93 results about "Spread Networks" patented technology

The invention provides an intelligent security and protection data analysis and decision method and system based on artificial intelligence. The method comprises the following steps: firstly, collecting heterogeneous data streams such as video frame sequences, infrared sensing signals, audio waveforms and equipment state logs uploaded by a plurality of security and protection monitoring equipment in a target area; multi-modal feature extraction is carried out through the spatial-temporal feature fusion model, and spatial-temporal feature vectors containing equipment deployment coordinates and the like are generated; dynamically dividing equipment clusters based on a self-adaptive clustering algorithm, so that the spatial-temporal feature vector similarity of the equipment in the same cluster is high; secondly, for each cluster, analyzing abnormal signal relevance by using an abnormal propagation network model to obtain a cluster-level risk prediction result and an equipment collaborative response instruction; and finally, according to the risk prediction result, dynamically adjusting the node weight of the abnormal propagation network model and the clustering center of the adaptive clustering algorithm, optimizing subsequent cluster division, and improving the accuracy and intelligence of intelligent security data analysis decision.

The invention provides a data association maintenance processing method and system based on file reference, and relates to the technical field of data association maintenance, which comprises the following steps: constructing a directed reference graph, identifying direct and indirect reference paths, and constructing a reference dependence intensity matrix in combination with a time sequence, an interaction amount and a reference propagation probability. And carrying out file clustering based on the matrix, and determining a core reference file set. And when the core file is updated, acquiring incremental update data by adopting data block level difference comparison, and merging the data blocks based on the content similarity and the semantic association degree. After the merged data blocks are segmented according to threshold values and semantic boundaries, updating is propagated to a target node through a multi-path updating propagation network, and the data consistency is ensured through version consistency check and a compensation updating mechanism. According to the method, data association between files can be effectively maintained, and the data updating efficiency and consistency are improved.

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 relates to an intelligent manufacturing management method and equipment based on a digital twinning technology, and a medium. The method comprises the following steps: constructing a collaborative model, collecting real-time data based on the collaborative model, establishing a manufacturing resource intelligent configuration simulation model, and generating an initial scheduling scheme; the initial scheme is optimized through a genetic algorithm, an executable scheduling scheme is generated, and the optimization target is to minimize the maximum completion time; applying the executable scheme to a workshop, monitoring a dynamic event and collecting data; when a dynamic event occurs, generating a rescheduling scheme based on an event-driven rescheduling strategy and Monte Carlo tree search; the rescheduling scheme is transmitted to a workshop, tasks are adjusted, and meanwhile process execution data are recorded; constructing a digital twinborn model based on recorded data, analyzing dynamic event influence, identifying key nodes in combination with a fault propagation network, and optimizing a dynamic scheduling strategy; and an optimized scheduling parameter and a model updating strategy are generated by using a digital twin simulation result, and closed-loop management and real-time response 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.

Network management systems and associated methods are described for providing intent-driven management of networks. A transaction journal of intents for a network may be maintained to define intended behavior of the network. A proposed intent may be received and verified, and the transaction journal may selectively be updated to include the proposed intent based on the verification. Changes for network devices and / or services may be propagated to control the network to adhere to the intents of the journal, including the proposed intent when verified. The network may be monitored to identify and mitigate violations of the intents of the journal or other issues.

The invention provides a social network key node identification method and system fusing propagation characteristics, and relates to the technical field of node identification, and the method comprises the steps: collecting various data in real time; the method comprises the following steps: identifying network propagation data to obtain key information data, constructing an enhanced recurrent neural network optimized based on a sodat swarm optimization algorithm, inputting the key information data, outputting to obtain propagation network characteristics, and analyzing the key information data to obtain node propagation performance data. And extracting a multi-factor influence coefficient from the external factor data, constructing a node evaluation system, and screening the preliminary nodes according to the multi-factor influence coefficient to obtain key nodes. According to the method, the recurrent neural network is enhanced, the propagation network features are extracted, the node evaluation system is constructed, the topological structure data are classified, and the key nodes are determined in combination with the multi-factor influence coefficient, so that the understanding and prediction capabilities of the propagation process are improved, and the key nodes are identified more accurately.

The invention discloses a public opinion propagation prediction method based on a dynamic time weighted Renyi entropy and a graph neural network, and the method comprises the steps: constructing a public opinion propagation network based on the hot topic information of a network platform; constructing a public opinion propagation prediction model based on the dynamic time weighted Renyi entropy and the graph neural network; and inputting the public opinion propagation network into the public opinion propagation prediction model to complete public opinion propagation prediction of the hot topic information. According to the method, the machine learning method, the complex network and the graph entropy theory are combined, a new view angle is provided for public opinion propagation prediction, a new thought is provided for application of the time weighted entropy features in complex network analysis, and the method has important theoretical value and practical significance.

The invention discloses a flood risk propagation assessment method, device and equipment and a storage medium, and the method comprises the steps: calculating a flood risk value of each sub-basin in a research region according to an obtained flood risk assessment index variable of the research region; grading the flood risk value to obtain a flood risk grade of each sub-basin in the research area; constructing a Markov chain space-time model; determining a flood risk space-time propagation rule according to the Markov chain space-time model; fitting a flood risk propagation function according to a flood risk space-time propagation rule; according to the flood risk propagation function, the flood control water conservancy project operation information data and the historical flood data, determining a flood risk transfer blocking effect of the flood control water conservancy project; constructing a flood risk space-time propagation network model with a space-time dependency relationship; and according to the flood risk space-time propagation network model, evaluating the research area to obtain a flood risk propagation evaluation result.

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 discloses a fault detection method and system for an EVTOL aircraft, and relates to the technical field of fault detection, and the method comprises the steps: extracting fault data from operation data through employing a radial basis function neural network, and building a fault data set; constructing a generative adversarial network, generating virtual fault data, and aligning the virtual fault data with actual fault data by using a domain adaptation technology to expand a fault data set; constructing a fault propagation model, simulating a dynamic propagation path of a fault between components in the electric vertical take-off and landing aircraft, and optimizing a fault recognition threshold by combining a federated learning framework; and constructing a hypergraph structure, processing a fault propagation path in the hypergraph structure by using a gated attention propagation network, and outputting a fault diagnosis result in combination with the optimized fault recognition threshold. According to the method, feature extraction and anomaly recognition are performed on the collected data through the radial basis function neural network, and the accuracy of fault detection and the adaptability to different working conditions are effectively improved.

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 relates to a rumor detection method based on dual-domain perception structure feature fusion learning, and belongs to the technical field of natural language processing and information spreading. The method comprises the following steps: acquiring a social media message event data set, constructing a post propagation network graph and a user social network graph, and extracting post propagation features and user social features; identifying a rumor diffusion key time window by using a propagation density peak value and slope division method, and extracting substructure features; processing the sub-features through a mutual attention mechanism to obtain fusion features; the method comprises the following steps: constructing a projection matrix through fusion features, carrying out weighted summation on the fusion features by adopting the projection matrix to obtain interaction features, further generating first weighted post propagation features and first weighted user social features, carrying out homogeneous interaction information modeling to obtain a final homogeneous interaction comprehensive representation, splicing the final homogeneous interaction comprehensive representation with original tweet features, and carrying out classification. And obtaining a rumor detection result. The rumor detection precision and robustness can be improved.

The invention discloses a turnout sleeper number determination method and device, and the method comprises the steps: obtaining turnout three-dimensional point cloud data, and carrying out the regional division of the turnout three-dimensional point cloud data based on the prior information of a steel rail structure; inputting the turnout three-dimensional point cloud data of different areas into the established three-dimensional point cloud segmentation model, and outputting point cloud data carrying semantic tags; constructing a three-dimensional point cloud segmentation model by taking the hierarchical feature network structure as a first-layer model and the point feature propagation network structure as a second-layer model; inputting the turnout three-dimensional point cloud data of different areas into the hierarchical feature network structure, and outputting point cloud features of the corresponding areas; inputting the point cloud features into a point feature propagation network structure, and outputting point cloud data carrying semantic tags; and determining the serial number of the sleeper according to the point cloud data carrying the semantic tag. According to the method, the positioning precision of the turnout sleeper can be improved, high-precision associated positioning of defect positions is realized, and the safety of railway operation is ensured.

Techniques are disclosed for disseminating network service-specific mapping information across administrative domains. In one example, a network device receives an indication of a route target and one or more underlay tunnels configured to support a service route. The service route is configured to transport network traffic associated with a first network service of a plurality of network services. The network device defines, based on the indication, a first transport class of a plurality of transport classes. The network device receives a service route for the first network service and stores a correspondence between the service route and the first transport class. The network device receives network traffic associated with the first network service and forwards, based on the correspondence, the network traffic along the underlay tunnels specified by the first transport class.

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.

The present invention belongs to the field of data mining technology and relates to a method for discovering the influence of key elements based on native-derivative topic transfer learning, including obtaining information including native topics and derived topics and related user information from an API interface provided by a social platform; constructing the early propagation network topology and propagation timing of derived topics, including using a joint distribution adaptive method to perform cross-domain feature adaptation on the content space of native topics and derived topics, and considering the sparsity of early data of derived topics, using an adversarial transfer learning method to compensate for the network structure; constructing a message-path-user ternary association graph of derived topics and performing cyclic iterative scoring to rank the influence of key elements of derived topics; the present invention can timely and accurately mine key elements in the early stage of the outbreak of derived topics, and the present invention can also be widely used in the precise placement of product advertisements, the discovery of important pathogenic genes, the prediction of popular research results, and the prevention of computer virus propagation.

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 invention discloses an influence maximization method based on direct connection and autoregression influence estimation, and relates to the technical field of information processing. Under a given propagation network G, modeling is performed on propagation dynamics according to propagation trajectory data of nodes in the network to obtain a propagation model M (x, G; x represents a node state, and theta represents a model parameter; utilizing a propagation model M (x, G; (theta) mapping the initial state x0 of the node to a propagation final state y, namely the final infection probability of the node, mapping the initial state x0 of the node to a continuous state vector z, and constructing an end-to-end propagation agent model y = Ms (z, G; theta); and fixing a model parameter theta, setting a propagation final state y as an optimization target, optimizing a continuous state vector z corresponding to the initial state x0 of the node through a gradient descent method, and obtaining a seed node set according to an optimization result. The invention provides an influence maximization method which can give consideration to the diffusion effect, the calculation efficiency and the application flexibility.

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.