34 results about "Community network" patented technology

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The embodiment of the invention provides an API (Application Program Interface) traffic data risk prediction method, device and equipment and a medium, which are used for solving the problem that the risk cannot be found in time when facing complex data security attacks in the prior art. The method comprises the following steps: determining whether API flow data has a candidate result of risk or not; based on the information carried by the API flow data and the candidate result, updating a weight value of an edge connected between corresponding nodes in the community network topological graph and a risk weight value of each node; and inputting the community network topological graph, the updated edge weight value in the community network topological graph and the risk weight value of each node into a community discovery model, obtaining whether the output API flow data has a risk or not, and if the output API flow data has the risk, outputting a prediction result of a target risk node with the risk and a corresponding associated risk node. Whether the risk exists or not can be accurately identified in the face of complex data security attacks, and security protection can be conveniently carried out by determining the target risk node with the risk.

A method and system for attribute community search based on a relation tree are provided. In the method, a data relation of a target domain is obtained, and an attribute information network of the target domain is constructed in the form of a graph. The attribute information network is loaded over a unified access interface, and data processing, subgraph construction, and algorithm operation and maintenance search are performed on the attribute information network. A community structure parameter k value and a search node q are received through an interface for attribute community search provided for a user, and a community that contains the search node q and satisfies structural cohesion and optimal attribute cohesion is returned. Rendering and interaction are performed on a user interface, which renders the community search result returned into a dynamic community network diagram and presents the dynamic community network diagram to the user.

Disclosed herein is a system for detection of a molecular community in a molecular network, the system comprising an information handling system that comprises at least one processor; and a non-transitory storage medium storing instructions readable and executable by the processor to perform a method comprising receiving molecular data from an analytical device and / or a data repository; generating an unpruned molecular network from the molecular data; treating the unpruned network with a community detection algorithm to form one or more molecular communities; parsing the molecular communities to prevent the formation of singletons; and identifying at least one molecule present in the molecular community by comparing the molecular community with reference spectra from a library of spectra or by using a molecular structure prediction tool.

The invention relates to the technical field of complex network analysis, and provides a social network overlapping community discovery method, which comprises the following steps: S1, calculating a potential influence value of each node, and screening out a set of core nodes in a network according to the influence values; s2, calculating the viewpoint convergence speed between the nodes by using the viewpoint value change in the DeGroot viewpoint dynamical model, and calculating the propagation weight between the nodes according to the average accumulated viewpoint difference value for measuring the viewpoint convergence speed; s3, constructing a membership degree vector for each node, and updating the membership degree vector in combination with the propagation weight in each round of propagation; s4, adaptively adjusting a community membership degree threshold value of label propagation according to the community network scale and the community characteristics, and allocating the community network attribution of the nodes according to the community membership degree threshold value; and S5, according to the propagation result of the initial label, carrying out community structure correction and redistribution of undistributed nodes. The defects of randomness, overlapping modeling and calculation efficiency of a traditional method are overcome.

1. Name of the product in this design: Intelligent Positioner. 2. Purpose of this design: Bluetooth anti-loss device, used to help users locate easily lost items such as keys, wallets, and backpacks. Its core function relies on Apple's "Find My" network or Google's "Find Hub" global community network to find items. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The present invention relates to the field of data processing technology, and in particular to a smart community integrated management platform based on big data analysis, comprising: a communication load analysis module, which obtains load data of the community network by analyzing the types and quantities of various terminals accessing the community base station within a preset time period; a special node positioning module, which identifies special network nodes in the community by dividing special service IP addresses and monitors the network behavior of special network nodes by setting data acquisition rules; a monitoring terminal connection module, which obtains data of each monitoring terminal by connecting to the community monitoring network; and a comprehensive management module, which determines whether there is a security vulnerability in community management by analyzing the matching degree between various terminal access characteristics, special node data flow characteristics and personnel flow characteristics, and outputs an alarm signal.

The invention discloses a key character recognition method and device, equipment, a storage medium and a product, and relates to the technical field of computers, and the key character recognition method comprises the steps: obtaining user feature information in response to a key character recognition request; storing the user feature information to a preset social relation graph through a hash function; predicting the social relation graph through a community network algorithm and a machine learning algorithm to obtain a social relation circle; and based on the social relation circle, according to a preset feature discovery engine, determining a target key figure. According to the method, the Hash function is adopted, the user feature information is stored in the social relation graph to comprehensively capture the social relation of the user, the hidden social relation can be effectively found through combination of a community network algorithm and a machine learning algorithm, and the social relation can be found according to the requirements of specific fields or scenes in combination with the preset feature finding engine. The key figures are screened in a customized manner, and the accuracy of key figure recognition is improved.

This invention discloses a dynamic quantile filtering method for product co-occurrence networks, comprising: firstly, constructing a product co-occurrence network based on the co-occurrence relationships of products in orders; then, employing a dynamic quantile filtering strategy to denoise the network, specifically: obtaining the co-occurrence frequency weights of all product pairs and forming a co-occurrence matrix; extracting all non-zero co-occurrence frequency weights for each node; defining a local adaptive threshold based on the quantiles of these weights; retaining edges greater than or equal to the threshold; resetting the weights of edges less than the threshold to zero; and retaining isolated nodes; symmetricizing the adjacency matrix using an intersection strategy to obtain a weighted adjacency matrix; updating the network to obtain the denoised product co-occurrence network; and finally, using the Louvain algorithm to perform community partitioning on the denoised network to obtain a product community network. This invention can adaptively filter out weak connections and noisy edges in the product co-occurrence network, effectively improving the accuracy and robustness of community detection.

The application provides a non-negative matrix factorization community detection method based on latent structure and attribute, and relates to the field of community network detection. The method mainly solves the problem that the existing method does not sufficiently utilize community network information. The method comprises the following steps: firstly, preprocessing community network data, and representing information in the network; calculating an adjacency matrix, an attribute matrix and a latent structure matrix; establishing a community detection model mainly based on direct topological structure and attribute information and supplemented by latent structure information; calculating an iterative updating rule; setting the number of iterations, initializing a community member matrix, two community-community matrices and the latent structure matrix, and adjusting a weight parameter; performing iteration to obtain a target matrix; and finally discovering a community according to the iteration result community member matrix.

The application discloses a fast community query system and method with precision guarantee under a large-scale network.The method is as follows: 1, obtaining attribute data from multiple different sources, involving target information of related fields, and constructing a large-scale community network of a target field in a graph manner according to the information; 2, setting precision constraints, confidence, structure constraints and a target node according to a query requirement, the target node being a searched object; then, attribute-aware sampling, approximate estimation, candidate community optimization and sampling parameter adjustment based on errors are sequentially performed on the large-scale community network, and finally, a query result of the community query is obtained.The application introduces attribute-aware sampling into the community query method to control the size of the queried community, and uses an approximate estimation method to ensure the community quality.Meanwhile, the application comprehensively considers the attribute association between entities, and has the close characteristics with high similarity.

The application relates to the technical field of network security, in particular to a network traffic adjustment method and device, electronic equipment and a storage medium; in the application, communication link information between each two autonomous domains existing in a communication link in a network system is detected; when community division conditions are met, the autonomous domains in the network system are subjected to community division processing based on the communication link information, and at least one community network is obtained; wherein each community network comprises at least one autonomous domain; when an abnormal autonomous domain exists in the network system, a target community network associated with the abnormal autonomous domain is subjected to network traffic adjustment; wherein the abnormal autonomous domain is an autonomous domain subjected to abnormal traffic attack in the network system. The application can adaptively and dynamically perform network traffic adjustment on the autonomous domain subjected to attack in the network system, effectively adjust the network traffic, improve the security of defense control, and reduce the influence of abnormal traffic attack.

The application discloses a speaker clustering method, device and equipment. The method comprises the following steps: dividing a speech to be processed into a plurality of speech segments; obtaining speaker features of the speech segments; constructing a community network by taking the speech segments as nodes and taking speaker feature similarities as edge values; and determining a speech segment set corresponding to each speaker according to the community network by using a community detection algorithm. The processing manner makes it possible to perform speaker clustering based on community detection, and the accuracy of speaker clustering can be effectively improved.

The invention discloses a user role analysis method and system in a network community, and relates to the field of social networks, and the method comprises the steps: respectively obtaining community network interaction data about a preset category viewpoint published in a preset social network platform; wherein the community network interaction data comprises a post and a posting user or a forwarding user of the post; determining an optimal issue on a preset category viewpoint; based on the social network interaction data, calculating a semantic polarization degree of an optimal topic of each user on a preset category viewpoint; and performing network community user role division on each user according to the semantic polarization degree of each user on the optimal issue to obtain a division result. According to the method, more detailed understanding and role division can be carried out on the community presenting the binary structure on the preset category viewpoint in the social network, and whether the user is living in the information cocoon house of the preset category viewpoint or not is analyzed.

The invention relates to a science and technology fusion measurement method and system and a medium thereof, and the method comprises the steps: obtaining a literature database comprising patent data and thesis data, and preliminarily constructing a science and technology knowledge network based on the literature database; perfecting the science and technology knowledge network by using a community division algorithm based on the preliminarily constructed science and technology knowledge network to obtain a science and technology knowledge community network; calculating the complementarity measure and the substitutability measure of each knowledge node in the science and technology knowledge community network; based on the complementarity measure and the substitutability measure, the fusion mode of the scientific knowledge and the technical knowledge is identified. The system and the medium are used for realizing the method. Compared with the prior art, objectivity and accuracy of science and technology fusion evaluation are improved.

The invention relates to the technical field of building talkback, in particular to a method and system for converting a community network into a home network. A main control screen and a plurality of sub-control screens are arranged in the same home network, a sub-control screen list composed of the plurality of sub-control screens is established in the main control screen, and the main control screen and a plurality of ladder way machines are arranged in the same community network; when the main control screen receives a call request initiated by the ladder way machine through the community network, the call request is forwarded to the sub-control screen list under the home network; when one sub-control screen in the sub-control screen list answers, an answering command is sent to the main control screen; and after receiving the answering command, the main control screen forwards the answering command to the ladder way machine, hangs up own call and calls to other sub-control screens at the same time, and establishes talkback communication between the ladder way machine and the sub-control screens. And the owner end only needs to carry a network cable as a main control screen to access the community network to forward data, so that the sub-control screen without carrying the network cable can receive the call of the ladder way machine.

The application relates to the technical field of big data, and particularly discloses a user behavior identification method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a community network to be processed, the community network comprising a plurality of nodes and a plurality of edges, each node being used for representing user data of a user to be identified, and each edge being used for representing association weight information between two nodes; using a community discovery algorithm to perform a graph reconstruction cycle on the community network in a plurality of processes until the modularity gain between the plurality of nodes in the community network is all lower than a gain threshold; wherein the community discovery algorithm is used for performing community merging on the plurality of nodes in the community network according to the plurality of edges in the community network, and performing sub-community splitting according to the connected state between the nodes in the merged community; and the user behavior of the user to be identified is inferred according to the optimized community network. The method can improve the identification accuracy of the user behavior.

The present invention relates to a method and system for processing high-net-worth customer information based on a relationship network. The method comprises establishing a first community network set based on a network model and a second community network set based on a knowledge graph, and combining the first community network set and the second community network set to obtain a network scenario determination result. The method then performs community discovery on the capital transaction network of high-net-worth customers, performs scenario discovery and determination through social relationships, and performs customized information processing within high-net-worth customers. This method breaks away from conventional supervised machine learning methods, starting from the high-net-worth customer transaction network and performing community discovery based on network dimensional features. This method not only reduces learning costs but also enables more accurate information processing, making it particularly suitable for meeting customer acquisition and product marketing needs.

The invention discloses a target influence maximization method and device based on influence probability joint modeling, and the method comprises the steps: removing non-key user nodes which have small influence on a target user in a social network, and obtaining a simplified target community network; calculating the influence of the candidate seed nodes on the target user in the target community network by using a target influence probability algorithm CATIP; calculating the influence of the candidate seed nodes on the non-target users by using a weighted collective influence algorithm WCI; and comprehensively evaluating the influence of the candidate seed nodes on the target and non-target user groups by using a joint influence probability CIP algorithm, and identifying and obtaining key seed nodes. According to the method, under the condition of equal resource consumption, the influence effect on the target user can be improved, the interference on the non-target user can be reduced, and the accurate access of the information can be realized. In addition, the method is low in time complexity, and is suitable for a target influence maximization task of a large-scale network.

The invention provides an insurance product recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: extracting an explicit feature vector of a to-be-recommended user according to the user information of the to-be-recommended user; based on the social relation of the to-be-recommended user in the community network, extracting an implicit feature vector of the to-be-recommended user; splicing the explicit feature vector and the implicit feature vector of the to-be-recommended user to generate a fused feature vector of the to-be-recommended user; determining a plurality of target reference users based on the similarity between the fusion feature vector of the to-be-recommended user and the fusion feature vector of the reference user; and based on the insurance type purchase list of the target reference user, determining an insurance type recommendation list of the to-be-recommended user.

The present invention relates to an intelligent fish community stability evaluation method and system based on a network model, the method comprising optimizing species distribution data based on the impact of a single species on community biodiversity and the impact of interspecific interactions on community structure, obtaining species distribution optimization data and constructing a community association network model; using a PCoA method to quantify the functional diversity overlap of species and construct a functional niche network model; constructing a comprehensive network model based on the community association network model and the functional niche network model; constructing a radar chart based on the community association network model, the functional niche network model and the comprehensive network model, and evaluating network stability based on the radar chart area method. The present invention can better reflect the lack of species interaction relationships caused by incomplete sampling, make the network structure more complex and closer to the actual situation, can deeply explore the interaction relationships between species, and improve the accuracy of evaluation results and the comparability between different biological community networks.

This invention discloses a method and system for analyzing user roles in online communities, relating to the field of social networks. The method includes: acquiring community network interaction data on preset social network platforms regarding preset category viewpoints; wherein the community network interaction data includes posts and the users who posted or forwarded the posts; determining the best topic on the preset category viewpoint; calculating the semantic polarization degree of each user on the best topic on the preset category viewpoint based on the community network interaction data; and classifying each user into online community user roles based on the semantic polarization degree of each user on the best topic, obtaining the classification result. This invention enables a more detailed understanding and role classification of communities in social networks that exhibit a binary structure on preset category viewpoints, and analyzes whether users are living in information cocoons of preset category viewpoints.

The invention discloses an abnormal object recognition method and device, a medium and equipment, and relates to the technical field of computers, and the method comprises the steps: obtaining the attribute information and behavior information of a to-be-recognized object in a target interaction scene; performing feature extraction processing on the attribute information to obtain first feature data of the to-be-identified object; performing statistical analysis processing on the behavior information, and determining second feature data of the to-be-identified object; based on a community network corresponding to the target interaction scene, determining third feature data of the to-be-identified object, the third feature data indicating correlation between the to-be-identified object and an abnormal sub-scene in the target interaction scene; the first feature data, the second feature data and the third feature data are input into an abnormal object recognition model for recognition processing, a recognition result for the to-be-recognized object is obtained, and the recognition result indicates whether the to-be-recognized object is an abnormal object in the target interaction scene or not. According to the invention, the recognition accuracy of the abnormal object can be improved.

The invention provides a community information dissemination system and method based on a random directed hypergraph theory, which introduces community structures and attributes on the basis of a traditional information dissemination model on the basis of the directed hypergraph theory, simulates high-order asymmetric interaction in a real society through a method of randomly adding directed hyperedges, and provides a community information dissemination system and method based on the random directed hypergraph theory. According to the method, roles and susceptibility degrees of different individuals in information propagation are distinguished, and a new social information propagation model and a theoretical calculation method of the influence density of the social information propagation model are constructed. In practical application, community network model parameters are set according to a to-be-analyzed information spreading scene, a random simulation experiment is carried out by using a Monte Carlo algorithm, so that the influence density approaches a theoretical value, and spreading of information, influence or infectious diseases in a complex network in a real society can be better simulated, understood and explained; the critical value of the key control parameter can be quickly found, and the control and management of the information spreading can be further realized.

This invention discloses a method, apparatus, equipment, and medium for flood risk assessment that integrates population networks. The method includes: constructing a flood physical risk layer based on hydrological and meteorological data and geographic information data of the target area; constructing a population network of multiple population network scenarios with individuals in the target area as nodes based on spatiotemporal trajectory data of the target area, and determining the target population network corresponding to the weakest population network scenario; performing spatial overlay analysis on the flood physical risk layer and the target population network to generate a flood risk distribution map representing the collaborative flood risk level of different spatial units in the target area; this invention uses the spatiotemporal trajectory data of individuals to represent the connections between individuals in the target area for collaborative flood risk analysis, which can effectively overcome the systematic bias caused by assessment based on a single physical dimension, and achieve accurate quantification and true reflection of urban flood resilience level.

Disclosed herein is a computer-implemented method of creating community groups for clinical trials and community engagement, each community group comprising a plurality of patients grouped based on a

The invention provides a cross-community service node resource allocation method and device based on a hierarchical evolutionary game. The method comprises the following steps: constructing a double-layer network based on a topological graph of a service network; randomly generating an inter-community initial allocation strategy based on the current total resource quantity of the service network; carrying out evolutionary game on the initial deployment strategy between the communities based on a pre-constructed evolutionary game model; calculating the inter-community network benefit under the inter-community deployment strategy obtained by each evolutionary game based on a pre-constructed network benefit function until the inter-community network benefit is maximized; randomly generating an intra-community initial deployment strategy of each community based on the total resource amount of each community; based on a pre-constructed evolutionary game model, performing evolutionary game on the intra-community initial deployment strategy of each community; and obtaining the optimal intra-community deployment strategies of the communities until the internal network benefits of the communities are maximized. According to the invention, the stability and balance of global optimization and local resource allocation are realized, and the overall network benefit is improved at the same time.

This paper relates to a cross-platform method and apparatus for detecting abnormal account behavior. It involves segmenting historical account behavior data from one or more platforms into community networks to obtain account behavior communities, and training a corresponding anomaly detection model for each community. During model training, a core platform set within each community is identified that meets the risk prediction requirements, and the amount of historical account behavior data within those communities is adjusted based on this core platform set. In the application phase, the target account behavior community to which the account to be detected belongs is determined, and its real-time behavior data is input into the corresponding model to obtain the abnormal behavior risk prediction result. The proposed method effectively identifies cross-platform collaborative abnormal behavior, significantly improving anomaly detection accuracy and model training efficiency.

The application discloses a kind of attribute community query method and system based on relationship tree, the method first obtains the data relationship of target field, constructs the attribute information network of target field in the mode of graph.Secondly, attribute information network is loaded through unified access interface, data processing, subgraph construction and algorithm operation and query are carried out on attribute information network.Then, the interface of attribute community query is provided to user, input community structure parameter k value and query target q, return a community containing query point q, satisfy structure cohesion and optimal attribute cohesion.Finally, the rendering and interaction of user interface, it presents the community query result returned by interface to user through graphical method, renders the query result as dynamic community network graph.The application does not need to give query attribute list, reduces user input, so that community query result is more in line with actual.

The application discloses a network reconstruction and overlapping community detection joint method, device, equipment and medium, the method comprises the following steps: obtaining binary time series data and community quantity of community network; initializing community membership matrix and community interaction matrix; using expectation maximization algorithm to iterate community membership matrix and community interaction matrix to obtain target community membership matrix; determining the community structure of the community network and the overlapping community in the community network based on the target community membership matrix. The application explicitly introduces the community membership variable in the parameter space, so that the network inference and community detection, which are regarded as independent problems, form a unified optimization architecture. The responsibility variable is used to track the specific contribution of each edge to the observed dynamics, ensuring the deep coupling and mutual promotion of network inference and community detection. The application not only realizes the detection of the underlying overlapping community structure of the complex network and the accurate reconstruction of the hidden network topology structure, but also ensures the reconstruction accuracy of the complex network.