58 results about "Dynamic network topology" patented technology

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

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 distribution network dynamic topology identification and self-healing control method and device, and the method comprises the steps: collecting operation data of a distribution network, carrying out the time synchronization processing, data quality evaluation and data restoration, and obtaining a high-quality network state data set; based on the data set, performing dynamic topology identification by adopting a path improved by a relaxation method and a forest enhancement algorithm to obtain a dynamic network topology model; constructing a backpack network model by adopting a cyclic data generation method, and evaluating distributed photovoltaic output and load conditions; network abnormity is monitored in real time, and a machine learning algorithm is adopted to assist in judging fault types, positions and influence ranges; power supply reliability and economy are comprehensively considered, and a multi-objective optimization algorithm is adopted to generate a switch operation sequence; switching operation is executed, a temporary micro-grid is formed or a power supply path is adjusted, and the execution effect is monitored and evaluated in real time. According to the method, the dynamic topology of the high-proportion distributed photovoltaic power distribution network can be accurately identified, and fault positioning and efficient self-healing control are realized.

The invention discloses an unmanned aerial vehicle data link communication system for beyond-visual-range cluster measurement and control, and relates to the field of communication transmission. The system comprises a dynamic topology construction module which constructs a dynamic network topology according to unmanned aerial vehicle node parameters and selects an optimal communication path; the spectrum resource management module is used for optimizing spectrum allocation of multiple frequency bands and dynamically allocating spectrum resources; the relay node selection module is used for selecting an optimal relay node and optimizing the energy consumption and the signal quality of the relay node; the clock synchronization module is used for performing high-precision clock synchronization through clock skew modeling and correcting frequency drift; the channel distribution module is used for dynamically distributing communication channels; and the fault detection module monitors the fault state of the communication link of the unmanned aerial vehicle in real time through a fault monitoring model, and performs anomaly detection. Through dynamic topology construction, intelligent relay node selection and multi-dimensional utility function optimization, the flexibility, stability and resource allocation efficiency of a communication network are effectively improved, and efficient and reliable communication of an unmanned aerial vehicle cluster is ensured.

The invention relates to the technical field of network communication, in particular to a network resource entry method and system based on a topology self-discovery technology, and the method comprises the following steps: a detection source device generates a detection data packet containing a device identifier, a TTL value and an accessed device list, and sends the detection data packet to an adjacent device hop by hop; after the intermediate equipment receives the probe packet, if the intermediate equipment does not exist in the accessed list, adding equipment information and a connection relationship and continuing forwarding until the TTL value is exhausted or no available port exists; the method has the beneficial effects that hop-by-hop collection of network equipment information is realized by designing an intelligent detection data packet, a dynamic network topology is constructed in combination with a graph theory algorithm, deep resource attributes are extracted by utilizing a machine learning technology, and finally automatic input and real-time updating of network resources are realized. According to the method, through detection-construction-extraction-entry-visualization five-dimensional architecture, the problems of low efficiency and poor real-time performance of traditional manual entry are solved.

The invention discloses an industrial internet attack and defense situation and risk early warning and sensing method, and particularly relates to the field of internet risk early warning and sensing, which comprises the following steps of: acquiring multi-source heterogeneous data, constructing a basic data set covering an attack, service and equipment ternary space, including attack characteristics, service influence and equipment control vectors, and solving the problems of data heterogeneity and dispersion; constructing a triple function based on the data set, respectively quantifying the attack comprehensive threat degree, the influence degree of the attack on the service and the malicious control risk of the equipment, and retaining the characteristics of each dimension; a dynamic network topology model is constructed, nodes, edges and edge weights are defined, an attack propagation path and influence intensity are described, and node state dynamic updating is achieved; and finally, a risk prediction model is constructed by fusing quantitative indexes and topological information, a global risk value is calculated, graded early warning is realized through triple dimensions, a corresponding response mechanism is matched, and the timeliness and effectiveness of industrial internet security protection are improved.

The invention relates to the technical field of electric power data monitoring, in particular to a fault diagnosis system and method based on an electric power dispatching data network, and the method comprises the steps: analyzing the equipment operation state of each network node based on the equipment operation data of each network node in the electric power dispatching data network, mining a fault association rule, and constructing a node fault knowledge base; predicting a high-probability fault node and a fault time period, and determining a node fault degree; constructing a dynamic network topology model of the nodes based on a graph theory, confirming a standby line set, analyzing the power supply stability of standby lines, and generating a line priority list; and switching the fault node into an optimal standby line confirmed by the line priority list, temporarily supplying power, performing real-time monitoring after the standby line is successfully switched, further executing a process of main and standby line switching-standby line no-load detection-resource release, and re-distributing the fault node into the power dispatching data network. And safe and stable operation of a power system is ensured.

The invention discloses a method, a system, equipment and a medium for improving reliability of high-frequency data acquisition in a multi-element load scene, and relates to the technical field of power system communication and data acquisition, comprising the following steps: broadcasting beacons to a station area and receiving terminal response, constructing dynamic network topology and channel state information, executing dynamic channel allocation based on the received information, and sending the dynamic network topology and channel state information to the station area. The method comprises the following steps: acquiring minute-level data packets, performing fragment coding on the acquired minute-level data packets, selecting an optimal path for each terminal for transmission in combination with a network state, collecting fragments at a receiving side, checking integrity, decoding and restoring when conditions are met, and otherwise, starting a local retransmission mechanism until the data packets are successfully recovered. According to the method, the reliability guarantee system is constructed by dynamically sensing the network state, cooperatively optimizing channel allocation and transmission paths and combining fountain code coding and a precise retransmission mechanism, and the success rate and the integrity of high-frequency data acquisition are remarkably improved in a multi-element load scene.

The invention discloses an advanced network threat intelligent detection and defense system and method in the technical field of network defense. The system comprises a data acquisition module which analyzes encrypted traffic through a deep packet inspection technology and marks space-time metadata; the feature extraction module is used for constructing a node behavior association graph by adopting a multi-scale sliding window and a graph neural network; the detection engine module integrates multi-model parallel analysis of LSTM, random forest and the like, and dynamically fuses detection results through an entropy weight method; and the defense execution module is used for realizing flow cleaning and virtual trapping node deployment based on an SDN (Software Defined Network) technology. The method comprises the steps of protocol extension field reverse analysis, covert channel time sequence correlation analysis and dynamic network topology adjustment. According to the method, the encryption APT attack detection accuracy reaches 98.7%, the false alarm rate is reduced to 0.15%, the defense strategy effective time is shorter than 1 second, and the method is suitable for complex network environments such as cloud computing and industrial Internet of Things.

The invention discloses a symbiotic radio network hybrid beamforming method and system based on assistance of a reconfigurable holographic surface (RHS), and the method comprises the steps: constructing a communication architecture with the RHS as a transmitting antenna, and eliminating dual path loss; a dual-mode signal demodulation strategy is designed, the strategy I adopts MMSE-SIC combined with MRC technology to realize demodulation of an environment signal, deletion of a received signal from an SN through SIC and demodulation of a backscattering signal one by one, and the strategy II adopts design of the environment signal and a transmission frame structure thereof, combination of zero-forcing beamforming and demodulation of the backscattering signal to avoid transmission of an environment signal demodulation error; the transmitting power is optimized through a gradient descent method, the digital beam forming non-convex constraint is processed through fractional programming, the RHS amplitude control matrix is optimized through a sequence parameter convex approximation algorithm, and efficient alternating optimization of the three is achieved. The method has the advantages that energy efficiency of a multi-user scene is improved, system power consumption is reduced, dynamic network topology is supported, high reliability is kept, and limitation of an existing single-user scheme is broken through.

The embodiment of the invention provides a VRB-based dynamic network topology sensing scheduling method, device and equipment and a storage medium, a dynamic network topological graph can be generated according to obtained transmission parameters, the structure of a current network, the connection relation between nodes and the real-time states of the nodes can be intuitively and accurately reflected, and the real-time performance of the nodes is improved. When the equipment state in the network changes, such as the load of a certain GPU server is too high or a certain link is congested, the dynamic network topological graph can be updated in time; the optimal transmission path can be selected for the data related to the task according to the real-time network state, the problem that in the prior art, unnecessary nodes or links are wound when the data related to the task is scheduled is solved, and the network transmission efficiency is improved.

The invention relates to an intelligent power grid optimization control method based on dynamic network topology reconstruction. The process comprises the following steps: establishing a mixed integer linear programming optimization model; screening the optimal deployment position of the loop closing device; actual power grid data are collected in real time, the net load fluctuation range of each power grid node is calculated, the flexible supply and demand state of a power grid line is evaluated, and if the flexible demand of the power grid line exceeds the supply or power flow out-of-limit, the optimal power regulation quantity and the optimal power grid network topology structure of a loop closing device are calculated; deploying the loop closing device according to the optimal deployment position and the optimal power regulation quantity of the loop closing device, changing a power grid connection structure according to the optimal power grid network topology structure, and optimally controlling an actual power grid; and calculating to obtain the power grid performance after the deployment of the loop closing device, and if any power grid performance is lower than a preset threshold value, carrying out the above process again. Compared with the prior art, the power of the network topology and the loop closing device can be quickly adjusted according to the actual power grid condition.

The invention provides a fault diagnosis method and device, equipment, a medium and a product, and belongs to the technical field of communication network fault detection, the method comprises the following steps: receiving multi-source real-time information of each functional layer in a communication network, the multi-source real-time information comprising a dynamic network topology relation and fault information of at least one functional layer; fusing the multi-source real-time information into a multi-modal feature vector; the multi-modal feature vectors are input into a large language model, a fault diagnosis report output by the large language model is obtained, and the fault diagnosis report comprises fault root cause nodes; wherein the large language model is a model obtained by performing reinforcement learning according to corpora in the communication field. Multi-source information in the communication network is fused, cross-layer fault reasoning is carried out by means of the large language model obtained based on corpus training in the communication field, fault root cause points and fault diagnosis reports with practical guiding significance are obtained, and efficient, high-precision and real-time fault diagnosis services are provided for the large-scale communication network.

The invention discloses a novel AI (artificial intelligence) cognitive fusion network (Cogntive Fusion Network) architecture and a realization method of the novel AI (artificial intelligence) cognitive fusion network architecture. The architecture comprises a dynamic attention mechanism, double seven-layer cognitive fusion processing units (perception, understanding, reasoning, decision making, execution, reflection and meta-cognition), a memory-calculation separation engine, and a dynamic network topology management and multi-time sequence parallel processing engine. Through the functions of a dynamic attention mechanism, multi-level cognitive processing, knowledge and calculation separation, dynamic network adjustment, parallel time sequence processing and the like, the advanced cognitive ability exceeding the traditional Transform architecture is realized. Compared with the prior art, the cognitive understanding accuracy is improved by 90%, the reasoning speed is improved by 182%, the concurrent processing capacity is improved by 450%, and the multi-time sequence processing efficiency is prompted by 8 times. The method can be widely applied to a plurality of cross-industry fields such as frontier scientific research, precise medical diagnosis, enterprise intelligent decision making, government public management, personal intelligent assistants and the like.

The invention discloses an unmanned aerial vehicle ad hoc network identification and multi-hop relay method and system based on dynamic optimization of an HCAR. According to the method, a dynamic network topology is constructed and maintained through periodic beacon interaction between nodes; after a target unmanned aerial vehicle identification signal is received, evaluating the direct link quality with a ground control center, and if the direct link quality is lower than a threshold value, starting a multi-hop relay; the optimal next-hop node and the communication channel are jointly selected according to the real-time topology and the channel state through the HCAR algorithm, and data are forwarded to a ground control center hop by hop. According to the invention, through the dynamic collaborative optimization of the routing and the channel, the transmission stability and the spectrum efficiency in a high-density scene are improved; link interruption rapid detection and self-healing capabilities are provided, and data transmission continuity is guaranteed; energy state monitoring and dynamic deployment are combined, and outdoor long-term reliable operation is supported.

This application provides a wireless ad hoc network transmission method and system for VOCs monitoring data from oil storage tanks, relating to the field of wireless transmission technology. The method includes: deploying monitoring nodes in the tank area to collect VOCs monitoring data and meteorological data, and deploying relay nodes to collect wind speed and direction data; correcting the VOCs monitoring data based on the meteorological data to generate a data packet to be transmitted, and simultaneously generating link state parameters based on the wind speed and direction data; then determining the stability parameters of the wireless link based on the link state parameters and constructing a dynamic network topology; selecting a first transmission path for the data packet to be transmitted; and finally updating the stability parameters when the number of consecutive wind direction data changes exceeds a preset threshold, selecting a second transmission path, and switching the data packet to be transmitted to the second transmission path, thus completing the wireless ad hoc network transmission of VOCs monitoring data. This application improves the continuity and reliability of data transmission under complex meteorological conditions.

According to the content caching method considering the dynamic network topology in the satellite edge calculation, when modeling is conducted on the content caching problem in the satellite edge calculation, the dynamic nature of the satellite network topology is considered, and dual optimization of transmission time delay and transmission energy consumption is achieved under the constraint condition of the satellite caching capacity and the repeated caching number; according to the method, the content caching problem is solved by utilizing multi-agent graph attention reinforcement learning, and the purpose of solving a caching strategy in a dynamic environment is achieved. And the low-orbit satellite selects to cache the content to the local, so that the transmission delay and transmission energy consumption of the user for acquiring the content are reduced, the increase of the transmission delay and the transmission energy consumption caused by the topology change of the satellite network is avoided, and the cache resources of the satellite are effectively utilized.

The invention discloses a low earth orbit satellite network deterministic multicast routing method based on a time expansion graph, which relates to satellite and communication technologies, and comprises the following steps: constructing a time expansion graph to represent time slot characteristics, dynamic network topology and time-varying network resources of a low earth orbit satellite network; pruning the original time expansion graph according to the working characteristics of the CSQF mechanism and multicast service transmission requirements, and providing a solution space for deterministic multicast routing calculation; and by taking the pruned and enhanced time extension graph as input, calculating a deterministic multicast route from a source node, and selecting a transmission edge with sufficient capacity and a storage edge conforming to a CSQF mechanism hop by hop until all virtual nodes are reached, so that the obtained route has the maximum bottleneck capacity on the premise of meeting the multicast service time delay upper limit requirement. According to the method, a low-complexity algorithm is designed, the link with the optimal resource and the transmission time slot with the time delay guarantee are selected hop by hop in the graph, and the multicast tree with the time attribute is constructed, so that end-to-multi-end deterministic guarantee is provided.

The invention relates to the technical field of wireless communication testing, and discloses an automatic testing method for a wireless ad hoc network, which comprises the following steps of: initializing a testing system, establishing connection between automatic testing software and a plurality of communication agent modules, and respectively controlling a tested ad hoc network module by the communication agent modules; through the communication agent module, the test network topology is constructed by adopting at least one of the following modes: 1, based on a preset path loss model, calculating and setting the minimum transmitting power capable of establishing a preset link for each tested module; 2, controlling a moving trolley bearing the tested module, and driving the moving trolley to a physical position capable of forming a preset link; and after the target network topology is constructed, executing an automatic test and collecting a test result. According to the method, high-precision, high-efficiency and high-reproducibility construction of the complex dynamic network topology is realized, and the automation level and reliability of the test are remarkably improved.

The invention relates to a network situation display and auxiliary decision-making system, and belongs to the field of communication networks and cloud computing. According to the invention, the situation display module realizes the comprehensive visualization of network resources, security and protection situations and target monitoring in a unified Web portal by fusing three kinds of data of network resources, network security and protection and target monitoring; the intelligent network resource allocation module generates an optimal resource allocation scheme through a multi-stage decision process based on the acquired network resource situation data, and ensures that network resource fragments are minimized; and the network security and protection intelligent analysis module provides potential attack path visualization and defense key points for the user by performing dynamic labeling in the dynamic network topology. According to the method, the minimization of network resource fragments is ensured, the situation elements are converted into executable strategies, and the effect of providing auxiliary decisions for users through situations is achieved.

The invention discloses an intelligent AI combined unmanned aerial vehicle air-ground integrated communication method, relates to the technical field of unmanned aerial vehicle communication, and is used for solving the problem of cooperative disorder in unmanned aerial vehicle cluster communication. According to the invention, the real-time position, speed, attitude and signal quality parameters of the unmanned aerial vehicle are obtained through the distributed sensing unit, and a dynamic network topological graph is constructed; performing role allocation, space-time resource allocation and ground-air link switching by adopting an intelligent decision combining a centralized mode and a distributed mode; high-reliability data transmission is realized based on multi-unmanned aerial vehicle cooperative beam forming, adaptive modulation and coding, priority queue scheduling and hybrid automatic retransmission; performance indexes are collected in real time through an AI learning unit, parameters are dynamically adjusted, and a self-optimization closed loop is formed; and the collaboration and the resource utilization efficiency of unmanned aerial vehicle cluster communication are remarkably improved.

The application discloses an unmanned aerial vehicle air-ground integrated communication method combined with intelligence AI, relates to the unmanned aerial vehicle communication technical field, and is used for solving the problem of cooperation confusion in unmanned aerial vehicle cluster communication; the application acquires real-time positions, speeds, postures and signal quality parameters of unmanned aerial vehicles through a distributed sensing unit, and constructs a dynamic network topological atlas; intelligent decision-making combining centralization and distribution is adopted to perform role allocation, space-time resource allocation and ground-air link switching; high-reliability data transmission is realized based on multi-unmanned aerial vehicle cooperative beam forming, adaptive modulation and coding, priority queue scheduling and hybrid automatic repeat request; performance indexes are collected in real time through an AI learning unit, and parameters are dynamically adjusted, so that a self-optimization closed loop is formed; and the cooperation and resource utilization efficiency of the unmanned aerial vehicle cluster communication are remarkably improved.

The application discloses a kind of power system fault severity assessment methods based on optical CT measurement, it is related to power system technical field, comprising the following steps: S1, construct optical CT measurement model;S2, multiscale decomposition and feature extraction;S3, space-time coupling fault location;S4, dynamic network topology reconstruction;S5, fault influence propagation analysis;S6, power element health state prediction;S7, fault severity quantitative index establishment.The power system fault severity assessment method based on optical CT measurement, by introducing wavelet transform, multiscale decomposition, space-time coupling algorithm and comprehensive severity quantitative index system, the accuracy of fault location and the accuracy of severity assessment are greatly improved, overcome the limitation of traditional technology in fault signal capture and propagation path analysis, can track the dynamic change of power system fault in real time, comprehensively.

A simulated network Kubernetes controller for dynamic network topology partitioning includes: a DQN-based network topology partitioning module, a Pod scheduling module, and a rolling upgrade-based Pod migration module. The DQN-based network topology partitioning module performs graph partitioning based on user-submitted network topology information to obtain the distribution of virtual network components on physical machines. The Pod scheduling module performs scheduling calculations based on the virtual network component information that needs to be modified during algorithm verification to obtain the results of the virtual network components to be migrated. The Pod migration module performs Pod migration based on the calculated list of virtual network components to obtain the modified network topology distribution. This invention implements a Kubernetes controller based on the KubeBuilder framework for actual scheduling. It is used to achieve overall system load balancing when verifying network resource scheduling algorithms in large-scale dynamic topology scenarios on a cloud-native simulated network platform.

The invention discloses a rail transit communication network routing state dynamic optimization method and system, and the method comprises the steps: constructing a closed-loop control system of perception-evaluation-decision-execution-verification, constructing a dynamic network topology model on the basis of whole-network high-precision clock synchronization, and collecting link performance indexes based on multi-source data. Performing time alignment, missing value filling and normalization processing; dynamically calculating each index weight by using an information entropy model to form a link comprehensive score; an optimal path is calculated in combination with an improved Dijkstra algorithm and a multi-constraint path cost function, and an optimization result is issued to a routing protocol module to realize dynamic routing adjustment; and meanwhile, a closed-loop feedback, performance verification and rollback mechanism is introduced, so that the switching stability is ensured. According to the method, the routing state of the rail transit communication network can be adaptively optimized in real time, the link utilization rate is improved, the end-to-end time delay is reduced, and the high reliability and the service quality of bearing services are guaranteed.

The invention discloses a massive alarm log noise reduction and correlation analysis method based on a dynamic aggregation log codebook. According to the method, association analysis based on the Apriori algorithm is achieved for the alarm logs, an association log sequence is generated, a specific data structure-log codebook (Log Code-book) is generated through regularization processing, and codebook of the automatic association relation is achieved. In addition, noise reduction processing and multi-device association analysis of the massive alarm logs of the devices are achieved through the log codebook, and the alarm log association processing effect is remarkably improved. According to the invention, through a double-period codebook aggregation framework based on period broadcasting and timing uploading, a relatively high log association analysis effect is maintained in a dynamic network topology environment, and relatively high robustness is represented in various network environments.

The invention discloses a psychological deterrence-based three-layer AI (artificial intelligence) agent collaborative network security active defense system and method, and the system adopts three AI agents (a way finder, a bait hand and an observer) to cooperatively work to realize a sensing-confusing-deterrence three-layer defense system. The system can actively identify, analyze and cope with network attacks through technologies of dynamic network topology management, a psychological battle strategy engine, an advanced anomaly detection algorithm, key management security enhancement, an authentication and authorization mechanism, user data security storage, an intelligent agent stealth mechanism and the like, and meanwhile, threat intelligence sharing is realized through a community joint defense mechanism. The core innovation of the method is that uncertainty is maximized, so that an attacker cannot confirm success after huge resources are input, and finally the attacker is forced to give up the attack. The problems of passive defense, slow response, high false alarm rate and the like of a traditional network security system are solved, and the initiative, accuracy and efficiency of network security defense are remarkably improved.

The invention discloses an evaluation method for detecting the influence of retired wind power blade milling on the environment, and the method comprises the steps: collecting environment influence source data, recognizing a pollution feature cluster, and constructing a three-dimensional pollution fingerprint spectrum; constructing a milling workshop-environment medium coupling digital twinborn body, and generating a three-dimensional dynamic database; generating a fiber characteristic exclusive parameter set by using a Bayesian optimization method; performing space-time quantum clustering and fiber trajectory simulation, and predicting settlement hotspots of dust in the terrain-weather coupling field; constructing a dynamic network topology structure, and generating a toxicity risk thermodynamic diagram; and generating an environmental impact assessment report, and generating an environmental impact traceability chain by using distributed storage of a private chain and an interstellar file system. According to the invention, the whole process from pollution feature identification, dynamic prediction to risk assessment is intelligent and precise. The method can effectively predict dust settlement hot spots and biotoxicity risks, provides a scientific basis for environment management of retired wind power blade milling, and promotes the wind power industry to develop in a green and sustainable direction.