164 results about "Structure mapping" patented technology

The invention discloses a bank loan business risk control system and method based on big data analysis, and relates to the technical field of financial risk control, and the method comprises the steps: collecting and preprocessing real-time behavior data, and obtaining a user behavior feature set; based on the user behavior feature set, calling a behavior map modeling engine to carry out structured mapping, matching with a risk anchor point rule base, identifying a potential risk mode and labeling an initial anchor point risk label; correcting the deviation between the initial risk anchor point tag and the actual default record by adopting a value function optimization method, and predicting the risk grade score of the current behavior of each user in combination with the historical behavior sample data and loan feedback data of the user; predicting probability distribution of migrating to a default state in the future through user risk grade scores and historical state evolution data; and in combination with the potential loss under each behavior path, evaluating the current loan business risk, and generating a risk control strategy through a risk level mapping rule and a strategy decision engine.

The invention discloses a multi-time-scale charging load and energy storage cooperation probability prediction method, and relates to the technical field of power systems and smart grids, and the method comprises the steps: obtaining historical multi-source data of a target system, and constructing factor sub-graphs of different time scales; coupling the cause sub-graphs into a time cause feedback atlas through a structure mapping function, wherein the atlas comprises explicit time scale control nodes; executing cross-time-scale causal effect calculation based on the time factor feedback atlas to generate a causal effect matrix; using the causal effect matrix to construct a joint probability prediction model, and outputting a first prediction result; and when it is monitored that the deviation of the first prediction result exceeds a dynamic threshold value, triggering a time scale switching mechanism to generate a second prediction result. According to the method, multi-scale causal modeling is realized by constructing the time cause feedback atlas containing the time scale control nodes, dynamic scale switching and a residual compensation mechanism are combined, the prediction precision and the scheduling adaptability are remarkably improved, and multi-time-scale collaborative optimization management is realized.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention relates to the technical field of safety monitoring of a nuclear reactor, and particularly discloses a multi-region pressure overrun leakage monitoring method for a reactor pressure vessel. The method comprises the steps of multi-region pressure data acquisition, real-time pressure field model construction, dynamic pressure field baseline modeling, pressure overrun anomaly detection, leakage source accurate positioning and evaluation based on spatial gradient analysis, topological structure mapping and multi-sensor data fusion, and graded early warning and response. By the adoption of the technical scheme, real-time and fine monitoring of pressure distribution in the reactor pressure vessel can be achieved, the rapid and accurate judgment and positioning capacity of leakage events is improved, the operation safety and reliability of nuclear power facilities are enhanced, and the reliability of the nuclear power facilities is improved. The defects of independent monitoring, local accurate positioning and complex working condition adaptability of multi-region pressure over-limit leakage in the prior art are overcome.

The invention discloses a data filling automation system and method based on a large model, and relates to the field of electric digital data processing, and the method specifically comprises the steps: monitoring and collecting the input behaviors of a user in different platform forms in real time, constructing a semantic state vector, and carrying out the aggregation to form a semantic global vector; constructing a structural disturbance tensor model, modeling the stable position of each field, and obtaining a stable state representation vector of each target field under disturbance through a tensor projection mode; and constructing a path cost function according to a matching relationship between the semantic global vector and a structure mapping result, solving an optimal execution path for field filling and reporting, and driving a browser to perform automatic filling and reporting according to a path sequence, thereby realizing automatic operation of data filling and reporting under semantic reasoning and structure disturbance. Automatic and intelligent cross-platform data filling and reporting are achieved, filling and reporting efficiency is effectively improved, platform independence, semantic adaptability and structure fault-tolerant ability are achieved, and data consistency and stability of the filling and reporting process are ensured.

The invention discloses an operating system containerization kernel compatibility method based on shadow structure mapping, which comprises the following steps of: constructing a multi-dimensional mapping rule base by analyzing the difference of data structures between a container system and a standard system, and creating a shadow structure comprising a data area, a metadata area and an expansion area when the container system runs on a host system; the host system allocates a shadow memory space and a memory pool, establishes mapping between a shadow structure and a physical memory page and bidirectional mapping between the shadow structure and a host structure, intercepts system call of a container application, obtains a target structure and allocates a shadow structure and a host structure memory, and sends the shadow structure and the host structure memory to the host system; according to the method, conversion from the target structure to the host structure is completed according to the multi-dimensional mapping rule base, the host machine completes calling through the host structure, then the calling result is reversely converted into the target structure and returned to the container application, and seamless compatibility of the container system and the host system is achieved under the condition that a user mode program of the container system is not modified.

The invention discloses a two-stage multi-agent cooperation strategy based on exploration reward molding, and belongs to the field of multi-agent reinforcement learning. Trajectory data (including environment states, rewards, rewards and actions) generated by interaction of the intelligent agent and the environment are stored in an experience buffer pool and are updated and maintained through increment. Subsequently, randomly sampling an environment state and a corresponding return from the experience pool, and constructing a conditional diffusion model by using the return as a condition to generate a high-return target state; thirdly, global environment states of different time steps in different trajectories are sampled, time structure mapping is learned, and states with similar time are mapped to hidden states with similar geometric space; according to the method, a double-end Q network is adopted, an exploration strategy is decoupled into a target exploration strategy and a behavior exploration strategy, reward functions corresponding to two stages respectively act on a decision-making network of an intelligent agent, and more effective exploration and collaboration are achieved.

The invention discloses an electric power production management system security situation awareness method based on a cryptographic algorithm. According to the invention, by deeply fusing SM2, SM3, SM4 and other national cryptographic algorithms and power system characteristics, a full-link autonomous and controllable security protection system is constructed. In a data acquisition stage, SM4 encryption transmission and SM3 hash evidence storage are adopted to ensure confidentiality and integrity of data from a source to processing, and eavesdropping and tampering risks in a transmission process are effectively resisted; in a core situation assessment link, point multiplication operation of SM2 elliptic curve cryptography is innovatively introduced into a node aggregation process of a graph neural network, and graph structure mapping of physical topology of a power system is combined, so that the model can accurately capture implicit association and cascade influence between equipment, and the situation assessment accuracy is improved. At the same time, the recognition capability of the hidden attack mode is enhanced by using the nonlinear transformation of SM4, and the perception depth and anti-attack toughness of the system to the complex threats in the power production scene are improved from the bottom layer of the algorithm.

The invention discloses a writing process analysis method based on computer vision, which relates to the technical field of handwritten character recognition, and comprises the following steps: carrying out perspective transformation on real-time writing data, obtaining a writing image frame sequence, carrying out motion blur correction and pen point coordinate positioning on the writing image frame sequence by adopting an RAFT (Reversible Addition Fragmentation Transform) optical flow algorithm, and forming a writing track data flow; and performing curvature segmentation on the writing trace data stream to obtain discrete stroke segments, and performing spatial topological correlation and geometric structure mapping on the discrete stroke segments to generate a writing stroke topological graph. Through the RAFT optical flow algorithm and the stroke semantic analysis model, the precision of stroke recognition is improved, and efficient and accurate analysis from dynamic visual input to structured character output is achieved.

The invention provides a nuclear power regulation human factor trap identification method and device based on a knowledge graph, and the method comprises the steps: determining a current nuclear power regulation according to a to-be-executed task, and extracting an operation target in the current nuclear power regulation; on the basis of a pre-constructed interface knowledge graph, according to the current nuclear power regulation, calculating all regulation paths from a task starting point node to a task end point node; the interface knowledge graph takes interaction elements related to operation in the interface of the nuclear power plant simulator as nodes and takes the relationship between the interaction elements as edges; the task end node is an operation target; and obtaining a human factor trap identification result of the current nuclear power regulation according to a path structure in the regulation path. According to the method, the interaction elements in the nuclear power plant simulator interface are subjected to structured mapping through the interface knowledge graph, automatic recognition of the structural human factor trap in the nuclear power regulation can be effectively achieved, data support and logic basis are provided for regulation optimization and interface design, and finally safety risks caused by human errors are reduced.

The invention relates to the technical field of power system automation, and discloses a power distribution network operation risk assessment regulation and control method based on a graph attention network and reinforcement learning, and the method comprises the steps: mapping a topological structure of a power distribution network into a graph structure; constructing a node feature matrix and an adjacent matrix based on the node features and the edge features; inputting the node characteristic matrix and the adjacent matrix into a short-circuit current prediction model based on a graph attention network (GAT), and outputting short-circuit current prediction values of a bus and a line in the power distribution network and corresponding short-circuit current prediction intervals after the distributed power supply is connected to the grid; constructing a reward function aiming at reducing the short-circuit current risk and guaranteeing the load power supply, and training a strategy network and a value function through a SoftActor-Critic algorithm so as to obtain a reinforcement learning model; and collecting the state of the power distribution network in real time in a preset scheduling period to realize dynamic regulation and control of the short-circuit current risk of the power distribution network. According to the method, topology and electrical association is accurately captured, and the prediction precision is improved.

The invention discloses a product image-text and document association identifier generation method fusing a PLM coding rule, relates to the technical field of product data management, and is used for solving the problem that an image-text and document association identifier is not clear. According to the method, document information vectors are constructed by fusing PLM coding rules and collecting document metadata of design drawings, process documents, quality management and bill-of-material systems, an image-text document structure mapping chain is generated through a structure mapping judgment model, a unique association identifier of the image-text document is generated in combination with a preset template, and the unique association identifier of the image-text document is obtained. And real-time updating is carried out during structure replacement, hierarchical reconstruction or configuration change, cross-system serial number semantic alignment is realized through a heterogeneous coding analysis rule base, heterogeneous naming conflicts are solved, identifiers are written into a product data management platform, an index relationship is established, hierarchical retrieval, version switching and historical backtracking are supported, and the product data management efficiency is improved. And the consistency and cooperation efficiency of product research and development and quality control are improved.

The invention relates to the technical field of industrial factory monitoring, in particular to a cloud-side collaborative factory digital twin monitoring modeling method and system. The method comprises the following steps: collecting communication link data and space coordinate data to construct a factory topological structure diagram and construct a factory digital twin monitoring model; based on the nested graph neural network, comparing existing and historical factory topology structure diagrams to obtain a factory topology difference structure diagram, and performing structure mapping consistency verification on the factory topology difference structure diagram by using a structure mapping residual function to generate an entity equipment structure change event set; obtaining a changed entity equipment operation state data set, and generating a factory performance offset index set; and the changed physical equipment and other physical equipment directly connected with the changed physical equipment in the factory are monitored. The invention provides a digital twin monitoring modeling method for a cloud-side collaborative factory, which integrates space structure information and communication link characteristics, has a dynamic self-adaptive capability, and realizes real-time state monitoring of the factory.

The invention discloses a power target damage propagation path modeling method based on an attack chain atlas, and the method comprises the steps: collecting multi-source heterogeneous data of a power system, and carrying out the standardized structure mapping and data quality optimization, thereby obtaining standardized behavior metadata; performing heterogeneous node extraction on the standardized behavior metadata, defining an association relationship between heterogeneous nodes to construct an initial atlas, optimizing the initial atlas in combination with manual verification and an automatic duplicate removal mechanism to obtain a heterogeneous atlas, and performing attack time sequence anchoring on attack behavior nodes in the heterogeneous atlas, the incidence relation of attack behaviors, system components and service logic in time and topology dimensions is mined, an attack path is reconstructed in a cross-layer mode, then a potential high-risk attack mode is identified, damage propagation prediction is carried out, and adaptive optimization is carried out on a damage propagation prediction result in combination with service priorities. The method has the effects of realizing structured restoration and damage propagation reasoning of the attack path and providing comprehensive technical support for safety protection of the power system.

The invention relates to the technical field of industrial digital twinning, and discloses a factory production monitoring method and system based on digital twinning, and the method comprises the steps: collecting the operation characteristic parameters of an entity production line and the simulation parameters of a digital twinning model, and constructing an entity-virtual mapping data set; calculating a structure coupling deviation rate and a time phase drift rate based on the time-structure mapping matrix; determining virtual-real mismatch types including a local lag type, a synchronous drift type and an energy reverse type according to the combination relation of the two types; performing regional response correction, time phase compensation or energy inversion correction for different types of mismatch; the correction result is fed back to the digital twin model to generate a corrected virtual operation curve, the stability index of the model is calculated, when the stability index does not reach a convergence interval, the deviation analysis and mismatch recognition steps are repeatedly executed, and dynamic coordination and high-precision production monitoring between the virtual model and the real model are achieved. According to the method, the dynamic coordination and high-precision production monitoring capability between virtual and real models is improved.

Provided is a quantum computing method for obtaining an optimal solution of a problem with multiple discrete variables, wherein the problem is represented by a cost function, the method comprising:—generating a graph structure from the cost function,—dividing the graph structure into at least two disjunct subgraph structures, wherein each subgraph structure comprises a subset of the multiple variables,—mapping each subgraph structure to a local cost function represented as local cost Hamiltonian,—determining, for each local cost Hamiltonian, all eigenstates corresponding to an energy below a predetermined cut off energy using a quantum processing device, wherein each variable of the subset of multiple variables is represented by a qubit of the quantum processing device,—recombining the determined eigenstates, and-approximating a ground state from the recombined eigenstates, wherein the ground state represents the optimal solution.

The invention provides an information visualization method and system, a medium, equipment and a program product, and relates to the technical field of information processing, and the method comprises the steps: obtaining heterogeneous input information; performing category detection on the heterogeneous input information by utilizing the classification model, and determining a category corresponding to the heterogeneous input information; calculating entity relevancy, information relevancy and information weight corresponding to the heterogeneous input information according to the category; inputting structured label information corresponding to the heterogeneous input information according to the entity relevancy, the information relevancy and the information weight; constructing an atlas data structure corresponding to the structured label information; and mapping the map data structure into visual elements to obtain visual information. According to the method, heterogeneous input information is converted into visual information, so that leap-type improvement from bottom-layer data quantification to top-layer cognitive perception is realized.

The invention discloses a multi-modal large model-based multimedia file generation method, and relates to the technical field of education content generation. Task alignment and evidence readiness, question analysis and question solving trajectory diagram construction, compiling into a unified instruction of a split white board and a parastyle, controlled generation, co-optimization in generation, posterior judgment, traceable release and learning write-back are sequentially completed; according to the scheme, a problem solving track graph is used as a unique true source, a semantic structure which can do a problem is mapped into a shot, blackboard writing and oral playing, and a suitable age target and a cognitive load are synchronously constrained; in the generation stage, the fact and safety scores are calculated according to the shot, local regeneration is triggered, and rework of the whole section is reduced; source credentials are written before publishing, and learning conditions are written back in combination with watching and evaluation, so that a correct, right-age, traceable and self-evolutionary closed loop is formed. And unified numbering, continuous input processing and output are realized, fixed-point backspacing and sampling inspection are supported, uncertainty and cost are reduced, and convenience is brought to campus distribution and home check verification.

The invention relates to an interface adaptation method and system for reconstruction and integration of a complex information system module. The method comprises the following steps: acquiring a protocol mapping rule and constructing a rule base; monitoring and receiving a calling request which is sent by a source module through a transmission layer protocol and carries source module side interface protocol format data; analyzing the request, stripping a source module side transport layer protocol header to extract application layer data, and packaging the application layer data into an internal general data model; performing structure mapping, data conversion and dynamic logic processing on the model through a rule execution engine according to a request mapping rule of a rule base, reconstructing the model into target request data according to a target module interface format, and sending the target request data to a target module through a target side transport layer protocol; receiving target response data, stripping a transport layer protocol header of the target response data, analyzing the target response data to the internal general data model, and performing reverse conversion according to a response mapping rule to enable the data to conform to a source side interface format; and packaging the back-to-source module side transport layer protocol back to the source module. By adopting the method, the adaptation flexibility and the maintenance efficiency can be improved.

The invention discloses a preparation optimization method and system for high-memory polyester lining cloth, and relates to the technical field related to textile fabrics, and the method comprises the steps: receiving a product application scene of target polyester lining cloth, executing scene adaptation analysis, and establishing a function demand matrix; performing adaptive analysis on the function demand matrix by using the structure response table; key index extraction is carried out on the hierarchical partition structure combination, and a multi-target evaluation function mapped with the hierarchical partition structure is established; establishing a parameter optimization candidate set; hierarchical control is carried out on the hierarchical partition structure combination, and response matching is carried out by utilizing finite element simulation; and establishing rotation deviation according to a response matching result, and updating the parameter optimization candidate set to complete preparation optimization. The technical problems that in the prior art, the polyester lining cloth is difficult to adapt to diversified application scenes, performance indexes are difficult to balance, and consequently the preparation scientificity and the production efficiency of the high-memory polyester lining cloth are poor are solved, and the technical effect of improving the preparation process scientificity and the production efficiency is achieved.

The invention relates to the technical field of content extraction, in particular to a multi-modal hybrid document OCR (Optical Character Recognition) and structured extraction method, which comprises the following steps of: acquiring an image text region bounding box and classifying a style, extracting a font or stroke sequence to generate a character positioning structure, dividing paragraph and sentence groups to classify semantic fields, and calculating a field matching relationship to generate structural mapping. According to the method, logic mapping is constructed through character two-dimensional coordinate sorting and paragraph contours, complete reconstruction of a page structure is enhanced, semantic field categories are extracted by using syntactic density of sentence paragraph division and inter-paragraph features, the accuracy of field classification is improved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. A field mapping relation is established through Jaccard similarity and part-of-speech consistency analysis between a head word and a standard field keyword, a field path index and a structure node link are clarified, field semantic affiliation and structure position output are unified, and the document structure reduction degree and field extraction accuracy are improved.

The invention discloses a multi-dimensional report automatic generation method based on business data fusion, and belongs to the field of intelligent report generation, and the method comprises the following steps: S1, carrying out hierarchical abstract modeling on historical reports with inconsistent structures, and extracting a standardized index field set by adopting a structure mapping and time sequence alignment mode; s2, carrying out grouping aggregation on the standardized index fields according to semantic similarity and dimension types; s3, taking the graph structure as input, executing a dimension correlation graph embedding operation, and constructing a weighted structured dimension graph; s4, according to the weighted structured dimension graph, constructing an index-driven multi-dimensional report component framework; s5, automatically executing a report instance generation process through a task graph scheduling module in combination with node grouping weights and resource constraints; s6, introducing a dimension traceability link backstepping mechanism, establishing a causal traceability chain for key indexes in a report result, and outputting an explanation; and S7, performing weight fine tuning and module iteration recommendation on the report structure. The method has the beneficial effects of improving report automation, multi-system fusion and interpretability.

The invention provides a software process document generation method and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: based on a document generation demand, retrieving retrieval information corresponding to the document generation demand from a preset retrieval enhancement generation knowledge base through a semantic understanding engine by adopting a retrieval enhancement generation technology; analyzing the target software process document template according to a document generation engine to obtain structure mapping information, and filling retrieval information corresponding to a document generation requirement into the target software process document template according to the structure mapping information to generate an initial software process document; according to the compliance verification engine, compliance verification and rule matching are conducted on the initial software process document, the target software process document passing verification is output, the target software process document passing verification is automatically generated, generation and verification integration is achieved, the generation efficiency of the target software process document can be improved, and the generation efficiency of the target software process document is improved. And the quality of the target software process document is ensured.

The invention provides a clothing pattern intelligent generation method and system based on multi-modal visual identification, and relates to the technical field of visual identification. The method comprises the following steps: firstly, acquiring clothing image data and text description data, performing unified preprocessing, obtaining a style category label, an appearance attribute label and a local contour key point set through multi-modal visual identification, generating structure semantic elements and geometric anchor points by utilizing a semantic-to-structure mapping knowledge base, and constructing a plate type structure diagram; further analyzing the structural constraint and executing constraint solution to generate a candidate pattern; and in combination with manufacturability verification and constraint correction, closed-loop optimization is formed, a target plate type file meeting the structure and manufacturing requirements is output, and effective connection from semantic recognition to plate type structure generation is realized.

The invention discloses an intelligent electronic nose gas recognition method and system, a medium and equipment. The method comprises the following steps: acquiring basic feature data required by electronic nose recognition; according to the basic feature data, information fusion is carried out based on a multilayer context structure mapping network, and context enhancement representation is obtained; according to the context enhancement representation, dynamic fusion of feature data and compensation features is achieved through a self-consistent gating strategy, and compensated input features are obtained; and according to the input features, generating a prediction probability of a final gas type based on a feature interaction vector, and performing model training to obtain a final gas recognition result. Through the joint effect of all the modules, the system has relatively high stability and adaptability in the aspects of known gas recognition, environmental interference suppression, unknown category detection and the like, so that an electronic nose intelligent recognition platform with strong interpretability and good expandability is constructed.

The invention relates to the technical field of large models, in particular to a method and equipment for converting a natural language into an SQL statement and a medium. The method comprises the following steps: performing semantic analysis on a natural language query request input by a user to obtain an analysis result; matching the analysis result with a pre-constructed domain knowledge graph to generate a structured mapping relation set; constructing an abstract syntax tree based on the structured mapping relation set; and converting nodes of the abstract syntax tree into parameterized SQL query fragments by using a predefined conversion rule, and assembling the SQL query fragments into a complete target SQL statement based on the query intention. By utilizing the method, the corresponding SQL statement can be automatically, accurately and efficiently generated from the natural language query statement of the user.

The invention provides an actuator fault diagnosis method and device based on a graph convolutional network and a medium, which are used for solving the problem of fault diagnosis of an actuator in an electromechanical system. According to the method, on the basis of a vibration signal, a CWRU data set is adopted as training and verification data, sliding window sampling and normalization processing are performed on the data set, and the generalization ability of a model is improved through a data enhancement technology. A graph construction method for vibration signals is designed, the signals are divided into nodes, edges between the nodes are constructed according to a time sequence and feature similarity, and mapping of the signals to a graph structure is achieved. And based on the constructed graph structure, designing a graph convolutional network (GCN) model to perform fault feature learning and classification. And finally, performing validity verification on the CWRU data set by using an actuator fault diagnosis method based on the graph convolutional network. The method has excellent diagnosis performance, can effectively identify faults, and shows relatively high robustness and accuracy under complex working conditions and noise environments.

The invention relates to the technical field of storage control and task scheduling, and discloses an annular buffer and state machine cooperative control method. According to the method, an external read-write instruction is obtained, and task packaging and structure mapping are carried out to generate an annular buffer area task queue; extracting a first task of the task queue and integrating the attribute information to generate a task object with an attribute identifier; performing dynamic sorting processing on the task object to generate an optimized task object; then starting an execution process including an access stage, a moving stage and a submission stage, and carrying out CRC (Cyclic Redundancy Check) verification and synchronous verification; and finally, updating the task queue and releasing resources according to a verification result. According to the method, efficient scheduling and reliable execution of tasks are realized, and the resource utilization rate of the buffer area and the system response efficiency are improved.

The invention discloses a large model operating environment adaptive deployment method based on strategy optimization, and the method specifically comprises the steps: S1, collecting the computing power capacity, the video memory capacity, the communication bandwidth and the communication time delay of a computing node, and constructing an operating environment diagram structure; s2, constructing a node set and an edge set, configuring state feature vectors, and forming a topological calculation graph; s3, performing multi-scale filtering and persistent coherence calculation on the topology calculation graph to generate a topology gating vector; s4, generating a candidate calculation structure set under the limitation of the topology gating vector; s5, configuring different rank parameters for the weight tensor according to topology complexity distribution, and executing tensor decomposition; s6, performing tensor re-parameterization on the candidate calculation structure to form a large model calculation structure; and S7, mapping the large model calculation structure to a calculation node to complete deployment execution. According to the method, topological gating and heterogeneous rank tensor decomposition are introduced, and self-adaptive deployment of the structure constrained by the environment is achieved.

The application discloses a kind of based on whitelist occupation monero network topology structure mapping method, the method includes: for each reachable node in monero network, the address in address set A is used to the white list of the reachable node is occupied, and after waiting for set time period, the white list of the reachable node is obtained;The address in the white list obtained is compared with address set A, and the first difference address set is obtained;According to the first difference address set, the outgoing connection node of the reachable node is determined;According to the outgoing connection node of each reachable node determined, the topology structure of the monero network mapped out is obtained.The application can not be necessary to send a large number of transactions to the network, avoid the invasiveness to network, and monero network topology structure can be accurately obtained at high speed.