Research on application system of electromechanical control knowledge graph construction based on semantic analysis

This paper proposes a model for constructing a mechatronic control knowledge graph based on multimodal semantic enhancement, aiming to improve the construction efficiency, accuracy and application value of the mechatronic control knowledge graph through the fusion and semantic analysis of multimodal data. The model uses multimodal information such as natural language processing (NLP), computer vision and sensor data to extract domain-specific entities, attributes and their complex relationships from unstructured and structured data such as technical literature, equipment manuals, operation videos, sensor data, etc. in the field of mechatronic control. First, semantic enhancement technology is used in combination with methods such as named entity recognition (NER) to accurately extract key entities and their semantic relationships in the field of mechatronic control. Then, through multimodal fusion technology, text, images, videos and sensor data are aligned across modalities to generate a unified domain knowledge representation. In order to ensure the reasoning ability and dynamic update of the knowledge graph, the model adopts a knowledge representation and reasoning framework based on graph neural network (GNN) to support real-time update and intelligent reasoning of the graph. In summary, the mechatronic control knowledge graph construction model based on multimodal semantic enhancement proposed in this paper not only solves the shortcomings of traditional knowledge management methods, but also provides an efficient and intelligent solution.