2 results about "Structural correlation" patented technology

The invention relates to the field of financial data, and discloses a medium-and-long-term adaptive transaction signal system based on a large model, which comprises the following steps: after obtaining target signal data, inquiring a hidden variable association graph of the target signal data, analyzing a structure association element and generating an evolution path, and by analyzing a path response sequence, identifying a sequence transition node and calculating a node chaos index, obtaining the target signal data; the method comprises the following steps: performing modal decomposition on target signal data to obtain a signal sub-data cluster, performing label coding to obtain a primary function label set, querying a sparse representation coefficient, marking a weight vector, coding the weight vector to a historical memory module, dynamically adjusting a hyper-parameter combination according to a convergence state, calculating a generalization performance ratio, generating a distribution characteristic key, and finally, performing data distribution on the basis of the distribution characteristic key. And constructing a self-adaptive transaction signal mechanism according to various identification elements in the secret key. According to the invention, the transaction signal adaptation accuracy can be improved.

The application discloses a large language model query optimization system and method for graph data, and belongs to the technical field of large language models and is applied to graph data processing in a resource-intensive environment. The application implements the method as follows: 1. a connection relationship in a text attribute graph is constructed, and a node classification task is assigned to an unlabeled node set; 2. token pruning is performed on a query set, specifically, text insufficiency is used to evaluate the query set nodes; neighbor text of a text sufficient query is removed to reduce overall token consumption; 3. query enhancement is performed on the query set after token pruning; specifically, the query set is executed in rounds in a cyclic iteration mode, and pseudo-labels generated in a previous round are used to enhance neighbor text of a subsequent query. Compared with the prior art, the application improves the accuracy of large-scale queries of a large language model in a resource-intensive environment graph data scenario by evaluating the text insufficiency of query nodes and utilizing the structural correlation characteristics of graph data.