Navigation knowledge graph construction and reasoning application method

Abstract

The invention provides a navigation knowledge graph construction and reasoning application method, which comprises the following steps of: performing preprocessing, time domain-to-frequency domain conversion, bandwidth acquisition by amplitude integral value segmentation and bandwidth information conversion on structured navigation data to obtain a characteristic value required by anomaly detection; extracting environmental information characteristics in the visual navigation data by adopting a neural network model; target entities in historical and text data are identified by adopting a bidirectional long-short-term memory recurrent neural network model and a conditional random field model, and a relation between the target entities is extracted by adopting a text-based convolutional neural network model. After the combined navigation data is extracted, constructing a navigation knowledge graph according to the extracted combined features and entity relationship information; the knowledge graph is subjected to relation learning and iterative updating by means of a graph convolutional neural network model, so that the knowledge graph obtains more comprehensive feature representation. And according to the integrated navigation knowledge graph and the current state characteristics calculated by the integrated navigation, carrying out cognitive reasoning and decision making on the current state of the integrated navigation.

Description

technical field [0001] The present invention relates to the field of aircraft navigation technology and knowledge map, specifically a navigation knowledge map construction and reasoning application method, which uses machine learning technology to carry out cognitive learning of different types of structured and unstructured data in a combined navigation system, mainly through A series of processes such as feature extraction, data mapping, and graph representation learning are used to build a navigation knowledge map, thereby improving the anomaly detection capability of the integrated navigation system and realizing high-level intelligent decision-making. Background technique [0002] Aircraft include: aircraft, missiles, etc. As an important component of national defense, it can complete specific strategic tasks. All kinds of aircraft operate in the air and need precise navigation to determine their own position in order to ensure that the flight attitude and flight positi...

AI Technical Summary

Problems solved by technology

[0008] 1) The lack of real abnormal data ignores the knowledge contained in a large number of texts and historical materials;

[0009] 2) The relationship between each navigation source data is not mined;

[0010] 3) A fixed mathematical model is used, which cannot cope with various situations;

[0011] 4) Only the navigation data is considered, but the environmental information is not considered

Images