29 results about "Clustering coefficient" patented technology

The invention provides a graph data communication intensity mapping method and system based on optical transport network topology. According to the method, an adjacent matrix is established by extracting graph data structure information, vertex centrality and a clustering coefficient are calculated, and a graph data communication feature matrix is generated; oTN network equipment information and optical link topology are collected, link parameters and wavelength resource states are measured, and an OTN physical network feature matrix is obtained; establishing a double-layer mapping relation matrix through a resource mapping algorithm; integrating the communication frequency, the time delay sensitivity, the bandwidth requirement, the wavelength resource and the topological matching degree, and calculating a communication strength weight matrix; generating a candidate layout scheme, performing multi-dimensional evaluation, and determining an optimized data layout scheme; and dynamically adjusting the mapping relation by adopting an incremental updating algorithm. According to the invention, the efficient collaboration of the graph computing task and the optical transport network resource is realized, and the performance and resource utilization efficiency of the distributed graph computing system are obviously improved.

The invention relates to an SDGs causal network toughness optimization method based on multi-modal data, and belongs to the technical field of sustainable development complex network data analysis, and the method comprises the following steps: obtaining multi-modal time sequence data representing SDGs, and carrying out missing value interpolation, standardization and time alignment processing on the data; predicting the future progress of each SDG based on a gated cycle unit model; identifying the causal relationship between the SDGs by using a panel vector autoregression model; calculating a potential interaction probability in combination with a local path index and a weighted random walk restart model, complementing missing links and forming a complete causal network; a multi-scene disturbance experiment is utilized, and a weighted clustering coefficient and weighted global efficiency are adopted to comprehensively calculate the overall toughness index of the network; and taking the toughness index as a fitness function, and performing global optimization by using a genetic algorithm to obtain an optimal network structure configuration and a priority management path. According to the method, the interaction relationship between the SDGs is quantitatively identified, the network structure and function toughness is improved, and technical support is provided for collaborative promotion and policy optimization of the SDGs.

The application discloses a circuit board thermal management method based on multi-level cooperation, comprising the following steps: constructing a thermal field real-time acquisition system based on a multi-modal sensor array, fusing spatial layout, thermal state data and functional attributes, and forming a structured thermal field sampling data set; normalizing, filtering and feature dimension reduction processing the thermal field data to improve modeling accuracy and sensitivity; establishing a circuit board thermal field topology atlas based on the processed data, extracting structural features such as node centrality, clustering coefficient and connectivity, and realizing thermal distribution pattern recognition; according to a topology feature matching priority strategy library, dynamically generating multi-objective optimization thermal regulation conflict resolution instructions, and continuously optimizing strategy mapping through feedback and online learning mechanism, and the application significantly improves the intelligence, self-adaptation and response efficiency of the circuit board thermal management.

This invention discloses a method and system for geological and mineral exploration, belonging to the field of geological and mineral technology. The solution includes three core steps: First, a quantum gravity gradiometer array is constructed at the target mining site to collect quantum gravity data in each area to mark drilling target areas that meet the criteria. Second, fluid data from nanorobots tracking each drilling target area is analyzed to determine the nanorobot penetration plan and collect ore-forming fluid data. Finally, a three-dimensional fracture network model of the target mining site is constructed based on the ore-forming fluid data. By calculating the total clustering coefficient corresponding to the model, the design and analysis of the mining plan for the target mining site are completed. This solution integrates quantum technology and nanorobot tracking technology, and through data acquisition and modeling analysis, provides an innovative technical path and systematic analytical framework for the scientific formulation of geological and mineral exploration and mining plans.

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 invention provides a complex network construction method and electronic equipment, and relates to the field of complex networks. Constructing at least two small-world networks, and combining the small-world networks into an integral network; selecting scale-free nodes and connection nodes from the small world nodes, and creating connection edges between the scale-free nodes and the connection nodes to obtain an initial complex network; the probability that the small-world nodes are selected as the connection nodes is positively correlated with the first degrees of the small-world nodes, so that the node degrees of the initial complex network follow power law distribution. And dynamically evolving the network based on the characteristic parameters of the initial complex network to obtain a target complex network. The target complex network has the characteristic of high clustering coefficient, and the node degrees of the target complex network follow power law distribution, so that the characteristics of close connection among individuals, rapid information propagation and huge influence of a small number of individuals can be reflected, and a complex interaction rule among the individuals can be accurately reflected. Moreover, the target complex network can simulate the dynamic change of the individual relationship through dynamic evolution, and the effectiveness is improved.

The invention relates to the technical field of data processing, in particular to a mass log data archiving method for a cross-border e-commerce platform, which comprises the following steps: receiving cross-border e-commerce transaction log streams, analyzing and extracting user session IDs and commodity SKU codes in logs, counting the frequency of sharing the user session IDs or the commodity SKU codes among different log entries in a buffer area, and storing the user session IDs or the commodity SKU codes in the buffer area. And establishing a log attribute association frequency table, and calculating log clustering coefficient indexes of different log entries on the clustering dimension according to the log attribute association frequency table. According to the method, the compressed sequence is generated through line number difference calculation, difference distribution density is counted to serve as a storage format decision basis, an array container format is selected to reduce storage space occupation when the distribution density is lower than a threshold value, and a bitmap container format is automatically switched to improve bit operation efficiency when the distribution density is higher than the threshold value. And the established adaptive sparse bitmap index can dynamically balance the storage cost and the retrieval speed in different data sparseness scenes.

The application provides a kind of urban rail transit multi-site associated area development status and potential matching degree calculation method, device and storage medium, and the calculation method includes: obtaining urban road network, building, POI and public transport data;Calculate the global network importance of rail transit station, the local clustering coefficient of rail transit station and the transfer level of rail and road public transport station, and construct rail transit station correlation index;The rail station correlation index is standardized and kernel density analysis, and the first data set of rail transit multi-site correlation degree distribution is constructed;Calculate the volume rate, functional mixing degree and road network density, and construct the second data set of built environment aggregation state;According to the first data set and the second data set, the matching degree of development status and potential is calculated.The application can realize the capture of urban rail transit key section dynamic boundary, the evaluation and prediction of development potential, and provide a basis for the delineation of urban renewal or development construction range and design direction.

This invention discloses a method for decomposing complex networks based on graph contrastive learning and multi-hop aggregation, comprising the following steps: collecting the number of neighbor nodes, connecting edges, and average clustering coefficients of a complex traffic network to construct a network decomposition model; inputting the original graph into a role graph generation module to obtain a role graph; inputting the original graph and the role graph into a multi-view representation learning module to obtain multi-angle graph representations of the role graph and the original graph; obtaining an importance score for each node; calculating and optimizing the joint loss function to train the network decomposition model; decomposing the traffic network to obtain the importance values ​​of traffic nodes in the traffic network, and setting stronger security measures for traffic nodes with high importance and weaker security measures for nodes with low importance. This invention uses intra-graph contrastive learning and cross-contrast learning to improve graph representation performance and proposes a multi-hop aggregation mechanism to predict node importance by combining multi-hop neighbor information, achieving high-performance network decomposition.

The invention discloses an ultra-deep crack plugging layer mechanical structure evaluation method based on a topological structure, and relates to the technical field of leakage control. The method comprises the following steps that reservoir conditions of a well leakage position are obtained, and a plugging layer of a plugging formula to be evaluated is obtained in combination with an indoor plugging experiment; cT scanning is carried out on the plugging layer, and the geometric center and the particle size of the particle material are obtained through a digital image processing technology; judging whether particles are in contact or not based on the geometric center and the particle size of the particle material, and constructing a particle material contact network topology model according to the contact relationship; and calculating the matching property, the particle node betweenness, the network betweenness centrality and the clustering coefficient of the particle contact network based on the particle material contact network topology model, and evaluating the plugging layer based on the parameters. The method disclosed by the invention is relatively simple and rapid, the core characteristics of the mechanical structure of the plugging layer can be extracted by directly analyzing the particle contact network representing the geometric skeleton of the plugging layer, and the evaluation process is more direct and efficient.

The invention discloses a structure health assessment method based on stress root spectrum and ultrasonic cluster system superposition sound. The method comprises the following steps: stress root spectrum analysis of experimental structure characteristics; solving a superposition coefficient of the stress root spectrum and the ultrasonic cluster system; solving a self-adaptive clustering coefficient of the superposition coefficient; and solving the structural health coefficient. According to the method, recessive damage characteristics in stress distribution and ultrasonic cluster system response of the large-scale structural member can be effectively extracted, separation of irrelevant interference in a multi-source heterogeneous signal and accurate judgment of damage sensitive information are realized through stress root spectrum analysis and cluster system superposition sound fusion, and adaptive clustering is completed according to response similarity on the basis; according to the method, the health degree quantitative index directly corresponding to the structural damage state can be generated in real time, so that the reliability level of the component is dynamically and accurately evaluated, the early recognition and early warning capability of potential damage is remarkably improved, and reliable guarantee is provided for safe operation and service life management of large industrial equipment.

The application provides a kind of based on the graph data communication intensity mapping method and system of optical transport network topology.The method is by extracting graph data structure information to establish adjacency matrix, calculate vertex degree centrality and clustering coefficient, generate graph data communication characteristic matrix;Collect OTN network equipment information and optical link topology, measure link parameters and wavelength resource state, get OTN physical network characteristic matrix;Through resource mapping algorithm to establish double-layer mapping relationship matrix;Comprehensive communication frequency, time delay sensitivity, bandwidth demand, wavelength resource and topological matching degree, calculate communication intensity weight matrix;Generate candidate layout scheme and carry out multidimensional evaluation, determine the optimized data layout scheme;Mapping relationship is dynamically adjusted using incremental updating algorithm.The application realizes the efficient cooperation of graph computing task and optical transport network resource, significantly improves the performance and resource utilization efficiency of distributed graph computing system.

The invention discloses a structural reliability evaluation method based on clustering ultrasonic load interaction blind extraction. The method comprises the following steps: separating useless and useful data of a large-scale structure based on ultrasonic information; blind extraction of structure reliability correlation based on useful ultrasonic information; solving a structural reliability multi-index correlation coefficient; solving a clustering coefficient based on interactive blind extraction; and calculating a reliability numerical value of the large structural member. Separation of useless information and extraction of related reliability information in ultrasonic load spectrum information of the large structural component can be realized, clustering of similarity degree is carried out on the obtained extraction information, and then the reliability of the structure is determined in real time through the reliability clustering value, so that the damage condition of the to-be-analyzed structural component is accurately obtained, and the reliability of the to-be-analyzed structural component is improved. And the operation safety degree of a large-scale structure is effectively ensured.

Embodiments of the present disclosure provide a keyword extraction method, comprising: first acquiring a text complex network corresponding to a to-be-processed text, the text complex network comprising nodes corresponding to a plurality of single characters in the to-be-processed text, weight values of connecting edges between the plurality of nodes, strengths of the plurality of nodes and in-out degrees of the plurality of nodes; then calculating a clustering coefficient of connections between the plurality of nodes based on the strengths of the plurality of nodes, the in-out degrees of the plurality of nodes and the weight values of the connecting edges in the text complex network; thereafter calculating a proportional mixing amount of the connections between the plurality of nodes based on the in-out degrees of the plurality of nodes, the in-out degrees of nodes connected by the plurality of nodes and the number of connecting edges in the text complex network; and calculating path lengths of the connections between the plurality of nodes based on the connecting edges between the plurality of nodes; and finally extracting keywords corresponding to the to-be-processed text based on the clustering coefficient of the connections between the plurality of nodes, the proportional mixing amount and the path lengths, thereby improving the accuracy and comprehensiveness of keyword extraction.

The invention relates to a granite residual soil slope evaluation method and system based on machine learning. According to the method, a standardized data set is constructed by collecting slope multi-source data, and a dynamic graph structure representing a fracture network with nodes and edges is established; simulating seepage-stress feedback for the first time by using a message passing mechanism of a graph neural network, and outputting predicted displacement and a water head; on the basis, fracture expansion behaviors are predicted, hydraulic parameters are updated, secondary feedback simulation is carried out after a graph structure is adjusted, and more accurate node features are obtained; recognizing a seepage front surface according to the secondarily updated water head data, calculating a Gaussian curvature and a clustering coefficient, and comparing the Gaussian curvature and the clustering coefficient with a critical value to realize self-adaptive discrimination of a failure mode; finally, the failure mode and the displacement data are combined to calculate the safety coefficient and trigger early warning, accurate tracking of fracture dynamic evolution is achieved, the numerical value divergence problem of a traditional method is solved, and the accuracy of slope stability evaluation and the early warning reliability are remarkably improved.

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

The application discloses a kind of crack seepage prediction methods of combining yield criterion and network topology, belong to rock mass engineering construction field, including the following steps: topological analysis is carried out to crack network based on complex network theory, determine the degree centrality of crack network node and crack shear stress transmission coefficient;Based on Mohr-Coulomb yield criterion, establish shear stress equation, determine the criterion of crack network damage leakage according to yield criterion and seepage stress coupling relationship;Considering the degree distribution and clustering coefficient of crack network node, the damage evolution equation considering the importance of node and crack damage characteristics is established;Based on the established damage evolution equation, the percolation region of crack seepage damage evolution prediction is carried out.The application uses complex network index to identify the importance of crack network node, and carries out failure calculation on crack edge and node in combination with yield criterion and seepage-stress coupling relationship, which effectively improves the efficiency of crack network damage evolution and seepage prediction.

The application discloses a unilateral limb motor imagery task electroencephalogram classification method, which sequentially comprises the following steps: A: constructing a unilateral limb motor imagery data set and preprocessing to obtain motor imagery electroencephalogram signals; B: using a deep neural network model to extract features and classify the motor imagery electroencephalogram signals to obtain a probability distribution matrix of the electroencephalogram signals; C: using a CSP method to process the motor imagery electroencephalogram signals to obtain a time-frequency graph data set, and then using a deep neural network model to extract features and classify the time-frequency graph data set to obtain a probability distribution matrix of the time-frequency graph signals; D: calculating an average clustering coefficient and optimizing the two probability distribution matrices; and then based on D-S evidence theory and using the two optimized probability distribution matrices to make a fusion decision to obtain a final classification result. The application can effectively improve the classification accuracy of unilateral limb motor imagery tasks and provides a data basis for the development of brain-computer interface technology.

The present application belongs to the technical field of uniformity analysis, and particularly relates to a perovskite film composition uniformity analysis method and system, to solve the technical problem that traditional technology is difficult to standardize quality monitoring of composition uniformity of large-area perovskite film. The analysis method comprises: S1, collecting photoelectric response signals returned by each to-be-tested point; S2, constructing a graph model based on the spatial positions of each to-be-tested point and the corresponding harmonic entropy; S3, calculating the clustering coefficient of each node in the graph model, and multiplying the clustering coefficient of the node by the harmonic entropy corresponding to the node to obtain a perturbation vector; S4, calculating the kurtosis of the perturbation vector, and linearly combining the kurtosis with the reference parameter to obtain an index representing the composition uniformity of the perovskite film. The analysis method provides an objective and reliable evaluation basis for quality control and process optimization of perovskite film.

The application discloses a kind of network equipment early warning evaluation method and system based on SNMP, belong to network equipment state monitoring early warning evaluation technical field, method includes: using SNMP protocol and equipment OID information acquisition network equipment's key state information and store to database;Real-time early warning is carried out to key state information based on early warning filter pool, and real-time early warning result is obtained;Current data, historical data are recalled and combined with real-time early warning result, and state evaluation is carried out using improved grey clustering algorithm, with early warning result as the determination reference of index abnormal degree and the basis for grey class correction, the state evaluation result associated with early warning result is output by calculating clustering coefficient and determining grey class.The application realizes the deep fusion of early warning and evaluation, improves the accuracy and timeliness of network equipment state evaluation, provides strong support for preventive maintenance, and can be widely applied to the operation and maintenance of various network equipment.

The invention discloses a steel production modeling method based on a complex network, and relates to the technical field of industrial production process modeling. The method comprises the following steps: deconstructing the whole process of a steel production system, and abstracting three types of units including production processing, energy / medium conversion and storage and material input / output / buffer into network nodes; extracting a device physical connection and production flow transmission relation, and defining a directed edge; a directed complex network model is constructed and visualized; and calculating degree distribution, a global average path length, an improved average path length and a clustering coefficient, and verifying scale-free and small-world characteristics. According to the method, through a'basic theory layer-engineering application layer 'double-layer index system, steel production whole-process global topological representation and system characteristic quantification are achieved, model support is provided for whole-process optimization, academic preciseness and engineering practicability are achieved, and the method can be popularized to cement, thermal power and other process industrial systems.

The invention discloses a power distribution area flexible capacity expansion regulation and control method based on a topological structure, and the method comprises the steps: firstly constructing a power grid topological graph with a power supply area as a node and a power transmission and distribution line as an edge, obtaining the attributes of the node and the edge, and calculating the normalization degree, the clustering coefficient and the betweenness centrality; combining the output power, the current, the voltage fluctuation, the power supply area and the population to obtain a first confidence coefficient and a first stability, and further forming a node importance index; the method comprises the following steps: periodically collecting various real-time power supply data, fusing node attributes and importance to construct first sample data, constructing label data according to historical outage times, outage frequency, outage duration and power supply availability, and training a capacity expansion strategy prediction model; and constructing a real-time sample by utilizing rated parameters of capacity expansion equipment and real-time power supply data during operation, inputting the real-time sample into a prediction model to obtain predicted power supply data of each transformer area, determining capacity expansion priority according to the predicted power supply data, and realizing graded flexible capacity expansion regulation and control of the power distribution transformer area.

The invention relates to the technical field of electric power system safety, and particularly discloses a power grid operation risk dynamic management and control method based on an ampere-wind system, and the method comprises the steps: monitoring the spatial electric field intensity distribution in real time, and obtaining the real-time spatial distance data between members of an operation group through an ultra-wideband positioning system; performing wavelet packet transformation on the electric field data to separate power frequency and high-frequency harmonic components, and generating an electromagnetic field distortion coefficient by calculating the density and the intensity of a field intensity gradient abrupt change point; the keyword frequency of the communication content is analyzed, a personnel interaction topological graph is constructed, and a behavior coupling degree coefficient is obtained by calculating a node degree distribution entropy and a clustering coefficient; inputting the two coefficients as input parameters into a pre-trained power grid operation risk model, and outputting a power grid operation risk value; and finally, risk levels are divided according to a comparison result of the risk value and a threshold value, and early warning and management and control are carried out on high-risk operation. According to the invention, a closed-loop management mechanism from monitoring to management and control is established, and the accuracy and timeliness of power grid operation safety management and control are improved.

This invention relates to the field of complex network technology. It provides a method for identifying key nodes in complex networks based on Shannon's theorem and the Matthew effect. The method includes: acquiring the topology of the complex network and modeling it as an undirected graph; calculating the structural features of each node; mapping the node's clustering coefficient and structural hole constraint to channel noise, and mapping the node degree and clustering coefficient to channel bandwidth; quantifying the node's information propagation capability as a relay using a signal-to-noise ratio formula; calculating the node's initial information based on its degree and normalized K-shell value; iteratively calculating to give nodes with high initial information a weight gain when aggregating neighbor contributions, simulating the cumulative process of network resources tilting towards core nodes, and obtaining a Matthew effect information aggregation value; obtaining the node's final influence score based on the Matthew effect aggregation value and information propagation capability; and sorting the nodes in descending order based on the final influence score to identify key nodes in the complex network.

The application discloses a bonding encapsulation equipment health state evaluation method based on twin data, classifies and combs three major functional components of an ultrasonic welding head system, a motion control system and a machine vision system of the bonding encapsulation equipment, establishes a hierarchical structure model and a fault tree, constructs a three-level health evaluation model including a component layer, a functional component layer and a whole machine layer, divides equipment health states into four qualitative grades of excellent, good, general and observation, introduces a health degree into quantitative characterization in combination with a fuzzy mathematics theory, adopts a multi-channel neural network model based on an attention mechanism to recognize the health state of a vibration signal, adopts a grey clustering model for health indexes which are not vibration type and are difficult to directly quantitatively evaluate, calculates clustering coefficients of the health indexes belonging to various health states based on a whitening weight function, and determines the health state; and adopts an entropy weight method based on twin data driving to determine the weight of each health index, carries out grey class weighted fusion, and outputs the real-time health degree and the health state grade of the equipment. The application improves the reliability, accuracy and equipment operation efficiency of the equipment health state evaluation, shortens the maintenance time, and reduces the production cost.

The invention discloses an SG-Lasso-based depression (DP) automatic detection method, which is characterized in that a super-brain function network (HBFN) is constructed and optimized through electroencephalogram (EEG) signals, and efficient DP detection is realized. The method comprises the following steps: inputting a multi-channel EEG signal, constructing an HBFN by using SG-Lasso, establishing a mathematical model based on sparsity and stability, optimizing the HBFN at different frequency bands, extracting different types of clustering coefficients in the HBFN, and checking and evaluating differences among clustering coefficient groups through nonparametric arrangement at different frequency bands. And selecting clustering coefficients with significant differences among the groups as potential markers for DP detection, and finally verifying the effectiveness of the potential markers on DP automatic detection by adopting a classifier. According to the method, the HBFN is optimized in combination with sparsity and stability, the accuracy of DP detection is remarkably improved, and a new technical support is provided for clinical DP auxiliary diagnosis.

This invention discloses a state assessment method and system for hydropower unit speed regulation systems based on LBWA-ROCOSD-OCC-IEGC. First, a three-level indicator system—target layer, project layer, and indicator layer—is constructed. Based on a unified assessment window, samples of multi-source, heterogeneous periodic indicators are constructed and time-aligned to obtain a historical sample matrix and samples to be assessed. Then, subjective weights are determined based on the LBWA method, objective weights are extracted based on the ROCOSD method, and a comprehensive weight vector is obtained through multiplication synthesis. Next, the comprehensive clustering coefficients for each state level are calculated based on the OCC-IEGC model. Finally, the state level determination results and quantitative scoring results are output, and the mean comprehensive score, standard deviation of the comprehensive score, result stability, and result acceptance conclusion are output through repeated execution of the state assessment.

The invention discloses an artificial universal intelligent security assessment method and system based on an AHP and a genetic algorithm. According to the scheme, firstly, a data acquisition module extracts data features related to security from an AGI model; then the index hierarchy construction module establishes a safety index system based on an analytic hierarchy process; the weight calculation module calculates an initial weight and performs consistency verification; if the consistency ratio (CR) does not meet the threshold value, the genetic algorithm module executes optimization and correction; the optimized weight is input into a grey clustering module to calculate a clustering coefficient and a comprehensive safety score; and finally, the result output module generates a safety assessment report and a risk level. According to the scheme of the invention, a hierarchical structure system of AGI security risks is established through AHP, and consistency correction and global optimization are carried out by using a genetic algorithm, so that evaluation weight calculation is more objective and stable; and meanwhile, a grey clustering model and a whitening weight function are combined to realize quantitative classification of different risk types, so that a comprehensive safety score with dynamic adjustability and interpretability can be output.

The application discloses a method for generating infrastructure network attack and defense game strategy based on multi-index fusion, and has the characteristics that the method comprises the following steps: acquiring the topology structure of an infrastructure network, determining the strategy set of player 1 and player 2, and constructing an infrastructure network game model; calculating the payoff matrix of player 1 and player 2 in the infrastructure network game under various strategy profiles respectively by taking three network performance indexes of the maximum connected piece scale, network efficiency and clustering coefficient; constructing three pairs of membership functions and non-membership functions according to the preference of a decision maker; converting the payoff matrix into an intuitionistic fuzzy set payoff matrix by using the three pairs of membership functions and non-membership functions, and obtaining a final payoff matrix by using an intuitionistic fuzzy weighted aggregation operator for weighted fusion; and combining the intuitionistic fuzzy set theory to convert the solution of the infrastructure network game model into the solution of a nonlinear programming problem, so as to obtain the mixed strategy Nash equilibrium solution of player 1 and player 2.