14 results about "Cluster coefficient" patented technology

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.

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 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 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 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.

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 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.