Medical packaging material and preparation method thereof

Abstract

The invention provides a medical packaging material and a preparation method thereof.The preparation method comprises the steps that S100, polypropylene, maleic anhydride grafted co-polypropylene, peroxide and filler are mixed, then melt extrusion and two-way stretching are conducted, and a printing layer is obtained; s200, respectively preparing a barrier layer and a heat sealing layer; and S300, the heat sealing layer, the blocking layer and the printing layer are sequentially stacked from inside to outside, and the packaging material is obtained. Through the preparation method of the packaging material, the prepared packaging material has excellent barrier property and antibacterial property while resisting high temperature, and can meet the requirements of the medical field.

Description

Technical Field

[0001] The present invention relates to the technical field of packaging materials, and in particular to a medical packaging material and a preparation method thereof. Background Art

[0002] Medical packaging materials are key elements to ensure that medical devices remain sterile during the disinfection and sterilization process, and they are directly related to the safety and effectiveness of medical devices. Medical devices will be exposed to high temperatures during the sterilization process. To ensure the safety and effectiveness of medical devices, medical packaging materials should generally have excellent barrier properties and high temperature resistance. At the same time, to maintain a sterile state for a long time, medical packaging materials should also have certain antibacterial properties. How to make packaging materials have both excellent high temperature resistance and antibacterial properties has always been the research goal of researchers in this field. Summary of the invention

[0003] The present invention aims to solve at least one of the above technical problems.

[0004] To this end, a first object of the present invention is to provide a method for preparing a medical packaging material.

[0005] The second object of the present invention is to provide a packaging material

[0006] To achieve the first objective of the present invention, the present invention provides a method for preparing a medical packaging material, wherein the packaging material comprises, from top to bottom, a printing layer, a barrier layer and a heat-sealing layer, wherein the heat-sealing layer comprises polypropylene and silver ions; the barrier layer comprises a layer at least partially covering the heat-sealing layer, wherein the barrier layer comprises polypropylene, silicon oxide and silver ions; the printing layer at least partially covers the barrier layer, wherein the printing layer comprises polypropylene, maleic anhydride grafted copolymerized polypropylene, peroxide and filler; the preparation method comprises: S100, mixing polypropylene, maleic anhydride grafted copolymerized polypropylene, peroxide and filler, and then melt-extruding and biaxially stretching to obtain a printing layer; S200, respectively preparing Prepare a barrier layer and a heat-sealing layer; S300, stack the heat-sealing layer, the barrier layer and the printing layer in order from the inside to the outside to obtain a packaging material; wherein the heat-sealing layer and the barrier layer, and the barrier layer and the printing layer are compounded by an adhesive; S200 includes: S210, prepare silver ion microcapsules; S220, mix part of the silver ion microcapsules with polypropylene evenly, heat and melt cast into a film, and obtain a heat-sealing layer; S230, mix the remaining silver ions with polypropylene evenly, heat and melt extrude and uniaxially stretch into a polypropylene film, and then sequentially perform vapor deposition treatment, annealing treatment, and drying treatment on the polypropylene film to obtain a barrier layer.

[0007] Compared with the prior art, the technical effects achieved by adopting the technical scheme are as follows: the packaging material prepared by the packaging material preparation method of the present invention consists of a printing layer, a barrier layer and a heat-sealing layer to form a multi-layer structure, which respectively endows the packaging material with different functions; by adding silver ions to the heat-sealing layer and the barrier layer, the packaging material has antibacterial properties, which helps to extend the shelf life of the packaging contents and is particularly suitable for pharmaceutical packaging; the barrier layer contains silicon oxide, which further improves the barrier properties of the material and prevents water vapor and oxygen from penetrating; polypropylene is used as the main material, which has good recyclability and low environmental impact, and meets the safety standards of pharmaceutical packaging; through the specific process of the packaging material preparation method of the present invention, a packaging material with an easy-to-tear function and easy to use can be prepared, the printing layer of the packaging material enhances the mechanical strength through a biaxial stretching process, so that the packaging material is more durable and can withstand certain pressure and impact; through the packaging material preparation method of the present invention, the prepared packaging material can have excellent barrier properties and antibacterial properties while being resistant to high temperatures, which can meet the needs of the medical field.

[0008] In the above technical scheme, S210 includes: S211, preparing wall material: adding porous cyclodextrin into deionized water and stirring evenly to obtain wall material emulsion; S212, preparing core material: adding dispersant into nanosilver solution and stirring evenly to obtain core material emulsion; S213, preparing silver ion microcapsules: after mixing the wall material emulsion and the core material emulsion evenly, spraying and drying them in sequence to obtain silver ion microcapsules.

[0009] Compared with the prior art, the technical effects achieved by adopting the technical scheme are as follows: by adding porous cyclodextrin to deionized water to prepare a wall material emulsion, and by adding a dispersant to a nanosilver solution to prepare a core material emulsion, uniform dispersion of the wall material and the core material is ensured, so that the finally prepared silver ion microcapsules have uniform structure and performance; the nanosilver solution as the core material has high-efficiency antibacterial properties, and the antibacterial effect of silver ions is further enhanced through microencapsulation treatment, so that the packaging material can maintain the antibacterial properties for a longer time during storage and use.

[0010] In any of the above technical solutions, in S212, the amount of the dispersant used is 1wt.%-10wt.% of the nanosilver solution; the dispersant is an ethanol solution of sorbitan monooleate.

[0011] Compared with the prior art, the technical effects achieved by adopting the technical solution are as follows: using an appropriate amount of sorbitan monooleate as a dispersant can effectively reduce the surface tension of nanosilver particles, promote their uniform dispersion in the solution, avoid particle agglomeration, and improve the uniformity and stability of microcapsules; the addition of the dispersant makes the nanosilver solution easier to operate in the subsequent processing process, thereby improving the efficiency and quality of the production process.

[0012] In any of the above technical solutions, in S211, the mass ratio of porous cyclodextrin to deionized water is (3-5):1; and / or in S213, the mass ratio of wall material emulsion to core material emulsion is 1:1.

[0013] Compared with the prior art, the technical effects achieved by adopting the technical scheme are as follows: the mass ratio of porous cyclodextrin to deionized water is 3-5:1, which can not only make the porous cyclodextrin form a more stable wall material structure, improve the physical stability of the microcapsule, and prevent rupture or deformation in the subsequent treatment process, but also more effectively coat the nanosilver particles, improve the encapsulation efficiency of silver ions, and avoid the loss of silver ions during storage and use; the mass ratio of the wall material emulsion to the core material emulsion is 1:1, which ensures the balance of the wall material and the core material in the microcapsule, so that the microcapsule has sufficient strength and stability and can effectively release silver ions.

[0014] In any of the above technical solutions, in S213, the spray particle size of the spray treatment is 10μm-20μm; and / or the temperature of the drying treatment is 70℃-90℃.

[0015] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: the particle size of the spray treatment is 10μm-20μm, which can ensure the uniform size of the generated microcapsules, improve the distribution consistency of the microcapsules, and enhance the overall quality of the product; the drying temperature is controlled within the range of 70℃-90℃, which can effectively remove moisture from the microcapsules without destroying active ingredients such as nanosilver and cyclodextrin at excessively high temperatures, thereby maintaining their antibacterial properties and structural stability.

[0016] In any of the above technical solutions, in S230, the evaporation treatment uses a silicon-containing target material for the evaporation treatment; and / or before the evaporation treatment, the method further includes coating an adhesion-enhancing coating on the polypropylene film.

[0017] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: the evaporation of silicon-containing target material can form a dense silicon oxide film on the surface of the polypropylene film, which significantly improves the barrier performance of the film and reduces the penetration of oxygen and water vapor; the silicon oxide layer has excellent chemical stability and mechanical strength, which can improve the durability and damage resistance of the film, making the packaging material more durable; coating the adhesion-enhancing coating on the polypropylene film can significantly improve the adhesion between the silicon oxide layer and the polypropylene substrate, prevent the evaporated layer from falling off or cracking, and ensure the stability and integrity of the coating.

[0018] In any of the above technical solutions, the mass ratio of polypropylene particles, maleic anhydride grafted copolymerized polypropylene particles, peroxide and filler is 1: (0.3-0.5): (0.01-0.1): (0.01-0.1); and / or the particle size of the filler is 0.01 μm-0.1 μm.

[0019] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: the above-mentioned proportion configuration can balance the functions of each component, so that the final polypropylene film has both good mechanical properties and excellent adhesion and heat sealing properties; the filler with a particle size of 0.01μm-0.1μm has good dispersibility and can be evenly distributed in the polypropylene matrix, ensuring the consistency and stability of the material performance.

[0020] In any of the above technical solutions, the adhesive is a polyurethane adhesive; and / or the dry weight of the adhesive is 3.5 g / m 2 -4.5g / m 2 ; and / or the coating thickness of the adhesive is 1μm-3μm.

[0021] Compared with the existing technology, the technical effect achieved by adopting this technical solution is: the polyurethane adhesive has excellent adhesion performance, can form a strong bond between different materials, improve the bonding force between layers, and ensure the structural stability of multi-layer packaging materials; the dry weight of the glue is 3.5g / m 2 -4.5g / m 2 Within the range of 1μm-3μm, it can ensure that the adhesive forms a uniform and firm bond between the layers, thereby improving the strength and durability of the overall packaging material; the coating thickness is within the range of 1μm-3μm, which can ensure that the adhesive is evenly covered on the film surface, avoid weak links, and improve the overall bonding strength.

[0022] In any of the above technical solutions, the thickness of the heat-sealing layer is 25 μm-70 μm; and / or the thickness of the barrier layer is 25 μm-50 μm; and / or the thickness of the printing layer is 15 μm-30 μm.

[0023] Compared with the existing technology, the technical effect achieved by adopting this technical solution is that by optimizing the thickness of each layer, the prepared packaging material has been significantly improved in terms of heat sealing strength, barrier properties, printing quality and overall durability. These improvement measures ensure the high performance and high quality of packaging materials in practical applications, meet various high-demand packaging needs, and provide reliable protection and excellent user experience.

[0024] To achieve the second purpose of the present invention, the present invention further provides a medical packaging material, which is prepared by the preparation method of any of the above technical solutions.

[0025] Furthermore, during the evaporation process in S230, a machine learning algorithm is introduced to monitor and predict the evaporation parameters in real time, automatically adjust the working state of the evaporation equipment, and ensure that each batch of barrier layers can achieve the ideal physical and chemical properties. This improves the density and consistency of the barrier layer, and further enhances the barrier properties of the packaging material.

[0026] After adopting the technical solution of the present invention, the technical effects that can be achieved are as follows:

[0027] 1. The method for preparing the packaging material of the present invention can make the prepared packaging material have excellent barrier properties and antibacterial properties while being resistant to high temperatures, and can meet the needs of the medical field;

[0028] 2. Through microencapsulation treatment, the antibacterial effect of silver ions is further enhanced, so that the packaging material can maintain its antibacterial properties for a longer time during storage and use;

[0029] 3. The use of silicon-containing target material evaporation can form a dense silicon oxide film on the surface of the polypropylene film, which significantly improves the barrier properties of the film and reduces the penetration of oxygen and water vapor.

[0030] 4. By adopting a gain adaptive control algorithm to dynamically adjust the temperature, pressure and deposition rate during the evaporation process, the system can accurately control the thickness and uniformity of the film layer to ensure that the quality of each batch of products meets the expected standards; this intelligent control strategy can not only respond to process fluctuations in real time and optimize production parameters, but also significantly reduce resource waste and defective rates in production, improve production efficiency and overall output; in addition, through fine adjustment and closed-loop feedback control, the system can continuously optimize process conditions, further improve product reliability and market competitiveness, and meet increasingly stringent quality standards and customer needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of the intelligent evaporation equipment control system designed by the present invention. DETAILED DESCRIPTION

[0032] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0034] like Figure 1 Shown is a specific flowchart of the implementation of the patent algorithm.

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below.

[0036] In the relevant technology, in order to ensure the safety and effectiveness of medical devices, medical packaging materials should generally have excellent barrier properties and high temperature resistance; at the same time, in order to maintain a sterile state for a long time, medical packaging materials should also have certain antibacterial properties.

[0037] In view of this, an embodiment of the present invention provides a packaging material and a method for preparing the same. By adding silver ions to the barrier layer and the heat-sealing layer and adding a silicon oxide layer on the surface of the barrier layer, the prepared packaging material has excellent barrier properties and antibacterial properties while being resistant to high temperatures.

[0038] Specifically, an embodiment of the present invention provides a packaging material, which includes, from top to bottom, a printing layer, a barrier layer and a heat-sealing layer, the heat-sealing layer including polypropylene and silver ions; the barrier layer includes at least partially covering the heat-sealing layer, the barrier layer including polypropylene, silicon oxide and silver ions; the printing layer at least partially covers the barrier layer, the printing layer including polypropylene, maleic anhydride grafted copolymer polypropylene, peroxide and filler.

[0039] Preferably, the packaging material is composed of a printing layer, a barrier layer and a heat-sealing layer to form a multi-layer structure, which respectively endows the packaging material with different functions. By adding silver ions to the heat-sealing layer and the barrier layer, the packaging material has antibacterial properties, which helps to extend the shelf life of the packaged contents and is particularly suitable for pharmaceutical packaging; the barrier layer contains silicon oxide, which further enhances the barrier properties of the material and prevents water vapor and oxygen penetration; polypropylene is used as the main material, which has good recyclability and low environmental impact, and meets the safety standards for pharmaceutical packaging.

[0040] Furthermore, the preparation method of the packaging material comprises: S100, mixing polypropylene, maleic anhydride graft copolymerized polypropylene, peroxide and filler, melt-extruding and biaxially stretching to obtain a printing layer;

[0041] S200, preparing a barrier layer and a heat-sealing layer respectively;

[0042] S300, stacking a heat sealing layer, a barrier layer and a printing layer in order from inside to outside to obtain a packaging material.

[0043] Preferably, a packaging material with an easy-to-tear function and easy to use can be prepared through the specific process of the preparation method of the packaging material of the present invention. The printed layer of the packaging material has enhanced mechanical strength through a biaxial stretching process, making the packaging material more durable and able to withstand certain pressure and impact. Through the preparation method of the packaging material of the present invention, the prepared packaging material can have excellent barrier properties and antibacterial properties while being resistant to high temperatures, and can meet the needs of the medical field.

[0044] Preferably, the heat sealing layer and the barrier layer, and the barrier layer and the printing layer are compounded by adhesive, the compounding speed is 70±20m / min, the oven temperature is 60-80°C, and the compounding is dried at 50°C for 10h-12h; when the heat sealing layer and the barrier layer are compounded, the compounding speed is 70±20m / min, the oven temperature is 60-80°C, and the compounding is dried at 50°C for 100h-120h.

[0045] Preferably, S200 includes:

[0046] S210, preparing silver ion microcapsules;

[0047] S220, mixing part of the silver ion microcapsules with polypropylene uniformly, heating and melting, and casting the mixture into a film to obtain a heat-sealing layer;

[0048] S230, after the remaining silver ions are uniformly mixed with the polypropylene, the mixture is heated, melted, extruded and uniaxially stretched into a polypropylene film, and then the polypropylene film is subjected to a vapor deposition treatment, an annealing treatment and a drying treatment in sequence to obtain a barrier layer.

[0049] Furthermore, S210 includes:

[0050] S211, preparing wall material: adding porous cyclodextrin into deionized water and stirring evenly to obtain a wall material emulsion;

[0051] S212, preparing a core material: adding a dispersant to the nanosilver solution and stirring evenly to obtain a core material emulsion;

[0052] S213, preparing silver ion microcapsules: uniformly mixing the wall material emulsion and the core material emulsion, and then sequentially performing spraying treatment and drying treatment to obtain silver ion microcapsules.

[0053] Preferably, porous cyclodextrin is added to deionized water to prepare a wall material emulsion, and a dispersant is added to a nanosilver solution to prepare a core material emulsion, thereby ensuring uniform dispersion of the wall material and the core material, so that the silver ion microcapsules finally prepared have uniform structure and performance. Preferably, modified porous cyclodextrin is used as the wall material, which helps to effectively encapsulate and protect silver ions, avoid the loss and oxidation of silver ions, and improve the stability and durability of silver ions.

[0054] Preferably, the nanosilver solution is used as the core material, which has efficient antibacterial properties. The antibacterial effect of silver ions is further enhanced through microencapsulation treatment, so that the packaging material can maintain the antibacterial properties for a longer time during storage and use; the nanosilver solution is prepared by adding sodium citrate or sodium borohydride to silver nitrate to reduce the silver ions in the silver nitrate solution into nanosilver ions.

[0055] Preferably, in S211, porous cyclodextrin is added to deionized water, and stirred at 50°C-80°C and 300-600 rpm for 3h-5h to obtain a wall material emulsion; in S212, a dispersant is added to the nanosilver solution, heated in a water bath at 50°C-80°C, and stirred at a speed of 200-300 rpm for 1h-1.5h to obtain a core material emulsion.

[0056] Preferably, in S212, the amount of the dispersant is 1wt.%-10wt.% of the nanosilver solution; the dispersant is an ethanol solution of sorbitan monooleate.

[0057] Preferably, using an appropriate amount of sorbitan monooleate as a dispersant can effectively prevent the precipitation of silver particles in the nanosilver solution and maintain the stability of the solution. Sorbitan monooleate can effectively reduce the surface tension of the nanosilver particles, promote their uniform dispersion in the solution, avoid particle agglomeration, and improve the uniformity and stability of the microcapsules. Preferably, an ethanol solution of span-80 is used as a dispersant; the addition of the dispersant makes the nanosilver solution easier to operate in the subsequent treatment process, thereby improving the efficiency and quality of the production process.

[0058] Preferably, in S211, the mass ratio of porous cyclodextrin to deionized water is 3-5:1, which can not only make the porous cyclodextrin form a more stable wall material structure, improve the physical stability of the microcapsule, and prevent rupture or deformation during subsequent treatment, but also more effectively coat the nanosilver particles, improve the encapsulation efficiency of silver ions, and avoid the loss of silver ions during storage and use; the mass ratio of the wall material emulsion to the core material emulsion is 1:1, which ensures the balance of the wall material and the core material in the microcapsule, so that the microcapsule has sufficient strength and stability and can effectively release silver ions.

[0059] Preferably, the particle size of the spray-treated particles is 10 μm-20 μm, which can ensure that the generated microcapsules are uniform in size, improve the distribution consistency of the microcapsules, and enhance the overall quality of the product; the drying temperature is controlled within the range of 70°C-90°C, which can effectively remove moisture from the microcapsules without destroying active ingredients such as nanosilver and cyclodextrin at excessively high temperatures, thereby maintaining their antibacterial properties and structural stability.

[0060] Preferably, in S230, the evaporation treatment uses a silicon-containing target material for the evaporation treatment; and / or before the evaporation treatment, the process further includes coating an adhesion-enhancing coating on the polypropylene film.

[0061] Preferably, the evaporation of the silicon-containing target material can form a dense silicon oxide film on the surface of the polypropylene film, which can significantly improve the barrier properties of the film and reduce the penetration of oxygen and water vapor. The silicon oxide layer has excellent chemical stability and mechanical strength, which can improve the durability and damage resistance of the film, making the packaging material more durable; an adhesion-enhancing coating is applied on the polypropylene film, and the adhesion-enhancing coating is preferably a silicon oxide coating with a thickness of preferably 1μm-3μm, which can significantly improve the adhesion between the silicon oxide layer and the polypropylene substrate, prevent the evaporated layer from falling off or cracking, and ensure the stability and integrity of the coating.

[0062] Furthermore, between the annealing treatment and the drying treatment, a protective coating needs to be further coated on the silicon oxide layer, preferably a modified polyvinyl alcohol coating with a thickness of 1μm-3μm; the annealing temperature is 55℃-75℃, and the drying temperature is 45℃-65℃. The drying treatment is preferably constant temperature drying, and the drying time is selected according to actual conditions, and no specific limitation is made here.

[0063] Preferably, the maleic anhydride graft copolymer polypropylene has good adhesion, which helps to improve the bonding force between the film layers and ensure the strong bonding between the printing layer and the barrier layer, and between the barrier layer and the heat-sealing layer. An appropriate amount of peroxide helps to initiate the cross-linking reaction of polypropylene, improve the processing performance and thermal stability of the material, and prevent the material from degrading during processing. The addition of fillers can enhance the rigidity and strength of the material, making the film more durable and tear-resistant. By setting the mass ratio of polypropylene particles, maleic anhydride graft copolymer polypropylene particles, peroxide and filler to 1: (0.3-0.5): (0.01-0.1): (0.01-0.1), the functions of each component can be balanced, so that the final polypropylene film has both good mechanical properties and excellent adhesion and heat-sealing properties.

[0064] Preferably, fillers with a particle size of 0.01 μm-0.1 μm have good dispersibility and can be evenly distributed in the polypropylene matrix to ensure the consistency and stability of material performance; micron-sized fillers can not only effectively increase the barrier path of the film, improve the barrier properties of the material, reduce the penetration of oxygen and water vapor, and protect the quality of the package contents, but also enhance the thermal stability of the material, prevent the film from deforming or degrading during high-temperature heat sealing, and ensure the high temperature resistance of the packaging material.

[0065] Preferably, the polyurethane adhesive has excellent adhesion properties, can form a strong bond between different materials, improve the bonding strength between layers, and ensure the structural stability of multi-layer packaging materials; the dry weight of the glue is 3.5g / m 2 -4.5g / m 2Within the range of 1μm-3μm, it can ensure that the adhesive forms a uniform and firm bond between the layers, thereby improving the strength and durability of the overall packaging material; the coating thickness is within the range of 1μm-3μm, which can ensure that the adhesive is evenly covered on the film surface, avoid weak links, and improve the overall bonding strength.

[0066] Preferably, the heat-sealing layer has a thickness of 25 μm-70 μm, which can provide sufficient material for heat sealing, improve the heat-sealing strength of the packaging material, and ensure the firmness of the seal; the barrier layer has a thickness of 25 μm-50 μm, which can provide sufficient physical thickness to prevent the penetration of gas and water vapor while ensuring the barrier performance, and protect the quality and freshness of the package contents; the printing layer has a thickness of 15 μm-30 μm, which can ensure the clarity of the printed pattern and the saturation of the color, and enhance the visual effect and brand value of the packaging; by optimizing the thickness of each layer, the prepared packaging material has been significantly improved in terms of heat-sealing strength, barrier performance, printing quality and overall durability. These improvement measures ensure the high performance and high quality of the packaging material in practical applications, meet various high-demand packaging needs, and provide reliable protection and excellent user experience.

[0067] Example 1

[0068] The present invention provides a method for preparing a packaging material, characterized in that the packaging material comprises a printing layer, a barrier layer and a heat-sealing layer from top to bottom, the thickness of the heat-sealing layer is 25 μm, the thickness of the barrier layer is 25 μm, and the thickness of the printing layer is 15 μm, and the preparation method comprises:

[0069] S100, polypropylene, maleic anhydride graft copolymer polypropylene, peroxide and filler are mixed in a mass ratio of 1:0.3:0.01:0.01, melt-extruded and biaxially stretched, and the filler particle size is 0.01 μm to obtain a printing layer;

[0070] S200, preparing a barrier layer and a heat-sealing layer respectively;

[0071] S300, stacking a heat-sealing layer, a barrier layer, and a printing layer in order from inside to outside to obtain a packaging material;

[0072] The heat seal layer and the barrier layer, as well as the barrier layer and the printing layer are laminated with polyurethane adhesive, and the dry weight of the adhesive is 3.5g / m 2 , the coating thickness is 1μm;

[0073] S200 includes:

[0074] S210, preparing silver ion microcapsules;

[0075] S220, mixing part of the silver ion microcapsules with polypropylene uniformly, heating and melting, and casting the mixture into a film to obtain a heat-sealing layer;

[0076] S230, after the remaining silver ions are uniformly mixed with polypropylene, the mixture is heated, melted, extruded and uniaxially stretched into a polypropylene film, a silicon oxide coating is coated on the polypropylene film, the thickness of the silicon oxide coating is 1 μm, the polypropylene film is heated to 150° C., silicon dioxide is used as a target material, a high-energy electron beam is used to irradiate silicon dioxide so that silicon dioxide is deposited on the tungsten hexaoxide coating, an annealing treatment is performed at 55° C., and a drying treatment is performed at 45° C. to obtain a barrier layer;

[0077] S210 includes:

[0078] S211, preparing wall material: adding porous cyclodextrin into deionized water, with the mass ratio of porous cyclodextrin to deionized water being 3:1, stirring at 50° C. and 300 rpm for 3 h to obtain a wall material emulsion;

[0079] S212, preparing a core material: adding a dispersant to the nano-silver solution, wherein the amount of the dispersant is 1 wt.% of the nano-silver solution, stirring at 50° C. and 200 rpm for 1 hour to obtain a core material emulsion;

[0080] S213, preparing silver ion microcapsules: mixing the wall material emulsion and the core material emulsion at a mass ratio of 1:1 for 0.5 h, spraying the mixture with a spray particle size of 10 μm, and drying the mixture at 70° C. to obtain silver ion microcapsules.

[0081] Example 2

[0082] The present invention provides a method for preparing a packaging material, characterized in that the packaging material comprises a printing layer, a barrier layer and a heat-sealing layer in order from top to bottom, the thickness of the heat-sealing layer is 70 μm, the thickness of the barrier layer is 50 μm, and the thickness of the printing layer is 30 μm, and the preparation method comprises:

[0083] S100, polypropylene, maleic anhydride graft copolymer polypropylene, peroxide and filler are mixed in a mass ratio of 1:0.5:0.1:0.1, melt-extruded and biaxially stretched, and the filler particle size is 0.1 μm to obtain a printing layer;

[0084] S200, preparing a barrier layer and a heat-sealing layer respectively;

[0085] S300, stacking a heat-sealing layer, a barrier layer, and a printing layer in order from inside to outside to obtain a packaging material;

[0086] The heat seal layer and the barrier layer, as well as the barrier layer and the printing layer are laminated with polyurethane adhesive, and the dry weight of the adhesive is 4.5g / m 2 , the coating thickness is 3μm;

[0087] S200 includes:

[0088] S210, preparing silver ion microcapsules;

[0089] S220, mixing part of the silver ion microcapsules with polypropylene uniformly, heating and melting, and casting the mixture into a film to obtain a heat-sealing layer;

[0090] S230, after the remaining silver ions are uniformly mixed with polypropylene, the mixture is heated, melted, extruded and uniaxially stretched into a polypropylene film, a silicon oxide coating is coated on the polypropylene film, the thickness of the silicon oxide coating is 3 μm, the polypropylene film is heated to 150° C., silicon dioxide is used as a target material, a high-energy electron beam is used to irradiate silicon dioxide so that silicon dioxide is deposited on the tungsten hexaoxide coating, an annealing treatment is performed at 75° C., and a drying treatment is performed at 65° C. to obtain a barrier layer;

[0091] S210 includes:

[0092] S211, preparing wall material: adding porous cyclodextrin into deionized water, with the mass ratio of porous cyclodextrin to deionized water being 5:1, stirring at 80° C. and 500 rpm for 5 hours to obtain a wall material emulsion;

[0093] S212, preparing a core material: adding a dispersant to the nano-silver solution, wherein the amount of the dispersant is 10 wt.% of the nano-silver solution, stirring at 80° C. and 300 rpm for 1.5 h to obtain a core material emulsion;

[0094] S213, preparing silver ion microcapsules: mixing the wall material emulsion and the core material emulsion at a mass ratio of 1:1 for 1 hour, spraying with a spray particle size of 20 μm, and drying at 90° C. to obtain silver ion microcapsules.

[0095] Example 3

[0096] The present invention provides a method for preparing a packaging material, characterized in that the packaging material comprises a printing layer, a barrier layer and a heat-sealing layer from top to bottom, the thickness of the heat-sealing layer is 65 μm, the thickness of the barrier layer is 43 μm, and the thickness of the printing layer is 25 μm, and the preparation method comprises:

[0097] S100, polypropylene, maleic anhydride graft copolymer polypropylene, peroxide and filler are mixed in a mass ratio of 1:0.3:0.08:0.08, melt-extruded and biaxially stretched, and the filler particle size is 0.05 μm to obtain a printing layer;

[0098] S200, preparing a barrier layer and a heat-sealing layer respectively;

[0099] S300, stacking a heat-sealing layer, a barrier layer, and a printing layer in order from inside to outside to obtain a packaging material;

[0100] The heat seal layer and the barrier layer, as well as the barrier layer and the printing layer are laminated with polyurethane adhesive, and the dry weight of the adhesive is 4.0g / m 2 , the coating thickness is 2μm;

[0101] S200 includes:

[0102] S210, preparing silver ion microcapsules;

[0103] S220, mixing part of the silver ion microcapsules with polypropylene uniformly, heating and melting, and casting the mixture into a film to obtain a heat-sealing layer;

[0104] S230, after the remaining silver ions are uniformly mixed with polypropylene, the mixture is heated, melted, extruded and uniaxially stretched into a polypropylene film, a silicon oxide coating is coated on the polypropylene film, the thickness of the silicon oxide coating is 2 μm, the polypropylene film is heated to 140° C., silicon dioxide is used as a target material, a high-energy electron beam is used to irradiate silicon dioxide so that silicon dioxide is deposited on the tungsten hexaoxide coating, an annealing treatment is performed at 75° C., a modified polyvinyl alcohol protective coating with a thickness of 1 μm is coated, and then a drying treatment is performed at 60° C. to obtain a barrier layer;

[0105] S210 includes:

[0106] S211, preparing wall material: adding porous cyclodextrin into deionized water, the mass ratio of porous cyclodextrin to deionized water being 4:1, stirring at 60° C. and 400 rpm for 4 hours to obtain a wall material emulsion;

[0107] S212, preparing a core material: adding a dispersant to the nano-silver solution, wherein the amount of the dispersant is 8 wt.% of the nano-silver solution, stirring at 70° C. and 250 rpm for 1 hour to obtain a core material emulsion;

[0108] S213, preparing silver ion microcapsules: mixing the wall material emulsion and the core material emulsion at a mass ratio of 1:1 for 0.5 h, spraying the mixture with a spray particle size of 15 μm, and drying the mixture at 80° C. to obtain silver ion microcapsules.

[0109] Example 4

[0110] The present invention provides a method for preparing a packaging material, characterized in that the packaging material comprises a printing layer, a barrier layer and a heat-sealing layer from top to bottom, the thickness of the heat-sealing layer is 62 μm, the thickness of the barrier layer is 45 μm, and the thickness of the printing layer is 28 μm, and the preparation method comprises:

[0111] S100, polypropylene, maleic anhydride graft copolymer polypropylene, peroxide and filler are mixed in a mass ratio of 1:0.4:0.08:0.08, melt-extruded and biaxially stretched, and the filler particle size is 0.05 μm to obtain a printing layer;

[0112] S200, preparing a barrier layer and a heat-sealing layer respectively;

[0113] S300, stacking a heat-sealing layer, a barrier layer, and a printing layer in order from inside to outside to obtain a packaging material;

[0114] The heat seal layer and the barrier layer, as well as the barrier layer and the printing layer are laminated with polyurethane adhesive, and the dry weight of the adhesive is 4.0g / m 2 , the coating thickness is 2μm;

[0115] S200 includes:

[0116] S210, preparing silver ion microcapsules;

[0117] S220, mixing part of the silver ion microcapsules with polypropylene uniformly, heating and melting, and casting the mixture into a film to obtain a heat-sealing layer;

[0118] S230, after the remaining silver ions are uniformly mixed with polypropylene, the mixture is heated, melted, extruded and uniaxially stretched into a polypropylene film, a silicon oxide coating is coated on the polypropylene film, the thickness of the silicon oxide coating is 3 μm, the polypropylene film is heated to 140° C., silicon dioxide is used as a target material, a high-energy electron beam is used to irradiate silicon dioxide so that silicon dioxide is deposited on the tungsten hexaoxide coating, an annealing treatment is performed at 65° C., a modified polyvinyl alcohol protective coating with a thickness of 3 μm is coated, and then a drying treatment is performed at 60° C. to obtain a barrier layer;

[0119] S210 includes:

[0120] S211, preparing wall material: adding porous cyclodextrin into deionized water, wherein the mass ratio of porous cyclodextrin to deionized water is 4:1, stirring at 65° C. and 400 rpm for 4 hours to obtain a wall material emulsion;

[0121] S212, preparing a core material: adding a dispersant to the nano-silver solution, wherein the amount of the dispersant is 6 wt.% of the nano-silver solution, stirring at 70° C. and 250 rpm for 1 hour to obtain a core material emulsion;

[0122] S213, preparing silver ion microcapsules: mixing the wall material emulsion and the core material emulsion at a mass ratio of 1:1 for 0.5 h, spraying the mixture with a spray particle size of 15 μm, and drying the mixture at 80° C. to obtain silver ion microcapsules.

[0123] Comparative Example 1

[0124] This comparative example provides a packaging process, which includes a heat sealing layer, a barrier layer and a printing layer from the inside to the outside, the heat sealing layer has a thickness of 15 μm, the barrier layer has a thickness of 20 μm, and the printing layer has a thickness of 70 μm. The preparation method of the easy-tear film includes the following steps:

[0125] S100, preparing a heat sealing layer, a barrier layer and a printing layer respectively;

[0126] S200, stacking a heat-sealing layer, a barrier layer, and a printing layer in order from inside to outside to obtain a packaging material;

[0127] Among them, the heat sealing layer and the barrier layer, and the barrier layer and the printing layer are compounded by adhesive; the adhesive is a polyurethane adhesive.

[0128] Comparative Example 2

[0129] The comparative example provides a packaging material, which includes a heat sealing layer, a barrier layer and a printing layer from the inside to the outside, the heat sealing layer has a thickness of 15 μm, the barrier layer has a thickness of 20 μm, and the printing layer has a thickness of 70 μm. The preparation method of the easy-tear film includes the following steps:

[0130] S100, preparing a heat sealing layer, a barrier layer and a printing layer respectively;

[0131] S200, stacking a heat-sealing layer, a barrier layer, and a printing layer in order from inside to outside to obtain a packaging material;

[0132] The heat sealing layer and the barrier layer, and the barrier layer and the printing layer are fixed by adhesive; the adhesive is polyurethane adhesive;

[0133] The method for preparing the barrier layer comprises:

[0134] S111, melt-extrude the polypropylene and uniaxially stretch it into a polypropylene film;

[0135] S112, heating the polypropylene film to 130°C, using silicon dioxide as a target material, and irradiating silicon dioxide with a high-energy electron beam to deposit silicon dioxide on the polypropylene film to obtain a polypropylene film having a silicon oxide layer, the thickness of the silicon oxide layer is 3 μm, and obtaining the barrier layer.

[0136] Performance Testing

[0137] 1. Cook the samples of Examples 1-4 and Comparative Examples 1-2 at 121°C for 20 min and observe whether stratification occurs;

[0138] 2. Detecting the water vapor transmission rates of the samples of Examples 1-4 and Comparative Examples 1-2 before and after steaming;

[0139] 3. According to GB / T 3917.2-2009, whether Examples 1-4 and Comparative Examples 1-2 have a straight-line tearing function;

[0140] 4. The packaging materials of Examples 1-4 and Comparative Examples 1-2 were cut into square samples of 1 cm×1 cm and cleaned to ensure sterility. The diluted Gram-negative non-spore-forming bacillus was dripped onto the samples, sealed and cultured at room temperature for 7 days.

[0141] Test Results

[0142] 1. The samples of Examples 1-4 and Comparative Examples 1-2 were steamed at 120°C for 20 min. The samples of Examples 1-4 were not stratified, while Comparative Example 1 was slightly stratified and Comparative Example 2 was not stratified;

[0143] 2. The water vapor transmission rates of the samples of Examples 1-4 and Comparative Examples 1-2 before and after steaming are shown in Table 1;

[0144] Table 1

[0145]

[0146] 3. According to GB / T 3917.2-2009, the tearing properties of Examples 1-4 and Comparative Examples 1-2 were tested. Examples 1-4 all had a straight-line tearing function, Comparative Example 1 did not have a straight-line tearing function, and Comparative Example 2 had a straight-line tearing function. The tear strengths of Examples 1-4 and Comparative Examples 1-2 are shown in Table 2.

[0147] Table 2

[0148]

[0149] 4. The aluminum foil packaging materials of Examples 1-4 were cut into square samples of 1 cm×1 cm, and diluted Gram-negative non-spore-forming Bacillus was dripped onto the samples. No bacterial colony grew, indicating that the samples had good antibacterial properties.

[0150] A packaging material, characterized in that, during the evaporation treatment process in S230, a machine learning algorithm is introduced to monitor and predict the evaporation parameters in real time, automatically adjust the working state of the evaporation equipment, and ensure that each batch of barrier layers can achieve ideal physical and chemical properties. Thereby improving the density and consistency of the barrier layer, and further enhancing the barrier properties of the packaging material. The specific implementation steps are:

[0151] Step A: Goal Setting Content

[0152] Determine key parameters:

[0153] Deposition rate: The deposition rate directly affects the thickness and uniformity of the film layer. The goal is to optimize the evaporation parameters to keep the deposition rate within the ideal range and ensure the uniformity of the barrier layer;

[0154] Thickness uniformity: Thickness uniformity is one of the important indicators for evaluating the quality of the barrier layer. The goal is to ensure the uniformity of the thickness of the barrier layer by real-time monitoring and adjusting process parameters, thereby improving the overall barrier performance of the material;

[0155] Temperature and pressure: These parameters have a significant impact on the physical and chemical reactions during the evaporation process and need to be precisely controlled during the process to optimize the structure and performance of the barrier layer;

[0156] Step B: Data collection and preprocessing

[0157] Historical data collection:

[0158] Collect relevant evaporation parameters and film performance data from past production processes. These data can be obtained through the factory's production records, experimental data and quality inspection reports; specifically, they include the following types of data:

[0159] Evaporation process parameters:

[0160] Temperature: The temperature of the working chamber in the evaporation equipment usually affects the evaporation rate of the material and the growth of the film layer;

[0161] Pressure: The air pressure during the evaporation process directly affects the migration and deposition of the evaporation material;

[0162] Deposition rate: The amount of material deposited on the substrate per unit time, which is a key parameter affecting the thickness and uniformity of the film;

[0163] Evaporation time: the duration of the entire evaporation process;

[0164] Target characteristics: purity and size of silicon target;

[0165] Film performance data:

[0166] Thickness: Evaluate the control accuracy of the evaporation process by measuring the film thickness;

[0167] Uniformity: Multi-point measurement is used to evaluate the thickness difference of the film layer at different positions, reflecting the uniformity of the film layer;

[0168] Density: The microstructure of the film layer was observed by scanning electron microscopy (SEM) to evaluate its density;

[0169] Real-time data collection:

[0170] Sensor deployment location: Temperature sensor: The temperature sensor should be deployed at multiple locations in the evaporation chamber, including near the evaporation source, on the substrate surface, and in different areas of the chamber to ensure that the uniformity and changes in temperature can be monitored; Model: Use a high-precision platinum resistance temperature sensor;

[0171] Pressure sensor: Deployment location: The pressure sensor should be installed on the top and side of the evaporation chamber, close to the vacuum pump, to monitor the pressure changes in the chamber; Model: Use a capacitive pressure sensor;

[0172] Deposition rate sensor: Deployment position: The deposition rate sensor is installed directly above the substrate to directly measure the deposition rate of the material on the substrate; Model: Use a quartz crystal microbalance (QCM) sensor;

[0173] Thickness measurement sensor: Deployment location: The thickness measurement sensor should be installed outside the evaporation chamber to detect the film thickness on the substrate surface through the window; Model: Use laser interferometer;

[0174] Real-time film layer detection: Online thickness gauge: Deploy an online thickness gauge to monitor the thickness change of the film layer in real time; Installation location: The online thickness gauge should be installed at the discharge port of the evaporation chamber and the substrate transmission path to ensure that the thickness measurement is performed immediately after the film layer is formed;

[0175] Data preprocessing

[0176] Data cleaning and normalization:

[0177] Data cleaning:

[0178] Outlier detection: Identify and remove outliers using box plots;

[0179] Missing value processing: For missing data, mean filling can be used to fill it in to ensure the integrity of the data;

[0180] Data normalization:

[0181] Standardization: The data is converted into a standard normal distribution with zero mean and unit variance according to the following formula to eliminate the dimensional differences between different parameters:

[0182]

[0183] Among them, X is the original data, μ is the mean, σ is the standard deviation, X norm is the normalized data;

[0184] Feature extraction:

[0185] Temperature change rate:

[0186] Characteristic description: During the evaporation process, the rate of temperature change will affect the migration and deposition rate of the evaporation material. By calculating the rate of temperature change, the characteristics of temperature fluctuations during the evaporation process can be captured;

[0187] Calculation method: Calculate the temperature change rate in each time period through the continuous sampling data of the temperature sensor;

[0188]

[0189] Among them, T t is the temperature at time t, Δt is the time interval, and ΔT is the temperature change rate;

[0190] Sedimentation rate changes:

[0191] Characteristic description: Fluctuations in deposition rate directly affect the thickness and uniformity of the film layer. By analyzing the changes in deposition rate, the stability of the film layer performance can be predicted; it can be calculated using the following formula:

[0192]

[0193] Among them, R t+1 , R t are the deposition rates at time t+1 and t, respectively, Δt is the time interval, and ΔR is the deposition rate change rate;

[0194] Step C: Model selection and training

[0195] Using Deep Belief Network (DBN) to complete the machine learning model selection and training in the evaporation process can better capture the complex nonlinear relationship in the evaporation process;

[0196] The deep belief network consists of a multi-layer restricted Boltzmann machine (RestrictedBoltzmannMachin e , RBM) stacked; each RBM layer consists of a visible layer and a hidden layer; the energy function of RBM is defined as:

[0197]

[0198] Among them, i and j represent the numbers of the visible layer and hidden layer neural units respectively, and v i and h j denote the neural units of the visible layer and the hidden layer, respectively, i and c j is the corresponding bias term, W ij is the weight between the visible layer and the hidden layer; E(v, h) represents the energy function value;

[0199] The joint probability distribution of RBM is expressed as:

[0200]

[0201] Where P(v, h) represents the joint probability distribution, which indicates the probability of the visible unit and the hidden unit appearing at the same time; E(v, h) represents the energy function value, and Z represents the partition function, which is used to normalize the probability distribution;

[0202] Model training:

[0203] In DBN, the hidden layer output of the first layer RBM is used as the input of the second layer RBM, and so on; each layer of RBM learns different levels of data features, and finally forms a deep network;

[0204] Supervised fine-tuning: After all RBM layers are pre-trained, a fully connected regression layer is finally connected to map the evaporation parameters to the thickness and uniformity of the film layer; it can be expressed as:

[0205]

[0206] In the formula, L represents the number of network layers, Indicates that the output is the predicted value of the film performance, W (L) , b (L) are the weight and bias of the output layer, respectively, h (L-1) is the hidden layer output of the L-1th layer;

[0207] The loss function arranged during the training process can use the mean square error to minimize the difference between the predicted value and the true value, which is expressed as follows;

[0208]

[0209] Where N is the number of training samples, is the predicted value of the i-th sample, y i is the actual film thickness and uniformity, represents the loss function, which measures the gap between the model's predicted value and the true value, and θ is the model's parameter, i.e., the set of variables optimized through the training process;

[0210] The gradient of the loss function for each weight is calculated through the back-propagation algorithm, and the weight is updated and fine-tuned using the gradient descent algorithm:

[0211]

[0212] Among them, η is the learning rate, L represents the number of network layers, and W (L) represents the weight, is the gradient symbol;

[0213] The four-vector optimization algorithm is used to optimize the hyperparameters of DBN. The four-vector optimization algorithm is an efficient global optimization method suitable for complex and multi-dimensional optimization problems. It can quickly find the optimal solution by searching and optimizing vectors in four directions at the same time. When using DBN for intelligent control of the evaporation process, the hyperparameters that need to be optimized include: the number of hidden units of each layer of RBM, the learning rate, and the number of training iterations.

[0214] Initial vector generation: Four initial vectors are randomly generated, representing four different hyperparameter combinations, namely X1, X2, X3, and X4;

[0215] The fitness function needs to combine the hyperparameters of the DBN model with the performance indicators of the model, as shown below:

[0216]

[0217] Among them, F(X) is the fitness function value, X is a hyperparameter vector combination, which consists of the hyperparameters that need to be optimized; N is the number of training samples, is the predicted value of the i-th sample under the hyperparameter combination X, y i is the actual film thickness and uniformity, l represents the number of the hidden layer, H l represents the number of units in the hidden layer of the lth layer; λ is the regularization coefficient, which is used to balance the prediction error and complexity of the model;

[0218] Vector update: Using the core idea of ​​the four-vector optimization algorithm, a new candidate vector is generated through linear combination and interpolation of four initial vectors, thereby optimizing the hyperparameter combination;

[0219] X new =X1+α(X2-X3)+β(X3-X4)

[0220] X new is the updated vector combination, α and β are adjustment coefficients that control the update direction and amplitude; the above process is continuously iterated to gradually approach the optimal hyperparameter combination by updating the vector. In each iteration, the vector with the smallest MSE is selected as the initial vector for the new round, and the iteration is continued until convergence, that is, the objective function value no longer decreases significantly;

[0221] Step D: Real-time monitoring and feedback control

[0222] Using the gain adaptive control algorithm to achieve real-time monitoring and feedback control during the evaporation process can better cope with the uncertainty of parameter changes during the production process and ensure the stability and accuracy of the system;

[0223] The design of the gain adaptive controller is based on the PID controller, and its gain parameters are automatically adjusted with the real-time input process parameters to adapt to different working conditions;

[0224] Gain scheduling formula: The gain adaptive controller automatically adjusts the gain parameters according to the changes in process parameters, as shown below;

[0225] K p (t) = K p0 ·f p (T(t), P(t), R(t))

[0226] K i (t) = K i0 ·f i (T(t), P(t), R(t))

[0227] K d (t) = K d0 ·f d (T(t), P(t), R(t))

[0228] Among them, K p (t), K i (t), K d (t) represent their respective gain parameters, K p0 , K i0 , K d0 are the initial proportional, integral, and differential gains, respectively, and f p (), f i (), f d () is the gain scheduling function, which adjusts the gain based on the current temperature T(t), pressure P(t), and deposition rate R(t);

[0229] The controller formula is as follows:

[0230]

[0231] ∈(t) is the error signal, u(t) is the adjusted control output, which is used to adjust the working state of the equipment;

[0232] In the real-time control of the evaporation process, by comparing the DBN model prediction value with the actual measurement value, it can be determined whether the process parameters need to be adjusted; according to the size and direction of the error, the controller will issue adjustment instructions to optimize the thickness and uniformity of the film layer;

[0233] Rules for controller adjustment process:

[0234] Temperature adjustment rules: When the film thickness error ∈(t) is greater than zero, it means that the actual film thickness is less than the predicted value, and the temperature needs to be increased; when the film thickness error ∈(t) is less than zero, it means that the actual film thickness is greater than the predicted value, and the temperature needs to be reduced; the controller can directly affect the temperature of the evaporation chamber by adjusting the power output of the heater;

[0235] Pressure adjustment rules: When the film uniformity deviation ∈(t) is greater than zero, it means that the film uniformity deviation is large; when the film uniformity deviation ∈(t) is less than zero, it means that the film uniformity deviation is small; the controller can adjust the pressure by adjusting the vacuum pump power;

[0236] Deposition rate adjustment rules: When the film thickness still does not reach the expected target after adjusting the temperature and pressure, especially when the film thickness is too thin and the deposition amount cannot be effectively increased by increasing the temperature, the deposition rate can be adjusted; the controller can achieve this by adjusting the supply rate of the material supply system; through these rules, the controller can dynamically adjust the process parameters during the evaporation process to ensure the quality and consistency of the film layer, thereby improving the overall performance of the product.

[0237] The detailed implementation cases are as follows:

[0238] Determine the process parameter targets: the target is a film thickness of 50nm and a uniformity deviation of 2%;

[0239] The initial process parameters are: temperature T0 = 205 °C, pressure P0 = 0.5 × 10 -3 , deposition rate R0 = 1.2 nm / min;

[0240] Building DBN model:

[0241] Data collection: 5,000 sample data sets were collected from historical evaporation processes, including temperature, pressure, deposition rate, and corresponding film thickness and uniformity data;

[0242] Initial structure of DBN model:

[0243] Input layer: 3 nodes (temperature, pressure, deposition rate);

[0244] Hidden layer: The number of hidden layers of RBM is 3, and the initial number of hidden units is [128-8] optimized;

[0245] Output layer: 2 nodes (film thickness prediction value and uniformity prediction value);

[0246] Four-vector optimization algorithm optimizes hyperparameters

[0247] Initial hyperparameter vector:

[0248] X1=[3, 64, 32, 16, 0.01, 100]

[0249] X2=[3,128,64,32,0.01,150]

[0250] X3=[3, 32, 16, 8, 0.005, 200]

[0251] X4=[3, 64, 32, 16, 0.02, 100]

[0252] Fitness function calculation: λ = 0.01

[0253] F(X1)=-2.8, F(X2)=-3.1, F(X3)=-2.5, F(X4)=-3.0

[0254] According to the formula, the vector is updated, and the adjustment coefficients α=0.5, β=0.3,

[0255] Calculate X1-X2:

[0256] [3-3, 64-128, 32-64, 16-32, 0.01-0.01, 100-150] == [0, -64, -32, -16, 0, -50]

[0257] Calculate X3-X4:

[0258] [3-3, 64-32, 32-16, 16-8, 0.02-0.005, 100-200] = [0, 32, 16, 8, 0.015, -100]

[0259] Adjust by factor:

[0260] α(X1-X2)=0.5×[0,-64,-32,-16,0,-50]=[0,-32,-16,-8,0,-25]

[0261] β(X4-X3)=0.3×[0, 32, 16, 8, 0.015, -100]=[0, 9.6, 4.8, 2.4, 0.0045, -30]

[0262] Add the basis vector X3:

[0263] X new =[3, 32, 16, 8, 0.005, 200] + [0, -32, -16, -8, 0, -25] + [0, 9.6, 4.8, 2.4, 0.0045, -30]

[0264] New vector:

[0265] X new =[3, 9.6, 4.8, 2.4, 0.0095, 145]

[0266] The updated optimal hyperparameter vector is: the number of hidden layers L = 3, the number of hidden units in the first layer H1 = 10, the number of hidden units in the second layer H2 = 5, the number of hidden units in the third layer H3 = 2, η = 0.0095, N iter=145;

[0267] By using the best hyperparameter combination, the DBN model is established and trained to predict the process parameters: predict the film thickness Predicted uniformity

[0268] Application of adaptive control algorithm: Based on the prediction results of DBN model, the adaptive control algorithm will calculate the required process parameter adjustment to reduce the deviation;

[0269] Calculate the error signal:

[0270] Film thickness error∈ d =50–48=2nm, the error is positive, indicating that the film thickness is insufficient;

[0271] Uniformity error ∈ U =3.5%-2%=1.5%, the error is positive, indicating that the uniformity deviation is too large;

[0272] The adaptive controller calculates the control output: a PID controller is used to calculate the adjustments for temperature, pressure, and deposition rate;

[0273] Temperature adjustment: proportional gain K pT =2, integral gain K iT =0.1, differential gain K dT =1.5;

[0274]

[0275] The temperature should increase by ΔT = 4.9°C, and the adjusted temperature is T = 205 + 4.9 = 209.9°C;

[0276] Pressure adjustment: proportional gain K pP =1×10 -3 , integral gain K iP =0.05×10 -3 , differential gain K dP =0.5×10 -3 ;

[0277]

[0278] The pressure should be reduced by ΔP = 1.725 × 10 -3 Torr, adjusted pressure - 1.225 x 10 -3 , but the pressure cannot be negative, so just adjust the pressure to a positive value close to zero;

[0279] Deposition rate adjustment: proportional gain K pR =0.5, integral gain K iR=0.02, differential gain K dR =0.1;

[0280]

[0281] The deposition rate should be increased by ΔR = 1.64 nm / min, and the adjusted deposition rate is R = 2.841 nm / min;

[0282] By implementing the above complete experimental process, the system calculates and adjusts the temperature, pressure and deposition rate according to the film thickness and uniformity deviation predicted by the DBN model, and finally makes the process parameters reach the target; this closed-loop feedback control significantly improves the accuracy and stability of the evaporation process and ensures the consistency of product quality.

[0283] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for preparing a medical packaging material, characterized in that: The packaging material comprises a printing layer, a barrier layer and a heat-sealing layer from top to bottom. The heat seal layer comprises polypropylene and silver ions; The barrier layer includes at least a portion covering the heat seal layer, the barrier layer including polypropylene, silicon oxide and silver ions; The printing layer at least partially covers the barrier layer, and the printing layer includes polypropylene, maleic anhydride grafted copolymer polypropylene, peroxide and filler; The preparation method comprises: S100, mixing the polypropylene, the maleic anhydride graft copolymerized polypropylene, the peroxide and the filler, melt-extruding and biaxially stretching to obtain the printing layer; S200, preparing the barrier layer and the heat-sealing layer respectively; S300, stacking the heat sealing layer, the barrier layer and the printing layer in order from inside to outside to obtain the packaging material; Wherein, the heat sealing layer and the barrier layer, and the barrier layer and the printing layer are both laminated by adhesive; The S200 includes: S210, preparing silver ion microcapsules; S220, mixing part of the silver ion microcapsules with polypropylene uniformly, heating and melting and casting to form a film, to obtain the heat sealing layer; S230, after the remaining silver ions are uniformly mixed with the polypropylene, the mixture is heated, melted, extruded and uniaxially stretched into a polypropylene film, and then the polypropylene film is subjected to a vapor deposition treatment, an annealing treatment and a drying treatment in sequence to obtain the barrier layer.

2. The preparation method according to claim 1, characterized in that: The S210 includes: S211, preparing wall material: adding porous cyclodextrin into deionized water and stirring evenly to obtain a wall material emulsion; S212, preparing a core material: adding a dispersant to the nanosilver solution and stirring evenly to obtain a core material emulsion; S213, preparing the silver ion microcapsules: uniformly mixing the wall material emulsion and the core material emulsion, and then sequentially performing spraying treatment and drying treatment to obtain the silver ion microcapsules.

3. The preparation method according to claim 2, characterized in that: In the S212, The amount of the dispersant is 1wt.%-10wt.% of the nano silver solution; The dispersant is an ethanol solution of sorbitan monooleate; In S211, the mass ratio of the porous cyclodextrin to the deionized water is (3-5):1; and / or In S213, the mass ratio of the wall material emulsion to the core material emulsion is 1:

1.

4. The preparation method according to claim 2, characterized in that: In the S213, The spray particle size of the spray treatment is 10 μm-20 μm; and / or The temperature of the drying treatment is 70°C-90°C.

5. The preparation method according to claim 1, characterized in that: In the S230, The evaporation process uses a silicon-containing target material to perform the evaporation process; and / or Before the evaporation treatment is performed, the method further comprises coating an adhesion-promoting coating on the polypropylene film.

6. The preparation method according to claim 1, characterized in that: The mass ratio of the polypropylene particles, the maleic anhydride graft copolymerized polypropylene particles, the peroxide and the filler is 1:(0.3-0.5):(0.01-0.1):(0.01-0.1); and / or The particle size of the filler is 0.01 μm-0.1 μm.

7. The preparation method according to claim 1, characterized in that: The adhesive is a polyurethane adhesive; and / or The dry weight of the adhesive is 3.5 g / m 2 -4.5g / m 2 ; and / or The coating thickness of the adhesive is 1 μm-3 μm.

8. The preparation method according to claim 1, characterized in that: The thickness of the heat-sealing layer is 25 μm-70 μm; and / or The thickness of the barrier layer is 25 μm-50 μm; and / or The thickness of the printing layer is 15 μm-30 μm.

9. The preparation method according to claim 1, characterized in that: During the evaporation process in S230, a machine learning algorithm is introduced to monitor and predict the evaporation parameters in real time, automatically adjust the working state of the evaporation equipment, and ensure that each batch of barrier layers can achieve the ideal physical and chemical properties, thereby improving the density and consistency of the barrier layer, and further enhancing the barrier properties of the packaging material. The specific implementation steps are: Step A: Goal Setting Content Determine key parameters: Deposition rate: The deposition rate directly affects the thickness and uniformity of the film layer. The goal is to optimize the evaporation parameters to keep the deposition rate within the ideal range and ensure the uniformity of the barrier layer. Thickness uniformity: Thickness uniformity is an important indicator for evaluating the quality of the barrier layer; The goal is to ensure uniformity of barrier layer thickness by real-time monitoring and adjustment of process parameters, thereby improving the overall barrier performance of the material; Temperature and pressure: These parameters have a significant impact on the physical and chemical reactions during the evaporation process and need to be precisely controlled during the process to optimize the structure and performance of the barrier layer; Step B: Data collection and preprocessing Historical data collection: Collect relevant evaporation parameters and film performance data from past production processes. These data can be obtained through the factory's production records, experimental data, and quality inspection reports; specifically, they include the following types of data: Evaporation process parameters: Temperature: The temperature of the working chamber in the evaporation equipment usually affects the evaporation rate of the material and the growth of the film layer; Pressure: The air pressure during the evaporation process directly affects the migration and deposition of the evaporation material; Deposition rate: The amount of material deposited on the substrate per unit time, which is a key parameter affecting the thickness and uniformity of the film; Evaporation time: the duration of the entire evaporation process; Target characteristics: purity and size of silicon target; Film performance data: Thickness: Evaluate the control accuracy of the evaporation process by measuring the film thickness; Uniformity: Multi-point measurement is used to evaluate the thickness difference of the film layer at different positions, reflecting the uniformity of the film layer; Density: The microstructure of the film layer is observed by scanning electron microscope (SEM) to evaluate its density; Real-time data collection: Sensor deployment location: Temperature sensors: Temperature sensors should be deployed at multiple locations in the evaporation chamber, including near the evaporation source, on the substrate surface, and in different areas of the chamber to ensure that the temperature uniformity and changes can be monitored; Pressure sensor: Deployment location: The pressure sensor should be installed on the top and side of the evaporation chamber, close to the vacuum pump, to monitor the pressure changes in the chamber; Deposition rate sensor: Deployment location: The deposition rate sensor is installed just above the substrate to directly measure the deposition rate of the material on the substrate; Thickness measurement sensor: Deployment location: The thickness measurement sensor should be installed outside the evaporation chamber to detect the film thickness on the substrate surface through the window; Real-time film layer detection: Online thickness gauge: Deploy an online thickness gauge to monitor the thickness change of the film layer in real time; Installation location: The online thickness gauge should be installed at the discharge port of the evaporation chamber and the substrate transmission path to ensure that the thickness measurement is performed immediately after the film layer is formed; Data preprocessing Data cleaning and normalization: Data cleaning: Outlier detection: Identify and remove outliers using box plots; Missing value processing: For missing data, mean filling can be used to fill in the missing data to ensure the integrity of the data; Data normalization: Standardization: The data is converted into a standard normal distribution with zero mean and unit variance according to the following formula to eliminate the dimensional differences between different parameters: Among them, X is the original data, μ is the mean, σ is the standard deviation, X norm is the normalized data; Feature extraction: Temperature change rate: Characteristic description: During the evaporation process, the rate of temperature change will affect the migration and deposition rate of the evaporation material. By calculating the rate of temperature change, the characteristics of temperature fluctuations during the evaporation process can be captured; Calculation method: Calculate the temperature change rate in each time period through the continuous sampling data of the temperature sensor; Among them, T t is the temperature at time t, Δt is the time interval, and ΔT is the temperature change rate; Sedimentation rate changes: Characteristic description: Fluctuations in deposition rate directly affect the thickness and uniformity of the film layer. By analyzing the changes in deposition rate, the stability of the film layer performance can be predicted; it can be calculated using the following formula: Among them, R t+1 , R t are the deposition rates at time t+1 and t, respectively, Δt is the time interval, and ΔR is the deposition rate change rate; Step C uses a deep belief network (DBN) to complete the machine learning model selection and training in the evaporation process, which can better capture the complex nonlinear relationship in the evaporation process; The deep belief network is composed of multiple layers of restricted Boltzmann machines (RBMs). Each RBM layer consists of a visible layer and a hidden layer. The energy function of RBM is defined as: Among them, i and j represent the numbers of the visible layer and hidden layer neural units respectively, and v i and h j denote the neural units of the visible layer and the hidden layer, respectively, i and c j is the corresponding bias term, W ij is the weight between the visible layer and the hidden layer; E(v, h) represents the energy function value; The joint probability distribution of RBM is expressed as: Where P(v, h) represents the joint probability distribution, which indicates the probability of the visible unit and the hidden unit appearing at the same time; E(v, h) represents the energy function value, and Z represents the partition function, which is used to normalize the probability distribution; Model training: In DBN, the hidden layer output of the first layer RBM is used as the input of the second layer RBM, and so on; each layer of RBM learns different levels of data features, and finally forms a deep network; Supervised fine-tuning: After all RBM layers are pre-trained, a fully connected regression layer is finally connected to map the evaporation parameters to the thickness and uniformity of the film layer; it can be expressed as: In the formula, L represents the number of network layers, Indicates that the output is the predicted value of the film performance, W (L) , b (L) are the weight and bias of the output layer, respectively, h (L-1) is the hidden layer output of the L-1th layer; The loss function arranged during the training process can use the mean square error to minimize the difference between the predicted value and the true value, which is expressed as follows; Where N is the number of training samples, is the predicted value of the i-th sample, y i is the actual film thickness and uniformity, represents the loss function, which measures the gap between the model's predicted value and the true value, and θ is the model's parameter, i.e., the set of variables optimized through the training process; The gradient of the loss function for each weight is calculated through the back-propagation algorithm, and the weight is updated and fine-tuned using the gradient descent algorithm: Among them, η is the learning rate, L represents the number of network layers, and W (L) represents the weight, is the gradient symbol; The four-vector optimization algorithm is used to optimize the hyperparameters of DBN. By simultaneously considering the vectors in four directions for search and optimization, the optimal solution can be found quickly. When using DBN for intelligent control of the evaporation process, the hyperparameters that need to be optimized include: the number of hidden layers, the number of hidden units in each layer of RBM, the learning rate, and the number of training iterations. Initial vector generation: Four initial vectors are randomly generated, representing four different hyperparameter combinations, namely X1, X2, X3, and X4; The fitness function needs to combine the hyperparameters of the DBN model with the performance indicators of the model, as shown below: Among them, F(X) is the fitness function value, X is a hyperparameter vector combination, which consists of the hyperparameters that need to be optimized; N is the number of training samples, is the predicted value of the i-th sample under the hyperparameter combination X, y i is the actual film thickness and uniformity, l represents the number of the hidden layer, H l represents the number of units in the hidden layer of the lth layer; λ is the regularization coefficient, which is used to balance the prediction error and complexity of the model; Vector update: Using the core idea of ​​the four-vector optimization algorithm, a new candidate vector is generated through linear combination and interpolation of four initial vectors, thereby optimizing the hyperparameter combination; X new =X1+α(X2-X3)+β(X3-X4) X new is the updated vector combination, α and β are adjustment coefficients that control the update direction and amplitude; the above process is continuously iterated to gradually approach the optimal hyperparameter combination by updating the vector. In each iteration, the vector with the smallest MSE is selected as the initial vector for the new round, and the iteration is continued until convergence, that is, the objective function value no longer decreases significantly; Step D uses a gain adaptive control algorithm to achieve real-time monitoring and feedback control during the evaporation process, which can better cope with the uncertainty of parameter changes during the production process and ensure the stability and accuracy of the system; The design of the gain adaptive controller is based on the PID controller, and its gain parameters are automatically adjusted with the real-time input process parameters to adapt to different working conditions; Gain scheduling formula: The gain adaptive controller automatically adjusts the gain parameters according to the changes in process parameters, as shown below; K p (t)=K p0 ·f p (T(t),P(t),R(t)) K i (t)=K i0 ·f i (T(t),P(t),R(t)) K d (t)=K d0 ·f d (T(t),P(t),R(t)) Among them, K p (t), K i (t), K d (t) represent their respective gain parameters, K p0 , K i0 , K d0 are the initial proportional, integral, and differential gains, respectively, and f p (), f i (), f d () is the gain scheduling function, which adjusts the gain based on the current temperature T(t), pressure P(t), and deposition rate R(t); The controller formula is as follows: ∈(t) is the error signal, u(t) is the adjusted control output, which is used to adjust the working state of the equipment; In the real-time control of the evaporation process, by comparing the DBN model prediction value with the actual measurement value, it can be determined whether the process parameters need to be adjusted; according to the size and direction of the error, the controller will issue adjustment instructions to optimize the thickness and uniformity of the film layer; Rules for controller adjustment process: Temperature adjustment rules: When the film thickness error ∈(t) is greater than zero, it means that the actual film thickness is less than the predicted value, and the temperature needs to be increased; when the film thickness error ∈(t) is less than zero, it means that the actual film thickness is greater than the predicted value, and the temperature needs to be reduced; the controller can directly affect the temperature of the evaporation chamber by adjusting the power output of the heater; Pressure adjustment rules: When the film uniformity deviation ∈(t) is greater than zero, it means that the film uniformity deviation is large; when the film uniformity deviation ∈(t) is less than zero, it means that the film uniformity deviation is small; the controller can adjust the pressure by adjusting the vacuum pump power; Deposition rate adjustment rules: When the film thickness still does not reach the expected target after adjusting the temperature and pressure, especially when the film thickness is too thin and the deposition amount cannot be effectively increased by increasing the temperature, the deposition rate can be adjusted; the controller can achieve this by adjusting the supply rate of the material supply system; through these rules, the controller can dynamically adjust the process parameters during the evaporation process to ensure the quality and consistency of the film layer, thereby improving the overall performance of the product.

10. A medical packaging material, characterized in that: The medical packaging material is prepared by the preparation method according to any one of claims 1 to 9.

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