160 results about "Complex network" patented technology

This invention relates to a train route identification method, apparatus, device, and medium, belonging to the field of rail transit technology. The method includes: acquiring train operation data and train network vector GIS data; inputting the train operation data and train network vector GIS data into a pre-trained graph neural network model, outputting feature data; associating and matching the feature data with a preset train network, and determining the train's corresponding route based on the latitude and longitude trajectory with the highest matching probability and a confidence level meeting a preset threshold. By using an improved graph neural network model to achieve deep fusion of train operation data and network GIS data, the association features between train operation and network topology are accurately captured, effectively solving the technical problem of mismatched train routes in complex network conditions.

The application relates to the technical field of wireless communication, and discloses a network switching method, system, device and medium based on multi-round simulation optimization, which comprises the following steps: acquiring real-time network state information to perform flow prediction, obtaining an overall flow prediction result; acquiring a load value from the overall flow prediction result, and if the load value is higher than a load threshold value, determining a high-load access point and redirecting flow to a nearby access point to obtain an initial network configuration; performing multi-round switching simulation to generate a candidate sequence, and if a preset load balancing condition is met, obtaining a preliminary switching scheme and calculating a reward value, selecting a switching scheme with the highest reward value as an optimized switching scheme; executing the optimized switching scheme, recording access point states; performing abnormality detection, and if there is an abnormal access point, adjusting a prediction method and predicting overall flow, optimizing network configuration according to a prediction result, and obtaining a final network switching configuration. The method can efficiently and stably switch wireless networks in a complex network environment.

The application discloses a kind of defense decision generation methods, systems and electronic equipment for dynamic threat posture, including dynamic threat posture modeling and defense knowledge base construction: target network topology, asset and threat event are structuredly described, dynamic threat posture representation is constructed, and defense knowledge graph is formed in combination with defense rule, response action and security constraint;Multi-stage decision strategy generation based on defense large model: based on defense knowledge graph, introduce defense large model as core reasoning engine, defense process is divided into multiple defense stages, and candidate defense strategy sequence is generated in each stage;Strategy evaluation feedback and closed-loop dynamic optimization output: candidate defense strategy is multidimensional evaluated, and constraint condition is dynamically adjusted according to execution feedback, strategy regeneration is triggered, and closed-loop defense decision mechanism is formed to output the sustainable optimization of strategy.The method of the application can significantly improve the automated defense decision capability under complex network environment.

This invention provides a lateral control method and system for autonomous vehicles in network attack scenarios, applied to the controller of an autonomous vehicle. The method includes: in response to the detection of a network attack, performing the following steps: using a pre-built incremental sparse Gaussian process model to predict and repair the measurement signals of vehicle sensors affected by the network attack, generating repaired observation signals; constructing a rolling time-domain estimator based on vehicle dynamics constraints, inputting the repaired observation signals into the rolling time-domain estimator, and obtaining the current optimal state estimate of the vehicle; based on the optimal state estimate, calculating the current control command of the vehicle through a pre-set hierarchical model predictive controller, executing the control command, and completing lateral control. This invention can improve the state estimation accuracy, control stability, and real-time feasibility of autonomous vehicles in complex network attack scenarios.

This application provides a converged terminal based on automotive-grade Ethernet and WiFi technology. It provides a UTP signal interface through a WiFi MCU module, 2.4G and 5G WiFi signals through a WiFi RF baseband module, and an RF signal interface through a 1000BASE-T1 communication module. Regardless of whether the existing network transmission method is coaxial RF-head, WiFi, or UTP cable, users can successfully connect to the existing network through a converged terminal as an intermediate device. The converged terminal of this application is particularly suitable for rapid network access in complex network environments.

The application discloses a method for adaptive secure consensus control of a multi-agent spatio-temporal dynamic system with unknown boundary nonlinearity under hybrid attacks, comprising the following steps: constructing a multi-agent spatio-temporal dynamic system model with unknown boundary nonlinearity and a virtual leader model; establishing a hybrid network attack model containing deception attacks and denial of service (Dos) attacks, and using an adaptive radial basis neural network to approximate the unknown boundary nonlinearity function; defining a consensus error signal to obtain a consensus state error system; designing a composite adaptive neural network secure boundary consensus control scheme, constructing a Lyapunov function for the error system, and obtaining a sufficient condition for the error system to realize mean square secure consensus control. The application effectively solves the problem that the traditional method is difficult to simultaneously cope with complex network attacks and unknown boundary nonlinear disturbances, and significantly improves the security and robustness of the system.

This invention relates to network coding technology and discloses a network coding method based on maximum rank distance code. The method includes: performing network coding on transmitted data to obtain coded data; performing linear addition and multiplication on the coded data to obtain coded data packets; using a preset parity check matrix to perform error detection and location on the coded data packets to obtain an error matrix; and performing rank error correction on the coded data packets based on the error matrix to obtain the original source data. This invention also provides a network coding apparatus, device, and medium based on maximum rank distance code. This invention can reduce the amount of redundant data transmitted in the transmission network and maintain low latency and high data recovery success rate even in complex network environments.

The application discloses an edge computing network service function chain embedding method based on K shortest path, and aims at a distributed service function chain embedding problem under time delay constraint. In the case of meeting the end-to-end time delay constraint, a service function embedding algorithm based on K shortest path algorithm is designed to minimize the deployment cost. The application firstly proposes a service function embedding problem modeling method based on a weighted layered graph, and converts the service function chain embedding problem into a minimum cost path problem under time delay constraint in the weighted layered graph. Secondly, a service function chain embedding mechanism under time delay constraint is designed based on the weighted layered graph and K-linear measurement aggregation algorithm. Compared with the prior art, the application has better high performance and operability, and has practical significance for complex network applications in edge computing networks.

The application discloses a climate risk zoning method based on a complex network, comprising the following steps: acquiring original space-time data of climate disaster-causing factors, exposure and vulnerability indexes of disaster-affected bodies in a region to be researched; performing homogenization preprocessing to generate a standardized grid data set; taking a grid unit as a node to construct a heterogeneous weighted complex network; based on the time sequence change of the edge weight between nodes, performing an adaptive threshold-freezing mechanism to update the heterogeneous weighted complex network; running a cross-scale hierarchical community discovery algorithm to obtain an initial climate risk zoning; inputting the initial climate risk zoning into a space-time convolutional graph neural network to perform transfer learning and fine-tuning by using zoning-loss coupling loss function, and outputting a final climate risk zoning result. The application systematically solves the core problems of high precision, high timeliness and low cost, and solves the problem that traditional climate risk zoning cannot consider both climate risk intensity and climate historical change process.

The application discloses a multi-ship lock cooperative ship navigation scheduling method based on complex scene self-adaption, comprising: acquiring static and dynamic data of a multi-ship lock system, constructing a basic complex network model, calculating by using the basic complex network model to obtain a basic data calculation result; acquiring new real-time data based on the basic data calculation result and preprocessing to obtain processed dynamic characteristic data; optimizing the basic complex network model by using the processed dynamic characteristic data, constructing a cascade effect prediction model; predicting congestion propagation based on the cascade effect prediction model to obtain a prediction result, executing cooperative scheduling, realizing intelligent monitoring and cascade effect prevention control of the multi-ship lock system. The application quantifies the correlation between ship lock operation synchronism and congestion diffusion, realizes accurate prediction of a risk propagation path, effectively reduces the congestion cross-ship lock propagation probability, and further shortens the average fault recovery time required.

This invention provides a rapid depression detection method based on a weighted degree transfer network, comprising: acquiring and preprocessing the EEG signals of a subject to obtain a one-dimensional EEG time series; mapping the one-dimensional EEG time series into a complex network using a weighted horizontal visualization algorithm; extracting degree and intensity sequences; using the deduplicated set of degree values ​​as nodes of the new network; tracing the degree transfer path between adjacent time points; calculating the product of the degree difference and intensity difference between adjacent time points as the edge weights of the degree transfer path, thereby constructing a weighted degree transfer network; normalizing the network to obtain a transition probability matrix; calculating its Shannon entropy and MPR statistical complexity as a joint feature vector; and using a machine learning classifier to identify the depression state. This invention does not require presetting parameters such as embedding dimension, and by fusing the dynamic difference features of degree and intensity, it can quickly and accurately capture abnormal nonlinear dynamic patterns in the EEG signals of depression.

The application discloses a computer network security risk identification system based on data processing, and relates to the technical field of access management. The system comprises a scene and behavior association rule construction module, a three-dimensional dynamic association and risk identification module, a three-dimensional risk tracing module, a risk linkage disposal module, a cross-scene permission dynamic adaptation module and a bidirectional feedback optimization module. The application establishes a three-dimensional association matching logic of scenes, behaviors and processes through the collaborative work architecture of scene and behavior association rule construction, three-dimensional dynamic association and risk identification, three-dimensional risk tracing, cross-scene permission dynamic adaptation, risk linkage disposal and bidirectional feedback optimization, realizes full-process closed-loop management and control from risk identification, tracing, permission adaptation, linkage disposal to rule optimization, and solves the problems of single risk identification dimension and insufficient cooperation of each link in the prior art, the inability to cover diversified risks in a multi-scene complex network environment, and the lack of systematicness and continuity in risk disposal.

This invention belongs to the field of network security technology and discloses a deep learning-based DDoS attack detection method, including S1, data preprocessing, which performs data cleaning, noise reduction, and format standardization on the acquired network traffic data stream to ensure that the data is suitable for subsequent processing; S2, feature selection, which transmits the preprocessed network traffic data to a feature selection module and uses a selective deep autoencoder to extract potential attack features from the network traffic data; S3, attack detection, which transmits the extracted network traffic data features to a DDoS attack detection module and uses a CNN-Self-Attention model to detect whether the network traffic data is DDoS attack traffic and identify its attack type. This invention can extract important characteristics from complex network traffic data, improve detection accuracy, and achieve high accuracy results, making it suitable for current DDoS attack detection systems.

The application discloses a fast re-routing method based on bidirectional potential energy field, comprising the following steps: constructing a forward potential energy field and a reverse potential energy field based on destination nodes in a network; generating a data packet forwarding rule according to the forward potential energy field and the reverse potential energy field; and switching between the forward potential energy field and the reverse potential energy field according to a link state in a data packet forwarding process. The application can realize efficient, stable and robust fault recovery in a complex network environment.

This application discloses a vulnerability remediation method for IoT terminals based on intrinsic security technology. First, the original message is decrypted and normalized to eliminate bit offsets before heterogeneous execution. This is then executed in parallel within an isolated heterogeneous environment to obtain a response set. Next, a multi-mode adjudication mechanism is used to accurately locate the damaged node. By decoupling trusted responses and cleansing feedback, the consensus result is echoed back to the terminal, simultaneously triggering isolation and cleansing instructions for dynamic node reconstruction and route loopback updates, forming a self-healing feedback control chain. This approach effectively eliminates bitstream misalignment interference during the adjudication process, significantly reduces the false interception rate, and achieves online isolation and reconstruction of damaged nodes without interrupting services. This greatly enhances the intrinsic security and service continuity of IoT terminals in complex network environments.

The embodiment of the invention provides a path searching method and device, and can solve the technical problem that the related technology cannot meet the path searching requirement in a complex network environment. The method comprises the steps of obtaining at least one search node based on search parameters corresponding to a path search request; forming a search path based on the search parameters and the at least one search node; generating a path comprehensive weight of the search path according to an adjacent node and a corresponding edge attribute of each search node in any search path; and under the condition that the search path meets the search path generation condition, in response to the condition that the path comprehensive weight meets a weight threshold value, determining a target path. According to the method, multiple path attributes are comprehensively considered in the path searching process, self-definition and dynamic adjustment of weights are supported, path selection can be flexibly adapted according to actual service requirements, threshold setting and efficient pruning operation are achieved, and the technical problems that an existing path searching strategy is single, weight configuration is rigid, and complex constraint scenes are difficult to adapt are solved.

The application discloses an edge network traffic prediction method based on redundancy reserve pool calculation, comprising the following steps: collecting traffic data of an edge base station, and constructing a training set and a test set according to the traffic data; inputting the constructed training set into an ESN model and training, and collecting a reserve pool state matrix; performing high-dimensional mapping on the reserve pool state matrix by using a ordinal pattern conversion network modeling method, calculating the ordinal complexity of reserve pool neurons according to a complex network measurement index, and determining key and non-key neurons of the reserve pool; balancing migrating the input weight and the internal weight of the key neurons of the reserve pool to the non-key neurons of the reserve pool in a redundant manner to form a new ESN model, retraining the new ESN model, and obtaining an optimal output weight matrix; deploying the trained new ESN model on an edge side network traffic management device, collecting traffic data of the edge base station in real time as input of the model, and obtaining a prediction value of future network traffic as output of the model.

This invention relates to the field of Internet audio data processing technology, specifically to a cross-terminal voice input and metadata repair method and system based on H5. The method includes: a data receiving step: receiving real-time segmented data streams and terminal environment fingerprints; a model building step: injecting the environment fingerprint to build an ideal model and generating a theoretical drift benchmark; a feature mapping comparison step: extracting real-world features and comparing them with the benchmark to determine the metadata deviation type; a repair execution step: adaptively verifying and repairing the data stream based on the deviation type; and a reconstruction generation step: rewriting the metadata header based on the repaired data payload to generate a standardized audio file. This invention, through reverse engineering using an ideal model, accurately distinguishes between hardware anomalies and network packet loss, improving the usability of Web audio acquisition in complex network environments.

This invention discloses a secure cross-regional command interaction system and method for a visually isolated industrial control system, aiming to solve problems such as complex network gateway configuration, potential risks, incompatibility with outdated protocols, and low visualization in existing technologies. The system includes a command encoding module, a physically isolated display channel, a command capture and execution terminal, and a feedback channel. The method includes generating dynamic command images, transmitting them to the production control area via a split-screen device, capturing and verifying the images, and performing simulated human-machine interaction operations. By adopting the above technical solution, this invention can eliminate the network attack surface, effectively resist replay attacks and content tampering, achieve zero-intrusion integration with existing legacy systems, realize visualized confirmation and complete audit traceability of command execution status, and provide a secure control command exit and status feedback entry for the decision-making platform in the management information area, realizing an intelligent closed loop of "perception-decision-execution-verification".

The application provides a physical guidance-based communication and network coordination partitioning method, and belongs to the technical field of power system optimization, and comprises the following steps: when the physical layer state of a coupled system changes, determining a physical layer change event based on a pre-constructed power distribution network physical layer complex network model; determining an optimal cross-domain transmission mapping based on the physical layer change event and a pre-constructed dual-domain coupled complex network model; evolving a current communication layer topology based on the optimal cross-domain transmission mapping and a pre-constructed sensing coordination communication layer complex network model to obtain an evolved communication layer structure; and converting topology reconstruction events and the like into communication layer structure adjustment bases directly by introducing the cross-domain optimal transmission mapping based on the physical layer state change, so that the physical layer can actively guide and dynamically link the communication layer, and the communication topology can evolve in real time and self-adaptively according to the physical state, and the dual-domain structure can be kept in highly coordinated matching.

The application provides a physical guidance-based communication and network coordination partitioning method, and belongs to the technical field of power system optimization, and comprises the following steps: when the physical layer state of a coupled system changes, determining a physical layer change event based on a pre-constructed power distribution network physical layer complex network model; determining an optimal cross-domain transmission mapping based on the physical layer change event and a pre-constructed dual-domain coupled complex network model; evolving a current communication layer topology based on the optimal cross-domain transmission mapping and a pre-constructed sensing coordination communication layer complex network model to obtain an evolved communication layer structure; and converting topology reconstruction events and the like into communication layer structure adjustment bases directly by introducing the cross-domain optimal transmission mapping based on the physical layer state change, so that the physical layer can actively guide and dynamically link the communication layer, and the communication topology can evolve in real time and self-adaptively according to the physical state, and the dual-domain structure can be kept in highly coordinated matching.

The application provides a network construction and characterization target echo parameter estimation method, converts a target echo parameter estimation problem into a signal characteristic analysis problem, introduces a complex network theory, completes mapping of an echo signal time-frequency domain to a complex network, further converts an echo signal characteristic analysis problem into network construction and characterization, and improves estimation precision of a target echo parameter. Experimental results of the application show that the method disclosed by the application is an effective method for target echo parameter estimation, and the parameter estimation precision is higher.

A system and method are provided for explaining ontological sub-graphs. The system and method include querying an ontology to determine a match between a query graph and a portion of an ontology graph. When there is a match, a subgraph representing the match is first translated into a simple summary using a simple language (e.g., triplets which include a subject and object corresponding to pairs of connected nodes in the subgraph and a verb / predicate representing a relation / edge in the subgraph that connect the pair nodes). This simple summary is then fed, as part of a prompt, to a large language model (LLM) that generates a human-readable summary based on the prompt.

The application discloses a kind of civil aviation information coordination system modeling and evaluation method based on complex network, for the lack of prior art, in combination with the structure and operating characteristics of civil aviation information coordination system, it is abstracted into the networked graph model of multiple types of nodes and its interconnection relationship constitutes, realize system standardization description;Design by network flexibility, structural robustness, information interaction efficiency three aspects multiple key indicators constitute the performance evaluation multilayer index system, and for the subjective weight of the performance evaluation first-level index obtained by fuzzy analytic hierarchy process and the objective weight of the performance evaluation second-level index obtained by inverse entropy weight method, with the principle of minimum deviation to carry out the subjective and objective fusion second-level index weighting, calculate the contribution of each index to the comprehensive performance of civil aviation information coordination system, finally realize the scientific quantification evaluation of the designed civil aviation information coordination system performance.The present application can provide theoretical support for future civil aviation information coordination system construction and optimization from top-level architecture construction.

This invention discloses a crowdsourced logistics overlapping community detection method based on graph convolutional networks and probabilistic decoding, applied to the field of overlapping community partitioning in complex networks. The method includes: obtaining a graph adjacency matrix and a worker node attribute matrix based on the relationships between worker nodes; performing feature encoding on worker nodes using an improved graph convolutional network to obtain a worker node-community membership matrix; introducing learnable community weight parameters to define the structural association strength between node pairs; and decoding the structural association strength as the probability of edges existing between node pairs using a Bernoulli-Poisson random graph generation mechanism; constructing a structural reconstruction loss function using a set of positive sample edges and a set of negative sample non-edges sampled from the graph adjacency matrix; and introducing community membership constraints and attribute consistency constraints, which are then weighted and combined to form the overall optimization objective function of the model. This invention achieves accurate overlapping community partitioning of worker nodes, effectively improving the accuracy and practicality of the partitioning.

Various embodiments herein described are directed to methods, apparatuses and computer program products configured for monitoring and improving the performance of complex network computer systems. In some examples, an example service health status monitoring server may generate service health status data objects based at least in part on system-monitored metric metadata of metric-based alert data object(s) and user-defined mapping metadata of metric-based rule data object(s). Additional examples provide various example user interfaces, such as, but not limited to, health status monitoring user interfaces, that facilitate various user inputs and software operations in a service health status monitoring system.

The application relates to a kind of unmanned plane distributed cooperative prediction control methods for communication impaired scene, establish the multiple unmanned plane system model under cooperative flight task, configure the network model under communication impaired scene and the corresponding each transmission channel buffer model for each unmanned plane;With the improved robust multivariable observer, the state after unmanned plane is disturbed and the interference information estimation in output channel are estimated, with the estimated state as the input of distributed cooperative prediction controller and compensating corresponding interference, obtain the control input generation and compensation strategy for communication impaired scene, realize the distributed cooperative prediction control of unmanned plane for communication impaired scene.The application effectively filters the measurement disturbance in output channel and FDI attack influence and obtains the estimated value of actual state, effectively deals with the communication impaired phenomenon that transmission channel exists random packet loss, reduces the calculation burden in cooperative flight task, and has higher reliability under unknown interference and complex network environment.

The application discloses a method for optimizing infrastructure network game strategy based on linear constraint, which comprises the following steps: obtaining the topology structure of the infrastructure network, determining the strategy set of the attack side and the defense side, and constructing a basic model of complex network game; determining an index representing network connectivity, calculating the income of the attack side and the defense side under various strategy profiles in the complex network game model, and thus obtaining an income matrix; adding a restriction condition to the strategy selection of the attack side and the defense side, and thus obtaining the strategy constraint set of the attack side and the defense side; converting the solution of the basic model of complex network game into the solution of a linear programming problem, obtaining a mixed Nash equilibrium solution, and thus obtaining the strategy optimization result of the attack side and the defense side. The method can greatly widen the application of complex network game research in practice by introducing the constraint matrix countermeasure into the infrastructure complex network game.

The invention belongs to the technical field of computer vision, and particularly relates to an end-to-end shape recognition method and system based on a complex network. The method comprises the following steps: carrying out dense discretization on the edge of a to-be-identified image to obtain an edge description point set; selecting a series of key points on the obtained edge description point set at equal intervals; extracting local features of a key point surrounding edge description point set by using a shape context; taking each key point as a node, defining edges in the network according to spatial correlation among the key points, and constructing a multi-layer complex network by adopting a dynamic evolution strategy; local features and topological features of the complex network are processed through a graph convolutional network, and robust expression of the shape classification task is obtained; and performing classification through a full connection layer network to obtain a classification result, and realizing end-to-end shape recognition. According to the method, the adaptability and robustness of the method to different shape classification tasks are enhanced.

This invention relates to the field of secure data transmission, and more particularly to an authentication method and system for secure data transmission. The method includes receiving commitment information from a communication node and associating and storing the commitment information with the communication node's identity identifier. Upon meeting preset trigger conditions, the method receives verification information from the communication node, which corresponds to the commitment information. The commitment information is verified based on the verification information; upon successful verification, an identity credential for the communication node is generated. The method also receives an authorization request from the communication node, which includes attribute information of the data to be transmitted. An authorization credential bound to the data to be transmitted is generated based on the authorization request. Finally, the method receives the data to be transmitted, the identity credential, and the authorization credential from the communication node. The validity of the identity credential and the authorization credential is verified; if both are valid, the data to be transmitted is received. By coupling authentication and authorization, this invention achieves fine-grained access control and can adapt to complex network environments such as high latency and intermittent connections.