1025 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 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 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 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 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 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.

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 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.

In an aspect, a UE and BS communicate data traffic in accordance with a first bandwidth part (BWP) configuration. The BS sends, to the UE in association with communication of a plurality of reference signals for positioning (e.g., PRS, SRS-P, etc.), an indication of a second BWP configuration that includes a different bandwidth than the first BWP configuration. The UE and BS communicate, for a duration of the communication of the plurality of reference signals for positioning, at least the plurality of reference signals for positioning in accordance with the second BWP configuration.

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.

The invention relates to a dynamic access method in a cross-domain cooperative scene, and belongs to the technical field of cross-domain cooperative communication. The method comprises the following steps: dividing cross-domain nodes into multiple clusters, defining intra-cluster and inter-cluster time slot tables, and carrying out initial resource allocation; each cluster head collects node positions, topological relations and flow data in the cluster in real time, a graph neural network and a DQN network are fused, and accumulated rewards of different communication node pairs in the cluster are calculated for each time slot; and based on a dynamic scene state, preferentially selecting a high-yield communication node to access a time slot, updating data traffic to be transmitted, performing a time slot resource allocation decision according to a time sequence until the current data traffic to be transmitted is transmitted, updating a time slot table and synchronizing the whole network. According to the method, the problems of resource competition conflict and poor dynamic topology adaptability in a cross-domain scene are solved, efficient coordination of node dynamic access is realized, the spectrum resource utilization rate and network robustness are improved, and the method is suitable for heterogeneous node cooperative communication scenes such as an unmanned aerial vehicle group and an unmanned fleet.

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.

This disclosure describes techniques for distributing security service by performing security policy-based routing among network devices. The techniques include determining a security function to be applied to a packet of a data traffic flow. The security function may be determined based on a security policy and an intended destination of the packet. The techniques may also include determining whether a network device is capable of performing the security function. A route for the packet to the destination may be determined based on whether the network device is capable of performing the security function. As such, distributing security service techniques may improve efficiency in data traffic routing, and may reduce cost and / or prevent redundancy in security service application.

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.

Concepts and technologies disclosed herein are directed to encrypted applications verification. According to one aspect disclosed herein, a device can execute an application verification module to perform operations. The device can receive a hash function from a master security server. The hash function can be used to verify an application installed on the device. The device can apply the hash function to application code of the application to determine a hash result. The master security server can determine whether the application is verified based upon the hash result. The device can execute the application thereby generating data traffic to be sent via a network to a destination. The device can provide the data traffic to the network. A network node can receive the data traffic and can allow or deny the data traffic access to the destination based upon a command received from the master security server.

Methods, systems, and devices for wireless communication are described. A user equipment (UE) may be configured to support multiple subscriber identity modules (SIMs) and multiple corresponding subscribers that may provide wireless services to the UE. Each of the SIMs may be associated with a corresponding protocol stack. The UE may be configured with a layer that is distinct from the protocol stack, such as a dual-steer layer. The dual-steer layer may manage the routing and steering of data traffic associated with one or more of the protocol stacks of the UE. The UE may process, via the dual-steer layer, the data traffic via one or more of the protocol stacks of the UE based on the first set of UE route selection policy (URSP) rules, traffic information associated with the data traffic, and capabilities of one or more of the protocol stacks.

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.