119 results about "Spread Networks" patented technology

The invention provides a three-dimensional point cloud data segmentation method and system based on multi-scale point features, and relates to the technical field of point cloud processing, and the method comprises the steps: obtaining original point cloud data, constructing a three-dimensional Hash index table, carrying out the self-adaptive hierarchical division through a local maximum value of a density distribution curve, and obtaining a local maximum value of a density distribution curve; noise point elimination is carried out based on a region density count value and a geometric distribution coefficient, an enhanced feature vector containing a main feature direction, a main curvature value and the like is constructed for non-eliminated points, a local geometric descriptor is constructed by adopting four spherical search regions with increasing radiuses, a three-branch space attention network is input to extract spatial features, and the spatial features are extracted; and fused feature data are obtained through a multi-level geometric feature extraction network and a bidirectional feature propagation network. And obtaining a final segmentation result through segmentation energy function optimization and a region merging strategy.

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 discloses a user social behavior-based public opinion propagation prediction method and system. The method comprises the following steps of S1, perfecting and dividing data set categories according to data set forms and contents; s2, performing sentiment analysis according to the content of the comments forwarded by the user, and performing preliminary clustering on themes of the tweets forwarded by the user; s3, fusing with user theme features, and performing feature construction; s4, constructing a feature module of the user node, and pruning the whole social network according to the propagation condition; s5, calculating the credibility degree and the susceptibility degree of the user according to the historical forwarding data to construct a mask matrix, and performing similarity evaluation on the propagation network of the specified event; and S6, constructing a propagation prediction result evaluation index. According to the method, the dependency and independence of the user are combined, the user topic features are combined, the user node features with more practical significance are obtained, and compared with traditional public opinion analysis and prediction, the method has higher accuracy.

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 relates to the technical field of content analysis, and particularly discloses a delivery analysis method and system based on content characteristics and propagation effects, and the method comprises the steps: obtaining a plurality of delivery contents based on a target keyword; multi-dimensional putting effect analysis is carried out, and a putting effect comprehensive score is generated; performing content characteristic analysis on the plurality of delivery contents to generate a content quality score; acquiring propagation data of a plurality of delivery contents, constructing a propagation network based on the propagation data, inputting the propagation network into a pagerank model, and outputting a propagation effect score; and generating a delivery analysis report according to the delivery effect comprehensive score, the content quality score and the propagation effect score. Through analysis of multi-dimensional indexes such as the delivery amount, the category, the time interval distribution and the re-delivery proportion, a delivery effect comprehensive score is generated based on standardization and a weight distribution mechanism, the overall performance of a delivery strategy can be evaluated, delivery effect feedback can be provided, systematic decision support is provided, and the delivery efficiency and the return rate are improved.

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 belongs to the technical field of internet application, and particularly relates to a key user discovery method oriented to subdivided fields and wide-area social contact, which comprises the following steps: acquiring user basic information, user historical behaviors and user social contact contents under a current topic, and extracting field information and social contact information from the user basic information, the user historical behaviors and the user social contact contents; calculating the domain recognition degree of one user to another user according to the domain information and the social information, and establishing a topic propagation network by taking the user as a node and the domain recognition degree as an edge weight; in the topic propagation network, walking based on a bias sampling strategy to obtain structural features of nodes; a two-layer graph convolutional neural network is established, the network is composed of a first graph convolutional layer, a Dropout layer and a second graph convolutional layer which are cascaded, and structural features of nodes are input into the network to predict whether the nodes have influence or not; according to the method, key users can be found in specific fields, key users with more influence are selected for customers to carry out product popularization, and the effectiveness of advertisement putting is improved.

The invention relates to the technical field of road transportation informatization systems, in particular to a road transportation market illegal behavior situation awareness system and a road transportation market illegal behavior situation awareness method. The collected data is preprocessed and stored; the relation matrix construction module is in communication connection with the data acquisition module; based on the preprocessed data, constructing a market subject relation matrix R; the propagation intensity calculation module is in communication connection with the relation matrix construction module; calculating an illegal behavior propagation intensity matrix P; the topology network construction module is in communication connection with the propagation intensity calculation module; constructing a topology propagation network G; the behavior evolution prediction module is in communication connection with the topology network construction module; based on the topology propagation network G, predicting the evolution of the violation behavior; the risk assessment module is in communication connection with the behavior evolution prediction module; and risk assessment is performed and early warning information is generated based on the illegal behavior evolution prediction result, so that a potential wind source can be identified as early as possible, and the early warning timeliness is greatly improved.

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.

Aspects of the subject disclosure may include, for example, a method that includes receiving, by a processing system, an uplink signal at a radio unit (RU) communicating over a fronthaul interface with a distributed unit (DU) of a communication network; receiving a demodulated reference signal (DMRS) configuration at the RU; performing a beamforming procedure at the RU for one or more beams generated at the RU; and determining, in accordance with an option selected by the DU, whether to perform an equalization procedure for the uplink signal at the RU resulting in equalized user data. In accordance with the equalization procedure being performed at the RU, equalization for the uplink signal is not performed at the DU. Other embodiments are disclosed.

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.