Data Element Ownership Confirmation and Circulation in the Intelligent Robot Industry Empowered by Generative AI
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Abstract
This paper proposes a generative artificial i ntelligence m ethod, R AG-CGM (Retrieval-Augmented Generation with Constrained Generation Model), to address the coexistence of multiple data-ownership definitions a nd t he l ow c irculation e fficiency of da ta el ements in th e in telligent robot industry. A semantic representation model is developed for heterogeneous data sources containing ownership, scenario, quality, and safety attributes, including sensor data, device logs, interaction records, and text documents generated in robotic systems. By modeling data-generation methods, participating entities, and mutual contributions among sources, the method builds a structured mapping for data-source traceability and ownership determination. After this mapping is established, a retrieval-augmented generation mechanism constructs the full context of data assets and demand semantics. Conditional generation then produces semantically aligned and interpretable supply-demand matches under ownership-boundary and compliancerule constraints. Finally, an optimization function based on generative scoring ranks candidate matches and dynamically updates validity conditions, supporting efficient circulation after ownership confirmation. Experimental results show that the method achieves an ownership identification accuracy of 0.913 and an F1 score of 0.901, about 4.4% higher than the second-place RAG baseline. The circulation success rate reaches 0.892, outperforming all comparison methods and providing interpretable ownership tracing for data-element governance in intelligent robotics.
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