4 results about "Entity–relationship model" patented technology

The invention provides a three-phase asynchronous motor database construction method and system, and the method comprises the steps: obtaining the source data of a three-phase asynchronous motor, and the source data comprises the common parameters, geometric dimensions and performance parameters of the motor; analyzing a data dependency relationship among the common parameters, the geometric dimensions and the performance parameters to determine a determination condition of each data set; creating a plurality of correlative data tables based on the data dependency relationship, wherein the data tables comprise a geometric dimension table group for storing geometric dimensions of the motor and a performance parameter table for storing performance parameters of the motor; and constructing an entity-relationship model of the database based on the association relationship among the plurality of data tables, and generating an SQL statement for data query. According to the technical scheme provided by the invention, structured and standardized storage of the motor data is realized, data redundancy is remarkably reduced, data reusability and query efficiency are improved, and industrial digital application is effectively supported.

The application discloses a multi-level emergency command intelligent decision-making method and system based on a knowledge graph. The method comprises the following steps: S1, extracting the exclusive features of text modalities, visual modalities and space-time modalities from multi-source heterogeneous data in the whole scene of multi-level emergency command; S2, constructing a cross-modal attention fusion network to output multi-modal fusion features; S3, constructing a multi-modal joint entity relationship Transformer model, taking the multi-modal fusion features as input, and outputting standardized entity-relation triplets; S4, based on the entity-relation triplets, constructing a three-level hierarchical knowledge graph; S5, generating an emergency decision-making scheme by using a three-layer reasoning framework, and outputting the decision-making comprehensive confidence of the scheme; and S6, according to a preset emergency response level, combining the decision-making comprehensive confidence, and executing corresponding human-machine collaborative rules. In this way, intelligent sensing, accurate judgment, collaborative decision-making, efficient disposal and closed-loop optimization of the whole life cycle of an emergency event are realized.

The invention relates to the technical field of data processing, and discloses a data accounting method and device, equipment and a medium, and the method comprises the steps: obtaining to-be-accounting business list data, and carrying out the standardization processing of the business list data based on an entity relation model, and obtaining a list data set; the list data set comprises an insurance type element, an account set type element and an accounting element; constructing a screening rule tree based on the list data set, executing database retrieval operation in a head node of the screening rule tree, and screening to obtain a target data subset; grouping each node in the target data subset through a graph neural network model to obtain an accreditation data group corresponding to each preset accounting criterion; and mapping accreditation data groups corresponding to the preset accounting criteria into a preset list to be accreditated, and generating an accounting data set corresponding to the business list data. The method can be applied to financial science and technology or medical care service program systems, and the accuracy and efficiency of data accounting can be improved.

This application provides a method and apparatus for generating a knowledge graph for cross-layer data processing activities. The method includes: defining entities and relationships corresponding to the business logic layer, data operation layer, and technical implementation layer, as well as cross-layer relationships between entities in adjacent layers; generating an entity-relationship model based on entities, relationships, and cross-layer relationships; extracting entity data from the data processing activity documents corresponding to the business logic layer, data operation layer, and technical implementation layer to obtain entity-relationship data; and generating a knowledge graph based on the entity-relationship model and entity-relationship data. This application achieves full-link association analysis through three-layer cross-layer entity modeling, forming a structured, traceable, high-quality knowledge graph. This provides accurate reference and data support for risk analysis of data processing behavior, helping to quickly identify abnormal operations and potential risks.