86 results about "Pagerank algorithm" patented technology

The invention relates to the field of enterprise industry chain evaluation, and discloses an artificial intelligence-based enterprise industry chain multi-dimensional evaluation system and method, and the system comprises a data collection module which is used for obtaining industry chain data from a structured database, an unstructured text and a third-party platform; the data fusion module is used for constructing a dynamically updated industrial chain knowledge graph through a natural language processing technology; the multi-dimensional evaluation module comprises a financial health evaluation sub-module, a supply chain toughness evaluation sub-module, an innovation power evaluation sub-module and a carbon emission evaluation sub-module, and each sub-module generates quantitative scores by adopting an LSTM neural network, a PageRank algorithm and a space-time diagram convolutional network model and weights the quantitative scores to obtain comprehensive scores; the risk early warning module is used for simulating a triple conduction effect of an external event on an industrial chain based on a graph neural network and generating a risk index and a coping strategy; and the visual interaction interface dynamically displays the evaluation result. According to the method, the problems of data splitting and poor dynamic adaptability in a traditional evaluation method are solved, and accurate evaluation of the industrial chain is realized.

The invention discloses a knowledge question-answering method and system based on topic knowledge graph retrieval enhancement, and the method comprises the steps: firstly extracting a local topic represented in a triple form based on an original document through employing a large language model, carrying out the clustering, and generating a global topic triple set representing the global perspective of the whole document; secondly, on the basis of the global topic triple set, topic-guided entity and relation extraction is adopted, and a mixed knowledge graph is constructed; secondly, providing a semantic perception personalized PageRank algorithm, matching query semantics with semantics of edges in the mixed knowledge graph, and dynamically adjusting the weight of score propagation between nodes; and finally, designing a three-level progressive retrieval mechanism, retrieving multi-level information related to user query from the mixed knowledge graph, and inputting the multi-level information into the large language model to generate a final answer. According to the method, the semantic integrity and retrieval precision of the knowledge graph are remarkably improved, and the accuracy, comprehensiveness and enabling performance of generated answers are ensured.

The invention provides an occupational development recommendation method based on a school friend relation chain and dynamic employment data, and the method comprises the following steps: integrating the academic data of a school student database and the dynamic employment data of an external recruitment platform, and achieving the credible storage and updating of a school friend occupational trajectory through a block chain technology, analyzing the unstructured text by using a natural language processing technology and extracting a standardized field; a dynamic time window model is constructed, employment information is graded and weighted according to data timeliness, a path clustering algorithm is adopted to analyze occupational transition trajectories of the same professional school friends, and three types of typical development mode libraries of a technical deep ploughing type, a management transition type and a cross-industry transition type are generated; constructing a school friend relation graph from hierarchy, industry and geography dimensions, analyzing position promotion rate and regional aggregation characteristics, identifying key inward-pushing nodes through an improved PageRank algorithm, and labeling associated enterprise resources; and constructing a four-dimensional target model based on the post matching degree, the school friend association strength, the development potential and the salary growth space.

The invention discloses an intelligent reasoning knowledge base construction method based on a knowledge graph, and relates to the technical field of government affair information processing and intelligent reasoning. Accurate extraction of strategy elements and formalized expression of logic relations are realized by constructing a structured strategy clause knowledge graph; a reliable data basis is provided for conflict detection; a method of combining a self-adaptive threshold mechanism and constraint satisfiability solution is adopted, so that the accuracy of conflict detection is ensured, and the method can adapt to the language characteristics and time evolution characteristics of a strategy text; clear decision support is provided for a strategy making department by establishing a complete conflict report generation mechanism; the timeliness and the consistency of the knowledge base are ensured by an incremental graph updating and version management function, and the department cooperation capability is further enhanced by the construction of the government affair responsibility sub-graph; the application of the multi-task learning model improves the intellectualization level of demand processing, and the personalized PageRank algorithm realizes accurate department recommendation.

The invention discloses a lateral movement detection method and device for deploying a time sequence perception graph neural network based on edge optimization, and the method comprises the steps: S1, capturing a host authentication log in real time through a log collection agent deployed on edge equipment, carrying out the event filtering and key field extraction through a rule matching engine, and forming a standardized entity data set; s2, designing an authentication graph structure by adopting an ontology graph modeling language, and definitely defining node types of a user, a host, a process and the like and a time sequence edge type containing a timestamp and an authentication type attribute; s3, mapping the entities into heterogeneous nodes based on a dynamic graph modeling technology, and constructing a complete transverse movement heterogeneous authentication graph through weighted time sequence edges; s4, importing the constructed authentication graph into a lightweight graph database Neo4j Embedded, and realizing dynamic topology maintenance by adopting an incremental PageRank algorithm; s5, defining each identity verification event as a multi-tuple structure, designing a time perception sub-graph generator, performing optimized and accelerated local neighborhood sampling by setting a time window, and generating a time sequence sub-graph sample required by training; s6, constructing a multi-scale attention coding framework, and integrating a local graph attention network and a global Transform encoder to realize multi-level feature fusion; and S7, aiming at the resource limitation of the edge equipment, implementing model cutting, a hierarchical quantification strategy and a streaming sub-graph loading mechanism, and ensuring the efficient operation of the system in a resource-limited environment.

The invention provides a large-scale knowledge graph visualization method and system, and relates to the field of knowledge graph visualization. The method comprises the following steps: acquiring knowledge graph data, and clustering the knowledge graph data through a modularity-based discovery algorithm; obtaining each sub-graph corresponding to the clustered knowledge graph data, and for each sub-graph, selecting a representative node based on a PageRank algorithm or a Leader Rank algorithm; carrying out force-oriented layout on the clustered knowledge graph data through a tree diagram space filling technology; and for the knowledge graph data subjected to the force-oriented layout, distributing priorities and use times of a Barnes-Hut algorithm and a random vertex sampling algorithm according to a preset mode, and dynamically displaying a visualization result in a layered manner through an affine transformation technology. According to the method and the device, the problem that the structural expression clarity of the drawn knowledge graph is greatly reduced due to the fact that semantic clusters and hierarchical organizations in the graph are difficult to accurately present in a traditional visualization method is solved.

The invention discloses a clothing style accurate matching distribution method and system, and relates to the technical field of clothing personalized recommendation, and the method comprises the steps: collecting a clothing matching coupling data set, carrying out the spatial-temporal feature decoupling, forming a style selection feature matrix, carrying out the semantic clustering analysis and manifold space mapping of the style selection feature matrix, and outputting a clothing style selection label. Inputting the clothes style selection labels and the user behavior data into a collaborative filtering recommendation engine, executing label similarity calculation and user behavior modeling, generating a clothes matching candidate set, and performing behavior frequency weighting and attribute preference coupling on the clothes matching candidate set to obtain behavior-preference coupling weight parameters. According to the invention, the clothing style selection label is generated through manifold mapping, and the personalized ability of clothing matching and distribution is improved. And meanwhile, through a space-time cooperation conversion rate estimation model and an improved PageRank algorithm, the matching accuracy of the recommendation result and the actual demand of the user is improved, and full-link accurate matching distribution of the clothing is realized.

The invention relates to the technical field of industrial chain analysis, and discloses an industrial chain key hub identification method based on social network analysis, comprising the following steps: collecting multi-source heterogeneous data and constructing an industrial chain node incidence matrix, the data including enterprise industrial and commercial information, transaction records, patent cooperation data and social interaction records; based on a directed weighted heterogeneous network model, mapping industrial chain nodes into network vertexes, mapping association among the nodes into directed edges with weights, and constructing an industrial chain network; calculating a node global influence score by using an improved PageRank algorithm, and identifying a community core node in combination with a Louvain community discovery algorithm; and fusing the global influence score and a community core node result, and determining an industrial chain key hub. The industrial chain key hub identification method based on social network analysis aims to accurately identify key hub nodes by constructing an industrial chain network model and combining a network topology structure and dynamic interaction data.

The invention discloses a double-high power distribution network harmonic collaborative governance method based on cloud edge collaboration and dynamic partitioning, and belongs to the field of power harmonic governance, and the method comprises the steps: constructing a time-varying harmonic collaborative governance architecture based on cloud edge collaboration, which comprises a cloud server, an edge server and governance equipment; constructing a derivative network link matrix; an observation node is selected by adopting an improved PageRank algorithm based on a derivative network link matrix; according to the harmonic state of the observation node and the state information of the governance equipment, a two-stage dynamic partitioning method is adopted to realize dynamic partitioning of a governance area of the governance equipment; a multi-time scale cloud edge collaborative optimization governance strategy is adopted to optimize governance parameters based on a dynamic partitioning result; and based on the treatment parameters of the long time scale, an improved NSGA-II multi-objective optimization algorithm is adopted to further dynamically correct the treatment parameters on the short time scale, and based on the treatment parameters optimized by the cloud server in the multi-time scale, the edge server controls the treatment equipment to realize time-varying harmonic collaborative treatment according to the treatment parameters. According to the invention, decentralized, whole-network and time-varying wave efficient, real-time and collaborative treatment of the double-high power distribution network can be realized.

The invention discloses an intelligent management system and method applied to an evidence pollution platform, and relates to the technical field of intelligent management.The method comprises the steps that historical data of the platform are collected and preprocessed, and then an evidence value evaluation model and a pollution risk evaluation model are constructed based on the historical data; the evidence value evaluation model is fused and combined with a PageRank algorithm and a weighted linear regression algorithm to generate a comprehensive evidence value score; the pollution risk assessment model is combined with a graph neural network algorithm and a random forest algorithm to output a comprehensive pollution risk score; the two scores are mapped to a two-dimensional coordinate system, the evidence value is taken as a horizontal axis, the pollution risk is taken as a longitudinal axis, four quadrants of high value-high risk, high value-low risk, low value-high risk and low value-low risk are divided, a dynamic evidence value-pollution risk double-coordinate decision-making mechanism is formed, and different processing is carried out.

The invention relates to the technical field of big data network communication, and discloses a big data network communication coordination method and device, and a storage medium. The method comprises the following steps: acquiring original communication data, cleaning and classifying the original communication data, and generating an initial coordination scheme; generating a parallel influence data set based on entropy quantization and a PageRank algorithm; generating a bandwidth demand value through normalization processing and dynamic correction; executing multi-level threshold judgment and selecting a content optimization or data compression strategy; integrating the initial scheme and the correction parameters to carry out strategy fusion and simulation evaluation, and generating a final coordination scheme; and monitoring a resource state through a protocol and generating a feedback optimization parameter. According to the invention, the data processing efficiency and the resource coordination capability of network communication are improved, and the system adaptability and the transmission stability are enhanced.

The invention discloses a network fault prediction method, and relates to the technical field of fault prediction. The method comprises the following steps: constructing a cross-level semantic topology model; screening a key link by using a node importance algorithm and extracting a structural fingerprint vector; calculating the deviation degree between the current link and the structure fingerprint vector, and judging that the structure is abnormal if the deviation degree is smaller than a threshold value; constructing an anomaly propagation rule base according to historical faults and operation and maintenance data; when a plurality of structure anomalies appear at the same time, the conflict resolution algorithm determines an anomaly dominant factor; potential signals are collected according to the abnormal dominant factors, weights are given according to a rule base, and weighting is carried out to obtain comprehensive potential signals; based on this, a fault propagation path is constructed, and a path weight is calculated by using a Pearch ranking algorithm; a fault chain is rehearsed through a depth-first search graph traversal algorithm; and verifying the rehearsal chain by using a preset test statistic circumstantial evidence to obtain a fault prediction result. According to the invention, on the basis of the fault prediction result, cross-level network fault prediction in network propagation is realized.

The invention relates to the technical field of data retrieval, in particular to a meat detection data management system and method, and the system comprises a data model dynamic adjustment module, a data relation mapping module, a database architecture evolution module, a data merging optimization module, a history tracking and analysis module, a data integrity recovery module and a system performance optimization module. According to the method, the decision tree algorithm and the Bayesian network play a key role in dynamic adjustment of the data model, the optimal data model structure is effectively recognized, the graph database technology and the PageRank algorithm are used for constructing and optimizing the data relation network graph in data relation mapping, the effectiveness of the data storage structure is enhanced, and the data storage efficiency is improved. The clustering analysis method and the K-means algorithm improve the efficiency of data processing, the data recovery algorithm and the transaction log analysis technology ensure the accuracy and integrity of data in data integrity recovery, and the overall performance and stability of the system are remarkably improved through reasonable resource allocation and load balancing of the system monitoring and tuning strategy.

The application discloses a power grid power transmission section identification method based on an improved PageRank algorithm and network conversion, first, an improved PageRank algorithm is used to calculate a node criticality index, and a network constraint coefficient is calculated according to a network topological structure and is used to correct a restart vector; second, a key power transmission section search topological graph is constructed through network conversion and node criticality index calculation, and identification of the key power transmission section is converted into search of a shortest path; finally, according to energy transaction between a generator node and a load node, a search range of the key power transmission section is set, a related cut set with the highest criticality is found, and through checking and correction, an identification result of the key power transmission section is obtained. The application can well improve the deficiency of the original PageRank algorithm when applied to the power grid, can accurately identify the power transmission section of the power grid, provides a key monitoring guide for power grid monitoring and dispatch personnel, and better guarantees the safe and stable operation of the power grid.

The application relates to the computer technology field, in particular to a processing method and device of an open source component, electronic equipment and a storage medium. The method comprises the following steps: receiving an open source project to be processed; the open source project comprises a plurality of open source components; core developer determination processing is performed on the plurality of open source components, and a core developer corresponding to each open source component is determined; based on a preset webpage ranking Pagerank algorithm and the core developer corresponding to each open source component, analysis component centrality determination processing is performed on each open source component, and a target core open source component is obtained; the analysis component centrality determination processing is used for comprehensively scoring the core developer corresponding to the open source component and a dependency relationship, and the target core open source component meeting a condition in comprehensive scoring is screened out. In this way, through refined analysis processing on the core developer in each open source component and the dependency relationship, the target core open source component of the open source project can be accurately determined.

This invention discloses a PageRank-based agent discovery method and system. The method involves initializing the agent discovery system's agent list, comprehensive scoring weight parameters, and system configuration; associating basic information with agent skill information to form a skill candidate list; calling an API to generate semantic vectors for candidate skills and task descriptions in the skill candidate list through semantic pre-screening, calculating cosine similarity as the basic matching degree, adding a matching bonus score to the basic matching degree to obtain a semantic enhancement score, and sorting the semantic enhancement scores; calculating and weighting the PageRank score, call frequency score, call success rate score, and recent activity score; sorting the scores and returning the top M results to the user. The method models the agent's historical call records as a weighted directed graph and uses the PageRank algorithm to quantify the global importance of nodes, transforming the retrieval process into a graph traversal guided by a "importance-relevance" dual-factor approach, significantly reducing the inclusion rate of irrelevant results.

The invention discloses a knowledge graph semantic enhancement generation method based on a large language model, and the method comprises the steps: evaluating the importance of an entity in a knowledge graph, calculating the structural importance of the entity in the knowledge graph based on a PageRank algorithm, extracting and capturing the feature of the entity and a complex semantic relation through the semantic importance, taking a PageRank value as an additional feature, and carrying out the semantic enhancement generation of the knowledge graph based on the PageRank value. Fusing with a graph attention mechanism to evaluate entity importance; dividing the entities into two levels according to an entity importance evaluation result and a knowledge graph long-tail distribution feature; carrying out multi-hop entity expansion and K-neighbor relation search on the entities of the important hierarchy to retrieve and obtain entity associated sub-graph information, and carrying out sub-graph simplification optimization structure overhead on the retrieved important entity sub-graph information; different Prompt templates are adopted for entities of different important levels to generate differentiated entity description content. According to the method, the entity description generation cost is reduced, and the problem that the entity of the knowledge graph lacks semantic description information is solved.

The invention relates to an online community creator feedback prediction method and system based on a dynamic reputation graph and text analysis. Relates to the technical field of feedback prediction. The method comprises the following steps: S1, acquiring and preprocessing historical interaction data containing comment texts, user IDs, timestamps and like and treading records; s2, constructing a like and treading double-view interaction map; s3, segmenting the data according to the day, and calculating a daily positive and negative reputation value through a PageRank algorithm; s4, introducing a time decay function, and carrying out weighted summation on the daily granularity reputation value in the close time window to obtain a dynamic reputation; s5, text semantic features are extracted through RoBERTa, the text semantic features and the dynamic reputation features are spliced and fused, and the full-connection neural network is input to output the like or treading probability. The method gives consideration to positive and negative reputation and time dynamics, makes up for the defects of plain text prediction, remarkably improves the feedback prediction precision, especially optimizes the click behavior prediction effect, and can provide support for community atmosphere guidance and network violent early warning.

The application relates to a knowledge graph question answering method combining a large model and a graph neural network, comprising a question decomposition module, a sub-question solving module and an answer reasoning module, wherein an iterative question decomposition method is proposed in the question decomposition module to solve the difficulty that the size of a subgraph in a multi-hop question cannot be determined by using a graph neural network method, the sub-question solving module is composed of a subgraph retrieval stage and a reasoning path construction stage, a similarity definition method is given in the subgraph retrieval stage, and a Personalized PageRank algorithm is used for calculation, and in order to improve the accuracy of GNN answers, the subgraph of a subsequent question is combined with the subgraph of a previous question to realize dynamic expansion of the subgraph. In the reasoning path construction stage, a candidate answer is obtained by using a pre-trained graph neural network, and a BFS algorithm is used to construct a reasoning path. Finally, the rationality of the reasoning path is judged by using the answer reasoning module to give a sub-question answer. The application provides a knowledge graph question answering framework combining a large model and a graph neural network, and effectively improves the performance of the model in a multi-hop question.

The invention provides an operation activity multi-account cheating identification method and system based on graph propagation. The method comprises the steps that a client side collects user behavior data and reports the data, a server side constructs a heterogeneous user relation graph based on the data and gives a cheating risk weight to an edge, the cheating probability is calculated by using a PageRank algorithm based on a seed blacklist user and is subjected to hierarchical disposal, and the client side dynamically adjusts operation activity interaction logic according to a hierarchical result; according to the system, the method is realized through cooperation of the client SDK module, the server graph construction and propagation module, the hierarchical decision module and the client anti-cheating execution module. According to the method, the defects that an existing supervision model is high in labeling cost, large in recognition delay and incapable of achieving front-end real-time intervention are effectively overcome, and the method is scientific, efficient and good in transverse transplantation capacity.

The invention discloses a self-adaptive multi-modal entity disambiguation method and system for enterprise big data, and relates to the technical field of enterprise big data analysis. The method comprises the following steps: constructing an influence graph by extracting stock right relation data and guarantee relation data, and screening core nodes by adopting a PageRank algorithm; adaptively adjusting the influence propagation weight based on the relation strength to obtain node score distribution; recognizing a fracture relationship in combination with historical name change, and repairing the atlas through multi-modal feature similarity; conflict intensity distribution is generated by comparing node score changes before and after restoration, and time sequence optimization is carried out on attenuation parameters in combination with enterprise business transformation events; and finally, enterprise entity identity determination is completed based on the optimized conflict intensity distribution. The entity disambiguation accuracy and stability in a complex enterprise network can be improved.

The present application relates to the technical field of teacher teaching, especially to a knowledge point sorting method based on multi-dimensional information. The steps are as follows: constructing initial knowledge point information through knowledge point acquisition; constructing a statistical basic graph through the frequency of knowledge points in the initial knowledge point information; sorting according to the textbook directory hierarchical structure and statistical information weighting; and constructing a machine learning classification algorithm model based on simple machine learning logistic regression. The present application provides a knowledge point sorting method based on multi-dimensional information, which comprehensively considers the importance of knowledge points based on multiple dimensions of data through statistical information arrangement, and at the same time constructs a classification model to classify the importance of knowledge points in business; the PageRank algorithm is used for accurate scoring, which ensures that the knowledge point directory structure is quickly interacted with the subject professional personnel and the model is continuously iterated, improves the labeling efficiency, and at the same time ensures the accuracy of the knowledge point sorting.

The invention discloses a power supply chain collaborative optimization method and device based on a knowledge graph, and a storage medium. The method comprises the following steps: performing knowledge extraction on heterogeneous data of a whole link of a power supply chain to obtain a first entity in a power field and a relationship between the entities; performing entity alignment on the first entity to obtain an aligned second entity; constructing a power supply chain knowledge graph based on the first entity, the relationship between the entities and the second entity; constructing a collaborative scheduling model; based on the power supply chain knowledge graph, calculating an equipment risk value of each equipment by using an improved PageRank algorithm; based on the power supply chain knowledge graph, performing output prediction by using a graph attention network model to obtain an output prediction result of an output node in the target area; and solving the collaborative scheduling model based on the target area, the target demand, the output prediction result and the equipment risk value to obtain a target scheduling decision. According to the invention, the real-time data intercommunication of the power supply chain is realized, and the collaborative optimization efficiency is improved.

The invention discloses a vacuum carburizing furnace fault propagation path identification method based on a stress-strength interference model, and the method comprises the steps: carrying out the construction of a layered topology directed graph of a vacuum carburizing furnace through employing a DEMATEL / ISM method, and obtaining a fault propagation path between subsystems; and a PageRank algorithm is used to solve the fault influence degree between the equipment subsystems. Mathematical modeling is carried out on the propagation intensity corresponding to the fault propagation path obtained by the layered topology directed graph, and the importance degree of the path is quantified by utilizing the propagation intensity numerical value. For any fault path of the vacuum carburizing furnace, the fault propagation intensity is represented by three variables, namely the fault influence degree, the fault probability of the subsystems and the edge betweenness of the path, and the propagation intensity is defined from three aspects, namely the mutual influence degree of the subsystems, the fault probability characteristic and the statistical characteristic. According to the method, the physical process of equipment failure is fully considered, and a new mathematical model is constructed for the reliability and the failure propagation strength of the vacuum carburizing furnace.

The invention discloses a power system key hub identification method based on degree centrality and density. The method comprises the following steps: S1, defining and calculating a degree centrality index based on the number of branches directly connected with nodes; s2, based on the degree centrality indexes, performing static structure inspection on the nodes, and screening out the nodes of which the degree centrality indexes meet a preset reliability standard; s3, weighting parameters are set based on node voltage levels, and weighting processing is carried out on the degree centrality indexes; s4, evaluating the degree that the node is referenced by other nodes by adopting a PageRank algorithm; and S5, fusing the weighted degree centrality index and the PageRank value to generate a comprehensive key index, sorting all candidate key nodes, and outputting a key hub identification result. According to the method, the comprehensiveness and accuracy of key hub node identification are remarkably improved, and a comprehensive and accurate decision basis is provided for differential prevention and control and planning of a power grid.

The invention discloses a historical painter dynamic relation network calculation method for historical learning, which comprises the following steps: firstly, obtaining multi-source ancient literature basic data and event information, realizing data enhancement and event expansion through a large language model, filling a data gap and removing repeated data; constructing a basic relationship strength model (BaseWave) by adopting Beta distribution, constructing an event pulse model (EventPulse) by adopting a Gaussian pulse function, and determining the initial influence of the figure in combination with an extended dynamic PageRank algorithm; splitting the positive and negative relation intensities, carrying out vectorization calculation, and describing the dynamic propagation intensity of the social relation through an adjacent matrix; and finally, outputting dynamic network data including relationship evolution, influence change and culture carrier association. According to the method, the crossing of the relation strength from a static label to a spatio-temporal dynamic curve is realized, the social link effect of a short-term culture interaction effect and a culture carrier is quantified, and a scientific and efficient quantitative analysis tool is provided for history learning and literature creation.

The invention provides a community recommendation method based on interest induction propagation. According to the method, the accuracy and reliability of group recommendation are improved. The method comprises the following steps: firstly, carrying out preprocessing, interest intensity calculation and interest stability calculation on interest tags of community users, and then carrying out social behavior analysis; thirdly, calculating the self-sensing trust degree and mutual sensing trust degree of the user, obtaining the interest sensing trust degree through fusion calculation, building a trust degree network according to the interest sensing trust degree, calculating the interest influence, simulating the influence of behavior interaction in the community on the interest of the user by utilizing a sorting algorithm, and in the trust degree network, calculating the interest sensing trust degree of the user; and simulating a transmission process of interest influence by adopting a PageRank algorithm, calculating group interestingness in the community, and realizing community information recommendation through the group interestingness. According to the method, individual selection and group influence factors of community user interest decision making are innovatively combined, and the accuracy and reliability of group recommendation are remarkably improved.

The invention discloses a power network-communication network coupled node state evaluation method and system and a storage medium, which consider the topological relation between nodes in the network, realize the topological importance calculation of power and communication nodes by constructing an internal weighted incidence matrix and combining with an improved PageRank algorithm, and provide an effective basis for subsequent node state evaluation. According to the method, the coupling relation between a power network and a communication network is considered, the cross-network coupling relation is captured by constructing a cross-network coupling matrix and iteratively updating matrix elements in combination with the node states of the two networks, and the vacancy of a traditional method in coupling evaluation of the node states is made up. And calculating node state scores of the power network and the communication network by combining a multi-dimensional quantization and graph convolutional neural network method, and finally screening out weak nodes of the network. Network node state evaluation can be realized by integrating multi-dimensional features, and the accuracy and reliability of node state evaluation are improved.

This invention provides a method, apparatus, equipment, and storage medium for identifying bill intermediaries, relating to the field of data processing technology. The method acquires bill data and behavioral characteristics of bill intermediaries, and based on this data and characteristics, generates a knowledge graph of the bill intermediaries. This graph can uncover typical behavioral characteristics of the bill intermediaries, establishing a behavioral network. Using this knowledge graph and the PageRank algorithm, the method can obtain the bill discounting institutions and their PageRank values. Target bill discounting institutions with PageRank values ​​greater than a preset threshold are identified as suspicious bill intermediaries. This allows business personnel to further audit these suspicious institutions, improving the accuracy of the judgment and significantly reducing labor and time costs.

The application discloses a kind of intelligent reasoning knowledge base construction method based on knowledge graph, it is related to government affair information processing and intelligent reasoning technical field, the present application realizes the accurate extraction of policy element and the formalized representation of logical relationship by constructing structured policy clause knowledge graph, provides reliable data basis for conflict detection;Adopt the method that adaptive threshold mechanism and constraint satisfiability solution are combined, both ensure the accuracy of conflict detection, and can adapt to the language characteristics and time evolution characteristics of policy text;By establishing complete conflict report generation mechanism, clear decision support is provided for policy making department;Incremental graph updating and version management function ensure the timeliness and consistency of knowledge base, and the construction of government affair responsibility sub-graph further enhances department cooperation capability;The application of multi-task learning model improves the intelligent level of appeal processing, and personalized PageRank algorithm realizes accurate department recommendation.