330 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 discloses an intelligent network equipment scheduling optimization method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing multi-source operation state data, and generating a scheduling input feature tensor; s2, constructing a Transform prediction model based on a multi-head self-attention mechanism, and outputting a task density and a resource pressure prediction value; s3, forming a search individual state vector by the predicted values, and initializing an individual population of the gravitational search algorithm; s4, constructing a fitness function and executing a gravitational search algorithm to generate an optimal task scheduling scheme; s5, issuing the optimal scheduling scheme to each device, executing task distribution, migration and scheduling, and collecting execution data; and S6, comparing an execution result with a predicted value, constructing a feedback data set, and jointly updating the model and the optimization mechanism. The invention aims to realize accurate prediction and global optimization of intelligent network task scheduling, improve the resource utilization rate and the system scheduling efficiency, and construct a closed-loop control mechanism with a self-learning capability.

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 present application relates to the technical field of chip verification, and relates to a software and hardware collaborative simulation verification platform for accelerating verification and positioning of a smart network interface card chip. The platform comprises: a host side QEMU virtual machine, which simulates a hardware device, sends an IO request, responds to the IO request, and calls a device driving program to implement driving execution of a host side hardware device; a host side thread adapter, which converts a multi-thread processing program of the host side QEMU virtual machine into a single-thread processing program; an UVM verification platform, which performs simulation reproduction of FPGA prototype verification of the smart network interface card chip to generate a simulation waveform and a simulation log; a network side data stream adapter, which performs data type conversion on an Ethernet message; and a network side data packet transceiver, which performs message legitimacy check on the Ethernet message returned by the network side data stream adapter. The platform of the present application can improve the problem positioning speed and the chip verification efficiency, shorten the overall chip development period, and improve the chip quality.

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 provides a communication method, device and system and electronic equipment, and relates to the technical field of network communication. The communication method comprises the following steps: executing an intelligent network card access network judgment operation to obtain a judgment result; when it is detected that the judgment result is access, an access request is sent to the switch, so that the switch broadcasts the access request to the intelligent network card located in the virtual local area network to which the static address of the target server substrate management controller belongs, and then the target intelligent network card responds to the access request and sends the access request to the switch. And performing network communication with the target server baseboard management controller through the static address of the target intelligent network card. The switch broadcasts the access request to the intelligent network card in the virtual local area network to which the static address of the substrate management controller of the target server belongs, which is equivalent to network isolation, so that network conflicts can be effectively avoided.

The invention provides an intelligent network prediction and dynamic optimization method based on machine learning, and relates to the technical field of network communication and optimization, and the method comprises the steps: predicting a future network condition change trend through a machine learning algorithm based on historical data and a detection result, so as to obtain a prediction result; constructing decision logic based on field professional knowledge and experience; and generating a deployment suggestion according to the prediction result and the decision logic, and selecting a deployment scheme according to the deployment suggestion.

The invention provides a shipborne intelligent network security protection architecture and method, and relates to the technical field of network security, and the architecture comprises a security domain division module which is used for dividing a shipborne network into three physically isolated security domains, including a key task domain, an operation management domain and a crew life domain; the longitudinal protection strategy module is used for generating a longitudinal strategy comprising a protection instruction, a communication control instruction and a risk quantification instruction based on the security domain structure; and the risk calculation module is used for detecting intra-domain equipment access behaviors by trapping addresses in the key task domain, collecting intra-domain network traffic and service unit logs, and outputting a service asset quantized value, a vulnerability severity quantized value and a threat behavior deviation degree according to a risk quantization instruction. According to the invention, accurate identification, graded response and efficient disposal of shipborne network threats are realized, and the safety of a ship key system is guaranteed.

The invention discloses an intelligent networked unmanned ship cluster control method and system, and relates to the technical field of intelligent control and cluster collaboration, and the method comprises the steps: constructing a multi-layer distributed unmanned ship cooperative control network structure, and dividing nodes according to the task attributes of unmanned ships. The method comprises the steps of establishing a connection topological graph in a cluster according to initial function distribution, calibrating nodes through the connection topological graph, collecting the energy state, the load capacity, the communication quality and the task pressure of each unmanned ship, establishing a multi-dimensional state matrix, and calculating a comprehensive decision value of each node according to the multi-dimensional state matrix. And switching node operation through an adjacent topological relation and a comprehensive decision value, synchronizing the updated node state and the connection diagram to an unmanned ship cluster, and carrying out self-adaptive distribution and network structure optimization. According to the method disclosed by the invention, the management orderliness of a cluster structure and the task cooperation efficiency among the nodes are improved by constructing a three-layer cooperative network of the control nodes, the execution nodes and the sensing nodes.

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.

Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic while applying injection limits to different traffic classes at an ingress edge port. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. Furthermore, an edge switch can dynamically allocate the ingress port bandwidth among the traffic classes that are active at a given moment.

The invention discloses an intelligent network card management and control method, device and system and electronic equipment, and relates to the technical field of computers, the method comprises the steps that temporary data of an intelligent network card is reserved and cached in a preset persistent cache region, when a system of target computing equipment is restarted or shut down, the data in the preset persistent cache region cannot be cleared, and the data in the target computing equipment cannot be cleared; when the intelligent network card enters the initialization stage after the system is started, data recovery is performed on the intelligent network card based on the reserved data cached in the preset persistent cache region, so that rapid initialization of the intelligent network card is realized, the initialization time of the intelligent network card is shortened, and important data of the intelligent network card is prevented from being lost.

The invention discloses a method for deploying a regular expression based on P4 in an intelligent network card / DPU. The method specifically comprises the following steps that the regular expression defined by a user is compiled into a finite-state machine; a matching table structure and a state register are defined in the P4 program and used for storing a state conversion rule of the regular expression, and a current matching state is recorded; and mapping a matching logic and a state register defined by the P4 into a hardware module of the intelligent network card / DPU, and realizing high-speed state conversion and matching by utilizing the parallel processing capability of the intelligent network card / DPU. According to the method for realizing regular expression matching based on P4 in the intelligent network card or the DPU, the regular expression is converted into the finite-state machine, and the programmability and the hardware acceleration capability of the P4 language are combined, so that the network flow detection and analysis efficiency is greatly improved, and the network flow detection and analysis efficiency is improved. And an effective solution is provided for next-generation network security and performance optimization.

The present application discloses a contact network device, a smart car, a smart network system and a transportation system. The contact network device is installed on the car. The smart network system includes a network information system and a network power supply system. The network power supply system includes a contact network connected to the contact network device. The contact network provides electricity and basic sensor information for the car. The network information system interconnects the contact network device, the smart car and the contact network information to form a vehicle network. The smart car equipped with the contact network device and the smart network system constitute a new transportation system. When the car is traveling on a road with smart network system facilities, the smart network system provides electricity and sensor information, and the car is driven by a person or automatically drives forward. When the car is traveling on a road without smart network system facilities, it is powered by a battery or an internal combustion engine, and can be driven by a person or a simple remote control driving system provides information to enable the car to move forward, solving the problems of electric vehicle charging, endurance and automatic driving application.

The invention provides a data processing method and device of an intelligent network card, equipment and a storage medium, and the method comprises the steps: carrying out the industrial protocol analysis processing of a received data packet, extracting an equipment identifier and request type information, and distributing the data packet to a fast path or a standard path according to the request type information; performing hotspot detection processing on the data packets in the fast path, performing hotspot prediction in combination with a detection result and a preset equipment association relationship, and generating a hotspot data set; performing dynamic management on multi-level caches of the intelligent network card according to the hotspot data set, and distributing the hotspot data to corresponding cache levels according to timeliness; and when a query request is received, performing response processing according to the request type information and the multi-level cache, and when the query content is hit, constructing a response data packet in the intelligent network card and directly sending the response data packet. According to the method, the data processing task is unloaded through the intelligent network card, CPU resource consumption is reduced, dynamic changes of data hotspots are adapted, and the system response speed is increased.

The invention discloses an intelligent network false information identification and early warning system based on a multi-modal behavior map, and particularly relates to the field of intelligent network false information identification and early warning. A specific implementation mode of the system comprises a dynamic behavior graph construction module which is configured to construct a dynamic behavior graph, monitor newly added behavior flow data corresponding to original user behavior data in real time, and update a topological structure of the dynamic behavior graph according to the newly added behavior flow data; the time sequence reasoning model training module is used for constructing a behavior sequence data set with a timestamp and training the initial false information time sequence reasoning model; and the risk early warning module is used for carrying out false information identification on the updated dynamic behavior map according to a false information time sequence reasoning model and a historical abnormal behavior map library so as to generate corresponding false identification information. According to the embodiment, dynamic behavior characteristics are captured through the time sequence reasoning model, normal operation and fraudulent behaviors are distinguished, and false alarms caused by static rules are reduced.

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.

A system and method are provided for implementing a network component, such as a software-defined wide area network, a firewall, a router, or a load balancer. The network component can be an embedded network edge device that is implemented, e.g., in software, in circuitry, or using hardware acceleration (e.g., a data processing unit (DPU), a smart network interface card (SmartNIC), etc.). The system can include multiple dataplanes, including a primary dataplane and a shadow dataplane. A packet dispatcher relays received data packets to a primary dataplane and the shadow dataplane. The primary dataplane applies a current version of the network component to data packets, and the secondary dataplane applies a new version of the network component to identical replicas of the data packets. A control plane agent compares performance data gathered from the respective dataplanes to perform verification testing on the new version of the network component.

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 discloses an intelligent network technology service management system and method based on big data, and particularly relates to the technical field of intelligent network technology services, and the system comprises a data collection module which is used for configuring a distributed crawler, an API interface and a sensor to obtain multi-source heterogeneous data from a user terminal, network equipment and a third-party platform, comprising user behavior data, network flow data, equipment state data and service quality logs; the data storage module is used for storing data by adopting a fusion framework of a column database and a time sequence database; a column database and a time sequence database are fused, a partition storage strategy is matched, unified storage of structured / unstructured data is achieved, cross-database ETL batch processing bottlenecks are eliminated, a data processing module integrates a streaming computing engine and a batch processing framework, high-concurrency real-time analysis is supported, query delay is reduced from the hour level to the second level, and the query efficiency is improved. And automatically triggering computing node / bandwidth elastic expansion when responding to burst traffic.

The disclosed systems, methods, and computer readable media relate to managing persistent memory within a smart network interface card (smartNIC) of a cloud-computing environment, or at storage device of the host instance on which the smartNIC operates and to which the smartNIC has access. The smartNIC may be configured with various modes that enable passthrough of input / output operations to or from non-volatile remote storage (e.g., a block storage data plane volume) or storage of at least some of the data corresponding to the input / output operations within the persistent memory managed by the smartNIC. The smartNIC may be configured to select a storage location for data between a pool of candidate storage including the persistent storage managed by the smartNIC or the non-volatile remote storage. These techniques improve communications between the smartNIC and the non-volatile remote storage and reduce the risk of network jitters and overall network latency.

The invention is suitable for the technical field of communication data scheduling and intelligent network management, and provides a communication data classification management method and system based on AI analysis, and the method comprises the steps: obtaining an original node cache sending window value of a communication node located in a target construction region of a target underground tunnel, historical construction and communication data of the target underground tunnel are acquired; acquiring a plurality of sub-regions which are matched with the background information of the target construction region and are constructed and local historical data corresponding to each sub-region from historical construction and communication data, and extracting construction degree values and effective uploading data utilization rates of the sub-regions in different construction stages; according to the method, a characteristic mode recognition mechanism based on historical construction and communication data is introduced, dynamic self-adaptive adjustment of communication parameters (node cache sending window values) between construction stages is achieved, and the method is remarkably different from an existing management mode depending on static configuration or experience setting.

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.

A smart network interface card (smartNIC) may receive first traffic for a first process configured with a first bandwidth limit. The smartNIC may receive second traffic for a second process configured with a second bandwidth limit, the second bandwidth limit corresponding to a larger value between a second transmit limit and a second receive limit associated with the second process. The smartNIC may queue the received traffic associated with the first process and the second process in a scheduler, the scheduler having a first set of queues configured to store traffic from the first process, and a second set of queues configured to store traffic from the second process. The smartNIC may forward queued traffic from the first set of queues or the second set of queues, a maximum amount of forwarded first process traffic corresponding to the first bandwidth limit minus an amount of forwarded second process traffic.

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 provides an intelligent network operation and maintenance method and system based on performance analysis and a large language model, and belongs to the field of network operation and maintenance. The method comprises the following steps: firstly, embedding a large language model into a network application performance analysis system; performance analysis platform authentication information and an API path are configured, a semantic analysis template, an inference rule base, a graph model and an alarm threshold value are initialized, and a report generation template and a scheduling strategy are configured; receiving an operation and maintenance problem, a query request or a diagnosis demand input by a user in a natural language form; based on a semantic analysis template configured in the large language model, intention recognition and keyword extraction are carried out, a special language DSL in the field of structured query is generated, network application performance analysis is carried out, performance feedback data is obtained, index analysis is carried out, an analysis result is fed back to the large language model, a natural language is generated according to the feedback result, and the natural language is stored in a database. And displaying according to the report generation template. The problem analysis efficiency, the automation degree and the network operation and maintenance efficiency and effect are improved.

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 bulk cargo intelligent logistics scheduling method and system based on artificial intelligence. The method comprises the steps that firstly, bulk cargo transportation tasks are acquired, and to-be-scheduled task features are extracted through a task feature extraction network; obtaining transport capacity attribute data of to-be-selected transport capacity, and generating to-be-selected transport capacity features through the transport capacity feature extraction network; screening the optimal transport capacity of which the scheduling priority coefficient is higher than a reference value, and extracting the characteristics of the optimal transport capacity; optimizing the optimal transport capacity feature based on the adaptation coefficient of the task feature and the optimal transport capacity feature; and determining a final scheduling transport capacity and distributing tasks through matching of the optimization features and the to-be-selected transport capacity features. According to the design, multi-dimensional feature extraction of tasks and transport capacity is realized by utilizing an artificial intelligence network, the adaptation precision is improved in combination with a dynamic optimization mechanism, the problems of low matching efficiency and poor real-time performance of traditional scheduling are solved, and the intelligent scheduling level of bulk cargo logistics is improved.