Intelligent diagnosis method and device based on ensemble learning framework, equipment and medium

Abstract

The invention relates to an intelligent diagnosis diagnosis method and device based on an ensemble learning framework, equipment and a medium, and the method comprises the steps of collecting multi-source operation data of a target metrological verification automatic system, and carrying out the preprocessing of the multi-source operation data to obtain a corresponding fault data sample set; constructing a readable target data set of a preset fault diagnosis model according to the fault data sample set, wherein the preset fault diagnosis model adopts an XGBoost model, training the XGBoost model according to the target data set, and taking the corresponding XGBoost model after training as a target fault diagnosis model; and acquiring online operation data of the target metrological verification automatic system in real time, and analyzing the online operation data according to the target fault diagnosis model to predict the fault condition of the target metrological verification automatic system. According to the invention, the fault condition of the metrological verification automatic system can be accurately predicted on line in real time, and the fault diagnosis efficiency and accuracy are improved.

Description

technical field [0001] The present invention relates to the technical field of power metering diagnosis, in particular to an intelligent diagnosis method, device, equipment and medium based on an integrated learning framework. Background technique [0002] With the continuous deepening of electric power information construction and the development of smart grid and artificial intelligence technology, the demand for intelligent metering equipment is increasing day by day, which makes the metering verification components tend to be automated, standardized and process-oriented. Promotion, people rely more and more on the verification assembly line. Once the verification line fails, the verification work errors and losses caused are several times or even dozens of times the labor cost. Therefore, it is important to do a good job in fault warning and quickly locate faults Realize the guarantee of full automation and intelligence. [0003] At present, the mainstream method of ver...

AI Technical Summary

Problems solved by technology

In actual production, the application effect of the expert system in the fault diagnosis of the verification system is not good. On the one hand, the verification system involves a lot of equipment, and the cause of the fault is not all caused by a single problem, but often a combination of multiple situations. The fault knowledge base It needs to be continuously updated and expanded; on the other hand, the fault diagnosis of the verification system mostly uses the method of comparing the equipment measurement point data with the preset threshold value. The size of the threshold value setting is not based on the change of the variable factors associated with it. However, most of the adjustments are determined through expert experience and remain unchanged for a long time, lacking objectivity

[0004] For the above-mentioned related technologies, the inventor believes that there is a defect that the actual fault diagnosis is not good. Therefore, the fault diagnosis method of the metrological verification automation system needs to be further improved

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