564 results about "Smart network" patented technology

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

Computer-implemented methods and systems make a generative AI system more explainable. A programmed computer system grows a generative AI system by adding one or more explainable network elements to the generative AI system. Each explainable network element can be trained to discriminate two or more explainable sets of training data items for the generative AI system. After adding the one or more explainable network elements, training of the generative AI system can be updated with the one or more explainable network elements added. Then the programmed computer system can determined whether continued growth of the generative AI system is required.

The invention relates to the technical field of data processing, and discloses a communication transmission management method and system for a heterogeneous network and a storage medium. The method comprises the following steps: processing a signal strength parameter and a data priority level in a data hierarchical configuration table to obtain a network resource allocation scheme; performing real-time monitoring processing on a 4G / 5G cellular network base station, a WiFi hotspot and a satellite communication node to obtain a network switching control instruction; performing transmission quality monitoring processing on the engine parameters, the navigation data, the airborne equipment state data and the environment monitoring data to obtain a transmission quality parameter table; and performing data mining analysis processing on the network switching control instruction and the transmission quality parameter table to obtain a transmission strategy optimization parameter containing network resource optimization configuration. In a heterogeneous network environment, intelligent network switching and resource allocation based on data priorities are realized, so that transmission requirements of data with different priorities are ensured, and meanwhile, the utilization efficiency of network resources is improved.

The invention discloses a power equipment data acquisition and processing system, which comprises a multi-protocol compatible acquisition module, an intelligent network detection module, a system service performance monitoring module and a high-concurrency processing module, the multi-protocol compatible acquisition module is responsible for adaptation and data acquisition of different power equipment protocols and normalization processing of the acquired data; the intelligent network detection module monitors a network link state, diagnoses a network fault reason and pushes fault information in real time; the system service performance monitoring module collects key performance indexes of a server, displays performance data through a visual interface, and triggers an alarm when the indexes are abnormal; the high-concurrency processing module compresses the collected data, realizes efficient pushing and priority processing of the data through a message queue, and guarantees the data processing efficiency in a high-concurrency scene; comprehensive monitoring of power equipment communication and system performance is realized, and data acquisition efficiency and system reliability are improved.

A system and method for accelerating a cybersecurity event detection and remediation includes extracting corpora of feature data from a suspicious electronic communication, wherein the corpora of feature data comprise at least one corpus of text data extracted from a body of the suspicious electronic communication; computing at least one text embedding value for the suspicious electronic communication; evaluating the text embedding values of the corpus of text data against an n-dimensional mapping of adverse electronic communication vectors, the n-dimensional mapping comprising a plurality of historical electronic communication vectors derived for a plurality of historical electronic communications; identifying whether the suspicious electronic communication comprises one of an adverse electronic communication based on the evaluation of the text embedding value, and accelerating a cybersecurity event detection by routing data associated with the suspicious electronic communication to one of a plurality of distinct threat mitigation routes.

The invention relates to the technical field of network security, in particular to an intelligent network security detection system and method based on big data analysis, and the method comprises the steps: generating adversarial data through a historical network data set, training a network security detection model through the adversarial data, and dynamically adjusting an adversarial training objective function in training. The network security detection model can balance normal loss and adversarial loss, and the robustness and accuracy of the network security detection model are balanced; the network flow data flow is analyzed through the machine learning model, the network state index is output, the network security detection model is optimized according to the output network state index, and the detection capability of the network security detection model to the antagonistic attack is improved; according to the method, traditional network attack data is extracted from a historical network data set, the traditional network attack data and adversarial disturbance generated through adversarial training are combined, and a network security detection model is trained, so that the network security detection model can detect traditional network attacks at the same time.

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 relates to the technical field of copper wire manufacturing control, in particular to a copper wire multi-pass rolling and annealing cooperative control method and system based on an intelligent network, and the method comprises the steps: setting a process control node according to an industrial control process, building a cooperative communication network based on the process control node, and collecting a multi-mode industrial control signal; setting a data transmission processing protocol, establishing an industrial control sensing platform, integrating multi-mode industrial control signals, and converting the multi-mode industrial control signals according to process positions to generate process operation parameters; analyzing non-linear correlation between process steps, and calculating process adjustment parameters by combining the copper wire performance indexes; according to the data transmission processing protocol, the process operation parameters are transmitted to a cooperative control center and compared with the process adjustment parameters for analysis, and a parameter cooperative control instruction is generated; and the parameter cooperative control instruction is fed back to the rolling control node and the annealing temperature control node based on the cooperative communication network for parameter correction. According to the invention, the problems of low process synergy efficiency and insufficient signal processing capability in copper wire manufacturing are effectively 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 discloses a machine room energy-saving operation and maintenance management method and system, and particularly relates to the technical field of machine room management. Computing resources are integrated in combination with a virtualization technology, virtual machine allocation is dynamically adjusted, migration flow is optimized by adopting an intelligent network bandwidth scheduling mechanism, and migration efficiency is improved; by combining a temperature and humidity control system and equipment operation temperature anomaly detection, the operation states of the air conditioner and the cooling equipment are dynamically optimized, energy consumption is reduced, early warning is performed based on the equipment operation temperature and the network load condition through a fault prediction model, equipment faults are prevented, and fuzzy logic control is adopted; based on the energy consumption efficiency and the fault prediction result, resource configuration is dynamically adjusted, the energy efficiency of the whole machine room is optimized, the utilization rate of computing resources is effectively improved, the influence of network migration on services is reduced, the energy consumption of the machine room is reduced, the equipment stability is improved, the management cost is optimized, and efficient, energy-saving and intelligent machine room operation and maintenance management is achieved.

Techniques for selective intelligent enforcement and / or selective intelligent offloading for mobile networks using a smart network interface card are disclosed. In some embodiments, a system / process / computer program product for selective intelligent enforcement and / or selective intelligent offloading for mobile networks using a smart network interface card includes monitoring network traffic in a core mobile network using a Smart Network Interface Card (NIC) of a network element in the core mobile network to identify a new session that attached to the core mobile network for mobile network communications; extracting meta information associated with the new session using the Smart NIC of the network element in the core mobile network; and applying selective intelligent enforcement and / or selective intelligent offloading using the Smart NIC of the network element if the extracted meta information associated with the new session matches a selective intelligent enforcement policy and / or a selective intelligent offload policy.

The invention discloses a low-voltage power grid residual current monitoring and early warning system and method based on an intelligent network, and belongs to the field of safety monitoring. The method comprises the steps that a residual current sensor is installed to monitor current change, and current fluctuation is intelligently analyzed; establishing a machine learning model based on historical data, and training normal fluctuation characteristics of the residual current; the sudden change of the current is judged through a time sequence anomaly detection algorithm, and a new data optimization model is regularly utilized; the residual current fluctuation is graded, when the current deviation reaches a threshold value, the system automatically sends a notification of the corresponding grade according to the early warning grade, the position of a fault node is automatically judged through the geographic position of a sensor and a network topology structure, and a fault section is judged in combination with power grid topology data; after early warning is triggered, the system performs statistical analysis on abnormal current data, and the system automatically optimizes the acquisition frequency and the early warning threshold according to the real-time current data and the fault probability. According to the invention, through monitoring and intelligent early warning, the safety and response speed of the power grid are significantly improved.

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 large language model reasoning KV prefetching method and system based on an intelligent network card, and relates to the field of machine learning, and the large language model reasoning KV prefetching method based on the intelligent network card mainly comprises the steps: migrating a historical KV cache to a remote storage device, setting a KV prefetching window in a local GPU, and storing the KV prefetching window in the remote storage device; according to the current waiting model reasoning task information, determining the next historical KV cache called into the KV prefetching window, and directly fetching the historical KV cache on the remote storage equipment to the KV prefetching window; and reusing the historical KV cache of the KV prefetching window to execute the reasoning task, updating the KV cache data and the corresponding state, and updating the KV cache data and the corresponding state to the remote storage device. By means of the large language model reasoning KV prefetching method and system based on the intelligent network card, efficient utilization of video memory resources and improvement of system performance can be achieved.

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.

Examples described herein relate to a network interface that includes an initiator device to determine a storage node associated with an access command based on an association between an address in the command and a storage node. The network interface can include a redirector to update the association based on messages from one or more remote storage nodes. The association can be based on a look-up table associating a namespace identifier with prefix string and object size. In some examples, the access command is compatible with NVMe over Fabrics. The initiator device can determine a remote direct memory access (RDMA) queue-pair (QP) lookup for use to perform the access command.

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 discloses a network security intelligent monitoring method and system for a bank shield key device, and belongs to the technical field of bank shield key devices. The method comprises the following steps: acquiring historical network flow data of the E-bank USB key in a working state; monitoring the network connection state of the bank shield key press, and obtaining the operation data of the bank shield key press; processing the historical use data of the E-bank USB key, analyzing the network data characteristics of the E-bank USB key, and generating the network use standard of the E-bank USB key; analyzing the network connection state of the bank shield key device through an intelligent network detection model, and calculating a network connection risk value of the bank shield key device; according to the network connection risk value of the bank shield key press, judging whether to carry out early warning on network security or not; and an optimization suggestion is sent to related personnel, and firewall rules are configured. By monitoring the operation behavior of the E-bank USB key in real time, abnormal behaviors can be found in time, and measures can be taken.

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.