65 results about "Spread Networks" patented technology

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention relates to a construction site environment dynamic regulation and control method and system fused with AIoT. According to the method, a multi-source environment sensor network is deployed to collect original data of a construction site environment in real time, a data set with aligned timestamps is generated, an abnormal event is detected by using an isolated forest algorithm after standardized denoising processing, and a specific parameter type and a time window are marked; a causal intensity matrix between parameters is constructed through Granger causal test on the basis of environment data intercepted in an abnormal time period, a directed weighted network adjacency matrix representing a pollution propagation path is generated accordingly, and an abnormal source is accurately positioned through node influence propagation calculation and timestamp verification; and finally, a regulation and control instruction sequence is dynamically generated according to the shortest influence path of the source node in the propagation network, so that autonomous analysis of implicit association among construction site environment parameters, accurate positioning of a pollution source and dynamic distribution and instant deviation correction of a multi-stage regulation and control strategy are realized.

The invention provides a power transmission and distribution line suspended foreign matter detection method and system based on image processing and deep learning. Wherein a historical point cloud set on the surface of the power transmission tower is acquired, a deformation gradient map is generated through dynamic registration of curvature differences of adjacent point clouds, a super-threshold region is marked as a deformation correlation region, and foreign matter contour parameters are extracted; performing time-frequency decomposition on the node strain signal, retaining a steady-state component, and mapping the steady-state component to an association region to form a strain map; calculating the displacement offset of the deformation association interval to construct a deformation propagation network, and extracting load over-threshold nodes to construct a stress diffusion network; and superposing the network, fusing the displacement and the load value in the superposition area to generate a risk index, and outputting early warning in combination with the foreign matter coordinates and the risk index. According to the technical scheme provided by the invention, the cross-modal dynamic analysis of deformation propagation and stress diffusion paths is fused, and the early warning efficiency of foreign matter attachment and structure abnormity risks of the power transmission tower is improved.

The invention discloses a false information intelligent detection and traceability method based on cross-modal consistency verification. The method specifically comprises the following steps: S1, multi-modal content feature extraction; s2, carrying out cross-modal consistency verification; s3, performing deep counterfeiting detection; s4, propagation anomaly detection; s5, checking knowledge enhancement facts; s6, information tracing and variation tracking; s7, comprehensive authenticity evaluation; s8, a model training strategy; a multi-task learning framework is adopted, and parameters of all modules are jointly optimized. According to the method, a multi-dimensional consistency verification system is constructed by fusing multi-source information such as video content, text semantics, a propagation network and an external knowledge base, and automatic identification, authenticity evaluation and information traceability of various false information are realized.

The invention relates to the technical field of power grid exception handling, and discloses a power grid exception event handling method and system based on a knowledge graph. The method comprises the following steps: collecting time sequence data flow and space coordinate information of a power grid abnormal event in real time through multi-source sensing equipment, and fusing equipment operation parameters to construct a dynamic event data pool; performing graph structure conversion on the dynamic event data pool based on a knowledge graph topological connection relationship, calculating event node propagation path intensity and generating an event propagation network model; key abnormal indexes are separated out from the model by adopting a multi-modal feature extraction technology, risk probability calculation is completed in combination with an adaptive learning algorithm, and an abnormal risk distribution diagram is output; and classifying event levels according to the graph, and automatically generating a processing instruction sequence. According to the method, omnibearing data perception and accurate analysis of the abnormal events of the power grid are realized, an event propagation rule is clearly presented, risk assessment is enabled to be closer to an actual operation state, and accurate guidance is provided for abnormal disposal.

The invention discloses a legal contract risk monitoring and early warning method, and particularly relates to the technical field of risk early warning. The method comprises the following steps: constructing a multi-contract risk propagation network by identifying transmissible terms in a plurality of legal contract texts, and generating a multi-contract propagation path topology network model by adopting a graph theory algorithm; identifying a cascade trigger mechanism of risk propagation terms between contracts by using time sequence causal analysis, and outputting a cascade risk propagation link; performing sensitivity grading on each risk propagation path to obtain propagation path sensitivity grading data; integrating the cascaded risk propagation link and propagation path sensitivity grading data, constructing a contract network risk propagation comprehensive evaluation model, and outputting a comprehensive risk evaluation result; comparing the comprehensive risk assessment result with a preset risk early warning threshold rule to generate corresponding risk prompt information; according to the method, the propagation trend and the diffusion path of the legal contract risk can be effectively predicted, and the timeliness and the accuracy of contract risk management and control are improved.

The invention discloses an intelligent product quality safety risk multi-dimensional information fusion evaluation method, which comprises the following steps: calculating raw material production data aggregation degree and deviation degree, carrying out abnormity identification to obtain an abnormity result, calculating a potential hardware risk coefficient, calculating a first influence degree and correcting the potential hardware risk coefficient to obtain a hardware risk coefficient, and carrying out evaluation. According to a matching abnormal result fed back by a user, feature enhancement is carried out to obtain risk features, data quality evaluation and cross enhancement are carried out to obtain a software integration risk coefficient, and a risk association propagation network and an intelligent product quality safety risk evaluation model are constructed; and inputting the multi-dimensional information of the intelligent product to be evaluated into the intelligent product quality safety risk evaluation model to obtain an intelligent product quality safety risk evaluation result. The method not only can improve the comprehensiveness, dynamism and accuracy of intelligent product risk evaluation, but also has important significance for improving the reliability of the intelligent product and the industry risk management and control level.

The embodiment of the invention provides a recommendation object propagation evaluation method and a recommendation object propagation planning method. The recommendation object propagation evaluation method comprises the steps of obtaining an initial network value of a propagation network of a target recommendation object and a propagation queue; selecting a first propagation probability from the propagation queue; based on the first propagation probability, determining a propagation value gain of the initiating user; determining the target user value of the initiating user based on the initial user value of the initiating user and the propagation value gain, and returning to execute the step of selecting the first propagation probability from the propagation queue until the propagation queue is empty, and determining the target network value of the propagation network based on the target user values of the multiple users; and determining a network value gain of the propagation network based on the target network value and the initial network value. And the real contribution of the dynamic propagation network is quantified, so that the network value evaluation is more accurate and reliable.

The invention discloses a multi-source data fusion aerospace electronic system reliability evaluation method based on design and manufacturing process constraints, and belongs to the technical field of electronic system reliability evaluation. The problem that the reliability evaluation accuracy of the spaceflight electronic system is poor under the condition of incomplete information is solved. The method comprises the following steps: firstly, establishing a digital prototype model of the aerospace electronic system, and establishing a parameter degradation model of key components of the electronic system in combination with a failure physical model so as to obtain a performance degradation reliability function of each key electrical performance parameter of the electronic system; establishing an electronic system function failure propagation network model based on a logical relationship between an electronic system function structure and basic function network nodes, performing state updating on each function network node of the electronic system by adopting a Bayesian inference method, and obtaining a function failure reliability function of each function network node of the electronic system; and then based on the two reliability functions, constructing a system-level reliability model to obtain an overall reliability curve of the electronic system.

The invention discloses a bidirectional adaptive video super-resolution method based on frame difficulty index, and belongs to the field of computer vision. The invention provides a frame-level dynamic reconstruction method for solving the problems that simple frame calculation is redundant and difficult frame reconstruction is insufficient due to the fact that an existing model adopts a fixed calculation strategy for video frames with different difficulties. According to the method, a motion detail decoupling propagation network is constructed, motion information is efficiently transmitted by utilizing a shallow forward propagation branch, and a deep backward propagation branch focuses on recovering texture details so as to decouple a time sequence propagation task; meanwhile, a frame reconstruction difficulty evaluation network is introduced to generate a global difficulty index, so that the receptive field weight of the adaptive time sequence fusion network and the refining depth of the dynamic refining network are regulated and controlled. According to the method, through explicit modeling frame-level reconstruction difficulty, adaptive matching of the model capacity and the video frame feature complexity is realized, and the video reconstruction performance is remarkably improved under limited computing power.

The invention discloses an underwater image enhancement method based on a prior guide network. The underwater image enhancement method comprises the following steps: S1, establishing a double-flow architecture network model based on a Transform U-shaped encoder-decoder network, a physical perception prior encoder and a multi-scale feature enhancement module; s2, taking an original underwater image X and a prior image Xp enhanced based on physical perception as inputs of a U-shaped network and a prior encoder respectively, and performing iterative training on a double-flow architecture network model; carrying out back propagation on network parameters by taking loss function convergence stability as a training cut-off condition, and obtaining an ideal double-current architecture model; and S3, collecting an actual underwater image, inputting the actual underwater image into the ideal double-flow architecture model, and automatically outputting an enhanced image Y. By using the double-flow architecture network model of the method, the underwater image enhancement effect can be improved, the image details and the local contrast can be enhanced, and more accurate and natural colors can be recovered.

The invention provides an industrial equipment predictive maintenance method based on the Internet of Things, and the method comprises the steps: monitoring the operation data of each equipment of an industrial system in real time, carrying out the standardization processing of the data, obtaining a multi-source monitoring data set, and recording the connection layout, electrical circuit distribution and mechanical transmission structure relation between the equipment; if a certain equipment node in the preliminary propagation network abnormally fluctuates on a transmission medium or a transmission path, analyzing a potential propagation path of a node fault through historical monitoring data, a process interruption range and a control signal transmission path; and analyzing the real-time operation load and the historical fault record of the related equipment of the fault propagation potential path, calculating the fault occurrence probability of each equipment node under the sharing of the environmental conditions, and obtaining a fault risk distribution diagram of the whole system.

The invention relates to the technical field of precision marketing analysis based on big data, and discloses a precision marketing analysis method and system based on big data. Sequentially executing transaction log preprocessing, extracting a customer transaction sequence and constructing a commodity transfer frequency matrix; constructing a commodity transition probability matrix based on the matrix, and calculating a diffusion retention coefficient; the commodity popularity is initialized in combination with a time attenuation mechanism; constructing a directed purchase network and calculating the diameter of the directed purchase network as an upper limit of diffusion round times; executing heat diffusion iteration according to the transition probability and the retention coefficient, and judging whether convergence occurs or not; and finally normalizing the popularity vector and sorting, and outputting a precise putting target. According to the method, modeling behaviors are extracted from original data, a propagation network is constructed, and indirect propagation of user interests is simulated in combination with time sequence popularity, so that automatic modeling from behavior data to a delivery target is realized. According to the scheme, the accuracy and the service decision-making efficiency are improved.

The invention discloses a public opinion tracing method based on multi-source heterogeneous data. The public opinion tracing method comprises the following steps: acquiring the multi-source heterogeneous data; normalizing and fusing the multi-source heterogeneous data to construct a public opinion propagation network; the step of constructing the public opinion propagation network comprises constructing a public opinion propagation tree set, processing the public opinion propagation tree set and constructing a multi-layer public opinion propagation network based on the public opinion propagation tree set; and determining a public opinion propagation source in the multilayer public opinion propagation network based on the multilayer local influence evaluation function and the shortest distance. The public opinion propagation source can be accurately positioned from the multi-source heterogeneous data.

The invention relates to a product security event evolution process prediction method and device, and electronic equipment. The method comprises the steps that a data set is built through multi-source heterogeneous data, and the multi-source heterogeneous data is data having an incidence relation with a product safety event; cleaning data and extracting event entities and attributes from the data set; constructing a affair graph by using a causal reasoning method of a time sequence association rule; taking entities in the affair atlas as nodes of a propagation network, and mapping causal relationships in the affair atlas as edges of the propagation network and weights on the edges; creating agent attributes and event state transition rules; and constructing a multi-agent model, and predicting the evolution process of the product security event by using the multi-agent model. According to the invention, the technical problem of lack of research on the evolution law of the product security event in the prior art is solved.

This disclosure provides methods, components, devices and systems for coordinated scheduling-based protocols for multi-hop traffic flows in a controller-based network. Some aspects more specifically relate to a coordinated scheduling of time slots for multi-hop traffic flows across both fronthaul and backhaul links in controller-based networks. In some examples, an access point (AP) within the network may provide information indicative of one or more established schedules of time slots to a network controller and the network controller may use such information for conflict resolution between different schedules of time slots. In accordance with the propagation of schedules of time slots to the network controller, the network controller may obtain a complete view of traffic patterns within the network and leverage such a complete view for the conflict resolution. Additionally, or alternatively, an AP may rely on a configuration by the network controller of one or more coordinated schedules of time slots.

The present invention provides an artificial intelligence-based intelligent security data analysis and decision-making method and system. First, heterogeneous data streams such as video frame sequences, infrared sensor signals, audio waveforms, and device status logs uploaded by multiple security monitoring devices in a target area are collected. Then, multimodal feature extraction is performed through a spatiotemporal feature fusion model to generate spatiotemporal feature vectors containing device deployment coordinates, etc. Based on this, an adaptive clustering algorithm is used to dynamically divide device clusters so that the spatiotemporal feature vectors of devices in the same cluster have high similarity. Then, for each cluster, an abnormal signal correlation is analyzed using an abnormal propagation network model to obtain cluster-level risk prediction results and device coordinated response instructions. Finally, the node weights of the abnormal propagation network model and the cluster centers of the adaptive clustering algorithm are dynamically adjusted according to the risk prediction results to optimize subsequent cluster divisions and improve the accuracy and intelligence of intelligent security data analysis and decision-making.

The invention discloses construction and application of an interpretability model for virus molecular propagation network clustering. The construction comprises the steps of sorting a sample data set, preprocessing the data set, constructing a model, training, selecting an optimal model for interpretability analysis and the like. The method has the advantages that sequences and epidemiological information are converted into digital signals at the same time, comprehensive information fusion is carried out, and interpretable comprehensiveness and accuracy are achieved; meanwhile, an SHAP black box model is used for explaining a framework, an optimal model is selected for interpretability analysis, the SHAP value of each sample feature is calculated, contribution of the features to a model prediction result is quantified, and composition and features of the network are more effectively explained.

The application provides a kind of online social platform topic propagation topology fingerprint graph construction method, comprising: constructing topic propagation network graph, determining source node, extracting maximum connected subgraph, calculating subgraph each node forwarding volume, node hop count, adjacent edge forwarding intensity;From each subgraph source node, with forwarding intensity as weight, the shortest weighted path from source node as filter function, forward search out connected component sequence, record each connected component start time step and duration, the same connected component is merged;From each subgraph recorded search end node, start reverse search, get ring sequence from reverse search, record each ring structure start time step and duration, the same ring structure is merged;According to connected component sequence set, ring sequence set constructs topic propagation fingerprint graph.The application can effectively extract the significant topology of connected path and loop formed in the process of online social platform topic propagation, and quantify its duration.

The application relates to a depth completion method, device and equipment based on radiation difference and spatial distance, a bilateral propagation network is constructed based on a multi-scale architecture; wherein, an ultra-network is designed in a preprocessing module, the bilateral propagation network has a preference for the nearest value in the radiation difference and the spatial neighborhood in the depth propagation process through filtering weights; the preprocessing module processes a color image and a sparse depth map to obtain corresponding image encoding and depth features under each scale; the image encoding and the depth features of the same scale are input into a multi-modal fusion module for fusion to obtain a dense depth map; a depth optimization module iteratively updates the depth propagated by the multi-modal fusion module based on the sparse depth map, and the completed dense depth map is obtained by weighted combination of the dense depth maps under each scale. The application solves the problems of ambiguity of sparse depth map representation and spatial invariance of convolution operation in the multi-modal fusion stage.

The application relates to the technical field of intelligent response to comment crisis, and discloses an intelligent response method and device for comment crisis, electronic equipment and a storage medium, wherein the method comprises the following steps: through deep semantic analysis and clustering of multi-source comment data, independent crisis event entities can be accurately and efficiently identified and aggregated from noise information; through construction of a time sequence social communication network model and quantification of user node influence, scientific prediction and evaluation of crisis diffusion trend and intensity are realized, so that a crisis index can more accurately reflect the severity of an event; and in combination with cultural characteristics related to the event and the crisis index, a first-level response language suitable for the event is dynamically generated.The application has the beneficial effect that the response strategy is improved in pertinence, timeliness and cultural suitability, so that a subject can more actively and effectively manage and resolve online comment crisis, and automation and intelligentization of a main process from identification to response are realized.

This disclosure provides a target model training method, a multimodal data processing method and apparatus therefor, an electronic device and a program, which can be applied to artificial intelligence-based content generation scenarios in the field of artificial intelligence technology, particularly in the fields of computer vision, deep learning and large-scale models. [Solution] The specific solution includes inputting sample data into a preset model to obtain initial multimodal features of the sample data, and, if the parameters of the image / text encoder in the preset model are fixed, using the initial multimodal features and preset noise features to train the N-layer spread network in the preset model to obtain a target model.

The invention provides a new energy station operation simulation assessment method and system based on comprehensive early warning, and relates to the field of new energy power generation, and the method comprises the steps: collecting operation and fault data, constructing a parameter propagation network to recognize fluctuation abnormity, and generating a risk propagation path; a turning point is determined according to the fluctuation change rate, and adjustment and disturbance time periods are divided; constructing a staged evolution scene, and injecting a control instruction and an interference signal; and evaluating the adjustment and anti-interference capability based on the response and the deviation between the instability trajectory and the safety boundary, and generating an assessment result. According to the method, early warning and targeted evaluation of the operation risk of the new energy station are realized.

The invention relates to the technical field of intelligent response of crisis comment, and discloses an intelligent response method and device of crisis comment, electronic equipment and a storage medium, and the method comprises the steps: carrying out the deep semantic analysis and clustering of multi-source comment data, can precisely and efficiently recognize and aggregate an independent crisis event entity from noise information, and improves the accuracy of the crisis event entity. By constructing a time sequence social communication network model and quantifying user node influence, scientific prediction and evaluation of crisis diffusion trend and intensity are realized, so that a crisis index can more accurately reflect the severity of an event, and an adaptive first-level response skill is dynamically generated in combination with cultural features associated with the event and the crisis index. The method has the beneficial effects that the pertinence, the timeliness and the cultural suitability of the response strategy are improved, so that the subject is helped to more actively and effectively manage and resolve the online comment crisis, and the automation and the intelligentization of the main process from identification to response are realized.

The present application belongs to the field of computer and online social network, and particularly relates to a propagation network reconstruction and prediction method combined with multiple types of dynamic social relationship. A social relationship representation method is provided in the present application, which represents social relationship by using a multi-layer social network, and the multi-layer social network G includes a relationship topology network corresponding to multiple relationship types one by one; the relationship topology network includes network nodes corresponding to users one by one, and edges of the relationship topology network are used to represent relationship strength between users. The social relationship representation method provided in the present application uses a multi-layer graph neural network to describe various different types of social interaction features. The present application comprehensively considers multi-level, multi-type social relationship and information propagation characteristics, and is suitable for various different complex social networks.

The invention discloses a drill jumbo toughness evaluation method based on personalized customization and environmental disturbance. The drill jumbo toughness evaluation method comprises the following steps: constructing a personalized demand library and an environmental disturbance library; constructing a forward propagation network of demand change, a reverse feedback network of environmental disturbance and a propagation network in which the two networks act at the same time, and identifying a component and a path which are jointly propagated and influenced by the demand change and the environmental disturbance; setting a first scene for environment disturbance assessment, a second scene for personalized customization demand change assessment, and a third scene for assessment when the environment disturbance and the personalized customization demand change act at the same time; establishing a toughness evaluation index system of the drill jumbo; and based on the calculated values of the four indexes, generating a comprehensive toughness evaluation result in the scene to be evaluated by adopting a weighted fusion method. According to the method, the toughness of the drill jumbo under the scene of environment disturbance and personalized customization demand change can be improved.

This invention discloses a convex clustering analysis method and system for assembly error propagation networks. The method includes the following steps: S1, acquiring the assembly error propagation network data to be clustered; S2, constructing a convex optimization problem based on the network data acquired in step S1, and transforming it into an equality-constrained optimization problem; S3, solving the equality-constrained optimization problem using the alternating direction multiplier method, iterating repeatedly until a preset iteration termination condition is met, and obtaining the cluster centers corresponding to each network data; S4, completing the clustering of the network data according to the distance between the cluster centers, and outputting the clustering results. This invention applies the convex clustering framework to the clustering problem of network sets, which can effectively handle graph-structured data and overcome the shortcomings of traditional clustering methods, such as being prone to getting trapped in local optima, being sensitive to initial values, and requiring a preset number of clusters, thus achieving efficient and robust network set clustering.

This invention discloses a method for mitigating social propagation risk and selecting robust groups based on extreme value dependency networks and maximally independent sets. The method includes: acquiring user interaction data from a social network and constructing a graph structure based on the user interaction data; obtaining target node pairs from the graph structure for a preset propagation activity period and calculating the extreme value dependency of the target node pairs; constructing propagation networks with target nodes as vertices and extreme value dependency as the edge construction criterion under different dependency thresholds, evaluating the propagation networks, and determining the extreme value dependency propagation network based on the evaluation results; dividing the extreme value dependency propagation network into communities and determining central nodes from the divided communities; constructing a minimum extreme value dependency group based on the extreme value dependency propagation network; and identifying high-risk node groups in the minimum extreme value dependency group using a propagation loss index to obtain the identification result. This invention can achieve the identification, isolation, and control of tail propagation linkage relationships.

The application discloses an influence maximization method based on pass-through and autoregressive influence estimation, relates to the technical field of information processing, and models propagation dynamics according to propagation trajectory data of nodes in a given propagation network G, obtains a propagation model M(x, G; theta), and is used for modeling a node state evolution process, wherein x represents a node state, and theta is a model parameter; an initial state x of a node is mapped to a propagation final state y, that is, a final infection probability of the node, by using the propagation model M(x, G; theta), the initial state x of the node is mapped to a continuous state vector z, an end-to-end propagation proxy model y=M(z, G; theta) is constructed, the model parameter theta is fixed, the propagation final state y is set as an optimization target, the continuous state vector z corresponding to the initial state x of the node is optimized by using a gradient descent method, and a seed node set is obtained according to an optimization result. 0 0 s 0 The application provides an influence maximization method which can consider diffusion effect, calculation efficiency and application flexibility.​​​

The application belongs to the field of social network information processing, and particularly relates to a key user identification method, device and system in a derivative topic network; the method comprises the following steps: obtaining data information in a topic network; extracting a native topic, a derivative topic user relationship network, a native topic and derivative topic propagation network, user basic attributes and user intimacy from the data information in the topic network; extracting a user feature vector representation through a community label propagation algorithm according to the user basic attributes and user intimacy; extracting a topic vector representation of the native topic and the derivative topic through a joint distribution adaptive algorithm according to the native topic and the derivative topic propagation network; inputting the user feature vector representation and the topic vector representation into a user influence dynamic space-time model based on an attention mechanism, and outputting an opinion leader user or a long tail user. The application can accurately identify key users in a derivative topic network.