Fault diagnosis method and system of intelligent electric meter, and electronic device

Abstract

The invention provides a fault diagnosis method and system of an intelligent electric meter, and an electronic device. The method comprises the following steps: acquiring test data of the intelligentelectric meter; preprocessing the test data to obtain a preprocessing result of the test data; performing online analytical processing on the preprocessing result to obtain an online analytical processing result corresponding to the test data; and inputting the online analytical processing result into a pre-trained fault diagnosis model, and outputting a fault diagnosis result of the intelligent electric meter, wherein the fault diagnosis model is obtained by training a machine learning model. The method realizes comprehensive analysis of data of fault cases of an electric meter through a machine learning algorithm, and a relevant identification model with high integrality and robustness is formed, so that the accuracy of fault diagnosis of the intelligent electric meter is improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps diagnose problems related to an electrical power supply network more accurately than existing methods such as manual inspections that require physical contact between components being tested (such as meters) and can only detect issues at specific points rather often causing delays during maintenance work. It also allows for faster testing times compared to traditional techniques like laboratory tests. Overall, this innovation provides technical benefits over current technologies including automated monitoring systems used in industrial settings.

Problems solved by technology

The technical problem addressed in this patents relates to improving the efficiency and precision of diagnosing failures (failure) in smart grids due to factors like complexity or lack of understanding about how humans work together effectively. Current methods rely heavily on manually annotating past records from previous decisions made through trial and error techniques which may result in incorrect conclusions. Therefore, an improved solution needs to address these issues while maintaining high levels of confidence in the results obtained.

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