214 results about "Smart network" patented technology

The invention relates to the field of wireless communication and intelligent networks, in particular to a software defined radio-based ad hoc network dynamic slicing architecture and a management and control method, comprising a basic communication layer, an intelligent control layer and a service execution layer. The basic communication layer adopts a multi-distribution multi-center clustering architecture; the intelligent control layer is responsible for intelligent management and resource scheduling of the self-organizing network, and the intelligent control layer comprises a plurality of command and control nodes; and the service execution layer adopts a micro-service-based architecture, and encapsulates different network functions and service logics into independent micro-service modules. According to the method, the technical problems of high plane coupling, resource scheduling rigidness, insufficient survivability and high hardware dependency in the prior art are solved.

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 an optical data network (10-1) for connecting a plurality of data network nodes (N1-1, N2-1, N3-1, N4-1). The optical data network (10-1) comprises: i) the plurality of data network nodes (N1-1, N2-1, N3-1, N4-1), the data (99) comprising data packets (99p), wherein each data network node (N1-1, N2-1, N3-1, N4-1) is provided with a network interface (Nl); and ii) a data connection network (100-1) comprising at least one data switch (SW) and a plurality of optical connections (50) for allowing establishment of data links (DL1, DL2) between pairs of said plurality of data network nodes (N1-1, N2-1, N3-1, N4-1). At least one of the at least one data switch (SW) is a smart data switch (SSW) comprising at least one packet switch (PS, EPS) and at least one circuit switch (CS, OCS), At least two network interfaces are smart network interfaces (SN, SNC) comprising a data classifier (25) configured for classifying the data packets (99p) into at least two traffic classes including first traffic class (D1) and a second traffic class (D2) in accordance with a classification scheme. The smart data switch (SSW) and the network interfaces (SN, SNC) are configured to route the data packets (99p) of the first traffic class (D1) to the packet switch (PS, EPS) and to route the data packets (99p) of the second traffic class (D2) to the circuit switch (CS, OCS).

The invention relates to the field of multimedia communication and network control, in particular to an intelligent network control method for low-delay video return and related equipment. The intelligent network control method comprises the following steps: acquiring network key indexes including bandwidth, delay, jitter and packet loss rate of a network link in real time, and providing real-time network environment data support for transmission strategy adjustment. According to the method, an intelligent control mechanism combining network state perception and video content feature recognition is constructed, so that the technical problem of low-delay video return in a complex network environment is effectively solved. Specifically, key indexes of a network link are collected in real time, and a lightweight CNN model is introduced to analyze the video content activeness, so that dual perception capabilities for a network environment and content features are formed, data support is provided for dynamic adjustment of coding parameters, and accurate balance between video quality and network adaptability is realized.

The invention discloses a network protocol analysis system based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an MCP module, a network analysis module, a data source module, a context module, an MCP interface, a data source interface and a network analysis interface. An MCP module, a network analysis module, a data source module and a context module. All the modules work cooperatively through standardized interfaces to form a unified network protocol analysis platform. The system supports a context-driven tool calling process and a double-channel execution structure, control logic and data transmission are decoupled, and efficiency and expandability in a large-size Pcap data processing scene are improved. Through a unified semantic modeling and context state tracking mechanism, through analysis of a multi-layer protocol, multi-round traceable execution of a task and structured expression of an analysis result are realized, and infrastructure support is provided for a modularized, pluggable and reusable intelligent network protocol analysis platform.

The invention belongs to the technical field of network scheduling, discloses an intelligent network scheduling method and system based on hybrid reinforcement learning, designs a multi-head attention strategy network based on Transform, can execute discrete protocol selection and continuous rate adjustment at the same time, and breaks through the limitation of a traditional scheduling model on action dimension division. Therefore, the scheduler can flexibly select the optimal strategy when facing a complex task, and the refinement and adaptability of the decision are remarkably improved. In order to overcome the problem of value over-estimation in reinforcement learning, a double-Q-Critic network structure is constructed, and double evaluation paths are introduced. In the training process, through minimizing the estimation value deviation of the two Critic values, the overhigh estimation is effectively inhibited, so that the convergence stability and the estimation value precision of the strategy are improved. In the reward mechanism, a structural causal model (SCM) is introduced to generate an anti-factual reward signal, and a real reward and an anti-factual reward are fused by constructing a causal intervention path for a specific action.

Disclosed are systems, apparatuses, methods, and computer-readable media for policy-based transparent packet inspection for last mile zero-trust workload protection. The method comprises receiving a packet on a network interface of a provisioned resource in a data center or a user device within a network; determining, by a first intercepting agent provisioned within the network interface, whether to inspect the packet based on rules received from a control plane of the network, wherein the network interface comprises a smart network interface card (SmartNIC) or a data processing unit (DPU) and is configured with the first intercepting agent based on the control plane; selectively invoking a deep packet inspection of the packet based on inspection of the packet by the first intercepting agent using the rules from the control plane; and blocking the packet at the network interface based on the deep packet inspection identifying malicious content within the packet.

Systems and methods for dynamic buffer management in switches that facilitate a data-driven intelligent networking system are provided. The system can accommodate dynamic traffic with fast, effective congestion control while providing efficient use of internal input buffer space.

The invention relates to the technical field of computing power management, in particular to an intelligent network computing power resource management system which comprises a node response load combing module, a task computing power grading module, a computing power scheduling adaptation module, a link load blocking detection module and a computing power flow direction reprogramming module. In the invention, by tracking the continuous change and inversion frequency of the node response direction, combining the computing power use state and the cache occupation ratio, dynamically evaluating the node stability, extracting the task logic depth and the cache interaction deviation to divide the computing power level, the hierarchical matching of the task demand and the node capability is realized; nodes with high cache load and unstable response are screened out, the resource scheduling accuracy and the integrating degree of task allocation are improved, the link abnormal growth trend and the queuing backlog proportion are monitored, the transmission blocking risk is recognized in time, and the flow direction is reconstructed by preferably selecting replaceable nodes based on the idle computing power and the bearing ratio; and the agility of resource calling and the continuity of task execution in a complex network are improved.

The invention relates to an intelligent SDN network abnormal flow detection method and system, and the method comprises the steps: collecting network flow data through an SDN switch, carrying out the preprocessing, and generating a standardized time sequence feature vector; performing multi-scale feature extraction on the standardized time sequence feature vector based on a multi-scale one-dimensional expansion convolutional neural network and a channel attention mechanism, capturing a context dependency relationship of abnormal traffic in combination with a bidirectional long-short-term memory network, and outputting a high-dimensional abnormal feature vector; and adopting a full connection layer and a Softmax activation function to classify the high-dimensional abnormal feature vector, optimizing the model performance through a composite loss function, and generating the classification probability of the abnormal traffic. According to the method, the detection precision of a complex attack mode is improved through multi-scale expansion convolution and a channel attention mechanism, and the robustness is enhanced by capturing time sequence dependence in combination with a bidirectional long-short-term memory network; the classification performance is optimized by adopting a composite loss function, and the false alarm rate is reduced; and realizing millisecond attack blocking based on a dynamic flow table rule of a classification probability.

The invention relates to the technical field of network scheduling, and discloses an agricultural heterogeneous perception and intelligent network scheduling method and system, and the method comprises the following steps: S01, a system initialization stage; step S02, a resource scheduling and strategy learning stage; step S03, a sensing data fusion and processing stage; s04, a security mechanism and life cycle management stage: adopting an end-to-end encryption and identity authentication mechanism in a data transmission process, and collecting feedback parameters for updating a scheduling strategy and releasing resources after a task is completed; the system comprises a multi-mode agricultural sensing terminal, intelligent gateway equipment, a 5G and LPWAN hybrid access network, a resource scheduling module and a scheduling strategy learning engine. According to the method, the multi-dimensional state characteristics such as the network congestion level, the edge node load, the data timeliness and the task priority are sensed in real time through the deep reinforcement learning model, and the optimal allocation decision of communication and computing resources is dynamically generated.

The invention discloses a satellite communication network construction method based on large language model reasoning, and relates to the technical field of satellite communication and intelligent network control, and the method comprises the steps: carrying out the deep semantic analysis of a high-level task instruction through a large language model engine, converting a fuzzy natural language demand into an accurate executable strategy framework, and carrying out the deep semantic analysis of a high-level task instruction; the fundamental transformation from how to configure network management and control to what is achieved is realized, and the intelligent level and the man-machine interaction efficiency of network management are greatly improved; a space-time diagram model is introduced to carry out prospective prediction on network topology, and a comprehensive link cost value function and a multi-target routing evaluation function are designed, so that a system can accurately predict and avoid network faults caused by cross joints and the like, the problem of communication continuity in special areas such as polar regions and the like is fundamentally solved, and the communication efficiency is improved. Excessive dependence on ground relay facilities is eliminated, and end-to-end transmission delay is effectively reduced.

The invention provides an encrypted traffic anomaly detection method based on unsupervised learning, and relates to the field of artificial intelligence network security. In order to solve the problems of dependence on a large number of labeled abnormal samples, poor model generalization ability and poor dynamic adaptability of the existing encrypted traffic anomaly detection method, the invention provides an encrypted traffic anomaly detection method based on unsupervised learning, which comprises the following steps: extracting effective features of an encrypted traffic data packet, generating a state sequence by using a KMeans clustering algorithm, and carrying out unsupervised learning on the state sequence; calculating the occurrence probability of the state sequence in combination with an n-order homogeneous Markov chain model; a dynamic adaptive threshold is constructed based on exponential weighted moving average (EWMA) and a sliding window mechanism, and abnormality judgment is realized by comparing the occurrence probability of a state sequence with the dynamic threshold. According to the method, only normal encrypted traffic is utilized for training, and effective detection of all encrypted traffic including abnormal traffic can be realized.

Systems and methods for intelligent network incident management, Root Cause Analysis (RCA), and automated remediation include receiving metrics, graphs, and historic logs associated with network performance of a tenant of the cloud system; identifying a network issue based on the received metrics, graphs, and historic logs; performing an automated RCA to determine a cause of the network issue; and remediating the identified network issue based on the determined cause. Various embodiments include training specialized Large Language Models (LLMs) for performing the automated incident identification, RCA, and remediation.

The invention discloses a 5G base station traffic prediction method based on a decomposable periodic mode network, and relates to the technical field of 5G base station traffic prediction. Comprising the following steps: an intelligent network management center acquires characteristic data related to 5G transmission in a 5G base station through a data acquisition module, and after preprocessing the acquired characteristic data, samples are divided into a training set, a verification set and a test set according to a time sequence; inputting the training set and the verification set into a 5G base station traffic prediction model based on the decomposable periodic mode network for training, wherein the 5G base station traffic prediction model based on the decomposable periodic mode network comprises a periodic mode modeling module, a component decomposition module and an xLSTM module; and after multiple times of training iteration, finally selecting the optimal weight parameter on the test set for prediction. According to the invention, the periodic time characteristics of the traffic data of each base station are fully utilized, and the precision of 5G base station traffic prediction is improved; data reference is provided for base station management and network optimization, and manpower and time costs are reduced.

This invention provides a method and system for vehicular data transmission based on spatiotemporal autonomous load balancing routing, belonging to the field of intelligent network routing technology. The method includes: sending vehicle driving data to a non-congested network relay device; filtering the vehicle driving data by the non-congested network relay device to obtain a reference trajectory path; sensing the location and storage information of neighboring network relay devices and outputting a set of candidate relay devices; calculating the spatiotemporal parameter set of the candidate relay device set, and obtaining the comprehensive data transmission capability of the candidate relay device set based on weights and the spatiotemporal parameter set; updating network status data and the congestion status of network relay devices; and arranging the data transmission order in ascending order according to the remaining lifetime, transmitting the vehicle driving data to the data transmission destination. This invention employs a weighted and spatiotemporal autonomous load balancing routing method to comprehensively evaluate the data transmission capability of network relay devices and select the optimal next-hop network relay device for the data.

The invention discloses an intelligent network big data secure transmission method and system based on cloud computing. The method comprises the following steps: performing block processing on multi-source network data to generate metadata; labeling the sensitivity level of the metadata through an NLP natural language model, and outputting a label data block carrying the sensitivity level; inputting the label data blocks and the risk indexes into a pre-trained decision tree model, and outputting an encryption algorithm combination of each label data block; dynamically adjusting encryption parameters of the encryption algorithm combination to obtain a target encryption algorithm combination; and according to the target encryption algorithm combination, calling an encryption library to encrypt the tag data block, outputting an encrypted data packet, and transmitting the encrypted data packet by using a data transmission module of the cloud computing platform. The encryption scheme can be quickly optimized, and the security and efficiency of data transmission are ensured.

The invention provides an OCP intelligent network card test platform and a test method, which are applied to the technical field of OCP product test development, and the OCP intelligent network card test platform comprises a platform base, a switching test board assembly and a CPU board assembly. The switching test board assembly is fixedly arranged on the platform base, and the switching test board assembly is provided with a high-speed connector socket and an OCP slot; the CPU board assembly comprises a CPU, a memory and a high-speed connector plug; wherein the CPU board assembly is connected with the high-speed connector socket through the high-speed connector plug in a plugging manner; and the OCP slot is used for inserting an OCP intelligent network card to be tested, so that the CPU board assembly communicates with the OCP intelligent network card through the switching test board assembly, the test cost and the occupied space are remarkably reduced, the deployment and maintenance convenience is greatly improved, and the problems that a large number of servers are needed for testing in the OCP product development test, and the test efficiency is greatly improved are fundamentally solved. The test cost is high, and the test space demand is large.

The application provides a switching method in a data storage system, a smart network card and a computing device, and belongs to the technical field of storage. In the switching method provided by the application, a plurality of storage nodes in the data storage system share the smart network card, and the smart network card has the functions of determining whether the storage nodes can provide services and analyzing read-write requests from messages, so that the smart network card can directly stop using a first storage node and send a read-write request to a second storage node different from the first storage node in the plurality of storage nodes without passing through a host connected to the data storage system in the case that it is determined that the first storage node cannot provide services. Since the path between the smart network card and the storage nodes is shorter than the path between the host and the storage nodes, the method can shorten the time consumed for determining that the storage nodes cannot provide services and for switching the storage nodes, and improve the efficiency of abnormal processing.

The disclosed systems, methods and computer readable media relate to managing Non-Volatile Memory Express (NVMe) over Transmission Control Protocol (TCP) (NVMeOTCP) connections between a smart network interface card (smartNIC) and a block storage data plane (BSDP) of a cloud computing environment. A software agent (“agent”) executing at the smartNIC may manage a number of network paths (active and, in some cases, passive network paths). The agent may monitor the network traffic (e.g., input / output operations (IOPS)) through the paths (e.g., using established NVMeOTCP connections corresponding to the paths). If a condition is met relating to a performance threshold associated with the monitored paths, the agent may increase or decrease the number established NVMeOTCP connections to match real time network conditions.

This disclosure is directed to methods and systems that establish a secure data channel between a host and a disaggregated hardware device (“DHD”) of a data center. The system comprises an initiator host that runs objects, such as virtual machines and containers. The host includes an initiator smart network interface card (“SNIC”). The initiator SNIC includes a virtual device, a trust platform module (“TPM”) and a security engine. The system also comprises a target host equipped with a DHD and a target SNIC. The target SNIC includes a TPM and a security engine. The TPM and the security engine of the initiator SNIC and the TPM and the security engine of the target SNIC establish a secure data channel between an object running on the host and the DHD.

1. The name of the design product: intelligent network measuring line instrument without switch. 2. The use of the design product: for network measuring line instrument. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: front view.

The invention discloses a vehicle-mounted network pressure testing method and device. The method comprises the steps that automatic testing software controls at least one hardware device to execute power-on operation of a vehicle to trigger awakening of the vehicle and execute power-off operation of the vehicle to trigger dormancy of the vehicle; injecting a message into a vehicle-mounted network through the automatic test software, wherein the message comprises a normal wake-up message or an abnormal message; acquiring vehicle-mounted network data of the vehicle through an intelligent network data acquisition instrument; and comparing the acquired vehicle-mounted network data with expected data, and judging whether the sleep time sequence or the wake-up time sequence is abnormal or not. According to the scheme, manual operation is reduced through automatic testing, testing cost and time cost can be reduced, and testing efficiency and accuracy are improved.

The application belongs to the technical field of network communication and relates to a smart network communication link design method, which designs a set of link quality maintenance mechanism: a joint feature vector containing a physical layer bit error rate and a logical layer queue depth is constructed, signal degradation acceleration and queue accumulation acceleration are captured based on second derivative calculation, adjacent link probability distribution differences are compared and layered sorted in a Pareto manner, and link quality fine evaluation and degradation judgment are realized; candidate backup links are screened based on physical disjoint constraints, real-time performance feedback of the candidate backup links is obtained through perturbation detection packets carrying traffic feature templates, service degradation risk probability is predicted in combination with quality scores of target links, resource margin and path cost information are interacted between network nodes based on a distributed negotiation mechanism, an optimal switching path is generated, and service flow is smoothly migrated, so that link maintenance is upgraded from passive response to active pre-inspection.

The application relates to the technical field of cloud computing, in particular to an intelligent network self-recovery method and system applied to a smart traffic system, which comprises the following steps: data collection, abnormality detection, fault diagnosis, automatic repair and self-learning optimization; the beneficial effects are that the intelligent network self-recovery method and system applied to the smart traffic system can immediately identify abnormal behaviors in the network and respond quickly through real-time monitoring and a quick abnormality detection mechanism, and the time for fault detection and response is greatly reduced; advanced fault diagnosis algorithms, including deep learning and pattern recognition technology, are used, and the system can accurately diagnose various complex faults, including unknown or novel fault modes.

The application discloses a kind of mobile network equipment forced authentication system and method based on multi-protocol perception, belong to wireless network portal authentication technical field, for the problem that non-HTTP protocol cannot trigger authentication in existing Captive Portal technology, system pop-up window can only trigger once and Wi-Fi connection is unstable and leads to authentication interruption, the present application works through delay authentication mark, dynamic DHCP lease management and intelligent network scoring protection cooperation, when the system detects the non-HTTP class protocol request of unauthenticated device, set delay authentication mark and block request, trigger authentication when its subsequent HTTP request or system detection;By allocating short lease for unauthenticated device, trigger system re-detection using lease expiration or IP address change, realize authentication pop-up window multiple trigger;And return normal network response to maintain high network score when user initial connection, prevent user equipment automatic switching network.

Smart Cybersecurity Computing Device for Real-Time Threat Analytics and Zero Trust Enforcement

Embodiments of the present application provide a cloud disk mounting method, a data processing method and related devices, and relate to the technical field of communication. The method comprises: obtaining configured cloud disk mounting information and cloud disk binding information; determining a target disk volume in a target storage server based on the cloud disk mounting information, and establishing a correspondence between the target disk volume and a smart network card. Based on the cloud disk binding information and the correspondence between the target disk volume and the smart network card, a correspondence between the target disk volume and a disk name in a host computer is established, so that the target disk volume is mounted to the smart network card. The present application thus realizes automatic mounting of the cloud disk, improves the accuracy of cloud disk mounting and the efficiency of cloud disk mounting.