A parameter optimization method for an automatic dosing packaging scale

Abstract

The present application relates to a kind of parameter optimization methods for automatic quantitative packaging scale, including first parameter optimization process and second parameter optimization process, the second fast feed end weight is optimized by slow feed flow rate and standard slow feed time, by obtaining second slow feed consumption time to judge whether the physical property of material changes, so that the parameter optimization method can respond to material characteristic change.This parameter optimization method can be automatically adjusted to optimal parameter, without operator intervention, more highly automated;At the same time, this parameter optimization method is efficient and simple, time-consuming is short, easy to implement, can realize capacity maximization on the basis of satisfying accuracy requirement.

Description

Technical Field

[0001] This invention relates to the field of automatic quantitative packaging scales, and in particular to a parameter optimization method for automatic quantitative packaging scales. Background Technology

[0002] The automatic quantitative packaging scale operates through two phases: a rapid feeding phase and a slow feeding phase. The rapid feeding phase offers fast feeding speeds but cannot guarantee accurate weighing and packaging. The slow feeding phase, while slower, allows for precise control of weighing and packaging accuracy. In practical use, the client needs to manually set the "Target Weight" and "Rapid Feed End Point (SP)." The Target Weight represents the client's desired weight per package, and the Rapid Feed End Point (SP) indicates the total weight to be achieved during the rapid feeding phase.

[0003] A Chinese invention patent has been published, publication number: CN110949706A, patent title: Automatic Tuning and Optimization Method for Working Parameters of Automatic Quantitative Packaging Scale, application date: 2019.11.26. It discloses an automatic tuning and optimization method for the working parameters of an automatic quantitative packaging scale. By first conducting a slow feeding test throughout the entire process, and then conducting a test of fast feeding followed by slow feeding, the relevant weight parameters of key nodes in the two tests are obtained. The optimal weights E1 at the end of fast feeding and E2 at the end of slow feeding are calculated using these weight parameters, thereby achieving self-tuning and optimization of working parameters. This eliminates the dependence on the operator's technical level and can achieve high accuracy and work efficiency.

[0004] However, this optimization method requires obtaining multiple weight parameters and taking average values ​​from multiple trials, which takes a long time to optimize the parameters and wastes time. At the same time, this optimization method eliminates the influence of material characteristics on the optimization results. When the physical characteristics of the material fed into the automatic quantitative packaging scale change during operation, the optimization method cannot respond and will not automatically adjust the parameters. In this case, the operator needs to manually adjust the parameters to ensure the weighing accuracy of the automatic quantitative packaging scale. Summary of the Invention

[0005] To address the shortcomings of existing production technologies, this applicant provides a reasonable parameter optimization method for automatic quantitative packaging scales. This method optimizes parameters by acquiring the time consumed by the second slow feed to determine if the physical properties of the material have changed. This allows the optimization method to respond to changes in material properties and automatically adjust to optimal parameters without operator intervention, resulting in a higher degree of automation. Furthermore, this parameter optimization method is efficient, simple, time-saving, and easy to implement, maximizing production capacity while meeting accuracy requirements.

[0006] The technical solution adopted in this invention is as follows:

[0007] A parameter optimization method for an automatic quantitative packaging scale, the parameter optimization method comprising the following steps:

[0008] The material type and target weight are obtained, and different standard slow feeding times are set for different material types in the control system.

[0009] The first parameter optimization process is carried out, and the fast feeding end weight is set as the first fast feeding end weight. The first fast feeding end weight is 50% of the target weight. Fast feeding is performed and the real-time weight in the package is obtained. When the real-time weight reaches the set fast feeding end weight, fast feeding is stopped and the weight of the first material in the package is obtained after stabilization.

[0010] Slowly feed the material and obtain the real-time weight in the package. Simultaneously start the internal timer to keep track of the time. When the real-time weight reaches the target weight, stop feeding and stop timing. Obtain the first slow feeding time. After stabilization, obtain the second material weight in the package at this time.

[0011] The difference between the weight of the second material and the weight of the first material is determined to be the weight required for the first slow feed.

[0012] The ratio of the weight required for the first slow feed to the time consumed by the first slow feed is determined as the slow feed flow rate;

[0013] The product of the slow feed rate and the standard slow feed time is determined as the weight required for the second slow feed.

[0014] The difference between the target weight and the weight required for the second slow feed is determined as the ending weight for the second fast feed, thus completing the first parameter optimization process;

[0015] The second parameter optimization process is carried out by setting the fast feeding end weight as the second fast feeding end weight, fast feeding and obtaining the real-time weight in the package, stopping fast feeding when the real-time weight reaches the set fast feeding end weight, and simultaneously starting slow feeding, activating the internal timer and obtaining the real-time weight in the package.

[0016] When the real-time weight reaches the target weight, stop feeding and stop timing, and obtain the second slow feeding time FST2;

[0017] If the second slow feed time is within the set range of the standard slow feed time, it means that the material properties have not changed, so the number of material property changes MC = 0.

[0018] If the second slow feed time exceeds the set range of the standard slow feed time, it indicates that the material properties have changed, so the number of material property changes MC = MC + 1.

[0019] If the number of times the material properties change (MC) is greater than or equal to 3, the first parameter optimization process is repeated.

[0020] If the number of times the material properties change (MC) is less than 3, continue with the second parameter optimization process.

[0021] Its further technical solution lies in:

[0022] The parameter optimization method can also obtain information such as whether it is the user's first time using the service.

[0023] If this is the user's first time using the service, the first parameter optimization process must be performed first. Only after the first parameter optimization process is completed can the second parameter optimization process be performed.

[0024] If the user is not a first-time user, the second parameter optimization process can be performed directly.

[0025] When the user is not using the service for the first time, the second parameter optimization process will use the fast feed end weight parameter that the user used last time.

[0026] Each time the internal timer is synchronously activated, it starts from 00:00.

[0027] The second slow feeding time needs to be controlled within the range of ±0.5 seconds of the standard slow feeding time.

[0028] The control system has preset standard slow feeding times for four material types, with the standard slow feeding time ranging from 3 to 5 seconds.

[0029] The four material types are: powdered materials, granular materials, irregularly shaped materials (flakes / strips / blocks), and other types of materials;

[0030] The standard slow feeding time for powdery materials is 4 seconds.

[0031] The standard slow feeding time for granular materials is 3 seconds.

[0032] The standard slow feeding time for powder / strip / block materials is 5 seconds.

[0033] The standard slow feeding time for other types of materials is 4 seconds.

[0034] Images of typical materials are provided for users' reference for each of the four material types.

[0035] During the first parameter optimization process, the system needs to be stabilized for 3 seconds before weighing the first and second materials.

[0036] The beneficial effects of this invention are as follows:

[0037] This invention is reasonable and efficient. By monitoring the material characteristics through the second slow feeding time, the parameter optimization method can respond to changes in material characteristics and automatically adjust to the optimal parameters without operator intervention, thus achieving a higher degree of automation. At the same time, this parameter optimization method has a short parameter optimization time, is easy to implement, and can maximize production capacity while meeting accuracy requirements.

[0038] The present invention also has the following advantages:

[0039] (1) It has a “first use” mode and a “daily use” mode. After using the “first use” mode, you can directly use the “daily use” mode. The design is reasonable and can shorten working time, making it more efficient and convenient.

[0040] (2) The present invention is simple to calculate, requires less data, has low requirements for the control system, and the optimization results are stable and reliable, and can also shorten the calculation time.

[0041] (3) The parameter optimization method provided by the present invention can achieve fully automatic optimization without human intervention, and has a higher degree of automation. Attached Figure Description

[0042] Figure 1 This is a structural block diagram of the control system of the present invention.

[0043] Figure 2 This is a schematic diagram of the information flow of the present invention. Detailed Implementation

[0044] The specific embodiments of the present invention will now be described with reference to the accompanying drawings.

[0045] Example 1:

[0046] like Figures 1-2 As shown, a parameter optimization method for an automatic quantitative packaging scale is described. Figure 1 In this system, the human-machine interface is used to obtain the material type and target weight input by the user; the feed rate control module is used to control the material feed rate; the internal timer is used for timing; the weight acquisition module is used to acquire the real-time weight of the material; and the main control chip is used to process and calculate the information returned by other modules and issue instructions to control the working status of other modules. The feed rate control module, the internal timer, and the main control chip together form the control system.

[0047] refer to Figure 2 The information flow diagram shown illustrates a parameter optimization method for automatic quantitative packaging scales, which includes:

[0048] Obtain the material type and target weight (Target), and control the system to set different standard slow feeding times (ST) for different material types.

[0049] The first parameter optimization process is carried out. The fast feeding end weight SP is set to the first fast feeding end weight SP1. The first fast feeding end weight SP1 is 50% of the target weight. Fast feeding is performed and the real-time weight in the package is obtained. When the real-time weight reaches the set fast feeding end weight SP, fast feeding is stopped and the first material weight FSW1 in the package is obtained after stabilization.

[0050] Start slow feeding and obtain the real-time weight of the package. Simultaneously start the internal timer to keep track of the time. Stop feeding and stop timing when the real-time weight reaches the target weight. Obtain the first slow feeding time FST1. After stabilization, obtain the second material weight SSW1 in the package at this time.

[0051] The difference between the weight of the second material SSW1 and the weight of the first material FSW1 is determined to be the weight W1 required for the first slow feed.

[0052] The ratio of the weight W1 required for the first slow feed to the time FST1 consumed by the first slow feed is determined as the slow feed flow rate SR;

[0053] The product of the slow feed rate SR and the standard slow feed time ST is determined as the weight W2 required for the second slow feed.

[0054] The difference between the target weight Target and the weight W2 required for the second slow feed is determined as the end weight SP2 of the second fast feed, thus completing the first parameter optimization process;

[0055] The second parameter optimization process is carried out by setting the fast feeding end weight SP to the second fast feeding end weight SP2, fast feeding is performed and the real-time weight in the package is obtained. When the real-time weight reaches the set fast feeding end weight SP, fast feeding is stopped and slow feeding is started at the same time. The internal timer is started synchronously and the real-time weight in the package is obtained.

[0056] When the real-time weight reaches the target weight Target, stop feeding and stop timing, and obtain the second slow feeding time FST2;

[0057] If the second slow feed time FST2 is within the set range of the standard slow feed time ST, it means that the material properties have not changed, so the number of material property changes MC = 0.

[0058] If the second slow feed time FST2 exceeds the set range of the standard slow feed time ST, it indicates that the material properties have changed, so the number of material property changes MC = MC + 1.

[0059] If the number of times the material properties change (MC) is greater than or equal to 3, the first parameter optimization process is repeated.

[0060] If the number of times the material properties change (MC) is less than 3, continue with the second parameter optimization process.

[0061] Parameter optimization methods can also obtain information such as whether it is the user's first time using the service.

[0062] If this is the user's first time using the service, the first parameter optimization process must be performed first. Only after the first parameter optimization process is completed can the second parameter optimization process be performed.

[0063] If the user is not a first-time user, the second parameter optimization process can be performed directly.

[0064] When the user is not using the service for the first time, the second parameter optimization process will use the fast feed end weight (SP) parameter that the user used last time.

[0065] The internal timer starts counting from 00:00 every time it is synchronously activated.

[0066] The second slow feed time, FST2, needs to be controlled within the range of standard slow feed time ST ± 0.5S.

[0067] The control system has preset standard slow feeding times (ST) for four material types, with the standard slow feeding time (ST) ranging from 3 to 5 seconds.

[0068] The four material types are: powdered materials, granular materials, irregularly shaped materials (flakes / strips / blocks), and other types of materials;

[0069] The standard slow feeding time for powdery materials is 4 seconds.

[0070] The standard slow feeding time for granular materials is 3 seconds.

[0071] The standard slow feeding time for powder / strip / block materials is 5 seconds.

[0072] The standard slow feeding time for other types of materials is 4 seconds.

[0073] Images of typical materials are provided for users' reference for each of the four material types.

[0074] During the first parameter optimization process, the weight of the first material FSW1 and the weight of the second material SSW1 need to be stabilized for 3 seconds before weighing.

[0075] This embodiment provides a parameter optimization method for an automatic quantitative packaging scale. It is simple, reliable, and easy to implement. It does not require human intervention and can automatically correct the "fast feeding end weight SP" throughout the process. At the same time, by monitoring the second slow feeding consumption time FST2, it can adapt to the changing working conditions of material characteristics, ensuring the stability of the weight accuracy of each package. It can also ensure that maximum production capacity is achieved under the condition of meeting the accuracy and stability requirements.

[0076] Example 2:

[0077] According to the parameter optimization method for an automatic quantitative packaging scale provided in Embodiment 1, Embodiment 2 takes the automatic quantitative packaging of 5 bags of rice, 25kg / bag, as an example to realize automatic quantitative packaging;

[0078] This is the first time that this method has been used to quantitatively package rice in 25kg / bags. The material properties have not changed during the quantitative packaging process, and it is necessary to meet the national standard of 0.2-level measurement accuracy. Therefore, the weight error of each bag of packaged rice needs to be controlled within ±50g.

[0079] Specifically, the steps include the following:

[0080] S1. To quantitatively package the first bag of rice, firstly, obtain the material type and target weight Target, and control the system to set different standard slow feeding times ST for different material types.

[0081] The user inputs the material type and target weight (Target) into the human-machine interface. First, the user needs to select the material type to be quantitatively weighed and packaged. There are four material type options. After the user selects the material type, the control system will automatically match the standard slow feed time (ST) based on the information pre-stored in the main control chip. The correspondence between material type and standard slow feed time (ST) is shown in the table below:

[0082] Material type Standard slow feed time ST powdery materials 4S Particulate materials 3S Flake-strip-block irregularly shaped materials 5S Other types of materials 4S

[0083] Each option is accompanied by a picture of a typical material for users to refer to, making it easy for users to compare the materials they need with the typical materials and helping them select the type of material.

[0084] In this embodiment, the material type is selected as "particulate material", and the corresponding standard slow feeding time ST is 3S, and the target weight Target is set to 25kg;

[0085] S2. After setting the material type and target weight, the user also needs to select whether it is the first time using the device on the human-computer interaction interface;

[0086] S2.1. When the user selects "Yes", the "First Use" mode is entered. The "First Use" mode includes the first parameter optimization process and the second parameter optimization process.

[0087] S2.2. When the user selects "No", the "Daily Use" mode is entered. The "Daily Use" mode first performs the second parameter optimization process, and the first parameter optimization process will only be performed when the corresponding conditions are triggered.

[0088] S2.3. In this embodiment, the user selects "Yes";

[0089] S3. Perform the first parameter optimization process;

[0090] S3.1. Set the fast feeding end weight SP to the first fast feeding end weight SP1. The first fast feeding end weight SP1 is 50% of the target weight Target, i.e. SP1 = Target * 50% = 12.5kg. Fast feed and obtain the real-time weight in the package. When the real-time weight reaches the set fast feeding end weight SP = 12.5kg, stop fast feeding, stabilize for 3 seconds, and obtain the weight of the first material in the package at this time, FSW1.

[0091] S3.2 Start slow feeding and obtain the real-time weight of the package. Simultaneously start the internal timer to start timing. The internal timer starts timing from 00:00. When the real-time weight reaches the target weight Target, stop feeding and stop timing. Obtain the first slow feeding time FST1. After stabilizing for 3 seconds, obtain the second material weight SSW1 in the package at this time.

[0092] S3.2. Determine the difference between the weight of the second material SSW1 and the weight of the first material FSW1 as the weight W1 required for the first slow feed, i.e., W1 = SSW1 - FSW1;

[0093] S3.3. The ratio of the weight W1 required for the first slow feed to the time FST1 consumed by the first slow feed is the slow feed rate SR, i.e., SR = SFW1 / FST1, in kg / s;

[0094] S3.4. Determine the product of the slow feed rate SR and the standard slow feed time ST as the weight W2 required for the second slow feed. Determine the difference between the target weight Target and the weight W2 required for the second slow feed as the weight SP2 at the end of the second fast feed, i.e., SP2 = Target - SR * ST.

[0095] S3.5. Complete the first parameter optimization process;

[0096] S4. At this point, the automatic quantitative packaging of the first bag of rice is completed;

[0097] S5. When packaging the second bag of rice, no user operation is required. The automatic production line places the packaging bag in the corresponding position, and the automatic quantitative packaging scale will automatically work to package the second bag of rice.

[0098] S6. When packaging the second bag of rice, the second parameter optimization process will be performed directly;

[0099] S6.1. Set the fast feeding end weight SP to the second fast feeding end weight SP2, fast feed and obtain the real-time weight in the package. When the real-time weight reaches the set fast feeding end weight SP, stop fast feeding and start slow feeding at the same time. Simultaneously start the internal timer and obtain the real-time weight in the package. The internal timer starts counting from 00:00 seconds.

[0100] S6.2. Stop feeding and stop timing when the real-time weight reaches the target weight Target, and obtain the second slow feeding time FST2;

[0101] S6.3. If the second slow feed time FST2 is within the set range of the standard slow feed time ST, it means that the material characteristics have not changed, so set the number of material characteristic changes MC = 0;

[0102] If the second slow feed time FST2 exceeds the set range of the standard slow feed time ST, it indicates that the material properties have changed, so the number of material property changes MC = MC + 1.

[0103] In this embodiment, the second slow feeding time FST2 needs to be controlled within the range of standard slow feeding time ST ± 0.5S, that is, FST2 >= ST - 0.5S and FST2 <= ST + 0.5S. In this embodiment, the material characteristics of rice will not change, so the number of material characteristic changes will always remain MC = 0.

[0104] S6.4. If the number of times the material properties change (MC) is greater than or equal to 3, repeat step S3, i.e. the first parameter optimization process;

[0105] If the number of times the material properties change (MC) is less than 3, continue to cycle through step S6, which is the second parameter optimization process, and continue to package the next 3 bags of rice.

[0106] In this embodiment, since MC=0, step S6, i.e. the second parameter optimization process, continues to be repeated.

[0107] S7. Since step S6 is repeated, the value of the second fast feed end weight SP2 remains unchanged. Continue to use the second fast feed end weight SP2 parameter to finish packaging the remaining three bags of rice.

[0108] This embodiment uses the first use of this optimized method to package 5 bags of 25kg / bag rice as an example to illustrate the optimization principle of this method. As long as the slow feeding time is within the range of 3-5 seconds, the automatic quantitative packaging scale can meet the national standard of 0.2-level measurement accuracy. The weight at the end of the slow feeding can be adjusted by calculating the material's slow feeding flow rate SR, and the weight at the end of the fast feeding can be adjusted accordingly. This achieves the purpose of parameter optimization, ensuring accuracy while maximizing production capacity due to the sufficiently short slow feeding time and sufficiently long fast feeding time.

[0109] Example 3:

[0110] The difference between this embodiment and embodiment two is that, based on embodiment two, another 10 bags of rice, the same 25kg / bag rice used in embodiment two, are packaged. During the packaging process, the material characteristics of the rice used for the first and second bags in this embodiment are the same as those of the rice in embodiment two. When packaging the third bag, the material characteristics of the rice used have changed due to the humid environment.

[0111] The differences between this embodiment and Embodiment 2 in terms of specific steps are as follows:

[0112] In step S2.3 of Example 2: In this example, if the user selects "No", then step S6 in Example 2 will be performed directly for automatic quantitative packaging;

[0113] After packaging the first two bags of rice, the third bag of rice was packaged. However, because the material characteristics of the third bag of rice differed from those in Example 2, the second slow feeding time FST2 exceeded the standard slow feeding time ST ± 0.5 seconds.

[0114] In step S6.3 of Example 2, it is necessary to set MC = MC + 1, then MC = 1 at this time;

[0115] Since MC = < 3 at this time, we continue with step S6 in Example 2 to package the fourth bag of rice;

[0116] When packaging the fourth bag of rice, since the material characteristics of the fourth bag of rice are still different from those of the rice in Example 2, we continue to set MC = MC + 1, at which point MC = 2;

[0117] If MC = < 3, then continue with step S6 in Example 2 to package the fifth bag of rice;

[0118] Until the sixth bag of rice was packaged, since the material characteristics of the sixth bag of rice were still different from those of the rice in Example 2, MC was set to MC+1, at which point MC=4;

[0119] At this point, MC>3, and it is necessary to repeat step S3 in Example 2 to re-optimize the rapid feeding end weight SP so that the re-optimized rapid feeding end weight SP can guarantee the packaging accuracy of rice after the material characteristics have changed.

[0120] Based on Example 2, this embodiment uses the packaging of 10 bags of 25kg / bag rice of the same material type as in Example 2 as an example. It illustrates that when the material characteristics of the rice change during the packaging process, this parameter optimization method can respond to whether the material has changed based on the second slow feeding consumption time FST2. At the same time, it sets the number of times the material characteristics change MC. Only when the material characteristics of 4 consecutive bags of rice are different from the material characteristics of the rice used when the second fast feeding end weight SP2 is set, will the first parameter optimization process be triggered, and the second fast feeding end weight SP2 will be optimized. This improves the working efficiency of the parameter optimization method and makes it more efficient and concise.

[0121] The above description is an explanation of the present invention and not a limitation thereof. The scope of the present invention is defined by the claims. Within the scope of protection of the present invention, any form of modification may be made.

Claims

1. A parameter optimization method for an automatic quantitative packaging scale, characterized in that: The parameter optimization method includes the following steps: The material type and target weight are obtained, and different standard slow feeding times are set for different material types in the control system. The first parameter optimization process is carried out, and the fast feeding end weight is set as the first fast feeding end weight. The first fast feeding end weight is 50% of the target weight. Fast feeding is performed and the real-time weight in the package is obtained. When the real-time weight reaches the set fast feeding end weight, fast feeding is stopped and the weight of the first material in the package is obtained after stabilization. Slowly feed the material and obtain the real-time weight in the package. Simultaneously start the internal timer to keep track of the time. When the real-time weight reaches the target weight, stop feeding and stop timing. Obtain the first slow feeding time. After stabilization, obtain the second material weight in the package at this time. The difference between the weight of the second material and the weight of the first material is determined to be the weight required for the first slow feed. The ratio of the weight required for the first slow feed to the time consumed by the first slow feed is determined as the slow feed flow rate; The product of the slow feed rate and the standard slow feed time is determined as the weight required for the second slow feed. The difference between the target weight and the weight required for the second slow feed is determined as the ending weight for the second fast feed, thus completing the first parameter optimization process; The second parameter optimization process is carried out by setting the fast feeding end weight as the second fast feeding end weight, fast feeding and obtaining the real-time weight in the package, stopping fast feeding when the real-time weight reaches the set fast feeding end weight, and simultaneously starting slow feeding, activating the internal timer and obtaining the real-time weight in the package. When the real-time weight reaches the target weight, stop feeding and stop timing, and obtain the second slow feeding time FST2; If the second slow feed time is within the set range of the standard slow feed time, it means that the material properties have not changed, so the number of material property changes MC = 0. If the second slow feed time exceeds the set range of the standard slow feed time, it indicates that the material properties have changed, so the number of material property changes MC = MC + 1. If the number of times the material properties change (MC) is greater than or equal to 3, the first parameter optimization process is repeated. If the number of times the material properties change (MC) is less than 3, continue with the second parameter optimization process.

2. The parameter optimization method for an automatic quantitative packaging scale as described in claim 1, characterized in that: The parameter optimization method can also obtain information such as whether it is the user's first time using the service. If this is the user's first time using the service, the first parameter optimization process must be performed first. Only after the first parameter optimization process is completed can the second parameter optimization process be performed. If the user is not a first-time user, the second parameter optimization process can be performed directly.

3. The parameter optimization method for an automatic quantitative packaging scale as described in claim 2, characterized in that: When the user is not using the service for the first time, the second parameter optimization process will use the fast feed end weight parameter that the user used last time.

4. The parameter optimization method for an automatic quantitative packaging scale as described in claim 1, characterized in that: Each time the internal timer is synchronously activated, it starts from 00:

00.

5. The parameter optimization method for an automatic quantitative packaging scale as described in claim 1, characterized in that: The second slow feeding time needs to be controlled within the range of ±0.5 seconds of the standard slow feeding time.

6. The parameter optimization method for an automatic quantitative packaging scale as described in claim 1, characterized in that: The control system has preset standard slow feeding times for four material types, with the standard slow feeding time ranging from 3 to 5 seconds.

7. The parameter optimization method for an automatic quantitative packaging scale as described in claim 6, characterized in that: The four material types are: powdered materials, granular materials, irregularly shaped materials (flakes / strips / blocks), and other types of materials; The standard slow feeding time for powdery materials is 4 seconds. The standard slow feeding time for granular materials is 3 seconds. The standard slow feeding time for powder / strip / block materials is 5 seconds. The standard slow feeding time for other types of materials is 4 seconds.

8. The parameter optimization method for an automatic quantitative packaging scale as described in claim 6, characterized in that: Images of typical materials are provided for users' reference for each of the four material types.

9. The parameter optimization method for an automatic quantitative packaging scale as described in claim 1, characterized in that: During the first parameter optimization process, the system needs to be stabilized for 3 seconds before weighing the first and second materials.

Images