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Cigarette product quality defect prevention and control learning system and method based on big data

A product quality and learning system technology, applied in neural learning methods, electronic digital data processing, digital data information retrieval, etc., can solve problems such as unfavorable enterprise production costs, cigarette product quality defects, cigarette brand influence, etc., to improve skills, The effect of high accuracy and accurate recommendation

Pending Publication Date: 2022-07-05
CHONGQING CHINA TOBACCO IND CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, such a requirement is very difficult for ordinary production personnel, and a large amount of work experience must be accumulated to achieve such proficiency
However, if we only rely on the staff to accumulate experience in work practice, it will take a long time, and it will also lead to an increase in the quality defects of cigarette products on the cigarette production line, which is not conducive to the control of production costs of enterprises, and the flow of quality-defective cigarettes into the market will also cause problems. Will have a negative impact on cigarette brands

Method used

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  • Cigarette product quality defect prevention and control learning system and method based on big data
  • Cigarette product quality defect prevention and control learning system and method based on big data
  • Cigarette product quality defect prevention and control learning system and method based on big data

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Experimental program
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Effect test

Embodiment 1

[0047] This embodiment is a big data-based cigarette product quality defect prevention and control learning system, such as Figure 1-6 As shown, it includes a data acquisition unit, a data processing unit, a data storage unit and a learning unit;

[0048] The data acquisition unit includes a camera 1 and a second display 2 installed on the cigarette production line. The camera 1 is electrically connected to a first communication sending module 3 that transmits images captured by the camera 1 , and the second display 2 is electrically connected to the first processor 6 . For connection, the second display 2 is provided with a USB interface 4 and a second control module 5 , and the second control module 5 is electrically connected to the first processor 6 .

[0049] The data processing unit includes a first processor 6, the first processor 6 is electrically connected with a first communication receiving module 7 and a second communication sending module 8, and the first communi...

Embodiment 2

[0054] This embodiment is a big data-based learning method for prevention and control of cigarette product quality defects, such as Figure 7-8 shown, including the following steps:

[0055] S1, the camera photographs the cigarettes on the production line to form a photographed image, and transmits the image to the image processing unit through the first communication transmission module and the first communication receiving module, and the image acquisition module collects the image;

[0056] S2. Manually enter the cigarette defect problem images found in the work into the defect data storage through the USB interface and the second control module, so as to enrich the entire defect storage and analysis database;

[0057] S3. The image preprocessing module performs filtering, enhancement, smoothing and sharpening processing on the collected image electronic signals, so as to make the image clear and ensure the image quality, wherein the method for preprocessing the image inclu...

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PUM

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Abstract

The invention relates to the technical field of quality control learning systems, and discloses a cigarette product quality defect prevention and control learning system and method based on big data, and the system comprises a data collection unit, a data processing unit, a data storage unit, and a learning unit. The data acquisition unit comprises a camera; the data processing unit comprises a first processor, the first processor is provided with an analysis system, and the analysis system comprises an image processing unit and a data analysis unit; the learning unit comprises a second processor, the second processor is electrically connected with a first display, a voice playing module and a first control module, and the second processor is provided with a multimedia interface. According to the method, the problems of cigarette physical quality, appearance defects and the like on the cigarette production line can be automatically collected, reasons and solutions are analyzed, a cigarette product quality defect prevention and control learning database is formed for workers to learn, and the service capacity of the workers is improved.

Description

technical field [0001] The invention relates to the technical field of quality control learning systems, in particular to a big data-based cigarette product quality defect prevention and control learning system and method. Background technique [0002] In the production process of cigarettes, in order to avoid the quality defects of the produced cigarette products being transferred to consumers, and the increase of the scrap rate caused by excessive defective products in production; therefore, all cigarette manufacturers are very concerned about the stability of production quality and the The issue of quality cigarette products that consistently meet industry requirements. [0003] In the field of cigarette physical quality and cigarette appearance quality monitoring in cigarette quality control, at present, multiple inspections such as post self-inspection, workshop quality personnel sampling inspection, and factory quality management department special inspection are usual...

Claims

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Application Information

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IPC IPC(8): G06V20/64G06V10/10G06V10/20G06V10/26G06V10/30G06V10/34G06V10/40G06V10/764G06V10/774G06V10/82G06N3/04G06N3/08G06K9/62G06F16/535G09B5/12
CPCG06N3/08G06F16/535G09B5/12G06N3/048G06N3/045G06F18/24G06F18/214
Inventor 李生春周森刘昌宏简敏黄勇黄卫江张志华
Owner CHONGQING CHINA TOBACCO IND CO LTD
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