1930 results about "Data traffic" patented technology

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

Provided are a device and a method for controlling data traffic by a core network control plane node. The method may include receiving, from an application function control entity, one or more pieces of information among help information on application traffic characteristics, a PDU-Set QoS parameter, an identifier for grouping flows belonging to a multi-modal application, and a synchronization threshold; transmitting, to a base station, one or more pieces of information among help information on application traffic characteristics, a PDU Set QoS parameter, an identifier for grouping flows belonging to a multi-modal application, and a synchronization threshold during a PDU session establishment or modification procedure; and instructing transmission of a user packet including PDU-Set information to a core network user plane node.

The invention relates to the technical field of intelligent traffic management, and discloses a road traffic signal lamp management system based on big data, which comprises a data acquisition and fusion module for acquiring traffic sensor data, visual data, GPS track data and external environment data, generating unified traffic state data through space-time alignment, data filtering and multi-modal data fusion; and the flow prediction module is used for predicting the traffic flow of a single intersection and a region by using a time sequence model and a graph neural network based on the traffic state data generated by the data acquisition and fusion module. Through multi-modal data acquisition and space-time alignment, Kalman filtering, Bayesian inference and other technologies, deep fusion of different data sources is realized, the comprehensiveness and accuracy of data are significantly improved, dynamic changes of a complex traffic scene can be truly reflected, high-quality basic data are provided for subsequent flow prediction and optimization control, and a core technical support is formed.

The invention discloses a load balancing control method based on data traffic, and relates to the technical field of data transmission. Comprising the following steps: constructing a flow monitoring module based on data flow, constructing a self-adaptive flow classification model, dynamically allocating weights to load nodes with balanced loads, dynamically adjusting a transmission path of the flow, carrying out real-time load balancing scheduling, and processing abnormal flow. According to the load balancing control method based on the data traffic, the self-adaptive traffic classification model and the multi-path traffic reconstruction algorithm are introduced, so that the defects in the prior art are fundamentally overcome. The real-time performance, the stability and the resource utilization rate of the system are remarkably improved through deep classification of the traffic characteristics and a multi-path dynamic segmentation strategy. According to the method, resource allocation can be optimized in a complex traffic scene, the load balancing efficiency is improved, meanwhile, the service quality of delay-sensitive and bandwidth-intensive services is guaranteed, and the availability and reliability of the whole system are enhanced.

This disclosure describes techniques and mechanisms for performing passive measurement for combined one-way latency, packet loss metrics along with liveness detection using customer data packets ingested at a sink node in hardware for Level 2 and Level 3 VPN services. The customer data packets are sampled and copied for measurement either at source node or sink node. The duplicated measurement packet headers are punted based on the IPV6 destination option type to hardware analytics engine at sink node for analytics that populates histogram bins using the timestamps from the packets. Using the transmitted packets during a period, and received packets in all the bins, packet loss is measured. Based on the packets received status, liveness state is detected by the sink node and notified to the source node.

The technology disclosed relates to detection of data traffic in computing environments, such as cloud environments. Example systems and methods detect a plurality of workloads in a virtual network in a computing environment and deploy a plurality of probe agents to the plurality of workloads. Each respective probe agent detects network traffic on a respective workload of the plurality of workloads, scans a data packet that is at least one of sent or received by the respective workload, generates a data classification relative to the data packet, and generates a scan result that includes packet payload information and an indication of the data classification. The scan results are received from the plurality of probe agents and a computing action is performed based on scan results.

Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and / or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.

The embodiment of the invention provides a micro-service flow anomaly monitoring method and device based on traffic data, and the method comprises the steps: receiving traffic data which is continuously uploaded by a road camera and comprises text data and image data, and storing and processing the traffic data through setting a micro-service, acquiring traffic log information when the traffic data is processed by the micro-service, calculating the volatility and the variation trend of the time sequence data, and constructing a model training data set by adopting a sliding time window method according to the traffic log information; collecting traffic log information of the micro-service per minute through a parallel mechanism, and inputting the traffic log information into the trained traffic data flow analysis model to obtain a peak ratio, a change rate and a duration output by the traffic data flow analysis model; processing different levels of early warning by adopting log recording, alarm pushing and automatic current limiting measures respectively; according to the invention, the micro-service flow anomaly monitoring efficiency and accuracy of traffic data can be effectively improved.

The invention discloses a dynamic load-oriented data processing task resource optimization method, device and system, and the method comprises the steps: constructing an optimization problem taking the total resource overhead of a system as an optimization target through continuously obtaining network time delay, the flow scale of a data processing task, the distribution of the data processing task in a network topology, and the data size of each client, and carrying out the optimization of the data processing task. Routing paths, aggregation positions and computing power resource allocation are continuously adjusted by solving corresponding optimization problems. According to the method, based on a dynamic load environment, resource configuration of a data processing task is optimized by adopting an intra-network aggregation technology, communication delay is reduced, and the overall performance of a system is improved. According to the dynamic load resource optimization system, the flexibility of the programmable switch is utilized, resources can be dynamically allocated and network flows can be adjusted according to changes of network conditions and data flows, and the utilization efficiency of network resources and the performance of data processing tasks are greatly improved.

The invention discloses a data flow monitoring method and system based on Internet communication, and relates to the technical field of data flow monitoring, and the method comprises the steps: carrying out the time-space modeling of historical features, constructing a flow mode library through three-dimensional features, combining dynamic period detection with multi-scale fusion, and extracting normal period behaviors and abnormal modes. Real-time exception collaborative verification is carried out, a threshold detector and a prediction detector which run in parallel are utilized, multi-dimensional scores are adjusted through scene perception dynamic weights, and real exceptions and false alarms are accurately distinguished; dynamic parameter correction is carried out, PID control parameters are optimized based on false alarm feedback and reinforcement learning, an exponential decay update mode library is combined, fusion of threshold adaptive adjustment and a long-term and short-term flow mode is realized, and the robustness of the system is improved. According to the invention, the precision and robustness of data traffic monitoring can be effectively improved, false alarms and missing alarms are reduced, and the method adapts to a complex and changeable network environment.

The invention relates to the technical field of financial data processing, in particular to a financial marketing platform data storage system and method, and the system comprises a data prediction regulation and control module, a hotspot recognition module, a storage medium adjustment module, a sensitive encryption module, a data backup module and a performance evaluation module. According to the method, by collecting and calculating the average value of the data traffic and the access time points of the financial marketing platform, the time period with high access frequency can be accurately predicted, the data traffic can be effectively managed, the probability of system overload can be reduced, and by matching suitable storage media such as SSD and HDD for different types of data, optimization is carried out according to the access speed requirement, the access speed of the financial marketing platform can be greatly improved. The data processing speed and cost efficiency are remarkably improved, differentiated encryption processing and backup are performed on the data, and a security policy is designed according to the sensitivity level of the data, so that the operation efficiency of data storage is effectively monitored and improved, and the efficiency and security of a data storage system are improved.

The invention provides an SDN (Software Defined Network) inter-domain traffic engineering method based on reinforcement learning, which comprises the following steps of: deploying a data traffic demand monitoring platform and a control system, and constructing a global network topological graph; calculating a short link identifier for the link in the network and distributing the short link identifier to each network device; flow judgment is carried out, upward notification is carried out according to requirements, and pre-operation of intelligent routing is cooperatively completed; deploying a reinforcement learning model in the total intelligent body, outputting an optimal cross-domain path strategy to the cooperative controller, disassembling the optimal cross-domain path strategy into flow table rules which can be executed by each domain, and issuing the flow table rules to local controllers of related domains; and each local controller pushes the flow table configuration to the domain switching equipment to complete the forwarding decision of the flow. According to the method, a complete closed-loop process of flow measurement, intelligent decision making, cross-domain control and path issuing is realized, feasible reference is provided for actual deployment of an intelligent network, and the method has good engineering popularization value and is suitable for intelligent scheduling scenes such as an operator backbone network, an industrial internet and metro edge cloud.

The invention relates to the field of group quality management, in particular to a group quality management system and method based on a dynamic data source. Comprising a data source management module, a quality data acquisition module, a quality data storage and management module, a quality data analysis and statistics module, a quality control and decision support module, a user management and authority control module and a system setting and management module which are connected through data interfaces, the data flow, the use frequency and the updating condition are monitored, and an early warning mechanism for data source connection abnormity, data access timeout and data conflict is arranged. According to the method, data integration and sharing can be improved, the accuracy of data quality is improved, user experience is improved, and safety is enhanced. According to the method, quality abnormity and fluctuation can be quickly and accurately found, targeted control measures are taken in time, quality problems are restrained in the bud state, and defective products and waste products are effectively reduced.

The invention discloses a multi-link fusion method and system based on an SD-WAN (Secure Digital Wireless Area Network) technology, which can timely discover and deal with the condition of network failure or performance reduction by monitoring network state parameters of a plurality of network links in real time, and is helpful for ensuring reliable network connection at any time point, thereby improving the reliability and the stability of the network, and improving the reliability and the reliability of the network. By dynamically adjusting distribution of data traffic, the optimal link combination can be selected for data transmission, so that congestion is avoided, delay is reduced, the packet loss rate is reduced, the utilization rate of network bandwidth is maximized, the data transmission efficiency is remarkably improved, the routing optimization step can quickly respond to service changes by utilizing flexibility and programmability of the SDN technology, and the service quality is improved. According to the method, the continuity and the high efficiency of services are ensured by optimizing data transmission routes, existing network resources can be fully utilized through multi-link fusion and intelligent scheduling, excessive dependence on a single link or an expensive high-performance link is avoided, and reduction of network construction and operation and maintenance cost is facilitated.

The invention discloses a mass data collection and storage method and system based on a distributed architecture, and relates to the technical field of distributed storage, and the method comprises the following steps: monitoring data traffic in real time, and collecting node resource states; according to the data traffic and the node resource state, the load condition and the data traffic change trend of each current node are analyzed, and according to the analysis result, resource allocation is dynamically coordinated through a distributed architecture; based on the coordinated resource allocation, evaluating the mobility level of the data and classifying the data, and performing adaptive migration and storage on the data according to a classification result; and data mobility changes are monitored in real time, and resource allocation and storage strategies are optimized. According to the method, the access efficiency of high-frequency access data is improved, resource allocation is optimized, and finally the storage efficiency and the overall performance are remarkably improved.

The invention relates to the technical field of network communication and route management, in particular to an intelligent router flow management method for a home scene, which comprises the following steps that: a home router collects network activity data of equipment, including data flow size, connection frequency and equipment type, performs cleaning and formatting processing, and stores the data flow size, the connection frequency and the equipment type; formatted device behavior data is generated. According to the method for analyzing the behavior data of the home network equipment based on the long and short-term memory network, the prediction precision of the periodicity and dependency of user behaviors is improved, and the allocation of bandwidth resources is more prospective and dynamic. Specifically, resources can be automatically adjusted according to predicted data requirements, and particularly in the peak period of requirements, it is ensured that key applications such as video conferences and online education preferentially obtain necessary network resources. According to the flow management technology, the overall operation efficiency of the network is improved by optimizing bandwidth utilization and reducing network congestion.

The invention discloses a data quality monitoring method and system, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a data flow fluctuation curve of a target data table, and constructing a data quality baseline library according to the data flow fluctuation curve; extracting abnormal data features based on the data quality baseline library, and generating a feature defect matrix corresponding to the abnormal data features; screening rule atoms from a preset rule pool according to the characteristic defect matrix, and constructing a rule atom link corresponding to the rule atoms; performing service scene adaptation on the rule atomic link to obtain a corresponding quality monitoring rule set; and performing data scanning on the target data table based on the quality monitoring rule set to obtain an abnormal data scanning result, and sending the abnormal data scanning result to a corresponding associated person. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of data quality monitoring is achieved.

The invention provides an intelligent gateway equipment interconnection method and system based on a Bluetooth ad hoc network protocol, and is applied to the field of communication data processing. Through adaptive node management, real-time updating of a network topology structure and optimization of a connection priority, communication stability and load balance are ensured, generation of a network bottleneck is avoided, data can be uniformly distributed among a plurality of intelligent gateways through intelligent data flow distribution, overload of a part of gateways is avoided, the overall system efficiency is improved, and the system performance is improved. And local execution of edge computing optimizes the response speed and reduces delay, and key control tasks such as equipment linkage, data distribution and abnormal response are cooperatively processed through a distributed computing architecture, so that the flexibility and the processing capability of the system are improved.

A method of providing clear text representing encrypted data to an entity that does not support an encryption / decryption protocol used to encrypt / decrypt the data is disclosed. A call to an encryption / decryption function implemented in a worker node is detected, wherein the call is to trigger encryption or decryption of a packet. In response to detecting the call, a clear text payload of the packet is captured from an entry point or an exit point of the encryption / decryption function. A modified packet is then created based on the captured clear text payload, including synthesizing a plurality of headers for the modified packet and appending the plurality of headers to the clear text payload. The modified packet is then sent to a processing entity that is external to the worker node.

Various embodiments include systems and methods of anomalous data transfer detection. Hotspots for an asset of an organization may be determined, corresponding to period(s) of time in which outbound data from the asset satisfies a hotspot threshold determined to be indicative of high outbound data traffic activity. Based on the outbound data, a first set of days are identified as “quiet” day(s); a second set of days are identified as “active” day(s); and “quiet” hour(s) of the day, associated with “active” day(s), are identified. The “quiet” day(s) and the “quiet” hour(s) are identified as a warmspot dataset, which may be utilized to detect anomalous data transfer activity associated with the asset. Detecting the anomalous data transfer activity includes computing one or more statistics on the warmspot dataset. Responsive to detecting the anomalous data transfer activity, an alert associated with the asset may be generated.

The invention discloses a multi-split cross-platform distributed monitoring system state data acquisition method, which belongs to the field of system operation and maintenance and data monitoring, and comprises the following steps: acquiring node data through a data acquisition module arranged on a distributed monitoring system node, and introducing a self-adaptive acquisition strategy into the data acquisition module, adaptively adjusting the node data acquisition frequency according to the resource utilization condition; a node data monitoring strategy is formulated according to user requirements, and the monitoring strategy is optimized by using a hybrid monitoring algorithm; a Delta compression technology is utilized to compress node data to obtain a compressed data packet, the compressed data packet is transmitted, and a multi-level transmission optimization mechanism is introduced in the transmission process of the compressed data packet. According to the method, the technologies such as message bus publishing and subscribing, a customized data acquisition strategy and data flow optimization are integrated, so that consistent state monitoring can be realized among different platforms and hardware architectures, and the efficiency and reliability of system operation and maintenance are greatly improved.

The invention relates to the technical field of intelligent electric energy meters, in particular to an intelligent electric energy meter system based on an RF net-shaped ad hoc network and a communication method of the intelligent electric energy meter system. The system adopts an intelligent electric energy meter with an integrated gateway function as a gateway node in an RF network and uploads power utilization data and state information to a head end system; independent gateway equipment does not need to be additionally deployed, so that the hardware procurement cost, the installation and debugging cost and the long-term operation and maintenance cost are remarkably reduced; meanwhile, according to the intelligent electric energy meter system communication method based on the RF net-shaped ad hoc network, the optimal main path and the optimal standby path are selected for data transmission by dynamically calculating the comprehensive routing metric value, the reliability of the network is greatly improved through the multi-path redundancy design, and the system has the higher fault self-healing capacity; in addition, the intelligent electric energy meter integrated with the gateway function can monitor and optimize the network topology structure, the data flow distribution and the node resource utilization rate in real time through a built-in network management system, and the data transmission efficiency and the network stability are further improved.

The invention discloses a multi-model fusion efficient intrusion detection method and system. The method comprises the steps of removing abnormal values in sample data, screening time features by using TSODE, screening fixed format features by using a user-defined regular specification, and screening statistic features by using Z-score. Using a sliding window method to confirm minority classes conforming to decision boundary properties, using BorderLine-SMOTE to generate samples, and using ENN verification to generate samples; extracting spatial features of the sample data by using TCN, and extracting traffic features of the sample data by using BiLSTM; fusing and normalizing the time and space features; counting sample mean values by using time windows with different time step lengths to generate an initial weight matrix, and calculating a QKV matrix; based on an attention mechanism result, outputting a classification result in a full connection layer; through deep extraction and fusion of time and space features by combining BiLSTM and TCN networks, a multi-head attention mechanism is proposed, and the problems of unbalanced sample distribution and unbalanced feature importance in traffic data are effectively solved.

The invention provides a trusted traffic data distributed storage system based on a main side chain, and the system comprises a traffic collection module which is used for carrying out the real-time collection of encrypted traffic data generated in a network, and carrying out the preliminary standardization processing of the encrypted traffic data, and obtaining the standardized encrypted traffic data; the traffic side chain certification module is used for carrying out integrity verification and authenticity construction on the standardized encrypted traffic data to prevent the data from being tampered or forged, so as to form verified encrypted traffic data; the traffic side chain data module is used for carrying out structured storage on the verified encrypted traffic data and reserving queriable records in a side chain; and the trusted main chain module is used for recording data abstracts, verification records, access behaviors and model voting result information on the side chain. According to the method, a block chain decentration trust mechanism and an encryption index mechanism are combined, encryption state organization and rapid retrieval of the data are achieved, and confidentiality and controllability of the data in the storage and circulation process are guaranteed.

The invention provides a service system disaster recovery system based on cloud computing, and relates to the technical field of disaster recovery backup recovery and cloud computing, and the system comprises a software application registration and arrangement module which is used for registering an entity object of a service application to a software warehouse, arranging a topological structure of the service application according to a dependency relationship, and configuring a disaster recovery strategy; the software application deployment disaster recovery module is used for automatically deploying a pull-up service application and backing up the pull-up service application according to a disaster recovery strategy; the fault monitoring and damage decision-making module is used for performing fault monitoring and damage decision-making on the application instance, and sending out an alarm notification when a fault or a disaster is monitored; and the system recovery disaster recovery switching module is used for executing system recovery operation when receiving the alarm notification, executing disaster recovery switching operation such as upstream data flow and service access entry redirection, and realizing system recombination recovery and service replacement. The invention further provides a service system disaster recovery method based on cloud computing, electronic equipment and a storage medium.

The invention relates to a valve fault diagnosis method and system based on a machine learning model and a mechanism model. The method comprises the steps that working condition classification marking is conducted on operation data of a valve through an ARX model; when the operation data under the same working condition is single-loop operation data, flow prediction is carried out in a machine learning model, deviation calculation is carried out on a generated flow prediction value and an actual flow value, and a flow deviation value under the current working condition is obtained; when the operation data under the same working condition is multi-loop operation data, analyzing the flow characteristics of the operation data by using the mechanism model, and performing deviation calculation on the obtained flow characteristic value and the flow characteristic reference value to obtain a flow characteristic deviation value under the current working condition; all the flow deviation values or the flow characteristic deviation values are traversed to be compared with a deviation value threshold value, and the fault type of the valve is determined according to a comparison result. According to the method, the accuracy, the real-time performance and the universality of valve fault detection in the process industry are improved.

The invention relates to an edge computing-oriented distribution task self-adaptive load prediction method and system and a medium, and aims to solve the problems that a traditional resource configuration mode is difficult to meet the quality requirement of content distribution along with explosive increase of mobile data traffic, large-scale invalid data transmission is often caused, and the service quality is influenced. According to the task-sending adaptive load prediction system based on edge computing, content caching and intelligent resource allocation are carried out at network edge nodes, so that the data transmission delay is remarkably reduced, and the response speed is increased. The system drives local workload prediction through incremental expression of historical service data, introduces a position information coding mode based on a Transform architecture, and effectively identifies and processes key features in load changes, so that the resource utilization rate of edge devices is improved, and efficient management of the edge devices is realized.

The invention discloses a data management system and method based on a heterogeneous platform, and belongs to the technical field of data management of a big data service platform, and the method comprises the steps: firstly collecting the protocol features and task metadata of a multi-source heterogeneous platform in the big data service platform, and constructing a dynamic knowledge graph according to the interaction relationship between the platforms; analyzing the uploaded data by using a knowledge graph, classifying and marking real-time levels according to a data format and task priorities, and formulating a priority scheduling strategy; data transmission paths and resource allocation are dynamically adjusted by monitoring platform data traffic, processor and memory usage in real time and combining data real-time performance and a scheduling strategy; on the basis, abnormal data in transmission are monitored and recognized, abnormal features are extracted, an abnormal data monitoring model is constructed, and abnormal information fed back by the model is used for dynamically updating the knowledge graph and continuously optimizing a data classification and scheduling strategy.

The invention relates to the technical field of communication route searching, in particular to a photovoltaic power station data transmission and monitoring system based on the 5G technology, and the system comprises a route state monitoring module which carries out the collection and statistics of the time delay value, bandwidth utilization rate and congestion degree value of each transmission link in a 5G base station service area, and generates a link state parameter set. According to the invention, the time delay value, the bandwidth utilization rate and the congestion degree value of each transmission link in the 5G base station service area are collected and counted, and the resource utilization efficiency is calculated based on the network load balance value and the energy consumption threshold value, so that the link state evaluation has dynamics and real-time performance, and the availability of different links can be described. And the links are calculated and screened based on the energy consumption load ratio to form a priority sequence of the transmission paths, so that the data traffic can be preferentially allocated to the link with the optimal transmission efficiency, and the occupation of the overall bandwidth by invalid traffic transmission is reduced.

The invention relates to the technical field of power production communication, and discloses an optical fiber transmission and multi-channel communication scheduling fusion method, system and device based on emergency communication switchover, and a medium, and the method comprises the steps: carrying out the unified protocol packaging of data of a production scheduling service, and obtaining a production scheduling service data independent transmission protocol stream; the method comprises the following steps: receiving a production scheduling service data independent transmission protocol stream to be transmitted, and pre-distributing a wavelength channel in an optical fiber transmission channel for the independent transmission protocol stream according to the priority and data traffic of a production scheduling service; carrying out independent transmission protocol stream transmission in the optical fiber transmission channel, adjusting the transmission adaptability of a target channel in multiple channels according to an emergency communication switching requirement, and switching the optical fiber transmission channel to the target channel; the target channel comprises a 5G private network channel and a satellite channel. According to the method, flexible adaptation and data consistency of production scheduling service transmission can be ensured, and the emergency scheduling communication capability and efficiency are improved.