A food packaging seal detection method

Abstract

The present invention discloses a food packaging seal detection method, which relates to the field of seal detection technology and solves the problem that the corresponding food packaging is not in a critical state of being squeezed, which will lead to an incomplete seal detection. The present invention identifies the sealability of the food packaging bag, does not require human supervision, but performs a self-test by the machine, and determines whether the food packaging bag is in a critical compression state based on the change of its characteristic points, so that the food packaging bag is in a critical state, which can not only achieve a better airtightness test effect, but also avoid the food packaging bag from being ruptured due to excessive negative pressure. This packaging bag sealability inspection method can achieve a better airtightness inspection effect and ensure the relevant accuracy in the airtightness inspection process.

Description

Technical Field

[0001] The invention relates to the technical field of seal detection, in particular to a food package seal detection method. Background Art

[0002] Food packaging seal testing is an important part of ensuring food safety and extending product shelf life. Generally, a food packaging seal tester is used to apply pressure and time to the sample according to the set parameters; during the test, the leakage of the sample is observed.

[0003] The application with publication number CN117110107A discloses a food packaging seal detection system, including a bottom plate, the bottom plate is fixedly connected to a circular ring frame, the circular ring frame is fixedly connected to a circular ring, the circular ring is fixedly connected to a half gear ring, the bottom plate is fixedly connected to a vertical plate, the vertical plate is provided with a vertical groove, a vertical round rod is provided in the vertical groove, the vertical plate is fixedly connected to the vertical round rod, the vertical plate is fixedly connected to a cross bar, the cross bar is fixedly connected to a cylinder, and the cylinder is provided with an annular bevel groove; the bottom plate is fixedly connected to a motor bracket, the motor bracket is fixedly connected to a motor, and the output shaft of the motor passes through the motor bracket and is fixedly connected to a main shaft. The invention relates to the technical field of food packaging detection, specifically, to a food packaging seal detection system. The technical problem to be solved by the invention is to provide a food packaging seal detection system to facilitate the seal detection of puffed packaging.

[0004] During the sealing test of food packaging, whether bubbles are generated in the food packaging is generally determined based on the negative pressure adjustment of the relevant tester, so as to finally assess whether there are relevant abnormalities in the food packaging. However, this assessment method is rather one-sided, and the corresponding food packaging is not in a critical state of being squeezed, which will lead to the sealing test being incomplete and unable to fully ensure the relevant accuracy of the sealing test. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a food packaging seal detection method, which solves the problem that the corresponding food packaging is not in a critical state of being squeezed, which may lead to incomplete seal detection thereof.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A food packaging seal detection method comprises the following steps:

[0007] S1. Place the food packaging bag in a corresponding sealing detector containing distilled water, and use machine vision equipment to lock the feature points of the food packaging bag. The specific method is as follows:

[0008] S11, obtaining a complete surface image of the food packaging bag based on a machine vision device, determining the center point of the packaging bag based on the complete image, and calibrating the determined center point as a main feature point;

[0009] S12, based on the complete surface image and the main feature point, determine the overall edge contour of the food packaging bag, and determine the point farthest from the main feature point on the overall edge contour, and mark this point as a secondary feature point. If there are one or more groups of secondary feature points, their specific existence is determined by the shape of the corresponding food packaging bag;

[0010] S2. Based on the normal negative pressure process of the sealing detector, the food packaging bag is tracked in real time to identify the change in the distance between the characteristic points. Based on the change in the relevant values ​​during the change in the distance between the characteristic points, it is determined whether the food packaging bag is abnormal in the test. At the same time, the straight-line distance and critical distance between the characteristic points of the corresponding food packaging bag are determined. The specific method is as follows:

[0011] S21, vacuum extraction is performed on the sealing detector based on preset parameters to gradually increase the negative pressure inside the sealing detector, and the straight-line distance Lc between the main feature point and the secondary feature point before vacuum extraction is confirmed. If there are multiple groups of secondary feature points, the straight-line distance Lc between the main feature point and the secondary feature point is determined by summing the multiple groups of straight-line distances;

[0012] S22. Based on the corresponding vacuum extraction process, the straight-line distance Lk between the main feature point and the secondary feature point of different time units is confirmed in real time, where k represents different time units, and k=1, 2, ..., n. When k is 1, it represents the first group of time units after the start time of vacuum extraction. When k is 2, it represents the second group of time units after the start time of vacuum extraction. The time unit is a preset unit. According to the different straight-line distances corresponding to different time units, the change distance value Z of adjacent time units is confirmed in real time. q , where q represents different time periods;

[0013] S23, confirm whether the adjacent change distance value satisfies: Z j >Z j+1 ×C1, where j∈q, C1 is a preset fixed coefficient factor. If the subsequent change distance value meets this evaluation condition, the subsequent change distance values ​​that appear again will be confirmed one by one until Z j ≤Z j+1 ×C1, the vacuum extraction process is stopped, and the straight-line distance Lk corresponding to this time unit is calibrated as the critical distance LL;

[0014] S24. Analyze and confirm whether the liquid level of the distilled water changes during the vacuum extraction period. If it does, generate and display an abnormal signal of the food packaging bag test for external personnel to view; if the liquid level of the distilled water does not change, perform subsequent analysis.

[0015] Preferably, it also includes:

[0016] S3. Confirm the linear distance and critical distance of the food packaging bags involved in the test one by one, confirm the distance interval of the corresponding food packaging bags, and then compare several groups of distance intervals. Based on the comparison process, determine the low-gas volume packaging bag or high-gas volume packaging bag. The specific method is as follows:

[0017] S31, according to the total time T of the corresponding food packaging bag from the straight-line distance Lc to the critical distance LL i , where i represents different food packaging bags, and then confirm the distance difference C i , where C i =Lc-LL, using C i ÷T i =V i Confirm the average speed V of the corresponding food packaging bag i ;

[0018] S32, after confirming the average speeds of the different food packaging bags in this batch one by one, sort them in ascending order to generate an average speed sequence, and then segment the sequence according to the preset value Ys to confirm the minimum value V of the average speed sequence i min and max values ​​V i max, based on the preset value Ys, generates a set of change intervals, the interval range distance of this change interval is Ys, and the minimum value of its change interval is V i min, the maximum value of its variation range is V i max, the variation of its variation range is: [V i min,V i min+Ys] to [V i max-Ys,V i max], the rate of each change is a unit rate, and the unit rate value is 0.1;

[0019] S33, according to the determined change interval, the change average speed in the average speed sequence belonging to the change interval is subjected to variance processing to determine the variance value F1, and then according to the change interval of real-time change, different change average speeds are subjected to variance processing to determine several groups of variance values, the minimum variance is selected from the several groups of variance values, and the change interval corresponding to the minimum variance is used as the standard interval;

[0020] S34, according to the average speed of change corresponding to the standard interval, and then according to the average speed of change, determine the corresponding critical distance LL, select the minimum value and the maximum value from several groups of critical distances LL, and determine the evaluation value interval;

[0021] S35, calibrating the food packaging bags whose critical distance is greater than the evaluation value range as high-gas volume packaging bags, and calibrating the food packaging bags whose critical distance is less than the evaluation value range as low-gas volume packaging bags;

[0022] S4. Based on the determined related numbers of high-gas volume packaging bags and low-gas volume packaging bags, the proportion of high-gas volume packaging bags and low-gas volume packaging bags is determined and displayed; the specific method for determining the proportion of high-gas volume packaging bags and low-gas volume packaging bags is:

[0023] Determine the total number of food packaging bags ZG in this batch, and then determine the number of high-gas volume packaging bags G1 and the number of low-gas volume packaging bags G2;

[0024] Use B1=G1÷ZG and B2=G2÷ZG to determine the proportion of high-gas volume packaging bags (B1) and the proportion of low-gas volume packaging bags (B2).

[0025] The present invention provides a food packaging seal detection method. Compared with the prior art, it has the following beneficial effects:

[0026] The present invention identifies the sealing property of the food packaging bag by confirming the degree of change of the packaging bag under the negative pressure state. No human supervision is required, but the machine performs the test by itself. Based on the change of the characteristic points, it determines whether the food packaging bag is in a critical compression state. By putting the food packaging bag in a critical state, not only a better airtightness test effect can be achieved, but also the food packaging bag can be prevented from being ruptured due to excessive negative pressure. This packaging bag sealing test method can achieve a better airtightness test effect and ensure the relevant accuracy in the airtightness test process.

[0027] After the air tightness test is completed, the average speed change is analyzed to determine the groups of average speeds with the lowest degree of dispersion. Based on the packaging bags to which the corresponding average speeds belong, the corresponding critical distance can be determined. Based on the relevant display of the critical distance, the corresponding evaluation value interval can be determined. Based on this evaluation value interval, the critical distance evaluation of other packaging bags can be used to evaluate the gas volume performance of the relevant packaging bags, and the high gas volume and low gas volume related evaluations can be performed for display.

[0028] Not only can the air tightness of packaging bags be tested, but the air volume of the corresponding batch of packaging bags can also be identified and displayed numerically based on the relevant data in the relevant testing process. External relevant personnel can make relevant response measures in a timely manner based on the relevant displayed numerical values. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the process of the present invention;

[0030] Figure 2 Schematic diagram of the method for evaluating high and low gas volume packaging bags of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Example 1

[0033] See also Figure 1 The present application provides a food packaging seal detection method, comprising the following steps:

[0034] S1. Place the food packaging bag in a corresponding sealing detector, wherein distilled water is contained in the sealing detector, and use a machine vision device to lock the feature points of the food packaging bag. Specifically, the machine vision device is arranged outside the sealing detector to monitor the negative pressure process of the food packaging bag, and based on the change process of the packaging bag, the feature points of the food packaging bag are tracked in real time to identify the parameter changes between the feature points. The specific method of locking the feature points is as follows:

[0035] S11. Obtain a complete surface image of the food packaging bag based on a machine vision device, determine the center point of the packaging bag based on the complete image, and calibrate the determined center point as the main feature point. Specifically, there are many common ways to obtain the center point based on relevant images. The current artificial intelligence can complete the relevant determination operation, so it will not be described in detail here. Common methods include centroid method, segmentation method and other different processing methods;

[0036] S12, based on the complete surface image and the main feature point, determine the overall edge contour of the food packaging bag, and determine the point farthest from the main feature point on the overall edge contour, and mark this point as a secondary feature point. There are one or more groups of secondary feature points, and their specific existence is determined by the shape of the corresponding food packaging bag. The reason for determining the secondary feature points here is to perform numerical analysis between subsequent feature points to analyze the change process of the corresponding packaging bag, determine whether the packaging bag is in the maximum pressure-bearing state, and identify whether its sealing performance meets the standard. When the packaging bag is a regular rectangle, the secondary feature points are the four corner points;

[0037] S2. Based on the normal negative pressure process of the sealing detector, the food packaging bag is tracked in real time to identify the change in the point distance of its feature points. Based on the change in the relevant numerical value during the point distance change process, it is confirmed whether the food packaging bag is tested abnormally, and the straight-line distance and critical distance between the feature points of the corresponding food packaging bag are confirmed. In the normal test process, the corresponding food packaging bag is generally placed inside the corresponding detector, and the detector is in a closed environment. The closed environment is vacuum-extracted using an external extraction tube, so that the environment inside the distilled water is in a negative pressure state. This negative pressure state will squeeze the food packaging bag, and the food packaging bag will be squeezed toward the middle during the squeezing process. The specific method of confirmation is:

[0038] S21, vacuum extraction is performed on the sealing detector based on preset parameters to gradually increase the negative pressure inside the sealing detector, and the straight-line distance Lc between the main feature point and the secondary feature point before vacuum extraction is confirmed. If there are multiple groups of secondary feature points, the straight-line distance Lc between the main feature point and the secondary feature point is determined by summing the multiple groups of straight-line distances;

[0039] S22. Based on the corresponding vacuum extraction process, the straight-line distance Lk between the main feature point and the secondary feature point in different time units is confirmed in real time, where k represents different time units, and k=1, 2, ..., n. When k is 1, it represents the first group of time units after the start time of vacuum extraction. When k is 2, it represents the second group of time units after the start time of vacuum extraction. The time unit is a preset unit, which is prepared in advance by the operator and generally takes a value of 100ms. According to the different straight-line distances corresponding to different time units, the change distance value Z of adjacent time units is confirmed in real time. q , where q represents different time periods;

[0040] S23, confirm whether the adjacent change distance value satisfies: Z j >Z j+1 ×C1, where j∈q, C1 is a preset fixed coefficient factor, the specific value of which is determined by the operator based on experience, and is generally 0.5. If the subsequent change distance value meets this evaluation condition, the subsequent change distance values ​​that appear again are confirmed one by one until Z j ≤Z j+1 ×C1, the vacuum extraction process is stopped, and the straight-line distance Lk corresponding to this time unit is calibrated as the critical distance LL;

[0041] S24, analyzing and confirming whether the liquid level of the distilled water changes during the vacuum extraction period. If so, generating and displaying a food packaging bag test abnormality signal for external personnel to view, indicating that there are related problems with the air tightness of the food packaging bag, resulting in gas leakage, thereby causing the liquid level of the distilled water to drop. If no change occurs, subsequent analysis is performed to assess the compression state of the food packaging bag during this stage, identify whether the numerical change trends of multiple groups of food packaging bags during this stage are the same, and assess whether the gas content of the food packaging bags is consistent;

[0042] Specifically, this method is used to identify the sealing of food packaging bags. There is no need for human supervision. Instead, the machine tests itself and determines whether the food packaging bag is in a critical compression state based on the changes in its characteristic points. This puts the food packaging bag in a critical state, which can not only achieve better airtightness test results, but also prevent the food packaging bag from rupturing due to excessive negative pressure. This packaging bag sealing inspection method can achieve better airtightness inspection results and ensure the relevant accuracy in the airtightness detection process.

[0043] Example 2

[0044] In the specific implementation process of this embodiment, compared with the above embodiment, this embodiment mainly analyzes the test data of the food packaging bags of the same batch, and the analyzed data is the relevant data of normal test;

[0045] The following steps are also included:

[0046] S3. Combination Figure 2 , confirm the straight-line distance and critical distance of the food packaging bags involved in the test in this batch one by one, confirm the distance interval of the corresponding food packaging bags, and then compare several groups of distance intervals. Based on the comparison process, determine the low-gas volume packaging bags or high-gas volume packaging bags. Specifically, based on the corresponding gas volume comparison process, it can be confirmed whether there are gas volume ratio-related problems in the production process of the corresponding packaging bags, and timely adjustments can be made to the related problems to package the overall quality of the corresponding food packaging bags in the production process;

[0047] The detailed process of the relevant comparison includes:

[0048] S31, according to the total time T of the corresponding food packaging bag from the straight-line distance Lc to the critical distance LL i , where i represents different food packaging bags, and then confirm the distance difference C i , where C i =Lc-LL, using C i ÷T i =V i Confirm the average speed V of the corresponding food packaging bagi ;

[0049] S32. After confirming the average speeds of the different food packaging bags in this batch one by one, sort them in ascending order to generate an average speed sequence, and then segment the sequence according to the preset value Ys, where the specific value of Ys is determined by the operator based on experience, and confirm the minimum value V of the average speed sequence. i min and max values ​​V i max, based on the preset value Ys, generates a set of change intervals, the interval range distance of this change interval is Ys, and the minimum value of its change interval is V i min, the maximum value of its variation range is V i max, the variation of its variation range is: [V i min,V i min+Ys] to [V i max-Ys,V i max], the rate of each change is a unit rate, and the unit rate is 0.1. Here we combine the example to understand: Assume that V i min is 6.0, where V i max is 8.1, and Ys is 0.5, then the first group of expression of the change interval is: [6.0, 6.5] changes to [6.1, 6.6]-[6.2, 6.7]-[6.3, 6.8], and stops when the change value reaches [7.6, 8.1], completing the entire change process;

[0050] S33, according to the determined change interval, the change average speed in the average speed sequence belonging to this change interval is processed with variance to determine the variance value F1, and then according to the real-time change change interval, different change average speeds are processed with variance to determine several groups of variance values, the minimum variance is selected from several groups of variance values, and the change interval corresponding to the minimum variance is used as the standard interval. Specifically, the average speed sequence changes from 6.0 to 8.1, and includes several groups of different change average speeds. Then, based on the first group of change intervals [6.0, 6.5], the relevant parameters belonging to this change interval can be determined to perform variance processing to determine the first group of variance values. After the processing is completed, the change interval is changed to [6.1, 6.6], so the second group of variance values ​​can be determined. And so on, one by one confirmation is performed to determine the relevant variance values. The variance processing process of the numerical value is relatively common, so it will not be described in detail here. The variance can indicate the discrete degree of the numerical value inside the corresponding interval.

[0051] S34, according to the average speed of change corresponding to the standard interval, and then according to the average speed of change, determine the corresponding critical distance LL, select the minimum value and the maximum value from several groups of critical distances LL, and determine the evaluation value interval;

[0052] S35. The food packaging bags whose critical distance is greater than the evaluation value range are calibrated as high-gas volume packaging bags, and the food packaging bags whose critical distance is less than the evaluation value range are calibrated as low-gas volume packaging bags. The larger the critical distance, the more gas there is inside the packaging bag, and the smaller the critical distance, the less gas there is inside the packaging bag.

[0053] S4. Based on the determined related numbers of high-gas volume packaging bags and low-gas volume packaging bags, the proportion of high-gas volume packaging bags and low-gas volume packaging bags is determined and displayed. The specific method for determining is:

[0054] Determine the total number of food packaging bags ZG in this batch, and then determine the number of high-gas volume packaging bags G1 and the number of low-gas volume packaging bags G2;

[0055] Use B1=G1÷ZG and B2=G2÷ZG to determine the proportion of high-gas volume packaging bags (B1) and the proportion of low-gas volume packaging bags (B2).

[0056] Specifically, during the normal negative pressure test, the average speeds of the packaging bags with similar gas volumes are not much different. Therefore, the average speeds with the lowest degree of dispersion can be determined by analyzing the average speeds. Based on the packaging bags with the corresponding average speeds, the corresponding critical distances can be determined. Based on the relevant display of the critical distances, the corresponding evaluation value intervals can be determined. Based on the evaluation of the critical distances of other packaging bags in this evaluation value interval, the gas volume performance of the relevant packaging bags can be evaluated, and the high gas volume and low gas volume can be evaluated for display.

[0057] Not only can the air tightness of packaging bags be tested, but the air volume of the corresponding batch of packaging bags can also be identified and displayed numerically based on the relevant data in the relevant testing process. External relevant personnel can make relevant response measures in a timely manner based on the relevant displayed numerical values.

[0058] Example 3

[0059] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0060] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0061] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A food packaging seal detection method, It is characterized in that The following steps are involved: S1. Place the food packaging bag in a corresponding sealing detector containing distilled water, and use a machine vision device to lock the feature points of the food packaging bag; S2. Based on the normal negative pressure process of the sealing detector, the food packaging bag is tracked in real time to identify the change in the distance between the characteristic points. Based on the change in the relevant values ​​during the change in the distance between the characteristic points, it is determined whether the food packaging bag is abnormal in the test. At the same time, the straight-line distance and critical distance between the characteristic points of the corresponding food packaging bag are determined. The specific method is as follows: S21, vacuum extraction is performed on the sealing detector based on preset parameters to gradually increase the negative pressure inside the sealing detector, and the straight-line distance Lc between the main feature point and the secondary feature point before vacuum extraction is confirmed. If there are multiple groups of secondary feature points, the straight-line distance Lc between the main feature point and the secondary feature point is determined by summing the multiple groups of straight-line distances; S22. Based on the corresponding vacuum extraction process, the straight-line distance Lk between the main feature point and the secondary feature point of different time units is confirmed in real time, where k represents different time units, and k=1, 2, ..., n. When k is 1, it represents the first group of time units after the start time of vacuum extraction. When k is 2, it represents the second group of time units after the start time of vacuum extraction. The time unit is a preset unit. According to the different straight-line distances corresponding to different time units, the change distance value Z of adjacent time units is confirmed in real time. q , where q represents different time periods; S23, confirm whether the adjacent change distance value satisfies: Z j >Z j+1 ×C1, where j∈q, C1 is a preset fixed coefficient factor. If the subsequent change distance value meets this evaluation condition, the subsequent change distance values ​​that appear again will be confirmed one by one until Z j ≤Z j+1 ×C1, the vacuum extraction process is stopped, and the straight-line distance Lk corresponding to this time unit is calibrated as the critical distance LL; S24, analyzing and confirming whether the liquid level of the distilled water changes during the vacuum extraction period. If it changes, generating a food packaging bag test abnormality signal and displaying it for external personnel to view; S3. Confirm the linear distance and critical distance of the food packaging bags involved in the test one by one, confirm the distance interval of the corresponding food packaging bags, and then compare several groups of distance intervals. Based on the comparison process, determine the low-gas volume packaging bag or high-gas volume packaging bag. The specific method is as follows: S31, according to the total time T of the corresponding food packaging bag from the straight-line distance Lc to the critical distance LL i , where i represents different food packaging bags, and then confirm the distance difference C i , where C i =Lc-LL, using C i ÷T i =V i Confirm the average speed V of the corresponding food packaging bag i ; S32, after confirming the average speeds of the different food packaging bags in this batch one by one, sort them in ascending order to generate an average speed sequence, and then segment the sequence according to the preset value Ys to confirm the minimum value V of the average speed sequence i min and max values ​​V i max, based on the preset value Ys, generates a set of change intervals, the interval range distance of this change interval is Ys, and the minimum value of its change interval is V i min, the maximum value of its variation range is V i max, the variation of its variation range is: [V i min,V i min+Ys] to [V i max-Ys,V i max], the rate of each change is a unit rate, and the unit rate value is 0.1; S33, according to the determined change interval, the change average speed in the average speed sequence belonging to the change interval is subjected to variance processing to determine the variance value F1, and then according to the change interval of real-time change, different change average speeds are subjected to variance processing to determine several groups of variance values, the minimum variance is selected from the several groups of variance values, and the change interval corresponding to the minimum variance is used as the standard interval; S34, according to the average speed of change corresponding to the standard interval, and then according to the average speed of change, determine the corresponding critical distance LL, select the minimum value and the maximum value from several groups of critical distances LL, and determine the evaluation value interval; S35, calibrating the food packaging bags whose critical distance is greater than the evaluation value range as high-gas volume packaging bags, and calibrating the food packaging bags whose critical distance is less than the evaluation value range as low-gas volume packaging bags; S4. Based on the determined related numbers of high-gas volume packaging bags and low-gas volume packaging bags, the proportion of high-gas volume packaging bags and low-gas volume packaging bags is determined and displayed.

2. A food packaging seal detection method according to claim 1, It is characterized in that In step S1, the specific method of locking the feature points is: S11, obtaining a complete surface image of the food packaging bag based on a machine vision device, determining the center point of the packaging bag based on the complete image, and calibrating the determined center point as a main feature point; S12. Based on the complete surface image and the main feature points, determine the overall edge contour of the food packaging bag, and determine the point farthest from the main feature point on the overall edge contour, and mark this point as a secondary feature point.

3. A food packaging seal detection method according to claim 2, It is characterized in that In the step S12, there are one or more groups of secondary feature points, and their specific existence is determined by the shape of the corresponding food packaging bag.

4. A food packaging seal detection method according to claim 1, It is characterized in that In step S24, if the liquid level of the distilled water does not change, a subsequent analysis is performed.

5. A food packaging seal detection method according to claim 1, It is characterized in that In step S4, the specific method of determining the ratio of the high-gas volume packaging bags and the low-gas volume packaging bags is: Determine the total number of food packaging bags ZG in this batch, and then determine the number of high-gas volume packaging bags G1 and the number of low-gas volume packaging bags G2; Use B1=G1÷ZG and B2=G2÷ZG to determine the proportion of high-gas volume packaging bags (B1) and the proportion of low-gas volume packaging bags (B2).

Images