162 results about "Information propagation" patented technology

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to the field of enterprise risk assessment, and discloses an enterprise risk assessment method and system based on a large language model and high-frequency graph convolution, and the method comprises the steps: S1, building an enterprise risk association graph: generating a structured joint weight between enterprises, and building the enterprise risk association graph; fusing the semantic weight of the large language model and the structured joint weight into a final edge weight; jointly inputting the fused risk map and the multi-modal feature into two-stage attention to generate a multi-modal attention feature; and S2, enterprise risk assessment: on the enterprise risk association graph endowed with the edge weight, carrying out information propagation and aggregation by adopting high-frequency graph convolution. According to the method, edge-level gating and fusion edge weight guide risk information to be preferentially diffused along a more credible and more relevant path, high-frequency signals such as financial mutation, negative news and major events can be quickly captured, and blind diffusion of the information in a network is reduced. And the amplification effect of market systematic impact is reduced.

The invention discloses a recommendation method based on semantic enhancement and a heterogeneous hypergraph network. The recommendation method comprises the following steps that semantic information in an explicit feedback text is coded and serves as an auxiliary signal of a recommendation task; classifying the articles into predefined categories by using LLM, constructing article-category association, and mining a potential co-occurrence relationship of the articles; constructing a heterogeneous hypergraph network; spreading and aggregating hypergraph information; performing semantic alignment and model training; and performing recommendation calculation based on the final representation of the user and the representation of the article, and outputting a recommendation result. According to the method, through technical paths of semantic coding, hypergraph modeling, information spreading and alignment supervision, comment semantics of LLM coding are aligned to the recommendation space through GAE, and the problem of degradation of LLM representation in the recommendation space is effectively solved.

The invention discloses a multi-modal fine-grained semantic alignment method and device based on a graph neural network, and relates to the field of multi-modal deep learning. Firstly, deep feature extraction is performed on input multi-modal original data, and then word-level text features and local image features are constructed into a cross-modal graph structure. And performing weighted aggregation on node neighborhood information of the cross-modal graph structure through the graph attention network. And finally, carrying out weighted fusion on the text alignment features and the image alignment features. According to the cross-modal feature fusion method, the word-level text features and the local image features are uniformly abstracted into the graph structure nodes for refined alignment, a more accurate cross-modal semantic corresponding relation can be captured, the heterogeneity problem in expression modes and semantic structures is effectively relieved, and the accuracy and reliability of cross-modal feature fusion are improved. The graph attention network can adaptively adjust the weight distribution of information propagation, highlights the effect of key features in the alignment process, and ensures that the model makes full use of important semantic relationships.

The invention belongs to the technical field of pipeline safety assessment, and discloses a multi-dimensional information interconnection and autonomous evolution collaborative pipeline full-state safety assessment method. And capturing a high-order relationship of data through double hypergraph reasoning of the instance-level hypergraph and the modal-level hypergraph to realize efficient interconnection. According to the method, mode-level and instance-level hypergraph information features are extracted through hypergraph information propagation, high-order correlation is mined through double-graph information aggregation, cross-mode and cross-instance consistency information and exclusive information are output after feature recombination, multi-dimensional data deep fusion is promoted, and high-quality data support is provided for follow-up pipeline full-state safety assessment. A two-stage autonomous evolution mechanism of intra-class progressive calibration and inter-class knowledge migration is respectively adapted to slight fluctuation and significant change scenes of the deep sea environment: precise adaptation of environment perturbation is realized through dual-branch feature extraction and dynamic weight adjustment in a domain; model parameter dynamic optimization is completed between domains through spatial-temporal feature clustering and cross-domain knowledge migration, and dynamic environment self-adaption can be achieved without manual intervention.

The invention relates to the technical field of thermal power plant abnormal data processing, in particular to a thermal power plant production monitoring system network security data set abnormal data identification and cleaning method, which comprises the following steps: mapping a spatial-temporal feature vector to a preset thermal power plant multi-level causal graph to obtain a data causal graph; performing information propagation and node updating on the data causal graph, and calculating an abnormal score of each node in a preset thermal power plant multilevel causal graph; identifying abnormal data according to the abnormal score and the network security data set; and cleaning the abnormal data according to the abnormal type and a preset data cleaning strategy. According to the method, correlation modeling of data on time and a system topological structure is realized through a multi-level cause and effect graph; an abnormal score is calculated through information spreading and node updating, and an abnormal event in the network security data can be accurately identified; the abnormal data is cleaned based on the preset data cleaning strategy, redundant, wrong and abnormal data can be effectively removed, and meanwhile key risk information is reserved.

The invention relates to the field of network public opinion management and control, in particular to a cross-modal network emergency command system based on a network space model. The method comprises the following steps: acquiring a network public opinion multi-modal data stream, positioning a malicious public opinion propagation source account based on the network public opinion multi-modal data stream, and performing network space cross-layer holographic portraying based on the malicious public opinion propagation source account to generate a source account holographic portraying information set; on the basis, multi-modal feature extraction and network space behavior model construction are carried out on hidden negative guidance behaviors, and an implicit negative information propagation mode model is generated; on the basis, potential malicious account active tracking and risk propagation network mining are carried out, and a potential risk propagation network information set is generated; on the basis, multi-level differential intervention strategies are generated and executed, and a network public opinion intervention strategy instruction set is generated. In the network public opinion emergency command process, pollution of false information to network space is reduced, and construction of healthy and sustainable network ecology is assisted.

The invention relates to the technical field of social network information propagation prediction, in particular to an event propagation prediction method based on group influence. The method comprises the following steps: constructing a social network graph of target event participating users, calculating an activation probability matrix among the users by using historical propagation data, and performing social graph structure enhancement based on a threshold value to obtain a de-noised social graph; applying a graph neural network on the de-noised social graph and introducing information bottleneck constrained self-supervised contrast learning optimization node embedding, and reserving features useful for group division and propagation prediction; dynamically dividing user groups related to propagation according to the similarity and propagation context of de-noising node embedding to form a group-level propagation sequence; based on the group representation, utilizing an attention mechanism and a group relation graph to carry out modeling on propagation influence among the groups to obtain global group representation of the event; global group representation is combined with candidate user embedding, propagation probability distribution is calculated, and future participating users of corresponding events are predicted.

The invention discloses a geometric topology fusion-based part assembly method, which comprises the following steps of: acquiring boundary representation models of at least two to-be-assembled CAD parts, and converting the boundary representation model of each CAD part into a structured heterogeneous geometric topology graph; performing feature coding on each node in the heterogeneous geometric topological graph to form node feature representation; inputting the node feature representation into a graph attention reasoning module, and obtaining a node embedding representation representing a geometrical relationship and a topological relationship in the CAD part through multi-layer residual graph structure information propagation and feature aggregation; based on the node embedding representations of the different parts, calculating association scores of node pairs among the different parts; and performing supervised training on the feature coding module and the graph attention reasoning module by using the labeled assembly constraint sample data. According to the method, fine geometric features can be extracted from a boundary representation B-Rep model, and a complex topological dependency relationship is inferred, so that robust assembly constraint inference is realized.

The invention provides a public opinion thermodynamic statistical method and system based on multi-AI agent collaboration, and the method comprises the steps: collecting public opinion data from a plurality of heterogeneous data sources, and carrying out the cleaning and standardization processing of the public opinion data, and obtaining the standardized public opinion data; identifying public opinion transaction events in the standardized public opinion data; for a public opinion transaction event, constructing a multi-dimensional thermal factor based on a large language model; wherein the multi-dimensional thermal factors comprise a volume factor based on an information propagation map, an emotion intensity factor based on text fine-grained emotion analysis and a source weight factor based on information source influence evaluation; according to the characteristics of the public opinion transaction event, configuring weights of the volume factor, the emotion intensity factor and the source weight factor; performing weighted fusion on the multi-dimensional thermal factors after weight configuration to obtain a comprehensive thermal value; and comparing the comprehensive thermodynamic value with a preset thermodynamic threshold value to obtain the public opinion thermodynamic level, thereby improving the accuracy of public opinion thermodynamic statistics.

A network device may have an interface configured as a trunk interface. The network device may receive information indicative of the state of the interface for enabling or disabling VLAN information propagation using the interface. Based on the received information, the network device may appropriately configure the interface to participate in a VLAN information propagation protocol.

The invention relates to the technical field of social network application, in particular to a social media viewpoint evolution simulation method and device based on coupling dynamics, and the method comprises the steps: extracting event topic interaction network data based on social network public information, carrying out the statistics of user historical interaction behaviors, and determining an information transmission network and an internal association network according to the data, the two forms a multi-layer association network; determining a transmission state of forwarding and a text viewpoint baseline for calculating a transmission state and a viewpoint value of a user in the social network data set; and inputting the propagation state and the viewpoint value into a coupling dynamics simulation model, carrying out iterative calculation until the viewpoint value converges so as to obtain a final propagation state and a final viewpoint value of the user, and generating forwarding situation distribution and user viewpoint distribution. Therefore, the problems that errors are generated, public opinion monitoring and early warning and public opinion guide strategy construction are affected and the like due to the fact that a viewpoint evolution model adopts a single-layer propagation structure and coupling modeling is not carried out on propagation and an internal correlation structure in the related technology are solved.

The invention relates to the technical field of information processing, in particular to an information cascade sequence prediction method and device, equipment and a medium, and the method comprises the steps: constructing multi-level data association through extracting a propagation track of target information, a historical behavior track of a user and a diffusion track of associated information; then, the steps of updating the information influence and updating the user influence are executed through iteration, so that the information spreading value and the user spreading capability are mutually enhanced in a loop: in each round of iteration, the information spreading potential is re-evaluated based on the updated user influence, and meanwhile, the user spreading efficiency is re-evaluated based on the updated information influence; and a continuously optimized feedback loop is formed. The problem of information loss of traditional one-way modeling is effectively solved, dynamic interaction of propagation content features, propagator attribute features and network structure features can be captured at the same time, and the information propagation prediction effect is improved.

The application provides a visual token pruning method based on graph information propagation, comprising the following steps: visual extraction is performed on an input image to obtain visual tokens; importance scores of the visual tokens are initialized; a graph structure about the visual tokens is constructed, each visual token is taken as a node, an adjacency matrix is calculated to construct connections between the visual tokens, and the graph structure is initialized; the adjacency matrix is updated through a preset similarity threshold to obtain visual token subgraph structures of different regions; each row of the adjacency matrix is normalized, node information is iteratively propagated, and final scores of each visual token are calculated; k visual tokens with the highest scores are selected according to the final scores of the visual tokens and are projected; the visual tokens obtained through the projection are spliced with text tokens, input into a large language model, and output results are obtained. The method can improve the calculation efficiency of the model, significantly reduces the calculation cost while maintaining the performance of the visual task.

The invention relates to the technical field of network communication, and discloses a network information dissemination method and system, and the method comprises the steps: an information relay node maintains a community feature matrix for each community, and dynamically represents a community information state; after to-be-propagated information is received, feature vectors of the to-be-propagated information are extracted, similarity calculation and fusion are carried out on the feature vectors and the current state of the matrix, and then the propagation value weight of the information to the community is quantized by calculating information entropy change before and after updating of the matrix; the nodes are combined with real-time community spatio-temporal context features to predict expected coverage utility of different forwarding strategies; and finally, collecting an actual propagation effect and resident feedback, carrying out difference analysis on the actual propagation effect and the resident feedback and a prediction result, and updating a prediction model and a community feature matrix on line, thereby realizing accurate quantification of information value, intelligent decision-making of scene self-adaption and continuous self-optimization of the system, and improving the efficiency of community information propagation and the resource utilization rate.

The embodiment of the invention discloses a multi-modal dialogue emotion recognition method and device based on course learning. A specific embodiment of the method comprises the steps of processing multi-modal dialogue data in response to a received man-machine interaction dialogue transmitted by a robot to obtain a multi-modal sample set and a multi-modal dialogue feature vector set; performing modal evaluation and emotion fusion to obtain an emotion modal membership degree set and an emotion fusion feature vector set; performing information spreading on the emotion fusion feature vector set to obtain a statement information set; fusing the difficulty signals of the multi-modal sample set to obtain a mixed difficulty feature set; distributing a training weight based on a model training server, and training the model in stages to obtain a dialogue emotion recognition model; and performing emotion recognition on the man-machine interaction dialogue to obtain a dialogue emotion recognition result, and performing interaction processing. According to the embodiment, the emotion of the user is quickly and accurately recognized, the man-machine interaction time of the robot is shortened, and the user experience is improved.

The invention relates to the technical field of social network information spreading prediction, and discloses an information spreading prediction method fusing public opinion field effect and a heterogeneous hypergraph. In order to solve the problems that in the prior art, only pairwise user relations are relied on, multi-user group influences cannot be described, different information is subjected to cascade independent processing, and multi-topic competition is not considered, the invention provides a prediction scheme fusing public opinion field effects and heterogeneous hypergraph learning. A heterogeneous hypergraph is constructed to obtain user multivariate relation representation, then a public opinion field effect is utilized to quantify attraction energy of different information topics to a user, attention competition among multiple topics is modeled, and a more real user propagation tendency is obtained; and finally, realizing joint prediction of user interest features and social influence features through an interactive fusion mechanism. The method can be used for scenes of information propagation trend analysis, public opinion monitoring, marketing recommendation, false information early warning and the like.

The invention discloses a user behavior analysis method and device based on a graph neural network and deep clustering. The method and device are applied to scenes such as social network information transmission analysis and disease transmission prediction. According to the method, social network data are abstracted into a graph structure, node features are extracted through an L-layer auto-encoder, data are reconstructed, the node features and an adjacent matrix are fused through an L-layer graph neural network, network interference is dynamically captured, deep clustering is carried out on graph network output to obtain exposure conditions, and a loss function is defined through a double-feedback network. Global loss function optimization model parameters including a loss function, mean square error loss and a variance item are designed; and finally, the causal effect is estimated by using an inverse probability weighted Hajak estimator. According to the method, dynamic adaptive exposure condition learning is realized, the problems of poor flexibility, low expandability and limited accuracy of a traditional method are solved, and the causal effect estimation precision in a complex network is improved.

The invention discloses a fine-grained privacy information propagation control strategy generation method and device, and belongs to the technical field of privacy control, and the method comprises the steps: obtaining an intimacy heterogeneous graph according to a social graph and a diffusion graph between users; updating the node embedding representation of the user node according to the graph neural network and the intimacy heterogeneous graph to obtain an updated node embedding representation; obtaining an initial user activation probability matrix according to the updated node embedding representation; obtaining a privacy disclosure score and a propagation scale according to the initial user activation probability matrix; constructing an optimization problem according to the privacy disclosure score and the propagation scale; and solving the optimization problem to obtain an optimal solution set, and taking a user in the optimal solution set as a node for next hop forwarding. According to the method, the accuracy of information diffusion prediction is higher and is closer to actual data, the static and rigid problems in the prior art are solved, a privacy control scheme with higher quality and more comprehensive selection is provided for the user, and the real optimal balance of effectiveness is realized.

The invention discloses an information propagation scale prediction method based on multi-feature fusion. The method comprises the following steps: firstly, preprocessing collected information propagation link data and constructing an attention network and a forwarding network; then, extracting multi-dimensional features from three dimensions of a space structure, a time evolution process and a text attribute of information diffusion; and finally, fusing the multi-dimensional features, and inputting the fused multi-dimensional features into a machine learning prediction model for training and prediction so as to realize automatic and accurate prediction of the propagation scale of the information on the social network. According to the method, through multi-feature fusion, the problems of incomplete feature mining and poor prediction precision in the prior art are solved, the information propagation process can be flexibly and effectively quantified, the prediction accuracy and interpretability are remarkably improved, and the method can be widely applied to the fields of network public opinion analysis, hotspot discovery and the like.

The invention relates to a social subject memory simulation system and method based on a large language model, and belongs to the technical field of computers. According to the method, online and offline multi-source information fusion is realized, and behavior and situation characteristics of social subjects can be comprehensively described; a large language model semantic comprehension capability is introduced, so that the intelligent level of memory retrieval and information fusion is improved; dynamic dump and index retrieval of long-term memory are supported, so that subjects can keep semantic coherence and behavior consistency in multiple rounds of interaction; the authenticity of information spreading and interaction between social subjects is enhanced, and technical support is provided for an intelligent social system, virtual human interaction and social simulation.

This invention relates to a biomimetic compound eye wide-area sensing and communication system for unmanned swarms, comprising a collaborative optical signal modulation and transmission unit, a wide-area biomimetic compound eye photoelectric sensing unit, and a spatial optical communication and positioning calculation unit. By structurally encoding control commands and employing frequency shift keying modulation, digital information is converted into near-infrared modulated optical signals, enabling spatial information propagation. The receiving end utilizes a biomimetic compound eye structure to converge wide-area field-of-view optical signals and completes photoelectric conversion through a spatial position sensor. Multi-channel signal processing is combined to achieve communication signal demodulation and feature extraction. Based on this, multi-dimensional features are constructed and spatial position calculation is performed, realizing integrated information interaction and relative position perception between nodes. This invention does not rely on radio frequency communication links and satellite navigation systems. In complex electromagnetic countermeasures and navigation-constrained environments, it can achieve stable communication and collaborative sensing for unmanned swarms, possessing advantages such as strong anti-interference capability, good concealment, and high environmental adaptability.

The present application relates to a kind of based on platform recommendation utility characterization implementation social platform information propagation prediction method, comprising the following steps: generating social content representation and social user representation;Social platform recommendation vector is built;According to network structure channel, the propagation process is predicted;Fusion platform recommendation channel;Decoding obtains subsequent time infected user.The present application also relates to a kind of based on platform recommendation utility characterization implementation social platform information propagation prediction device, processor and its computer readable storage medium.The momentum updating mode is used to iteratively update platform recommendation vector in the present application based on platform recommendation utility characterization implementation social platform information propagation prediction method, device, processor and its computer readable storage medium, can keep the stability of feature semantic distribution, using the idea of gated network, platform recommendation mechanism is regarded as parallel channel with friend propagation, when carrying out propagation prediction, the influence of platform recommendation mechanism is introduced.

The application provides an AI false information network propagation scene analysis method and system based on a large model, and relates to the technical field of deep learning; the method comprises the following steps: generating an AI information set; randomly extracting at least one group of AI information in the AI information set multiple times to obtain multiple subsets, and separately training the large model by taking the multiple subsets as training sets to generate multiple different user large models; randomly placing the different user large models on user nodes in a network structure for simulation, selecting an arbitrary user node in the network structure to put AI false information, simulating different user individuals by using the corresponding user large models on each user node, and realizing the analysis of AI false information in the network propagation scene by analyzing the state conversion of each user node in the network structure. The application can solve the problems of low efficiency of network user node feature learning and poor generalization and flexibility of false information propagation analysis methods.

The application relates to the technical field of network analysis, and discloses a network community discovery system and method through matrix analysis, which comprises the following modules: a network module, which converts a static network topology into an information propagation model, and establishes a node state time sequence dynamic model by defining a node information processing rule and a multi-round propagation mechanism; a phase space reconstruction module, which maps high-dimensional time sequence data to low-dimensional phase space through a nonlinear dimension reduction method to form node trajectory distribution data; a community feature module, which analyzes the convergence, oscillation mode and attractor feature of the node trajectory, and generates community structure feature data through trajectory similarity; and a community division module, which identifies a community boundary through density clustering, and constructs a hierarchical community organization through multi-scale analysis. The application can deeply mine the internal community structure of a network from the perspective of dynamic information propagation, and overcomes the limitation that traditional methods only consider static topology.

The invention relates to a sensitive topic propagation and control method based on subjective emotion and blocking tolerance of a user, and belongs to the technical field of information propagation control. Firstly, an information entropy theory is introduced to mine objective influence of sensitive information on user cognition, and meanwhile, a popularity algorithm method is introduced to track dynamic change of information flow in real time; measuring message influence based on user topic cognition and information traffic accuracy; a collaborative filtering algorithm and a Jaccard coefficient are introduced to quantify topic attention, interests and preferences of the users and credibility among the users respectively, subjective emotion influence factors of the users are designed, the effects of subjective emotion and message influence are considered, an evolutionary game theory is introduced to design an H hesitant state, and a multi-state propagation dynamic model based on subjective emotion is constructed; and proposing a comprehensive blocking index based on influence-tolerance to reveal the propagation situation of the sensitive topics, and effectively controlling the propagation of the sensitive topics.

The invention discloses a new media content propagation path optimization system based on AI analysis, and belongs to the field of data processing, the new media content propagation path optimization system based on AI analysis comprises a common sense analysis module used for starting an AI analysis model when an uploader uploads a popular science video to a platform, the video content is scanned based on a pre-trained common sense database, whether the video content contains pseudo science popularization features or not is recognized, and if the video content is judged to violate scientific common sense, uploading of the video is refused, an uploader is notified, and false information spreading is prevented; compared with the prior art, the method has the beneficial effects that pseudo science popularization preliminary screening is completed through the AI analysis model, the video is pushed to an authentication expert in the related field for uncertain video content, whether the video is taken off is determined on the basis of feedback of the authentication expert, a science popularization content screening system is constructed, a false information propagation chain is blocked, and the screening efficiency is improved. And group cognitive anxiety is relieved.

The application discloses a knowledge reasoning path selection and evaluation method, system, device and medium based on reinforcement learning, belongs to the technical field of path selection and evaluation, and comprises the following steps: representing a knowledge graph as a graph structure, adopting a deep reinforcement learning framework; generating node embedding vectors by using graph contrast learning and an adaptive enhancement mechanism; constructing a generation network and a discrimination network to obtain a candidate reasoning path; constructing a multi-objective reward function which fuses a topological connectivity reward and a semantic consistency reward, and performing reinforcement learning; calculating dynamic propagation weights of nodes in a reasoning process by using an information propagation model, and integrating the dynamic propagation weights into node feature representation; and adopting a deep reinforcement learning method to perform end-to-end training on an agent, and outputting a reasoning path and a path evaluation score. The application is suitable for multiple actual reasoning scenes, and provides a technical path for deep knowledge discovery and intelligent decision-making of a large-scale knowledge graph.

The invention relates to an information guidance strategy generation method, device and equipment in a multi-subject interaction scene, and relates to the technical field of reinforcement learning. The method comprises the following steps: defining a plurality of execution subjects participating in information guidance, and constructing a social network basic model; building a multi-agent strategy iteration framework, and initializing a strategy pool of each execution main body; generating a strategy interaction matrix through multiple rounds of simulation; solving an equilibrium strategy of each execution main body under the strategy interaction matrix, training an optimal response strategy and supplementing the optimal response strategy to a strategy pool to obtain an optimization strategy set; according to the optimization strategy set, the budget constraint and the node selection cost rule, the execution subjects select seed nodes from the social network basic model in sequence to deploy an information spreading starting point; and after the budget of all the execution subjects is used up, completing viewpoint evolution of all nodes in the social network basic model according to a viewpoint updating mechanism, and outputting an information guide strategy. According to the invention, a multi-agent cooperation strategy, dynamic viewpoint updating and resource optimization distribution can be integrated.