  As one of the key equipment in the distribution system, the distribution
transformer directly affects the reliability of the user power supply. The
probability of accidents occurring in the operation of transformer equipment is
high, so it has become a focus of material inspection in recent years. However,
the large amount of raw data from sample testing is not being used effectively.
Given the above problems, this paper aims to mine the relationship between the
unqualified distribution transformer inspection items by using the association
rule algorithm based on the distribution transformer inspection data collected
from 2017 to 2021 and sorting out the key inspection items. At the same time,
the unqualified judgment basis of the relevant items is given, and the internal
relationship between the inspection items is clarified to a certain extent.
Furthermore, based on material and equipment inspection reports, correlations
between failed inspection items, and expert knowledge, the knowledge graph of
material equipment inspection management is constructed in the graph database
Neo4j. The experimental results show that the FP-Growth method performs
significantly better than the Apriori method and can accurately assess the
relationship between failed distribution transformer inspection items. Finally,
the knowledge graph network is visualized to provide a systematic knowledge
base for material inspection, which is convenient for knowledge query and
management. This method can provide a scientific guidance program for operation
and maintenance personnel to do equipment maintenance and also offers a
reference for the state evaluation of other high-voltage equipment.
