Research on Fault Diagnosis and Disposal Suggestions Method of Power Communication Network Based on Dynamic Event Driven Knowledge Graph
Why this work is in the frame
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Bibliographic record
Abstract
With the development of smart grids, the intelligent operation and maintenance of power communication networks urgently need to evolve from passive response to active warning. Although knowledge graphs provide a global perspective for this, their static characteristics are difficult to cope with real-time and changing network states. The article paper aims to address the limitations of static knowledge graphs in real-time fault diagnosis. The proposed method converts real-time monitoring data into timestamp events and constructs spatiotemporal correlation sessions, achieving active perception and collaborative analysis of multi-source asynchronous faults. Based on this method, the system can not only accurately locate the root cause of faults, but also generate interpretable disposal suggestions automatically based on topology and business logic, providing new ideas for building intelligent power communication operation and maintenance systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it