Distribution network data asset protection considering multiple topology and multi-dimensional knowledge graph

The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions, exacerbating security risks, such as unauthorized access, data tampering, and forgery. In response to these challenges, this study introduces a novel framework that enhances the protection of data assets. It incorporates a multi-dimensional knowledge graph (MDKG) to refine access control and overcome current limitations by integrating a comprehensive set of data asset attributes, roles, policies, and permissions. This approach fosters the development of a nuanced and adaptable access-control mechanism. Furthermore, the framework integrates multiple topology (MTP) for holistic security risk detection, leveraging attention mechanisms, and cross-fusion to adapt to the dynamic data security landscape. Empirical evaluations affirm the effectiveness of MDKG-based access control, whereas comparative experiments demonstrate the superiority of the MTP-based security risk model over existing models. The framework was proven to be effective in countering security risks. This study provides innovative perspectives on data asset protection and establishes a solid foundation for the advancement of smart grid technology.