54 results about "Clustering coefficient" patented technology

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 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 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 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 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 method for analyzing the anti-gastric cancer effect of baicalein by combining network pharmacology and Mendel randomization. The method comprises the following steps: S1, collecting potential target spots of baicalein; s2, obtaining exposure data and outcome data; s3, Mendel randomization analysis is carried out, and MR analysis is carried out by using a TwoSampleMR software package of R; s4, performing difference analysis; s5, establishing a protein-protein interaction network: importing the potential action target information obtained in the step S3 into a String database to obtain protein interaction data so as to obtain a PPI network diagram, analyzing a result by using Cytoscape Version 3.9. 1 software, constructing the protein interaction network, and screening a hub gene (TOP10) scored by Clustering Coefficient by using a Cytohubba plug-in; s6, carrying out enrichment analysis on GO and KEGG pathways; s7, molecular docking verification: taking the screened gene target as a receptor, finding a receptor 3D structure file in a PDB database, taking baicalein as a ligand, and finding a ligand 3D structure file by utilizing a PubChem database; molecular docking is carried out through AutoDock software, and a docking result is visualized through PyMOL software.

The application discloses an AI permission intelligent allocation system and method based on behavior prediction, and relates to the technical field of permission monitoring and management, and comprises the following steps: collecting a plurality of permission use data records in history; taking the collected a plurality of permission use data records in history as a base and taking a pre-defined permission unique identifier as a node to construct a permission co-occurrence graph; analyzing the shortest path between permissions according to the permission co-occurrence graph, and performing operation sequence modeling; performing irregular combination detection and permission intelligent allocation on real-time permission use data records according to the operation sequence modeling; obtaining real-time a plurality of permission use data records, constructing a real-time permission co-occurrence graph, analyzing the clustering coefficient of the system module to which the permission in the real-time permission co-occurrence graph belongs, and performing permission risk management; and visually displaying the real-time permission co-occurrence graph. The application significantly improves the dynamic nature and intelligent level of permission management.

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 discloses a battery cabin fire automatic early warning method and system, electronic equipment and storage medium, which are used for realizing early and accurate early warning of battery cabin thermal runaway. The method first collects voltage time series of the battery monomer charging and discharging process, reconstructs the phase space through mutual information method and false nearest neighbor point method, constructs a recurrence plot matrix and extracts five-dimensional chaotic features such as recurrence rate and determination rate. Relying on the battery electrical connection and space thermal coupling topology, a graph convolution network is built to calculate the abnormal score of the battery monomer. Through the sliding window combined with the first and second derivative analysis, the real abnormal monomer is accurately identified. A local anomaly propagation network is built around the abnormal monomer, the average path length and clustering coefficient of the network are calculated, and the fault diffusion trend is quantified. Finally, according to the change rate threshold of the two topological parameters, the abnormal spread degree is judged, and the fire warning signal is triggered. The method can capture early implicit voltage anomalies of the battery, and effectively identify the fault deterioration and propagation situation.

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 provides a routing method of the Internet of Vehicles, electronic equipment and a computer readable storage medium, the limitation of traditional single-hop sensing is broken through by introducing a multi-hop neighborhood potential energy model, firstly, static constraints of neighborhood density and node centrality on link stability are quantized by using inertial potential energy; the dynamic influence caused by the consistency of the traffic flow speed and direction is depicted through the fluid potential energy; the potential energy is corrected on the basis of structural characteristics such as a subnet clustering coefficient and a path length, and the local stability characteristic of the network topology is accurately reflected; and finally, aggregating multi-hop potential energy information through a graph neural network, and constructing a link stability index LSS capable of comprehensively reflecting space-time relevance and nonlinear dynamic characteristics in combination with link instantaneous physical survivability, thereby fundamentally solving the technical problem of inaccurate link stability quantization in a high dynamic scene, and improving the link stability quantization accuracy. And an accurate and reliable stability measurement basis is provided for routing decision.

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 application discloses a new energy transmission and distribution collaborative consumption intelligent control method and system, relates to the technical field of new energy, and comprises the following steps: collecting real-time monitoring data of each node in a power system and performing pretreatment; constructing a power grid complex network model according to an adjacency matrix to calculate the power weight of each power transmission line; and storing all data to a relational database and performing management. The application accurately models the nodes and edges of the power system, constructs an adjacency matrix, comprehensively reflects the power grid topology structure and the power flow of the power transmission line, accurately assesses the power grid load and the operation state, avoids the error in the traditional method, calculates the degree, clustering coefficient and extended number of the nodes, combines a propagation dynamics model, simulates the propagation process of the fault in the power grid in real time, predicts the rate and range of the fault spread, and thus realizes multi-level early warning, improves the accuracy of the power grid vulnerability assessment, and enhances the stability and emergency response capability of the power grid.