88 results about "Clustering coefficient" patented technology

The invention relates to the technical field of platform management, and discloses an internet-based financial service platform management method, which comprises the following steps: collecting static identity data, dynamic behavior data, biological characteristics and environmental parameters of a user in real time, and generating a multi-dimensional dynamic identity chain after desensitization aggregation; the attack-defense engine simulates an attack strategy by using a diffusion model, analyzes causal contradictions and topology aggregation coefficients in combination with a space-time diagram convolutional network, marks high-risk nodes, and iteratively optimizes model parameters everyday through reinforcement learning; quantum signature and alliance chain verification are adopted in the transaction link, transactions involving high-risk countries or transactions with the fund abnormal transfer probability exceeding 95% are automatically frozen, and compliance channels are switched; the biological monitoring module analyzes the physiological signal in real time and triggers a multi-mode challenge; and aggregating edge node data through a federated learning framework, and automatically rolling back to a historical optimal version when the false alarm rate of the updated global risk control model exceeds the standard. According to the invention, efficient management of the financial service platform can be realized.

The invention discloses a community fire-fighting toughness evaluation method and system based on an association rule and a complex network, and the method comprises the steps: analyzing the disaster cause of an accident case based on a community fire accident case, and determining a community fire-fighting toughness evaluation index system from four dimensions: defending, rescuing, recovering and adapting; an Apriori algorithm is adopted to mine a coupling relationship between indexes, an association rule is generated, and a community fire-fighting toughness index complex network is constructed; key factors influencing the community fire-fighting toughness are determined by calculating the degree of a community fire-fighting toughness node, the intermediary centrality, the clustering coefficient and the like; and finally, making a fire-fighting toughness improvement scheme according to the key factors. According to the method, the invisible relation between the toughness indexes can be mined, the key factors influencing the community fire-fighting toughness can be determined, the community fire-fighting toughness improvement scheme can be formulated, the problem that community fire-fighting toughness evaluation mainly depends on subjective experience at present is effectively solved, and improvement of the overall fire-fighting toughness of the community is facilitated.

The invention provides a mask-masked transverse federal heterogeneous graph learning method and device. The method comprises two stages of client local training and global aggregation. The client deploys a local model based on the initial parameters distributed by the global server, shades random shading input features by using dynamically generated masks to mine a high-order topological structure, and optimizes the topology perception capability through edge reconstruction loss; meanwhile, node attributes are randomly masked, the masking proportion is dynamically adjusted, and semantic robustness is enhanced in combination with consistency semantic loss; and jointly updating local parameters by combining the node classification loss. In the global aggregation stage, the server calculates a clustering coefficient to measure the compactness of a graph structure according to an edge set under an appointed meta path of each client, dynamically distributes aggregation weights in combination with the number of nodes, fuses client parameters rich in topological information, and iteratively optimizes a global model. According to the method, the problem of semantic inconsistency caused by dynamic change of client data can be solved, the local graph structure value is captured, and the global generalization ability and convergence efficiency are improved.

The invention requests to protect a supply chain network toughness evaluation method based on a complex network theory, and aims to evaluate and optimize the toughness of a supply chain network under different attack strategies (such as random attack and deliberate attack) by establishing an evolution model of the supply chain network. According to the method, a network model with enterprises as nodes and cooperation relations as edges is established, key parameters (such as node degree, shortest path length and clustering coefficient) in a network structure are analyzed, and the stability and recovery capability of a supply chain under external impact are evaluated. By simulating different risk scenes, the structural performance, the efficiency performance and the recovery capability of the network are calculated and evaluated. The toughness evaluation method provides a new theoretical basis and technical means for supply chain risk management, and is helpful for improving the toughness of the supply chain network.

The invention discloses a new energy transmission and distribution collaborative consumption intelligent control method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting real-time monitoring data of each node in a power system, and carrying out the preprocessing; constructing a power grid complex network model according to the adjacent matrix to calculate the power weight of each power transmission line; and storing all data into a relational database and managing the data. According to the method, the nodes and the edges of the power system are accurately modeled, the adjacent matrix is constructed, the power flow of a power grid topological structure and a power transmission line is comprehensively reflected, the load and the operation state of the power grid are accurately evaluated, and errors in a traditional method are avoided; the propagation process of the fault in the power grid is simulated in real time, and the fault propagation rate and range are predicted, so that multi-stage early warning is realized, the accuracy of power grid vulnerability assessment is improved, and the stability and emergency response capability of the power grid are enhanced.

The invention discloses a method for identifying connected damaged areas of a complex network of fractures, and relates to the technical field of rock mass fracture identification. The method for identifying connected damaged areas assigns weight values ​​to fracture network nodes according to the length of the fracture, the Euclidean distance of the fracture, and the fracture width, and determines the fracture network adjacency matrix through the node weights; then, based on complex network theory and the adjacency matrix, the degree value and clustering coefficient of the fracture network are determined, and the fracture connected damaged area is identified based on the maximum connected area algorithm of graph theory; finally, the maximum connected area of ​​the fracture is determined according to the network node degree value, the clustering coefficient, and the weights of the nodes and edges.

The invention discloses a connected damage area identification method of a fracture complex network, and relates to the technical field of rock mass fracture identification, the connected damage area identification method comprises the following steps: according to the fracture length, fracture Euclidean distance and fracture width, assigning a weight value to fracture network nodes, and determining a fracture network adjacency matrix according to the node weight; then, based on a complex network theory and an adjacent matrix, determining a degree value and a clustering coefficient of a fracture network, and based on a maximum connected region algorithm of a graph theory, identifying a fracture connected damage region; and finally, according to the network node degree value, the clustering coefficient and the weights of the nodes and the edges, determining the maximum connected region of the fracture.

The invention belongs to the technical field of power system operation, and provides a collaborative optimization method and system for a multi-region interconnected power system, and the technical scheme is as follows: determining a direct consumption coefficient matrix and a final demand vector according to the power production and consumption data of each region; combining the consumption coefficient matrix and the final demand vector to construct a power quasi input-output model constraint; analyzing a node degree, a clustering coefficient and a shortest path based on the obtained power grid topological graph, calculating an importance index of nodes based on the obtained node degree, the clustering coefficient and the shortest path, performing identification to obtain key nodes, and performing vulnerability evaluation based on the key nodes obtained through identification to obtain a transmission optimization constraint; and designing a double-layer optimization structure, introducing the power quasi input-output model constraint into upper-layer optimization, and respectively introducing the transmission optimization constraint into double-layer optimization to obtain an optimized multi-region interconnected power system collaborative optimization scheme. The impact of cross-regional power transmission on carbon emissions is evaluated.

The invention provides a medical insurance fund health monitoring method and system based on a neural network, and a medium. Comprising the steps that multi-source heterogeneous medical insurance data are collected and input into a two-channel neural network model in parallel after space-time normalization processing, a time sequence prediction channel predicts a fund sustainability index through a long and short-term memory network in combination with an attention mechanism, an anomaly detection channel calculates a medical fund abuse risk probability and a regional circulation balance degree through a graph neural network, and the medical fund abuse risk probability and the regional circulation balance degree are calculated. And finally, fusing the indexes to generate a health index, comparing the health index with a dynamically adjusted threshold value to realize three-level early warning, constructing a medical risk propagation network, generating a sensitivity coefficient based on a clustering coefficient, betweenness centrality and historical risk intensity, dynamically optimizing an early warning threshold value, and enabling the system to support anti-factual causal analysis and generate policy intervention suggestions. According to the method, the problems of insufficient medical insurance fund space-time heterogeneity modeling, neglect of a risk conduction mechanism and poor static threshold adaptability of a traditional method are solved, and accurate monitoring and active prevention and control of fund health are realized.

The invention discloses an AI authority intelligent distribution system and method based on behavior prediction, and relates to the technical field of authority monitoring management, and the method comprises the steps: collecting a plurality of authority use data records in history; the method comprises the following steps of: constructing a permission co-occurrence graph by taking a plurality of collected historical permission use data records as a substrate and taking a predefined permission unique identifier as a node; according to the permission co-occurrence graph, analyzing the shortest path between permissions, and carrying out operation sequence modeling; performing illegal combination detection and intelligent permission distribution on real-time permission use data records according to operation sequence modeling; acquiring a plurality of real-time permission use data records, constructing a real-time permission co-occurrence graph, analyzing a clustering coefficient of a system module to which the permission belongs in the real-time permission co-occurrence graph, and performing permission risk management; and visually displaying the real-time permission co-occurrence graph. According to the method and the device, the dynamic and intelligent level of authority management is remarkably improved.

The invention belongs to the technical field of offshore wind power networking sending-out scheme evaluation, and discloses a large-scale offshore wind power networking sending-out multi-dimensional comprehensive evaluation method, system and equipment based on grey cloud whitening weight clustering and a medium, so as to solve the problem that the evaluation result of the existing method is difficult to comprehensively reflect the actual performance of an offshore wind power networking sending-out scheme. The method comprises the following steps: constructing a multi-dimensional comprehensive evaluation system; performing quantitative evaluation on each evaluation index in the multi-dimensional comprehensive evaluation system by adopting a microscopic evaluation model; weighting each evaluation index in the multi-dimensional comprehensive evaluation system; constructing a grey cloud whitening weight clustering macroscopic comprehensive evaluation model; on the basis of the grey cloud whitening weight clustering macroscopic comprehensive evaluation model, grey cloud clustering coefficients, belonging to grey classes, of evaluation indexes are calculated, after normalization, the grey cloud clustering coefficients and comprehensive weights are subjected to weighted synthesis to obtain comprehensive clustering coefficients, and therefore the evaluation result of the offshore wind power networking sending-out scheme is calculated. According to the method, the evaluation accuracy and reliability of the offshore wind power networking sending scheme are improved.

The invention discloses a fracture seepage prediction method combining a yield criterion and network topology, and belongs to the field of rock mass engineering construction, and the method comprises the following steps: carrying out topology analysis on a fracture network based on a complex network theory, and determining the degree centrality of fracture network nodes and a fracture shear stress transfer coefficient; establishing a shear stress equation based on a Mohr-Coulomb yield criterion, and determining a criterion of fracture network damage leakage according to the yield criterion and a seepage stress coupling relationship; considering degree distribution and clustering coefficients of fracture network nodes, and establishing a damage evolution equation comprehensively considering node importance and fracture damage characteristics; and based on the established damage evolution equation, carrying out fracture seepage damage evolution to predict the percolation region. According to the method, the importance of the fracture network nodes is identified by using complex network indexes, and failure calculation is performed on fracture edges and the nodes in combination with the yield criterion and the seepage-stress coupling relationship condition, so that the fracture network damage evolution and seepage prediction efficiency is effectively improved.

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 an unmanned aerial vehicle cluster network node security isolation and re-access method, and the method comprises the steps: obtaining the behavior deviation attribute data of an unmanned aerial vehicle cluster node, and constructing an anomaly detection model in combination with a cluster topological structure attribute; calculating the comprehensive behavior deviation degree of each node, judging that the node is an abnormal node when the comprehensive behavior deviation degree exceeds a preset threshold value, and performing isolation processing on the abnormal node; behavior data and software and hardware state log data of the abnormal nodes are obtained, and diagnosis and restoration are carried out; obtaining state data of the repaired abnormal node, and dynamically verifying whether the node state of the repaired abnormal node is recovered to be normal or not; and the connection relation of the nodes passing the verification is adjusted according to the centrality of the nodes and the clustering coefficient, and the nodes are re-fused into a cluster topological structure. According to the invention, identification, isolation and repair of abnormal nodes of the unmanned aerial vehicle cluster and re-access to the network after security verification can be realized on the premise of ensuring network security, and the robustness, security and efficiency of the unmanned aerial vehicle cluster are improved.

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 provides a method for detecting abnormal users of a social network based on graph flow triangle counting, relates to the technical field of abnormal detection of the social network, and adopts an approximate calculation technology based on sampling to efficiently complete triangle counting of dynamic graph flow under the constraint of a limited memory. The real-time performance and accuracy of triangle counting are remarkably improved, and the real-time processing requirement of large-scale graph flow is met; according to the method, by dynamically calculating the clustering coefficient of the nodes and combining the abnormal behavior characteristics, the abnormal vertexes in the social network can be effectively identified, and the method has higher accuracy in the aspect of detecting abnormal users and abnormal social modes; according to the method, efficient distribution and parallel execution of calculation tasks are achieved through distributed calculation, the bottlenecks of single-machine memory and processing capacity are broken through, the real-time estimation precision of the algorithm is improved, and meanwhile the communication overhead is further reduced through load balancing.

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 invention discloses an airport-high-speed rail traffic infrastructure double-layer network anti-disaster toughness recovery analysis method. The method comprises the following steps: establishing an airport-high-speed rail traffic infrastructure double-layer network model, and carrying out characteristic analysis on the model; calculating relative importance of a field and a high-speed rail network layer and intra-layer importance of nodes; calculating global importance of nodes in the model; selecting airport and high-speed rail nodes with large node degree values, aggregation coefficient values and global importance values for damage to obtain failure nodes in the airport-high-speed rail traffic infrastructure double-layer network model; the model toughness is evaluated by calculating the change of the double-layer network efficiency before and after node failure; and determining an optimal recovery sequence of the failure nodes based on a genetic algorithm. According to the method, the disaster resistance toughness of an airport-high-speed rail traffic infrastructure double-layer network is effectively improved, and an analysis method is provided for improvement of the operation efficiency of comprehensive three-dimensional traffic infrastructures in China and overall allocation of recovery resources under disasters.

The invention belongs to the technical field of circuit breakers, and particularly relates to a circuit breaker health state evaluation method and computer equipment. The method comprises the following steps: obtaining a measured value of each secondary index under each primary index reflecting the health state of the circuit breaker, and calculating a deterioration degree used for judging the aging degree of the measured value of the secondary index according to the measured value of each secondary index; constructing whitening weight functions of different grey classes of the second-level indexes, and substituting the degradation degrees of the second-level indexes into the whitening weight functions of different grey classes to obtain grey clustering coefficients of the second-level indexes; and obtaining an evaluation result of the health state of the circuit breaker according to the gray clustering coefficient of each secondary index and the weight corresponding to each primary index. The accuracy of the health state evaluation result of the circuit breaker is improved, the reliability of the circuit breaker is improved, faults are avoided, and normal operation of the circuit breaker is ensured.

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 belongs to the technical field of uniformity analysis, and particularly relates to a perovskite thin film component uniformity analysis method and system, so as to solve the technical problem that the component uniformity of a large-area perovskite thin film is difficult to carry out standardized quality monitoring in the prior art. The analysis method comprises the following steps: S1, collecting a photoelectric response signal returned by each to-be-detected point; s2, constructing a graph model based on the spatial position of each to-be-measured point and the corresponding harmonic entropy; s3, calculating a 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 disturbance vector; and S4, calculating the kurtosis of the disturbance vector, and carrying out linear combination on the kurtosis and the reference parameter to obtain an index representing the component uniformity of the perovskite thin film. The analysis method provided by the invention provides an objective and reliable evaluation basis for quality control and process optimization of the perovskite thin film.

The present application discloses a graph data partitioning method, device, electronic device and storage medium based on label characteristics, the method comprising: extracting N weakly connected components from the graph data; wherein a weakly connected component comprises a descriptive label between entity objects in the graph data, and N is an integer greater than 1; calculating the clustering coefficient and the minimum non-zero eigenvalue of the first weakly connected component of any one of the N weakly connected components in M ​​preset partitions; wherein the clustering coefficient is used to characterize the label density of the first weakly connected component in each of the M preset partitions, and the minimum non-zero eigenvalue is used to characterize the connectivity of the first weakly connected component in each of the M preset partitions; wherein M is an integer greater than 0; and determining the target partition of the first weakly connected component in the M preset partitions based on the clustering coefficient and the minimum non-zero eigenvalue of each of the M preset partitions.

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 discloses a kind of distributed mass node community division methods based on graph theory, belong to social network detection field, by the random sampling of complex network relation, cut is carried out in combination with weighted logarithm GN algorithm, interval calculation module degree and distributed finite iteration, can complete 100,000+ magnitude node relation complex network cutting in short time, and WS small world verification is carried out to the community after cutting, this method can effectively overcome the defects of existing method, can ensure accurate efficient real social network detection, the present application retains logarithm weighted relationship weight and finite random iteration distributed cutting, realize accurate efficient cutting of mass complex network, using small world theory ensures that the clustering coefficient of division community meets real social network.

The invention belongs to the technical field of quality detection, particularly relates to an online quality detection method and system for casting powder, and aims to solve the technical problems of poor accuracy and reliability of a detection method in the prior art. The detection method comprises the following steps: S1, obtaining a spectral signal from which a background is deducted, and calculating the global signal-to-noise ratio of the spectral signal; s2, calculating a second derivative spectrum of the spectral signal; selecting an effective spectrum peak; s3, classifying a plurality of effective spectrum peaks smaller than a preset clustering coefficient into a spectrum peak cluster according to the ratio of the distance between the center positions of the adjacent effective spectrum peaks to the sum of the respective estimated full width at half maximum; performing synchronous fitting on each spectrum peak cluster by adopting linear superposition of a plurality of asymmetric Lorentz functions; and S4, based on the peak area of the characteristic spectral line of each element obtained by fitting, establishing a working curve between the peak area and the content of the corresponding element in the casting powder, and determining the component content of the casting powder. And the accuracy of quantitative analysis results of the element content of the casting powder is ensured.

The invention provides a matching degree calculation method and device for the development status and potential of urban rail transit multi-station associated districts and a storage medium. The calculation method comprises the following steps: acquiring urban road network, building, POI and public traffic data; calculating the global network importance of the rail transit stations, the local clustering coefficient of the rail transit stations and the transfer level of the rail and the road public transport stations, and constructing a rail transit station association index; performing standardization processing and kernel density analysis on the rail station association index, and constructing a first data set of rail transit multi-station association degree distribution; calculating a plot ratio, a function mixing degree and a road network density, and constructing a second data set of an environment aggregation state; and according to the first data set and the second data set, calculating the matching degree of the development current situation and the potential. According to the method, capturing of the dynamic boundary of the key section of the urban rail transit and evaluation and prediction of the development potential can be realized, and a basis is provided for delimiting and designing a city updating or development and construction range.

The present invention belongs to the field of network information analysis technology, and is specifically a method for generating a high-order network model based on embedding multiple small group structures. The present invention includes: given an initial matrix, obtaining an edge probability matrix through Kronecker inner product iteration, and generating an initial edge graph, obtaining the first-order degree k1 and generalized degree k of each node in the initial graph. m and maximum degree k1‑max; starting from the 2nd-order clique, select the degree-dependent function based on the characteristic attributes of the network, select nodes with smaller generalized degrees, and obtain a labeled edge graph containing labeled nodes; then generate the order m of the clique based on the first-order degree k1, and then obtain an m-order clique structure, and embed the clique structure to obtain the generated graph; gradually increase the order of the embedded clique structure, and repeat the above steps of node labeling and clique structure embedding until a k1‑max-order clique structure is embedded. This method can well simulate real networks with different clustering coefficients and characterize the high-order structural characteristics of real networks.