258 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 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 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 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 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 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 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 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.

The invention belongs to the technical field of pressure pipeline monitoring, and discloses a pressure pipeline damage space-time positioning method and system based on multi-modal data collaboration. The method comprises the following steps that a cloud data center constructs a pressure pipe network three-dimensional simulation model, a multi-modal monitoring data analysis model and a pressure pipeline damage space-time positioning model; the monitoring data acquisition equipment is used for acquiring real-time multi-modal monitoring data; the edge computing gateway is used for preprocessing the real-time multi-modal monitoring data and uploading the real-time multi-modal monitoring data to the cloud data center by using an OPC UA protocol; the cloud data center is used for carrying out multi-modal feature fusion and data analysis; the cloud data center is used for carrying out damage space-time positioning; and the cloud data center carries out visual display. According to the method, the problems of dependence on a single data mode, insufficient real-time performance, inaccurate damage positioning and insufficient visual display in the prior art are solved.

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

The invention discloses a river valley area bridge earthquake vulnerability intelligent evaluation and earthquake resistance optimization analysis method, and relates to the technical field of bridge structure earthquake resistance analysis. The method comprises the following steps: constructing a geological model of an area where a bridge is located and a three-dimensional structure model of the bridge; a plurality of monitoring points are arranged in a bridge and a river valley area where the bridge is located, and collected data are uploaded to a cloud data center through a wireless transmission subsystem; carrying out seismic oscillation time history analysis based on the digital twinborn body, and visualizing the weak part of the structure through a cloud picture by adopting an artificial wave and actual measurement wave mixed input mode; and generating a multi-objective optimization scheme according to the vulnerability curve. According to the method, seismic oscillation time history analysis is carried out based on the digital twin, an artificial wave and actually measured wave mixed input mode is adopted, a weak part of a structure is visualized through a cloud picture, a multi-target optimization scheme is generated according to monitoring data, the seismic monitoring efficiency and supervision strength of a bridge in a river valley area are improved, and a bridge reinforcement method is rapidly provided.

The invention belongs to the technical field of data management, and discloses an inspection sample data tracking identification method and system based on the Internet of Things. The method comprises the following steps: a cloud data center constructs a sample tracking identification model and a sample data analysis model, and deploys a block chain network; the Internet of Things gateway is used for collecting real-time handover records and sending the real-time handover records to the cloud data center; the cloud data center performs tampering verification and generates real-time test sample data; the cloud data center is used for performing sample tracking and identification by using the sample tracking and identification model; the cloud data center is used for performing sample data analysis by using a sample data analysis model; and the cloud data center is used for storing the real-time test sample data, the real-time sample tracking identification result and the real-time sample data analysis result of the detection sample by using a block chain network. According to the invention, the problems of easy data tampering, difficult tracking, inaccurate analysis, low management efficiency and low storage reliability in the prior art are solved.

The application relates to a service scheduling and deployment method for Internet of Things edge computing node resources, which is realized based on an Internet of Things edge computing system. The system comprises a cloud data center, a convergence layer core network and a plurality of access layer switch networks connected in sequence. Each access layer switch network is in communication connection with a plurality of edge computing nodes, and each edge computing node is in communication connection with a plurality of data acquisition terminals. The application comprehensively considers the current running state of the edge computing system, network delay problems and running results after deployment, and performs service scheduling and deployment of Internet of Things edge computing node resources on each real-time data stream computing application. The application is suitable for the decision environment of the real-time computing task load and bandwidth dynamic change of the Internet of Things edge computing node, realizes global optimization from the system level, takes into account the single node load capacity, and realizes the optimal scheme and application planning and deployment decision suggestion of the long-period stable operation and real-time task flexible scheduling strategy.

The invention discloses a logistics life cycle shortening method and system in an Internet of Things environment, and relates to the technical field of Internet of Things, and the method comprises the steps: carrying out the identification of a cargo through an RFID tag, collecting the comprehensive data of the cargo, and carrying out the preprocessing; state information of goods is monitored in real time, a storage environment is adjusted, a storage position is allocated based on comprehensive data of the goods, an emergency degree value is allocated for each piece of goods, shelf layout and the storage position are adjusted, and a cloud data center receives data of each warehouse, carries out global inventory management and path planning and generates inventory layout data and path planning data. The multi-source sensor is used for monitoring the cargo state in real time, dynamically adjusting the storage environment and optimizing the warehouse space utilization rate, the shelf layout is adjusted in combination with emergency degree value distribution and an intelligent algorithm, it is ensured that high-priority cargos are quickly delivered out of a warehouse, the warehousing efficiency and the overall operation intelligence level are remarkably improved, and the labor intensity of workers is lowered. And a scientific basis and a dynamic optimization capability are provided for efficient shortening of the logistics life cycle.

The invention relates to the technical field of data processing, and discloses a green cloud data center energy saving method and system based on a sensor network. The method comprises the following steps: acquiring environmental parameters and energy consumption data of a data center by utilizing a multi-type sensor network; encrypting the data through a quantum key distribution technology; inputting a multivariate regression analysis model to construct an energy consumption evaluation model; adjusting ventilation and refrigeration parameters according to an analysis result; monitoring a renewable energy source state and formulating a priority strategy; load distribution and energy allocation are optimized, and intelligent control is achieved. Through safe and efficient data acquisition and processing, multi-dimensional energy consumption analysis and intelligent cooperative control, the problems of low energy efficiency and high carbon emission of a traditional data center are solved.

The invention relates to the technical field of cloud computing and network security crossing, in particular to a data plane multi-tenant traffic cleaning method and system oriented to a cloud data center, and the method comprises the steps: transmitting the summary information of a data packet to a control plane through a data plane; the control plane calculates a feature vector corresponding to each tenant based on the summary information, wherein the feature vectors reflect service traffic features and attack traffic features of the tenants; clustering the tenant feature vectors through a clustering algorithm to generate a cluster set; and generating a tenant decision tree mapping table and a tenant decision tree set according to the clustering cluster set, and deploying the tenant decision tree mapping table and the tenant decision tree set in a data plane. According to the method, the number of data plane decision trees is reduced by clustering similar tenants, the tenant capacity is improved under the limitation of switch hardware resources, and meanwhile, the generalization ability of a multi-tenant decision tree model is reserved.

The invention discloses a cloud data center virtual machine adaptive integration method based on Autoformer and an enhanced double-Q network, and relates to the technical field of cloud computing resource management and optimization, the resource utilization rate of each physical host is monitored in real time, historical load time sequence data is collected, a pre-trained Autoformer model is loaded, and double-Q network parameters and environment state variables are initialized; and predicting a host resource utilization rate based on an Autoformer model, and dividing an overload host, a low-load host and a normal host in combination with the current host resource utilization rate. According to the method, the Autoformer model is adopted for load prediction, trend terms and period terms are decomposed through an autocorrelation mechanism, the multi-scale periodicity in the cloud load is effectively captured, compared with a traditional LSTM model, the Autoformer is higher in modeling capacity for a complex time sequence mode, the misjudgment rate of an overload / low-load host can be remarkably reduced, a more accurate host state detection basis is provided for virtual machine integration, and the method has the advantages of being high in practicability and easy to popularize. Therefore, a resource allocation strategy is optimized.

The invention belongs to the technical field of image acquisition, and discloses a field image acquisition method and system. The method comprises the following steps: acquiring real-time field images and videos by using an edge computing gateway, and extracting a plurality of real-time key frame images; image segmentation is carried out, and the real-time attention of the real-time image area is obtained; lossless compression and dynamic encryption are carried out; collecting real-time network quality data of the transmission network, and generating a real-time data transmission strategy; uploading the plurality of encrypted real-time image compression packets to a cloud data center according to a real-time data transmission strategy; and restoring the plurality of encrypted real-time image compression packets of each real-time foreground image by using the cloud data center to obtain a restored real-time field image video. According to the invention, the problems of low data transmission efficiency, insufficient security, centralized processing bottleneck, high energy consumption and poor transmission reliability in the prior art are solved.

The invention relates to the technical field of intelligent medical treatment, in particular to a chronic disease whole-process health management system and method based on data driving, and the system comprises a doctor management end, a cloud data center and a patient intelligent terminal. The doctor management end has the following functions: a voice interaction module, a communication module, a data analysis module, a closed-loop management module and a dynamic feedback module, and the communication module is used for transmitting a real-time data instruction to the cloud data center. The patient intelligent terminal has the following functions: a voice interaction module, a communication module, a data analysis module, a data acquisition module and a dynamic feedback module, and the communication module is used for transmitting a real-time data instruction to the cloud data center. According to the invention, a chronic disease management system taking data as a core is constructed, full-closed-loop management is realized, and the problems of intervention lag, model staticization and low cross-modal data utilization rate are solved.

The invention belongs to the technical field of wind power dispatching, and discloses a distributed wind power real-time dispatching method and system based on edge calculation. The method comprises the following steps: a cloud data center constructs a wind power network model, a multi-modal data analysis model and a distributed wind power scheduling model, and deploys the multi-modal data analysis model to an edge computing gateway; the edge computing gateway is used for collecting real-time monitoring multi-modal data, and performing multi-modal data analysis by using a multi-modal data analysis model; the cloud data center is used for performing distributed wind power scheduling by using a distributed wind power scheduling model according to the wind power network model and the plurality of real-time data analysis results; and the cloud data center sends the real-time distributed wind power dispatching scheme to the wind power plant server of the wind power plant through the edge computing gateway. According to the invention, the problems of insufficient real-time performance, high system complexity and maintenance cost, limited data processing capability and lack of flexibility of a scheduling scheme in the prior art are solved.

The invention discloses a video image generation method and system driven by edge computing, and relates to the technical field of computer vision and edge computing, and the method comprises the steps: a terminal device initiates a generation task and perceives resources; dynamically dividing the generated task into a plurality of sub-tasks through a decision model based on the perception information, and deciding the execution position of each sub-task at the terminal, the edge node or the cloud; the nodes cooperatively execute the distributed subtasks and exchange intermediate data; and a final synthesis result is fed back to the terminal. The system comprises a terminal module, an edge node module, a cloud data center module and a collaborative management module. According to the invention, through terminal-edge-cloud cooperative computing, delay of a generation task and cloud bandwidth consumption are effectively reduced, data privacy protection is enhanced, system resource utilization efficiency and expandability are improved, and the method is especially suitable for video image generation application scenes with high real-time performance requirements.

The invention discloses a target detection pre-labeling method based on a fast algorithm, and relates to the technical field of computer vision and geometric processing, and the target detection pre-labeling method based on the fast algorithm comprises the following steps: S1, data acquisition and preprocessing; and the collected image data is transmitted to a cloud data center in real time through wireless transmission. According to the target detection pre-labeling method based on the fast algorithm, image information can be locked and recognized more accurately through operation of two times of pre-labeling, so that the collected image information can be recognized and screened more accurately, a certain workload can be reduced for subsequent manual auditing and correction, and the accuracy of target detection and pre-labeling is improved. And meanwhile, it is also ensured that the pre-labeling model can carry out accurate labeling processing on a relatively fuzzy or shielded image, and through operation of a YOLOv5 algorithm, the pre-labeling model can be more efficient and faster when carrying out image recognition.

The invention relates to an abnormal traffic detection method based on a sparse matrix, and belongs to the technical field of computer network security. Through sparse matrix compression storage, dynamic feature updating and an efficient calculation framework, the resource consumption is reduced, and the detection precision is improved. Experiments show that the detection delay is less than 30ms under the 100Gbps flow environment, the storage occupation is reduced by 87.5%, the F1-score on CIC-IDS2017 and UNSW-NB15 data sets reaches more than 96%, and the false alarm rate is less than 2%. The method is suitable for large-scale network scenes such as a 5G core network, a cloud data center and the Internet of Things, and the real-time anomaly detection efficiency and the resource utilization rate can be remarkably improved.

The invention discloses a system for predicting the bearing capacity of a prestressed concrete pile through a static pressure method. The system comprises a static pressure pile machine construction monitoring module, a digital static sounding module, a data receiving module, an interactive touch screen module, a bearing capacity calculation module and a cloud data center. The invention further discloses an implementation method of the static pressure method prestressed concrete pile bearing capacity prediction system, the feed-forward neural network model is used for correlation analysis of bearing capacity prediction data, input original data can be efficiently analyzed, after each time of model iteration, the updated model is transmitted to the on-site algorithm analysis unit through the 5G module, and the prediction result of the bearing capacity of the static pressure method prestressed concrete pile is obtained. Dynamic updating and optimization of the field model are achieved, the real-time performance and accuracy of prediction of the ultimate bearing capacity of the prestressed concrete piles are effectively improved through the mechanism, the limitation that a traditional acceptance method only depends on static load tests of a few piles is overcome, the bearing capacity of all the piles can be comprehensively evaluated, and potential safety hazards of construction are reduced.