5 results about "Relational encoding" patented technology

This invention proposes a method, system, device, and medium for relation completion in cement-based materials, belonging to the field of cement-based composite materials technology. The method includes: constructing a set of triples for a material spectrum based on original text samples of the cement-based material's formulation, process, and properties; training an encoder based on the triple set to obtain a first relation encoding model; mixing unlabeled samples into the original text samples and using the first relation encoding model as a model base, semi-supervised training of the first relation encoding model using a self-adversarial loss function to obtain a second relation encoding model, and extracting the source node embedding, target node embedding, and relation embedding of all triples; constructing a positive and negative sample pair input self-interference decoder to predict missing relations in the material spectrum; traversing the material spectrum, inputting the embeddings of any two nodes, and completing the missing relations based on the self-interference decoder. This invention achieves relational semantic reasoning for cement-based materials, thereby improving the accuracy of relation completion.

This invention discloses a method and system for constructing relationships between water conservancy objects based on dynamic weights and relational coding, belonging to the field of water conservancy information technology. The method includes: systematically classifying and defining water conservancy objects; constructing a water conservancy-specific relational coding system based on the definitions of water conservancy objects and relationships; performing relational calculations based on a generalized water system map and a business attribution map, and introducing a multi-factor dynamic weight adjustment mechanism; using a spatiotemporal graph database for integrated storage; and achieving iterative optimization of the parameters of the water conservancy object relationship construction system through an online learning and feedback mechanism. This invention achieves knowledge retrieval and matching through a water conservancy-specific relational coding system, introduces a multi-factor dynamic weight model to enable relational calculations to adapt to different hydrological conditions and business scenarios, realizes domain knowledge-driven intelligent reasoning based on a generalized water system map and a business attribution map, and constructs an online learning and feedback mechanism to enable the system to continuously optimize from historical data and real-time monitoring.

This invention discloses a network data exchange method and system based on data storage, relating to the field of network data exchange technology. The method includes: constructing a field dependency graph by extracting dependencies between data fields in a source data set, and performing hierarchical labeling based on node connectivity; decomposing the data into fragment sets and dividing them into parallel groups according to the field dependency graph; establishing a bidirectional index of physical storage addresses and logical access identifiers, and encoding the topology relationships to generate a storage mapping table; decoding and obtaining the dependency topology relationships and setting access preconditions to generate a synchronization sequence; reading data fragments in the same group in parallel according to the synchronization sequence, and reading data fragments in different groups sequentially; and reassembling the data based on the dependency graph to generate a target set. This invention improves data exchange efficiency and optimizes storage resource utilization.

The application discloses a drug adverse reaction prediction method based on KEPRF and KGNN-DW fusion, and aims to solve the problems of large noise in unstructured text extraction of pharmacopoeia and difficult prediction of implicit adverse reactions. The technical key points are as follows: a knowledge enhancement extraction framework KEPRF is constructed, visual structural features and progressive reasoning are used to realize high-precision structured extraction, and calibration is carried out in combination with a MedDRA term library; a physical-semantic knowledge graph fusing page code interval features is further constructed, a KGNN-DW model is used for multi-relation coding and attention fusion, different weights are given to different relations, and dynamic weighted fusion of relations is realized; finally, end-to-end prediction and weighted voting strategy are combined to realize accurate prediction. Experiments show that the method can effectively improve the prediction accuracy of implicit adverse reactions and the drug risk identification ability.