3348 results about "Networked system" patented technology

The invention provides a network security penetration detection method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining real-time network flow data containing multi-dimensional communication behavior records and protocol metadata of a target network system, and then extracting network behavior sequence features capable of describing communication behavior time relevance and a protocol interaction mode from the network flow data; inputting the network behavior sequence features into a pre-trained multi-modal threat prediction model, generating a potential attack vector prediction result containing an attack intention label, penetration path probability distribution and a threat level score, and dynamically verifying the potential attack vector prediction result based on a threat knowledge base, and finally, generating a self-adaptive defense strategy according to the penetration threat detection result, triggering defense interface deployment of a target network system, updating attack mode characteristics and a defense rule set of a threat knowledge base, and realizing intelligent and efficient network security penetration detection and defense.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention relates to the technical field of fault positioning, and is used for solving the problem that a fault event positioning system in the prior art has limitation and cannot adapt to dynamic changes. The invention provides a fault event positioning method based on topological model tracking analysis. The method comprises the following steps: S1, constructing a topological model based on system information and setting an edge weight; s2, tracking a fault propagation path by using time sequence data, and updating a path weight; s3, a fault propagation path is optimized through a shortest path algorithm, the shortest path algorithm adopts an A * algorithm, and a fault source is verified in combination with machine learning; and S4, outputting fault source nodes, related path information and confidence evaluation. The method is suitable for various complex network systems, is high in universality and expansibility, can adapt to the dynamic change of the topological structure and the running state of the system by dynamically updating the edge weight of the topological model, tracking the path and the machine learning model in real time, and always maintains high positioning accuracy and efficiency.

The invention relates to the technical field of resource scheduling control, and discloses a network resource hierarchical coordination method and system based on multiple agents. The method comprises the following steps: monitoring a network node load to obtain a resource state parameter; virtually constructing a resource pool and dividing a regulation domain; deploying an intelligent agent to construct a self-adaptive control system; extracting load characteristics to generate an analysis report; executing a double-layer adjustment strategy recording execution process; and analyzing the energy consumption data to optimize control strategy parameters. According to the application, the efficient utilization of network resources and energy consumption optimization can be realized while the performance of a computer network system is guaranteed.

A neural network system is provided. The system includes an autoencoder configured to encode input data into a latent space representation; a generator neural network configured to receive a noise vector and the latent space representation and output a set of routing coefficients; a discriminator neural network configured to evaluate the effectiveness of the routing coefficients by measuring the performance of a capsule network utilizing said routing coefficients; and a capsule network comprising a first capsule layer and a second capsule layer, wherein the routing coefficients are used to dynamically route outputs from the first capsule layer to the second capsule layer.

The invention relates to the technical field of intelligent ship control, in particular to an intelligent ship moving control method and system based on a self-adaptive algorithm. The method comprises the following steps: acquiring hardware equipment data required by ship movement control; configuring a network topology structure of a computer network system, winch equipment and an encoder according to hardware equipment data, and constructing an electrical ring network; carrying out initialization setting and communication test on the electrical ring network, and establishing a data sharing mechanism between devices to obtain system initial configuration data; collecting the winding and unwinding length and tension parameters of a steel wire rope through a sensor of winch equipment based on initial configuration data of the system, and constructing a real-time monitoring model of the position and posture of the ship; and performing ship state monitoring by using the real-time monitoring model to generate ship real-time state data. According to the invention, the electrical ring network with a redundant backup mechanism is adopted, the communication real-time performance and reliability of the system are improved, and the stability of control signal transmission is ensured.

The invention relates to the technical field of power system monitoring and fault diagnosis, and discloses a partial discharge detection network system based on an adaptive attention mechanism. Comprising a data acquisition module, a signal preprocessing and time-frequency image generation module, a deep learning network of an adaptive attention mechanism, a fault classification and diagnosis module and a real-time monitoring and alarm module, the data acquisition module acquires a multi-mode partial discharge signal in real time, and preprocessing is performed through the signal preprocessing and time-frequency image generation module. The deep learning network of the adaptive attention mechanism carries out feature extraction and classification on the time-frequency image, finally, the fault classification and diagnosis module outputs a diagnosis result, and the real-time monitoring and alarm module carries out real-time monitoring and automatic alarm according to the diagnosis result. According to the system, multi-mode partial discharge signals are collected in real time, feature extraction and classification are carried out through a deep learning network, the attention degree of different features can be dynamically adjusted through a self-adaptive attention mechanism, and the accuracy and robustness of detection are improved.

The invention provides a network security threat intelligent identification method based on a generative large model, and belongs to the technical field of network security. Comprising the steps of obtaining system demand information, and determining a threat identification target and a system coverage range; collecting network system data, wherein the network system data comprises equipment log information, network flow information and terminal behavior information; preprocessing the network system data to obtain standardized data; selecting and training a machine learning model based on the standardized data to obtain a threat identification model; network behaviors are analyzed, abnormal behaviors are identified, and threat classification is carried out; executing corresponding safety protection measures according to the threat classification result; performing manual auditing on the abnormal behavior, and formulating a protection strategy in a targeted manner; and periodically evaluating the system operation state to generate a safety report. According to the invention, automatic response type intelligent identification of network security threats is realized, and the threats can be effectively detected under the condition of attack escape detection.

The invention discloses a multi-dimensional computing power dynamic perception routing decision-making method and system based on SRv6 driving. The method comprises the following steps: firstly, screening an optimal target computing power node based on a service demand and a node real-time load; and then, based on the dynamic network topological graph fusing the service sensitivity, calculating an optimal network path reaching the computing power node by using an improved SPFA algorithm, and realizing a global collaborative decision of selecting the most suitable computing point and searching the most efficient connection path. Meanwhile, based on an SRv6 driving service chain dynamic generation and closed loop execution method, a target computing power node and necessary network functions are abstracted into a programmable SID, an SID sequence (service chain) is dynamically constructed according to a double-stage decision result and is packaged in an SRH head, second-level path issuing and state monitoring are achieved through a programmable controller, and a real-time monitoring result is obtained. According to the method, the problems of poor real-time performance, single dimension, weak cooperative capability and the like in the prior art are solved, and efficient, stable and intelligent development of a future-oriented distributed intelligent computing network system can be promoted.

The invention discloses a multi-class network security threat perception and active and passive cooperative response processing system and method, and the system comprises a multi-class threat perception module which is used for perceiving a plurality of security threats existing in a network environment, and transmitting an obtained security event to a threat information association module; the threat information association module is used for analyzing and integrating various security events and extracting threat information from the security events; the attack graph-based threat path analysis module is used for mining vulnerability information in a network system, describing a network topology structure and an operation state, constructing an attack graph and obtaining active defense nodes in combination with a currently occurring security event and an analysis result of the attack graph; and the strategy generation module generates a corresponding response processing strategy according to the threat information and the property and the emergency degree of the security event, and generates an active defense strategy in combination with the active defense node, thereby realizing active defense and advanced deployment and control of potential threats. According to the invention, network security threats can be timely and effectively found and coped with.

The invention relates to the technical field of wireless communication, in particular to a wireless communication network system and method for intelligently fusing a multi-mode heterogeneous network. Comprising a multi-mode access unit; a protocol compatibility and conversion unit; the heterogeneous network intelligent fusion unit completes cross-network dynamic flow collaboration and resource optimization decision through cross-layer feature association mining, service-network two-dimensional weight calibration and a double-closed-loop feedback optimization mechanism, generates a resource scheduling instruction and transmits the resource scheduling instruction to the intelligent scheduling and resource control unit; an intelligent scheduling and resource control unit; and a system control and cooperation unit. According to the invention, the service-network two-dimensional dynamic fusion weight is constructed through the intelligent fusion unit of the heterogeneous network, and bandwidth, frequency spectrum and power resources are allocated according to the priority and margin balance principle in combination with the intelligent scheduling unit, so that the problem of insufficient two-dimensional coordination of resource scheduling is solved, the resource scheduling is matched with the service demand and the network load, and the resource scheduling efficiency is improved. And resource waste or incapability of meeting business requirements is avoided.

A method for authenticated asset assessment is provided. The method involves executing a scan assistant on an asset to allow a remote scan engine to execute one or more scan operations on the asset for determining a state of the asset. The scan assistant may verify the identity of the scan engine by checking that a certificate received from the scan engine is signed with a private key associated with the scan engine. In some embodiments, the authentication may be performed as part of a TLS handshake process that establishes a TLS connection between the scan engine and the scan assistant. Once the scan engine is authenticated, the scan engine may communicate with the scan assistant according to a communication protocol to collect data about the asset. Advantageously, the disclosed technique reduces security risks associated with authenticated scans and improves the performance of authenticated scans.

The invention discloses an adaptive game-driven network defense method and system, and relates to the technical field of network security. The method comprises the following steps: generating a multi-modal bait according to network context information, and screening an optimal bait through credibility evaluation; constructing a time sequence feature tensor according to the network event sequence information of the bait, and obtaining predicted attack information by adopting a pre-trained attack prediction model; combining the network event sequence information of the bait and the predicted attack information to construct a defense income matrix, and carrying out iterative equilibrium solution to obtain an optimal strategy candidate pool; and taking the defense hybrid strategy of the optimal strategy candidate pool as an initial population, performing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain a Pareto optimal strategy set, and performing screening to obtain an execution strategy set for dynamic defense decision making. According to the invention, the dynamic property, intelligence and self-adaptability of network defense are realized, and the ability of a network system to cope with complex attacks is effectively improved.

A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost / benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost / benefit indicators.

The invention provides a low-power-consumption self-repairing and energy self-supplying mine dust monitoring sensor network system, which comprises a plurality of sensor nodes and a central monitoring platform, and is characterized in that data of all the sensor nodes are transmitted to the central monitoring platform in real time; the sensor network synchronizes environmental parameters, and performs data analysis, processing and visual display on the central monitoring platform; a plurality of sensors are integrated in each sensor node, and the sensors are used for monitoring different environmental parameters in a mine environment; the types of the sensors comprise a dust concentration sensor and a temperature and humidity sensor, and the environmental parameters comprise dust concentration, temperature and humidity; the problems of long-time operation, insufficient energy supply, node failure, unstable data transmission and the like of a dust monitoring system in a complex mine environment are solved. The system integrates a multi-source environment energy collection module, and the technologies of mechanical vibration, airflow kinetic energy, thermoelectric power generation and the like are adopted, so that self-power supply of sensor nodes is realized, and the requirement of frequently replacing batteries or wiring is avoided.

Devices, networks, systems, methods, and processes for identifying, classifying, and scheduling Low Latency, Low Loss, and Scalable throughput (LAS) traffic are described herein. A device may receive a data flow comprising one or more data packets. The device may parse the one or more data packets to check for a congestion indicator. Upon detecting the congestion indicator in any data packet, the device can classify or mark the data flow as LAS. The device may schedule the data flow by utilizing an Active Queue Management (AQM) queue for L4S data flows. The device can also mark other uplink or downlink data flows associated with the data flow as L4S. The device can also facilitate LAS capability sharing while associating with one or more wireless devices. The device may utilize a management frame with an extended capabilities field to inform the LAS capability of the device.

An example network system includes processing circuitry and one or more memories coupled to the processing circuitry. The one or more memories are configured to store instructions which, when executed by the processing circuitry, cause the network system to receive connection data related to an egress connection of an application service of an application. The instructions cause the network system to analyze the connection data to determine that the egress connection is an anomalous connection. The instructions cause the network system to generate a notification indicative of the egress connection being an anomalous connection and send the notification to a computing device.

A method is provided for processing data in a neural network system. The method includes receiving input data; processing the input data through a first set of neural network layers configured to perform data processing using MatMul-free techniques to produce intermediate data; further processing the intermediate data through a second set of neural network layers configured to simulate spiking neural network (SNN) functionalities using MatMul-free techniques; and outputting a result based on the processed data from the second set of neural network layers.

This application provides a communication method and a communication apparatus. The method includes: A first network unit sends first information to a second network unit, where the first information includes first indication information, and the first indication information indicates a configuration type of a first radio bearer of a remote terminal device. In this way, the second network unit may determine, based on the configuration type of the first radio bearer, whether to provide a configuration of a first radio link control (RLC) bearer corresponding to the first radio bearer on a link between the remote terminal device and the second network unit, to provide correct configuration information of the radio bearer for the remote terminal device. This helps improve data transmission efficiency of the radio bearer, improve radio resource utilization of a mobile network system, and improve system performance.

The invention discloses a soft network system capable of expanding a transmission mode and an implementation method, and the system comprises an equipment resource mapping management unit which automatically generates a unique channel code based on a native hardware interface and establishes a multi-dimensional mapping relation; the heterogeneous interface data acquisition unit is used for monitoring various hardware interfaces in a kernel space in parallel and realizing zero-copy data transmission through a three-buffer lockless annular buffer area; the transmission abstraction and dynamic expansion unit realizes transmission mode modularization in a plug-in form and supports atomic hot plug during operation; the protocol analysis and service processing unit is used for realizing unified scheduling and fault isolation in a single process through a dynamic link mechanism; and the cross-layer monitoring and self-healing unit realizes automatic switching based on a session state machine and a redundant channel. According to the method, the code complexity is reduced from O (N * M) to O (N + M), and the time lt is extended; fault switching is carried out at 1s; and the hardware cost is reduced by more than 40%, so that the method is particularly suitable for power and environment monitoring scenes with more than 1000 devices and more than 15 protocols, and has high creativity.

The invention relates to the technical field of network management, and discloses an intent-driven network management method and system based on a large language model (LLM). The method comprises the following steps: receiving a natural language intention of a network administrator, analyzing the intention by utilizing a pre-trained large language model, extracting a key target and constraint, generating a configuration scheme of a TCP / IP or QUIC protocol based on an analysis result and a network protocol knowledge base, and ensuring the effectiveness and safety of configuration through a network constraint verification module. And the configuration passing the verification is applied to the network system. The system comprises an intention input module, an LLM analysis module, a configuration generation module, a verification module, an application module and the like. According to the invention, through conversion from automation intention to configuration, the network management process is simplified, the management efficiency and flexibility are improved, the method is especially suitable for multi-parameter optimization scenes of protocols such as TCP / IP and QUIC, and intelligent network management is realized.

The invention relates to the technical field of coal mine fully mechanized mining, in particular to a coal mine communication network system based on a time-sensitive network, and the system comprises a wired communication system, the backbone network of the wired communication system is composed of a plurality of TSN network switches which are in ring network topology connection, and the wired communication system is used for accessing coal mine equipment as a terminal to a TSN network; the wireless communication system is connected with the wired communication system, consists of wireless communication equipment compatible with a TSN protocol, and is used for accessing the coal mine equipment into a TSN network through wireless connection; a communication system based on a time sensitive network (TSN) is established, a real-time, determined and reliable communication network is provided for a coal mine production system, wired and wireless communication systems are adopted, the whole coal mine production control system is covered together, and the requirement of automatic control for data communication is met.

The invention relates to a DDRL-based aircraft mission offloading method in a low earth orbit satellite network. The method comprises the following steps: (1) constructing an LEO low earth orbit satellite network system into an LEO satellite mobile edge computing network system; (2) modeling an LEO low earth orbit satellite network system utility maximization problem into a joint decision problem for optimizing task unloading and resource allocation; and (3) converting a joint decision problem into a Markov decision process MDP, learning an optimal task unloading decision by using a dual depth Q network algorithm DDQN under a dual depth reinforcement learning DDRL framework, and obtaining an optimal resource allocation strategy by using a time difference triple strategy gradient algorithm TD3PG on this basis. Simulation experiments show that compared with a reference algorithm, the scheme can effectively access and cooperatively compute concurrent tasks, and has better convergence and superiority under different environment variables.

The invention discloses a virtual power plant optimal scheduling method based on artificial intelligence, and further reduces the power operation cost through an integrated intelligent management and scheduling strategy. The system is composed of a power generation end, an energy storage end, a power utilization end, a system management platform, a power grid, a power market and other key components. The core lies in that a depth deterministic strategy gradient (DDPG) algorithm is utilized to construct a simulation environment model, and a reward function is designed to evaluate the economic benefit of the strategy. By initializing Actor and Critic neural networks, the system can generate and evaluate a scheduling strategy, and continuously iteratively updates the network through an experience playback mechanism to optimize economic benefits. The invention provides an innovative virtual power plant optimal scheduling method based on artificial intelligence, and maximization of the benefits of the virtual power plant is realized by reducing the operation cost.

The invention provides a power distribution network and traffic network cooperative operation method considering multi-subject benefit coupling, and belongs to the technical field of network system control, and the method comprises the following steps: S1, constructing a three-layer power traffic coupling network cooperative operation model, comprising a lower-layer traffic network user balance model, a middle-layer charging network operator optimal pricing model and an upper-layer power distribution network second-order cone relaxation optimal power flow model; s2, converting the lower-layer traffic network user equilibrium model into a group of constraints by applying a KKT condition, thereby converting the three-layer electric power traffic coupling network collaborative operation model into a double-layer model; and S3, solving the double-layer model by using a CCG algorithm. According to the power distribution network and traffic network cooperative operation method considering multi-agent benefit coupling, the system operation efficiency is optimized, the cooperative interaction among the operation agents is ensured, and the overall cooperative effect of the power distribution network and the traffic network is improved.

The invention relates to the technical field of information security, in particular to an electronic information security attack detection method and device based on a graph neural network. According to the electronic information security attack detection method and device based on the graph neural network, data are collected from a target electronic information network system, and an initial network topological graph structure is generated; feature selection and optimization are carried out based on a gold washing optimization algorithm, an optimized network node feature matrix and an optimized network edge feature matrix are generated, and an improved graph neural network model is established; and dynamically updating a network topological graph structure, inputting the optimized improved graph neural network model, analyzing features of network nodes and network edges, detecting abnormal behaviors, classifying attack types, generating attack paths, triggering an early warning mechanism, and outputting an attack detection result. According to the electronic information security attack detection method and device based on the graph neural network, accurate and efficient detection of electronic information security attacks is realized, and important technical support is provided for defense of complex network attacks.

The invention provides a class-independent motion prediction network system and method combining space-time and frequency domain features, equipment and a medium, and belongs to the field of automatic driving. The problem that the space-time dependency relationship and the dynamic characteristics cannot be fully captured in a complex dynamic scene in the moving target prediction is solved; comprising construction of a feature coding module, a hierarchical space-time integration module, a frequency-space fusion module and a feature decoding module, and the HTSIM fuses space-time information step by step through a multi-level space-time feature interactive learning mechanism, and can capture a long-term space-time dependency relationship and local dynamic change, so that a complex motion mode is modeled more accurately; fSFM combines the characteristics of a frequency domain and a space domain, multiband decomposition is carried out on input characteristics through two-dimensional discrete wavelet transform, and the capturing capability of dynamic characteristics is enhanced through deep fusion of the characteristics of the frequency domain and the space domain; the method is suitable for real-time track prediction tasks in intelligent traffic systems such as automatic driving and the like.

The invention discloses an intelligent networked passenger car network system architecture design method based on link redundancy, which comprises the following steps of: constructing a CAN (Controller Area Network) line network module and a vehicle-mounted Ethernet module, and connecting a plurality of control nodes in a passenger car to obtain a connection link; judging connection link connectivity and transmission quality of the plurality of control nodes by utilizing a heartbeat packet mechanism to obtain link quality; and carrying out link switching according to the link quality to obtain the network system architecture of the intelligent networked passenger car. The invention relates to the technical field of intelligent traffic, and solves the technical problems of low error-tolerant rate, insufficient reliability and easy threat to driving safety of the existing single-chain design or double-chain design during link fault.

This disclosure describes techniques for providing application assurance by validating expected packet flows for an application against a model of a network that is to transport the expected packet flows. For example, a method includes obtaining, by an application assurance system, a flow definition that describes a packet flow for an application; determining, by the application assurance system by querying a network model of a network system that transports packet flows for applications, using the flow definition, a set of network nodes and links of the network system that are expected to transport or process the packet flow for the application; obtaining, by the application assurance system, an indication that a network node or link, from the set of network nodes and links, is in an operational state that could affect performance of the application; and in response to obtaining the indication, by the application assurance system, outputting an alert.

The invention provides a distributed multi-sensor tracking method based on privacy protection and anomaly suppression. The distributed multi-sensor tracking method based on privacy protection and anomaly suppression comprises the following steps: acquiring original measurement information of local sensors; according to the original measurement information, adopting a pre-established robust Kalman filter to suppress random abnormal interference to obtain a local state estimation value; and encrypting the local state estimation value by adopting a dynamic mask mechanism, and fusing the encrypted estimation values transmitted between the adjacent sensor nodes by adopting an average consensus filter according to the topological structure of the network system, thereby obtaining global consistency state estimation of the multi-sensor system. According to the method, the local state estimation value is encrypted, privacy information is protected, and the method is applied to a multi-sensor scene through global consistency state estimation of a multi-sensor system, so that the technical problem that a distributed sensor tracking method is lacked in the prior art to avoid privacy leakage is solved.