  The quality of power grid equipment forms the material foundation for the
safety of the large power grid. Ensuring the quality of equipment entering the
grid is a core task in material management. Currently, the inspection of
incoming materials involves the generation of sampling plans, sampling,
sealing, sample delivery, and testing. Due to the lack of a comprehensive
control system and effective control measures, it is not possible to trace the
execution process of business operations afterward. This inability to trace
hampers the investigation of testing issues and risk control, as it lacks
effective data support. Additionally, a significant amount of original record
information for key parameters in the testing process, which is based on
sampling operation standards, has not been effectively utilized. To address
these issues, we conduct researches on key monitoring technologies in the
typical material inspection process based on the Internet of Things (IoT) and
analyze the key parameters in inspection results. For purpose of complete the
above tasks, this paper investigates the use of Long Short-Term Memory (LSTM)
algorithms for quality prediction in material equipment based on key inspection
parameters. In summary, this paper aims to provide professional and reliable
quality data support for various business processes within the company,
including material procurement, engineering construction, and equipment
operation.
