מושגי ליבה
The author aims to mine the relationship between unqualified distribution transformer inspection items using association rule algorithms, sorting out key inspection items and providing a basis for judgment. By constructing a knowledge graph, correlations between failed inspection items are clarified, offering a scientific guidance program for equipment maintenance.
תקציר
The content discusses the importance of material inspection in ensuring power grid safety, focusing on distribution transformers. It highlights the use of association rule algorithms to analyze unqualified inspection items and construct a knowledge graph for efficient material inspection management. The FP-Growth algorithm is favored over Apriori for its speed and accuracy in assessing relationships between failed inspection items.
סטטיסטיקה
Support AB = P A ∪ B = 35.5%
Confidence AB = P B|A = 95.6%
Support AB = P A ∪ B = 10.21%
Confidence AB = P B|A = 95.21%
Support AB = P A ∪ B = 27.34%
Confidence AB = P B|A = 95.14%
Support AB = P A ∪ B = 15.25%
Confidence AB = P B|A = 92.32%
Support AB = P A ∪ B = 35.25%
Confidence AB = P B|A=92.18%
ציטוטים
"Power grid material quality inspection connects material production and engineering construction."
"The FP-Growth algorithm is significantly better than the Apriori method."
"Knowledge graphs efficiently organize large-scale network information with minimal cost."