73 results about "Graph theoretic" patented technology

The application provides a sewer network monitoring method and system, the method comprising: collecting multi-source monitoring data of the sewer network in real time; constructing a directed weighted graph topology model based on basic geographic information data, basic structure data and a graph theory algorithm; identifying a topology structure problem of the sewer network according to the directed weighted graph topology model and correcting the topology structure problem; diagnosing a functional defect state of the sewer network and an internal physical defect state of the sewer network; extracting a liquid level sequence in the multi-source monitoring data and predicting a water level change trend of a key node of the sewer network; mapping the corrected standard topology structure file, the functional defect state, the internal physical defect state and the water level change trend to a hydraulic model to construct a digital twin body that is real-time synchronized with the sewer network. The application effectively monitors the sewer network by fusing multi-source monitoring data, identifying a pipe network background structure problem, diagnosing a functional defect and predicting a water level change trend.

This invention belongs to the interdisciplinary field of digital twins and artificial intelligence, specifically disclosing an automatic generation method for manufacturing equipment control logic based on neural-symbolic collaboration and intermediate representation verification. This method utilizes a neural-symbolic collaboration strategy, employing four-element semantic model constraints and thought chain reasoning to transform unstructured natural language process instructions into structured hierarchical state machine (HSM) intermediate representations. Through linear temporal logic (LTL) formal verification and graph theory connectivity analysis, static security verification of the control logic is performed, effectively improving the system's temporal determinism and deadlock resistance. Based on this, the system performs dynamic physical simulation in a digital twin environment, extracting spatial coordinate deviations when mechanical interference occurs, and inversely mapping these deviations into structured error correction prompts, which are then fed back to the large language model for logic reconstruction. This invention constructs a cross-modal "generation-verification-correction" closed-loop mechanism, achieving deep integration of model cognitive intent and underlying physical space constraints, ultimately deterministically compiling the verified intermediate representation into highly reliable industrial standard control code.

This invention discloses a multi-scale method for identifying and controlling the propagation paths of airworthiness risks for autonomous vessels. The method includes: hierarchically modeling multi-source airworthiness risk factors based on ontology to construct a structured knowledge graph; establishing logical relationships between risk factors using structural equation modeling and converting them into a Bayesian network; dynamically evaluating input vessel operation data and identifying multi-scale airworthiness risk propagation paths and key risk nodes using graph theory topology analysis; and finally, generating a hierarchical control strategy based on the identification results. This invention enables dynamic and probabilistic assessment of the initial and ongoing airworthiness of autonomous vessels, effectively identifying risk propagation mechanisms and key links, and providing support for the safe design, operation, and supervision of autonomous vessels.

The application discloses a weather-aware dominant sub-regional-sub-level-grid source-load interaction seasonal supply-demand prediction method, and belongs to the technical field of power transmission and distribution network optimization. The method comprises the following steps: S1, a topological model is constructed based on graph theory, voltage, branch capacity and topological radial constraint are determined, and operation parameters are obtained through power flow calculation; S2, a state space of DQN is designed, a node voltage, a switch state and the like, an action space, an effective switch operation and a multi-objective reward function containing a violation penalty are designed, and a model is trained by using experience replay, target network updating and a greedy strategy; S3, the trained model is used to select optimal switch operation to adjust the topology; and S4, an optimization result is output. The application can process high-dimensional states by DQN, can consider loss reduction and compliance, can be stably trained and converges fast, can cope with load fluctuation to realize dynamic reconstruction, can reduce line loss after optimization, and can improve power supply economy and safety.

This invention discloses a method and apparatus for fine extraction of aquatic vegetation and landscape connectivity assessment in rivers and lakes based on high-resolution remote sensing, belonging to the field of environmental remote sensing monitoring and water ecology assessment technology. It includes: acquiring and preprocessing high-resolution satellite imagery to obtain a standardized remote sensing image dataset; extracting water body masks and aquatic vegetation coverage areas through hierarchical threshold segmentation; constructing a dual-threshold discrimination rule based on the normalized vegetation index and soil-regulated vegetation index to finely divide the distribution areas of floating and submerged plants; constructing a binary raster map of vegetation distribution based on the distribution areas; combining a preset connectivity distance threshold; calculating the landscape connectivity index of vegetation patches based on graph theory; determining the ratio of equivalent connected area to effective connected area; and completing a comprehensive assessment of the ecological effectiveness of the aquatic vegetation community. This invention is mainly used for remote sensing monitoring of aquatic vegetation in urban rivers and lakes and for assessing the effectiveness of water ecology restoration, effectively mitigating spectral interference from complex water body sediment backgrounds.

This invention relates to a method, device, and electronic equipment for underwater vehicle formation control. The method includes: constructing a formation configuration maintenance framework based on rigid graph theory, and determining the relative positional relationships between vehicles through distance constraints; establishing an obstacle avoidance control model based on the artificial potential field method, calculating the potential field force based on the distance between the vehicle and the obstacle and the distance between vehicles, thereby achieving obstacle avoidance and collision prevention between vehicles; performing online identification and compensation of unmodeled dynamics and external disturbances in the vehicle dynamics model based on a radial basis function neural network, using a Gaussian function as the activation function; and vector-fusing the formation maintenance control force, potential field force, and compensation output of the radial basis function neural network to generate control commands for each vehicle, thereby achieving formation control. This invention can achieve high-precision formation maintenance, effective obstacle avoidance, and adaptive disturbance compensation capabilities in complex underwater environments.

The application discloses a kind of based on graph theory's unmanned plane formation control method, belong to graph theory relevant application technical field;Including S1, the parameter of initialization unmanned plane formation, simultaneously to the operation environment of unmanned plane formation is modeled;S2, construct based on graph theory's unmanned plane adaptive dynamic communication topology, establish the communication and cooperation between unmanned plane;S3, based on graph theory consistency algorithm generation unmanned plane formation control signal;S4, based on S3 unmanned plane formation control signal fusion virtual force model carries out unmanned plane formation path optimization and obstacle avoidance;S5, formation consistency verification and parameter adaptive adjustment are carried out;S6, whether to complete formation task is judged, after task completion, enter termination control process, the application adopts above-mentioned method, constructs the adaptive dynamic graph topology and dynamic weight matrix updating mechanism of adaptation unmanned plane flight characteristic, solves the problem of poor robustness in the existing based on graph theory's unmanned plane formation control method in topology fixed.

The present application relates to the technical field of computer storage service high availability, and discloses a data distribution method, device and equipment of a distributed storage system and a storage medium, the method comprising: configuring a plurality of object storage devices for a distributed storage system cluster, and calculating a total number of expected PGs of the cluster based on the expected number of PGs of each object storage device; calculating a number of non-repeating PGs that can be accommodated within a polynomial time based on the number of object storage devices; distributing the cluster PGs based on the size relationship between the number of non-repeating PGs that can be accommodated and the total number of expected PGs of the cluster and using mathematical graph theory, until all of the total number of expected PGs of the cluster are distributed. If two disks are damaged, at most only one PG cannot be written, and if three disks are damaged, at most only one PG cannot be read, thereby reducing the risk of data loss. If more disks are damaged, the amount of data that can be lost will also be greatly reduced, thereby reducing the range of PGs that can be affected when multiple data disks are damaged.

This invention relates to the field of contract management technology in the construction and real estate industry, and particularly to an intelligent element extraction method and system for contracts in the construction and real estate sector. The invention includes: standardizing the format and extracting the content of contract documents to obtain extracted files; defining semantic feature rules for the extracted files to obtain a graph structure; performing divide-and-conquer parallel processing on the graph structure to obtain processed subtasks; and performing deep validation on the processed subtasks to obtain structured data. This invention, through the formal representation of graph theory structures, enables rules to handle nested, branching, and looping logic, thereby significantly improving the accuracy and coverage of extraction and reducing missed and false extractions.

The present application relates to a kind of metabolic pathway extension network based on structural feature automatical establishment method, belong to bioinformatics and computational chemistry technical field.The purpose is to solve the problem of existing KEGG database incomplete, annotation lag of metabolite.Method includes: obtaining KEGG skeleton and modified group construction seed library;Through substructure matching and set difference calculation to determine the attribution of derivative;Iterative expansion forms structure association set;Combining stereochemistry rule and mass difference verification screening derivative;Merging KEGG reaction and extended relationship constructs network, and remove redundant path by graph theory method, finally output extended pathway network.The method greatly improves the annotation rate of metabolite pathway, supports in-depth functional analysis of metabolomics data.

The present disclosure provides an AI-based clinical decision support system comprising an input module configured to receive clinical information comprising brain scan data, an analysis module configured to parse the clinical information using statistical measures from functional connectivity analysis with counterfactual explanations to identify brain connectivity patterns associated with brain disorders, and an output module configured to present a recommended diagnosis and explanation comprising attribution information identifying connectivity features contributing to the diagnosis. The brain scan data comprises functional magnetic resonance imaging, electroencephalography, and magnetoencephalography data. The statistical measures comprise functional connectivity analysis and graph theory metrics including degree metrics, betweenness centrality measures, and clustering coefficients. The analysis module comprises a functional connectivity engine configured to process brain scan data and generate connectivity features, a feature bank configured to store connectivity features, and modeling backbones configured to analyze connectivity features using machine learning techniques.

The application relates to the technical field of smart parks, in particular to a smart park intelligent equipment interconnection management method and system, real-time state data and position coordinate data of equipment are collected through a sensor network, after sudden task requirements are classified, a dynamic relationship graph among the equipment is constructed by using a graph theory algorithm, a cooperation path is extracted and a high-risk path is marked; a cooperation priority sequence is generated based on a priority queue algorithm in combination with a response time weight and a task urgency, resource allocation is dynamically adjusted through position coordinate data to eliminate competition; a confusion index set is constructed by using execution feedback data, a dynamic relationship graph is updated in a closed loop, and an equipment interconnection management configuration is output after verification. The corresponding system comprises eight function modules, and realizes full-process automatic collaboration. The application solves the problems of weak dynamic adaptation, extensive resource scheduling and poor cooperation stability in the prior art, significantly improves equipment cooperation efficiency and resource utilization, and guarantees the reliability of park normal operation and emergency disposal.

The application discloses a design method and system of a two-dimensional material loaded metal monatomic catalyst, comprising the following steps: firstly, generating a substrate structure by using a random strategy, a space group and graph theory; then, removing and optimizing the substrate structure according to atomic environment and lattice constant comparison; then, confirming a stable substrate structure by using a density functional perturbation theory method and ab initio molecular dynamics calculation; then, loading a monatomic transition metal on the substrate to obtain a monatomic catalyst model, and constructing a catalyst attribute database by using first principle calculation; finally, designing a monatomic catalyst by machine learning and database interpretation analysis. The application can quickly obtain a target two-dimensional material loaded metal monatomic catalyst by combining high-throughput screening method, first principle calculation and machine learning.

The application discloses a layout and wiring method and system for multi-row CFET standard cell automatic generation, performs Euler path guided transistor partitioning and combines multi-strategy graph theory pruning to obtain an area-optimal row partitioning scheme; generates intra-partition layout based on Euler paths, and completes inter-partition layout according to a wireability index of the number of integrated pins and wire length. M0 layer pin access is optimized based on a multi-commodity flow SMT model, and intra-cell wiring is completed in combination with integer linear programming, the layout result is combined with the final wiring result, and a final CFET standard cell layout is generated. The application effectively solves the problems of internal wiring resource shortage and pin access difficulty caused by aggressive area shrinkage under the multi-row CFET architecture, and realizes the synergistic optimization of layout area, wiring quality and generation efficiency under the premise of ensuring complete compliance with design rules.

The application provides a three-dimensional weaving pattern generation method based on graph theory and neural networks, comprising: acquiring line segment data in a traditional pattern, constructing a topological graph composed of nodes and edges, determining the hierarchical order of multi-level relationships, and obtaining a hierarchical structure model; extracting three-dimensional rendering parameters from the topological graph according to the hierarchical structure model, and generating a diversified pattern skeleton set using a graph partitioning method; obtaining fusion data of the diversified pattern skeleton set and the hierarchical structure model to obtain a pattern image with visual depth; and fusing rendering parameters of the interlaced logical representation data in the pattern image and the hierarchical structure model to generate a reconstructed three-dimensional weaving pattern. Through the synergistic effect of the graph theory algorithm and the neural network, the application realizes efficient conversion from the pattern skeleton to the three-dimensional image, significantly improves the automation level and visual performance of the pattern design, and provides important technical support for the digital protection and innovative application of the traditional pattern.

This invention provides a statistical method and system for the digital integration degree of water pipeline network operation and maintenance information. The method defines the statistical boundaries of the entire operation and maintenance process, extracts key information nodes to construct a process node mapping table, constructs a multi-dimensional indicator system covering digital coverage, integration connectivity, and value creation, and uses a subjective and objective combined weighting algorithm to determine the dynamic comprehensive weight of the indicators. It collects underlying business data, constructs a directed graph of data flow and a digital elevation space model, calculates topology and spatiotemporal correction coefficients based on graph theory topology anomaly pruning diagnosis and spatial gradient penetration algorithms, and then corrects the basic score to obtain the comprehensive integration degree. Finally, it classifies the levels and outputs a directional optimization sequence including key speed-limiting nodes. This invention deeply integrates the physical spatial characteristics of the pipeline network with the underlying graph theory topology of the system, effectively filtering out pseudo-integration and redundant data transmission, accurately quantifying the true interconnection level of the entire business process, and providing a scientific diagnostic basis for the optimization of the water digital architecture.

This invention provides a method and system for optimizing mining methods in metal mines, relating to the field of mining method optimization technology. The method first constructs a candidate mining method library and an evaluation index system for mining method optimization; secondly, it sets multi-level quality grades for mining methods and establishes grading standards for the evaluation indicators; further, it constructs a relationship network of evaluation indicators and uses graph theory analysis and breadth-first search algorithms to achieve objective weighting of the evaluation indicators; it constructs a TOPSIS evaluation model to calculate the proximity of each mining scheme to its ideal optimal value; it constructs a cloud model to calculate the membership matrix of each mining method; finally, it obtains the final score through a joint probability function and selects the mining method with the highest score. This invention can reduce the influence of subjective human weighting on the evaluation results, improve the accuracy of the evaluation results, and enable the evaluation process to present more diverse information, providing a quantitative basis for scientific decision-making in mining methods.

This application relates to a flexible collaborative method and system for heavy-haul train platoons to resist malicious information degradation, belonging to the field of heavy-haul railway transportation technology. The method includes: modeling the communication topology of the heavy-haul train platoon using graph theory, treating each heavy-haul train as an independent intelligent agent that interacts only with its neighboring trains; establishing a dynamic model for each train and performing equivalent linearization based on pseudo-partial derivatives to obtain a data-dependent linearized model; constructing a channel fading model and a transmission state model to describe the impact of random fading and malicious information degradation on communication performance, respectively; and designing a distributed data-driven control algorithm based on the communication topology, linearized model, channel fading model, and transmission state model, including pseudo-partial derivative iterative estimation and parameter reset mechanisms, to generate control force commands for each train. This method can effectively resist malicious information degradation during communication, enabling bounded collaborative trajectory tracking of the platoon under information degradation environments, ensuring operational safety and transportation efficiency.

This invention relates to the field of database technology and discloses a dynamic data management method oriented towards business complexity. This method parses business logs to construct a real-time business interaction topology graph, calculates the structural entanglement degree representing logical coupling and the access discrete entropy representing the degree of hotspot dispersion, and generates a business state vector. It uses a time series model to predict future states, employs a recursive cost function considering lock blocking diffusion and resource contention effects to calculate execution costs, and generates policy instructions when the net utility meets a lag threshold. Responding to the instructions, it executes dynamic materialized views or transient vertical splitting operations including hot / cold separation, and performs nonlinear adaptive synchronous flow control based on real-time load. This invention quantifies business complexity through graph theory features and combines a predictive decision-making mechanism to achieve proactive data architecture optimization, effectively solving the performance degradation problem in highly coupled and hotspot scenarios.

The application relates to a risk assessment method and device for an urban lifeline pipe network, and relates to the field of urban pipe network system safety monitoring; the method can accurately quantify pipe network risks and provide data support for urban risk decision-making. The method comprises the following steps: acquiring attribute parameters of each pipe section in a pipe network system and soil body attributes, and constructing a finite element model of the pipe network system; acquiring seismic motion records of a region where the pipe network system is located, performing dynamic analysis based on amplitude-modulated acceleration time histories, and obtaining stress data of the pipe sections under different seismic motion peak accelerations; determining the damage levels of the pipe sections through the stress data, and counting the number of times when the damage levels reach preset levels to obtain failure probabilities of the pipe sections; training a machine learning agent model through the seismic motion records and the failure probabilities, and determining the failure probabilities of to-be-measured pipe sections through the model; determining importance indexes of the to-be-measured pipe sections through a weighted graph theory model; and determining risk assessment results of the to-be-measured pipe sections based on the failure probabilities and the importance indexes.

This invention establishes a method and system for three-dimensional facies-controlled sand body modeling and connectivity evaluation. The method is based on a dimensionality-reduction modeling architecture: a planar sedimentary facies distribution map is generated through probability-guided sequential indicator simulation; under the constraint of planar facies zones, a one-dimensional vertical sequence of interbedded sand and mud layers is independently generated point-by-point using probabilistic sampling; the planar facies map and the vertical sequence are stitched together in three-dimensional space; then, based on graph theory undirected graph algorithms, the connectivity of the three-dimensional sand body is identified using vertical depth interval overlap and non-interlayer occlusion as dual criteria; finally, connectivity uncertainty is quantified through Monte Carlo iteration. Compared with traditional full-3D grid sequential Gaussian simulation, this method is applicable to clastic reservoirs with gradual lateral variation and dominant vertical accretion (lateral variation coefficient of planar facies zones less than 0.3). It eliminates the computationally expensive three-dimensional spatial interpolation and complex structural modeling, forming a simplified engineering path of "trading computational power for uncertainty assessment," which can be used for reservoir connectivity evaluation and development scheme optimization.

A collaborative fault handling system and method for provincial and local power grids based on multi-source data includes: a multi-source data processing unit that collects heterogeneous data from multiple sources, and forms a power grid state feature vector through normalization processing and knowledge graph construction; a topology construction unit that generates a power grid operation topology map through a depth-first search algorithm; a fault intelligent diagnosis unit that uses wavelet transform and graph theory search algorithms to locate fault nodes and uses the Newton-Raphson method to calculate the fault impact range; and a collaborative handling decision-making unit that optimizes fault handling plans through a case-based reasoning model to obtain an operation instruction sequence, and then relies on instant messaging and a workflow engine to achieve collaborative handling by provincial and local multi-level dispatching agencies. This invention, through multi-source data integration and multi-algorithm collaborative application, fully leverages the complementary value of real-time power grid operation, management, fault recording, and external environmental data compared to traditional fault diagnosis methods that rely on single data or simplified algorithms.

The application discloses a kind of decoupling methods, devices and storage medium of enterprise data architecture, it is related to the technical field of data management, the method of the application comprises: obtaining data object in target data architecture and the dependent relationship between the data object;According to the data object and the dependent relationship, data architecture directed graph is constructed;According to the data architecture directed graph, the multidimensional centrality index and weight value of each data object are calculated;According to the multidimensional centrality index and the weight value, determine target coupling node, and execute corresponding decoupling strategy for the target coupling node.The application determines the corresponding decoupling node by abstracting data architecture into directed graph and applying graph theory centrality principle, and executes corresponding decoupling scheme, solves the problem that data model coupling degree is high in enterprise-level data warehouse, link is rigid, improves the maintainability of data architecture and the reuse value of model.

The application relates to the technical field of brain science image processing, and discloses a brain tissue neuron morphology three-dimensional reconstruction and connection group analysis method and system. The method comprises the following steps: feature point extraction and matching and optical flow registration are performed on continuous ultrathin section electron microscope images to obtain three-dimensional image body data; a three-dimensional deep convolutional neural network is used for semantic segmentation of cell bodies, dendrites, axons and dendritic spines, and secondary registration is driven through confidence feedback; synapses are detected and classified into excitatory or inhibitory types; topological perception skeletonization reconstruction is performed, and segmentation correction is constrained through topological anomaly feedback; and a synapse connection matrix is constructed, and graph theory analysis is performed.

This invention discloses a method and system for lightning fault location in hybrid collector lines of wind farms in mountainous areas. The method includes: a graph-theory-based collector line topology model; construction of a full-node propagation time matrix; synchronous acquisition of transient traveling wave signals of lightning faults at multiple preset measurement points, and extraction of the initial arrival time of the traveling wave recorded at each measurement point; determination of the lightning fault section based on the arrival time difference of the traveling wave between different measurement points, combined with the full-node propagation time matrix, and through interval constraints; within the lightning fault section, establishing an error function between the theoretical propagation time difference and the actual measurement time difference, using the location of the lightning fault point as a variable; solving for the minimum value of the error function to obtain the location of the lightning fault point on its corresponding line segment, and outputting the lightning fault distance. This method and system can improve the accuracy of lightning fault location.

The present application belongs to the field of big data processing and data analysis, and particularly relates to a product ecological value conversion optimization method based on big data analysis. The method comprises: constructing a structured user behavior feature set through multi-source data acquisition and processing; constructing a multi-dimensional product ecological value conversion graph based on graph theory, and giving a multi-dimensional attribute vector to the directed edge; using a graph neural network to perform global representation learning and automatically mine high-value conversion path patterns; simultaneously performing reverse churn analysis on the graph to construct negative value decay path patterns; establishing a real-time optimization decision model coupled with positive path reinforcement and negative path blocking, and dynamically generating resource allocation weights and intervention strategies for each node. The present application realizes dynamic identification of conversion paths and accurate allocation of resources through the construction of a heterogeneous graph and a coupled optimization mechanism, inhibits user churn while improving the overall value transmission efficiency of the ecological system.

This invention relates to the field of power grids and discloses a power line topology identification method integrating dual-mode communication and Bluetooth technology. The method includes the following steps: The communication master node (CCO) and communication sub-nodes (STAs) are powered on and initialized, automatically forming a high-speed power line carrier and low-power wireless hybrid communication network, establishing a dual-mode communication link; the CCO collects outage event information recorded by each STA through this link, and filters out the set of STA nodes that experienced outages within the same time window based on a timestamp matching algorithm, generating a preliminary electrical topology relationship; each node's Bluetooth module periodically broadcasts and records RSSI values, which the CCO then aggregates and spatially groups the STA nodes using a density clustering algorithm; further, the physical distance between key node pairs is obtained, optimizing the physical location grouping; finally, the CCO integrates electrical topology and physical location information and uses a graph theory algorithm to generate a complete power line topology map; this method achieves effective identification of the topology structure of low-voltage distribution substations, possessing both electrical and spatial dimensions of information.

This application relates to the field of pipeline construction technology, specifically a method and system for integrated construction layout of outdoor comprehensive pipeline networks combining temporary and permanent structures. This includes acquiring geological survey data of the construction area. By constructing a three-dimensional geological-pipeline coupled digital model, integrating geological, topographical, and existing pipeline data, and utilizing triangular mesh Boolean operations to accurately identify hard collisions, soft collisions, and geological conflicts, construction risks are effectively avoided, and design changes and rework are reduced. A graph theory algorithm is used to comprehensively consider length, geology, cost, and workload weight, automatically generating the optimal combination path for temporary and permanent pipelines, and introducing an obstacle penalty mechanism to ensure the path is scientifically sound and economical. Based on real-time monitoring data, soil settlement is predicted, and combined with a back-analysis algorithm, excavation depth and support parameters are dynamically adjusted to effectively control trench stability. Furthermore, through the "permanent-temporary combination" mode, the land occupation of temporary facilities and foundation pressure calculations are optimized, reducing material waste and secondary excavation.

A power distribution network online security analysis, early warning and auxiliary decision method and system, comprising: collecting multi-source graph model data and real-time operation data of main network, distribution network and microgrid, identifying the topological connection relationship of power grid equipment based on graph theory algorithm, and dynamically maintaining through incremental updating algorithm to construct full-voltage-level power grid topology full graph; performing base state power flow calculation and static N-1 safety analysis on the topology full graph, identifying the risk of section overrun and equipment overload, and generating an enhanced topology full graph with risk level, type and trend by using a load prediction model; based on the enhanced topology full graph, the dynamic adaptive fault credibility evaluation model is used to determine the fault point by fusing the topological influence factor, the operation risk trend factor and the equipment health degree attenuation factor. The application realizes the global dynamic presentation of the power grid structure, and improves the fault positioning accuracy and disposal efficiency.

The application relates to a method for identifying a hybrid topology of a transformer distribution network and a microgrid. The method comprises collecting voltage time series recorded by intelligent electricity meters of each node in the distribution network and the microgrid to be identified; preprocessing the voltage time series to obtain aligned voltage time series; calculating a similarity coefficient based on the aligned voltage time series of each pair of nodes; constructing a dissimilarity complete connection graph based on the similarity coefficient; obtaining a graph theory feature of each pair of nodes based on the dissimilarity complete connection graph; constructing an input set and an output set of the node pair based on the similarity coefficient and the graph theory feature of each pair of nodes; inputting the input set and the output set into a binary classification algorithm model to output a classification result of whether the node pair is connected; and reconstructing a topology structure of the distribution network and the microgrid based on the classification result of each node pair. The application can restore the topology structure of the distribution network or the microgrid with high precision.