CNN (convolutional neural network)-based method for monitoring internal component surface of nuclear pile

Abstract

The invention discloses a CNN (convolutional neural network)-based method for monitoring internal component surface of a nuclear pile. The method comprises the following steps: S1, acquiring video data of the internal component surface of the nuclear pile; S2, obtaining image data; S3, dividing the image data into a training dataset and a testing dataset; S4, inputting the training dataset into aCNN for training to obtain a feature recognition model and output primary recognition features; S5, classifying the primary recognition features according to known corrosion features to obtain a corrosion type; S6, inputting the testing dataset into the feature recognition model for testing to output secondary recognition features; S7, judging whether the secondary recognition features accord withthe corrosion type; S8, displaying the corrosion type on a human-computer interaction interface of a monitoring and early warning module. The method solves the problems that labor input is large andefficiency is low due to manual monitoring and requirements for timeliness and continuity of material surface corrosion monitoring cannot be met in the prior art.

Description

technical field [0001] The invention relates to the technical field of the nuclear industry, in particular to a method for monitoring the surface of nuclear reactor internal components based on a convolutional neural network. Background technique [0002] Regular inspection and maintenance of in-service equipment in high-radiation underwater environment of nuclear power plants is an important guarantee for the safe operation of in-service nuclear power plants, and it is also a dangerous, hard and time-consuming job, which needs to solve the problems of high radiation dose and underwater operation feasibility. [0003] Judging from the current development situation in our country, to improve the safety and reliability of nuclear implementation, reduce the radiation dose of operators as much as possible, improve the working environment, and solve some hidden dangers that seriously threaten the safety of nuclear power plants in my country, it is necessary to vigorously develop a...

AI Technical Summary

Problems solved by technology

[0008] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a highly practical nuclear reactor internal component surface monitoring method based on convolutional neural network that saves manpower input, high efficiency, high timeliness and continuity, and solves the problem Manual monitoring with existing technology leads to large manpower input and low efficiency, and cannot guarantee the timeliness and continuity of material surface corrosion monitoring

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