Repair method and repair apparatus for flexible panel, device and storage medium

A device for repairing a flexible panel, comprising:

• a processor;
• and
• a memory capable to store computer programs executable by the processor;
• wherein the processor implements a repair method for a flexible panel when executing the computer programs, the method comprising:
• obtaining first image information of a flexible panel to be repaired;
• determining a first classification tag of the flexible panel to be repaired based on a deep learning model and the first image information;
• and
• determining an operating parameter of a repair device according to the first classification tag, and repairing the flexible panel to be repaired based on the operating parameter;
• wherein the method comprises:
• determining the deep learning model based on a preset number of flexible panel samples to be repaired,
• wherein determining the deep learning model based on a preset number of flexible panel samples to be repaired comprises:
• dividing the preset number of flexible panel samples to be repaired into training flexible panel samples and verification flexible panel samples according to a preset ratio;
• training with the training flexible panel samples to obtain a first model;
• inputting the verification flexible panel samples into the first model to obtain a first prediction accuracy rate of the first model;
• and
• determining the deep learning model based on the first prediction accuracy rate;
• wherein training with the training flexible panel samples to obtain the first model comprises:
• obtaining second image information of each training flexible panel sample;
• for each training flexible panel sample, obtaining third image information of a repaired flexible panel sample after the training flexible panel sample is repaired based on the adjusted candidate operating parameter;
• determining whether the third image information meets a repair standard for each repaired flexible panel sample;
• in response to the third image information of the repaired flexible panel sample not meeting the repair standard, adjusting the candidate operating parameter and repairing the training flexible panel sample based on the adjusted candidate operating parameter, until obtained image information of the repaired flexible panel sample meets the repair standard;
• determining a candidate operating parameter corresponding to the obtained image information meeting the repair standard as a second classification tag of the training flexible panel sample;
• obtaining the second image information and a second classification tag of each training flexible panel sample, to form a second image information collection and a second classification tag collection;
• determining the second image information collection and the second classification tag collection as training sample data;
• and
• obtaining the first model by training with the training sample data.

A non-transitory computer-readable storage medium having stored therein instructions that, when executed by a processor, causes the processor to perform a repair method for a flexible panel, the method comprising:

• obtaining first image information of a flexible panel to be repaired;
• determining a first classification tag of the flexible panel to be repaired based on a deep learning model and the first image information;
• and
• determining an operating parameter of a repair device according to the first classification tag, and repairing the flexible panel to be repaired based on the operating parameter;
• wherein the method comprises:
• determining the deep learning model based on a preset number of flexible panel samples to be repaired,
• wherein, determining the deep learning model based on a preset number of flexible panel samples to be repaired comprises:
• dividing the preset number of flexible panel samples to be repaired into training flexible panel samples and verification flexible panel samples according to a preset ratio;
• training with the training flexible panel samples to obtain a first model;
• inputting the verification flexible panel samples into the first model to obtain a first prediction accuracy rate of the first model;
• and
• determining the deep learning model based on the first prediction accuracy rate;
• wherein training with the training flexible panel samples to obtain the first model comprises:
• obtaining second image information of each training flexible panel sample;
• for each training flexible panel sample, obtaining third image information of a repaired flexible panel sample after the training flexible panel sample is repaired based on the adjusted candidate operating parameter;
• determining whether the third image information meets a repair standard for each repaired flexible panel sample;
• in response to the third image information of the repaired flexible panel sample not meeting the repair standard, adjusting the candidate operating parameter and repairing the training flexible panel sample based on the adjusted candidate operating parameter, until obtained image information of the repaired flexible panel sample meets the repair standard;
• determining a candidate operating parameter corresponding to the obtained image information meeting the repair standard as a second classification tag of the training flexible panel sample;
• obtaining the second image information and a second classification tag of each training flexible panel sample, to form a second image information collection and a second classification tag collection;
• determining the second image information collection and the second classification tag collection as training sample data;
• and
• obtaining the first model by training with the training sample data.