A power data security risk assessment method and device, a storage medium and an electronic device
By identifying abnormal electricity consumption behavior using a hybrid model of unsupervised clustering and Dirichlet process, and assessing the risk of indirect leakage of power data, this approach solves the problems of abnormal electricity consumption behavior and privacy information leakage in low-voltage distribution networks, and enables security risk assessment and automated response for power data.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID JIBEI ELECTRIC POWER CO LTD
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies cannot effectively identify abnormal electricity consumption behavior of users in low-voltage distribution networks, and lack a systematic quantitative assessment of the risk of indirect leakage in the security risk assessment of power data, resulting in a high risk of privacy information leakage.
By acquiring the electricity consumption information of target users, a high-dimensional spatial probability density model is constructed using unsupervised clustering methods and a Dirichlet process hybrid model to identify abnormal electricity consumption behavior. Based on the characteristics of electricity consumption behavior, the indirect leakage risk of power data is assessed, and the risk state quantity is dynamically updated to achieve automated assessment and response.
It enables accurate identification and risk assessment of abnormal electricity consumption behavior, reduces the risk of indirect leakage of electricity data, protects users' electricity information security, and avoids indirect leakage of privacy information.
Smart Images
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