43 results about "Community based" patented technology

An electronic gaming device for providing a community-based electronic game including a community matrix for providing community gameplay of the community-based electronic game. The community-based electronic game includes a base game including a first set of reels and the community matrix displayed within a display interface of the community-based electronic game. One or more social trigger symbols associated with play of the base game by a plurality of players are randomly determined and presented on one or more reel columns of the first set of reels. For each of the one or more social trigger symbols, one or more community social trigger symbols are presented on one or more columns of the community matrix, each of the one or more community social trigger symbols being attributed to respective one or more players of the plurality of players of the base game associated with at least one social trigger symbol.

This invention discloses a method for community detection in social networks with unknown community cardinality. The method first constructs an encoder, trains it using an arbitrary unsupervised method, maps the input social network to Euclidean space, and obtains the representation vectors of the vertices. It then calculates the similarity matrix of vertex pairs, sorts the similarities, selects vertex pairs with high similarity as positive samples, and selects vertex pairs with low similarity as negative samples, assigning pseudo-labels to each to form the training set. Next, it constructs a community relationship prediction model, trains it using a binary cross-entropy loss function, predicts the community relationships of vertex pairs, and optimizes hyperparameters. Finally, it constructs a structure graph based on the model's prediction results, inputs the structure graph into the Liu-Wen algorithm to obtain the final community detection result. Experiments show that, without providing community cardinality, this method can accurately detect the community structure in social networks.

This invention relates to the field of computer network security technology, specifically to a CWE (Cost Evidence-Based Weapon) segmentation method, device, and storage medium based on a community discovery algorithm, aiming to improve the benefits of vulnerability classification for vulnerability intelligence analysis. The proposed CWE segmentation method based on a community discovery algorithm includes: constructing a knowledge graph based on the relationships between different CWEs; assigning each CWE in the knowledge graph to a separate community; and using a modularity-based community discovery algorithm to progressively merge communities until the modularity no longer increases, resulting in the final segmented CWE communities. Furthermore, this invention proposes, from an attack benefit perspective, constructing a joint CAPEC and CWE knowledge graph, and using a community discovery algorithm to segment CWEs into communities, significantly improving the benefits of vulnerability classification for vulnerability intelligence analysis.

The present disclosure includes embodiments of a systems and methods of a community-based project platform for providing a collaborative project platform for projects. The embodiments of the methods may include determining one or more available projects for a first set of one or more users, based on a tag for each of the first set of one or more users and a type of the one or more available projects. In response to the determining of the one or more available projects for the first set of one or more users, the methods may additionally include, displaying the one or more available projects to the first set of one or more users.

The method includes: grouping multiple cloud service components in a cloud platform into communities based on call relationships, with intra-community call closeness no more less than a preset value and inter-community closeness less than the value; if at least one target component in a first community has performance below a preset level (meeting the first preset condition), creating a second community identical to the first to share part of its load.

The invention discloses a differentiated privacy protection method and system based on social propagation force perception, and the method comprises the following steps: S1, constructing a social graph, and carrying out the preprocessing and community division of the graph, and obtaining community tags; s2, calculating the propagation degree, the influence and the propagation weight of the node based on the community label; s3, grouping the nodes according to the comprehensive risk score, and presetting an initial differential privacy parameter for each group; s4, based on a grouping result, adaptively adjusting the noise intensity of each group through a water level type strategy, and carrying out differential training; and S5, after training is completed, constructing a strong black box node member to infer attacks, calculating node-level attack advantages and spreading weighted privacy risks, and evaluating and visualizing a privacy protection effect. Under the same global privacy budget, the effective noise variance needing to be superposed is lower than that of an independent Gaussian noise scheme, so that the model training stability can still be maintained in a strong privacy scene.

This invention provides an online retail method, apparatus, device, and storage medium based on community group buying. The method includes: providing community group buying product information to a user on a community group buying client; receiving the user's operation on the community group buying product on the community group buying client, generating a community group buying order or adding the community group buying product to a shopping cart; responding to the user's self-pickup address having a retail store identifier, providing the user with retail product information associated with the self-pickup address on the community group buying client, wherein the user's self-pickup address is the self-pickup address of the community group buying order; receiving the user's operation on the retail product on the community group buying client, generating a retail product order, and marking the retail product order as a self-pickup order. This application combines the retail business of retail stores with community group buying online.

The application belongs to the field of network community, and discloses a network community hiding method based on user attribute characteristics and related devices, which comprises the following steps: obtaining the edge addition selection probability of each community pair according to the separation degree, attribute similarity and the number of nodes of each community in the community pair; obtaining the edge deletion selection probability of each community according to the cohesion degree, attribute uniqueness and the number of node pairs; randomly selecting a community pair as a target community pair according to the edge addition selection probability of each community pair, and adding an edge to the non-edge with the minimum product of the sum of node degrees and (1-attribute similarity) in the target community pair; and randomly selecting a community as a target community according to the edge deletion selection probability of each community, and deleting an edge with the maximum product of the sum of node degrees and (1-attribute similarity) in the target community. The method breaks through the limitation of a specific target community mode, effectively overcomes the drawbacks that existing algorithms generally ignore node attribute information, and improves the community hiding performance.

The invention belongs to the technical field of computer software testing, and particularly relates to a Linux kernel vulnerability mining method based on diversity guidance. According to the method, firstly, collected PoCs are expressed by using a customized abstract syntax tree, clustering is carried out on the PoCs based on a Louvain community discovery algorithm, an initial diversity seed bank is constructed, and seeds are divided into a plurality of communities with different functions; in order to quantify seed diversity, a community prevalence rate index (CPR) is introduced; designing a double-layer multi-arm tiger machine scheduling framework based on the CPR, wherein the framework is used for efficiently allocating variable resources between communities and in the communities; a CPR-guided seed variation strategy is adopted to preferentially carry out rapid variation and expansion on high-diversity seeds, so that the coverage speed and efficiency of vulnerability triggering are improved. Experimental results show that compared with a current most advanced kernel fuzzy test tool, the method has the advantages that the code coverage rate is averagely increased by 17.4%, and the vulnerability discovery number is averagely increased by 9.1 times.

The application belongs to the technical field of comprehensive energy optimization operation, and provides a comprehensive energy optimization control method and system based on community game, comprising: obtaining a directed weighted graph of comprehensive energy; taking all nodes in the obtained directed weighted graph as game participants, and constructing a detection model based on community game; hierarchically solving the constructed detection model based on community game to obtain single-layer community structure; merging the obtained single-layer community structure to form comprehensive energy community structure; performing cooperative game benefit distribution in the formed comprehensive energy community structure, processing multi-objective optimization priority through a hierarchical sequence method to obtain optimal weight coefficients and income distribution of all nodes, and completing optimization control of comprehensive energy based on community game.

The application discloses a community cross-domain recommendation system based on multi-modal large model alignment, and relates to the technical field of artificial intelligence recommendation. The system includes multi-modal alignment, prompt construction and teacher-student alignment modules. First, the multi-modal large model is used to convert the heterogeneous images and text data of interest points into unified structured semantic labels, realizing cross-modal feature alignment. Second, the user's auxiliary travel history is introduced and combined with the thinking chain reasoning mechanism to accurately decouple the preference differences of users in the resident mode and the tourist mode. Finally, a two-stage strategy of supervised fine-tuning and direct preference optimization is adopted to efficiently transfer the reasoning ability of the large teacher model to the lightweight student model. The application effectively solves the semantic gap and preference deviation problem in off-site recommendation, realizes real-time recommendation within seconds while ensuring high accuracy, and is suitable for online travel services and location service scenarios.

This invention provides a blockchain storage method and system based on social networks. The method includes: constructing a network transaction graph based on node transaction data, mining the social relationships between nodes from the graph to represent the probability of future transactions; dividing nodes into different communities based on social relationships, with each node storing related transactions from nodes within the same community, avoiding the burden of storing the entire blockchain ledger; modeling the total storage and query costs of the entire network into a multi-objective optimization model based on the storage and query costs of individual nodes, minimizing the storage and query costs in the network; and employing a multi-swarm ant colony algorithm to solve the problem, allocating transaction storage locations by adjusting the storage saving rate and query cost tolerance rate to achieve a balance between storage and query costs in the network. This invention solves the technical problems of high node storage burden, low storage and query efficiency, and low degree of storage cost balance.

The invention discloses a programming community work recommendation method based on a thermodynamic characteristic value, and relates to the technical field of work recommendation, and the method comprises the following steps: obtaining the capability portrait data of a current user and the dynamic characteristic data of a to-be-recommended programming work; according to the ability portrait data, the dynamic feature data and the historical recommendation feedback data, obtaining the preliminary adaptation degree of the works, and constructing a dynamic thermodynamic value sensing system based on the real-time popularity change of the programming works, the user demand evolution trend and the technical iteration condition; obtaining a thermodynamic characteristic value of the to-be-recommended programming work based on the dynamic thermodynamic value sensing system, the heat thermodynamic influence coefficient, the capability adaptation thermodynamic coefficient and the aging thermodynamic influence coefficient; a target recommendation sequence is obtained according to the preliminary adaptation degree of the works and the thermodynamic characteristic value, the thermodynamic characteristic value is calibrated through the target recommendation sequence and the user behavior feedback data to obtain a programming work recommendation result, and the effect is that subsequent recommendation better fits the real preference of the user.

The invention provides a GraphRAG-based legal knowledge graph analysis method and device and a medium, and relates to the technical field of knowledge graphs. The method comprises the following steps: analyzing a multi-modal law decision case document to obtain a case primitive set; constructing one or more case primitive communities based on the case primitive set and a primitive community clustering rule, and generating a cross-community association map according to each case primitive community and a preset cross-community association rule; and based on each case primitive community and the cross-community association graph, constructing a dynamic legal knowledge graph. In response to a user query request, determining multi-dimensional retrieval information corresponding to a user query intention from the dynamic legal knowledge graph through a preset GraphRAG retrieval engine, and sending the multi-dimensional retrieval information to the user terminal; and updating a primitive community clustering rule, a preset cross-community association rule and / or a preset reasoning strategy of a GraphRAG retrieval engine based on feedback information from the user terminal.

The system according to this embodiment aims to improve the IT literacy of elderly people. [Solution] The system according to the embodiment comprises a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, and a community management unit. The learning plan generation unit generates a learning plan. The dialogue unit engages in dialogue based on the plan generated by the learning plan generation unit. The guide unit provides practical guidance based on the information obtained by the dialogue unit. The security education unit provides security education based on the guidance provided by the guide unit. The collaboration unit collaborates with families based on the education provided by the security education unit. The community management unit manages the community based on the information obtained by the collaboration unit.

This invention proposes an influence blocking maximization algorithm based on overlapping interest community detection. It includes a multi-factor competitive propagation model and an algorithm based on this model and overlapping interest community detection, used for modeling the competitive propagation process of multiple information in social networks and selecting positive information seed nodes. Modeling the competitive propagation process and maximizing influence blocking are hot topics in social network analysis. Traditional models, based on IC and LT, are difficult to accurately depict the real-world propagation process; traditional community-based algorithms divide communities based on topological structure, ignoring user interests and preferences. This invention comprehensively considers user interests, information interaction latency between users, and trust levels, enabling better modeling of the competitive propagation process; it extends the traditional community concept to interest communities, improving community detection accuracy; and it guides the selection of positive information seed nodes through the overlapping interest community structure, achieving the desired negative information influence blocking effect in a short time.

This application provides an information retrieval method, apparatus, and storage medium, including retrieving a preset number of community summaries from a pre-built community summary index library in response to a user query; constructing prompt words based on the community summaries and the user query; and generating an answer covering global information using a large language model. This invention eliminates the reliance on raw text slices, using semantically coherent and structurally clear community summaries as the basic unit for retrieval and reasoning, effectively solving the information fragmentation problem in traditional RAGs, thereby enhancing the global inductive ability of the large language model and improving the accuracy of the generated global response.

This invention belongs to the field of computer science, specifically relating to a community search method for product recommendation in an e-commerce network. The method includes: converting the e-commerce network into an undirected attribute graph G; initializing parameters and calculating the attribute-enhancing conductance of each node in the undirected attribute graph based on the initialization parameters; extracting candidate coarse communities based on the attribute-enhancing conductance; selecting coarse communities from the candidate coarse communities; processing the coarse communities using a trained neural community optimization model to obtain an encoding state; optimizing the coarse communities based on the encoding state to obtain refined communities; and recommending products based on online community search results. This invention combines deep learning technology with traditional predefined structural constraints. This method organically combines the advantages of both, thereby significantly improving the quality of community search.

One example method includes identifying, in a knowledge graph that represents a ZT (zero trust) environment, communities that each comprise one or more entities, computing a risk of granting access to one of the entities, when the risk exceeds a threshold, alerting the ZT environment in its entirety about a possible threat involving the entity regarding which the risk was computed, and propagating information about the threat to the entities that are members of the community to which the one entity belongs.

This invention belongs to the field of financial risk control technology and discloses a method and system for identifying abnormal mobile phone bill recharges based on big data. The method includes: collecting a set of recharge relationship records; constructing a cross-account recharge network topology based on the recharge relationship record set; dividing the cross-account recharge network topology into communities to obtain multiple recharge communities, and calculating the community feature vector of each recharge community; matching the community feature vector of each recharge community with preset abnormal community judgment rules, identifying abnormal communities in the recharge communities based on the matching results, and extracting a set of abnormal accounts based on the abnormal communities; outputting the set of abnormal accounts and the corresponding community feature vectors to the operator's risk control platform, and reconstructing the fund flow path. This invention can achieve full-process coverage of the discovery, characterization, location, and tracing of group-based abnormal behavior in operator mobile phone bill recharge scenarios, improving the comprehensiveness, accuracy, and traceability of abnormal operator mobile phone bill recharge identification.

The application discloses a life circle planning rationality evaluation method and system based on people flow prediction, and belongs to the field of city planning. The life circle planning rationality evaluation method based on people flow prediction comprises the following steps: collecting multi-source data, and constructing a community space-time database; constructing a community people flow trajectory prediction model based on the community space-time database, and predicting a community inflow trajectory vector set and an outflow trajectory vector set; simulating a community life circle based on the community inflow trajectory vector set and the outflow trajectory vector set; performing planning rationality evaluation on the simulated life circle; and outputting an evaluation result. The method uses OD flow prediction data to simulate a life circle, obtains community outflow and inflow trajectories, and further predicts a life circle service range, thereby breaking through the technical method that can only identify a community life circle based on present data, providing a simulation and rationality evaluation method for a planning community life circle after a community planning scheme is implemented, and improving evaluation accuracy.

The application provides a community service network resource scheduling method and device based on hierarchical reinforcement learning, comprising: dividing a sample community service network to obtain a plurality of non-overlapping sample communities; constructing a high-level strategy model and a low-level strategy model, constructing a global reward function of the high-level strategy model with global benefit as the target, and constructing a local reward function of the low-level strategy model with local scheduling satisfaction as the target; updating parameters of the high-level strategy model and the low-level strategy model by using reward values of each layer until each model converges; after obtaining the high-level and low-level strategy models, dividing a to-be-scheduled community service network to obtain a plurality of non-overlapping to-be-scheduled communities; and scheduling each to-be-scheduled community based on the trained high-level and low-level strategy models to obtain a resource allocation strategy of each to-be-scheduled community, thereby improving scheduling efficiency, reducing calculation complexity, and enhancing structure perception and generalization ability of the strategy.

This invention proposes a method and system for detecting social robot groups based on a network social platform, comprising: constructing strong features for individual social robot identification, identifying accounts on the network social platform that conform to the strong features as seed accounts; performing extended line analysis based on the social network relationships of the seed accounts, and combining nickname detection to discover and summarize communities with similar nickname paradigms, thereby obtaining social robot communities based on seed accounts; employing a graph convolutional clustering community discovery method to integrate the graph structure and text signals of the network social platform into community detection, and using the social robot communities and seed accounts to identify key communities with robot coverage exceeding a threshold as the social robot group detection result.

The application discloses a media delivery method and system based on water purification equipment data analysis, relates to the technical field of media delivery, and divides an advertisement resource set into multiple class resource sets according to business information through a gradient boosting decision tree, links all the class resource sets according to business association to construct an advertisement resource library; information text of a target community is acquired based on equipment installation information, is classified into a corresponding class resource set by adopting a text classification algorithm, and a horizontal migration recommendation algorithm or random extraction is adopted to select a recommended advertisement for the target community based on whether or not there is an advertisement interaction record of the same type community; drinking water statistical data is acquired by counting and collecting water taking records in each adjustment period, and a personnel scale degree is calculated by fuzzy reasoning to determine a similar community; an advertisement is newly recommended for the target community by fusing and filtering a recommendation algorithm and considering the advertisement interaction records of the target community and the similar community, and effective combination of water purification equipment data and advertisement media is realized, and advertisement delivery precision and effect are improved.

This invention provides an AI-based method, system, and platform for automatic multi-document parsing. The method includes: acquiring a candidate set of ghost references and performing cyclical sequential joint inference using a conditional random field to obtain a ghost reference set; acquiring four-dimensional feature vectors based on the ghost reference set and the original document, constructing a Bayesian network using these vectors, and obtaining the inference result through inference via the Bayesian network; creating a document heterogeneous graph based on the ghost reference set and the original document, generating aggregated text node vectors through attention aggregation based on multiple edge types, and obtaining community results using a community adversarial strategy; constructing a document dynamic graph based on the community results and temporal data, obtaining hidden states by processing the temporal and spatial dimensions of the document dynamic graph, and outputting the parsing result. This method uses ghost references as a unified analytical clue and central feature to construct a multi-dimensional joint evaluation intelligent parsing framework to improve the depth of document parsing.

The invention relates to the field of data system design and evaluation, in particular to future community planning design based on a quantitative data system. The invention aims to provide the vehicle-mounted integrated gateway controller, a plurality of core function modules are systematically integrated in a single vehicle-mounted gateway controller through functional modular design, and the vehicle-mounted integrated gateway controller supports various heterogeneous communication protocols, so that the defects of single communication capability, poor fault-tolerant capability, long time delay and the like in the prior art are overcome; the universality of the gateway is improved, the application range of the gateway is expanded, and the gateway is particularly applied to large data volume and low time delay of a high-order intelligent driving system.

A server 30 comprises a community action information acquisition unit 32, a determination unit 36, and an output unit 40. The community action information acquisition unit 32 acquires information about a decarbonization action implemented by a community related to a user, the information being transmitted from an input device 22. The determination unit 36 determines a decarbonization action to be recommended to the user, on the basis of the information about the decarbonization action implemented by the community. The output unit 40 outputs information about the recommended decarbonization action determined by the determination unit 36 to a user terminal 12.

The invention discloses a key node identification and price prediction method and system for a cryptocurrency market, and belongs to the technical field of cryptocurrency market analysis. The method comprises the following steps: firstly, acquiring historical price data of various cryptocurrency, and constructing a cryptocurrency price association network based on symbol correlation; secondly, performing community detection on the network by adopting an EDGly algorithm, and calculating and identifying key nodes in each community based on a Shapley value; and finally, acquiring historical price data and social media emotion data of the key nodes, inputting the historical price data and the social media emotion data into the trained Tuned BiLSTM-Sentient model, and outputting a future price prediction result. The system comprises a corresponding data input layer, a network construction module, a community and key node identification module and a price prediction module. Through organic integration of network science, game theory and deep learning, the problems that in the prior art, a market structure is not deeply depicted, key nodes are not completely recognized, and prediction precision is limited are solved, and a systematic solution is provided for market risk early warning and investment decision making.

The invention discloses a temporary employment scheduling and community autonomy method and device based on a block chain, and a storage medium. The method comprises the steps of performing data mining on historical service data when service personnel serve a community based on a data mining and machine learning algorithm to obtain a service knowledge rule, and constructing a knowledge base according to the service knowledge rule; setting an optimization problem and constructing an intelligent optimization scheduling model based on knowledge driving based on the optimization problem; obtaining a to-be-scheduled service order task, and obtaining a service personnel order receiving allocation scheme of the to-be-scheduled service order task according to the intelligent optimization scheduling model based on knowledge driving and all service personnel registered in the system, so as to realize order receiving of the to-be-scheduled service order personnel; and meanwhile, the order receiving record of the service order task to be scheduled is uploaded to the block chain. According to the invention, the problems of difficulty in personnel identity verification, poor personnel scheduling efficiency and the like in the existing resident autonomous cross-community process can be solved.

Disclosed are systems and methods for community-based multi-sensor fused data processing to determine natural hazard risk. In one embodiment, a system comprises one or more memory units storing instructions and one or more processors configured to execute the instructions to receive localized data from a distributed multi-sensor network, the distributed multi-sensor network including a plurality of sensor devices associated with a community, receive property data, community infrastructure data and environmental data from at least one external repository, generate combined data using data fusion, the combined data being based on the localized data from the distributed multi-sensor network and at least one of the property data, community infrastructure data or the environmental data, determine a community risk score for a natural hazard by implementing a machine learning method on the combined data, and perform a mitigating action based on the community risk score.