100 results about "Network analytics" patented technology

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

The invention discloses a city management AI dispatch algorithm and system based on historical mining and responsibility matching, and the method comprises the steps: building and dynamically updating a city management element evolution graph through obtaining the multi-mode description information of a city management case and the real-time state data of disposal resources; calculating potential disposal effects of different candidate dispatching schemes by using a causal inference engine, and generating a comprehensive efficiency estimation vector; on the basis, a multi-target reinforcement learning strategy is adopted to generate an optimal dispatch instruction, and system parameters are continuously optimized through online element learning during execution; cooperative processing network analysis is activated for sudden complex events, and responsibility atlas reconstruction is triggered when the matching efficiency is low. According to the method, the accuracy and efficiency of case disposal are remarkably improved, disposal timeliness optimization, resource load balancing and improvement of the first solution rate are realized, and meanwhile, the adaptive capacity and continuous optimization efficiency of the system to complex scenes are enhanced.

According to the privacy enhancement type (k, d)-truss community search method for the social network, a novel KTG tree structure is constructed by fusing k-truss and G-tree indexes. According to the index structure, hierarchical community decomposition of a social graph and social distance information are fused, a refined boundary vector coding scheme is designed to support efficient distance calculation, and a double-cloud-server non-collusion architecture integrating improved homomorphic encryption and matrix encryption technologies is constructed. Through a two-stage security query process of first structure filtering and then distance verification, on the premise of protecting full-process privacy of a graph structure, a query intention, a distance matrix and an intermediate calculation result, efficient and accurate search of a close community in a large-scale social network is realized. The method is suitable for various scenes such as social recommendation, risk control, public opinion analysis and anti-fraud, and the problems of privacy disclosure and calculation efficiency in social network analysis are effectively solved.

The invention relates to the technical field of data management, in particular to a project data management system for research and development of small and medium-sized enterprises, which comprises a data acquisition module for acquiring research and development project data of the small and medium-sized enterprises through a multi-source data fusion technology; the quality diagnosis module analyzes research and development data by applying a deep learning network, and automatically identifies and predicts data quality abnormity and a potential risk mode; the process collaboration module is combined with artificial intelligence and a process engine technology, and automatically adjusts research and development task processes and authority distribution according to project progress and member collaboration requirements; the resource interaction module coordinates resource allocation, sharing and recovery between the research and development project and the enterprise internal resource pool; and the decision optimization module coordinates data of each module and a research and development project target to realize collaborative optimization of project planning, resource allocation and risk control, so that the problems of data fragmentation, low flow efficiency and unreasonable resource allocation in the research and development process of small and medium-sized enterprises are solved, and the research and development efficiency and the achievement transformation capability are improved.

Systems, methods, apparatuses, and computer program products for network data analytics profiling for a user equipment (UE) are provided. One method may include obtaining data related to a UE and generating analytics for the UE based on the obtained data, generating a UE analytics profile for the UE using at least one of the obtained data and / or the analytics generated for the UE, and storing the UE analytics profile in a repository. The method may also include receiving, from a network node in the visited network, a request to retrieve the UE analytics profile for the visited network. The method may then include generating the visited network UE analytics profile, and providing the visited network UE analytics profile to the network node in the visited network.

The invention relates to the field of financial risk protection, in particular to a financial risk protection method and system based on data mining, and the method comprises the following steps: collecting enterprise multi-dimensional financial logs, carrying out the deep mining of transaction behaviors, and constructing an enterprise financial behavior map; performing multi-signal correlation degree calculation and liability structure change perception based on the enterprise financial behavior map, and marking potential fund chain vulnerabilities; upstream and downstream supply chain information of an enterprise is identified, supply chain ecological topology network analysis and risk effect calculation are carried out, and a key risk critical link is identified; carrying out multi-scene financial risk simulation on the potential fund chain vulnerability points and the critical links of the key risks, and constructing a financial risk simulation sand table; and carrying out impact bearing capability evaluation based on the financial risk simulation sand table. According to the method, the potential financial risk of the enterprise is accurately identified in advance, a protection decision is made in a targeted manner, the risk protection capability of the enterprise is improved, and enterprise assets are effectively protected.

The invention discloses an urban public facility layout optimization method and system based on multi-source space-time big data, and relates to the technical field of urban planning. Comprising the following steps: firstly, acquiring and fusing multi-source space-time big data such as mobile equipment signaling data and point-of-interest data; identifying and delimiting an urban public service three-level center system based on the fusion data through a spatial clustering and network analysis algorithm, and determining each center level and a spatial influence range; constructing a differentiated service demand quantification model for each level of service center; constructing a multi-stage collaborative layout optimization model, and completing facility hierarchical assignment and site selection optimization; evaluating the layout scheme in the simulation environment and outputting a grading evaluation report; through dynamic analysis and multi-level collaborative optimization driven by multi-source space-time big data, accurate matching of public facility layout and service requirements is achieved, and meanwhile the service coverage rate and cross-level connection efficiency are improved.

The invention relates to a node injection attack method based on adaptive target selection, and the method comprises the following steps: S1, target node selection: calculating a comprehensive score of a node based on uncertainty and topology centrality, and dynamically selecting a target node set of a current attack round; s2, feature generation: using an adaptive feature generator to generate node features which are similar to target node distribution and have strong aggressiveness; and S3, disturbance edge construction: selecting an optimal disturbance edge connection mode for the injection node according to strategy network output in reinforcement learning. According to the method, the attack flexibility can be improved through dynamic target selection, the attack performance can be remarkably enhanced through combination of disturbance characteristics and structures, and the method has good concealment, expandability and generalization ability. The method is widely applied to security evaluation and defense research fields related to graph neural networks, such as social network analysis, recommendation systems, knowledge graphs and the like.

A method for NADD-assisted dynamic configuration of HTTP parameter settings at NFs includes receiving, at the NADD, SBI message feeds from a plurality of producer NFs. The method further includes determining, by the NADD and from at least one of the SBI message feeds, an HTTP parameter setting for one of the producer NFs. The method further includes communicating, by the NADD, the HTTP parameter setting to the producer NF. The method further includes receiving, by the producer NF and from the NADD, the HTTP parameter setting. The method further includes using, by the producer NF, the HTTP parameter setting to control traffic flow from a consumer NF to the producer NF.

The application discloses a cross-department collaboration efficiency optimization method fused with social network analysis, comprising the following steps: multi-modal data acquisition and space-time labeling: collecting communication data, behavior data and physiological signal data in cross-department collaboration, adding time stamp and node identification to each data, and associating the node identification with department attributes and role characteristics in the social network; dynamic identification of cognitive bias: based on the social network node interaction data, adopting natural language processing and sentiment analysis technology to detect logical fallacy of the communication text, and generating bias type label and propagation intensity index in combination with physiological signal wave, the application improves collaboration efficiency, accurately identifies the cognitive bias propagation path in cross-department collaboration through multi-modal data fusion and social network analysis, dynamically intervenes to reduce invalid communication, shortens the task response and dispute resolution time, enhances collaboration resilience, and the space-time enhancement and critical state early warning mechanism of the propagation graph can avoid the risk of network structure imbalance in advance.

Analytics equipment (14) for a communication network (10) stores a stream (18) of data records (20) from the communication network (10). The analytics equipment (14) samples the stored stream (18T) to obtain a sampled stream (18S) that includes fewer data records (20) than the stored stream (18T). The analytics equipment (14) explores the sampled stream (18S) to identify characteristics (28) of data records (20) to be used for insight creation. Based on the identified characteristics (28), the analytics equipment (14) filters the stored stream (18T) to obtain a filtered stream (18E) that includes data records (20) with the identified characteristics (28). The analytics equipment (14) then creates one or more insights (16) about the communication network (10) using the filtered stream (18E).

The method for automatic identification, classification and development trend analysis of net red villages based on multi-source data fusion and natural language processing comprises the following steps: UGC data is crawled from Xiaohongshu and Douyin through a distributed master-slave architecture, de-duplicated based on SimHash, and normalized in time and coding format; a text semantic fingerprint is generated, and multi-level semantic cache fingerprint matching is performed; for unassigned text, its complexity is calculated, and a large language model API is adaptively called to automatically complete and extract five-level administrative divisions; weights are determined based on the analytic hierarchy process, interaction indicators such as likes, comments, collections and forwards are integrated, and a comprehensive network heat index of the village is obtained; an external text mining tool is connected, and batch word frequency analysis, semantic network analysis and sentiment tendency evaluation are performed; a document-term matrix is constructed, TF-IDF weighting is performed, and unsupervised clustering algorithm is used for clustering analysis of village characteristics; cross-dimension analysis is performed on the clustering results, and a development portrait, advantage mining and operation suggestion warning are automatically generated in combination with the SWOT model.

A model training method includes: a first network analytics function receiving first model information transmitted from at least one second network analytics function; performing computation, aggregation, or processing based on the first model information to obtain second model information; transmitting the second model information to each of the at least one second network analytics function. The second model information is used by each of the at least one second network analytics function to update a respective local model. The at least one second network analytics function is respectively configured in at least one of: a user equipment (UE), a radio access network (RAN), a network function (NF), or an application function (AF).

The invention discloses an edge graph neural network edge point modeling method based on dynamic graph optimization, and belongs to the technical field of graph neural networks and deep learning. According to the method, a graph structure containing task related nodes and auxiliary concept nodes is constructed, an iterative refining mechanism of edge point joint updating is introduced, and dynamic mutual optimization of node features and edge features is achieved. In the node updating stage, the edge features serve as weights to guide neighborhood information aggregation; in the edge updating stage, updated node features are used for recalculating edge features to form closed-loop optimization of edge guiding points and point updating edges. According to the method, the static problem of edges in a traditional graph neural network is solved, the utilization ability of the model to dynamic relations and prior knowledge is improved, and the method is suitable for complex reasoning tasks such as small sample learning, social network analysis and recommendation systems.

The invention belongs to the technical field of network analysis, and particularly relates to a crowd-sourcing high-order network critical threshold prediction method and device based on a graph neural network. The method comprises the following steps: integrating local structure features of nodes, position codes of positions of the nodes in a whole graph and parameters influencing the global state of a system, and providing comprehensive and three-dimensional cognition on the network for a model; in a network information processing level, a local and global mixed GNN architecture is adopted, and a GIN module and a global self-attention module are arranged in parallel: the former is used for accurately capturing a local neighborhood structure of a node, and the latter is used for capturing a long-distance dependency relationship in a whole graph range; by fusing the information of the two dimensions, the model can understand how the local structure affects the global dynamics more deeply. The problems that the prior art depends on dynamic evolution data, and critical threshold prediction cannot be accurately carried out on the high-order network are solved.

This application relates to the field of relational network analysis in artificial intelligence, specifically to a user differentiation method, apparatus, device, and medium based on content and network features, comprising: acquiring a social network graph; acquiring node features of each node based on the social network graph; inputting the node features into a Markov random field model to obtain a first classification result of the node; extracting content features from the content information; inputting the content features into a trained content classification model to obtain a second classification result of the content features; and determining the user type of the node based on the first classification result and the second classification result. This application combines the different characteristics of content features and network features, utilizing more comprehensive user information to detect whether users on social network platforms are spam accounts, making it less likely for malicious users to bypass the detection.

This invention discloses a molecular docking analysis method for obtaining core targets based on the RCSB database, comprising: screening drug-disease-immunity intersection targets through multiple databases; screening core targets through protein interaction network analysis and multi-topology algorithms; obtaining core target structure files from the RCSB database and downloading active ingredient structure files from the PubChem database; performing molecular docking through the CB-DOCK2 database; screening effective binding pairs using binding energy as an indicator and visualizing the results; and finally outputting the results through functional annotation and pathway enrichment analysis. This method improves the accuracy of core target screening and the reliability of molecular docking through multi-database integration, multi-algorithm collaboration, and multi-dimensional evaluation, forming a complete technical chain and providing efficient technical support for the analysis of the mechanisms of action of traditional Chinese medicine compound prescriptions.

The invention discloses a land value evaluation method and device based on a deep convolutional neural network, and the method comprises the steps: building a land value evaluation index system of a target region, building a network analytic hierarchy model, carrying out the sensitivity analysis of the land value evaluation index system, obtaining an impact factor weight matrix corresponding to the land value evaluation index system, and obtaining an impact factor weight matrix; and taking the influence factor weight matrix corresponding to the historical land value index evaluation index system of the target area as an input parameter, taking the historical land value index of the target area as an output parameter, and training the deep convolutional neural network to obtain a land value evaluation model. And obtaining an influence factor weight matrix corresponding to the land value evaluation index system of the target area planning, and evaluating the land value index of the target area planning based on the land value evaluation model. The method is suitable for the situation that the house price and land price samples are few, the correlation between the land value and the evaluation index can be accurately and adaptively constructed, and the land value is updated.

A method for network analytics data director (NADD)-informed configuration of a 3gpp-Sbi-Max-Rsp-Time header value includes receiving, at the NADD and from network functions (NFs), NF configuration details and copies of service-based interface (SBI) messages transmitted to and received by the NFs, determining service operation processing times of the NFs, and communicating, to an NF service consumer, NF analytics data including the service operation processing times and the NF configuration details. The method further includes automatically determining, by the NF service consumer and using the NF analytics data, a 3gpp-Sbi-Max-Rsp-Time header value for an SBI request message, adding, by the NF service consumer, the 3gpp-Sbi-Max-Rsp-Time header value to the SBI request message, and transmitting, by the NF service consumer, the SBI request message to a destination.

The invention discloses a social perception maximization method based on differentiatable graph optimization, and relates to the technical field of data mining and network analysis, and the method comprises the steps: obtaining social network cascade data, and constructing a weighted bigraph of a user perception event; constructing a social perception forward propagation model according to the weighted bigraph, defining an activation state variable for each user, defining a total activation amount for each event, and introducing a function to evaluate the perception quality of each event; the activation state variables of all the users form an activation vector, the process of searching the optimal activation vector is constructed into a differentiable optimization task, and the optimization target is to maximize the perceived quality sum of all the events; and according to the optimal activation vector, generating a final seed user set as an optimal solution for maximizing social perception. According to the method, the problems of local optimization and model simplification in the prior art are solved, global optimization is realized, and the user influence can be accurately modeled.

This invention relates to the field of social network analysis technology and proposes a social network sentiment evolution model that integrates user influence and activity. To address the shortcomings of existing technologies, such as inaccurate simulations and inflexible predictions in current network sentiment analysis and sentiment evolution processes, this invention provides a social network sentiment evolution model that integrates user influence and activity. The model analysis method involves: collecting text sentiment data from target users; collecting user influence data from each user; collecting the activity level of the target social network at time t; collecting the forgetting probability of each user at time t as a measure of sentiment evolution capability; establishing a dynamic model for the sentiment evolution of the social network; optimizing the evolution probability of the information propagation model based on text sentiment; and analyzing the evolution process of the dynamic model for the sentiment evolution of the social network based on the optimized information propagation model. This model is suitable for application in social network analysis.

Embodiments provides a method executed by a server computer executing a supply chain network analysis application of a supply chain model. The method includes receiving supply chain network data associated with a supply chain network having one or more supply chain nodes. The method then programmatically executes inferences on the supply chain network data using one or more machine learning models and one or more heuristic algorithms to implement descriptive analytics, diagnostic analytics, and prescriptive analytics to create and store one or more scenario prescriptions that specify one or more changes to the one or more supply chain nodes. The method performs steps for programmatically executing inferences on the supply chain network data that includes extracting one or more data features at a path level, the one or more data features indicating descriptive insights related to one or more paths in the supply chain network. The method includes a step of identifying, using one or more path-level machine learning models, one or more cost drivers of the one or more paths in the supply chain network by computing a feature score of each of the one or more data features at the path level. The method includes creating and storing, using the one or more path-level machine learning models and the feature score of each of the one or more data features, one or more digital representations of the one or more scenario prescriptions. The method includes generating and displaying one or more visualizations of one or more updated network models that implements the one or more scenario prescriptions.

Embodiments of the present disclosure disclose a method for generating a risk assessment model, comprising: preprocessing a plurality of declaration information to obtain a plurality of first entries included in the plurality of declaration information; performing graph network analysis on information related to the plurality of first entries to generate a first result; performing feature engineering processing on the plurality of first entries to generate a second result; and training a neural network using the first result and the second result to obtain a risk assessment model.

The application provides a public safety multi-source risk factor correlation identification analysis method based on a knowledge graph, relates to the technical field of risk identification, and comprises the following steps: obtaining multi-source risk factor data, extracting information from unstructured data, constructing an initial correlation network, performing feature analysis and calculating a similarity matrix, identifying closely correlated subgroups through community discovery, constructing a multi-level correlation network, analyzing a transmission path and calculating a weight, and finally generating risk warning information. The application can effectively identify the complex correlation between public safety risk factors and improve the risk prediction accuracy.

The invention discloses a subgraph editing distance calculation method based on a graph neural network, and relates to the field of graph calculation and artificial intelligence, and the method comprises the following steps: obtaining a query graph and a target graph; generating node-level and edge-level representations for the query graph and the target graph through a unified graph isomorphism encoder to capture the influence of node and edge specific editing operation on graph topology; inputting the target graph into a self-adaptive graph mask module, generating mask scores of nodes and edges through a gating attention mechanism in combination with query graph representation, shielding irrelevant parts and dynamically balancing graph scale differences; and performing multi-head mask processing on the target map based on the mask score to generate a plurality of candidate substructures. According to the method, asymmetry can be effectively processed, fine-grained structure differences are captured, calculation precision and efficiency are improved, and the method is suitable for scenes such as drug discovery, social network analysis and recommendation systems.

The invention relates to the technical field of social network analysis, and discloses a method for identifying important nodes in social network data, a terminal and a storage medium. The method comprises the following steps: acquiring social network data and calculating semantic similarity between posters; the method comprises the following steps: mapping users, stickers and topics into nodes, mapping attention, interactive behaviors and similarity relationships into edges, and constructing a multi-relationship heterogeneous graph; secondly, defining a meta path to capture high-order topological dependence, performing multi-hop neighborhood aggregation on nodes, and fusing different path characteristics by using a self-attention mechanism to obtain high-order structure characteristics; meanwhile, a unified semantic space is constructed based on a pre-training language model, and semantic embedding representation of nodes is extracted. And finally, the high-order structure features and the semantic features are deeply fused to generate joint representation, and scores are calculated based on the joint representation so as to accurately identify important nodes. According to the method, the hierarchy and potential influence of the nodes in the network can be comprehensively described, so that the accuracy and robustness of important node identification are improved.

The invention discloses a small sample learning method suitable for single power grid project investment execution risk management evaluation, which relates to the technical field of power grid investment management, and comprises the steps of establishing an evaluation index system, screening and extracting key indexes, and establishing a small sample learning method based on a semi-supervised prototype network. According to the method, a semi-supervised form of a prototype network is further defined on the basis of the prototype network, then typical annotations are generated by using kernel density estimation, and finally empirical analysis is carried out. Through screening and defining of multi-stage key evaluation indexes, in combination with social network analysis and an interpretation structure model, process type, driving type and result type key indexes are comprehensively identified; the semi-supervised prototype network and kernel density estimation are innovatively applied, so that risk classification and management can be effectively carried out under the condition of small sample data, and the actual problem of lack of big data is solved.

This invention discloses a graph mining-based questionnaire analysis method, system, electronic device, and medium, relating to the field of data processing. The method includes: acquiring questionnaire data to be analyzed; establishing a questionnaire matrix based on the questionnaire data; cleaning the questionnaire matrix to obtain a scoring matrix; calculating the similarity between different attribute scores based on the scoring matrix to establish a core attribute adjacency matrix; using attributes in the core attribute adjacency matrix as nodes in the network to establish a core attribute network; performing graph mining on the core attribute network to obtain a minimal core attribute network; and analyzing the influence of different core attributes based on the minimal core attribute network to obtain the questionnaire analysis results. This invention can reduce the influence of extreme data in questionnaires and improve the interpretability of the data in questionnaires.

The present disclosure provides a method for setting a value of an inactivity timer for transitioning between states of a data session in a network comprising a first entity and a second entity providing network analytics. The method comprises obtaining, by the second entity, input data comprising communication description information of at least one user equipment (UE), and providing, by the second entity, output analytics generated based on the input data to the first entity, the output analytics comprising UE communication analytics per data session, wherein the output analytics are used to determine whether to update the value of the inactivity timer of the data session.

Mechanisms are provided to perform application modernization through data modularization by using complex networking analysis. A static network of a data model is generated comprising nodes for database objects. Use case information is collected for use cases and community detection is performed on the static network to generate communities of database objects. A cohesion index for each community is determined and the use case information is integrated into the static model to generate a dynamic model having use case node(s) and edges representing interactions between the use case with database objects of the static model. A dispersion index is generated for each use case node and, in response to the dispersion index having a predetermined condition, the communities are dynamically optimized based on the cohesion index and the dispersion index to thereby generate a decomposed data model which is used for application modernization and / or migration.