A large-scale model-based antibacterial aluminum plate production control method and system

Abstract

The present invention belongs to the field of factory control technology. A method and system for controlling the production of antibacterial aluminum plates based on a large model are provided. The method comprises: obtaining production preparation data and target adhesion index related to the antibacterial aluminum plates to be produced; inputting the target adhesion index, the surface roughness of the aluminum substrate and the type of additives into a key production parameter prediction model constructed based on the large model, and the key parameter prediction model outputs a predicted set of key production parameters; and coating the silver ion coating on the aluminum substrate according to the key production parameters to produce an antibacterial aluminum plate that meets the target adhesion index. The present invention can formulate specific key production parameters based on the surface roughness of the aluminum substrate, the type of coating and the type of additives, and perform a curing treatment of the silver ion coating according to the key production parameters, so as to obtain an antibacterial aluminum plate that is adapted to the target adhesion index expected by the customer, thereby realizing the personalized customization of the antibacterial performance of the antibacterial aluminum plate by the customer.

Description

Technical Field

[0001] The present invention relates to the field of factory control technology, and in particular to a large model-based antibacterial aluminum plate production control method and system. Background Art

[0002] Antibacterial aluminum sheet refers to aluminum sheet containing special antibacterial coating (such as silver ion coating, such as Figure 1 As shown, it is non-toxic and has been verified by strict physical and chemical experiments. Some antibacterial aluminum sheets can produce more than 99.9% antibacterial rate against Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus, and the antibacterial effect of the product does not decrease with surface cleaning and remains effective within the life of the paint film. Antibacterial aluminum sheets are widely used in food processing, biopharmaceuticals, hospitals and other fields.

[0003] The long-term antibacterial performance of the antibacterial aluminum plate with silver ion coating is mainly related to the adhesion of the silver ion coating on the aluminum substrate. Good adhesion can ensure that the silver ion coating acts on the aluminum surface for a long time, thereby providing a continuous antibacterial effect. In order to ensure that the silver ion coating on the antibacterial aluminum plate produced has good adhesion, the existing technology is to clean the surface of the aluminum substrate to ensure that there is no oil and impurities, optimize the coating formula, micro-treat the surface of the aluminum substrate and activate the surface, for example, use specific additives (such as 16-mercaptohexadecanoic acid and silane coupling agent KH560) in the coating formula.

[0004] However, in addition to the above-mentioned production processes, the curing process in the production process has a great influence on the adhesion of silver ion coating on the antibacterial aluminum plate. After searching, it was found that there is no research in the prior art to formulate the optimal curing control parameters based on the specific conditions of silver ion coating and aluminum substrate, which leads to insufficient adhesion of silver ion coating on antibacterial aluminum plate, thus affecting the antibacterial performance of antibacterial aluminum plate. Therefore, how to determine the appropriate production process based on the real-time dynamic parameters of antibacterial aluminum plate production is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In response to the above technical problems, the present invention provides an antibacterial aluminum plate production control method, system, electronic equipment, computer storage medium and computer program product based on a large model.

[0006] The invention discloses a large-scale model-based antibacterial aluminum plate production control method, which is characterized in that the method comprises the following steps: obtaining production preparation data and target adhesion index related to the antibacterial aluminum plate to be produced, wherein the production preparation data comprises the surface roughness of an aluminum substrate, the coating type of a silver ion antibacterial coating, and the type of auxiliary agent in the silver ion antibacterial coating formula; inputting the target adhesion index, the surface roughness of the aluminum substrate, the coating type and the auxiliary agent type into a key production parameter prediction model constructed based on the large-scale model, wherein the key parameter prediction model outputs a set of predicted key production parameters; wherein the key production parameters comprise the coating curing temperature and the coating curing time; and coating the silver ion coating on the aluminum substrate according to the key production parameters to produce the antibacterial aluminum plate meeting the target adhesion index.

[0007] Optionally, production preparation data and a target adhesion index related to the antibacterial aluminum plate to be produced are obtained, including: receiving the coating category of the silver ion antibacterial coating related to the antibacterial aluminum plate to be produced, the type of additives in the silver ion antibacterial coating formula and the target adhesion index transmitted by a terminal device via a network; wherein the target adhesion index is determined by: inputting antibacterial aluminum plate performance description data into the terminal device, using a semantic analysis model to perform semantic analysis on the antibacterial aluminum plate performance description data to obtain an expected remaining amount of the silver ion coating on the antibacterial aluminum plate after a set number of cleanings, and obtaining the target adhesion index by matching the expected remaining amount with a preset comparison table; and, receiving the surface roughness of the aluminum substrate related to the antibacterial aluminum plate to be produced transmitted by the terminal device via a network, or receiving the surface roughness of the aluminum substrate related to the antibacterial aluminum plate to be produced transmitted by an automatic roughness measuring device via a network.

[0008] Optionally, the target adhesion index, the surface roughness of the aluminum substrate, the coating category and the additive type are input into a key production parameter prediction model constructed based on the large model, and the key parameter prediction model outputs a predicted set of key production parameters, including: using the BERT model to convert the target adhesion index, the surface roughness of the aluminum substrate, the coating category and the additive type into production statements in natural language related to the antibacterial aluminum plate to be produced; inputting the production statements into the key production parameter prediction model, and the key production parameter prediction model outputs a predicted set of preliminary production parameters; obtaining surface texture information of the aluminum substrate used to produce the antibacterial aluminum plate, and evaluating and deriving a set of adjustment coefficients based on the surface texture information; using the adjustment coefficients to optimize the coating curing temperature and the coating curing time corresponding to the preliminary production parameters, respectively, to obtain the key production parameters.

[0009] Optionally, surface texture information of an aluminum substrate used for producing an antibacterial aluminum plate is obtained, and a set of adjustment coefficients is obtained based on the surface texture information evaluation, including: taking surface images of the aluminum substrate used for producing the antibacterial aluminum plate from multiple angles, extracting surface texture information of the aluminum substrate from each of the surface images, and evaluating the texture regularity of the aluminum substrate based on the surface texture information; obtaining the type of passivator and the type of coating process used for passivation of the surface of the aluminum substrate, and obtaining a set of adjustment coefficients based on the type of passivator, the type of coating process, and the matching of the texture regularity; wherein the curing temperature coefficient and the curing time coefficient in the adjustment coefficient are both positively correlated with the texture regularity.

[0010] Optionally, the key production parameter prediction model is trained in the following manner, including: collecting a set of small sample training data sets, each training data included in the training data set includes the surface roughness of the aluminum substrate, the coating category, the additive type, the curing temperature, the curing time and label data, and the label data is an adhesion index; using the small sample training data set to fine-tune the large model to obtain the key production parameter prediction model.

[0011] Optionally, before coating the silver ion coating on the aluminum substrate according to the key production parameters, the method further includes: using simulation software to simulate the key production parameters multiple times to determine the average deviation degree between the actual adhesion index of the antibacterial aluminum plate and the target adhesion index, and if the average deviation degree is lower than the deviation degree threshold, generating a confirmation signal, and the confirmation signal is used to trigger coating the silver ion coating on the aluminum substrate according to the key production parameters.

[0012] The present invention also discloses an antibacterial aluminum plate production control system based on a large model, the system includes a processing device and a storage device, the computer code stored in the storage device is called and executed by the processing device to implement the following steps: obtaining production preparation data and target adhesion index related to the antibacterial aluminum plate to be produced, the production preparation data including the surface roughness of the aluminum substrate, the coating type of the silver ion antibacterial coating, and the type of auxiliary agent in the silver ion antibacterial coating formula; inputting the target adhesion index, the surface roughness of the aluminum substrate, the coating type and the auxiliary agent type into a key production parameter prediction model constructed based on the large model, the key parameter prediction model outputs a predicted set of key production parameters; wherein the key production parameters include the coating curing temperature and the coating curing time; coating the silver ion coating on the aluminum substrate according to the key production parameters to produce an antibacterial aluminum plate that meets the target adhesion index.

[0013] The present invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement any of the above methods.

[0014] The present invention also discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement any of the above methods.

[0015] The present invention also discloses a computer program product, which includes computer codes. When the computer codes are executed by a processor of an electronic device, any of the above methods is implemented.

[0016] The beneficial effect of the present invention is at least that: the above-mentioned scheme of the present invention can formulate specific key production parameters based on the surface roughness of the aluminum substrate, the coating type of the silver ion antibacterial coating, and the type of additives in the silver ion antibacterial coating formula, and perform a curing treatment of the silver ion coating according to the key production parameters, thereby obtaining an antibacterial aluminum plate that is suitable for the target adhesion index expected by the customer, thereby realizing the customer's personalized customization of the antibacterial performance of the antibacterial aluminum plate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a schematic diagram of the structure of the antibacterial aluminum plate disclosed in an embodiment of the present invention.

[0019] Figure 2 It is a flow chart of a large-model-based antibacterial aluminum plate production control method disclosed in an embodiment of the present invention.

[0020] Figure 3 It is a structural schematic diagram of an antibacterial aluminum plate production control system based on a large model disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0022] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0023] The long-term antibacterial performance of the antibacterial aluminum plate with silver ion coating is mainly related to the adhesion of the silver ion coating on the aluminum substrate. Good adhesion can ensure that the silver ion coating acts on the aluminum surface for a long time, thereby providing a continuous antibacterial effect. In order to ensure that the silver ion coating on the antibacterial aluminum plate produced has good adhesion, the existing technology is to clean the surface of the aluminum substrate to ensure that there is no oil and impurities, optimize the coating formula, micro-treat the surface of the aluminum substrate and activate the surface, for example, use specific additives (such as 16-mercaptohexadecanoic acid and silane coupling agent KH560) in the coating formula.

[0024] However, in addition to the above-mentioned production processes, the curing process in the production process has a great influence on the adhesion of the silver ion coating on the antibacterial aluminum plate. After searching, it was found that there is no research in the prior art to formulate the optimal curing control parameters based on the specific conditions of the silver ion coating and the aluminum substrate, which leads to insufficient adhesion of the silver ion coating on the antibacterial aluminum plate, thus affecting the antibacterial performance of the antibacterial aluminum plate.

[0025] Therefore, how to determine the appropriate production process based on the real-time dynamic parameters of antibacterial aluminum plate production is a technical problem that needs to be solved urgently.

[0026] like Figure 2 As shown, in response to the above technical problems, an embodiment of the present invention discloses an antibacterial aluminum plate production control method based on a large model, the method comprising the following steps: obtaining production preparation data and target adhesion index related to the antibacterial aluminum plate to be produced, the production preparation data comprising the surface roughness of the aluminum substrate, the coating type of the silver ion antibacterial coating, and the type of auxiliary agent in the silver ion antibacterial coating formula; inputting the target adhesion index, the surface roughness of the aluminum substrate, the coating type and the auxiliary agent type into a key production parameter prediction model constructed based on the large model, the key parameter prediction model outputs a predicted set of key production parameters; wherein the key production parameters include the coating curing temperature and the coating curing time; coating the silver ion coating on the aluminum substrate according to the key production parameters to produce an antibacterial aluminum plate that meets the target adhesion index.

[0027] Compared with the prior art solutions, the present invention can comprehensively formulate the optimal curing treatment parameters according to the dynamic production preparation data related to the antibacterial aluminum plate to be produced and the expected target adhesion index, so that the prepared antibacterial aluminum plate can meet the target adhesion index required by the user. The specific explanation is as follows: First, whenever a new batch of antibacterial aluminum plates needs to be produced, the production preparation data and the target adhesion index related to the antibacterial aluminum plates to be produced are first obtained. Among them, the production preparation data includes the surface roughness of the aluminum substrate, the coating category of the silver ion antibacterial coating, and the type of additives in the silver ion antibacterial coating formula. Among them, the surface roughness of the aluminum substrate is measured by manual or automatic detection equipment, and the silver ion antibacterial coating includes water-based silver ion antibacterial coatings, solvent-based silver ion antibacterial coatings and other categories. The types of additives in the silver ion coating formula include but are not limited to 16-mercaptohexadecanoic acid and silane coupling agent KH560. In addition, the target adhesion index is formulated based on customer needs. The target adhesion index refers to the remaining amount (such as thickness) of the silver ion coating on the antibacterial aluminum plate after a set number of cleanings. The larger the remaining amount, the higher the corresponding target adhesion index.

[0028] Then, the target adhesion index, aluminum substrate surface roughness, coating type and additive type obtained above are input into the key production parameter prediction model, and the model outputs a set of predicted key production parameters, including coating curing temperature and coating curing time. Among them, the key production parameter prediction model is built based on a large model, and the large model here refers to general large models such as GPT model and BERT model.

[0029] Finally, using the above key production parameters as a reference, the temperature and curing time of the silver ion coating are controlled, and finally an antibacterial aluminum plate with the target adhesion index that meets the customer's needs can be obtained.

[0030] Therefore, the above scheme of the present invention can formulate specific key production parameters based on the surface roughness of the aluminum substrate, the coating type of the silver ion antibacterial coating, and the type of additives in the silver ion antibacterial coating formula, and perform the curing treatment of the silver ion coating according to the key production parameters, so as to obtain an antibacterial aluminum plate that is suitable for the target adhesion index expected by the customer, thereby realizing the customer's personalized customization of the antibacterial performance of the antibacterial aluminum plate.

[0031] Optionally, production preparation data and a target adhesion index related to the antibacterial aluminum plate to be produced are obtained, including: receiving the coating category of the silver ion antibacterial coating related to the antibacterial aluminum plate to be produced, the type of additives in the silver ion antibacterial coating formula and the target adhesion index transmitted by a terminal device via a network; wherein the target adhesion index is determined by: inputting antibacterial aluminum plate performance description data into the terminal device, using a semantic analysis model to perform semantic analysis on the antibacterial aluminum plate performance description data to obtain an expected remaining amount of the silver ion coating on the antibacterial aluminum plate after a set number of cleanings, and obtaining the target adhesion index by matching the expected remaining amount with a preset comparison table; and, receiving the surface roughness of the aluminum substrate related to the antibacterial aluminum plate to be produced transmitted by the terminal device via a network, or receiving the surface roughness of the aluminum substrate related to the antibacterial aluminum plate to be produced transmitted by an automatic roughness measuring device via a network.

[0032] In this embodiment, the antibacterial aluminum plate manufacturer is equipped with terminal equipment, transmission network and automatic measurement equipment. The factory staff can input the customer's performance requirement information for the antibacterial aluminum plate into the terminal equipment. The performance requirement information can be based on natural language description. The semantic analysis model in the terminal equipment performs semantic analysis on it to obtain the above-mentioned expected residual amount. At the same time, the preset comparison table stores a plurality of comparison relationships between the residual amount and the target adhesion index. By looking up the table, the target adhesion index corresponding to the performance requirement of the customer can be quickly determined. In addition, the type of additives in the silver ion coating formula can also be directly input into the terminal equipment by the factory staff, and then transmitted to the production management system.

[0033] At the same time, for the surface roughness of the aluminum substrate in the production preparation data, manual measurement and automatic measurement by roughness automatic measurement equipment can be adopted as needed. If manual measurement is adopted, the factory staff will input the roughness into the receiving terminal device after completing the roughness measurement, and the terminal device will transmit it to the production management system through the network. If the roughness automatic measurement equipment is used for measurement, the roughness automatic measurement equipment will automatically transmit it to the production management system through the network after completing the measurement.

[0034] Optionally, the target adhesion index, the surface roughness of the aluminum substrate, the coating category and the additive type are input into a key production parameter prediction model constructed based on a large model, and the key parameter prediction model outputs a predicted set of key production parameters, including: using a BERT model to convert the target adhesion index, the surface roughness of the aluminum substrate, the coating category and the additive type into production statements that conform to natural language and are related to the antibacterial aluminum plate to be produced; inputting the production statements into the key production parameter prediction model, and the key production parameter prediction model outputs a predicted set of preliminary production parameters; obtaining surface texture information of the aluminum substrate used to produce the antibacterial aluminum plate, and evaluating a set of adjustment coefficients based on the surface texture information; and using the adjustment coefficients to optimize the coating curing temperature and the coating curing time corresponding to the preliminary production parameters, respectively, to obtain the key production parameters.

[0035] In this embodiment, since the target adhesion index, aluminum substrate surface roughness, coating category and additive type obtained above are all isolated indicator information, if they are directly input into the key production parameter prediction model, the key production parameter prediction model is likely to have misunderstandings, or completely fail to understand the meaning of these indicator information. In this regard, it is necessary to first use the BERT model to convert it into a production statement that conforms to natural language and is related to the antibacterial aluminum plate to be produced. The production statement is, for example, "The customer's expected target adhesion index for this batch of antibacterial aluminum plates is A, and the surface roughness of the aluminum substrate has been measured to be B. The antibacterial silver ion coating is a water-based silver ion antibacterial coating, and the type of additive used in the silver ion coating is C. Based on these conditions, a set of optimal key production parameters is generated, including coating curing temperature and coating curing time". The production statement is input into the key production parameter prediction model, and the model can predict a corresponding set of preliminary production parameters.

[0036] At the same time, the surface texture of the aluminum substrate will also affect the adhesion of the silver ion coating applied thereon. The specific reasons are: 1) When the surface of the aluminum substrate has textures, these textures (such as grooves, protrusions, pores, etc.) will increase the actual contact area between the coating and the substrate. More contact area means more physical bite points. The coating can more firmly "grasp" the surface of the substrate after curing, thereby improving adhesion; 2) The presence of texture can also change the surface energy of the aluminum substrate surface and affect the wettability of the coating. Suitable textures can make the coating easier to spread and penetrate on the surface of the aluminum substrate, forming a good wetting effect, thereby improving adhesion. Therefore, the present invention further sets a set of adjustment coefficients based on the surface texture information of the aluminum substrate, and then uses this set of adjustment coefficients to optimize the coating curing temperature and coating curing time in the preliminary production parameters, so as to obtain more accurate key production parameters.

[0037] It should be noted that the preliminary production parameters predicted by the key production parameter prediction model and the key production parameters obtained by the above optimization are actually parameters in a reasonable range, that is, whether the silver ion coating is cured according to the preliminary production parameters or the key production parameters, it can be ensured that the adhesion index of the silver ion coating on the antibacterial aluminum plate produced is within the preset range, which is obtained by floating a certain value on the basis of the target adhesion index. However, the difference between the two is that the key production parameters are "weaker" than the preliminary production parameters (that is, the adjustment coefficient is a value less than 1), for example, the coating curing temperature in the key production parameters is 90°C, and the coating curing temperature in the preliminary production parameters is 93°C; the coating curing time in the key production parameters is 15 minutes, and the coating curing time in the preliminary production parameters is 18 minutes.

[0038] Among them, appropriately increasing the temperature and curing time will increase the curing speed and obtain better curing effects, while appropriately reducing the temperature and curing time will reduce production costs. Therefore, the above-mentioned set of adjustment coefficients includes the curing temperature coefficient and the curing time coefficient. The curing temperature coefficient and the curing time coefficient are both values ​​less than 1, which are used to appropriately adjust the coating curing temperature and coating curing time in the preliminary production parameters to lower values ​​within the preset range, so as to reduce the production cost of the antibacterial aluminum plate.

[0039] Optionally, surface texture information of an aluminum substrate used for producing an antibacterial aluminum plate is obtained, and a set of adjustment coefficients is obtained based on the surface texture information evaluation, including: taking surface images of the aluminum substrate used for producing the antibacterial aluminum plate from multiple angles, extracting surface texture information of the aluminum substrate from each of the surface images, and evaluating the texture regularity of the aluminum substrate based on the surface texture information; obtaining the type of passivator and the type of coating process used for passivation of the surface of the aluminum substrate, and obtaining a set of adjustment coefficients based on the type of passivator, the type of coating process, and the matching of the texture regularity; wherein the curing temperature coefficient and the curing time coefficient in the adjustment coefficient are both positively correlated with the texture regularity.

[0040] In this embodiment, first, a high-definition camera is used to shoot the surface of the aluminum substrate from multiple angles, so as to extract and integrate the surface texture information of the aluminum substrate, that is, which areas of the aluminum substrate have textures, the specific shapes and trends of these textures, etc. The regularity of the texture of the aluminum substrate can be obtained by evaluating the regularity of all surface texture information. Obviously, the higher the consistency of the surface texture, the higher the corresponding texture regularity, and vice versa.

[0041] On this basis, the curing temperature coefficient and curing time coefficient are both positively correlated with the texture regularity, that is, the higher the texture regularity of the aluminum substrate, the worse the adhesion of the silver ion coating after curing on the aluminum substrate. At this time, the curing temperature coefficient and curing time coefficient are set to a value closer to 1 (for example, 0.9) to avoid a significant reduction in the curing temperature and curing time, which causes the actual adhesion of the silver ion coating to fail to reach the target adhesion index; and when the texture regularity of the aluminum substrate is lower, the adhesion of the silver ion coating after curing on the aluminum substrate is higher. At this time, the curing temperature coefficient and curing time coefficient are set to a value further away from 1 (for example, 0.7), which can reduce the curing temperature and curing time to achieve the purpose of reducing production costs as much as possible while achieving the target adhesion index. Among them, the evaluation of the texture regularity of the aluminum substrate can be achieved through a rule-based classifier (such as a decision tree algorithm), which will not be described in detail.

[0042] In addition, the type of passivating agent used in the passivation treatment of the aluminum substrate surface will affect the curing effect of the silver ion antibacterial coating on the aluminum substrate, and different coating processes (such as brush coating, roller coating, spray coating, etc.) will also affect the curing effect of the silver ion antibacterial coating on the aluminum substrate. Therefore, when the present invention pre-constructs the positive correlation between the above adjustment coefficient and the texture regularity, it is determined separately based on the specific type of passivating agent and coating process.

[0043] Optionally, the key production parameter prediction model is trained in the following manner, including: collecting a set of small sample training data sets, each training data included in the training data set includes the surface roughness of the aluminum substrate, the coating category, the additive type, the curing temperature, the curing time and label data, and the label data is an adhesion index; using the small sample training data set to fine-tune the large model to obtain the key production parameter prediction model.

[0044] In this embodiment, for general large models such as GPT and BERT, there is no need to use large sample level (for example, more than 500) training data for training. Instead, small sample training data (for example, 100) can be used to fine-tune the training. The specific training process will not be described in detail.

[0045] In addition, during fine-tuning training, prompt information can be added to guide the training direction of the large model. The prompt information can also be information that conforms to natural language and is set manually.

[0046] Optionally, before coating the silver ion coating on the aluminum substrate according to the key production parameters, the method further includes: using simulation software to simulate the key production parameters multiple times to determine the average deviation degree between the actual adhesion index of the antibacterial aluminum plate and the target adhesion index, and if the average deviation degree is lower than the deviation degree threshold, generating a confirmation signal, and the confirmation signal is used to trigger coating the silver ion coating on the aluminum substrate according to the key production parameters.

[0047] In this embodiment, after the key production parameters are obtained, they can also be verified by simulation. If the simulation results show that the average deviation between the actual adhesion index of the simulated antibacterial aluminum plate and the target adhesion index is lower than the deviation threshold, it means that the key production parameters are available, and the coating and curing of the silver ion coating on the aluminum substrate can be triggered according to the key production parameters. Otherwise, the coating and curing of the silver ion coating on the aluminum substrate according to the key production parameters are not triggered, but the key production parameter prediction model is triggered to re-predict until the above-mentioned condition of being lower than the deviation threshold is met. The above-mentioned simulation software can be finite element (FEA) software, which is not specifically limited.

[0048] like Figure 3 As shown, an embodiment of the present invention also discloses an antibacterial aluminum plate production control system based on a large model, the system includes a processing device and a storage device, the computer code stored in the storage device is called and executed by the processing device to implement the following steps: obtaining production preparation data and target adhesion index related to the antibacterial aluminum plate to be produced, the production preparation data including the surface roughness of the aluminum substrate, the coating type of the silver ion antibacterial coating, and the type of auxiliary agent in the silver ion antibacterial coating formula; inputting the target adhesion index, the surface roughness of the aluminum substrate, the coating type and the auxiliary agent type into a key production parameter prediction model constructed based on the large model, and the key parameter prediction model outputs a predicted set of key production parameters; wherein the key production parameters include the coating curing temperature and the coating curing time; coating the silver ion coating on the aluminum substrate according to the key production parameters to produce an antibacterial aluminum plate that meets the target adhesion index.

[0049] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the above embodiment.

[0050] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0051] An embodiment of the present invention further discloses a computer program product, which includes computer code. When the computer code is executed by a processor of an electronic device, the method described in the above embodiment is implemented.

[0052] The computer-readable storage medium described above may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0053] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0054] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0055] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A large-scale model-based antibacterial aluminum plate production control method, characterized in that: The method comprises the following steps: Acquire production preparation data related to the antibacterial aluminum plate to be produced and a target adhesion index, wherein the production preparation data includes the surface roughness of the aluminum substrate, the coating type of the silver ion antibacterial coating, and the type of additives in the silver ion antibacterial coating formula; The target adhesion index, the surface roughness of the aluminum substrate, the coating type and the additive type are input into a key production parameter prediction model constructed based on the large model, and the key production parameter prediction model outputs a set of predicted key production parameters; wherein the key production parameters include coating curing temperature and coating curing time; Applying the silver ion coating on the aluminum substrate according to the key production parameters to produce an antibacterial aluminum plate that meets the target adhesion index; The target adhesion index, the surface roughness of the aluminum substrate, the coating type and the additive type are input into a key production parameter prediction model constructed based on a large model. The key production parameter prediction model outputs a set of predicted key production parameters, including: Using the BERT model, the target adhesion index, the surface roughness of the aluminum substrate, the coating category, and the additive type are converted into production statements in natural language related to the antibacterial aluminum plate to be produced; Inputting the production statement into the key production parameter prediction model, the key production parameter prediction model outputting a set of predicted preliminary production parameters; Acquire surface texture information of an aluminum substrate used to produce an antibacterial aluminum plate, and evaluate and obtain a set of adjustment coefficients based on the surface texture information; wherein the adjustment coefficients include a curing temperature coefficient and a curing time coefficient; The adjustment coefficients are used to optimize the coating curing temperature and the coating curing time corresponding to the preliminary production parameters, respectively, to obtain the key production parameters.

2. The method for controlling the production of antibacterial aluminum plates based on a large model according to claim 1 is characterized in that: Obtain production preparation data related to the antimicrobial aluminum sheet to be produced and target adhesion indicators, including: Receiving the coating type of the silver ion antibacterial coating related to the antibacterial aluminum plate to be produced, the type of additives in the silver ion antibacterial coating formula, and the target adhesion index transmitted by the terminal device through the network; Wherein, the target adhesion index is determined by the following method: Inputting the antibacterial aluminum plate performance description data into the terminal device, using the semantic analysis model to perform semantic analysis on the antibacterial aluminum plate performance description data to obtain the expected remaining amount of the silver ion coating on the antibacterial aluminum plate after a set number of cleanings, and obtaining the target adhesion index according to matching the expected remaining amount with a preset comparison table; And, receiving the surface roughness of the aluminum substrate related to the antibacterial aluminum plate to be produced transmitted by the terminal device through the network, or receiving the surface roughness of the aluminum substrate related to the antibacterial aluminum plate to be produced transmitted by the roughness automatic measuring device through the network.

3. The method for controlling the production of antibacterial aluminum plates based on a large model according to claim 1 is characterized in that: The surface texture information of the aluminum substrate used for producing the antibacterial aluminum plate is obtained, and a set of adjustment coefficients are obtained based on the surface texture information, including: Taking surface images of an aluminum substrate used to produce an antibacterial aluminum plate from multiple angles, extracting surface texture information of the aluminum substrate from each of the surface images, and evaluating the texture regularity of the aluminum substrate based on the surface texture information; The type of passivation agent and the type of coating process used for the surface passivation of the aluminum substrate are obtained, and a set of adjustment coefficients are obtained according to the type of passivation agent, the type of coating process, and the texture regularity matching; wherein the curing temperature coefficient and the curing time coefficient in the adjustment coefficient are both positively correlated with the texture regularity.

4. The method for controlling the production of antibacterial aluminum plates based on a large model according to claim 3 is characterized in that: The key production parameter prediction model is trained in the following manner, including: Collect a set of small sample training data sets, where each piece of training data in the training data sets includes surface roughness of an aluminum substrate, coating type, additive type, curing temperature, curing time, and label data, where the label data is an adhesion index; The large model is fine-tuned using the small sample training data set to obtain the key production parameter prediction model.

5. The method for controlling the production of antibacterial aluminum plates based on a large model according to claim 4 is characterized in that: Before coating the silver ion coating on the aluminum substrate according to the key production parameters, the method further comprises: The key production parameters are simulated multiple times using simulation software to determine the average deviation between the actual adhesion index of the antibacterial aluminum plate and the target adhesion index. If the average deviation is lower than the deviation threshold, a confirmation signal is generated, and the confirmation signal is used to trigger the coating of the silver ion coating on the aluminum substrate according to the key production parameters.

6. A large-scale model-based antibacterial aluminum plate production control system, the system comprising a processing device and a storage device, characterized in that: The computer code stored in the storage device is called and executed by the processing device to implement the following steps: Acquire production preparation data related to the antibacterial aluminum plate to be produced and a target adhesion index, wherein the production preparation data includes the surface roughness of the aluminum substrate, the coating type of the silver ion antibacterial coating, and the type of additives in the silver ion antibacterial coating formula; The target adhesion index, the surface roughness of the aluminum substrate, the coating type and the additive type are input into a key production parameter prediction model constructed based on the large model, and the key production parameter prediction model outputs a set of predicted key production parameters; wherein the key production parameters include coating curing temperature and coating curing time; Applying the silver ion coating on the aluminum substrate according to the key production parameters to produce an antibacterial aluminum plate that meets the target adhesion index; The target adhesion index, the surface roughness of the aluminum substrate, the coating type and the additive type are input into a key production parameter prediction model constructed based on a large model. The key production parameter prediction model outputs a set of predicted key production parameters, including: Using the BERT model, the target adhesion index, the surface roughness of the aluminum substrate, the coating category, and the additive type are converted into production statements in natural language related to the antibacterial aluminum plate to be produced; Inputting the production statement into the key production parameter prediction model, the key production parameter prediction model outputting a set of predicted preliminary production parameters; Acquire surface texture information of an aluminum substrate used to produce an antibacterial aluminum plate, and evaluate and obtain a set of adjustment coefficients based on the surface texture information; wherein the adjustment coefficients include a curing temperature coefficient and a curing time coefficient; The adjustment coefficients are used to optimize the coating curing temperature and the coating curing time corresponding to the preliminary production parameters, respectively, to obtain the key production parameters.

7. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 5.

8. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that: The computer program product includes computer codes, and when the computer codes are executed by a processor of an electronic device, the method according to any one of claims 1 to 5 is implemented.

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