1361 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.

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 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 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 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.

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 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.

Methods and apparatus for providing L4S support with regard to non-3GPP untrusted / trusted accesses in which a user equipment (UE) is enabled to monitor for uplink and / or downlink congestion with regard to L4S uplink data traffic flows at the UE, detect congestion, and perform explicit congestion notification (ECN) markings or communicate congestion information to perform ECN marking to another device are described. In some embodiments, the UE device indicates detected congestion, at the UE, in an uplink (UL) traffic flow via performing ECN marking of an innermost IP header ECN field. In some other embodiments, the UE device communicates detected congestion information, via a GRE message to a core network interface device, which communicates, via GTP-U, the detected congestion information to a UPF, which then uses the received UE detected congestion information to performs ECN marking in the innermost IP header of an uplink data packet.

The invention discloses a method and a system for realizing hosted API automatic testing by an API gateway, belongs to the technical field of computer software, and aims at solving the technical problems of how to realize non-intrusive and automatic API testing, ensuring that monitoring and verification can be directly carried out on an API flow level, improving the testing efficiency and the coverage rate and improving the testing efficiency. According to the technical scheme, the method comprises the steps of test case generation, wherein an interface structure is automatically analyzed based on an API document, and a basic test case set is generated; flow capturing: monitoring the actual calling condition of the API in real time, and recording the specific content of each request and the corresponding response data; carrying out flow playback: carrying out playback test based on the captured historical flow, and verifying the compatibility of the API; assertion verification: assertion judgment is performed on indexes of the return value and the response time through an assertion mechanism, and abnormity is found; and test report generation: aggregating test result data, generating a test report containing success rate, response time and error log detailed information, and supporting HTML / PDF / JSON export.

Apparatuses, methods, and systems are disclosed for multipath QUIC for ATSSS using stream-aware steering mode. One method includes establishing a multiaccess data connection with a mobile communication network. The method includes establishing a plurality of QUIC connections, each QUIC connection supporting data transmission with a mobile communication network over first and second access networks. The method includes detecting a data packet to be transmitted via the multiaccess data connection and selecting a first steering rule from a configured set of steering rules. The method includes selecting a first QUIC connection of the plurality of the QUIC connections. The method includes allocating a new stream in the first QUIC connection, where the new stream is configured to transmit data traffic using a steering mode of the first steering rule and sending the data packet using the new stream.

The invention provides a method and system for judging suspicious traffic in encrypted traffic, and the method comprises the steps: collecting to-be-detected encrypted traffic, and extracting the encrypted traffic features of the to-be-detected encrypted traffic; wherein the encrypted traffic feature comprises a first traffic feature and a second traffic feature, and the second traffic feature comprises domain name popularity; the second traffic feature is determined based on a malicious neighborhood value determined according to the relation graph; the relation graph comprises domain name nodes, entity nodes, edges connecting the entity nodes and edges connecting the entity nodes and the domain name nodes, and the edge attributes of the edges of the relation graph comprise communication related data and traffic types; determining the traffic type of the to-be-measured encrypted traffic based on the encrypted traffic feature of the to-be-measured encrypted traffic, the traffic type including normal traffic and suspicious traffic; and in response to the fact that the traffic type of the to-be-tested encrypted traffic is suspicious traffic, performing subsequent decryption analysis on the to-be-tested encrypted traffic through a decryption DPI module.

There is provided an apparatus in a user equipment, the apparatus comprising: means for receiving, from a core network entity, at least one user equipment route selection policy rule, wherein the at least one user equipment route selection policy rule comprises a component with information associated with a source device that is able to request data traffic via the user equipment, and means for receiving, from an application of the source device, a request for uplink data traffic, wherein the request comprises information about the source device. The apparatus further comprising means for determining how to route the data traffic from the application, using the at least one user equipment route selection policy rule and based on the received information about the source device in the request, to determine a route, and means for routing the data traffic according to the determined route.

The invention discloses a scheduling instruction real-time monitoring system for streaming data processing, which relates to the technical field of streaming data processing and comprises a data access module, an instruction analysis module, a dynamic scheduling strategy module, a real-time monitoring module, a scheduling execution module, a visual monitoring interface, an exception handling module and an extension module. The data access module is used for receiving a multi-source heterogeneous data stream in real time and carrying out protocol analysis and priority marking on input data, and the instruction analysis module is connected with the data access module and is used for extracting a scheduling instruction in the data stream and converting the scheduling instruction into executable operation; and the dynamic scheduling strategy module generates a dynamic scheduling strategy based on the real-time data traffic, the system load and a preset rule. Through the dynamic scheduling strategy module and real-time monitoring feedback, the system can automatically adjust resource allocation according to real-time load prediction, and dynamic balance of high resource utilization rate and low delay is achieved.

The invention provides a large-scale encrypted traffic frame-by-frame clustering analysis method based on a big data architecture, and relates to the technical field of big data, and the method comprises the steps: carrying out the data partitioning and storage of target encrypted data obtained through the preprocessing of original encrypted traffic data; based on a clustering visualization result obtained by visualizing a target clustering result obtained by carrying out frame clustering analysis on the target encrypted data, judging whether a data traffic abnormal behavior exists or not, and when the data traffic abnormal behavior exists, generating an abnormal analysis report; and an abnormal analysis report is transmitted to safety management personnel so as to take corresponding measures in time for defense. The method comprises the following steps: visualizing a target clustering result obtained by carrying out frame clustering analysis after processing and partition storage on encrypted traffic data based on a big data architecture, identifying traffic data exception, generating an exception analysis report when the exception exists, and transmitting the exception analysis report to safety management personnel to take corresponding measures for defense. The network threat identification capability is effectively enhanced, and the overall protection level of network security is further improved.

The invention relates to the technical field of computers, in particular to a multi-path routing system and system based on traffic prediction, and the system comprises a traffic prediction module which is used for obtaining historical data of network traffic; predicting the network flow in a period of time in the future based on the historical data; the route selection module is in communication connection with the traffic prediction module and receives predicted traffic data sent by the traffic prediction module; a plurality of paths meeting the bandwidth requirement are selected for data transmission; the data transmission module is in communication connection with the route selection module and executes data transmission according to the multiple paths selected by the route selection module; the network monitoring module is in communication connection with the data transmission module and monitors data traffic in a network in real time; sending the real-time flow data to a routing selection module; the route adjustment module is in communication connection with the route selection module and the network monitoring module and is used for receiving the real-time flow data sent by the network monitoring module; and dynamically adjusting the path selected by the routing module based on the real-time flow data.

The invention discloses a medical data center network risk assessment method based on a random walk model. The method comprises the following steps of 1, obtaining topological structure information and a node set of a medical data center network; 2, constructing a weighted undirected graph according to the topological structure and the data traffic information between the nodes; 3, in the weighted undirected graph, performing simulation based on a random walk model to obtain a transition probability matrix between nodes; 4, calculating a node risk score of each node according to the transition probability matrix; 5, performing aggregation processing on the node risk scores of the nodes to obtain an overall network risk score; and step 6, generating an assessment report according to the overall network risk score. According to the method, the risk conduction probability between the nodes is quantified by constructing the combination of the weighted undirected graph and the random walk model, the limitation of a traditional static assessment method is broken through, and a cross-node risk propagation path caused by data flow can be identified.