64 results about "Initial topology" patented technology

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention discloses a power distribution area topology identification method and system based on an intelligent fusion terminal and correlation coefficients, and the method comprises the steps: collecting the multi-source data of a power distribution area through an edge fusion terminal, and carrying out the data preprocessing and feature extraction at a terminal side; carrying out parallel calculation on a voltage fluctuation Pearson's correlation coefficient and a load change Kendall rank correlation coefficient, and constructing a correlation coefficient matrix in different time periods; calculating an adaptive weight based on the load fluctuation entropy, and fusing a dual-mode correlation coefficient; carrying out topology generation by using a graph neural network, and outputting an edge existence probability and a node hierarchy; performing physical constraint optimization on the initial topology by adopting a genetic algorithm; the system comprises an edge fusion terminal cluster, a cloud analysis platform, a topology verification module and a terminal management platform. According to the method, topology recognition precision and dynamic adaptability are remarkably improved, bimodal correlation coefficients and graph neural network space modeling are creatively fused, and a complex topological structure is precisely restored.

The invention discloses a pixelated matching circuit design and optimization method based on a large language model, and relates to the technical field of radio frequency circuits, and the method comprises the steps: obtaining a target demand and a physical constraint of a matching circuit; generating an initial topological structure based on the target demand and the physical constraint, and performing parameter estimation on the initial topological structure to obtain an initial binary matrix constrained by the initial topological structure and the parameter; obtaining an optimization problem based on the initial binary matrix and the target demand; wherein the optimization problem comprises distribution of optimization parameters, optimization objectives and solution spaces; and obtaining a key performance index and an optimization algorithm according to the optimization problem, and carrying out iterative search in the optimization algorithm according to the key performance index to obtain an optimal solution for solving the optimization problem so as to generate an optimal pixelated topology. According to the method, the optimal pixelated topology can be generated, and efficient global optimization is realized.

The invention provides an electric two-wheeled vehicle topological structure optimization method and system based on a W algorithm, and relates to the technical field of electric two-wheeled vehicle topological structure optimization. The method comprises the following steps: collecting performance data, structural parameter data and working condition data of the whole electric two-wheeled vehicle and key parts; and constructing a topological structure initial model based on the improved convolutional neural network, and inputting core features to output a preliminary topological design scheme. And constructing a topology scheme evaluation model based on a fuzzy analytic hierarchy process, and obtaining a comprehensive evaluation score of the preliminary topology design scheme. And constructing a multi-target topological optimization model, and solving to generate a plurality of groups of candidate topological optimization schemes. And solving to obtain an optimal topological structure scheme. According to the method, the multi-target model is used, the W algorithm is used for solving, the improved convolutional neural network and the fuzzy analytic hierarchy process are combined, the performance cost balance is achieved, the design efficiency is improved, the scheme practicability is enhanced, the algorithm framework is extensible, data can be reused, and enterprise innovation is assisted.

The invention discloses a heterogeneous Internet of Things topology key node identification method in combination with a mutation theory, and belongs to the field of computers and information, and the method comprises the steps: firstly constructing an initial topology of a heterogeneous Internet of Things, generating a topology set through random disturbance, and calculating performance indexes such as robustness, redundancy, communication efficiency and a global clustering coefficient; then normalizing the indexes by using a mutation series method, evaluating the correlation between the indexes based on a Pearson's correlation coefficient, and obtaining a network performance mutation value through layer-by-layer fusion; simulating a deliberate attack process, establishing a corresponding relationship between a node deletion sequence and performance evolution, and fitting to obtain a performance evolution function; and finally, establishing a cuspidal point mutation model, and identifying critical nodes causing network performance mutation by analyzing the equilibrium state, the singular set and the bifurcation set of the model, thereby breaking through the limitation of traditional static network indexes, being capable of accurately capturing the mutation behavior of the network performance, being high in objectivity, and providing a theoretical basis for topological optimization and fault-tolerant design of the Internet of Things.

The invention discloses a fully mechanized coal mining hydraulic support electrohydraulic control system based on deep learning, and the system comprises a data collection and topology construction module which is used for collecting multi-source real-time data and constructing a support topology adjacency matrix; the data preprocessing and dynamic topology updating module is used for preprocessing the original data set and dynamically updating the initial topology adjacency matrix; the space-time diagram convolution depth autoregression modeling module is used for outputting a future multi-step prediction result set based on a space-time diagram convolution depth autoregression network model; the underground safety constraint judgment module is used for performing underground safety constraint judgment; the control instruction generating and issuing module is used for generating an electro-hydraulic control instruction set; and the execution feedback and model closed-loop updating module is used for collecting real-time state data, updating the preprocessed data set and enhancing the spatial-temporal characteristic sequence. According to the invention, the space-time diagram convolution depth autoregressive network is adopted to realize intelligent control of the fully-mechanized mining hydraulic support.

The invention relates to a power distribution network topology correction method fusing topology anomaly recognition and structure credible reasoning. The method comprises the following steps: recognizing an abnormal region of a topological structure, and recognizing and generating candidate edges and unobserved nodes; constructing a structure behavior joint feature tensor considering behavior collaboration between nodes, adjacent structure similarity and sudden change synchronism, and inputting the structure behavior joint feature tensor into a topological connection credibility scoring model to obtain an edge credibility score; and carrying out credibility correction on the initial topological structure by combining the edge credibility score obtained by the topological connection credibility scoring model, the candidate edge and the unobserved node, and generating a final topological structure. Compared with the prior art, the method has the advantages that omission edges, error edges and unmodeled nodes in the original topological structure are comprehensively repaired, and the integrity and credibility of the topological structure of the power distribution network are remarkably improved.

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.

The invention provides a spatial-temporal feature fusion-based power distribution network topology dynamic reconstruction method. The method comprises the steps of obtaining first time sequence data, second time sequence data and a line operation state; generating a first spatial-temporal characteristic parameter based on the first time sequence data and the line operation state; generating a second spatial-temporal characteristic parameter based on the second time sequence data and the line operation state; generating a local initial topology reconstruction strategy based on the first spatial-temporal characteristic parameter and the second spatial-temporal characteristic parameter; 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 based on the global topology reconstruction strategy, generating a corresponding switching operation instruction and outputting the switching operation instruction to an instruction execution end of the power distribution network. According to the method provided by the invention, effective fusion of the spatial-temporal characteristics of the power supply side and the load side in the urban-rural junction power distribution network is realized, the operation flexibility and efficiency of the power distribution network are improved, and waste of electric power resources is reduced.

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 present invention discloses a method and system for collaborative fault-tolerant tracking control of a multi-agent system based on topology reconstruction, which is used to solve the problem of maintaining the performance of a multi-agent system under actuator failure. The method specifically includes the following steps: Step 1, clarifying the description of the multi-agent system under fault conditions; Step 2, constructing the initial topology structure and nominal controller of the multi-agent system under fault-free conditions; Step 3, designing a topology reconstruction strategy to deal with various fault scenarios; Step 4, integrating various topology reconstruction strategies and forming a collaborative fault-tolerant control method. The present invention can ensure the performance of the multi-agent system by adjusting the topology structure according to the fault information when a fault occurs in the actuator of the multi-agent system, thereby weakening the adverse effects of the fault on the multi-agent system and improving the reliability of the multi-agent system.

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.

A method, a device and a computer program for repairing a defect of a mask for lithography, in particular an EUV mask, are described.A method of repairing a defect of a mask for lithography, in particular an EUV mask, comprises the following steps: (a.) carrying out a first repair step on the defect using a first repair dose, wherein the defect transitions from an initial topology to a first defect topology as a result; (b.) determining an influence of the first repair step on the topology of the defect; (c.) determining a second defect topology for the defect, which is intended to be achieved by way of a second repair step on the defect; and (d.) determining a second repair dose for the second repair step, at least in part on the basis of the determined influence of the first repair step on the topology of the defect and the second defect topology. The method may further comprise step (e.) of carrying out the second repair step using the second repair dose.

This invention discloses a method, apparatus, and storage medium for generating a fault inspection strategy for a distribution network. The method includes: acquiring single-line diagram data, geographical location data, and initial state data of multiple hardware devices in the distribution network when the distribution network is in a fault condition; determining the initial topology of the distribution network based on the single-line diagram data and geographical location data; inputting the initial topology and initial state data into a target topology model of the distribution network for analysis to obtain multiple areas and devices to be inspected; inspecting the areas and devices based on the target inspection resource information, target inspection constraints, and target inspection function of the distribution network to obtain inspection results; and generating a fault inspection strategy for the distribution network based on the inspection results. This invention solves the technical problem of low efficiency in fault inspection of distribution networks.

The invention relates to a fully-decoupled movable piezoelectric driving compliant parallel mechanism topological optimization method, which comprises the following steps of: obtaining a density field distribution condition of a compliant parallel mechanism based on participation parameters through an independent point density interpolation method; the sum of the expected output displacements corresponding to all the branch chains of the flexible parallel mechanism is maximized to construct a target function; the target function is corrected on the basis of preset constraints, an initial topological optimization model is obtained, and preset constraint conditions include the volume fraction of the compliant parallel mechanism, the piezoelectric actuator and the main design domain of the compliant parallel mechanism do not interfere with each other, and the movable platform does not rotate in a plane under the action of a load; and based on the initial topological optimization model, solving a topological optimization problem through a gradient-based moving asymptote optimization algorithm. According to the flexible parallel mechanism, the problems that the motion precision is reduced and the controllability is poor due to the input and output coupling effect of the flexible parallel mechanism can be solved.

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 discloses a layout method, device and equipment of a precast beam field and a medium, and relates to the technical field of building engineering construction planning. According to the precast beam field layout method, firstly, a position topological graph of each facility unit is obtained through prediction based on basic data of a precast beam field through a layout generation model, and then the facility units are distributed to a topographic map according to a topological relation and sub-region division is carried out; according to the method, a multi-objective optimization function based on logistics cost and land utilization rate is established, and in a multi-objective optimization solution process, constraints of each facility unit are limited in a sub-region, so that a function relation and a logic relation among the facility units in an initial topology are reserved in an optimization process, an optimization search space is greatly reduced, optimization efficiency is improved, and optimization efficiency is improved. And meanwhile, the layout efficiency, flexibility and adaptability are improved.

The application provides a rule base-based power grid maintenance plan conflict automatic avoidance method and system, and belongs to the technical field of power system automation. The method comprises the following steps: obtaining equipment measurement data, equipment model data, equipment graphic data and equipment operation state data of a power grid platform, and constructing an initial topology graph; obtaining a topology connected path set through iterative deep search traversal, combining with graph neural network for electrical correlation enhancement analysis, and generating a power supply topology tree; constructing a comprehensive maintenance rule base, a maintenance conflict rule base and a maintenance mode arrangement rule base based on the power supply topology tree; automatically matching the maintenance plan with the three rule bases, and outputting a maintenance plan and an operation guidance scheme capable of avoiding maintenance conflicts. The application solves the problems of low maintenance plan checking efficiency, repeated power outage, mode conflict and risk omission in the prior art, and improves the efficiency and safety of power grid maintenance plan management.