815 results about "Dynamical network" patented technology

Some embodiments automatically and proactively adjust network device configuration settings during network operation, based on correlations between device performance and device configuration. Correlations are computed using statistics routines or computed by a machine learning module. Some embodiments share adjusted configuration values via a cache, and some persist adjusted values through an application restart. In some embodiments, the cache is hierarchical and different kinds of reconfiguration data are shared at different levels. In some embodiments, the configuration value is shared only between application instances that have sufficiently similar contexts. Some embodiments detect a correlation loss and fall back to a known good configuration setting or a default configuration setting. Some embodiments optimize network internode communications by making dynamic adjustments which are not available from static configuration settings or from static configuration rules.

The invention discloses a self-adaptive communication method and system for ubiquitous electric power Internet of Things intelligent metering equipment, relates to the technical field of Internet of Things communication, and solves the problems of inaccurate quantitative communication, insufficient communication efficiency and poor flexibility in existing intelligent metering equipment communication. According to the invention, the accuracy and reliability of data transmission are improved through a dynamic network self-organizing algorithm and a multi-hop communication mechanism, the communication efficiency is optimized through multi-path transmission and dynamic spectrum management, and the flexibility and adaptability of the network are enhanced through an adaptive modulation coding model and a real-time optimization link selection strategy. And secure transmission of the data is further guaranteed through a multi-layer security mechanism, and the communication performance and flexibility of the intelligent metering equipment are remarkably improved.

The invention discloses a wide-narrow integrated ad hoc network method, and belongs to the technical field of network communication. The method comprises the following steps: a network node generates dynamic topology information based on a narrow-band communication module periodic broadcast state beacon; according to the link quality and the node load in the topological information, coordinating the resource allocation of broadband and narrowband channels by adopting a load-aware time slot allocation algorithm; data are classified according to service types, broadband data are modulated and transmitted by adopting orthogonal frequency division multiplexing, and narrowband control instructions are modulated and transmitted by adopting differential phase shift keying; when the link quality is deteriorated, the link is switched, and the transmitting power and the frequency band parameters are dynamically adjusted; and adaptive switching and data distribution of wide and narrow band communication modes are realized. According to the method, the problems of difficulty in topology maintenance and low bandwidth and narrowband resource allocation efficiency in a high-dynamic network scene are solved, the network adaptability, the anti-interference capability and the resource utilization rate are remarkably improved through a dynamic cooperative bandwidth and narrowband communication mode, and meanwhile, the protocol switching overhead and energy consumption are reduced.

The invention discloses a video transmission method and system based on streaming processing, and relates to the technical field of video transmission, and the method comprises the steps: collecting a video stream, carrying out the preprocessing, extracting a video frame, packaging the video frame into a picture stream, carrying out the data fragmentation, distributing an initialization path for fragmented data, and calculating an initial traffic proportion; and optimizing path selection and adjusting path flow distribution according to network feedback to obtain a path optimization result, and transmitting the fragmented data according to the path optimization result. Through multi-level feature extraction and key frame selection of video frames, fragmented packaging of video data is realized, a dynamic path optimization technology is combined, path selection and flow distribution are dynamically adjusted based on a Q learning algorithm and a luminescence firefly algorithm, the transmission efficiency is improved, video image processing and a dynamic network optimization technology are fully combined, and the video image processing efficiency is improved. And a brand new solution is provided for efficient and stable transmission of high-definition video streams.

The invention relates to a cerebrovascular three-dimensional evaluation method based on multi-scale feature fusion, and the method constructs a multi-scale feature fusion model which comprises a pilot segmentation network, a fusion segmentation network and a topology-style loss module. The pilot segmentation network is based on a dynamic Unet network, the response of the model to the complex boundary of the cerebrovascular image is enhanced, and the pathological detection performance is improved. The fusion segmentation network introduces a feature selection module, and effectively fuses the long-range dependence feature of the Transform layer and the local context information of the CNN layer, so that the model can accurately extract the cerebrovascular features under different scales. The topology-style loss module optimizes model parameters by combining a topology loss function and style self-consistency loss, restrains the topology continuity of a segmentation result, and effectively solves the problem that blood vessels are easy to break in a traditional segmentation method. The method effectively improves the cerebrovascular image segmentation precision.

The invention provides an energy storage cabin terminal remote upgrading method and system based on communication protocol optimization, and relates to the technical field of digital information transmission, and the method comprises the steps: deploying a bandwidth detection mechanism, adjusting a self-adaptive block transmission strategy, and dividing upgrading firmware into M dynamic data packets; constructing a dual-path transmission layer, and configuring a dual-path redundancy check mechanism; a missing block query rule is adopted, and a local flash memory is used for caching Hash check values of the received N dynamic data packets; analyzing a mapping relation between the block index and a multi-path redundancy check mechanism, and establishing a check mapping table; and performing real-time verification and state marking, and configuring a remote upgrading process to execute the upgrading operation of the energy storage cabin terminal equipment after incremental recombination is performed by comparing and verifying abnormal data packets. According to the invention, the technical problem of insufficient reliability of data transmission caused by lack of adaptability to a dynamic network environment due to the fact that most of energy storage cabin remote upgrading technologies in the prior art depend on a single communication path to carry out data transmission is solved.

The invention discloses a simulation test method for a vehicle-mounted communication network. The method comprises the following steps: S1, initializing a vehicle-mounted communication environment; s2, generating a dynamic network topology; s3, environment adaptability dynamic interference is generated; s4, co-simulation of the dynamic network and interference; s5, testing and evaluating communication performance; and S6, visualizing and exporting a test result. According to the invention, the network topology reflecting the real-time motion state of the vehicle is dynamically generated, the communication change between the vehicles can be reflected in real time in different traffic environments, and the interference conditions in various complex environments are simulated in combination with the environment adaptability dynamic interference generation module, so that the performance of the vehicle-mounted communication network can be comprehensively evaluated.

The invention discloses a reinforcement learning intrusion detection method and system based on dynamic network feature screening, and the method comprises the steps: constructing a self-adaptive reward reinforcement learning network based on Transform framework enhancement, carrying out the network flow feature screening in combination with a crayfish optimization algorithm, and constructing a self-adaptive reward reinforcement learning network based on Transform enhancement. Through combination of a Transform mechanism and reinforcement learning, adaptive learning and decision optimization capabilities in a dynamic network environment are significantly enhanced. A crayfish optimization algorithm is combined to perform feature extraction on the high-dimensional network traffic to capture a feature set which is most valuable for intrusion detection, so that a most representative feature subset can be efficiently extracted from complex and changeable network traffic data, and the automation and precision of a feature selection process are improved; the method effectively overcomes the defects of sample imbalance, feature selection, dynamic adaptability and the like in the prior art, and provides a more intelligent and efficient intrusion detection solution.

The invention provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

The invention discloses a multi-agent collaborative data exchange dynamic routing optimization method and system, and relates to the technical field of network communication, and the method comprises the steps: obtaining the real-time state information of each node in a network, and outputting a node state data set; constructing a historical state sequence of each node, calculating a load change trend, and outputting a routing adjustment signal when the load change trend reaches a load critical value; calculating a collaborative weight according to the network contribution degree of each agent, establishing a task allocation mapping relationship among the agents based on the collaborative weight, and outputting a collaborative scheduling result; determining a candidate path set, performing index evaluation on paths in the candidate path set, and outputting an optimal routing path; and executing data transmission, obtaining a quality difference between an actual transmission effect and an expected effect, and performing parameter correction. According to the invention, active optimization and multi-target balance of network routing are realized, and routing efficiency and system stability in a dynamic network environment are improved.

The invention provides an information resource distribution method and system applied to a dynamic network environment, and the method comprises the steps: firstly obtaining a real-time state feature set of a target distribution network, determining a resource distribution constraint condition set in a current network environment based on the real-time state feature set, carrying out the priority analysis of a real-time resource request sequence of a target user group, and obtaining a real-time resource distribution constraint condition set; obtaining resource timeliness grade distribution and a request path optimization feature set, generating a dynamic distribution strategy parameter set matched with a resource distribution constraint condition, triggering a resource redirection operation, and enabling the resource request response rate of the target user group to be improved to a preset threshold interval; and the information resource distribution efficiency and stability in the dynamic network are improved.

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.

The invention discloses a hydrogen fuel engine electronic thermostat heating assembly control method based on the Internet of Things, particularly relates to the technical field of hydrogen fuel engine control, and is used for solving the problems of cooling response lag and action conflict caused by disconnection of control logic and a real-time network state of an existing temperature control system in a dynamic network environment. Electronic thermostat temperature data and network state data are collected in real time to generate a dynamic synchronization coefficient, network topology toughness evaluation and causal influence analysis are combined to predict instruction conflict areas and divide priorities, and gradient correction is performed on heating power and valve opening based on a target deviation compensation value. And a correction instruction is issued through the distributed control unit and a communication reliability weight is synchronously updated, so that dynamic coordination of a network state and a temperature control parameter is realized, and efficient and stable operation of the hydrogen fuel engine under a complex working condition is ensured.

The invention relates to the technical field of power distribution network dispatching. By providing a power grid load dynamic prediction and optimal scheduling method, device, equipment and medium, the method comprises the following steps: performing multi-source heterogeneous fusion processing on meteorological parameters, historical load curves and new energy output data to generate a dynamic load prediction map; constructing a dynamic network model, and performing power flow distribution simulation processing based on the dynamic network model to obtain a stability margin calculation result and a preset safety threshold boundary; performing stage decomposition processing on the global scheduling target to generate a progressive scheduling stage sequence; performing matching processing on the response characteristics of the power generation equipment to generate a self-adaptive progressive scheduling instruction sequence; and executing an adaptive progressive scheduling instruction sequence, performing feedback processing on the real-time state of the power grid, and generating a dynamic adjustment instruction so as to realize multi-dimensional data association modeling, stability margin quantitative analysis and dynamic instruction optimization, thereby improving the load prediction precision and reducing fault diffusion and transient oscillation.

The invention discloses an enterprise digital management method based on data mining. The method comprises the following steps: acquiring information flow data and resource allocation data; analyzing according to the information flow data and the resource allocation data to obtain an initial network diagram; performing clustering analysis according to the initial network diagram, and performing path optimization to obtain a dynamic network diagram; performing feature extraction on the dynamic network diagram to obtain prediction analysis data; according to the dynamic network diagram and the prediction analysis data, network nodes are identified, and key nodes and high-risk nodes are obtained; in combination with the key nodes and the high-risk nodes, identifying a key propagation path to obtain the key propagation path; and based on the key nodes, the high-risk nodes and the key propagation paths, predicting key positions where resource bottlenecks and conflicts occur, and generating resource optimization suggestions. According to the method, the dynamic change of the implicit dependency relationship of the enterprise can be identified, high-risk nodes and key propagation paths are positioned, and resource configuration is optimized.

The invention discloses a network protection method and system based on an attack and defense game model, and relates to the field of network protection, and the method comprises the steps: firstly constructing a dynamic network graph through integrating an original log, asset information and threat intelligence; and then, identifying and predicting potential Top-K attack hypotheses from the dynamic atlas by using the trained graph neural network model. Then, the attack hypotheses are used as new evidences to update the Bayesian belief network, and the cognition of the intention and ability of the attacker is further improved. Most importantly, aiming at each attack hypothesis, a sub-game model is generated, and on the basis of the updated Bayesian belief network, real-time defense strategy solving is carried out by utilizing MCTS. The introduction of the MCTS enables the optimal defense strategy to be efficiently explored and recommended in incomplete information and complex decision space, avoids the infeasibility of exhaustion calculation, and achieves the real-time efficient defense decision of a large-scale network.

The invention relates to the technical field of remote control, in particular to an electrical automation remote control system which comprises an energy receiving and converting module, a dynamic network management module, a node fault diagnosis module, an energy distribution control module and a remote control execution module. According to the method, communication quality is optimized through signal fluctuation analysis, data packet routing and bandwidth allocation are dynamically adjusted, accurate transmission of remote control instructions is ensured, network structure is optimized through real-time network flow analysis, congestion is reduced, transmission efficiency is improved, fault diagnosis is carried out according to a load adjustment result, accurate identification, automatic isolation and routing reconstruction are achieved, and the method is suitable for large-scale popularization and application. And energy management dynamically adjusts energy output based on node supply and demand, ensures stable operation of key nodes, optimizes energy utilization, combines remote control execution with real-time monitoring, ensures accurate instruction issuing, and improves reliability and safety of remote control.

The invention provides a traffic flow spatio-temporal feature adaptive extraction method based on a dynamic Kolmogorov-Arnold network, and aims to solve the defects of a traditional model in the aspects of dynamic spatio-temporal modeling, structural adaptability and feature expression efficiency. According to the method, a network structure is dynamically adjusted through differential topology search and a parameterized primary function library, a double-flow coupling architecture is designed to extract space and time dependent features respectively, and adaptive fusion of spatial and temporal features is realized by using a gating mechanism. The method comprises the following specific steps: performing space-time normalization and graph structure coding to generate a node feature matrix; extracting multi-scale spatial-temporal features in parallel by the dynamic graph convolution KANs and the time sequence convolution KANs; feature alignment and cooperative enhancement are realized based on a local correlation matrix and a bidirectional interactive attention mechanism; and the lightweight KAN decoder is combined with the dynamic basis function library to output a prediction result. Experiments show that the MAE is reduced to 1.78 vehicles per minute and the reasoning speed is improved by 1.8 times in a traffic accident emergency scene on a PeMS08 data set, and the method is suitable for an intelligent traffic management and vehicle-road cooperation system.

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

The invention discloses an adaptive distributed network threat detection and response system and method, and the system comprises a data collection module which is used for capturing network traffic in real time and converting the network traffic into structured data; the data analysis module is used for performing parallel analysis on the structured data by adopting a plurality of anomaly detection algorithms; the algorithm fusion module is used for counting the abnormal judgment times of each algorithm on the same sample through a voting mechanism, and outputting a final classification result according to a preset threshold value; the method comprises the following working steps: concurrently capturing network traffic through distributed nodes, analyzing the network traffic into structured data, operating an isolated forest algorithm, a DBSCAN algorithm and a K-means algorithm in parallel, and outputting an anomaly judgment result; and fusing multi-algorithm results and generating final classification through a voting mechanism. The invention provides an efficient, intelligent and distributed network security protection system, which can effectively detect and respond to various network threats, and is particularly suitable for large-scale distributed environments and dynamic network security defense requirements.

The invention belongs to the technical field of emergency rescue management, and discloses a multi-disaster-point emergency rescue scheduling method, which comprises the steps that a multi-disaster-point emergency rescue dynamic network is constructed according to real-time information of disaster-affected points, and the real-time information of the disaster-affected points comprises positions, disaster-affected time and emergency rescue decisions of the disaster-affected points; formulating an initial rescue path of each rescue vehicle in the multi-disaster-point emergency rescue dynamic network based on a preset constraint condition; and drawing a directed graph based on the formulated initial rescue path, and solving an optimal scheduling strategy of the multi-disaster-point emergency rescue dynamic network under the minimum number of vehicles in the directed graph based on a Hopcroft-Karp algorithm. According to the technical scheme of the invention, by constructing the multi-disaster-point emergency rescue'time-space-decision 'dynamic network, the problem of'minimum vehicle number' in the multi-disaster-point emergency rescue dynamic network can be solved.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

The invention discloses an intelligent driving vehicle dynamic network redundancy configuration and switching method in a complex information source scene, and relates to the technical field of intelligent traffic and communication. According to the method, communication quality data of a plurality of communication links at different positions of the unmanned taxi in the driving process are detected, the data are broadcasted to other unmanned taxies around through a vehicle-mounted communication system, and communication quality data broadcasted by other unmanned taxies are received; the method does not depend on a centralized server or a cloud platform, and a signal map is fused and generated in a decentralized manner only based on self-detected and received data; dynamically configuring a network redundancy strategy according to the signal map and a preset task priority; after the strategy is executed, scores are recorded and broadcasted, scores of other vehicles are received, and the strategy is optimized and adjusted in advance. According to the method, the problem of unstable communication in a complex information source scene is solved, and the communication reliability and efficiency of the intelligent driving vehicle are improved.

Methods and apparatus for using an artificial intelligence engine to automatically predict what network slices will be needed are described. After the network slice prediction is made network slices are automatically generated in accordance with AI machine learned prediction. This new automated approach ensures the network slices are up to date and devices assigned the network slices properly as the new devices get activated on the network. This new approach can also add elasticity to the network slices based on the devices assigned to them by changing the amount of resources allocated to slices of a particular type as predicted resource needs change.

The invention relates to a dynamic network reconstruction strategy of a digital electric power energy storage system in the technical field of electric power energy storage. The strategy comprises the following steps: S1, building an energy storage system; the system consists of an intelligent layer, a control layer and a data layer, s2, modeling an energy storage network; learning an optimal control strategy of the energy storage network by adopting a graph capsule neural network; s3, dynamic network reconstruction; generating a fault emergency response model based on a Markov decision process; by systematically optimizing a topological structure and an optimal control strategy of the energy storage network, rapid isolation of a fault area and dynamic adjustment and reconfiguration of working modes of energy storage equipment and load equipment are realized, the energy storage network is ensured to still operate stably in a fault situation, and the energy storage efficiency is improved. The flexibility and elasticity (fault tolerance) of the energy storage system in a dynamic environment are improved; by reducing the energy loss in the transmission process, balancing the network load and preventing local faults, the technical problems existing in the prior art are solved, and the sustainable development of a novel renewable energy system is promoted.

The invention belongs to the technical field of network self-adaption, and particularly relates to a weak network self-adaption algorithm system based on dynamic network monitoring, which comprises the following steps of: deploying a monitoring agent on a network node; extracting network bandwidth, delay and packet loss rate from the network state report, preprocessing the extracted data, and extracting feature vectors from the preprocessed data; adjusting the size of a buffer area for data transmission according to the network bandwidth and delay; and controlling the sending and receiving of data according to the adjusted algorithm and parameter, monitoring the data transmission state in real time in the data transmission process, feeding back the data transmission state information, and adjusting the algorithm and parameter in time according to the feedback information. The problem of how to improve the efficiency and reliability of data transmission under the conditions of complex and changeable network environment, low bandwidth, high delay, serious packet loss and the like in the prior art is solved.

The invention relates to the field of intelligent building safety, and discloses an intelligent fire evacuation guiding method which comprises the following steps: collecting fire information and crowd information, and establishing a dynamic evacuation network model; dynamically calculating the weight of an evacuation path based on the fire information and the crowd information; according to the dynamic weight, an optimal evacuation path in the evacuation area is planned, and an intelligent fire evacuation guidance system is further provided and comprises a data acquisition module used for acquiring fire information and crowd information; the dynamic modeling module is used for constructing a dynamic network model of the evacuation area; the path planning module is used for planning an optimal evacuation path based on the dynamic network model; and the light control module is used for controlling the display state of the path guiding light. According to the invention, through dynamic weight calculation, real-time path optimization, light dynamic adjustment and crowd flow prediction, an efficient, safe and flexible intelligent evacuation guidance effect in a fire scene is realized.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention provides a time service precision evaluation method and system for a network time server, and relates to the technical field of computer networks, and the method comprises the steps: 1, collecting the operation data of the network time server in a dynamic network environment in real time, including core index data and auxiliary index data, carrying out the normalization of the core index data, and carrying out the calculation of the auxiliary index data; generating a standardized core index data set; step 2, dynamically integrating the standardized core index data set according to the fluctuation characteristics and relevance of each core index to generate a comprehensive core index characterization value; and step 3, in combination with auxiliary index data, carrying out dynamic compensation correction on the comprehensive core index representation value, and generating a comprehensive time service precision evaluation value. According to the invention, comprehensive, accurate and dynamic evaluation and abnormity diagnosis of the time service precision of the network time server are realized, and the network time service quality and the operation and maintenance efficiency are effectively improved.