Intelligent flaw detection data analysis system

Abstract

The invention relates to the field of rail traffic and steel rail flaw detection data analysis, in particular to an intelligent flaw detection data analysis system. The system comprises a hardware layer, an entity layer, an architecture layer, a service layer and an application layer which are mutually connected and communicated through different levels of architectures, the data analysis capability of the system is improved, the damage recognition capability is improved through an algorithm, a previous manual data analysis mode is converted into an intelligent algorithm-based and manual auxiliary judgment mode, the working efficiency is greatly improved. The system reliability is improved. The working quality is improved, accurate judgment of damage is ensured, and guarantee is provided for safe operation.

Description

technical field [0001] The invention relates to the field of rail transit and rail flaw detection data analysis, in particular to an intelligent analysis system for flaw detection data. Background technique [0002] Rail flaw detection and analysis is a highly technical, highly professional, and demanding work that involves multiple links such as data playback analysis and damage review. The "manual" management method of flaw detection can no longer meet the needs of railway transportation. [0003] Existing flaw detection and analysis instruments have fixed modes, unstable analysis, and frequent false positives and false positives. The false positive rate is as high as 99%. It needs to rely on a large number of human eyes for analysis, which is slow, labor-intensive, and costly. At the same time, there are a series of problems such as lack of concentration, insufficient work stability, lack of care, and lack of responsibility after manual inspection for a long time, which ...

AI Technical Summary

Problems solved by technology

[0002] Rail flaw detection and analysis is a highly technical, highly professional, and demanding work that involves multiple links such as data playback analysis and damage review. The "manual" management method of flaw detection can no longer meet the needs of railway transportation

[0003] Existing flaw detection and analysis instruments have fixed modes, unstable analysis, and frequent false positives and false positives. The false positive rate is as high as 99%. It needs to rely on a large number of artificial eyes to analyze, which is slow, labor-intensive, and costly.

At the same time, after a long time of manual inspection, there are a series of problems such as lack of concentration, insufficient work stability, carelessness, and lack of responsibility, which bring huge hidden dangers

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