505 results about "Dynamical network" patented technology

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 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 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.

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 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.

The invention relates to the field of artificial intelligence cross-modal retrieval, and provides a video text cross-modal retrieval method and system based on spatio-temporal feature fusion. The method comprises the following steps: carrying out key frame sampling and time sequence partitioning on an input video, extracting static visual features through a spatial feature network, and extracting motion features through a time dynamic network; a self-adaptive gating fusion module is adopted to dynamically calculate spatial-temporal feature weights and perform weighted fusion; extracting text semantic features by using a pre-training language model; constructing a double-flow projection network to map video fusion features and text features to a unified measurement space, and optimizing a feature distance by adopting a contrast loss function containing difficult negative sample mining and intra-modal constraint; and outputting a retrieval result according to the cosine similarity sequence. The system comprises four units, wherein the gating fusion module is integrated with an FPGA acceleration circuit. According to the method, mAP (at) 10 is equal to 0.78 in a UCF-101 data set, the time sequence action retrieval accuracy rate is 92.8%, and the single video retrieval delay is 23 milliseconds.

The invention provides a dynamic network security defense method and system oriented to real-time network state adaptation, and the method comprises the steps: building a standardized state vector sequence through collecting key indexes, such as network flow rate, abnormal connection ratio, topology change frequency, protocol distribution deviation degree and the like; based on the sequence, utilizing a time sequence prediction model embedded with a structured attention mechanism to predict attack risk trends of a plurality of time windows in the future in a prospective manner; and in combination with the current state and the risk trend, dynamically selecting an optimal strategy path in a predefined defense action map, mapping the optimal strategy path into a standardized control command which can be executed by equipment, and dispatching, issuing and executing the standardized control command according to the priority. According to the method, closed-loop self-adaptive defense from state perception and risk prediction to strategy generation and execution is realized, and the real-time performance, intelligence and engineering deployability of a network security system are remarkably improved.

The invention belongs to the technical field of dynamic network risk prediction based on knowledge graph driving, and discloses a dynamic network risk prediction method and system based on knowledge graph driving, and the method comprises the steps: firstly obtaining a network security event and a context to construct a knowledge graph, and then detecting entity semantic drift through a preset rule and a deep semantic model, and carrying out node splitting, fusion, renaming or label updating and other structural remodeling on the knowledge graph based on a detection result, and finally carrying out risk prediction by utilizing a graph neural network model. The problem that in a traditional method, a static model cannot adapt to dynamic semantic changes is solved, the recognition capacity of a novel attack mode is improved, the risk detection false report and missing report rate is reduced, and the risk prediction efficiency is improved.

A system and method for adaptive neural network architecture implementing sophisticated supervision and signal transmission capabilities. The system comprises a layered neural network monitored by a hierarchical supervisory system that collects operational data and implements architectural modifications. A meta-supervisory system oversees the supervisory process, tracking adaptation patterns and extracting generalizable principles from successful modifications. The system implements novel signal transmission pathways that enable direct communication between non-adjacent network regions through adaptive transformation components and coordinated timing mechanisms. This multi-level approach enables dynamic network adaptation while maintaining operational stability through careful monitoring and controlled modification procedures. The system's innovative architecture allows neural networks to evolve their processing capabilities during operation while preserving reliable performance through sophisticated supervision and controlled signal propagation.

The invention relates to the field of aviation voice signal processing, and discloses a voice recognition compensation pilot earphone hearing protection method and system, and the method comprises the following steps: collecting multi-modal data, and separating a sound source through tensor decomposition; inferring a pilot state by using a dynamic network; performing context recognition and semantic evaluation on the attention target voice; and finally, dynamically modulating the sound field based on deep reinforcement learning, enhancing the voice in a personalized manner, and outputting after noise suppression. The system comprises a multi-mode perception data acquisition module, a sound source decoupling module, a pilot state inference module, a voice processing and semantic evaluation module and a sound field modulation and output module. According to the invention, high-fidelity speech extraction is realized through multi-modal perception and tensor decomposition; evaluating priority key information in combination with attention and semantics; and deep reinforcement learning and model prediction control are adopted to dynamically optimize the sound field, so that the voice recognition accuracy and the pilot information acquisition efficiency are improved.

The invention discloses a new energy automobile battery pack thermal management system multi-target adaptive control method, which comprises the following steps: constructing a thermal resistance-thermal capacity dynamic network model of a battery module and a cooling flow channel, and constructing a reinforcement learning virtual training environment; constructing a multi-target composite reward function; constructing a neural network model based on depth deterministic strategy gradient, and guiding gradient updating of strategy network parameters by using a multi-target composite reward function until the neural network model converges; solidifying the trained and converged strategy network parameters, deploying the solidified strategy network parameters to a vehicle thermal management controller, and outputting the optimal cooling liquid flow rate under the current working condition; and mapping the optimal cooling liquid flow rate into an electronic water pump rotating speed instruction. The method can solve the problems that in the prior art, due to the fact that the calculation load is large, the requirement for millisecond-level real-time response under the extreme working condition is difficult to meet, limitation is caused by linear deduction of temperature distribution only depending on head and tail modules, and a dynamic adjustment strategy cannot be fed back in real time.

The invention discloses a short temporary rainfall forecasting method and system based on a dynamic neural network architecture. According to the method, training sample distribution is optimized through a space-time gradient driven resampling strategy, a time-frequency double-branch dynamic network architecture is adopted, a time domain branch extracts multi-scale space features through an encoder-decoder structure, a frequency domain branch adaptively activates an FFT calculation module through a content awareness dynamic network to capture multi-scale time features, and the multi-scale time features are obtained. And two key problems of meteorological data distribution imbalance and deep learning model optimization imbalance are solved in combination with a balance regression loss function. According to the method, adaptive computing resource allocation for meteorological events with different complexities is realized, the forecasting precision and computing efficiency of heavy rainfall events are remarkably improved, and an efficient and reliable technical solution is provided for short temporary rainfall business forecasting.

The invention relates to the field of high polymer materials, and discloses a high-wear-resistance elastomer sole material and a preparation method thereof.The material comprises an elastomer matrix and surface-modified boron nitride nanosheets; the elastomer matrix comprises a first dynamic network and a second dynamic network which are formed through crosslinking of dynamic covalent bonds, and the first dynamic network and the second dynamic network are based on different types of dynamic covalent bonds and have different dissociation activation energy barriers or external stimulus response conditions. The surface-modified boron nitride nanosheet and the second dynamic network form a selective interface enhancement effect; the preparation method comprises the steps of polymer functionalization, nanosheet surface modification, melt blending reaction net forming, forming and the like. The obtained elastomer sole material not only has excellent wear resistance and mechanical properties, but also can realize self-repair of graded response aiming at damages of different degrees, and the service life of the material is remarkably prolonged.

The invention discloses a Mesh routing selection method based on a graph neural network and reinforcement learning. The Mesh routing selection method comprises the following steps: step S1, constructing a Mesh network graph structure model; s2, extracting an embedded representation of each router node by using a graph neural network; s3, designing a routing decision mechanism based on Q-learning, and generating a Q value in combination with the current router node state and neighbor information; s4, introducing a Masking mechanism to process the dynamic action space, and shielding illegal next-hop action; and step S5, defining a multi-target reward function which is used for guiding a Q-learning training process. The invention provides a Mesh routing selection method based on a graph neural network and reinforcement learning, and aims to solve the problems of poor adaptive capacity to a dynamic network environment, low routing efficiency and lack of intelligent decision support in the prior art and improve the data transmission performance of a Mesh network.

A dynamic grid allocation system for power distribution in the development of integrated circuits in the sub-five-nanometer range, wherein the system comprises the following: a placement processor unit configured to receive a synthesized netlist comprising a variety of standard cells and macros, each associated with switching activity data derived from simulation or synthesis activity profiles, and to generate a spatial cell placement arrangement by grouping cells into microzones based on activity correlation, time sensitivity, and connection proximity; a power density calculation unit coupled to the placement processor unit and configured to calculate the local power density and instantaneous current demand for each microzone based on the spatial distribution of switching operations, capacity load, and effective switching frequencies; a dynamic network generation unit coupled to the power density calculation unit and configured to generate a multilayer power distribution network with variable network topology, where the width, spacing, via density and metal layer assignment of the power network are adaptively determined as continuous functions of the localized power density and power demand calculated for each microzone; a topology control processor configured to monitor voltage drop and electromigration data obtained from signoff analyses or predictive models in real time and dynamically adjust the power grid topology parameters to ensure compliance with predefined reliability thresholds for voltage drop and electromigration; a predictive current modeling unit configured to receive historical switching and voltage data, train a predictive model for transient current peaks, and provide predictive power boost instructions to the dynamic grid generation unit before power integrity violations occur; and A feedback synchronization controller is communicatively connected to the placement processor unit and the topology control processor, whereby the feedback synchronization controller continuously exchanges updated power density information and placement constraints, so that cell placement and network topology adapt together in real time, thereby minimizing routing congestion, ensuring a uniform voltage distribution, and guaranteeing compliance with electromigration and IR drop regulations under various activity conditions.

The invention relates to a real-time data encryption transmission method and a real-time data encryption transmission device for a weak current remote monitoring system, in particular to the field of weak current remote monitoring systems, and effectively avoids the problem of update failure caused by link fluctuation in a dynamic network environment by intelligently predicting a stable state of a network to accurately trigger key update. A lightweight distributed consensus mechanism is adopted to generate a security key block, so that the communication overhead is remarkably reduced while the Byzantine fault-tolerant capability is guaranteed; a precise distribution strategy based on topological difference identification greatly reduces redundant transmission, and efficient delivery of keys is realized; the innovative chain verification and zero knowledge synchronization mechanism ensures that the offline node safely recovers the key state, and the overall scheme significantly optimizes the transmission efficiency and the resource utilization rate while improving the system security, and is especially suitable for large-scale and high-dynamic Internet of Things monitoring scenes.

The invention relates to the technical field of network attack and defense, in particular to a multi-view fused dynamic network attack and defense deduction system which comprises a node initial positioning module, a communication behavior detection module, a behavior direction marking module, a role conversion judgment module and a main chain fusion construction module. According to the method, chain independence identification is realized through target transformation analysis in path evolution, a reverse propagation paragraph in a path is reversely deduced in combination with a behavior sequence and direction, role reconstruction logic is deduced through path direction change, a node role label and a sorting structure are dynamically adjusted, the deviation of path judgment under a single view angle can be effectively eliminated, and the accuracy of path judgment is improved. The method is advantaged in that attack chain integrity identification capability is enhanced, information continuity expression in a path evolution process is improved, automatic determination capability of a key behavior combination in an attack path is enhanced, and restoration and tracking of an attack leading chain under multipath information fusion are realized. And the authenticity, precision and behavior recognition accuracy of the attack and defense deduction result in the dynamic environment are remarkably improved.

The invention belongs to the technical field of image reconstruction and classification, and particularly relates to a radar image adaptive reconstruction and classification method based on intelligent hierarchical learning, and the method comprises the steps: obtaining an original radar image and radar platform parameters, and obtaining a standardized preprocessing image; outputting a high-fidelity reconstructed image through hierarchical processing; carrying out Sobel gradient calculation to obtain a gradient image and carrying out region division, carrying out differential dynamic window local enhancement, and then carrying out global gray balance and secondary filtering to obtain an enhanced and optimized image; carrying out local / global feature extraction, carrying out weighted fusion by utilizing semantic association, and outputting a multi-layer fusion feature map; outputting a high-confidence identification result by utilizing candidate region acquisition and difficulty evaluation, dynamic network switching identification and confidence closed-loop verification; and integrating the original radar image, the high-fidelity reconstructed image and the high-confidence identification result, and outputting a structured report. According to the method, the optimal balance of radar image speckle noise suppression and detail reservation can be realized.

The invention provides a real-time scheduling method for a wireless network based on a large language model, the wireless network comprises a scheduling node and a plurality of user nodes, and the scheduling node is provided with a scheduling agent, a historical memory pool, an reflection agent and a suggestion buffer area. The method comprises the following steps executed in each time slot: step S1, the scheduling node acquires global information of a wireless network under the current time slot and calculates states of all data streams under the current time slot according to a preset calculation rule; and S2, the scheduling agent generates a scheduling decision of the current time slot according to a preset scheduling prompt word by using a large language model configured by the scheduling agent based on the states of all data streams under the current time slot and decision suggestions stored in the suggestion buffer area and by taking a maximized reward as a target. According to the technical scheme of the invention, by introducing the large language model, the problem of insufficient adaptability in a dynamic network environment in the prior art is solved.

A computer system for adaptive neural network architecture implementing sophisticated supervision, pruning, and signal transmission capabilities. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operation patterns, implements architectural changes, detects network sparsity, coordinates pruning decisions, and manages resource redistribution. A meta-supervisory system tracks supervisory behavior patterns, stores successful modification and pruning patterns, and extracts generalizable principles from these patterns. The system manages signal transmission pathways that enable direct communication between non-adjacent network regions through signal modification and temporal coordination. This multi-level approach enables dynamic network adaptation and efficient resource utilization through pruning while maintaining operational stability. The system's innovative architecture allows neural networks to evolve their processing capabilities during operation while preserving reliable performance through sophisticated supervision and controlled modification.

The invention discloses a heterogeneous network embedding method based on dynamic heterogeneous network decomposition and a hierarchical space-time attention mechanism. The method comprises the following steps: dynamic network decomposition: decomposing a heterogeneous network sequence into independent subgraph sets according to edge types; multi-order node attention aggregation: for each sub-graph, fusing information of nodes and different edge types; semantic level attention is embedded, wherein global semantic embedding is generated in combination with global relation type weight and meta-path long-short-term memory network coding; time dynamic modeling: respectively calculating a time attention weight and a Monte Carlo sampling approximate Horkes intensity, and then aggregating and splicing results of the time attention weight and the Monte Carlo sampling approximate Horkes intensity to obtain a final embedding result. According to the method, through cross-granularity semantic modeling and efficient time sequence dependence learning, an innovative solution is provided for node representation of the dynamic heterogeneous network.

The invention belongs to the technical field of Internet of Vehicles control, and discloses an Internet of Vehicles channel congestion distributed closed-loop control method. According to the method, a closed-loop feedback channel resource congestion control strategy is provided, and a closed-loop feedback control framework adopted by the strategy is composed of reference model construction and adaptive feedback control model construction. And constructing a data packet acceptance rate model and a network effect maximization model in the reference model, and solving an optimal data packet reception probability as an ideal state of the Internet of Vehicles. In the adaptive closed-loop feedback control model, an error between an ideal state and an actual network state of the Internet of Vehicles is adjusted through a PI controller, so that the actual network state infinitely approaches the ideal network state. Network parameters are dynamically adjusted based on model reference adaptive control, the reliability of communication between vehicles in a dynamic network environment is ensured, and expected stability and robustness of a system can be kept in a dynamically changing environment.

The invention discloses a network address configuration method and electronic equipment, and relates to the technical field of network address configuration, and the method comprises the following steps: setting a special static network address configuration item in a preset storage area; when the system is started, the static network address is read from the special static network address configuration item, and the static network address is configured to a network interface of the baseboard management controller by using an adding command; initiating a dynamic host configuration protocol request to obtain a dynamic network address, and intercepting default configuration operation of a dynamic host configuration protocol request process on a network interface; configuring the dynamic network address to a network interface of the baseboard management controller by using an adding command; the static network address and the dynamic network address are configured at the same network interface of the baseboard management controller. The technical problem that static network address configuration and dynamic network address configuration are not compatible in the prior art is solved, and the substrate management controller can be accessed through at least one of the dynamic network address and the static network address.

A kind of image classification method based on class incremental learning of multi-normalization and dynamic network, comprising the following steps;Step 1: build new base network;Step 2: based on the initial image classification task D 1 of class incremental learning training of new base network, obtain initialization model, and freeze initialization model as the old model of next stage image classification task D 2 of class incremental learning;Step 3: when processing new class incremental learning image classification task, build feature enhancement model;Step 4: the size of feature enhancement model is controlled by model distillation technology, and the target compression model is obtained by distilling feature enhancement model;Target compression model is used as the old model when processing the next class incremental task;Step 5: on test sample, using target compression model carries out image classification precision detection.The present application is aimed at image classification task in the field of class incremental learning, used to relieve model catastrophic forgetting problem, improve the accuracy of model downstream image classification task.

A system for dynamic cybersecurity scoring using traffic fingerprinting and score improvement, that uses a web crawler that sends message prompts to external hosts and receives responses from external hosts, a time-series data store that produces time-series data from the message responses, and a directed computational graph module that analyzes the time-series data to produce a weighted score representing the overall cybersecurity state of an organization.

Systems and methods are described for automated anomaly detection in computer networks using a dynamic network graph. Network data describing communications among computing entities are received, and a dynamic graph is constructed and maintained whose nodes represent the entities and whose edges represent observed communications. Behavior characteristics are computed for the nodes, and the nodes are clustered using a clustering algorithm to obtain cluster assignments. Anomalies are detected by identifying nodes whose behavior characteristics deviate from those of their assigned clusters, and alerts or security actions are generated in response. The system supports incremental updates to graph structure and cluster assignments as network conditions evolve, improving detection latency and accuracy.

Systems and methods for providing dynamic network slicing resource reselection for wireless communication are described. User equipments (UEs) may determine that network slicing resources utilized with respect to one or more applications are to be changed in run time, such as for more efficient operation in light of a change in power mode, throughput mode, latency mode, etc. A message indicating a cause for reselection of a current packet data unit (PDU) session association with an application may be provided to a UE route selection policy (URSP) manager. The URSP manager may reevaluate a current PDU session association with the application to determine if it is a best match with the cause. If no, a switch to a new PDU session association with the application may be made. If yes, the current PDU session association with the application may be maintained. Other aspects and features are also claimed and described.

Systems and methods for configuring and managing a network device with a transformer model under control of a network control function (NCF) are disclosed. A processor of the NCF receives a request that identifies a network management task and associated service targets. The processor forms a token set of schema-defined tokens that represent network context, applies positional encodings to generate an ordered token sequence, and invokes the transformer model to produce a configuration patch. The configuration patch is validated against a schema-constrained decoder that enforces device grammar and is applied to the target network device. Device state and telemetry are read back to obtain a read-back state, which is evaluated against the service targets. When telemetry deviates, the processor generates a further configuration patch that modifies a bounded subset of parameters relative to the read-back state to restore compliance

The invention relates to the technical field of industrial internet of things and mobile edge computing crossing, in particular to a computing task unloading and service caching collaborative optimization method, which comprises the following steps of: acquiring an IIOT equipment state, channel quality and edge server resources in real time by constructing a dynamic network awareness architecture, and analyzing a task DAG topological structure; designing a service cache delay compensation mechanism, parallelizing a cache process and task execution, predicting future required services based on a task dependency relationship, and calculating a delay compensation factor; a deep reinforcement learning algorithm based on Transform is adopted, and an unloading decision is generated in combination with a hierarchical attention mechanism and an improved DDQN network; tasks are dynamically allocated to the local, the edge or the cloud through a three-level unloading decision tree, task completion time is minimized, a resource constraint modeling and task preemption mechanism is further included, and the system response capability and the resource utilization rate are improved.

The invention relates to an agricultural economy cross-border cooperation English negotiation auxiliary decision-making system, in particular to the field of data processing and analysis, and the system can capture multi-source policy data in real time, construct a policy chain reaction network through dynamic network modeling, effectively evaluate the trade dependency relationship between participating countries, and improve the accuracy of the trade dependency relationship between the participating countries. The system generates a multi-dimensional policy risk score vector based on historical policy data through a risk prediction engine, constructs a multi-objective optimization decision model in combination with a preset negotiation constraint condition, and can output accurate English negotiation policy scripts including clause revision suggestions and interaction sequential logic through a policy optimization generation module. Therefore, accurate decision support is provided for cross-border cooperation negotiation, the cross-border agricultural economic cooperation negotiation method has remarkable advantages in the aspects of improving the decision efficiency of cross-border agricultural economic cooperation, reducing policy risks and optimizing negotiation strategies, the negotiation effect and the agreement achievement rate can be remarkably improved, and smooth proceeding of international trade is promoted.