Roll-packing machine comprehensive quality capability evaluation method and system, storage medium and equipment

Abstract

The invention provides a roll-packing machine comprehensive quality capability evaluation method and system, a storage medium and equipment. The method comprises the following steps: collecting quality data of a roll-packing machine under a preset index; obtaining a process capability index and a defect rate of the roll-packing machine based on the quality data, carrying out mean value processing on the process capability index and the defect rate within a preset duration, and obtaining an updated process capability index and an updated defect rate based on the process capability index and the defect rate of the current month and the mean value within the preset duration; and obtaining a single quality capability evaluation value under the corresponding index based on the preset allocation weight of each index and in combination with the updated process capability index and the defect rate. According to the roll-packing machine comprehensive quality capability evaluation method and system, the storage medium and the equipment, the comprehensive quality capability of the roll-packing machine can be more comprehensively understood, measures are taken according to the comprehensive quality capability, the product quality is improved, the stability and consistency of products are guaranteed, and the requirements of consumers are met.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of tobacco mechanical equipment, and particularly relates to a method and system for evaluating the comprehensive quality capability of a cigarette making machine, a storage medium and equipment. BACKGROUND

[0002] In the cigarette manufacturing process, the cigarette making process is a crucial link. For a multi-unit cigarette making mode, the comprehensive quality level of the cigarette making machine determines the control level of the cigarette product. At present, the quality evaluation of the cigarette making product in the industry is generally based on GB 5606-2005 "Cigarette" national standard, and completely adopts or formulates an enterprise internal control standard based on the national standard. The evaluation of the quality of the cigarette product is based on the cigarette product, and the main content is the appearance, physical measurement, smoke, and perception of the cigarette. Such a quality evaluation method often only focuses on some single indicators of the cigarette, and lacks a comprehensive evaluation method for the comprehensive quality capability of the cigarette making machine, and cannot objectively reflect the comprehensive quality in the cigarette making process.

[0003] Therefore, it is particularly important to establish a scientific, objective, and comprehensive evaluation method for the comprehensive quality capability of the cigarette making machine, so as to better evaluate the comprehensive quality capability level of the cigarette making machine and improve the quality stability in the cigarette making process. SUMMARY

[0004] In view of the above-mentioned shortcomings of the prior art, the present application aims to provide a method and system for evaluating the comprehensive quality capability of a cigarette making machine, a storage medium and equipment, which can more comprehensively understand the comprehensive quality capability of the cigarette making machine, and accordingly take measures to improve the product quality, ensure the stability and consistency of the product, and meet the needs of consumers.

[0005] In a first aspect, the present application provides a method for evaluating the comprehensive quality capability of a cigarette making machine, which comprises the following steps:

[0006] Collecting quality data of the cigarette making machine;

[0007] Based on the quality data, obtaining process capability indicators and a defect rate of the cigarette making machine, and performing mean value processing on the process capability indicators and the defect rate within a preset time period, obtaining updated process capability indicators and a defect rate based on the process capability indicators and the defect rate of the current month and the mean values within the preset time period;

[0008] Based on the preset allocation weight of each indicator, and in combination with the updated process capability indicators and the defect rate, obtaining an individual quality capability evaluation value under the corresponding indicator.

[0009] In an implementation form of the first aspect, the quality data of the cigarette making machine comprises cigarette weight data, cigarette appearance defect data, machine quality rejection data, process equipment parameter data, small case appearance defect data, and small case online appearance defect data.

[0010] In an implementation form of the first aspect, the process capability index of the cigarette making machine is calculated based on the quality data by using the following formula:

[0011] CPK = CP x (1 - |Ca|)

[0012]

[0013] wherein CPK represents the process capability index of the cigarette making machine, Ca represents the process accuracy index, X represents the sample average value, μ represents the specification center, CP represents the process capability index, T represents the specification tolerance, and σ represents the sample standard deviation.

[0014] In an implementation form of the first aspect, the defect rate of the cigarette making machine is calculated based on the quality data by using the following formula:

[0015] P1 = defect number / detection times

[0016] P2 = defect number / defect opportunity

[0017] wherein P1 represents the defect rate calculation for the piecework data product, and P2 represents the defect rate calculation for the pointwork data product.

[0018] In an implementation form of the first aspect, the mean value processing is calculated by using the following formula:

[0019]

[0020] wherein y ij represents the normalized value of a single sample of a single index, x ij represents the value of a single sample of a single index, represents the sample mean value of a single index, and m represents the sample number.

[0021] In an implementation form of the first aspect, the preset allocation weight based on each index specifically comprises:

[0022] allocating the weight to the cigarette weight data index and the cigarette appearance defect data index according to the product internal control standard;

[0023] allocating the weight to the machine quality rejection data index according to the probability of machine quality defect occurrence;

[0024] allocating the weight to the process equipment parameter data index according to the priority of the flat disc position and the tight end amount.

[0025] According to the product internal control standard, the weight of the appearance defect data index of the small box package is allocated.

[0026] In an implementation form of the first aspect, the comprehensive quality capability evaluation of the rolling machine station specifically comprises process implementation capability evaluation and equipment performance capability evaluation; wherein the process implementation capability evaluation comprises cigarette comprehensive quality capability evaluation and package comprehensive quality capability evaluation.

[0027] In a second aspect, the present application provides a rolling machine station comprehensive quality capability evaluation system, corresponding data acquisition module, data processing module and capability evaluation module;

[0028] The data acquisition module is used for acquiring quality data of the rolling machine station.

[0029] The data processing module is used for acquiring process capability indexes and defect rates of the rolling machine station based on the quality data, and performing mean value processing on the process capability indexes and the defect rates within a preset time length, and acquiring updated process capability indexes and defect rates based on the process capability indexes and the defect rates of the current month and the mean values within the preset time length.

[0030] The capability evaluation module is used for acquiring single quality capability evaluation values under corresponding indexes based on preset allocation weights of the indexes and the updated process capability indexes and defect rates.

[0031] In a third aspect, the present application provides an electronic device, comprising a processor and a memory;

[0032] The memory is used for storing a computer program.

[0033] The processor is used for executing the computer program stored in the memory, so that the electronic device executes the above-mentioned rolling machine station comprehensive quality capability evaluation method.

[0034] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by an electronic device to realize the above-mentioned rolling machine station comprehensive quality capability evaluation method.

[0035] As described above, the rolling machine station comprehensive quality capability evaluation method, system, storage medium and electronic device have the following beneficial effects:

[0036] 1. The comprehensive judgment of the process quality level of the rolling machine station is realized, and multiple quality control dimensions are reflected through an integrated comprehensive quality index.

[0037] 2. The comprehensive quality index is formulated based on internal quality control requirements, and the quality evaluation is more intuitive and efficient through a unified data processing method.

[0038] 3. The comprehensive quality index is mean processed by taking the dynamic periodic quality index as a standard value, and the evaluation standard is iteratively updated following the adjustment of the standard value, avoiding the inadaptability of the evaluation standard after the improvement of the process technology level.

[0039] 4. The method adopts the function of horizontal comparison (between machines) combined with longitudinal comparison (different periods), and the quality evaluation index is evaluated with 100 points as the standard. If the score is lower than 100 points, it indicates that the quality evaluation index level is lower than the average level of the statistical period, and if the score is higher than 100 points, it indicates that the quality evaluation index level is higher than the average level of the statistical period, and the measurement standard is relatively simple.

[0040] 5. By layer-by-layer decomposition of the comprehensive quality index, the method can identify the specific weaknesses of the wrapping machine, so as to realize accurate evaluation and rapid improvement of the quality capability of the machine.

[0041] In summary, the present application provides a comprehensive, dynamic and efficient solution for the quality monitoring and management of the wrapping machine, which helps enterprises to improve production efficiency and market competitiveness while ensuring product quality. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A flow chart showing the wrapping machine comprehensive quality capability evaluation method of the present application in an embodiment;

[0043] Figure 2 An evaluation structure block diagram showing the wrapping machine comprehensive quality capability evaluation method of the present application in an embodiment;

[0044] Figure 3 A structure schematic diagram showing the wrapping machine comprehensive quality capability evaluation system of the present application in an embodiment;

[0045] Figure 4 A structure schematic diagram showing the electronic device of the present application in an embodiment. DETAILED DESCRIPTION

[0046] The embodiments of the present application will be described in detail below with specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure. The present application can also be implemented or applied by different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following examples and features in the examples can be combined with each other without conflict.

[0047] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0048] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0049] like Figure 1 As shown, in one embodiment, the comprehensive quality capability evaluation method for the wrapping machine of the present invention includes steps S11-S13.

[0050] Step S11: Collect quality data of the roll-up machine under preset indicators.

[0051] Specifically, the quality data collected from the cigarette packaging machine under preset indicators includes physical data of cigarettes, data on cigarette appearance defects, data on machine quality rejections, data on process equipment parameters, data on appearance defects of small box packaging, and data on online appearance defects of small boxes.

[0052] Cigarette physical testing is a series of tests performed on cigarettes and their components (such as filters), including but not limited to weight, circumference, length, hardness, draw resistance, and total ventilation.

[0053] Size inspection: This includes measuring the length, circumference, and other dimensional parameters of the cigarette and filter rod to ensure that the product meets the design specifications;

[0054] Weight inspection: Measure the weight of a single cigarette or filter to ensure product consistency and meet standard requirements;

[0055] Smoke resistance test: This refers to the force required to smoke, which is related to the smoker's experience.

[0056] Air permeability test: measures the ventilation performance of the filter tip, which affects the filtration effect on harmful substances in flue gas;

[0057] Pressure drop test: refers to the static pressure difference between the two ends of the filter tip in a completely sealed state. This is an important indicator for evaluating the function of the filter tip.

[0058] Total ventilation test: When a cigarette is inserted into the test device at the specified depth, the total amount of air passing through the filter section is measured, which is related to the degree of smoke dilution.

[0059] Cigarette appearance defect data is used to assess the type and severity of cigarette appearance defects, including but not limited to:

[0060] Printing errors: including blurry or incorrect patterns and text, color deviations, etc.

[0061] Physical damage: such as scratches, indentations or other forms of surface damage.

[0062] Structural issues: such as a loose filter or a bent cigarette.

[0063] Humidity issues: Cigarettes that are too wet or too dry will be considered defective.

[0064] Impurities: Any unintended substance in the paper or filter of a cigarette is considered a defect.

[0065] To effectively control and improve product quality, modern cigarette production lines typically employ automated inspection systems, such as computer vision-based technologies and deep learning models, to detect and classify appearance defects in cigarettes. These technologies can accurately identify various defects on high-speed production lines, thereby improving inspection efficiency and accuracy. Furthermore, detected defects are usually classified according to their severity, such as Class A, Class B, and Class C defects. Class A defects are serious quality defects that severely affect the product's appearance or functionality. Class B defects are relatively serious quality defects that significantly affect the product's appearance or functionality. Class C defects are general quality defects that have a negative impact on the product's appearance or functionality. Through statistical analysis of these defects, manufacturers can better understand potential problems in the production process and take corresponding measures to improve the production process and reduce the probability of defects occurring.

[0066] Machine quality rejection data typically refers to the monitoring and analysis of product quality using various sensors and detection systems during automated production processes, in order to reject defective products before they reach the end user. In cigarette production lines, this involves a series of detection and rejection mechanisms to ensure that only products that meet quality standards are packaged and shipped. For example, products with empty heads or leaks must be rejected.

[0067] Process equipment parameter data refers to various parameters used to control and optimize equipment performance during production. This data is typically provided by the equipment manufacturer and adjusted based on actual production needs. It includes, but is not limited to:

[0068] Equipment Model: The model and specifications of the equipment determine its basic performance and functions;

[0069] Operating parameters: Parameters that need to be set during equipment operation, such as temperature, pressure, speed, etc.

[0070] Production efficiency: The production speed and output of equipment, usually measured by the number of products produced per unit of time;

[0071] Energy consumption data: The energy consumed by the equipment during operation, including the consumption of energy sources such as electricity and gas;

[0072] Failure rate: The frequency with which equipment fails within a certain period of time, usually expressed as a percentage;

[0073] Maintenance records: The equipment's maintenance and upkeep history, including the date, content, and results of each maintenance session;

[0074] Part life: The expected lifespan of consumable parts in the equipment, used to predict and plan replacement cycles;

[0075] Quality control parameters: Parameters related to product quality, such as product dimensions, weight, appearance, and other standards;

[0076] Safety performance: Equipment safety performance indicators, such as safety protection measures and emergency shutdown mechanisms;

[0077] Environmental impact: The environmental impact of the equipment during operation, such as emissions and noise.

[0078] By monitoring and analyzing these parameter data in real time, equipment can be optimized and adjusted to improve production efficiency and product quality, reduce energy consumption and costs, and ensure the safe and stable operation of the equipment. In modern industrial production, many devices are equipped with intelligent monitoring systems that can achieve real-time monitoring and automatic adjustment of these parameters. In one embodiment, the acquisition of flattener position and dense end measurement data is crucial. The former is one of the important indicators for measuring the surface quality of a workpiece, while the latter usually refers to the measurement of densely packed parts in a material or component. Flatness is very important for control in fields such as machining, automobile manufacturing, and aerospace. Flatness refers to the smoothness of the workpiece surface, determined by measuring the height difference of the workpiece surface. Commonly used measurement methods include contact and non-contact methods. Contact measurement methods use a contact head to contact the surface of the object being measured, and the displacement is converted into an electrical signal by a sensor for measurement. This method is suitable for smaller and higher-precision workpieces. Non-contact measurement typically uses a laser probe, which does not require direct contact with the surface of the object being measured. In the tobacco industry, "closed-end quantity" refers to the amount of tobacco packed at both ends of a cigarette, with one end used for lighting and the other for attaching the filter. This measurement is crucial for ensuring product quality and consistency.

[0079] Small box packaging appearance defect data typically refers to data used during the product packaging process to detect and record quality issues related to the appearance of small box packaging. This data is crucial for ensuring packaging quality, improving the consumer experience, and maintaining brand image.

[0080] Automated visual inspection systems (such as cameras and image processing software) are used to detect defects on small packaging boxes, or machine vision technologies, including image capture, feature extraction, and pattern recognition, are applied to identify and classify different defects. Various defect types that may appear on the small packaging boxes are recorded, such as scratches, stains, wrinkles, misalignments, and damage. Detected defects are typically classified according to their severity, such as Class A, Class B, and Class C defects. Class A defects are serious quality defects that severely affect the product's appearance or functionality; Class B defects are relatively serious quality defects that significantly affect the product's appearance or functionality; and Class C defects are general quality defects that have a negative impact on the product's appearance or functionality. Through statistical analysis of these defects, manufacturers can better understand potential problems in the production process and take corresponding measures to improve the production process and reduce the probability of defects occurring.

[0081] Online appearance defect data for small boxes (10 items, including misaligned seals, folded corners of side seams, and folded lids) typically refers to data on appearance quality issues of small box packaging detected and recorded in real time on the production line. This data is crucial for timely detection of production defects, ensuring product quality, improving production efficiency, and reducing costs. Online appearance defect detection systems typically employ machine vision technology combined with intelligent software algorithms, such as deep learning models, to achieve high-speed, high-precision detection of small box packaging appearance. The system can automatically collect and store image data, which is essential for subsequent data analysis and model optimization. Through online sample annotation, the detection model can be continuously optimized, improving the accuracy and efficiency of detection. The online detection system can identify appearance defects in real time and automatically reject defective products, reducing the defect rate. The system can also provide intelligent alarms, promptly identifying problems in the production process and assisting decision-makers in taking corrective action.

[0082] Step S12: Based on the quality data, obtain the process capability index and defect rate of the packaging machine, and perform average processing on the process capability index and defect rate within a preset time period. Based on the process capability index and defect rate of the current month and the average within the preset time period, obtain the updated process capability index and defect rate.

[0083] Specifically, process capability refers to the extent to which the output of a process meets predetermined quality requirements under steady-state conditions. Defect rate refers to the proportion of non-conforming products to the total number of products produced; it directly reflects problems that occur during the production process.

[0084] Step S121: Based on the quality data, the process capability index of the wrapping machine is calculated using the following formula:

[0085] CPK = CP × (1 - |Ca|)

[0086]

[0087] CPK represents the process capability index of the wrapping machine.

[0088] Ca represents the process accuracy index (the difference between the actual center and the specification center; the smaller the difference, the higher the accuracy).

[0089] X represents the sample mean, μ represents the specification center, CP represents the process capability index, and T represents the specification tolerance.

[0090] σ represents the sample standard deviation.

[0091]

[0092] T = USL - LSL

[0093]

[0094] Where n represents the number of samples, USL represents the upper specification limit, and LSL represents the lower specification limit. c represents the mean of the sample bias, and c4 represents the retouching coefficient, which is related to the sample n and is obtained by looking up a table.

[0095] Step S122: The defect rate of the wrapping machine is calculated based on the quality data using the following formula:

[0096] P1 = Number of defects / Number of inspections

[0097] P2 = Number of defects / Defect opportunities

[0098] Where P1 represents the defect rate calculation for piece-rate data products, and P2 represents the defect rate calculation for point-rate data products.

[0099] Step S123: Perform average processing on the process capability index and defect rate over a preset time period, and obtain updated process capability index and defect rate based on the average of the process capability index and defect rate for the current month and the preset time period.

[0100] In one embodiment, a 6-month period is used as the preset duration for averaging. Process capability indicators are metrics that measure the stability and reliability of the production process. The monthly process capability indicator refers to the production process capability within a specific month, while the average of the 6-month process capability indicator is the average process capability over the past six months. The averaging process is calculated using the following formula:

[0101]

[0102]

[0103] The data after mean normalization is between (0,1), where y ij x represents the normalized value of a single sample for a single indicator. ij This represents a single sample value for a single indicator. This represents the sample mean of a single indicator, where m represents the number of samples.

[0104] In one embodiment, the present invention automatically collects quality data of the wrapping machine; calculates the process capability index and defect rate of the wrapping machine based on the collected quality data; performs average processing on the process capability index and defect rate over a preset period of time to determine the long-term performance trend; and obtains updated process capability index and defect rate by combining the process capability index and defect rate of the current month with the average over the preset period of time to reflect the current production performance.

[0105] Step S13: Based on the preset weights of each indicator, and combined with the updated process capability indicators and defect rate, obtain the individual quality capability evaluation value under the corresponding indicator.

[0106] Step S131, the preset allocation weights based on various indicators include:

[0107] The weights for the physical test data indicators and appearance defect data indicators of cigarettes are allocated according to the product internal control standards: As shown in Table 1, the weight allocation of the physical test data is based on the "Internal Control Standard for Cigarette Products", and the proportion of the deduction value corresponding to the individual physical test indicators (weight, circumference, length, hardness, draw resistance, and total ventilation) in the product enterprise standard is used as the allocation weight; As shown in Table 2, the weight allocation of the appearance defect data is based on the deduction standard for cigarette appearance defects in the "Internal Control Standard for Cigarette Products", and the proportion of the deduction value corresponding to the appearance defect indicators in the product enterprise standard is used as the allocation weight.

[0108] Table 1. Weighting Allocation of Cigarette Physical Tests

[0109] Detection item Weight Circumference Length Hardness Resistance to draw Total ventilation Penalty value 0.2 0.5 0.2 0.5 1 0.2 Weight 7.7％ 19.2％ 7.7％ 19.2％ 38.5％ 7.7％

[0110] Table 2. Weighting of Cigarette Appearance

[0111] Detection item A B C Penalty value 2 0.5 0.2 Weight 74.1％ 18.5％ 7.4％

[0112] The machine quality rejection data indicators are weighted according to the probability of machine quality defects occurring: as shown in Table 3, the weighting of machine quality rejection is based on the probability of machine quality defects occurring.

[0113] Table 3. Weight Allocation for Machine Quality Rejection

[0114]

[0115]

[0116] The process equipment parameter data indicators are weighted according to the priority of the flattening plate position and the close-end quantity: the weighting of the process equipment parameters is based on the importance ranking of the flattening plate position and the close-end quantity, with each parameter having a weight of 50%.

[0117] The weights for the small box packaging appearance defect data indicators are assigned according to the product internal control standards: as shown in Table 4, the weight allocation for small box packaging appearance defects is based on the deduction standards for packaging appearance defects (Class B and C) in the "Internal Control Standards for Cigarette Products", and the proportion of the deduction value corresponding to the small box appearance defect indicators in the product enterprise standards is used as the allocation weight.

[0118] Table 4. Weighting of Appearance Defects in Small Box Packaging

[0119] Detection item B C Penalty value 1.5 1 Weight 60.0％ 40.0％

[0120] The present invention records only the defect rate of the small box online appearance defect, and no longer assigns weights to the inspection items.

[0121] Step S132: Combine the updated process capability indicators and defect rate to obtain the individual quality capability evaluation value under the corresponding indicators.

[0122] The individual quality capability evaluation values ​​include: cigarette physical quality capability score, cigarette appearance quality capability score, machine quality rejection score, process equipment parameter score, small box packaging appearance quality capability score, and small box online appearance quality capability score. The comprehensive evaluation system is formed by considering all individual quality capabilities. For example... Figure 2 As shown, the comprehensive quality capability evaluation of the cigarette rolling and packaging machine specifically includes process implementation capability evaluation and equipment performance capability evaluation; wherein, the process implementation capability evaluation includes comprehensive cigarette quality capability evaluation and comprehensive packaging quality capability evaluation. Further, the comprehensive cigarette quality capability evaluation includes cigarette quality (cigarette physical testing, cigarette appearance defects), machine quality rejection, and process equipment parameters; the comprehensive packaging quality evaluation includes the appearance quality of small box packaging and the online appearance quality of small boxes.

[0123] Specifically, the physical quality of cigarettes is a quality capability score calculated from six physical tests within the evaluation period. The calculation formula is as follows:

[0124] Z 重量 =y 重量 ×100

[0125] Z 圆周 =y 圆周 ×100

[0126] Z长度 =y 长度 ×100

[0127] Z 硬度 =y 硬度 ×100

[0128] Z 吸阻 =y 吸阻 ×100

[0129] Z 通风率 =y 通风率 ×100

[0130] Z 物测 =Z 重量 ×weight 重量 +Z 圆周 ×weight 圆周 +…Z 总通风率 ×weight 总通风率

[0131] In one embodiment, the physical quality test data of cigarettes from three cigarette packaging machines of a certain brand from March to August 2023 were used as a sample to calculate the physical quality test capability score of cigarettes. The calculation results are shown in Tables 5-7:

[0132] Table 5. Performance Indicators of Cigarette Material Testing Process

[0133]

[0134] Table 6. Individual Scores for Cigarette Physical Tests

[0135]

[0136]

[0137] Table 7. Scores for Cigarette Material Quality Measurement Capability

[0138]

[0139] Specifically, the appearance quality of cigarettes is a quality capability score calculated based on the pass rate of the appearance of the three types of cigarettes within the evaluation period. The calculation formula is as follows:

[0140] Z A =Y A ×100

[0141] Z B =Y B ×100

[0142] ZC =Y C ×100

[0143] Z 烟支外观 =Z A ×weight A +Z B ×weight B +Z C ×weight C

[0144] In one embodiment, cigarette appearance data from three cigarette packaging machines of a certain brand from March to August 2023 were used as samples to calculate the cigarette appearance quality capability score. The calculation results are shown in Tables 8-10:

[0145] Table 8. Cigarette Appearance Pass Rate

[0146]

[0147] Table 9. Individual Scores for Cigarette Appearance

[0148]

[0149] Table 10. Cigarette Appearance Quality Score

[0150]

[0151] Specifically, the machine quality rejection capability is a quality capability score calculated by combining the rejection pass rates of two machine quality items within the evaluation period. The calculation formula is as follows:

[0152] Z 空头 =Y 空头 ×100

[0153] Z 漏气 =Y 漏气 ×100

[0154] Z 机台质量剔除 =Z 空头 ×weight 空头 +Z 漏气 ×weight 漏气

[0155] In one embodiment, the quality rejection pass rate of three roll packaging machines of a certain brand from March to August 2023 was used as a sample, and the quality rejection score of the computer machine was calculated. The results are shown in Tables 11-13:

[0156] Table 11. Machine Quality Rejection Rate

[0157]

[0158] Table 12. Scores for Machine Quality Elimination Items

[0159]

[0160] Table 13. Machine Quality Rejection Score

[0161]

[0162] Specifically, the process equipment parameters are quality capability scores calculated by assessing the compliance rates of two process equipment parameters within the evaluation period. The calculation formula is as follows:

[0163]

[0164] Z 平整盘位置 =Y 平整盘位置 ×100, Z 密端量 =Y 密端量 ×100

[0165] Z 工艺设备参数 =Z 平整盘位置 ×weight 平整盘位置 +Z 密端量 ×weight 密端量

[0166] In one embodiment, the compliance rate of process equipment parameters of three wrapping machines of a certain brand from March to August 2023 was used as a sample to calculate the process equipment parameter scores. The calculation results are shown in Tables 14-16.

[0167] Table 14. Compliance Rate of Process Equipment Parameters

[0168]

[0169] Table 15. Individual Scores of Process Equipment Parameters

[0170]

[0171]

[0172] Table 16. Scores of Process Equipment Parameters

[0173]

[0174] Specifically, the appearance quality capability of small box packaging is a quality capability score calculated by using the appearance pass rate index of two types of small boxes within the evaluation period. The calculation formula is as follows:

[0175]

[0176] Z B =y B ×100, Z C =yC ×100

[0177] Z 小盒包装过程外观 =Z B ×weight B +Z C ×weight C

[0178] In one embodiment, the pass rate of small box packaging appearance quality of three roll packaging machines of a certain brand from March to August 2023 was used as a sample to calculate the small box packaging appearance quality capability score. The calculation results are shown in Tables 17-19:

[0179] Table 17. Pass Rate of Appearance Quality of Small Box Packaging

[0180]

[0181]

[0182] Table 18. Individual Scores for Appearance Quality of Small Box Packaging

[0183]

[0184] Table 19. Quality Scores for the Appearance of Small Box Packaging

[0185]

[0186] Specifically, the online appearance quality of the small box is a quality capability score calculated by assessing the pass rate of 10 online tests of the small box within the evaluation period. The calculation formula is as follows:

[0187]

[0188] Z 小盒在线外观 =y×100

[0189] In one embodiment, the online appearance quality pass rate of small boxes on three wrapping machines of a certain brand from March to August 2023 was used as a sample to calculate the online appearance quality capability score of small boxes. The calculation results are shown in Tables 20-21:

[0190] Table 20. Online Appearance Quality Pass Rate of Small Boxes

[0191]

[0192]

[0193] Table 21. Online Appearance Quality Capability Score for Small Boxes

[0194]

[0195] Specifically, the equipment performance capability is a quality capability score calculated by assigning a score to the equipment performance capability of the coiling machine within the evaluation period. The calculation formula is as follows:

[0196]

[0197] Z 设备性能能力 =y×100

[0198] In one embodiment, the equipment performance capability scores of three wrapping machines of a certain brand from March to August 2023 were used as samples to calculate the equipment performance capability scores. The calculation results are shown in Tables 22-23.

[0199] Table 22. Equipment Performance Capability Scoring Values

[0200]

[0201] Table 23. Equipment Performance Capability Scores

[0202]

[0203]

[0204] In one embodiment, the results of the quality capability evaluation of the 667# winding and sealing machine in March are used as a sample, and the overall quality capability evaluation of the winding and sealing machine is illustrated in the schematic structural block diagram. Figure 2 The calculations were performed, and the results are shown in Table 24:

[0205] Table 24. Evaluation of the Overall Quality Capability of the Rolling and Packaging Machine

[0206]

[0207]

[0208] As shown in the table above, the overall quality capability evaluation of the 667# machine in March exceeded 100 points, and the overall quality level was higher than the benchmark level. However, the scores for equipment performance capability, online appearance of small boxes, machine quality rejection, and cigarette appearance were relatively low, indicating that there is still room for further improvement in quality level.

[0209] The scope of protection of the comprehensive quality capability evaluation method for wrapping machines described in this embodiment is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this invention is included within the scope of protection of this invention.

[0210] This invention also provides a comprehensive quality capability evaluation system for roll wrapping machines. The comprehensive quality capability evaluation system for roll wrapping machines can implement the comprehensive quality capability evaluation method for roll wrapping machines described in this invention. However, the implementation device of the comprehensive quality capability evaluation system for roll wrapping machines described in this invention includes, but is not limited to, the structure of the comprehensive quality capability evaluation system for roll wrapping machines listed in this embodiment. All structural modifications and substitutions of the prior art made according to the principles of this invention are included within the protection scope of this invention.

[0211] like Figure 3 As shown, in one embodiment, the comprehensive quality capability evaluation system for the wrapping machine of the present invention includes a data acquisition module 31, a data processing module 32, and a capability evaluation module 33.

[0212] The data acquisition module 31 is used to collect quality data of the roll-up machine under preset indicators.

[0213] The data processing module 32 is connected to the data acquisition module 31 and is used to obtain the process capability index and defect rate of the packaging machine based on the quality data, and to perform average processing on the process capability index and defect rate within a preset time period, and to obtain updated process capability index and defect rate based on the process capability index and defect rate of the current month and the average within the preset time period.

[0214] The capability evaluation module 43 is connected to the data processing module 42 and is used to obtain the single quality capability evaluation value under the corresponding indicator based on the preset weight allocation of each indicator and the updated process capability indicators and defect rate.

[0215] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0216] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of the present invention, depending on actual needs. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0217] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0218] This invention also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0219] This invention also provides an electronic device. The electronic device includes a processor and a memory.

[0220] The memory is used to store computer programs.

[0221] The memory includes various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0222] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device performs the above-described method for evaluating the comprehensive quality capability of the packaging machine.

[0223] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0224] like Figure 4 As shown, the electronic device of the present invention is embodied in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors or processing units 41, a memory 42, and a bus 43 connecting different system components (including the memory 42 and the processing unit 41).

[0225] Bus 43 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0226] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0227] Memory 42 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 421 and / or cache memory 422. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 423 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 43 via one or more data media interfaces. Memory 42 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0228] A program / utility 424 having a set (at least one) of program modules 4241 may be stored, for example, in memory 42. Such program modules 4241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 4241 typically perform the functions and / or methods described in the embodiments of the present invention.

[0229] The electronic device can also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 44. Furthermore, the electronic device can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 45. Figure 4 As shown, network adapter 45 communicates with other modules of the electronic device via bus 43. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0230] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for evaluating the comprehensive quality capability of a wrapping machine table, characterized in that, The method comprises the following steps: Collecting quality data of the cigarette packing machine; Obtaining process capability index and defect rate of the cigarette packing machine based on the quality data, and performing mean value processing on the process capability index and the defect rate within a preset time length, obtaining updated process capability index and defect rate based on the process capability index and the defect rate of the current month and the mean value within the preset time length; Obtaining single quality capability evaluation value under corresponding indexes based on preset allocation weight of each index and in combination with the updated process capability index and the defect rate.

2. The method according to claim 1, characterized in that: The quality data of the cigarette packing machine includes cigarette physical measurement data, cigarette appearance defect data, machine quality rejection data, process equipment parameter data, small box packaging appearance defect data and small box online appearance defect data.

3. The method according to claim 1, wherein: The process capability index of the cigarette packing machine based on the quality data is calculated by the following formula: CPK = CP × (1 - |Ca|) Wherein, CPK represents the process capability index of the cigarette packing machine, Ca represents the process accuracy index, X represents the sample average value, μ represents the specification center, CP represents the process capability index, T represents the specification tolerance, and σ represents the sample standard deviation.

4. The method according to claim 1, wherein: The defect rate of the cigarette packing machine based on the quality data is calculated by the following formula: P1 = defect number / detection times P2 = defect number / defect opportunity Wherein, P1 represents the defect rate calculation for piecework data products, and P2 represents the defect rate calculation for point data products.

5. The method according to claim 1, wherein: The mean value processing is calculated by the following formula: where y ij represents the single sample normalized value of the single item index, x ij represents the single sample value of the single item index, represents the sample mean value of the single item index, and m represents the sample number.

6. The method according to claim 1, wherein: The preset allocation weight of each index specifically comprises: Allocating weight to the cigarette physical measurement data index and the cigarette appearance defect data index according to product internal control standards; Allocating weight to the machine quality rejection data index according to the probability of machine quality defect occurrence; Allocating weight to the process equipment parameter data index according to the priority of the flat disc position and the tight end amount; Allocating weight to the small box packaging appearance defect data index according to product internal control standards.

7. The method according to claim 1, wherein: The single quality capability evaluation value includes cigarette physical measurement quality capability score, cigarette appearance quality capability score, machine quality rejection score, process equipment parameter score, small box packaging appearance quality capability score and small box online appearance quality capability score.

8. A comprehensive quality capability evaluation system for a wrapping machine table, characterized by, The system comprises a data collection module, a data processing module and a capability evaluation module; The data collection module is used to collect quality data of the cigarette packing machine; The data processing module is used to obtain process capability index and defect rate of the cigarette packing machine based on the quality data, and perform mean value processing on the process capability index and the defect rate within a preset time length, and obtain updated process capability index and defect rate based on the process capability index and the defect rate of the current month and the mean value within the preset time length; The capability evaluation module is used to obtain single quality capability evaluation value under corresponding indexes based on preset allocation weight of each index and in combination with the updated process capability index and the defect rate.

9. An electronic device, comprising: The electronic device comprises a processor and a memory; The memory is used to store a computer program; The processor is used to execute the computer program stored in the memory, so that the electronic device performs the cigarette packing machine comprehensive quality capability evaluation method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the electronic device to implement the method for evaluating the comprehensive quality capability of the cigarette making machine according to any one of claims 1 to 7.

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