Automatic production system and method of extrusion cover for sauce packaging

Abstract

The invention provides an automatic production system and method of an extrusion cover for sauce packaging, and the method comprises the steps: obtaining the production characteristics of the extrusion cover according to the original parameters of raw materials, factory environment parameters and customer demand parameters, and constructing an extrusion cover production machine control model according to the production characteristics of the extrusion cover and a corresponding extrusion cover machine control method; a real-time extrusion cover machine control method is obtained according to input of current extrusion cover production characteristics, obtained through order processing, of the to-be-produced extrusion cover, and related management personnel of an extrusion cover production line are assisted to adjust facility parameters of the extrusion cover machine according to the extrusion cover machine control method, so that the automatic production quality of the extrusion cover is managed. Through special processing of client side and producer side data parameters influencing the extrusion cover production effect and through an improved machine automation parameter adjustment model, the production control precision of the extrusion cover is improved, the production quality of the extrusion cover is improved, and the satisfaction degree of clients on extrusion cover products is improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent manufacturing technology, and in particular relates to an automated production system and method for a squeeze cap for sauce packaging. Background Art

[0002] In the squeeze cap manufacturing industry, to achieve high-quality development, save energy, and improve product delivery quality, tailoring squeeze cap production to the production environment and customer needs is key to improving production quality, saving electricity, and extending machine life. Traditional equipment production parameter adjustment methods rely primarily on operator experience and judgment, and fail to consider the specific parameters of raw materials, customer needs, and the blowing parameters during the squeeze cap connection process to control the equipment during the squeeze cap production process, ensuring that the squeeze caps produced can be delivered to customers in an environmentally friendly, efficient, and high-quality manner.

[0003] Manually debugging the parameters of modular equipment related to the production of squeeze caps is not only time-consuming and labor-intensive, but also often difficult to accurately control the squeeze cap production equipment, making it impossible to produce squeeze cap-related products that maximize the satisfaction of manufacturers and even consumers. In addition, although the development of information technology and automation technology in recent years has provided new means for equipment monitoring and control, such as installing various sensors to collect equipment operation data in real time and using computer systems for data monitoring and analysis, these technologies often focus on collecting and monitoring data to accurately and quickly obtain the relevant setting parameters of the equipment. However, the model's scenario applicability is not strong, and the processing and analysis of data from the production, client, and consumer ends are not considered. Moreover, the model is difficult to apply to specific demand scenarios.

[0004] Through the above analysis, the existing technology has the following problems that need to be solved:

[0005] How to consider the three-party influence on the production of squeeze-squeeze cap related data, and perform scenario-based application processing on the three-party data, how to construct a specialized simple and practical machine learning model based on the three-party data for the actual control scenario of the squeeze-squeeze cap production machine, so as to automatically generate the control method of each module of the squeeze-squeeze cap production machine to assist the squeeze-squeeze cap production machine manager to adjust the working parameters of the squeeze-squeeze cap production machine and manage the squeeze-squeeze cap production quality. How to perform targeted and effective parameter setting processing on the various data parameters that affect the squeeze-squeeze cap production effect, and how to improve the simple and practical machine learning model to improve the control accuracy of the squeeze-squeeze cap production machine, and screen the most suitable model after training, so as to assist in the squeeze-squeeze cap production quality management, reduce labor costs, improve the squeeze-squeeze cap production quality, and improve the B-end customers and C-end consumers' satisfaction with the high quality of the squeeze-squeeze cap products. To this end, an automated production system and method for squeeze-squeeze caps for sauce packaging are proposed. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides an automated production system and method for squeeze lids for sauce packaging.

[0007] In a first aspect of the present invention, there is provided a method for automatically producing a squeeze cap for sauce packaging, the method comprising:

[0008] Obtain the original parameters of raw materials, factory environment parameters, and customer demand parameters that affect the production of squeeze caps for sauce packaging; and obtain the corresponding squeeze cap machine control method;

[0009] Obtaining squeeze cap production characteristics based on the original parameters of the raw materials, the factory environment parameters, and the customer demand parameters;

[0010] Constructing a squeeze cover production machine control model based on the squeeze cover production characteristics and the corresponding squeeze cover machine control method;

[0011] Using historical squeeze cap production related parameters to test the control method generation accuracy of the squeeze cap production machine control model;

[0012] Based on the squeezed cover production machine control model with an accuracy greater than a threshold, a machine control method for the squeezed cover to be produced is generated, and relevant managers of the squeezed cover production line perform automated production quality management of the squeezed cover according to the machine control method for the squeezed cover to be produced.

[0013] Furthermore, the original parameters are obtained by processing the material type, density, melt index and tensile impact index.

[0014] Furthermore, the factory environmental parameters refer to the feeding track environmental parameters, which are specifically obtained by processing the blowing temperature, relative humidity and average wind speed of the feeding track.

[0015] Furthermore, the customer demand parameters include a customer packaged sauce category value and an applicable population parameter, and the customer packaged sauce category value S is obtained based on liquid and semi-solid.

[0016] Furthermore, the applicable population parameters are calculated based on the consumer's grip strength level, age, and gender correction coefficient.

[0017] Furthermore, the original parameters of the raw materials, the factory environment parameters and the customer demand parameters are used to obtain the squeeze cover production characteristics using a vector splicing method, and the vector splicing method includes vector horizontal splicing and vector vertical splicing.

[0018] Furthermore, the control method generation accuracy of the squeeze cap production machine control model is tested using historical squeeze cap production related parameters, wherein the historical squeeze cap production related parameters include historical original parameters used outside the training model, historical factory environment parameters, and historical customer demand parameters, as well as historical actual squeeze cap machine control methods;

[0019] The accuracy rate is calculated by inputting the historical original parameters, the historical factory environment parameters and the historical customer demand parameters into the squeeze cover production machine control model to obtain the historical squeeze cover machine control method and the historical actual squeeze cover machine control method.

[0020] Furthermore, the squeeze cap production machine control model utilizes a classifier based on the Fisher criterion that is modified based on factory environment parameters and customer demand parameters.

[0021] Also disclosed is an automated production system for squeeze-on caps for sauce packaging, comprising a raw material parameter acquisition module, a factory environment parameter acquisition module, an order receiving module, a squeeze-on cap production feature processing module, a squeeze-on cap production machine control model construction module, and a squeeze-on cap terminal control module, characterized in that:

[0022] The raw material parameter acquisition module is used to acquire the original parameters of the raw materials that affect the production of squeeze caps for sauce packaging;

[0023] The factory environment parameter acquisition module is used to acquire factory environment parameters that affect the production of squeeze caps for sauce packaging;

[0024] The order receiving module is used to upload and obtain customer demand parameters that affect the production of squeeze-on lids for sauce packaging;

[0025] The squeeze cap production feature processing module is configured to obtain squeeze cap production features based on the original parameters of the raw materials, the factory environment parameters, and the customer demand parameters;

[0026] The squeeze cap production machine control model construction module is used to construct a squeeze cap production machine control model using squeeze cap production characteristics and corresponding squeeze cap machine control methods;

[0027] The squeezed cover terminal control module is connected to the squeezed cover production machine control model construction module, uses historical squeezed cover production related parameters to test the control method generation accuracy of the squeezed cover production machine control model, and generates the squeezed cover machine control method to be produced based on the squeezed cover production machine control model with an accuracy greater than the threshold. The relevant management personnel of the squeezed cover production line perform automated production quality management of the squeezed covers according to the squeezed cover machine control method to be produced.

[0028] The present invention takes into account and objectively calculates the three-party influencing data on the production of squeezed covers, performs scenario-based application processing on the three-party data, and specifically constructs a simple and practical machine learning model based on the three-party data for the control scenarios required for the actual production of squeezed cover production machines, thereby automatically generating control methods for each module of the squeezed cover production machine to assist squeezed cover production machine managers in adjusting the working parameters of the squeezed cover production machine and managing the production quality of the squeezed covers.

[0029] The present invention specifically performs targeted and effective parameter setting processing on various data parameters that affect the production effect of squeezed covers. According to the experience of technical personnel and the setting guided by historical results, a simple and practical Fisher model is used to perform targeted parameter setting and improvement of the scene to improve the control accuracy of the parameters of each module of the squeezed cover production machine, and relevant accuracy thresholds are set to screen the most suitable model after training, thereby assisting in the production quality management of squeezed covers, reducing labor costs, improving the production quality of squeezed covers, and improving the high-quality satisfaction of B-end customers and C-end consumers with squeezed cover products.

[0030] More embodiments and improved effects of the present invention will be further introduced in conjunction with the drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a diagram showing the use of a model for training an automated production method of a squeeze lid for sauce packaging according to the present invention;

[0032] Figure 2 This is a schematic diagram of an automated production system for a squeeze cap for sauce packaging according to the present invention;

[0033] Figure 3 It is a schematic diagram of the feed track module in the present invention;

[0034] Figure 4 This is an example diagram of a squeeze cover for packaging in the present invention;

[0035] Figure 5 It is a schematic diagram of the improved classifier based on Fisher criterion in the present invention. DETAILED DESCRIPTION

[0036] The invention is further described below with reference to the accompanying drawings and specific implementation methods.

[0037] In a first aspect of the present invention, there is provided a method for automatically producing a squeeze cap for sauce packaging, the method comprising:

[0038] Obtain the original parameters of raw materials, factory environment parameters, and customer demand parameters that affect the production of squeeze caps for sauce packaging; and obtain the corresponding squeeze cap machine control method;

[0039] Obtaining squeeze cap production characteristics based on the original parameters of the raw materials, the factory environment parameters, and the customer demand parameters;

[0040] Constructing a squeeze cover production machine control model based on the squeeze cover production characteristics and the corresponding squeeze cover machine control method;

[0041] Using historical squeeze cap production related parameters to test the control method generation accuracy of the squeeze cap production machine control model;

[0042] Based on the squeezed cover production machine control model with an accuracy greater than a threshold, a machine control method for the squeezed cover to be produced is generated, and relevant managers of the squeezed cover production line perform automated production quality management of the squeezed cover according to the machine control method for the squeezed cover to be produced.

[0043] In this embodiment Figure 1 It shows the flowchart of specific model training and use.

[0044] Furthermore, the original parameters are obtained by processing the material type, density, melt index and tensile impact index, and the calculation formula is:

[0045]

[0046] Where, P r is the original parameter, [K] represents the compensation value of the raw material type K. If the raw material is polyethylene, [K] is 0.05, and if the raw material is polypropylene, [K] is 0.072. Here is the optimal raw material type compensation value set according to the input characteristics and output characteristics during model training of the present invention, K ρ is the density of raw material K, |K ρ | represents the density of the dimensionless raw material K, M F The melt index of the raw material indicates the mass of the plastic melt passing through a specific aperture within 10 minutes under standard conditions, and the unit is g / 10 minutes (g / 10min). P It is the ratio of the tensile strength to the impact strength of the raw material. The raw materials used to produce squeeze covers have an important influence on the control method of the production machine. According to the targeted processing of the original parameters of the raw materials, the subsequent model can be used to enhance the accuracy of the control method of the squeeze cover machine.

[0047] Furthermore, the factory environmental parameters refer to the feed track environmental parameters, which are specifically obtained by processing the blowing temperature, relative humidity and average wind speed of the feed track. The processing formula is:

[0048]

[0049] Where, PE is the factory environment parameter, V w The average wind speed is generally 0.5-1.5 m / s, and the purpose of blowing is to keep the connection of the outer cover at a certain temperature to prevent the connection of the outer cover from being too hard due to too rapid cooling and breaking during the subsequent closing process. In view of its variation range and its low impact on the production of squeezed covers, the temperature is similar. Therefore, the present invention uses a logarithmic function for constraint control to control the slight changes that affect the accuracy of the subsequent model. The present invention takes into account the subtle changes in the production process of squeezed covers, making the model control method more refined. H is the humidity of the feed track, E H Optimal humidity for closed lid.

[0050] Furthermore, the customer demand parameters include customer packaged sauce category values and applicable population parameters. The customer packaged sauce category value S is obtained based on liquid and semi-solid, with the liquid value being 1 and the semi-solid value being 0.9. It is set according to the difficulty of squeezing out the packaged sauce. Liquid is easy to squeeze out, so the value is greater than the semi-solid value to facilitate the classification processing of the subsequent model.

[0051] Furthermore, the applicable population parameters are calculated based on the consumer's grip strength level, age, and gender correction coefficient:

[0052]

[0053] Where, P A is the applicable population parameter, δ is the gender correction coefficient, and the grip strength of women is generally 80% of that of men. Based on the equal ratio of men and women in the applicable population, the value of δ is 0.9. P is the consumer's grip strength level. Generally, the grip strength level of boys is 50 kg, and the grip strength level of girls is 40 kg. The consumer's grip strength level is calculated based on the average grip strength ratio level. If the ratio of boys and girls is the same, P is 45 kg. N is the age of the consumer group with the largest usage, which is 30 years old here.

[0054] Furthermore, the original parameters of the raw materials, the factory environment parameters and the customer demand parameters are used to obtain the squeeze cover production characteristics using a vector splicing method, and the vector splicing method includes vector horizontal splicing and vector vertical splicing.

[0055] Furthermore, the control method generation accuracy of the squeeze cap production machine control model is tested using historical squeeze cap production related parameters, wherein the historical squeeze cap production related parameters include historical original parameters used outside the training model, historical factory environment parameters, and historical customer demand parameters, as well as historical actual squeeze cap machine control methods;

[0056] The accuracy rate is calculated by inputting the historical original parameters, the historical factory environment parameters and the historical customer demand parameters into the squeeze cover production machine control model to obtain the historical squeeze cover machine control method and the historical actual squeeze cover machine control method. The threshold value is 90%.

[0057] Furthermore, the squeeze cap production machine control model utilizes a classifier based on the Fisher criterion that is modified based on factory environment parameters and customer demand parameters.

[0058] Furthermore, the calculation formula of the classifier based on the Fisher criterion modified based on the factory environment parameters and customer demand parameters is as follows:

[0059]

[0060] Where M(C) is the output of the squeeze cover machine control method, WT is the normal vector perpendicular to the hyperplane, obtained through model training, C is the squeeze cover production feature, P E is the factory environment parameter, S is the customer packaged sauce category value, P A For the applicable population parameter, the present invention outputs the squeezing cover machine control method according to the M(C) value, which is divided into four categories of squeezing cover machine control methods, namely, M(C) value is less than -1, M(C) value is greater than or equal to -1 and less than 0, M(C) value is 0, and M(C) value is greater than 0.

[0061] The production process of squeeze caps specifically includes the following steps:

[0062] 1. Injection molding of outer cover.

[0063] The outer cover is formed by injection molding using a horizontal injection molding machine in the prior art.

[0064] 2. Transfer to 3 vibration plates for cover sorting.

[0065] The injection-molded outer covers are transported to three vibration plates through a conveyor track for sorting. The vibration plates are set inside the white cylindrical shell.

[0066] The material is transmitted from the vibration plate to the feed track. There are multiple air outlets arranged obliquely above and below the feed track (the lower air outlet is not shown, and it is symmetrically arranged on the upper and lower sides of the feed track with the upper air outlet). The upper air outlet blows hot air to make the connection of the outer cover have a certain temperature to prevent the connection of the outer cover from being too hard due to too rapid cooling and breaking during the subsequent closing process. The wind blown out by the upper and lower air outlets drives the outer cover to move forward. There are also 3 feed tracks. A mobile feed channel is provided in the middle of the feed track. A sensor is provided on each feed track. When the outer covers on the feed track are arranged to reach the sensor position, the number of outer covers arranged on the feed track reaches a predetermined number, and the mobile feed channel moves to the corresponding feed track, so that the outer covers that have reached a predetermined number move forward through the mobile feed channel until they move into the closing mechanism.

[0067] The cover closing mechanism includes a turntable, and a plurality of fixed claws are provided on the outer periphery of the turntable to fix the outer cover. A cover closing block is also provided on the outer periphery of the turntable. The outer cover passes through the cover closing block under the drive of the turntable to close the outer cover. The closed outer cover is then transported to the inner and outer cover assembly mechanism.

[0068] At the same time, the soft cover and the soft cover bracket are first assembled together in the inner and outer cover assembly mechanism. The inner and outer cover assembly mechanism includes a turntable, and a plurality of positioning pins are provided on the outer peripheral side of the turntable. One of the vibration disks feeds the soft cover onto the positioning pins, and a cross scratch is provided in the middle of the soft cover; the other vibration disk also feeds the soft cover bracket onto the positioning pins and is located above the soft cover. The pressure rod above the turntable presses down to combine the soft cover bracket and the soft cover together, and the combined two are taken out of the positioning pins and moved upward. The pressure rod is pressed down again to press the soft cover bracket and the soft cover into the closed outer cover, completing the assembly of the inner and outer covers. The assembled covers are transported to the soft aluminum film assembly mechanism.

[0069] The soft aluminum film assembly mechanism includes an unwinder and a rewinder. The lid is transported to the stamping platform, and the punch above it stamps the soft aluminum film into the inner cavity of the lid, completing the assembly of the lid and the soft aluminum film. After that, the material is cut out to complete the production of the squeezed lid.

[0070] In this embodiment, a specific squeeze cover machine control method includes corresponding working parameters of each module of the squeeze cover production machine equipment, such as the horizontal injection molding machine, the vibration plate, the feed channel transmission rate, the lid closing mechanism turntable speed, the inner and outer lid assembly mechanism, and the soft aluminum film assembly mechanism. The specific value range of the corresponding working parameters of each module is set according to the value of M(C). This is the setting required by those skilled in the art and will not be repeated here.

[0071] Also provided is an automated production system for squeeze-on caps for sauce packaging, comprising a raw material parameter acquisition module, a factory environment parameter acquisition module, an order receiving module, a squeeze-on cap production feature processing module, a squeeze-on cap production machine control model construction module, and a squeeze-on cap terminal control module, characterized in that:

[0072] The raw material parameter acquisition module is used to acquire the original parameters of the raw materials that affect the production of squeeze caps for sauce packaging;

[0073] The factory environment parameter acquisition module is used to acquire factory environment parameters that affect the production of squeeze caps for sauce packaging;

[0074] The order receiving module is used to upload and obtain customer demand parameters that affect the production of squeeze-on lids for sauce packaging;

[0075] The squeeze cap production feature processing module is configured to obtain squeeze cap production features based on the original parameters of the raw materials, the factory environment parameters, and the customer demand parameters;

[0076] The squeeze cap production machine control model construction module is used to construct a squeeze cap production machine control model using squeeze cap production characteristics and corresponding squeeze cap machine control methods;

[0077] The squeezed cover terminal control module is connected to the squeezed cover production machine control model construction module, uses historical squeezed cover production related parameters to test the control method generation accuracy of the squeezed cover production machine control model, and generates the squeezed cover machine control method to be produced based on the squeezed cover production machine control model with an accuracy greater than the threshold. The relevant management personnel of the squeezed cover production line perform automated production quality management of the squeezed covers according to the squeezed cover machine control method to be produced.

[0078] The present invention takes into account and objectively calculates the three-party influencing data on the production of squeezed covers, performs scenario-based application processing on the three-party data, and specifically constructs a simple and practical machine learning model based on the three-party data for the control scenarios required for the actual production of squeezed cover production machines, thereby automatically generating control methods for each module of the squeezed cover production machine to assist squeezed cover production machine managers in adjusting the working parameters of the squeezed cover production machine and managing the production quality of the squeezed covers.

[0079] like Figure 4 As shown, the squeeze cover includes an outer cover, an inner cover and a soft aluminum film, wherein the inner cover includes a soft cover (with a cross scratch in the middle as an opening) and a soft cover bracket. The soft cover is assembled in the soft cover bracket, and the soft cover bracket is then connected to the outer cover.

[0080] The present invention specifically performs targeted and effective parameter setting processing on various data parameters that affect the production effect of squeezed covers. According to the experience of technical personnel and the setting guided by historical results, a simple and practical Fisher model is used to perform targeted parameter setting and improvement of the scene to improve the control accuracy of the parameters of each module of the squeezed cover production machine, and relevant accuracy thresholds are set to screen the most suitable model after training, thereby assisting in the production quality management of squeezed covers, reducing labor costs, improving the production quality of squeezed covers, and improving the high-quality satisfaction of B-end customers and C-end consumers with squeezed cover products.

[0081] Of course, it can be understood that each embodiment of the present invention can achieve one of the effects alone, and a combination of multiple embodiments of the present invention can achieve all of the above effects, but it is not required that every embodiment of the present invention achieve all of the above advantages and effects, because each embodiment of the present invention can constitute a separate technical solution and make one or more contributions to the existing technology.

[0082] For any module structure not specifically defined in this invention, the prior art shall prevail. The prior art mentioned in the aforementioned background and specific embodiments of this invention may be considered as part of this invention and used to understand the meaning of certain technical features or parameters. The scope of protection of this invention shall be based on the actual content of the claims.

Claims

1. An automated production method for a squeeze cap for sauce packaging, characterized in that: The method comprises: Obtain the original parameters of raw materials, factory environment parameters, and customer demand parameters that affect the production of squeeze caps for sauce packaging; and obtain the corresponding squeeze cap machine control method; Obtaining squeeze cap production characteristics based on the original parameters of the raw materials, the factory environment parameters, and the customer demand parameters; Constructing a squeeze cover production machine control model based on the squeeze cover production characteristics and the corresponding squeeze cover machine control method; Using historical squeeze cap production related parameters to test the control method generation accuracy of the squeeze cap production machine control model; Based on the squeezed cover production machine control model with an accuracy greater than a threshold, a machine control method for the squeezed cover to be produced is generated, and relevant managers of the squeezed cover production line perform automated production quality management of the squeezed cover according to the machine control method for the squeezed cover to be produced.

2. The automated production method of a squeeze cap for sauce packaging according to claim 1, characterized in that: The original parameters are obtained by processing the material type, density, melt index and tensile impact index.

3. The automated production method of a squeeze cap for sauce packaging according to claim 1, characterized in that: The factory environmental parameters refer to the feed track environmental parameters, which are specifically obtained by processing the blowing temperature, relative humidity and average wind speed of the feed track.

4. The automated production method of a squeeze cap for sauce packaging according to claim 2, characterized in that: The customer demand parameters include a customer packaged sauce category value and applicable population parameters, and the customer packaged sauce category value S is obtained based on liquid and semi-solid.

5. The automated production method of a squeeze cap for sauce packaging according to claim 4, characterized in that: The applicable population parameters are calculated based on the consumer's grip strength level, age, and gender correction coefficient.

6. The automated production method of a squeeze cap for sauce packaging according to claim 1, characterized in that: The original parameters of the raw materials, the factory environment parameters and the customer demand parameters are used to obtain the squeeze cover production characteristics using a vector splicing method, and the vector splicing method includes vector horizontal splicing and vector vertical splicing.

7. The automated production method of a squeeze cap for sauce packaging according to claim 1, characterized in that: The control method generation accuracy of the squeeze cap production machine control model is tested by using historical squeeze cap production related parameters, wherein the historical squeeze cap production related parameters include historical original parameters used outside the training model, historical factory environment parameters and historical customer demand parameters, and historical actual squeeze cap machine control methods; The accuracy rate is calculated by inputting the historical original parameters, the historical factory environment parameters and the historical customer demand parameters into the squeeze cover production machine control model to obtain the historical squeeze cover machine control method and the historical actual squeeze cover machine control method.

8. The automated production method of a squeeze cap for sauce packaging according to claim 2, 3 or 5, characterized in that: The squeeze cap production machine control model utilizes a classifier based on the Fisher criterion that is modified based on factory environment parameters and customer demand parameters.

9. An automated production system for squeeze-on caps for sauce packaging, comprising a raw material parameter acquisition module, a factory environment parameter acquisition module, an order receiving module, a squeeze-on cap production feature processing module, a squeeze-on cap production machine control model construction module, and a squeeze-on cap terminal control module, characterized in that: The raw material parameter acquisition module is used to acquire the original parameters of the raw materials that affect the production of squeeze caps for sauce packaging; The factory environment parameter acquisition module is used to acquire factory environment parameters that affect the production of squeeze caps for sauce packaging; The order receiving module is used to upload and obtain customer demand parameters that affect the production of squeeze-on lids for sauce packaging; The squeeze cap production feature processing module is configured to obtain squeeze cap production features based on the original parameters of the raw materials, the factory environment parameters, and the customer demand parameters; The squeeze cap production machine control model construction module is used to construct a squeeze cap production machine control model using squeeze cap production characteristics and corresponding squeeze cap machine control methods; The squeezed cover terminal control module is connected to the squeezed cover production machine control model construction module, uses historical squeezed cover production related parameters to test the control method generation accuracy of the squeezed cover production machine control model, and generates the squeezed cover machine control method to be produced based on the squeezed cover production machine control model with an accuracy greater than the threshold. The relevant management personnel of the squeezed cover production line perform automated production quality management of the squeezed covers according to the squeezed cover machine control method to be produced.

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