generating a virtual-real residual error and updating the digital twinborn body and time sequence evolution twinborn model; and the risk and linkage module is used for executing risk identification, grading and life cycle linkage according to the corrected time sequence characteristic representation. According to the invention, a state space-Kalman enhanced time sequence evolution twinborn method is adopted, virtual-real self-correction and full-life-cycle early warning of the cloud data center are realized, and the method has the advantages of high precision, strong robustness and self-optimization.