Lead-acid storage battery non-contact liquid leakage detection device and method based on machine learning

Abstract

The invention discloses a lead-acid storage battery non-contact type liquid leakage detection device and method based on machine learning, and belongs to the field of battery detection. The device comprises a rack; and an image processor mounted on the rack is connected with an infrared camera, a longitudinal rotation motor and a transverse rotation motor and controls the infrared camera, the longitudinal rotation motor and the transverse rotation motor to operate. The method comprises the following steps of: 1, regulating an angle of the infrared camera on the rack to enable the infrared camera to be aligned with a lead-acid storage battery; 2, controlling the infrared camera to acquire a plurality of images by the image processor; 3, carrying out denoising on the images acquired in the step 2; 4, identifying liquid leakage regions in the images obtained in the step 3; and 5, marking the liquid leakage regions obtained in the step 4. The lead-acid storage battery non-contact type liquid leakage detection device and method based on machine learning adopt a non-contact method to detect liquid leakage, and are high in detection speed, stable and efficient; the lead-acid storage battery non-contact type liquid leakage detection device and method based on machine learning adopt logistic regression and linear regression algorithms of machine learning to judge a liquid leakage stateand identify the liquid leakage regions, and are high in identification rate; and the lead-acid storage battery non-contact type liquid leakage detection device and method based on machine learning are low in cost, simple to mount and construct and wide in application range.

Description

technical field [0001] The invention relates to the field of battery detection, in particular to a non-contact leakage detection method for lead-acid storage batteries based on machine learning. Background technique [0002] With the development of society and the advancement of science and technology, reserve power has become a very important part of daily life and production. Reserve power can be divided into button batteries, dry batteries, lithium batteries, lead-acid batteries, etc. according to the capacity from small to large. Button batteries Commonly used in small electronic mechanical equipment, such as electronic watches, dry batteries are often used in small old electronic equipment, such as radios, electronic toys, etc., lithium batteries are often used in small mobile electronic equipment, such as mobile phones, laptops, etc., lead-acid batteries are often used in small portable Mobile electric equipment and large non-movable equipment, such as electric vehicle...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the above-mentioned technical problems existing in the prior art, the present invention provides a technical solution of a non-contact leakage detection method for lead-acid batteries based on machine learning, which solves the problem that lead-acid batteries cannot be used as backup power sources for non-contact automatic Detect the problem of liquid leakage, and realize the non-contact automatic detection of liquid leakage of lead-acid batteries. It is applicable to a wide range of scenarios and easy to install.

Images