6 results about "Message propagation" patented technology

The application discloses a kind of wisdom agricultural trade information management system based on multi-agent cooperation, comprising: data acquisition processing module, for collecting business data and pre-processing;Multi-agent construction module, for building multi-agent, and setting state space, action space and interaction space;Local state generation module, for structure coding, and cutting, while performing dimension and order constraint;Cooperative decision module, for generating action intention vector, performing message propagation and multi-round merging processing, and performing dependency analysis and matrix assembly on the merging result;Instruction generation module, for hierarchical analysis, behavior binding and structure rearrangement on joint action matrix, generating instructions and issuing to corresponding terminal;Strategy updating module, for collecting feedback data, and iteratively updating action generation parameters.The application can realize the cooperative processing and dynamic management of multi-source business in the wisdom agricultural trade scene, improve the information management precision.

This application relates to the field of healthcare informatics technology, providing a drug recommendation method and related equipment based on drug representation and dynamic user modeling. User features are generated based on the acquired user's historical health records and current health status. Diagnostic features and procedural features are sequentially input into a GRU network and a Transformer network, respectively, to generate user representations through dynamic modeling. Drug features are input into a pre-constructed graph attention network to construct a heterogeneous graph between drug attributes and molecular motifs. Drug representations are generated by message propagation and stacking on this heterogeneous graph. User and drug representations are input into a pre-constructed feedforward neural network, outputting fused features between the drug and the user. The fused features are used to generate probabilities through an activation function, and recommendation information is generated based on the target drugs corresponding to these probabilities. This method can accurately match user health needs and provide personalized and effective drug recommendations.

This invention belongs to the field of online transaction security technology. Specifically, it discloses a method and system for online order fraud based on in-vehicle camera analysis. This invention effectively addresses novel unlabeled fraud by employing dual verification through distribution deviation detection and behavioral logic detection, combined with scenario-based dynamic weight adjustment, reducing reliance on labeled data. By constructing a dynamic group association graph with timestamps, and combining the message propagation mechanism and time decay factor of graph neural networks, it accurately extracts highly cohesive fraud subgraphs, locating core control nodes and collaborating accounts, overcoming the limitations of isolated analysis. By analyzing a first risk score representing the degree of abnormality in the current order operation behavior and a second risk score representing the degree of abnormality in account nodes, it achieves pre-emptive interception of fraudulent activities, transforming risk identification from post-transaction tracing to pre-transaction prevention, thus improving the security and reliability of mobile payments in in-vehicle scenarios.

Embodiments of the present specification provide a group management method, device and equipment, wherein the method comprises: acquiring a plurality of message clusters, the message cluster comprising group messages from a plurality of groups to be managed; determining a target user existing a cross-group message propagation behavior among the users based on the number of message clusters associated with the user corresponding to the group message; and filtering out a target message cluster from the plurality of message clusters based on the number of occurrences of the group message corresponding to the target user in each of the message clusters; extracting overlapping users between the target message clusters; determining a target risk object according to the extracted overlapping users; generating a group control strategy for each of the groups based on the target risk object contained in each of the groups; and performing risk control processing on the groups based on the group control strategy.