Boiling phenomenon judgment device and method based on deep learning and optical reflection structure

Abstract

The invention discloses a boiling phenomenon judgment device based on deep learning and an optical reflection structure, the device comprises a real-time image acquisition device, a data caching device and a machine learning device, wherein the real-time image acquisition device is used for acquiring a video stream in liquid in real time and sending the video stream to the data caching device; thedata caching device is used for storing the video stream data in the liquid acquired by the real-time image acquisition device; the machine learning device is used for extracting the video stream data in the data caching device, analyzing and processing the video stream data and judging the boiling grade of the liquid according to an analysis and processing result, used for carrying out self-optimization based on updated data of the data caching device. On the basis of artificial intelligence training, the judgment efficiency and accuracy of liquid boiling are improved, and judgment is not affected by the chemical property of a solution or the external environment.

Description

technical field [0001] The invention relates to the technical field of image information processing, in particular to a device and method for judging boiling phenomena based on deep learning and optical reflection structures. Background technique [0002] As a basic physical phenomenon, liquid boiling exists in the fields of metallurgy, pharmacy, and chemical industry where it is necessary to accurately judge whether boiling occurs. [0003] The existing boiling judgment technical scheme mainly judges the boiling phenomenon of the object under test indirectly by monitoring the upper limit and change of the temperature of the solution. When the chemical properties of the solution are unstable, or the external environment (pressure, temperature, etc.) It is difficult to ensure the accuracy of the method of judging the boiling phenomenon by indirect parameters. For example, if the temperature sensor is used to judge whether the liquid in the pot is boiling. The internal pressu...

AI Technical Summary

Problems solved by technology

[0003] The existing boiling judgment technical scheme mainly judges the boiling phenomenon of the object under test indirectly by monitoring the upper limit and change of the temperature of the solution. When the chemical properties of the solution are unstable, or the external environment (pressure, temperature, etc.) It is difficult to ensure the accuracy of the method of judging the boiling phenomenon by indirect parameters. For example, if the temperature sensor is used to judge whether the liquid in the pot is boiling. The internal pressure increases, and after boiling for a period of time, the liquid no longer boils, because the increase in pressure causes the boiling point to increase; later, due to the increase in temperature, the properties of the liquid change, which will cause the boiling point to change again. Therefore, this type of boiling is judged based on indirect parameters. The accuracy of the phenomenon method is difficult to ensure, and the obtained data information cannot meet the demand

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