57 results about "Complete graph" patented technology

The invention relates to the technical field of three-dimensional modeling in engineering surveying, in particular to a point cloud building component modeling method and system based on feature extraction. Three-dimensional point cloud data of building components are obtained, a multi-scale local neighborhood calculation covariance matrix is constructed with each point as the center, eigenvalues are decomposed, an edge probability graph is generated, corresponding component categories are semantically output, and corresponding key feature point sets are screened; taking the feature point cloud as a control point, constructing a three-level B-spline surface model, and adjusting the corresponding spatial distribution density; a weighted complete graph is formed through a key feature point set, a non-planar area is identified and processed through a Kurtowski theorem, a QEM algorithm is applied to carry out lightweight processing on a three-dimensional model, screening and optimization are carried out through automatically extracting key feature points of building components, and a concise and accurate lightweight model is constructed based on feature point topological optimization and a graph theory algorithm. The automation degree and efficiency of modeling are improved, and the contradiction between model lightweight and precision is effectively solved.

The invention relates to the technical field of artificial influence weather, and discloses an artificial influence weather knowledge graph construction method and system based on a large model. The method comprises the steps that multi-source meteorological data are collected and preprocessed to generate a standardized data set; extracting a meteorological feature set by using the pre-trained large model; detecting a data dynamic change frequency, and starting a dynamic strategy to switch and mark an abnormal data segment when the data dynamic change frequency exceeds a threshold value; in combination with a similarity matching result of the historical knowledge graph and the current features, decomposing the feature set of the non-abnormal data segments to generate knowledge feature subsets; correcting the knowledge feature subset based on the coupling relationship between the meteorological kinetic parameters and the environmental parameters; and reversely backtracking the key influence factors along the weather influence propagation map, and constructing the knowledge map. According to the method, multi-source data are integrated through standardized processing, abnormal data are dynamically identified, historical knowledge and physical mechanism are combined to correct features, key factors are finally backtracked to complete graph construction, and the method is suitable for knowledge integration and analysis in the field of artificial influence weather.

The invention discloses a milling path planning method and system for stress gradient steepest descent, and the method comprises the steps: carrying out the discretization of a residual stress field based on a lightweight bearing member, and mapping the discretized residual stress field to a milling model, so as to form a high-dimensional matrix; calculating a gradient vector and stress streamline distribution of the residual stress field, and according to the gradient vector and the stress streamline distribution, based on a minimum potential energy principle and a stress steepest descent strategy, performing milling path planning degradation to obtain an optimized baseline; and according to the high-dimensional matrix and the optimized baseline, milling path planning is converted into graph complete traversal with the graph theory as guidance, a steepest descent scheme of strain energy and rigidity is optimized, and an optimal milling path scheme is obtained. The method serves for precision machine manufacturing process design, and the problems of array geometric feature milling path planning and machining deformation control of current lightweight force bearing components are solved; and the method is particularly suitable for light-weight bearing components with array geometric characteristics and high dimensional precision.

The invention discloses a multi-unmanned aerial vehicle cooperative regional patrol path planning method based on hierarchical optimization, and the method comprises the steps: firstly, abstracting a task space containing a base station and a plurality of target points into a weighted undirected complete graph, and building a mathematical model with a target of minimizing the total completion time of a system and the standard deviation of task time; then solving is carried out through a hierarchical strategy; firstly, k-means clustering is adopted to carry out initial task allocation; solving a traveling salesman problem for each task cluster by using a simulated annealing algorithm, generating an initial path, and performing local optimization by using a 2-OPT algorithm; and finally, carrying out global collaborative optimization by adopting adaptive large-scale neighborhood search, dynamically adjusting task allocation and paths through a cross-unmanned aerial vehicle damage and repair operator, and carrying out iterative optimization according to a composite acceptance criterion and an adaptive mechanism to generate a near-optimal scheme. According to the method, through tight coupling of task allocation and path planning, the defect that a traditional method is prone to local optimization is effectively overcome, and joint optimization of completion time and load balancing is achieved.

This invention relates to a user interface layout generation method based on a latent diffusion model, belonging to the field of image generation. This invention extracts components and constraints from interface requirement description text using the BERT model, utilizes a graph convolutional network module to predict complete constraint relationships and borders to generate a complete graph, and generates the user interface layout based on the latent diffusion model. On one hand, introducing a latent diffusion model into user interface generation can meticulously capture the complex relationships and details between UI elements, thereby generating a more accurate user interface that conforms to interaction specifications. On the other hand, introducing a graph convolutional network into user interface generation can effectively predict complete constraint relationships between components, effectively capturing and modeling the complex dependencies and interactions between user interface elements; through graph structures, multi-dimensional constraints such as the position, alignment, and function of elements are applied, generating a more coordinated layout that conforms to design specifications; improving the aesthetics and usability of the generated interface, and enhancing the user experience.

The invention discloses a redundant space robot inverse kinematics solving method based on a graph neural network, and relates to the technical field of robot motion control and artificial intelligence crossing. A distance geometric graph is constructed to represent the configuration of the mechanical arm, an inverse kinematics problem is converted into a completion problem of a partial graph, joint configuration is sampled, and data pairs of a complete graph and the partial graph are stored as a data set; training based on an isotropic graph neural network and a conditional variation auto-encoder, and training a model based on a loss function of an evidence lower bound; and in the reasoning process, constructing a partial graph, inputting the partial graph into the trained prior network, outputting hidden variables meeting Gaussian mixture distribution, extracting sampling points, and generating a reconstructed complete graph to obtain joint angle information. The mechanical arm structure is represented through the distance geometric diagram, the inverse kinematics problem is converted into the complementation problem of partial diagrams, probability distribution of a solution space is learned through a conditional variation auto-encoder frame, multi-solution generation of different mechanical arm configurations is supported, and the solving precision and efficiency are high.

The invention provides a CTSP solving method and system based on a convolution enhanced agency attention mechanism, and relates to the technical field of data processing, and the method comprises the steps: obtaining city data and salesperson data; based on the city data and the salesman data, constructing a complete graph used for describing CTSP problem instances; based on the complete graph, constructing a constrained Markov decision process model, and modeling a path generation process as a step-by-step decision mechanism; a neural strategy network based on convolution enhancement and an agency attention mechanism is constructed, and the neural strategy network is used for solving a constrained Markov decision process model; training the neural strategy network based on the convolution enhancement and agency attention mechanism by using a reinforcement learning algorithm; generating a preliminary optimal path scheme through the trained neural strategy network based on convolution enhancement and an agency attention mechanism; and inputting the optimal path scheme into a traditional optimizer for fine optimization to obtain a final optimal path scheme.

The invention discloses a big language model Cypher generation method and system based on graph pattern alignment enhancement, and the method comprises the steps: constructing a data set which comprises a plurality of types of training data; supervising and finely tuning the large language model; sampling the supervised and fine-tuned large language model to obtain a sampling data set; training the large language model after supervision and fine tuning; obtaining a question and a graph database mode input by a user; related sub-graph modes are selected from the complete graph modes according to user questions and are verified; the large language model generates a corresponding Cypher query using a selected mode and a given question. According to the big language model Cypher generation method and system based on graph pattern alignment enhancement, after the big language model is enabled to better adapt to a Cypher generation task through supervision and fine tuning of various data, the model is trained by model sampling after supervision and fine tuning, the capability of the big language model to generate the Cypher is enhanced, and the big language model and the graph pattern are effectively aligned.

Disclosed in the present invention are a dynamic maximal clique enumeration device and method based on an FPGA with an HBM, the method including: the HBM stores a dynamic edge flow, a complete graph adjacency matrix, and candidate cliques; a matrix computing unit updates the complete graph adjacency matrix based on the dynamic edge flow, transmits the updated complete graph adjacency matrix to the HBM for storage, and determines header nodes, of which the corresponding candidate clique needs to be updated; a sequence computing unit constructs, according to the updated complete graph adjacency matrix and each header node to be updated, the sorted data set for reconstructing candidate cliques by data block sequencing; and an update computing unit executes, in parallel, an update task of the candidate clique corresponding to each header node to be updated based on the sorted data set, transmits the updated candidate cliques to the HBM for storage, and transmits the updated candidate cliques to the PC host to extract maximal cliques by means of a filtering operation. The present invention supports the computation of pipelined-type incremental maximal clique, thereby improving the overall computing efficiency of the task.

The invention relates to the field of quantum machine learning and high-energy physics, in particular to a particle flow classification method based on a quantum complete graph self-attention network, and the method comprises the steps: obtaining particle features in a data set, screening out four representative particles with the maximum transverse momentum from each particle flow to replace the characteristics of the whole particle flow so as to form a complete graph; performing self-attention coefficient calculation and weighted summation on particle features in the complete graph by using a QGAT model constructed by a self-attention mechanism to complete updating of the particle features; and the updated quantum state of the particle features is subjected to dimension reduction representation through the quantum convolutional network and then is used as the input of a quantum classifier for result prediction, so that the particle classification precision and the model expression ability are greatly improved.

The embodiment of the invention provides a wireless charging method for uncertain sensor node positions, and is applied to the technical field of wireless rechargeable sensor networks. The method comprises the following steps: actively detecting sensor node and obstacle distribution based on a transmitting coil array of the mobile charging equipment; a magnetic wave beam vector is formed by independently adjusting the voltage of each coil, and the accurate position of a node and the contour of an obstacle are determined by combining impedance measurement and wave beam intersection positioning; discretizing the region, constructing a vertex visible graph and generating a collision-free path complete graph; and calling a TSP solver to generate an initial charging path by taking minimization of movement consumption as a target, inputting the initial charging path to the trained reinforcement learning model, dynamically optimizing a track and a charging plan through real-time interaction, outputting an optimal path, and issuing the optimal path to the charging equipment. In this way, the problem of selecting a proper charging point in a complex scene with obstacles and uncertain node positions can be solved, accurate positioning of the nodes is achieved, and the charging efficiency is remarkably improved.

The application discloses an airport crowd route guidance scheme optimization method and device and a storage medium, and the method comprises the following steps: acquiring layout information of an airport, and abstracting an airport scene into a complete graph according to the layout information; screening edges in the complete graph according to a preset screening condition, so as to delete edges that do not meet the condition; abstracting an airport crowd route guidance scheme optimization problem into a combination optimization problem according to the screened complete graph; and using a discrete differential evolution algorithm to optimize the combination optimization problem, so as to obtain a final optimization result. The application performs mathematical modeling on an airport departure layer scene, and automatically generates and optimizes an airport crowd route guidance scheme by using a discrete differential evolution algorithm, so that a high-quality solution can be obtained, and the guidance of the crowd route in the airport can be effectively assisted. The application can be widely applied to the fields of intelligent transportation and evolutionary calculation.

The invention relates to the technical field of neural network acceleration, and relates to a CPU-FPGA-oriented graph neural network training acceleration method and system. The method comprises the steps that a CPU stores complete graph feature data and distributes different feature data to storage modules of different FPGAs through data channels according to a load balancing algorithm; partitioning the GNN network model by the CPU, and matching a partitioned model operator with a corresponding FPGA (Field Programmable Gate Array); the CPU completes issuing and sampling of a partition model according to a sampler algorithm, and sends the partition model to the FPGA; reading feature data after the FPGA is started, completing a calculation task by a load execution module, and writing a result back to a CPU memory; the CPU completes data updating and weight updating; and resending the updated weight data to the corresponding FPGA, and carrying out iterative training. According to the invention, operation of the graph neural network on a CPU-FPGA platform is successfully realized, and the overall acceleration effect is excellent.

The invention discloses a power distribution network load flow calculation method based on graph synchronization parallel and forward-backward substitution, and the method comprises the steps: building a graph model in a graph database based on a power network, and enabling the graph model to be divided into a plurality of sub-graph models; setting an initial state for nodes in the sub-graph model, traversing the sub-graph model in a graph node parallel computing mode, and recording a node layer number and a node path of the sub-graph model; and in combination with the sub-graph model, completing graph calculation through forward tracking current calculation and backward tracking voltage calculation in sequence by utilizing a forward-backward substitution method, and judging whether an absolute value of a voltage difference value of nodes in the sub-graph model is smaller than the maximum iteration convergence precision or not. According to the method provided by the invention, the graph model is established, and graph calculation is carried out in a graph parallel mode in combination with a forward-backward substitution method, so that the calculation speed can be obviously improved.

The application discloses a water body saliency target detection method based on multi-graph learning, relates to the technical field of saliency target detection, and comprises the following steps: step one: data acquisition: water body information is acquired through unmanned aerial vehicle images or remote sensing images; step two: data preprocessing: an adjacency matrix is generated based on SLIC superpixel segmentation of the acquired images, and a complete graph is constructed; step three: multi-feature extraction: (1) color feature map extraction; (2) contrast feature map extraction; step four: saliency detection by a manifold ordering method; step five: combination of multiple saliency maps; step six: saliency optimization: the images are processed through a saliency optimization framework, and a new saliency effect drawing is obtained; the color feature map, the contrast feature map and the saliency detection map obtained by the manifold ordering method are weighted and fused with different weights, a new saliency map is obtained, and finally saliency optimization is performed, so that a new saliency target detection method based on a multi-graph learning model is realized.

This invention discloses an adaptive graph clustering method based on feature adversarial and graph Transformer, belonging to the field of image processing technology. The original graph is optimized by a feature adversarial autoencoder module to match prior distributions of representation features, obtaining attribute-level features. The feature adversarial autoencoder module includes an autoencoder and a feature adversarial module. The original graph is then processed by a graph Transformer autoencoder module to perform the convergence and fusion of local features and global structure, and information propagation, obtaining structural-level features. Adaptive fusion of attribute-level and structural-level dual-source features from the feature adversarial autoencoder module and the graph Transformer autoencoder module completes graph clustering. The training of the feature adversarial autoencoder module and the graph Transformer autoencoder module employs a self-supervised learning approach. A joint optimization loss function guides the joint optimization of graph representation learning and cluster assignment. The joint optimization loss function includes a feature adversarial loss function, an optimization loss function, and a self-supervised learning loss function. The feature adversarial loss function includes the feature reconstruction loss function of the autoencoder and the minimum cross-entropy loss optimization function of the feature adversarial module.

The invention is suitable for the technical field of electric power Internet of Things, and relates to a power grid real-time topology identification method and system, a medium and a terminal, and the method comprises the steps: S10, taking all measurement points in a power grid as nodes, constructing a virtual edge between any two nodes, and obtaining a virtual complete graph; s20, constructing a node feature vector based on the electrical measurement data of each measurement point, and constructing an edge feature vector corresponding to a virtual edge based on a space-time correlation feature and an instantaneous difference / ratio feature between the measurement data of the paired measurement points; s30, reasoning the virtual complete graph through a pre-trained physical information fusion graph neural network model, and outputting the probability that each virtual edge is a real physical connection; and S40, judging the real topological structure of the power grid according to the probability. The method is simple in process and convenient to operate, high-precision identification of the power grid topological structure is achieved, and reliability and safety of an identification result are guaranteed.

The application discloses a high-speed railway station crowd dispersing route optimization method, system and device and a medium, wherein the method comprises the following steps: acquiring layout information of a high-speed railway station, representing a high-speed railway station scene as a complete graph according to the layout information; screening edges in the complete graph according to a preset screening condition; according to the screened complete graph, representing a high-speed railway station crowd dispersing facility layout design problem as an optimization problem of solving a multi-dimensional binary coding vector; acquiring historical passenger flow data of the high-speed railway station, constructing a simulation model of the high-speed railway station scene according to the historical passenger flow data and the optimization problem, optimizing the simulation model through a discrete competitive particle swarm algorithm, and obtaining a final crowd dispersing route. The application abstractly simplifies the crowd flow guiding facility design into an optimization problem for solving, and adopts the competitive particle swarm algorithm for optimization, so that the high-speed railway station crowd dispersing facility layout optimization problem can be better solved, and the application can be widely applied to the fields of system simulation and intelligent calculation.

The present invention provides a CTSP solution method and system based on a convolution-enhanced proxy attention mechanism, relating to the field of data processing technology. The method comprises: obtaining city data and salesperson data; constructing a complete graph for describing a CTSP problem instance based on the city data and the salesperson data; constructing a constrained Markov decision process model based on the complete graph, and modeling the path generation process as a step-by-step decision mechanism; constructing a neural policy network based on the convolution-enhanced and proxy attention mechanism, wherein the neural policy network is used to solve the constrained Markov decision process model; using a reinforcement learning algorithm to train the neural policy network based on the convolution-enhanced and proxy attention mechanism; generating a preliminary optimal path plan through the trained neural policy network based on the convolution-enhanced and proxy attention mechanism; and inputting the optimal path plan into a traditional optimizer for fine optimization to obtain a final optimal path plan.

The invention discloses an explicit dependency modeling method and device under an incomplete multi-mode learning condition, and belongs to the technical field of modeling. The method comprises the following steps: acquiring a multi-modal data set based on a task demand, and constructing a learnable directed complete graph according to all modalities contained in the data set by using a graph neural network; pruning the directed complete graph according to the modal inclusion condition in the training sample to obtain a pruned graph; inputting modal features of modals contained in the training sample and the corresponding pruning graph into a feature fusion network, so that the feature fusion network outputs a task result based on multi-modal fusion features; and updating network parameters of the graph neural network and the feature fusion network at the same time by using the output task result and a first comparison loss constructed by the label of the training sample to obtain a trained graph neural network. The directed complete graph output by the graph neural network can explicitly express the dependency relationship between modals.

This invention relates to the field of data processing and resource management technology, specifically disclosing a method and system for calculating the offset of succession trajectory in degraded forest habitats. The invention acquires multi-period three-dimensional laser point cloud data, generates a set of static physical anchor points through morphological elevation filtering and density clustering for noise reduction, calculates spatial affine transformation parameters using singular value decomposition based on the initial phase, and performs inverse rigid body transformation on the point cloud to generate aligned data. It extracts the centroid of vegetation trunks to construct an undirected complete graph, runs a minimum spanning tree algorithm to obtain the total length of the current habitat structure, and compares it with the baseline value to generate the topological offset. Then, it orthogonally decouples this data with soil sensor data to generate three-dimensional deviation vectors for water deficit, nutrient loss, and increased plant spacing. Finally, it solves for the projection coefficients using a system of linear equations to generate a precise allocation list for irrigation water, compound fertilizer, and seedlings, and schedules automated terminals to execute this list, achieving closed-loop intelligent management.

The present invention discloses a method for optimizing the drawing of massive data based on the maximum triangle three-segment algorithm. The present invention first downsamples the data based on the maximum triangle three-segment algorithm, attempts to compress the original data and maintain the detailed features as much as possible. And for locally steep segmented data, an improvement of dynamic adjustment of the segmentation is made. Then, the data whose magnitude exceeds the threshold is sliced, and for each sliced data, ECharts will create an instance separately for drawing to obtain a chart of the sub-sliced data. Finally, for the obtained sub-charts, the cascading style sheet language is used to locate them to the same position, and they are superimposed to form a unique and complete graph. By adopting the present invention, the data points used for drawing can be greatly reduced without losing the details of the original data as much as possible, thereby visualizing the industrial massive data with high efficiency and high performance.

The application provides a kind of based on betweenness centrality and geometric consistency graph contrast learning method and device, it is related to graph classification technical field.The method includes: according to the betweenness centrality of graph data node and edge, enhanced graph data is constructed to delete probability;Get the node feature of graph data and enhanced graph data, obtain graph representation by aggregating node feature based on Gaussian kernel reading method;According to the topological structure and node feature of graph data, construct multi-scale geometric consistency loss, construct normalized temperature scale cross-entropy loss according to graph representation, and then construct total loss to realize graph classification.The application introduces graph betweenness centrality into graph contrast learning, so as to retain important structural information of graph in graph enhancement process;Introducing multi-scale geometric consistency loss makes the structure information of graph can better guide the generation of graph feature;Optimize aggregated node feature based on Gaussian kernel reading method to obtain complete graph representation, to obtain more discriminative graph-level features.

The application discloses a kind of knowledge graph and sliding bearing surface damage degree evaluation method of big model linkage, belong to mechanical condition monitoring technical field.The application collects sliding bearing fault unstructured text and field damage data, combined with big model and FMEA framework, constructs four tuple initial knowledge graph;Through subgraph decomposition, value pruning and semantic anchor fusion, complete graph topology optimization.Damage information is standardized entity alignment, and optimized knowledge subgraph is obtained by constraint screening;Adopt nonlinear function quantification single damage failure risk, introduce coupling gain coefficient to establish multi-damage coupling evaluation system, and calculate comprehensive failure risk.Relying on hierarchical prompt template constraint big model reasoning, generate interpretable damage assessment report.The application solves the problems that traditional evaluation relies on artificial, hard threshold value determination has poor stability, multi-coupling risk is easily underestimated, and intelligent diagnosis lacks machine interpretation.The evaluation precision is high, and it is suitable for heavy machinery sliding bearing intelligent operation and maintenance scene.

The invention discloses a vector retrieval method and device for a balanced graph topological structure, and relates to the field of vector retrieve.The method comprises the steps that firstly, a graph is initialized into a minimum directed complete graph with any vector meeting the target in-out degree, a gradual vector adding mode is adopted, a forward optimization multi-level edge selection strategy and an edge refining mode are combined, and the minimum directed complete graph with the target in-out degree is obtained; and finally, constructing a neighbor graph of a balanced graph topological structure, and then performing greedy approximate nearest neighbor retrieval on the neighbor graph for the query vector to obtain a query result. According to the method, the balance of the topological structure of the neighbor graph is realized, the problem of deflection of the topological structure of the neighbor graph caused by unbalanced vector distribution is eliminated, and the accuracy and efficiency of vector retrieval are effectively improved. Compared with a current mainstream neighbor graph composition method, the composition method does not depend on reverse edge adding operation to update neighbors for existing vectors in the graph, and composition complexity and time expenditure are reduced.

The application discloses a subway station crowd transfer route optimization method and device and a storage medium, wherein the method comprises the following steps: acquiring layout information of a subway station, and abstracting a subway station scene into a complete graph according to the layout information; screening edges in the complete graph according to a preset screening condition, so as to delete edges that do not meet the condition; constructing a mathematical model according to the screened complete graph, and determining an optimization target of the model; and according to the optimization target, optimizing the model by using an ant colony algorithm, so as to obtain a final transfer route scheme. The ant colony algorithm is applied to the subway station crowd transfer route optimization scheme, a real subway station scene simulation model is established by coding entity facility nodes in the subway station scene, a corresponding crowd transfer route facility design is abstracted into a path planning optimization problem to be solved, and an effective crowd transfer route design scheme can be better provided. The application can be widely applied to urban subway traffic and intelligent computing fields.

This invention discloses an automated migration method and system for graph database metadata, belonging to the field of graph database data migration technology. The method includes: acquiring metadata from a source graph database, including structural definitions of vertex and edge types; performing full sampling analysis on vertex and edge data in the source graph database to construct a distribution mapping relationship from vertices to types and a connection mapping relationship from edges to vertex types; generating graph structure definition statements for the target graph database based on the metadata, distribution mapping relationship, and connection mapping relationship, combined with a preset migration strategy; and sending the graph structure definition statements to the target graph database for execution to complete graph structure modeling. This application can meet the migration needs between different versions of graph databases and ensure data consistency and integrity, thus having broad application prospects.

The present invention relates to a distribution transformer fault diagnosis method based on vibration signals, comprising the following steps: obtaining the distribution transformer vibration signal, processing the distribution transformer vibration signal using a combination of adaptive noise complete set empirical mode decomposition and Hilbert transform, respectively obtaining marginal spectra of different frequency bands to construct eigenvectors; constructing a Gaussian function-weighted undirected complete graph for the eigenvector matrix, obtaining an adjacency matrix, and building a multi-channel and multi-connected graph convolutional neural network model for mining deep features and fault classification; in the graph convolutional neural network model, optimizing the Gaussian kernel bandwidth using an improved Grey Wolf optimization algorithm with a perturbation factor of a sine function to obtain an optimal diagnostic model; and performing fault identification on the object to be identified using the obtained optimal diagnostic model. This method is advantageous for improving diagnostic accuracy and identifying unknown types of faults.

The present invention belongs to the field of computer application technology, and discloses a frequent pattern mining method based on spatial index, including step 1: data preprocessing of geographic social network; step 2: constructing a tree-like spatial index NaR‑Tree; step 3: frequent pattern mining; step 4: returning a result set containing k patterns. The present invention constructs a spatial index of a geographic social network and uses a tree-like index structure to store the geographic location information of the network and the structural information within the regional range. After constructing the index structure, the target search area can be efficiently located and the subgraph information within the area can be obtained. Through the spatial index structure, the present invention refines the scope of frequent pattern mining work from the complete graph to a certain area in the graph. By comparing the frequent patterns of different areas, it can help analyze the differences in characteristics such as user behavior patterns and social preferences between regions.

The application relates to a kind of distributed topology time-varying formation control method and system, and relates to the field of agent formation control, method includes: according to the communication relationship of each node in multi-agent formation, construct initial undirected complete graph;Each node is agent;Based on the initial undirected complete graph, according to distributed algorithm, generate global optimal rigid communication topology;Based on the global optimal rigid communication topology, according to the communication relationship of each node, the corresponding edge of each node is directed, and the in-degree of each node is less than or equal to 3, obtain optimal persistent communication topology;The optimal persistent communication topology is used to carry out communication control to multi-agent formation.The application reduces the complexity of multi-agent communication.