3298 results about "Data hub" patented technology

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

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.

A fabric control protocol (FCP) is a data transmission protocol that enables spraying of individual packets for a given packet flow across a data center from an ingress interface of the source data processing unit (DPU) across a plurality of parallel data paths of a logical tunnel in the network fabric to the egress interface of the destination DPU. The FCP has congestion control mechanisms used to determine a degree of congestion at the egress interface of the destination DPU and to modify a send window size at the source DPU based on the degree of congestion. Reliable FCP (rFCP) extensions provide reliability enhancements and improved failure resilience within the data center. The rFCP extensions provide an unsolicited mode for low latency operation with enhanced reliability mechanisms. The rFCP extensions provide failure resilience mechanisms to identify and avoid failed paths among multiple parallel data paths within the logical tunnel.

The invention discloses a flywheel energy storage system control method based on data center photovoltaic power supply, and belongs to the technical field of power system and new energy grid-connected control, and the method comprises the steps: obtaining the output prediction data of photovoltaic power generation, the real-time load power, the voltage fluctuation parameter of a commercial power grid, and the real-time rotating speed state of flywheel energy storage; generating a power gap predicted value by taking the real-time rotating speed state of flywheel energy storage as a dynamic constraint variable; generating a flywheel commercial power cooperative control strategy according to the commercial power voltage fluctuation parameter; the temperature change rate of the flywheel motor is detected in real time, and a control instruction is generated; and synchronously sending the control instruction to the photovoltaic inverter and the flywheel motor, adjusting the parameters of the photovoltaic inverter and the flywheel rotating speed response data, and dynamically updating the control parameter set. According to the invention, a dual adjustment mechanism of flywheel rotating speed dynamic constraint and voltage fluctuation ratio weight distribution is adopted, and a temperature gradient harmonic collaborative suppression strategy is combined, so that millisecond-level power compensation response can be realized, the power grid dependence degree is reduced, and the service life of an energy storage system is prolonged.

The invention provides a computing-electric-thermal coupled data center integrated energy system collaborative optimization method, and relates to the field of data center energy-saving management and control, and the method comprises the steps: firstly obtaining real-time data collected by a data center; secondly, a data center load model is constructed based on the relation among real-time data quantification calculation power, electric power and heating power; constructing a collaborative optimization model according to the data center load model, and solving the collaborative optimization model to obtain an optimization result; and finally, adjusting calculation task distribution, a power supply strategy and cooling system operation parameters according to an optimization result to form a calculation-electricity-heat coupled data center integrated energy system collaborative optimization strategy. According to the calculation-electricity-heat coupled data center comprehensive energy system collaborative optimization method, collaborative optimization is carried out on the data center energy system through multi-energy complementation, and energy conservation and efficiency improvement of the data center are achieved.

The invention relates to the technical field of network service testing, in particular to a testing method and system based on optical fiber characteristics. The method comprises the following steps: acquiring multi-dimensional optical fiber characteristic data of an optical fiber link through optical fiber characteristic acquisition equipment; performing characteristic data hierarchical integration on the multi-dimensional optical fiber characteristic data to obtain an optical link characteristic vector group; performing temperature-error code wavelet transform association according to the optical link feature vector group to obtain a temperature-error code wavelet feature set; constructing an association prediction matrix according to the temperature-error code wavelet feature set; performing a service delay exception test according to the association prediction matrix to obtain a delay tail exception event set; and performing micro-bend three-dimensional feature recognition according to the delayed tail abnormal event set to obtain micro-bend mode features. According to the invention, the operation and maintenance efficiency, reliability and toughness of the optical fiber network of the data center are improved through full-link test analysis from optical fiber physical characteristics to storage service delay.

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 relates to a traffic anomaly detection and network security reinforcement method for a full data center, and the method comprises the steps: achieving the precise recognition and active blocking of an attack chain through the real-time collection of full network traffic, the construction of a multi-dimensional time sequence model based on historical attack chain features, and the combination of a dynamic judgment threshold value and spatial and temporal distribution feature analysis. The method specifically comprises the steps of analyzing traffic in real time to generate an attack chain feature sequence; performing clustering and correlation analysis based on the multi-dimensional time sequence model; dynamically adjusting the monitoring density to focus the high-risk area; and generating a threat blocking instruction and executing network isolation. Aiming at the problems of high attack chain omission ratio, resource contention conflict and insufficient dynamic defense capability caused by dependence on a static rule and a fixed threshold value in the traditional scheme, the method solves the problem of weak perception capability of the traditional technology on a complex attack chain, remarkably reduces the omission rate and the error blocking rate, guarantees the service continuity through dynamic resource scheduling, and improves the service performance of the system. The method is suitable for real-time security protection of a large-scale data center.

A fabric control protocol (FCP) is a data transmission protocol that enables spraying of individual packets for a given packet flow across a data center from an ingress interface of the source data processing unit (DPU) across a plurality of parallel data paths of a logical tunnel in the network fabric to the egress interface of the destination DPU. The FCP has congestion control mechanisms used to determine a degree of congestion at the egress interface of the destination DPU and to modify a send window size at the source DPU based on the degree of congestion. Reliable FCP (rFCP) extensions provide reliability enhancements and improved failure resilience within the data center. The rFCP extensions provide an unsolicited mode for low latency operation with enhanced reliability mechanisms. The rFCP extensions provide failure resilience mechanisms to identify and avoid failed paths among multiple parallel data paths within the logical tunnel.

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.

Provided in embodiments of the present disclosure are a method for scheduling workloads of a data center in an electricity market environment, and a device. In the embodiments of the present disclosure, a multi-stage random optimization solution that allows a data center to participate in a multi-level electricity market is provided. The types and data dependency relationship of workloads are taken into consideration, and a load scheduling model, in a time dimension, for the workloads is constructed; on the basis of electricity source information of the data center, a multi-stage electricity cost optimization model is constructed, so as to comprehensively consider the exogenic uncertainty of an electricity price and carbon emissions and the endogenic uncertainty of the workloads; on the basis of the multi-stage electricity cost optimization model, multi-stage optimization solving is performed on the load scheduling model, so as to obtain a load scheduling scheme and an electricity purchase strategy. Therefore, a more accurate and more reliable estimation is made concerning the electricity flexibility of the data center, such that the electricity cost of the data center can be reduced, support is provided for the reduction of carbon emissions, and the consumption of renewable energy sources is encouraged, thereby achieving the energy use flexibility of the data center in the multi-level electricity market environment.

The invention relates to the technical field of water conservancy system design, in particular to a water conservancy informatization data collection and transmission system and method based on a distributed ad hoc network, and the method comprises the steps: deploying monitoring nodes at each monitoring point of a water conservancy project to collect hydrological data and environmental data; peripheral monitoring nodes are automatically found and connected through a distributed ad hoc network module, a multi-hop wireless network is formed, and communication between the monitoring nodes and gateway nodes is achieved. Specifically, a dynamic routing algorithm is adopted to monitor the delay and signal stability of a link in real time and monitor node residual energy, and an optimal transmission path is calculated; when a node or a link has a fault, a routing path is automatically recalculated according to a fault processing strategy, and disaster recovery transmission is realized; the energy consumption of the monitoring node is reduced through a sleep wake-up mode and an energy-aware routing algorithm; and converging the data of each monitoring node at the gateway node, and uploading the data to a cloud data center. And the flexibility and reliability of data transmission are enhanced.

The invention relates to the technical field of data center storage management, and discloses a data center storage space dynamic monitoring and early warning method comprising the following steps: S1, collecting and monitoring operation state data of a data center storage device in real time; s2, based on the collected historical storage data, predicting a future storage demand through a deep learning model; s3, based on the storage demand prediction result, dynamically scheduling the storage resources by adopting a reinforcement learning algorithm, and automatically adjusting the configuration of the storage device to optimize the use of the storage resources; and S4, when the storage device has a potential fault, performing early warning by using the fault prediction model. Through deep learning storage demand prediction, reinforcement learning resource scheduling optimization and intelligent fault prediction, the configuration efficiency of data center storage resources and fault early warning accuracy are improved, the storage cost and performance are optimized, and efficient and stable operation of the system is ensured.

The invention relates to the field of data storage and backup, and discloses a method and a system for transmitting disaster recovery backup capability of an emergency recovery unit. The method comprises the steps of establishing a quantum encryption transmission channel, monitoring a network index in real time and predicting transmission quality to start preventive optimization, calculating a link quality score to carry out link switching, verifying integrity and consistency of received data and executing retransmission, carrying out multi-mode data backup, and switching to an emergency recovery unit when a main data center fails. The system comprises a quantum encryption transmission module, a transmission monitoring and switching module, a data verification and retransmission module, a multi-mode backup module and an emergency switching and synchronization module. Through the technical scheme, the problems of data transmission security and high efficiency can be effectively solved, the disaster recovery backup capability of the emergency recovery unit is improved, the service continuity is ensured, and the method and the device are suitable for scenes with high requirements on data security and service continuity.

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 embodiment of the invention provides a data center energy consumption prediction method and system based on DCIM. Real-time operation parameters of an electric quantity monitoring module, a refrigeration equipment control module and an environment sensing module are synchronously obtained through a heterogeneous data bus, a dynamic topology calibration mechanism is adopted to carry out space-time alignment processing so as to generate a dynamic topology matrix, and a propagation path weight matrix is constructed; according to a timestamp sequence of a historical equipment start-stop event and an energy consumption fluctuation curve phase offset, an event trigger signal is injected into a propagation path weight matrix to form a multi-dimensional prediction map, a key propagation path identifier is positioned by using a path traceability mechanism based on transient peak distribution and real-time environmental parameter phase deviation in the prediction map, and a target propagation path is obtained. And adjusting an impedance coefficient and a pressure loss threshold value in the propagation path weight matrix so as to generate a refrigeration equipment power gradient adjustment instruction and a power supply link impedance matching instruction. According to the technical scheme provided by the embodiment of the invention, the energy efficiency management and operation stability are effectively improved.

The invention belongs to the technical field of communication, and discloses a network transmission management method and system, and the method comprises the steps that a data collection module collects multi-dimensional data in real time; the decision planning module optimizes a network route; the decision planning module performs protocol and encryption switching; the execution module dynamically adjusts the transmission strategy; the safety protection module performs four-dimensional dynamic protection on the data; the hybrid cloud deployment module guarantees the confidentiality of core data; the multi-active data center module realizes data synchronization among a plurality of data centers; the cross-domain collaborative module realizes privacy protection collaborative training between edge nodes through federated learning; and the block chain auditing traceability module records the key operation log. According to the method, routing and flow prediction are optimized through a mixed quantum classical calculation engine, multi-protocol heterogeneous scheduling and neural mimicry congestion control are combined, the network transmission efficiency is greatly improved, the data transmission speed is improved, and the data leakage risk is reduced through application of a zero-trust security matrix and a quantum key distribution technology.

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, which relates to the technical field of data security, discloses a power grid enterprise power data storage security risk assessment system comprising the following components: a virtual environment monitoring module, a risk assessment and isolation module, an early warning and response module and a security policy management module. According to the invention, omnibearing real-time monitoring is carried out on a virtual server and a virtual network of a power grid enterprise data center through the virtual environment monitoring module, an abnormal behavior detection model is continuously optimized by using a machine learning algorithm, and high-precision identification of novel abnormal behaviors is realized. Meanwhile, the risk assessment and isolation module introduces a specific risk factor weight in combination with the unique characteristics of the power data of the power grid enterprise, ensures that an assessment result highly accurately reflects the actual safety risk of power data storage, and remarkably improves the safety and stability of power data storage of the power grid enterprise.

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.