Decision optimization method fusing enhanced multi-modal learning and knowledge graph

By building multimodal embedding space and layered reinforcement learning agents, the problems of interpretability and semantic deviation of the knowledge graph are solved, dynamic feature fusion and intelligent update are realized, and the transparency and intelligence of the knowledge graph are improved.

CN120409642AActive Publication Date: 2025-08-01BEIJING SHANGCHENG ZHIYIN ROBOT TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510497457.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-21
Publication Date
2025-08-01
Estimated Expiration
2045-04-21

AI Technical Summary

Technical Problem

The existing knowledge graph has poor interpretability in application, and the fusion of static features leads to semantic deviations and is untraceable, and lacks intelligent growth.

Method used

By building a multimodal embedding space, building a layered reinforcement learning agent, defining a dual action space, building a multi-objective reward function, and generating a differential interpretation report through adversarial training mechanism and counterfactual reasoning, and performing incremental knowledge graph updates.

Benefits of technology

The dynamic fusion of multimodal features is realized, the semantic accuracy and interpretability are improved, the false alarm rate of output results is reduced, and the advantages of transparency and intelligent growth are provided.

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Abstract

The invention discloses a decision optimization method fusing enhanced multi-modal learning and a knowledge graph, and relates to the technical field of artificial intelligence and knowledge graphs. According to the decision optimization method for fusing enhanced multi-modal learning and the knowledge graph, dynamic fusion of multi-modal features is realized through a dynamic weight adjustment and semantic alignment constraint mode, semantic precision and interpretability are improved, the semantic deviation problem caused by traditional static feature fusion is effectively solved, and the method has the advantages of being high in robustness and high in reliability. And by recording an intelligent reasoning path selected by a hierarchical reinforcement learning agent, interactive graph structure display can be carried out, the advantage of transparency is achieved, meanwhile, the interpretability of the path is further improved in cooperation with a multi-target reward function, intelligent updating of the knowledge graph is carried out in cooperation with comprehensive confidence, intelligent growth of the knowledge graph is achieved, and the intellectual property of the knowledge graph is improved. And a fine-grained interpretable report is generated through an adversarial training mechanism and anti-factual reasoning, so that the false alarm rate of an output result is further reduced, and the decision transparency is improved.
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