37 results about "Initial topology" patented technology

The invention provides a power distribution network topology construction splicing method based on recursive grouping and a graph attention network. The method comprises the following steps: acquiring electrical attribute information of node equipment in a power grid by using a monitoring sensor; recursive grouping is carried out, nodes and branches in the feeder line of the power distribution network are identified by using the constructed impedance matrix and distance matrix, an initial topological graph is constructed, and a node feature matrix and an initial adjacent matrix are obtained; a GATv2 graph attention network is introduced, dynamic attention calculation is performed by using the node feature matrix and the initial adjacent matrix, and a final topological graph of the feeder is generated; a landmark matching method and Pearson's correlation coefficient calculation are adopted, and boundary points of each independent topology are identified and matched; performing accurate alignment on the topological graph generated by each feeder line by using a graph embedding synchronization technology, and finally performing global topology processing; and carrying out topology verification on the constructed global topological graph. Construction and efficient splicing of line topologies in different areas of the power distribution network are achieved, and efficient operation and intelligent development of a modern power distribution network are supported.

The invention relates to the technical field of mode recognition, in particular to a cardiac image data processing system based on deep learning, which comprises an initial map construction module, an artifact branch pruning module, a fracture path repairing module and a topological smooth output module. According to the method, initial topology containing node coordinates is constructed, the maximum inscribed circle area is calculated to generate circulation attributes, virtual blood flow injection simulation and flow resistance calculation are utilized to accurately quantify branch traffic capacity, artifact interference is eliminated according to the matching relation between the blood flow proportion and the flow resistance, and suspended node geometric features and a path texture sequence are combined, so that the branch traffic capacity is accurately quantified. A graph neural network is utilized to predict a connection probability to repair a fracture path, a node coordinate weighted updating mechanism is matched to guarantee smoothness and continuity of a topological structure, the problems of artifact confusion and path fracture in a traditional method are effectively solved, and the construction accuracy and automation level of a heart structure model are remarkably improved.

This invention provides a self-correcting method, apparatus, electronic device, and storage medium for network topology, relating to the field of network management technology. The method includes: collecting multi-source topology data of a target network device; performing fusion processing on the multi-source topology data to generate an initial topology relationship set, the initial topology relationship set including topology relationships; calculating the confidence level corresponding to each topology relationship; identifying the topology relationship to be verified from the initial topology relationship set based on the confidence level corresponding to the topology relationship; performing targeted re-probing on the topology relationship to be verified; updating the initial topology relationship set based on the re-probing results; obtaining a corrected target topology relationship set; and finally outputting the network topology graph corresponding to the target topology relationship set. This comprehensively improves the completeness and accuracy of network topology discovery and realizes automatic, closed-loop verification and correction of topology relationships, enabling the network topology to intelligently adapt to dynamically changing network environments.

The application discloses a wireless resource allocation parameter determination method and device and electronic equipment. The method comprises the following steps: obtaining an initial topology structure corresponding to a wireless network; determining a plurality of resource allocation relationships corresponding to a plurality of links according to the initial topology structure; determining a target topology structure corresponding to the wireless network according to the plurality of links and the plurality of resource allocation relationships corresponding to the plurality of links, wherein the target topology structure comprises points obtained according to the plurality of links and edges obtained according to the corresponding resource allocation relationships; and determining a resource allocation parameter corresponding to the wireless network according to the target topology structure, wherein the resource allocation parameter represents a resource parameter allocated by an access end to a corresponding device end. The application solves the technical problem of unreasonable resource allocation caused by unreasonable resource allocation parameter determination in the prior art.

A method for building a neural network configured to be run on a destination computing unit is implemented by a system including a computer and a memory storing a learning dataset. The method includes providing a neural network having an initial topology, and training the initial topology over the learning dataset. The topology of the neural network is optimized, which includes at least one iteration of the following steps: for each of a plurality of candidate topological changes, estimating the variation induced by the candidate topological change on: the neural network's error, and a value of at least one physical quantity needed for executing the neural network on the destination processing unit, selecting at least one of the candidate topological changes based on said estimation and updating the topology of the neural network according to said selected topological change, and training the updated neural network over the learning dataset.

The application relates to a fully decoupled movable piezoelectric drive flexible parallel mechanism topology optimization method, which comprises the following steps: obtaining the density field distribution of a flexible parallel mechanism based on participation parameters and through an independent point density interpolation method; constructing a target function based on the density field distribution and taking the sum of expected output displacements of each branch of the flexible parallel mechanism as maximum; modifying the target function based on preset constraints to obtain an initial topology optimization model, wherein the preset constraint conditions comprise the volume fraction of the flexible parallel mechanism, the non-interference between a piezoelectric driver and a main design domain of the flexible parallel mechanism, and the non-rotation of a moving platform in a plane under the action of a load; and solving a topology optimization problem through a gradient-based moving asymptote optimization algorithm based on the initial topology optimization model. Through the application, the problems of input and output coupling effects of the flexible parallel mechanism, reduced motion accuracy and poor controllability can be improved.

According to the wind turbine generator part topology-parameter collaborative optimization method fused with artificial intelligence, design is carried out based on actual working conditions, loads and spatial constraints of wind turbine generator parts, it is ensured that an initial topology configuration conforms to an engineering application scene, a variable density method is adopted to solve a material optimal distribution field, and the optimal distribution field is obtained. Materials are distributed in a key stress area in a concentrated mode, non-key areas are reduced reasonably, engineering geometric reconstruction is carried out on an initial topological configuration, key structure parameters are extracted, a parameterized model is established, and a core performance response is obtained by establishing a mapping relation between the key structure parameters and core performance responses. Performance evaluation is converted into model rapid prediction from high-fidelity simulation time consumption calculation, the single performance evaluation time is shortened to a minute level from tens of hours, an optimal parameter combination is obtained based on the AI agent model and in combination with a multi-objective optimization algorithm, the optimization iteration speed is greatly increased, the reliability of the optimization effect is guaranteed, and the performance evaluation efficiency is improved. And the research and development period is further shortened.

The invention provides a network equipment operation and maintenance monitoring method and equipment based on a novel network architecture, and a medium. The method comprises the following steps: firstly, acquiring an operation state of a network device; constructing an initial topological structure according to the stored equipment information; constructing a whole network topology according to the operation state and the initial topology information; and finally, determining the security state of the industrial control network according to the equipment fault knowledge graph, the whole network topology and the deep learning model. According to the invention, through constructing the whole network topology and combining the preset fault knowledge graph and the deep learning model, the network topology structure is optimized, the dynamic state of the network can be mastered at any time, and the functions of real-time monitoring, network maintenance early warning, network fault alarm and the like of the whole network of the workshop are realized.

The invention belongs to the technical field of power distribution network operation and control, and particularly relates to a power distribution network topology switching path generation method and system based on loop matching, and the method comprises the steps: accurately obtaining a to-be-closed branch and a to-be-disconnected branch of a topology network through the structural difference between an initial topology structure and a target topology structure; the perfect matching between each to-be-closed branch and each to-be-disconnected branch is obtained by constructing the operation bipartite graph, and the complex topology constraint relationship between the branches can be converted into the matching and sorting problem in the graph theory, so that the computing power consumption in the topology switching path acquisition process can be obviously reduced; therefore, the generation efficiency of the topology switching path can be improved; through the timing constraint graph and topology connectivity verification, the non-executable topology switching paths can be screened out, so that the feasible topology switching paths can be accurately reserved. Therefore, the method and the system provided by the invention can efficiently and accurately generate the feasible topology switching path.

The invention discloses a three-dimensional model file importing method and system, which is used for importing IGES / IGS files and comprises the following steps: reading a three-dimensional model file to be imported to generate an initial topological structure; performing foundation repair on the initial topological structure to obtain an intermediate topological structure keeping geometric continuity; reconstructing the intermediate topological structure to obtain an optimized topological structure; the optimized topological structure is output, and file import is completed; wherein the initial topological structure generation process comprises the steps of replacing a default fixed tolerance of a three-dimensional model to be imported with a dynamic tolerance, and generating an initial topological structure according to the dynamic tolerance. Aiming at the problem existing in the process of importing IGES / IGS files into CAE in the field of three-dimensional models, the defect of model analysis distortion caused by fixed tolerance in the prior art is overcome. By means of the method, the accuracy of model importing is effectively improved, and the completeness of complex models such as thin-wall parts and curved surface overlapping parts after importing is ensured.

The application discloses a kind of based on OSM data's expressway interflow node identification and network construction method, first acquisition and pretreatment OSM expressway network and service area data, extract the main line of specified attribute, ramp element and uniform projection coordinate system;Again, by calculating the physical intersection of main line and ramp, the best clustering radius is determined in combination with K distance diagram, interflow area is obtained by DBSCAN clustering and the first type of cutting point is generated, while merging uplink and downlink service area and projecting to generate the second type of cutting point;Based on two kinds of cutting points, the main line section is divided, the end point of the section is matched with the cutting point with an error value of 1.0 meter, interflow, service area and temporary node are generated, and an initial topology graph is constructed;Finally, delete the temporary node connected to only two road sections and simplify the network by merging edges, output structured node and edge data.The application solves the problems of inaccurate interchange identification, road network redundancy and missing service area topology in traditional methods, and realizes the automatic and high-precision construction of expressway logical road network, which is suitable for macroscopic traffic analysis and path planning.

The application discloses a power distribution area topology identification method and system based on intelligent fusion terminals and correlation coefficients, the method comprising collecting power distribution area multi-source data through edge fusion terminals, performing data preprocessing and feature extraction on the terminal side, calculating voltage fluctuation Pearson correlation coefficients and load change Kendall rank correlation coefficients in parallel, and constructing correlation coefficient matrices in time periods; calculating adaptive weights based on load fluctuation entropy, and fusing double-mode correlation coefficients; using a graph neural network to generate topology, outputting edge existence probability and node level; using a genetic algorithm to optimize the initial topology under physical constraints; the system comprises an edge fusion terminal cluster, a cloud analysis platform, a topology verification module and a terminal management platform. The application significantly improves topology identification accuracy and dynamic adaptability: innovatively fusing double-mode correlation coefficients and graph neural network spatial modeling, and accurately restoring complex topology structures.

This invention relates to the field of artificial intelligence computing models and intelligent decision control technology, and discloses a system and intelligent decision-making method based on an AI multi-dimensional model, including a feature perception module, a topology reconstruction engine, a feedback scheduling unit, and a transformation processing unit. The feature perception module extracts feature vectors of each component of a high-dimensional heterogeneous data stream, calculates the fusion value of the gradient, and determines the active feature dimension. The topology reconstruction engine generates a dynamic mask based on the active feature dimension and performs edge pruning on the initial topology graph to construct a task-sensitive topology structure. The feedback scheduling unit uses reinforcement learning operators to adjust the pruning threshold and update the dynamic mask coverage. The transformation processing unit performs weighted mapping operations based on the updated dynamic mask. This invention transforms full matrix operations into sparse topology operations through feature-guided logical pruning, reducing the overhead of invalid digital processing.

This invention belongs to the field of multi-scale optimization of composite materials, and discloses a multi-scale optimization design method for composite vehicle bodies considering multiple working conditions, including the following steps: S1. Constructing a multi-working-condition comprehensive objective function: Based on the SIMP topology optimization framework, the performance indicators of each working condition are treated as independent terms and assigned weight coefficients, and extended into a polynomial weighted form to achieve a unified optimization description for multiple working conditions; S2. Optimizing the vehicle body topology; S3. Correcting the topology: Combining manufacturing processes and assembly constraints, the initial topology is corrected; S4. Multi-scale optimization of composite material layups; S5. Differentiated allocation of layup schemes: Taking into account the mechanical performance requirements, safety margins, and manufacturing processes of the vehicle body, the layup thickness and layup sequence are rationally allocated. This invention achieves multi-scale integrated optimization of macroscopic topology and microscopic layups by introducing a multi-working-condition objective function and composite material layup parameters into the SIMP topology optimization framework.

The invention provides a drainage pipe network spatial topology dynamic reconstruction method based on RTK and on-site survey fusion. The method comprises the following steps: firstly, dividing troubleshooting blocks based on a pump station service range and a water catchment unit; establishing communication connection between RTK equipment and a mobile acquisition terminal, acquiring three-dimensional coordinate data of key nodes of the pipe network, synchronously acquiring field images, and generating a pipe network space sketch in real time; inputting attribute information on the basis, and constructing an initial vector pipe network topology model; the core is to obtain on-site on-site survey feature data, construct a multi-source evidence confidence correction model, perform logic conflict detection and dynamic correction on an initial topology model by using the model, and intelligently identify and repair anomalies such as reverse slopes, disconnection and dark connection. And finally, mapping the corrected model into a standardized GIS pipe network database and outputting the standardized GIS pipe network database. By fusing high-precision physical actual measurement and on-site microscopic trace evidence, the problem of topology distortion caused by old pipe network drawing missing and settlement is effectively solved, and the accuracy of pipe network reconstruction is remarkably improved.

The application claims a kind of inter-satellite reliable routing method based on fault domain model, belong to satellite communication technical field.Aiming at the problems that inter-satellite link is influenced by space environment and human interference, leading to transmission failure and routing interruption, a centralized inter-satellite reliable routing method based on inter-satellite link attribute is proposed.According to the real-time state and failure reason of inter-satellite link, the inter-satellite link is classified, the initial topology model containing global network information is constructed, the boundary diffusion method is designed combined with the state of boundary node of fault link, the fault link is aggregated into block to form fault domain topology model to limit the influence range of fault.Analysis of the influence of buffer queue capacity, link signal-to-noise ratio and path length on routing, construct comprehensive link utility function to quantify link quality, propose multi-attribute bypass path selection method to solve the problem of load imbalance caused by single path selection criterion, reduce system packet loss rate and end-to-end delay, improve the fault response capability of satellite network.

The invention relates to a power line communication network topology reconstruction method and device for single-port missing. The method comprises the following steps: receiving and transmitting a preset detection signal through a plurality of known tail ports in a power line network, determining the distance between the plurality of known tail ports according to a signal transmission moment and a signal first arrival moment, and constructing an initial topology network according to the distance; receiving and transmitting virtual signals through a plurality of virtual ports of the initial topology network, and determining the position of an unknown branch point according to the signal difference between a real signal and the virtual signals; receiving and transmitting virtual swept-frequency signals through a plurality of known tail ports and a plurality of virtual ports, and determining an unknown branch length according to a signal difference between a real swept-frequency signal and the virtual swept-frequency signal; and updating the initial topology network according to the position of the unknown branch point and the unknown branch length to obtain the topology network of the power line network. The power line communication network topology can be accurately reconstructed under the condition of single-port missing.

The invention discloses a wide-forbidden-band inverter distributed parasitic inductance network modeling and parameter extraction method. The method comprises the steps of establishing a distributed parasitic inductance network initial topology according to a physical structure of an inverter; designing a measurement method based on a two-port parallel straight-through S parameter and a measurement clamp following a common loop additional parasitic minimization and mutual inductance coupling minimization principle; performing two-port measurement, and converting the S parameter into an impedance curve; adaptively selecting an inductance extraction mode according to impedance curve resonance characteristics and correcting the model; measurement is carried out by constructing various terminal interconnection topologies, multiple groups of equivalent inductances are obtained, and parasitic inductances of all distributed branches are obtained through solving; and finally, fusing the distributed parasitic inductance network with the device parasitic capacitance, and constructing a distributed parasitic parameter network model for switch transient behavior prediction. According to the method, high-precision and high-robustness extraction of the nH-level parasitic inductance is realized, and the accuracy of switch behavior prediction is improved.

The application discloses a method for identifying key nodes of a heterogeneous Internet of Things (IoT) topology based on mutation theory, and belongs to the field of computers and information. First, an initial topology of the heterogeneous IoT is constructed, a topology set is generated by random disturbance, and performance indexes such as robustness, redundancy, communication efficiency and global clustering coefficient are calculated. Then, the indexes are normalized by using a mutation series method, the correlation between the indexes is evaluated based on a Pearson correlation coefficient, and a network performance mutation value is obtained by layer-by-layer fusion. Next, a deliberate attack process is simulated, a corresponding relationship between a node deletion sequence and performance evolution is established, and a performance evolution function is fitted. Finally, a cusp mutation model is established, critical nodes causing network performance mutation are identified by analyzing the equilibrium state, singular set and bifurcation set of the model, and the application breaks through the limitations of traditional static network indexes, can accurately capture the mutation behavior of network performance, is objective, and provides a theoretical basis for topology optimization and fault-tolerant design of the IoT.

The invention discloses a communication networking method, device and equipment of a computing power server and a storage medium, and relates to the technical field of communication, and the method comprises the steps: constructing a standardized node feature set through multi-dimensional parameter collection and principal component analysis, carrying out the quantitative evaluation of the performance, carrying out the dimensionality reduction, and laying a foundation for the topology; the improved Prim algorithm is used for generating an initial topology according to the composite weight and the connection number limitation, and the performance is optimized to prevent single-point overload; the graph neural network aggregates neighbor information, predicts link efficiency and global vulnerability, and intelligently evaluates the network. A self-adaptive link priority is established by fusing the real-time load and the prediction, and the load change is responded; the deep Q network is subjected to multi-action iterative optimization, the efficiency and robustness are balanced, and self-optimization dynamic topology is output; the multi-path configuration selects an independent path without a shared intermediate node, so that the fault tolerance and reliability are improved; the whole system adopts data driving and an intelligent algorithm, network self-sensing, self-optimization and self-recovery are realized, and a high-throughput, low-delay and fault-tolerant communication infrastructure is provided for a high-computing-power cluster.

This application relates to a method, apparatus, computer device, computer-readable storage medium, and computer program product for generating structured patterns on curved surfaces. The method includes: acquiring a curved surface model and topology editing operation data; performing topology design on an initial topology based on the topology editing operation data to obtain an intrinsic topology of the pattern; designing pattern elements based on preset design parameters and the intrinsic topology of the pattern to obtain a two-dimensional pattern result; parametricizing the curved surface model to obtain a parametric plane and a bidirectional mapping relationship; wherein the bidirectional mapping relationship indicates the mapping relationship between the curved surface model and the parametric plane; performing planar pattern overlap processing based on the two-dimensional pattern result and the parametric plane to obtain target pattern coordinate data; and performing inverse mapping based on the target pattern coordinate data and the bidirectional mapping relationship to obtain a structured pattern on the curved surface. This method can efficiently design the regular arrangement of three-dimensional elements and generate structured patterns.

A method, apparatus and computer program for repairing defects of lithographic masks, in particular EUV masks, are disclosed. A method for repairing defects of a lithographic mask, in particular an EUV mask, comprises the following steps: (a.) performing a first repairing step on the defect with a first repairing dose, such that the defect transitions from an initial topology to a first defect topology; (b.) determining the influence of the first repair step on the defect topology; (c.) determining a second defect topology at which a second repair step performed on the defect should reach; and (d.) determining a second repair dose of a second repair step, wherein at least one part of the determination is based on the measured influence of the first repair step on the defect topology and the second defect topology. In addition, the invention may also include a step (e.) of performing a second repair step with a second repair dose.

The invention discloses a data network dynamic topological graph intelligent generation method and system, and relates to the technical field of data network dynamic topological graphs. According to the method, the change data of the equipment is determined by comparing the real-time operation data of the network equipment with the historical operation data, topological calculation is performed on the initial topological matrix based on the change data, the updated topological matrix is obtained, and the target topological graph is drawn according to the updated topological matrix. The method does not need to wait for the manual triggering of a user, and achieves the automatic and real-time data collection. Only the target operation data after real-time preprocessing is compared with the historical data to determine the change data, the data comparison amount is reduced, the data processing time consumption is reduced, and it is ensured that network changes can be captured in time; moreover, the calculation amount is reduced, the network risk and the server burden can be reduced, and the high efficiency and stability of network operation and maintenance are ensured. The technical problems that in the prior art, generated topological graphs are low in efficiency and poor in real-time performance, manual triggering is needed due to interface limitation, consumed time is long, and the network state is difficult to reflect in real time are solved.

The application relates to a power distribution network topology correction method combining topology anomaly identification and structure reliable inference, and the steps comprise: identifying a topology structure abnormal area, identifying a candidate edge and an unobserved node; a structure behavior joint feature tensor considering the behavior cooperation between nodes, the adjacent structure similarity and the mutation synchronism is constructed, and is input into a topology connection reliability scoring model to obtain an edge reliability score; the edge reliability score obtained by the topology connection reliability scoring model is combined with the candidate edge and the unobserved node, the initial topology structure is reliably corrected, and a final topology structure is generated. Compared with the prior art, the application realizes comprehensive repair of the missing edge, the wrong edge and the unmodeled node in the original topology structure, and significantly improves the integrity and reliability of the power distribution network topology structure.

The application provides a power distribution network topology dynamic reconstruction method based on space-time feature fusion, comprising: acquiring first time series data, second time series data and line operation state; generating first space-time feature parameters based on the first time series data and the line operation state; generating second space-time feature parameters based on the second time series data and the line operation state; generating a local initial topology reconstruction strategy based on the first space-time feature parameters and the second space-time feature parameters; performing neighborhood correction on the initial topology reconstruction strategy to obtain a first topology reconstruction strategy; performing consistency judgment on the first topology reconstruction strategy to generate a global topology reconstruction strategy; and generating corresponding switch operation instructions based on the global topology reconstruction strategy and outputting the instructions to an instruction execution end of the power distribution network. The method provided by the application realizes effective fusion of space-time features of the power supply side and the load side in the urban-rural junction power distribution network, improves the operation flexibility and efficiency of the power distribution network, and reduces the waste of power resources.

The embodiment of the invention discloses a multi-target topological structure optimization method applied to a micro-channel radiator. The method comprises the following steps: acquiring a current state parameter and a thermal lattice Boltzmann model of the micro-channel radiator; based on the current state parameter and a thermal lattice Boltzmann model, determining a current flow velocity and a current temperature value corresponding to a calculation area in the micro-channel radiator; determining a to-be-replaced fluid unit and a to-be-replaced solid unit in the calculation area based on the current flow velocity, the current temperature value and each preset heuristic criterion, and determining a reference topological structure after replacement iteration in the micro-channel radiator based on the to-be-replaced fluid unit and the to-be-replaced solid unit; and determining a target topological structure corresponding to the micro-channel radiator based on the current temperature mean value corresponding to the reference topological structure and the initial temperature mean value corresponding to the initial topological structure of the micro-channel radiator. Through the technical scheme of the embodiment of the invention, the topological structure can be adjusted and optimized by considering multiple targets.

The invention discloses an intelligent topological graph generation method and system based on node attributes, and relates to the technical field of topological graph generation, and the method comprises the steps: collecting inherent attributes (identification, type and the like) and associated attributes (connection relation and the like) of a target node; cleaning and standardizing attribute data; matching a layout algorithm based on node attributes; inputting data to generate an initial topological graph, and combining connection strength to render lines; and outputting after optimization. The system comprises a data acquisition module, a preprocessing module, a rule matching module and the like, and supports dynamic updating and rule self-definition. The layout is driven through attributes, manual intervention is not needed, and the generation efficiency is improved; nodes and connections are rendered differentially, and the readability of the topological graph is enhanced; and multi-format export and scene adaptation are supported, and the flexibility is high. The problems that a traditional method is low in efficiency, poor in readability and insufficient in adaptability are solved, and the method is suitable for multiple fields such as network management and system architecture.

The application discloses a kind of three-dimensional model file import method and system, for IGES / IGS file import, comprising: reading the three-dimensional model file to be imported to generate initial topology structure;The initial topology structure is repaired to obtain the intermediate topology structure of keeping geometric continuity;Intermediate topology structure is reconstructed to obtain optimized topology structure;Optimized topology structure is output, and file import is completed;Wherein, initial topology structure generation process includes, with dynamic tolerance instead of the default fixed tolerance of the three-dimensional model to be imported, and initial topology structure is generated according to dynamic tolerance.This application is aimed at the problems existing in the CAE process of IGES / IGS file import in the field of three-dimensional model, solves the defects of model analysis distortion caused by fixed tolerance in prior art.Through the method of the application, the accuracy of model import is effectively improved, and the integrity of complex models such as thin-walled parts, curved surface overlapping parts and the like after import is ensured.

The embodiment of the invention discloses an IP network simulation method and device, equipment, a medium and a program product. The method comprises the following steps: obtaining a digital twin description of a target IP network and a physical resource description of a simulation bed; determining a simulation device of each network element in the target IP network, and determining a simulation device port corresponding to each link in the target IP network to obtain an initial topology mapping relationship; for a target network element simulated by physical equipment, simulation instruments used by a plurality of network elements which are interconnected with the target network element and belong to the same equipment role are aggregated into the same simulation instrument, and different virtual local area network sub-interfaces are distributed for a plurality of links which need to share the same simulation equipment port due to aggregation. Obtaining a target topology mapping relation; generating a twinborn simulation network description based on protocol information, routing information and service flow information in the digital twinborn description and the target topology mapping relation; and using the twin simulation network description to drive the simulation bed to simulate the target IP network.

The present application relates to IPv6 lightweight network device mapping method based on behavior fingerprint and hop verification;Including the following steps: collecting the metadata of original traffic in IPv6 network;Selecting multi-dimensional feature vector from metadata and processing, generating unique and stable fingerprint identification for each device;Sending probe packet to target device, calculating forward path hop count, estimating return path hop count for response packet return path hop count, detecting path by comparing forward path hop count and return path hop count;Fingerprint identification is used as topology node, and bidirectional path hop detection value is used as the topology edge weight between devices, the initial topology skeleton is constructed by minimum spanning tree algorithm, and the network topology structure is obtained by clustering optimization combined with auxiliary data;According to fingerprint identification, node hop count, connection density and behavior consistency, the hierarchical relationship is automatically inferred, and the interactive logical topology graph is generated, which is used for displaying abnormal nodes and associated path.