A multi-view intelligent seal control method based on tensor learning

Through the multi-view intelligent seal control method based on tensor learning, the problem of insufficient multi-view data processing in the existing technology is solved, and accurate monitoring and early warning of seal usage behavior is realized, and scientific decision-making support is provided.

CN119476742BActive Publication Date: 2025-03-28NANJING UNIV OF POSTS & TELECOMM +1
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Patent Information

Application Number
CN202510054493.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-01-14
Publication Date
2025-03-28
Estimated Expiration
2045-01-14

AI Technical Summary

Technical Problem

When faced with complex multi-view data, existing intelligent seal control technology has problems such as inaccurate abnormal detection and limited clustering algorithm performance, making it difficult to accurately monitor the seal usage behavior.

Method used

The multi-view intelligent seal control method based on tensor learning is adopted, and the key features used by seals are extracted through low-rank representation and tensor singular value decomposition, a multi-view subspace clustering model is constructed, the affinity matrix is ​​optimized, and the real-time spectral clustering analysis and early warning mechanism are realized.

Benefits of technology

It has improved the ability to monitor seal usage behavior, enhanced the early warning and control capabilities of abnormal usage behavior, provided more scientific decision-making support, and provided detailed seal usage reports for management.

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Abstract

The present invention discloses a multi-view intelligent seal control method based on tensor learning, which belongs to the technical field of tensor calculation and data mining; including: collecting data and processing the data; decomposing the data by tensor singular value decomposition to obtain the tensor nuclear norm; constructing a multi-view subspace clustering model based on tensor low-rank learning to optimize the representation of the affinity matrix; using the optimized affinity matrix to perform real-time spectral clustering to warn and control abnormal usage patterns; generating a seal usage report. The present invention analyzes and clusters different subspaces in the data by constructing a multi-view subspace clustering model; optimizes the representation of the affinity matrix so that the relationship between the data more accurately reflects the actual seal usage; through this model, the monitoring capability of seal usage behavior can be enhanced, and a more efficient early warning mechanism can be provided for abnormal usage behavior.
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