Complex ecological smart brain-driven data knowledge graph construction method and system

Through a complex ecological intelligent brain-driven method, deep feature perception networks and graph neural networks are used to build knowledge graphs, and combined with multimodal features and cross-modal attention mechanisms, the problems of low efficiency in knowledge graph construction and difficulty in multimodal data processing in the existing technology are solved, and efficient and accurate knowledge graph construction and optimization are achieved.

CN119719388AActive Publication Date: 2025-03-28北京科杰科技有限公司
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Patent Information

Application Number
CN202510218158.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-26
Publication Date
2025-03-28
Estimated Expiration
2045-02-26

AI Technical Summary

Technical Problem

The prior art has problems such as inefficiency, poor scalability, difficulty in handling multimodal data and performing complex multi-hop reasoning when building knowledge graphs.

Method used

The data knowledge graph construction method driven by complex ecological intelligent brain is adopted, and the initial graph structure is constructed through deep feature perception networks and graph neural networks. Combined with multimodal feature extraction and cross-modal attention mechanisms, a unified multimodal knowledge representation vector is generated, and the propagation probability is calculated through SHAP method and causal reasoning to optimize the graph features.

Benefits of technology

It improves the efficiency and accuracy of knowledge graph construction, enhances knowledge discovery and reasoning capabilities, realizes continuous optimization and adaptation of knowledge graphs, and improves its robustness and generalization capabilities.

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Abstract

The invention provides a data knowledge graph construction method and system driven by a complex ecological smart brain, and relates to the technical field of complex ecology, and the method comprises the steps: collecting scene data through a depth feature sensing network, and generating an initial graph structure; entity similarity is calculated based on the map feature fingerprints, related entity pairs are screened, multi-hop path reasoning is carried out, and entity association confidence indexes are generated; generating a multi-modal knowledge representation vector based on multi-modal feature extraction and a cross-modal attention mechanism; the method comprises the following steps: identifying influence characteristics, calculating a propagation probability in combination with a problem evolution diagram and causal reasoning, performing comparative analysis on the propagation probability and a map characteristic fingerprint to generate an optimization target, generating an error correction strategy by using an Adam optimizer, dynamically adjusting the reasoning depth through a double-DQN network, forming an optimization closed loop, and constructing a data knowledge map. According to the method, the accuracy and integrity of the knowledge graph can be effectively improved, the reasoning ability is enhanced, and the method can better adapt to knowledge discovery and application in a complex ecological scene.
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