354 results about "Cloud data center" patented technology

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 discloses an unmanned aerial vehicle agent end side cloud hierarchical decision-making method and system, and the method comprises the steps: S1, deploying a compressed lightweight deep learning model at an unmanned aerial vehicle terminal, and executing a real-time preliminary decision of a simple or emergency task; s2, constructing a dynamic task unloading mechanism, and unloading the complex task from the unmanned aerial vehicle terminal to an edge computing node; s3, carrying out preprocessing and local decision making on the task by the edge computing node, and further judging whether the task needs to be forwarded to the cloud data center or not; s4, the cloud data center carries out global optimization decision making for calculation-intensive or data-intensive tasks; and S5, monitoring network and computing node states in real time, and dynamically adjusting end, edge and cloud computing resource allocation to realize dynamic balance of decision precision and real-time response. According to the technical scheme disclosed by the invention, the computing power and energy consumption bottleneck of the unmanned aerial vehicle terminal can be effectively relieved, the computing resource allocation is dynamically optimized, and the real-time response capability and decision-making precision of complex task processing of the unmanned aerial vehicle are improved.

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

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 cloud computing and data center energy efficiency optimization, and discloses a carbon neutralization-oriented cloud data center energy efficiency optimization method, which comprises the following steps of: 1, acquiring server computing load, power consumption, cooling state and carbon emission data of a data center based on a distributed sensor network; step 2, constructing an energy efficiency mathematical model of the data center based on a deep learning model of AI training; 3, training a task scheduling and cooling system optimization strategy based on a reinforcement learning agent; 4, analyzing the real-time carbon emission condition of the data center by using an AI prediction algorithm; 5, based on the carbon emission trend predicted by AI, triggering an intelligent carbon compensation strategy in combination with the real-time energy consumption condition of the data center; and step 6, dynamically adjusting task scheduling, a cooling control strategy and a carbon compensation scheme by adopting an AI adaptive learning method based on data feedback. And dynamic scheduling of calculation tasks of the data center is realized through a reinforcement learning agent trained by AI.

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 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 monitoring operation and maintenance management method and system for a cloud data center, and relates to the field of monitoring operation and maintenance of the cloud data center, and the method comprises the steps: obtaining the historical data of a cloud data monitoring index, and building a cloud data monitoring database; obtaining short-term fluctuation, medium-term fluctuation and long-term trend of each cloud data monitoring threshold based on wavelet transform; establishing a hardware performance index evaluation model, and obtaining the influence of hardware performance reduction on the cloud data monitoring threshold; correlation among the cloud data monitoring indexes is acquired, and a cloud data monitoring threshold adaptive adjustment and alarm model is established; determining the dynamic response time of each cloud data monitoring index, and setting a cloud data monitoring threshold alarm level; and establishing a cloud data monitoring threshold alarm frequency model, and dynamically adjusting cloud data monitoring threshold alarm times in unit time. The high false alarm rate caused by business complexity is effectively reduced, the relevance and environmental adaptability of each cloud data monitoring threshold are improved, and the comprehensive quality of monitoring operation and maintenance is improved.

The invention belongs to the technical field of medical education, and discloses a medical education guidance method and system based on artificial intelligence. The method comprises the following steps: setting a medical education resource library in a cloud data center, and constructing an artificial intelligence model; performing feature extraction and classification on a plurality of medical education resource data in the medical education resource library; generating a user portrait according to the real-time basic information and the real-time behavior data of the user; performing matching in the plurality of real-time resource classification results according to the real-time user portrait; performing medical education guidance according to the real-time user portrait and the plurality of matched real-time resource data features; sending the real-time medical education strategy and the plurality of target medical education resource data to a user terminal; and performing parameter optimization on the artificial intelligence model according to real-time feedback data sent by the user terminal. According to the invention, the problems of high cost investment, low efficiency, poor effect, lack of customization and low intelligent degree in the prior art are solved.

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.

The invention belongs to the technical field of data storage, and discloses a jewelry transaction data secure storage method and system based on a block chain. The method comprises the following steps: a cloud data center deploys a block chain consensus and storage network and constructs a data security storage engine; the data server carries out encryption and uploads the encrypted data to the cloud data center; the cloud data center carries out decryption; the cloud data center is used for generating a real-time retrieval tag, a real-time data analysis result, a real-time storage resource scheduling scheme and a real-time data storage strategy of the decrypted real-time jewelry transaction data by using a data security storage engine; and the cloud data center is used for safely storing the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tag and the real-time data analysis result by using a block chain consensus and storage network. According to the invention, the problems of insufficient security, poor traceability, low storage efficiency and waste of storage resources in the prior art are solved.

The invention discloses a power equipment defect identification method and system based on edge cloud collaboration, and relates to the technical field of power equipment defect identification, and the method comprises the steps that a data collection device collects monitoring data of power equipment in real time, and sends the monitoring data to an edge computing gateway in a monitoring area; the edge computing gateway receives the monitoring data, pre-processes the monitoring data, obtains the pre-processed monitoring data and uploads the pre-processed monitoring data to the cloud data center; the cloud data center receives the preprocessed monitoring data, performs electrical equipment defect identification on the received data by adopting a multi-mode defect identification model to obtain a real-time defect identification result, and processes the real-time defect identification result by adopting a maintenance strategy generation model to obtain a real-time maintenance strategy; and sending the real-time maintenance strategy to the edge computing gateway. According to the method, an edge cloud cooperation mechanism is adopted, power equipment defect identification is performed on monitoring data of power equipment timely and efficiently, and a defect identification result is analyzed to obtain a corresponding maintenance strategy.

The invention discloses a camera monitoring system based on wireless communication and a communication protocol optimization method, and relates to the technical field of communication optimization, the camera monitoring system is composed of a monitoring camera group, a wireless communication network, an edge computing unit, a cloud data center and a user interaction terminal; the monitoring camera group simulates human eye vision, adjusts a visual angle and a resolution ratio according to needs, and collects data; a wireless communication network is combined with quantum encryption and a traditional technology, and a communication mode is intelligently selected; the edge calculation unit processes data with low power consumption by using a neuromorphic chip; the cloud data center fuses the block chain and federal learning analysis data; the user interaction terminal has holographic projection and emotion perception functions and provides personalized services. According to the invention, the modules are collaboratively innovated, the pertinence of collected data is strong, the communication is efficient and safe, the processing is fast and accurate, and the interaction experience is good; the system has the functions of intelligent analysis, energy-saving management and the like, the intelligence and reliability of the system are improved, and the system has wide application prospects and market value.

The invention belongs to the technical field of energy consumption analysis, and discloses a resident energy consumption behavior analysis method and system based on multi-modal data. The method comprises the following steps: constructing a regional user portrait generation model, a multi-modal feature fusion model, a resident energy consumption behavior analysis model and a regional electric energy scheduling model based on a cloud data center; real-time resident information and real-time multi-mode monitoring data are collected and preprocessed; performing regional user portrait generation by using a regional user portrait generation model; performing multi-modal feature fusion by using a multi-modal feature fusion model; performing resident energy consumption behavior analysis by using the resident energy consumption behavior analysis model; and performing regional electric energy scheduling by using the regional electric energy scheduling model. According to the invention, the problems of insufficient accuracy, insufficient data processing capability and lack of response mechanism in the prior art are solved.

The invention belongs to the technical field of industrial data analysis, and discloses an industrial sensor data analysis method and system. The method comprises the following steps that: a cloud data center sets an OPC UA instance, and constructs a data analysis model and a fault maintenance guidance model; the distributed edge cluster is used for carrying out calculation task distribution; the distributed edge cluster preprocesses a plurality of pieces of real-time industrial sensor data according to a real-time calculation task allocation scheme; the cloud data center is used for writing a plurality of preprocessed real-time industrial sensor data into an OPC UA instance, extracting corresponding real-time OPC UA dynamic data, and performing data analysis by using a data analysis model; and the cloud data center is used for carrying out fault maintenance guidance by using a fault maintenance guidance model according to the real-time data analysis result. The problems that in the prior art, the data processing and analysis capacity is insufficient, the data format is not uniform, the calculation pressure is large, and a response mechanism is lacked are solved.

The invention discloses a self-adaptive cloud edge-end cooperative task unloading method based on multi-agent reinforcement learning, and belongs to the technical field of cloud edge cooperative task unloading. The method comprises the following steps: constructing a cloud edge-end three-layer cooperation model; the terminal user optimizes the edge device selected to cooperate and the unloading task amount, and the edge end server comprises a fixed edge server and an unmanned aerial vehicle server; the unmanned aerial vehicle server optimizes a flight path according to the scheduling position of the cloud; the cloud data center optimizes and dispatches the unmanned aerial vehicle position according to the load condition; mobile equipment energy consumption and task completion time delay in the cloud side end cooperative task unloading process are modeled into a comprehensive system cost optimization problem, and the comprehensive system cost optimization problem is further converted into a Markov decision process; designing a multi-agent reinforcement learning algorithm to optimize a task unloading strategy; on the premise of meeting various constraints, the task execution efficiency can be effectively improved, meanwhile, the energy consumption of the mobile equipment is reduced to the maximum extent, and particularly, the service duration and quality of the unmanned aerial vehicle are improved.

The invention discloses an intelligent beef cattle breeding monitoring management system based on whole industry chain data. The system comprises a data acquisition module which is used for deploying sensors and data acquisition terminals in each link of a beef cattle whole industry chain for data acquisition; the data transmission and integration module is used for transmitting data acquired in each link to a cloud data center for processing by using a wireless communication technology; the intelligent analysis module is used for mining the integrated data and adjusting breeding processes such as seed selection and matching, a nutrition formula and a management strategy; the monitoring and early warning module is used for setting a key index threshold value, and when the key index exceeds the threshold value, the system automatically sends out early warning information to a mobile phone terminal or a computer client of a culture worker; and the breeding decision support module is used for providing guidance for the breeding direction according to long-term accumulated data and analysis results and screening out good beef cattle individuals and families thereof. The system has the advantages of high breeding efficiency, high beef cattle variety improvement speed and the like.

The invention belongs to the technical field of intelligent retrieval, and discloses an archive data intelligent retrieval method and system based on a multi-modal large model. The method comprises the following steps that a cloud data center builds an archive data intelligent retrieval platform, deploys a block chain network and builds an archive data intelligent retrieval engine; the cloud data center is used for carrying out data processing on the real-time multi-modal file big data; the cloud data center is used for generating a knowledge graph according to a plurality of real-time retrieval themes; the cloud data center is used for carrying out data clustering; the cloud data center uses a block chain network to carry out distributed storage; the user terminal is used for uploading real-time query data; the cloud data center is used for performing intelligent retrieval by using an archive data intelligent retrieval engine; and the cloud data center visually displays the target archive data. According to the method, the problems of low retrieval efficiency, insufficient accuracy, weak multi-modal data processing capability and insufficient security in the prior art are solved.

The invention provides an intelligent pipeline detection system based on the Internet of Things technology, and relates to the field of intelligent pipeline management, and the system collects the time sequence of state parameter data such as a temperature value, a pressure value and a vibration amplitude value of a monitored pipeline through a sensor network, and transmits the time sequence to a cloud data center through the Internet of Things technology. And the cloud data center processes the time sequence data of the state parameters to obtain a pipeline state detection result of the monitored pipeline, and if the pipeline state detection result shows that the pipeline state is abnormal, an early warning signal and a detection result are sent to a manager. Thus, intelligent monitoring of the pipeline state can be achieved, abnormal conditions can be found in time, and then safety and stability of pipeline operation can be ensured.

The invention belongs to the technical field of authority management, and discloses a human resource authority management method and system. The method comprises the following steps: in a cloud data center, constructing a behavior pattern baseline model, a user portrait generation model and a dynamic permission adjustment model; using a data acquisition device to acquire a real-time service mode and real-time behavior data of the user, and uploading the real-time service mode and the real-time behavior data to a cloud data center; extracting real-time behavior data characteristics of the real-time behavior data, and performing abnormal behavior analysis on the real-time behavior data characteristics by using a behavior mode baseline model according to a real-time service mode; and generating a real-time user portrait by using a user portrait generation model according to the real-time behavior data characteristics, and generating real-time human resource permission data by using a permission dynamic adjustment model according to the real-time user portrait. According to the invention, the problems of low practicability, large labor cost investment, low efficiency and low intelligent degree in the prior art are solved.

The invention provides a cloud-edge collaborative intelligent tool magazine tool life prediction method and system, and relates to the field of fault prediction and health management.The method comprises the steps that in a cloud data center, a current tool health index is obtained according to workpiece machining quality information and tool surface image evaluation; according to the current cutter health index, a cutter damage monitoring and early warning mechanism is configured and sent to an edge processing unit; according to the adaptive monitoring frequency, a sensor is controlled to conduct data monitoring, multi-source monitoring data are judged according to the adaptive early warning indexes, if not triggered, dynamic updating of a tool damage monitoring and early warning mechanism is conducted in an iteration mode, and if triggered, tool damage early warning is conducted. The invention aims to solve the technical problem that the accuracy and the reliability of tool damage early warning are insufficient due to the fact that a traditional early warning mechanism is difficult to adapt to the dynamic change of a tool machining state, and can improve the adaptation degree of a monitoring early warning mechanism and the tool machining state and remarkably improve the accuracy and the reliability of early warning by introducing a dynamic monitoring early warning mechanism.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

The invention relates to a PLC-bridging gateway internet-of-things automatic identification method, system and device and a medium. The method comprises the following steps: sequentially sending handshake detection signals to a plurality of PLC devices and receiving response messages; forming a candidate handshake data combination set, and completing handshake matching through a priority algorithm and an adaptive polling mechanism; verifying the response frame, analyzing the equipment information, and confirming the active state and communication parameters of the PLC equipment; generating a drive binding strategy according to a matching result, and distributing a session identifier for the drive binding strategy; generating a session identifier of the target PLC equipment, and updating a session routing table in the bridging gateway; and translating the industrial data collected by the target PLC device into an Internet of Things protocol data packet and uploading the Internet of Things protocol data packet to a cloud data center. According to the invention, automatic identification and rapid adaptation of various interfaces and protocols can be realized; the communication stability and real-time performance are improved through dynamic scheduling and route updating; uniform data uploading is supported, and efficient deployment of cloud analysis and the industrial Internet of Things is facilitated.

The invention discloses an ecological fish culture system capable of automatically purifying water. Comprising a user terminal, a cloud data center platform, a purification system, a real-time water quality detection module, a data acquisition and transmission module, an intelligent data analysis and management module, a remote monitoring and early warning module, an abnormal early warning processing module, a water purification linkage module, a data analysis visualization module and a culture system. Compared with the prior art, the aquaculture system has the advantages that the aquaculture system is added, circulating aquaculture of ecological fishes is achieved, a filter tank is established through a biological filter tank module in the aquaculture system by means of microbial populations, and the ecological fishes can be cultured in a circulating mode through the biological filter tank module in the aquaculture system, so that the aquaculture efficiency is improved, and the aquaculture cost is reduced. Harmful substances are converted into harmless substances through specific microbial populations, the harmful substances in water are absorbed by aquatic plants through the multi-stage purification module, and suspended particles and impurities in the water are filtered through mechanical objects such as a filter screen and filter cotton.

The invention belongs to the technical field of all-optical network management, and discloses an all-optical network visual management method and system. The method comprises the following steps that: a cloud data center constructs an all-optical network digital twin model and an artificial intelligence model; the edge computing gateway is used for collecting real-time equipment operation monitoring data and extracting real-time equipment dynamic monitoring data; the edge computing gateway is used for performing equipment fault diagnosis and equipment management strategy generation by using an artificial intelligence model; the edge computing gateway encrypts and transmits the real-time equipment operation monitoring data, the real-time equipment dynamic monitoring data, the real-time equipment fault diagnosis result, the real-time equipment management strategy and the real-time alarm signal of the optical network information transmission equipment to the cloud data center; and the cloud data center is used for performing visual display by using an all-optical network digital twinborn model. According to the invention, the problems of lack of comprehensive real-time monitoring capability, insufficient fault diagnosis precision, low visualization degree and low data processing efficiency in the prior art are solved.

The invention discloses a medical network collaborative penetration test system and method based on artificial intelligence, and belongs to the technical field of medical information security, and the method comprises the steps: integrating a hospital intranet, medical equipment, cloud data center data and historical network delay data to construct a medical network data center; analyzing association between network vulnerabilities and delay data through a graph neural network, constructing an attack path analysis model containing delay weights, and quantifying risk scores to generate a test target priority list; defining a protocol vulnerability and a delay threshold, designing a test scene and generating an attack vector, matching an attack tool to perform a simulation test, calculating a vulnerability triggering success rate and evaluating a risk; generating a penetration test case according to the test target priority list and the risk level, executing multiple rounds of attack tests and generating a report; and monitoring the change of the vulnerability triggering success rate by dynamically adjusting the delay parameter, setting an early warning threshold value, and monitoring and sending a risk early warning notification in real time.

A method performed by an ingress router of an edge cloud data center to optimize mass switching triggered by site failures or degradation at the data center. The method includes receiving a Border Gateway Protocol (BGP) UPDATE message that includes a site-reference identifier (ID) and operating capacity information, where the site-reference ID corresponds to a group of routes within a site of the cloud data center; attaching the site-reference ID to the group of routes in a routing table; selecting a path for forwarding traffic corresponding to the group of routes based on the operating capacity information; and forwarding traffic for selected services along the path.

A method for detecting network attack based on kernel operating characteristics of a software switch, comprising: collecting kernel operating characteristic data of the software switch during network management and packet forwarding of the software switch, wherein the kernel operating characteristic data of the software switch can reflect characteristic data of a fine-grained operating state of a network; preprocessing the kernel operating characteristic data of the software switch; and inputting the preprocessed kernel operating characteristic data of the software switch into a pre-trained attack detection model to detect a potential attack behavior in a network. The method is applicable to all scenarios where the software switch is applicable, especially a software-defined network and a cloud data center, an industrial Internet, 5G, edge computing based on the software-defined network. More accurate and timely detection and early warning of potential anomalies and attack behaviors in the network can be implemented.