765 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.

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 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 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.

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.

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 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.

The invention relates to a data real-time processing method based on elastic aggregation expansion of edge computing resources. A global resource view is constructed by collecting hardware resource data and software state data of edge nodes; logically aggregating the plurality of edge node resources to form a logic resource pool; monitoring a resource pool load rate and a task average time delay in real time, predicting data traffic through an LSTM model, and performing resource capacity expansion when any trigger condition is met; acquiring to-be-processed data through the edge gateway, and performing task scheduling in combination with the global resource view, the expanded logic resource pool and a preset scheduling rule; node load and task state are monitored in real time, and a task migration mechanism is started when a threshold value is exceeded. According to the method, the resource utilization rate and the dynamic adaptability are improved through elastic resource aggregation and capacity expansion; a task scheduling strategy is optimized, and task processing efficiency and execution of high-priority tasks are guaranteed; and the data processing accuracy, the global cooperation capability and the system reliability are enhanced.

The invention relates to the technical field of data processing, and discloses an Internet of Things gateway data grading processing method and system based on edge computing, and the method comprises the steps: collecting original operation data and a data traffic value received by an Internet of Things gateway; performing feature extraction and clustering grouping according to the original operation data, calculating an average influence coefficient of samples in a group in combination with a preset service weight, and determining an importance level corresponding to the group; important data is screened out for distribution immediate evaluation; performing aging characteristic analysis according to the important data, and predicting a data processing priority; analyzing resource demand allocation according to the priority sequence and the data flow value, and constructing a resource allocation proportion; according to a resource allocation proportion and an allocation proportion lower limit, identifying nodes with insufficient proportions and optimizing the priority sequence to obtain a final scheduling scheme; according to the final scheduling scheme, the processing sequence is applied to the original operation data, and data grading output is obtained. According to the method, efficient and accurate data processing is realized.

Techniques are described for routing traffic through an interconnect cloud gateway based on cloud traffic routing indicators. The interconnect cloud gateway can advertise the cloud traffic routing indicators, which can include cloud indicators and transport gateway indicators. The cloud indicators can include cloud tags utilized to route cloud traffic. The transport gateway indicators can include transport gateway flags utilized to identify private networks utilized to route the cloud traffic. The cloud traffic can routed during normal private network operation through private networks, which can be dynamically replaced by public networks due to occurrences of failures preventing the data traffic from being routed through the private networks and to cloud networks.

An electronic device is provided. The electronic device includes a communication module, and a processor connected thereto. The processor is configured to identify at least one of a plurality of parameters of traffic of data transmitted and received in a specific executing application, by using first short-range wireless communication, detect a data traffic pattern based on the identified parameter, and execute, based on the detected pattern, a communication mode including at least one configuration value related to the first short-range wireless communication. Executing the communication mode includes at least one of being capable of operating in a same frequency band as the first short-range wireless communication, and adjusting a scanning cycle between the first short-range wireless communication and second short-range wireless communication including another short-range wireless communication, setting a priority order of a transport packet, adjusting a channel scanning ratio of the first short-range wireless communication, or establishing boosting for operations of the traffic of the data.

The invention relates to the technical field of network communication, in particular to a multipath transmission load balancing method and system based on network state analysis. The method comprises the steps of collecting network state information of each transmission link from network equipment, calculating and obtaining the weight of each transmission link according to the collected network state information, setting a flow distribution strategy according to the weight obtained by calculation of each transmission link, and distributing data flow to each transmission link in proportion, and collecting the network state information of each transmission link in real time, updating the weight of each transmission link according to the change of each parameter in the network state information, performing threshold judgment on the updated weight, and dynamically adjusting the flow distribution strategy according to the weight meeting the threshold requirement. According to the method, the defects of a traditional static strategy can be effectively overcome, link congestion or idle is avoided, high efficiency and stability of data transmission are guaranteed, and finally the requirement of a user for high-quality network service is met.

The invention relates to the technical field of software development, in particular to a zero-code protocol analysis and full-link visualization commissioning and testing method for a heterogeneous terminal, which comprises the following steps of: acquiring protocol configuration information of the heterogeneous terminal input by a user through a visual interface, analyzing communication parameters and message rules in the protocol configuration information, and performing full-link visualization commissioning and testing on the protocol configuration information. Constructing a standardized protocol model instance; and initializing a communication server according to the communication parameters in the protocol model instance, and establishing a one-to-one mapping binding relationship between the network monitoring port and the protocol model instance. According to the method, the data flow of a network monitoring port is monitored, the corresponding protocol model instance is called based on the protocol routing rule to execute bidirectional data automatic conversion and full-link adjustment and test, a transparent test channel which is running as configuration is constructed, and the black box operation defect that rule definition and effect verification are separated in the past is overcome; and fusion and integration of massive stock terminals to a unified service platform are realized.

A system for AI-supported cybersecurity and regulatory compliance in energy distribution networks, consisting of: a hardware-embedded data acquisition module configured to intercept, capture, and time-stamp operational data streams and to control data traffic from SCADA (Supervisory Control and Data Acquisition) systems, AMI (Advanced Metering Infrastructure) systems, and energy management systems (EMS) via multiple communication protocols without operational latency; an FPGA-based deep packet inspection unit coupled with the data acquisition module, wherein the FPGA firmware is configured to perform line rate filtering, protocol decomposition and metadata extraction of the acquired data and forwards preprocessed packet data to an AI processing unit; an AI processing unit consisting of a multi-core central processing unit (CPU), a dedicated AI accelerator selected from a graphics processing unit (GPU) or a tensor processing unit (TPU), and a volatile memory buffer; a response orchestration module that is communicatively coupled with network management devices and operations controllers, wherein the response orchestration module is configured to perform automated security and compliance remediation measures, including network isolation of compromised segments, enforcement of protocol encryption, and privilege revocation; and an immutable audit logging subsystem configured to record all detected events, compliance assessments, and corrective actions in a blockchain-based distributed ledger, with each log entry cryptographically anchored with a secure hash value and digitally signed with keys stored in a secure hardware enclave.

OF THE DISCLOSURE Apparatuses, methods, and systems are disclosed for establishing a multi-access data connection with an external access path. One apparatus (500) includes a transceiver (525) that communicates (705) with first and second mobile networks and a processor (505) that sends (710) a request message to the first mobile network to establish a multi-access data connection, the request message containing an external access path request. The processor (505) receives (715) a response message that accepts the request to establish a multi-access data connection and that contains a set of rules indicating how the apparatus (500) is to route data traffic across a set of access networks in the first mobile network and across an external access path over the second mobile network. The processor (505) communicates (720) with a UPF in the first network over the set of access networks and the external access path.

A method performed by a user equipment (UE) for dynamically allocating data transmission resources in a massive multiple input multiple output (MEMO) system is disclosed. The UE receives from a network node at least one initial channel state information reference signal (CSI-RS) resource set comprising at least an initial number of ports. A sounding reference signal (SRS) is transmitted to the network node. The network node transmits to the UE a dynamically allocated number of CSI-RS ports, or alternatively, a trigger state for a predefined CSI-RS resource set and a beamformed CSI-RS. The UE computes one or more parameters corresponding to channel state information (CSI) from the beamformed CSI-RS. A CSI report comprising the one or more parameters is transmitted to the network node. The UE then receives user data over a data traffic channel using the dynamically allocated number of CSI-RS ports, or alternatively the pre-defined CSI-RS resource set.

The invention provides a performance test and evaluation method, system and device for a power grid credential environment and a medium, and the method comprises the steps: obtaining real-time data of each node through employing a distributed test probe and a data flow distribution strategy, and constructing a digital twinborn body of the power grid credential environment; the digital twin body comprises twin nodes corresponding to the nodes; extracting multi-dimensional performance test data of each twin node under the performance test load by adopting a classification dimension convergence method; and based on the performance test prediction data of each twin node, performing risk assessment on each twin node by adopting an entropy weight-comprehensive sorting algorithm. According to the method, dynamic load balance of each node is ensured through a data traffic distribution strategy, normal operation of the power grid credential environment is prevented from being influenced during test evaluation, and the method is suitable for a complex scene in which the power grid credential environment is easily impacted by a high load; data evaluation is carried out through performance test prediction data, data exception can be captured, and potential performance risk bottlenecks of a power grid credential environment can be found in advance.

The invention relates to the technical field of communication network optimization, and discloses a communication network performance monitoring optimization method and system. The method comprises the following steps: acquiring performance nodes influencing transmission quality in a communication network, and constructing a network transmission quality model; configuring a network monitoring probe model, performing performance test, acquiring the time delay detection capability and the packet loss rate detection precision, and integrating the network monitoring probe model and the packet loss rate detection precision into a network transmission quality model; calculating the total data flow load in a preset time window, obtaining a theoretical monitoring load in combination with the time delay detection capability of the probe, generating a data sampling frequency control instruction based on the theoretical monitoring load and the packet loss rate detection precision, and dynamically adjusting the working frequency of a probe data acquisition module according to the data sampling frequency control instruction. And the optimization effect of the method on the network transmission quality is evaluated. According to the method, by dynamically adjusting the sampling frequency, accurate adaptation to the network state is achieved, the real-time performance of monitoring and the resource utilization rate are improved, meanwhile, multi-dimensional performance indexes are integrated, and comprehensive support is provided for network transmission quality optimization.

The invention discloses a network data packet processing method, system and device based on an XDP and DPDK collaboration mechanism and a storage medium, and the method comprises the steps: carrying out the filtering of network traffic through an XDP program in a network card drive layer of a kernel mode, and redirecting to a DPDK program of a user mode; and performing related operations, such as forwarding, encryption and decryption, NAT and the like, on the redirected data packet through a DPDK program in a user mode according to the heat preservation processing logic of the DPDK configuration file or the set processing logic. According to the method, different XDP programs can be loaded for different network ports, and the method has good function extensibility; and irrelevant or malicious messages are discarded in a kernel mode, so that the occupancy rate of a memory and a CPU (Central Processing Unit) is greatly reduced, and then the data traffic forwarding rate is improved through the DPDK.

The invention relates to the technical field of wireless communication, in particular to a vehicle clustering and inter-cluster resource set scheduling method and system, and the method comprises the steps that a vehicle node maintains a neighbor table containing a neighbor motion vector, a relative distance and wireless resource perception in real time; calculating a cluster head factor fusing movement stability and a resource utility factor based on the table, selecting a node with the minimum cluster head factor as a cluster head, and broadcasting a clustering invitation; the free node calculates a clustering factor based on the speed difference with the cluster, the distance and the signal receiving strength, and determines whether to apply for clustering; the cluster members collaboratively perceive the resource occupation state and report the cluster head, and the cluster head selects a static or dynamic division mode to allocate a cluster resource set according to a data traffic scene. According to the method, while the stability of the cluster structure is ensured, the resource sensing accuracy is remarkably improved, the selection conflict is reduced, the resource requirements of different traffic scenes are efficiently adapted, and the resource availability and utilization efficiency are improved.

The embodiment of the invention provides a tag data processing method, RFID middleware and a storage medium. The method comprises the following steps: receiving and caching tag data of a plurality of tags; compressing the label data according to a predetermined compression mode to obtain compressed data; wherein the compression mode is determined according to a data compression level and a label inventory rate; and sending the compressed data. According to the embodiment of the invention, the data processing efficiency is improved, the equipment resource occupancy rate, the data traffic and the bandwidth occupancy of network transmission of middleware at a high inventory rate are greatly reduced, and meanwhile, the mass data processing pressure of an upper platform is reduced. According to the embodiment of the invention, the platform side can receive total label data, and algorithm application development is facilitated. In addition, according to the embodiment of the invention, the packet number and the data compression level are adjusted according to different service requirements, and the method is suitable for service scene requirements with different data volume and time delay sensitivity.

The invention relates to the technical field of photovoltaic power station safety monitoring, in particular to a mountain flexible photovoltaic support monitoring method and system, and the method comprises the steps: collecting the vibration frequency, strain, inclination angle and other multi-dimensional data of a support in real time through a distributed sensor array, and transmitting the data to a cloud platform through an encryption transmission protocol; after the cloud platform performs cleaning, normalization and standardization processing on data, real-time analysis is performed based on a predefined safety threshold, accurate identification and early warning of an abnormal state are realized, a distributed time sequence database is established for long-term data storage, multi-dimensional query and analysis are supported, a containerized elastic framework is adopted, resources can be dynamically allocated according to data traffic, and the data flow is dynamically allocated. And perfect remote equipment management and visual monitoring functions are provided. According to the invention, all-weather intelligent monitoring of the mountain flexible photovoltaic support is realized, the monitoring efficiency and the system reliability are significantly improved, and effective guarantee is provided for safe operation of a photovoltaic power station.

The invention discloses a traffic shaping scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target service type of a first data stream at a first time, and obtaining a second data stream at a second time corresponding to the target service type; the second time is before the first time and comprises flow data of a plurality of time periods; determining flow characteristic data of a third data stream at a third time according to the second data stream; the flow characteristic data is used for describing the flow rate and the burst degree of the third data flow; and determining a control instruction of a token bucket shaper of the flow queue based on the flow characteristic data, and adding a token to the token bucket shaper based on the control instruction to meet the flow transmission of the third data flow. According to the invention, through real-time adjustment of the token bucket parameters, accurate control of network congestion is realized.

The invention relates to the technical field of network monitoring, in particular to a computer network data flow monitoring system and method, and the system comprises a data flow collection module, a protocol layer analysis module, a time sequence feature extraction module, a defect compensation reconstruction module and a monitoring result output module. According to the method, a continuous difference track can be formed by extracting protocol fields and marking change time points in data stream acquisition, a cross-layer synchronization index is generated by calculating the interval and the sequence of adjacent fields, so that a cross-layer relationship is quantized in a dynamic process, quantile regression is adopted in feature processing to unify a numerical interval, and the accuracy of the cross-layer relationship is improved. Compared with the prior art, the method guarantees the comparability under different time scales, can rapidly identify an abnormal section when cross-layer synchronization is broken, deduces a potential mode in combination with feature values before and after abnormity, reconstructs defect information in a probability mode, enables the result to have the abnormity recognition precision and the flow monitoring integrity, and improves the network management efficiency and safety.

The invention discloses a data traffic scheduling method and a communication system based on traffic characteristics and path selection, and the method comprises the steps: presetting a large flow path and a small flow path according to the historical state information of each path, and enabling the path blocking risk and the path stability of the large flow path to meet a preset first requirement; the path blockage risk and the time delay of the small flow path meet preset second requirements, and the path blockage risk, the path stability and the time delay are determined according to historical state information; predicting whether the data stream sent by the sending end is a large stream or a small stream according to the traffic characteristics of the data stream historically sent by the sending end, and if the data stream is the large stream, determining that the sending end is a large stream sending end and distributing the data stream to a large stream path; and if the small stream is predicted, determining that the sending end is a small stream sending end and distributing the data stream sent by the small stream sending end to a small stream path. The bandwidth utilization rate and the response speed of a communication system can be effectively improved, and congestion and delay are reduced.

This application relates to port scan detection. A method includes identifying, in network data traffic, a set of source node and destination node (26) pairs, each pair having a given source node, a given destination node, and one or more ports (40) accessed in traffic between the nodes in each pair, and computing, for each pair, a respective baseline indicating a first number of ports accessed on the given destination node by source nodes other than the given source node in the pair during a first time period. For each pair, a respective test score is computed indicating a difference between a second number of ports accessed on the given destination node by the given source node in the pair during a second time period and the baseline, and a preventive action is initiated for the given source node for which the test score for any of the pairs is greater than a threshold.

Provided is a system for network access control. The system includes an authentication server configured to perform a basic authentication procedure for a user by communicating with at least one user terminal, a service server configured to provide a service to at least one user terminal passing through the basic authentication procedure, a collection device configured to acquire traffic data of the at least one user terminal passing through the basic authentication procedure from at least one of the service server and a network interface connected to the service server, acquire a traffic dataset by refining the acquired traffic data in accordance with correlation, and extract time-series feature points of the traffic dataset, and an artificial intelligence (AI) management device configured to train at least one AI model to define a traffic character template of the user on the basis of the time-series feature points.

The present invention relates to a system (101) and method for primary storage write traffic management, which can improve the overall system on chip (SoC) data traffic efficiency between the processor (103), direct memory access (DMA) channel (111) and the main memory (113), by minimizing the latency to write the data to the main memory (113). This is done by reducing the number of writes from the processor (103) to the main memory (113) without sacrificing data consistency between the processor (103) and the DMA channel (111).

Systems and methods for multicast data traffic service continuity under multicast-broadcast services (MBS) operation during a radio resource control (RRC) state or mode transition are described. In some embodiments, a user equipment (UE) receives, from a network, while in an RRC connected mode, an RRC release message comprising an RRC inactive mode point to multipoint (PTM) configuration that uses an MBS session to receive multicast data traffic while the UE is in an RRC inactive mode; enters the RRC inactive mode from the RRC connected mode in response to the RRC release message; and uses the RRC inactive mode PTM configuration to receive, from the network, the multicast data traffic of the MBS session while the UE is in the RRC inactive mode. Cases where a UE instead re-uses an RRC connected mode PTM configuration after transitioning to the RRC inactive mode are also described. Analogous network behaviors are described.