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This invention provides a method for constructing and reasoning a ship navigation knowledge graph based on multi-source heterogeneous data, involving the intersection of artificial intelligence and maritime technology. The method includes: Step S1, establishing a ship navigation knowledge model; Step S2, acquiring and preprocessing multi-source heterogeneous data; Step S3, performing named entity recognition on the preprocessed data based on a BiLSTM-CRF hybrid model incorporating domain dictionary features; Step S4, extracting entity relationships from the entities identified in Step S3 using a pre-trained BiLSTM hybrid model incorporating interactive attention mechanisms; Step S5, fusing knowledge from the entities and entity relationships extracted in Steps S3 and S4 to construct a preliminary knowledge graph; Step S6, performing link prediction on the preliminary knowledge graph based on an RGCN model incorporating temporal constraints and rule logic to achieve knowledge completion and reasoning. This invention improves the ability to respond to risks in complex navigation scenarios.

The application discloses an advertisement creative dynamic evaluation and intelligent decision system based on multi-source data fusion, and relates to the technical field of advertisement design. The system comprises a state perception and graphing module, an online decision module, an execution feedback module and a collaborative evolution updating module. The state perception and graphing module is used for acquiring real-time interactive behavior and external environment data, mapping the data to a dynamic creative feature graph and outputting a graph structured state vector. The online decision module takes the state vector as input, calls an incremental learning decision model to calculate expected performance values and decision uncertainty values of each candidate creative combination, and generates a creative selection instruction according to the decision uncertainty values through a strategy function. The execution feedback module outputs the instruction and receives corresponding actual performance data. The collaborative evolution updating module synchronously updates the weights of related nodes and edges in the dynamic creative feature graph and the internal parameters of the decision model according to the performance data, the selection instruction and the state vector. The application realizes real-time evaluation, intelligent decision and collaborative self-evolution of advertisement creatives in a dynamic delivery environment.