Method and device for determining operating parameters of ceramic cast film and terminal equipment

Abstract

The invention provides a method and a device for determining operating parameters of a ceramic cast film and terminal equipment. The method comprises the following steps: determining the operating parameters of the ceramic cast film by utilizing a multi-objective optimization function; the operation parameters comprise a first operation parameter for controlling slurry characteristics and a second operation parameter for controlling film thickness characteristics; respectively outputting a corresponding slurry characteristic prediction result and a film thickness characteristic prediction result based on the operation parameters by using the first prediction model and the second prediction model; determining whether the slurry characteristic prediction result and the film thickness characteristic prediction result respectively reach a slurry target characteristic value and a film thickness target characteristic value; and if not, determining an operation parameter adjustment value according to the slurry characteristic prediction result and the film thickness characteristic prediction result, and adjusting the operation parameter by using the operation parameter adjustment value. According to the method, the operation parameters of the ceramic cast film can be efficiently determined, and when the ceramic cast film is produced and prepared based on the operation parameters, it is guaranteed that the ceramic cast film reaches the optimal slurry characteristic and film thickness characteristic.

Description

Technical Field

[0001] The present application relates to the field of industrial manufacturing technology, and in particular to a method, device and terminal equipment for determining operating parameters of a ceramic cast membrane. Background Art

[0002] Ceramic cast film has shown important academic value and application prospects in many high-tech fields due to its unique dielectric properties and high-temperature stability.

[0003] In the process of producing and preparing ceramic cast film, the operating parameters will directly affect the slurry properties and film thickness properties of the ceramic cast film. Therefore, it is necessary to accurately control the operating parameters and determine the optimal combination of operating parameters to achieve the best slurry properties and film thickness properties. In the current technical solution, the operating parameters are manually adjusted by technicians based on practical experience to control the slurry properties and film thickness properties of the ceramic cast film. However, the operating parameters of the ceramic cast film determined in this way are not only inefficient, but also cannot ensure that the ceramic cast film achieves the best slurry properties and film thickness properties.

[0004] Therefore, how to efficiently determine the operating parameters of the ceramic cast membrane and ensure that the ceramic cast membrane achieves optimal slurry properties and membrane thickness properties when producing and preparing the ceramic cast membrane based on the operating parameters is a technical problem that technical personnel in this field currently need to solve. Summary of the invention

[0005] The purpose of the present application is to provide a method, device, terminal equipment, computer-readable storage medium and computer program product for determining the operating parameters of a ceramic cast membrane, aiming to efficiently determine the operating parameters of the ceramic cast membrane and ensure that the ceramic cast membrane achieves optimal slurry properties and membrane thickness properties when producing and preparing the ceramic cast membrane based on the operating parameters.

[0006] In a first aspect, the present application provides a method for determining operating parameters of a ceramic cast membrane. The method comprises:

[0007] Determining the operating parameters of the ceramic cast film using a multi-objective optimization function; the operating parameters include a first operating parameter for controlling slurry properties and a second operating parameter for controlling film thickness properties;

[0008] Outputting corresponding slurry property prediction results and film thickness property prediction results respectively based on the operation parameters using the first prediction model and the second prediction model;

[0009] determining whether the slurry property prediction result and the film thickness property prediction result reach the slurry target property value and the film thickness target property value, respectively;

[0010] If not, an operation parameter adjustment value is determined according to the slurry property prediction result and the film thickness property prediction result, and the operation parameter is adjusted using the operation parameter adjustment value.

[0011] In one of the embodiments, the first prediction model is a slurry characteristic dynamic linearization data model, and the second prediction model is a film thickness characteristic dynamic linearization data model.

[0012] In one embodiment, the using of the first prediction model and the second prediction model to output the corresponding slurry property prediction results and film thickness property prediction results based on the operating parameters respectively includes:

[0013] Inputting the first operating parameter and the second operating parameter into the slurry characteristic dynamic linearization data model, extracting slurry characteristic related features according to the first operating parameter and the second operating parameter using the slurry characteristic dynamic linearization data model, and outputting corresponding slurry characteristic prediction results according to the first operating parameter and the slurry characteristic related features;

[0014] The first operating parameter and the second operating parameter are input into the dynamic linearization data model of the film thickness characteristic, and the first operating parameter and the second operating parameter of the dynamic linearization data model of the film thickness characteristic are used to extract the relevant features of the film thickness characteristic, and the corresponding film thickness characteristic prediction result is output according to the first operating parameter and the relevant features of the film thickness characteristic.

[0015] In one embodiment, determining an operating parameter adjustment value according to the slurry property prediction result and the film thickness property prediction result, and adjusting the operating parameter using the operating parameter adjustment value comprises:

[0016] Determining a first difference value according to the slurry property prediction result and the slurry target property value;

[0017] determining a second difference value according to the film thickness characteristic prediction result and the film thickness target characteristic value;

[0018] determining an operating parameter adjustment value according to the first difference and the second difference;

[0019] The operating parameter is adjusted using the operating parameter adjustment value.

[0020] In one embodiment, the first operating parameter and the second operating parameter are input into the slurry characteristic dynamic linearization data model, the slurry characteristic dynamic linearization data model is used to extract slurry characteristic related features according to the first operating parameter and the second operating parameter, and the corresponding slurry characteristic prediction result is output according to the first operating parameter and the slurry characteristic related features, including:

[0021] Inputting the first operating parameter and the second operating parameter into the slurry characteristic dynamic linearization data model, using the slurry characteristic dynamic linearization data model to perform data fusion on the first operating parameter and the second operating parameter to obtain slurry characteristic related features, and outputting corresponding slurry characteristic prediction results according to the first operating parameter and the slurry characteristic related features;

[0022] The step of inputting the first operating parameter and the second operating parameter into the film thickness characteristic dynamic linearization data model, extracting film thickness characteristic related features using the first operating parameter and the second operating parameter of the film thickness characteristic dynamic linearization data model, and outputting corresponding film thickness characteristic prediction results according to the first operating parameter and the film thickness characteristic related features, comprises:

[0023] The first operating parameter and the second operating parameter are input into the dynamic linearization data model of film thickness characteristics, and the dynamic linearization data model of film thickness characteristics is used to perform data fusion on the first operating parameter and the second operating parameter to obtain film thickness characteristic related features, and the corresponding film thickness characteristic prediction results are output according to the first operating parameter and the film thickness characteristic related features.

[0024] In one embodiment, the determining the operating parameters of the ceramic cast membrane using a multi-objective optimization function comprises:

[0025] A multi-objective optimization function is constructed according to the characteristics of ceramic cast film;

[0026] The multi-objective optimization function is optimized and solved to calculate corresponding operating parameters.

[0027] In one embodiment, the method further comprises:

[0028] Determine model influencing factors; the model influencing factors include ambient temperature, ambient humidity and machine wear;

[0029] The slurry characteristic dynamic linearization data model and the film thickness characteristic dynamic linearization data model are adjusted according to the model influencing factor.

[0030] In a second aspect, the present application also provides a device for determining operating parameters of a ceramic cast film. The device comprises:

[0031] An operating parameter determination module, used to determine the operating parameters of the ceramic cast film using a multi-objective optimization function; the operating parameters include a first operating parameter for controlling slurry characteristics and a second operating parameter for controlling film thickness characteristics;

[0032] A prediction module, configured to respectively output corresponding slurry property prediction results and film thickness property prediction results based on the operation parameters using the first prediction model and the second prediction model;

[0033] A judgment module, used to determine whether the slurry property prediction result and the film thickness property prediction result reach the slurry target property value and the film thickness target property value respectively; if not, calling the adjustment module;

[0034] The adjustment module is used to determine an operation parameter adjustment value according to the slurry property prediction result and the film thickness property prediction result, and adjust the operation parameter using the operation parameter adjustment value.

[0035] In a third aspect, the present application further provides a terminal device, wherein the terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0037] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0038] The present application provides a method for determining the operating parameters of a ceramic cast film, which uses a multi-objective optimization function to determine the operating parameters of the ceramic cast film, wherein the operating parameters include a first operating parameter for controlling the slurry properties and a second operating parameter for controlling the film thickness properties, and then uses a first prediction model and a second prediction model to output the corresponding slurry property prediction results and film thickness property prediction results based on the operating parameters, that is, the operating parameters determined by the multi-objective optimization function are used as the input initial values ​​of the first prediction model and the second prediction model. Since the slurry properties and the film thickness properties are considered at the same time when determining the initial input values, the operating parameters can efficiently make the slurry property prediction results and the film thickness property prediction results reach the slurry target property value and the film thickness target property value, respectively. Therefore, the present method can efficiently determine the operating parameters of the ceramic cast film, and when the ceramic cast film is produced and prepared based on the operating parameters, it can ensure that the ceramic cast film reaches the best slurry properties and film thickness properties.

[0039] It can be understood that the operating parameter determination device, terminal equipment, computer-readable storage medium and computer program product of a ceramic cast membrane provided in the embodiments of the present application have the same beneficial effects as the above-mentioned operating parameter determination method of the ceramic cast membrane, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A flow chart of a method for determining operating parameters of a ceramic cast membrane provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the structure of a device for determining operating parameters of a ceramic cast film provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0045] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0046] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0047] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0048] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0049] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways. "Multiple" means "two or more".

[0050] Ceramic cast film has shown important academic value and application prospects in many high-tech fields due to its unique dielectric properties and high-temperature stability.

[0051] In the electronics industry, the application of ceramic cast film is particularly prominent, especially in the fields of multilayer ceramic capacitors (MLCC), integrated circuit substrates and high-temperature superconducting films. As microelectronic devices are developing towards high integration and miniaturization, cast film technology can produce high-precision, high-density circuit substrates and capacitor layers, greatly promoting the miniaturization and high performance of electronic products.

[0052] In the energy field, ceramic cast film technology also plays an important role. It is widely used in energy devices such as solid oxide fuel cells (SOFC) and lithium-ion batteries. In SOFC, ceramic membrane acts as an electrolyte material and plays the role of an ion conductor, ensuring the efficient operation of the fuel cell. In lithium-ion batteries, ceramic cast membranes can be used as diaphragms or electrode materials to improve the thermal stability and safety of the battery. As global energy research focuses on high-efficiency, low-emission and long-life energy devices, ceramic cast membrane technology can help improve the performance of these devices, reduce dependence on fossil energy, and promote the development of renewable energy.

[0053] In addition, the application of ceramic cast membranes in the field of environmental protection is gradually emerging, especially in gas separation and water treatment technology. Due to the high strength and chemical stability of ceramic cast membranes, they are suitable for gas purification and separation under high temperature, high pressure and corrosive environments, as well as filtering of particles and pollutants in water. Research on high-performance ceramic cast membrane materials can provide efficient filtration materials for environmental protection and promote the development of green technology.

[0054] In the biological and medical fields, ceramic membranes are beginning to be used in biological and medical devices due to their chemical inertness, biocompatibility and high surface activity. For example, ceramic membranes are used in biosensors, drug release systems or artificial bone and tooth implants, providing a stable structure and long-term durability. Through in-depth research on the biocompatibility and surface modification of ceramic cast membranes, their application in the biomedical field can be further promoted, providing a solid foundation for future medical technology advances.

[0055] In the process of producing and preparing ceramic cast film, the operating parameters will directly affect the slurry properties and film thickness properties of the ceramic cast film. Therefore, it is necessary to accurately control the operating parameters and determine the optimal combination of operating parameters to achieve the best slurry properties and film thickness properties. In the current technical solution, the operating parameters are manually adjusted by technicians based on practical experience to control the slurry properties and film thickness properties of the ceramic cast film. However, the operating parameters of the ceramic cast film determined in this way are not only inefficient, but also cannot ensure that the ceramic cast film achieves the best slurry properties and film thickness properties.

[0056] Therefore, how to efficiently determine the operating parameters of the ceramic cast membrane and ensure that the ceramic cast membrane achieves optimal slurry properties and membrane thickness properties when producing and preparing the ceramic cast membrane based on the operating parameters is a technical problem that technical personnel in this field currently need to solve.

[0057] The embodiment of the present application provides a method for determining the operating parameters of a ceramic cast film, which uses a multi-objective optimization function to determine the operating parameters of the ceramic cast film, wherein the operating parameters include a first operating parameter for controlling the slurry properties and a second operating parameter for controlling the film thickness properties, and then the first prediction model and the second prediction model are used to output the corresponding slurry property prediction results and film thickness property prediction results based on the operating parameters, that is, the operating parameters determined by the multi-objective optimization function are used as the input initial values ​​of the first prediction model and the second prediction model. Since the slurry properties and the film thickness properties are considered at the same time when determining the initial input values, the operating parameters can efficiently make the slurry property prediction results and the film thickness property prediction results reach the slurry target property value and the film thickness target property value, respectively. Therefore, the present method can efficiently determine the operating parameters of the ceramic cast film, and when the ceramic cast film is produced and prepared based on the operating parameters, it can ensure that the ceramic cast film reaches the best slurry properties and film thickness properties.

[0058] A method for determining multiple characteristics of a ceramic cast film provided in an embodiment of the present application can be executed by a processor of a terminal device when running a corresponding computer program.

[0059] Figure 1This is a flow chart of a method for determining multiple characteristics of a ceramic cast film provided in an embodiment of the present application. For ease of explanation, only the part related to this embodiment is shown. The method provided in this embodiment includes the following steps:

[0060] S100: Determine operating parameters of the ceramic cast membrane using a multi-objective optimization function; the operating parameters include a first operating parameter for controlling slurry properties and a second operating parameter for controlling membrane thickness properties.

[0061] Among them, the multi-objective optimization function refers to the need to consider two or more objective functions at the same time in the optimization problem. There are usually contradictions or conflicts between these objective functions, that is, optimizing one objective function may harm the performance of other objective functions. In the production process of ceramic cast film, multiple operating parameters need to be considered at the same time, such as slurry viscosity, casting speed, temperature, etc. These operating parameters affect each other and jointly determine the final quality of ceramic cast film. The multi-objective optimization function can help determine the best combination of these operating parameters to achieve the best slurry characteristics and film thickness characteristics.

[0062] It should be noted that the operating parameters will directly affect the slurry properties and film thickness properties. The operating parameters in this embodiment include a first operating parameter for controlling the slurry properties and a second operating parameter for controlling the film thickness properties; the first operating parameter includes stirring speed, additive amount and mixing time, etc., and the second operating parameter includes slurry flow rate, pressure and drying temperature, etc. This embodiment does not limit the specific contents of the first operating parameter and the second operating parameter.

[0063] Slurry properties refer to the physical and chemical properties of the slurry, including the uniformity of particle distribution in the slurry and the stability of the slurry, such as viscosity, density, surface tension, etc. These properties directly affect the formation and performance of the cast film; film thickness properties refer to the thickness-related properties of the cast film, such as uniformity.

[0064] S200: using the first prediction model and the second prediction model to output corresponding slurry property prediction results and film thickness property prediction results respectively based on the first operating parameter and the second operating parameter.

[0065] The first prediction model is used to output corresponding slurry characteristic prediction results based on the first operating parameter and the second operating parameter; the second prediction model is used to output corresponding film thickness characteristic prediction results based on the first operating parameter and the second operating parameter.

[0066] In this step, the first prediction model and the second prediction model can be a neural network model or a dynamic linearization data model, which is not limited in this embodiment. In practical applications, it is necessary to first train the first prediction model and the second prediction model, and then after determining the first operating parameters and the second operating parameters, the corresponding slurry characteristic prediction results and membrane thickness characteristic prediction results can be directly output based on the first prediction model and the second prediction model.

[0067] The slurry property prediction result refers to the predicted slurry property output by the first prediction model; and the film thickness property prediction result refers to the predicted film thickness property output by the second prediction model.

[0068] S300: Determine whether the slurry property prediction result and the film thickness property prediction result reach the slurry target property value and the film thickness target property value, respectively.

[0069] Among them, the target characteristic value of the slurry refers to the ideal characteristic value that the slurry corresponding to the ceramic cast membrane hopes to achieve during the production and preparation process of the ceramic cast membrane; the target characteristic value of the film thickness refers to the ideal characteristic value that the film thickness corresponding to the ceramic cast membrane hopes to achieve during the production and preparation process of the ceramic cast membrane.

[0070] In this step, the slurry characteristic prediction result is compared with the slurry target characteristic value to determine whether the slurry characteristic prediction result reaches the slurry target characteristic value, and a first comparison result is obtained; the film thickness characteristic prediction result is compared with the film thickness target characteristic value to determine whether the film thickness characteristic prediction result reaches the film thickness target characteristic value, and a second comparison result is obtained.

[0071] S400: If not reached, determining an operating parameter adjustment value according to the slurry property prediction result and the film thickness property prediction result, and adjusting the operating parameter using the operating parameter adjustment value.

[0072] In this embodiment, if the slurry property prediction results and the membrane thickness property prediction results both reach the corresponding slurry target property and membrane thickness target property values, it means that a ceramic cast membrane that meets the expected property requirements can be prepared according to the operating parameters. Therefore, production and preparation can be carried out directly based on the operating parameters to obtain the corresponding ceramic cast membrane.

[0073] In this step, when the slurry property prediction result does not reach the slurry target property value, or the film thickness property prediction result does not reach the film thickness target property value, or both the slurry property prediction result and the film thickness property prediction result do not reach the corresponding slurry target property and film thickness target property value, it means that the ceramic cast film that meets the expected property requirements cannot be prepared according to the operating parameters, so the operating parameter adjustment value is determined according to the slurry property prediction result and the film thickness property prediction result; wherein the operating parameter adjustment value refers to the parameter for adjusting the operating parameter, that is, taking the current operating parameter as the reference value, adjusting it according to the operating parameter adjustment value on the basis of the reference value to obtain the final operating parameter. The operating parameters are adjusted based on the slurry property prediction result and the film thickness property prediction result to reduce the accumulation of deviations.

[0074] The embodiment of the present application provides a method for determining the operating parameters of a ceramic cast film, which uses a multi-objective optimization function to determine the operating parameters of the ceramic cast film, wherein the operating parameters include a first operating parameter for controlling the slurry properties and a second operating parameter for controlling the film thickness properties, and then the first prediction model and the second prediction model are used to output the corresponding slurry property prediction results and film thickness property prediction results based on the operating parameters, that is, the operating parameters determined by the multi-objective optimization function are used as the input initial values ​​of the first prediction model and the second prediction model. Since the slurry properties and the film thickness properties are considered at the same time when determining the initial input values, the operating parameters can efficiently make the slurry property prediction results and the film thickness property prediction results reach the slurry target property value and the film thickness target property value, respectively. Therefore, the present method can efficiently determine the operating parameters of the ceramic cast film, and when the ceramic cast film is produced and prepared based on the operating parameters, it can ensure that the ceramic cast film reaches the best slurry properties and film thickness properties.

[0075] On the basis of the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the first prediction model is a dynamic linearization data model of slurry characteristics, and the second prediction model is a dynamic linearization data model of membrane thickness characteristics.

[0076] In this embodiment, the first prediction model is a slurry characteristic dynamic linearization data model, and the second prediction model is a film thickness characteristic dynamic linearization data model.

[0077] Specifically, in the slurry property control of ceramic cast film, the dynamic linearization data model is established by locally linearizing the complex nonlinear process. Control and analysis are simplified by converting the nonlinear system into a linear model at each sampling moment. The core of this method is to construct a linearized data model that is exactly equivalent to the original nonlinear system based on the input and output (I / O) measurement data of the system at each time point, that is, to obtain the dynamic linearization data model of slurry properties. Among them, the dynamic linearization data model of slurry properties is a model-free adaptive predictive control model (Model-Free Adaptive Predictive Control, MFAPC); it is used to describe the relationship between slurry properties and operating parameters, and can predict how different operating parameters affect the slurry properties of ceramic cast film. Specifically, the dynamic linearization data model of slurry properties is a data model based on the equivalent conversion of the full-format dynamic linearization method, which can dynamically adjust operating parameters such as stirring speed, additive dosage, mixing time, etc. to ensure the dispersion uniformity and stability of the slurry.

[0078] In practical applications, the dynamic linearization data model of slurry characteristics can obtain process data (slurry characteristics) such as slurry uniformity and particle distribution in real time according to the current process conditions (operating parameters), and establish a data-driven dynamic feature model in a data-driven way to predict the future state of the system, and make real-time adjustments to the operating parameters based on the predicted future state. For example, if the real-time operating parameters indicate that the viscosity of the slurry increases, the dynamic linearization data model of slurry characteristics predicts that particle agglomeration may occur in the future, so the stirring speed is adjusted in advance or additives are added to prevent the problem; if the dynamic linearization data model of slurry characteristics predicts that uneven particle distribution may occur in the future, the stirring time or speed is adjusted in advance to ensure a more uniform mixing effect.

[0079] In a specific embodiment, the process of determining the dynamic linearization data model of slurry characteristics is as follows:

[0080] Acquire the first historical operating parameter; the first historical operating parameter refers to the first operating parameter for controlling the slurry characteristics corresponding to the historical moment; collect the first operating parameter through each sensor; after collecting multiple historical operating parameters, use principal component analysis (PCA) or partial least squares regression (PLS) to extract key influencing factors, reduce the dimensions of the operating parameters and construct a variable set reflecting the slurry characteristics, that is, screen out the operating parameters related to controlling the slurry characteristics, and obtain the first historical operating parameter;

[0081] Dividing the first historical operation parameter into a training set and a test set;

[0082] Conduct preliminary training using the training set; learn the historical patterns of slurry properties changing with operating parameters, and establish a mapping relationship from input operating parameters to output slurry properties;

[0083] After the model is initially trained, it is necessary to verify and evaluate the model. Specifically, first perform cross-validation: use the test set to use the cross-validation method to evaluate the prediction performance of the model; by tuning the model's hyperparameters (such as learning rate, depth, etc.), ensure that the model has good generalization capabilities under different working conditions. Then perform error analysis and tuning: analyze the model's prediction error, especially the error distribution in different production stages or under different working conditions. Based on the error analysis, the model can be further optimized, and the feature selection or training method can be adjusted to improve the accuracy of the model.

[0084] In practical applications, by continuously acquiring real-time operating parameters, using the real-time operating parameters to update the model's weights or parameters, the dynamic linearization data model of slurry characteristics is continuously updated to cope with process drift and equipment aging, thereby improving the adaptability of the dynamic linearization data model of slurry characteristics to current working conditions. This can be achieved specifically through incremental learning or online learning technology. In a specific example, the model parameters of the dynamic linearization data model of slurry characteristics can be dynamically updated through online learning algorithms such as recursive least squares, recursive Bayesian method or Kalman filtering. This dynamic adjustment process based on historical operating parameters and real-time feedback ensures that the dynamic linearization data model of slurry characteristics can accurately control the slurry characteristics under changing process conditions and achieve high-precision slurry characteristic control.

[0085] Specifically, in the film thickness characteristic control of ceramic cast film, the dynamic linearization data model is established by locally linearizing the complex nonlinear process. Control and analysis are simplified by converting the nonlinear system into a linear model at each sampling moment. The core of this method is to construct a linearization data model that is exactly equivalent to the original nonlinear system based on the input and output (I / O) measurement data of the system at each time point, that is, to obtain the dynamic linearization data model of film thickness characteristics. Among them, the dynamic linearization data model of film thickness characteristics is also a model-free adaptive predictive control model (Model-Free Adaptive Predictive Control, MFAPC); it is used to describe the relationship between film thickness characteristics and operating parameters, and can predict how different operating parameters affect the film thickness characteristics of ceramic cast film. Specifically, the dynamic linearization data model of film thickness characteristics is a data model based on the equivalent conversion of the full-format dynamic linearization method, which can dynamically adjust the operating parameters such as slurry flow rate, pressure, drying temperature, casting speed and coating knife spacing to ensure the uniformity of film thickness.

[0086] In practical applications, the dynamic linearization data model of film thickness characteristics can obtain film thickness characteristics in real time according to current process conditions (operating parameters), and establish a data-driven dynamic feature model to predict the future state of the system in a data-driven manner, and make real-time adjustments based on the predicted future state. For example, if the predicted film thickness uniformity deviates from the preset value, the operating parameters such as the casting speed and the coating knife spacing will be adjusted in time to improve the uniformity of film thickness.

[0087] In a specific embodiment, the process of determining the dynamic linearization data model of film thickness characteristics is as follows:

[0088] Acquire a second historical operating parameter; the second historical operating parameter refers to a second operating parameter for controlling the film thickness characteristic corresponding to a historical moment; collect the second operating parameter through each sensor; after collecting a plurality of historical operating parameters, principal component analysis (PCA) or partial least squares regression (PLS) is used to extract key influencing factors, reduce the dimensions of the operating parameters and construct a variable set reflecting the slurry characteristics, that is, screen out the operating parameters related to controlling the slurry characteristics, and obtain the first historical operating parameters;

[0089] dividing the second historical operation parameters into a training set and a test set;

[0090] Perform preliminary training using the training set; learn the historical pattern of the film thickness characteristics changing with the operating parameters, and establish a mapping relationship from the input operating parameters to the output film thickness characteristics;

[0091] After the model is initially trained, it is necessary to verify and evaluate the model. Specifically, first perform cross-validation: use the test set to use the cross-validation method to evaluate the prediction performance of the model; by tuning the model's hyperparameters (such as learning rate, depth, etc.), ensure that the model has good generalization capabilities under different working conditions. Then perform error analysis and tuning: analyze the model's prediction error, especially the error distribution in different production stages or under different working conditions. Based on the error analysis, the model can be further optimized, and the feature selection or training method can be adjusted to improve the accuracy of the model.

[0092] In practical applications, by continuously acquiring real-time operating parameters, using the real-time operating parameters to update the weights or parameters of the model, the dynamic linearization data model of slurry characteristics is continuously updated to cope with process drift and equipment aging, thereby improving the adaptability of the dynamic linearization data model of slurry characteristics to current working conditions. Specifically, this can be achieved through incremental learning (Incremental Learning) or online learning (Online Learning) technology. In a specific example, the model parameters of the dynamic linearization data model of membrane thickness characteristics can be dynamically updated through online learning algorithms such as recursive least squares, recursive Bayesian method or Kalman filtering. This dynamic adjustment process based on historical operating parameters and real-time feedback ensures that the dynamic linearization data model of membrane thickness characteristics can accurately control the membrane thickness characteristics under changing process conditions and achieve high-precision membrane thickness characteristics control.

[0093] Considering that in practical applications, the collected first historical operating parameters and second historical operating parameters may include abnormal values, missing values ​​or noise data, etc., these data may have a negative impact on determining the dynamic linearization data model of slurry characteristics or the dynamic linearization data model of membrane thickness characteristics. In this embodiment, the collected first historical operating parameters and second historical operating parameters (collectively referred to as historical operating parameters) are cleaned to remove abnormal values, missing values ​​or noise data, and the abnormal values, missing values ​​or noise data in the collected historical operating parameters are removed to ensure that the data quality of the first historical operating parameters used to determine the dynamic linearization data model of slurry characteristics and the second historical operating parameters used to determine the dynamic linearization data model of membrane thickness characteristics is high, reduce data errors, and improve the quality of the dynamic linearization data model of slurry characteristics and the dynamic linearization data model of membrane thickness characteristics.

[0094] Among them, outliers refer to data that is inconsistent with the overall pattern of the collected historical operating parameters, which may be caused by measurement or input errors; missing values ​​refer to data that is not recorded or missing in the historical operating parameters; noise data refers to data that is wrong or deviates from the actual value in the historical operating parameters, which may be inaccurate data caused by environmental interference or sensor accuracy limitations. Specifically, for outliers, statistical methods (such as Z-score or IQR method) can be used to identify outliers in the data, and the outliers can be deleted or modified using statistical methods; for missing values, records containing missing values ​​can be deleted, filled with mean or median, or a prediction model can be used to estimate missing values; for noisy data, smoothing techniques (such as moving average or median filtering) can be used to reduce noise in the data.

[0095] In practical applications, trend analysis is performed based on long-term production data (operating parameters) and quality feedback (slurry property prediction results and membrane thickness property prediction results), and the slurry property dynamic linearization data model and the membrane thickness property dynamic linearization data model are continuously optimized to improve the overall prediction accuracy of the slurry property dynamic linearization data model and the membrane thickness property dynamic linearization data model.

[0096] In practical applications, the corresponding dynamic linearization data model of slurry characteristics and the dynamic linearization data model of film thickness characteristics can be trained according to different operating parameter intervals. For example, the stirring speed includes three operating parameter intervals of low speed, medium speed and high speed. For each operating parameter interval, the operating parameters corresponding to the operating parameter interval are used to train the corresponding dynamic linearization data model of slurry characteristics and the dynamic linearization data model of film thickness characteristics. After determining the operating parameters of the ceramic cast film using a multi-objective optimization function, the corresponding dynamic linearization data model of slurry characteristics and the dynamic linearization data model of film thickness characteristics are determined according to the operating parameter interval corresponding to the operating parameters. Therefore, it is possible to use a more accurate dynamic linearization data model of slurry characteristics and a dynamic linearization data model of film thickness characteristics to output the corresponding slurry characteristic prediction results and film thickness characteristic prediction results.

[0097] In this embodiment, the slurry characteristic dynamic linearization data model is used to output the corresponding slurry characteristic prediction results, and the film thickness characteristic dynamic linearization data model is used to output the film thickness characteristic prediction results, which can efficiently and accurately output the slurry characteristic prediction results and the film thickness characteristic prediction results.

[0098] On the basis of the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the first prediction model and the second prediction model are used to output the corresponding slurry property prediction results and film thickness property prediction results based on the operating parameters, including:

[0099] Inputting the first operating parameter and the second operating parameter into the slurry characteristic dynamic linearization data model, extracting slurry characteristic related features according to the first operating parameter and the second operating parameter using the slurry characteristic dynamic linearization data model, and outputting corresponding slurry characteristic prediction results according to the first operating parameter and the slurry characteristic related features;

[0100] The first operating parameter and the second operating parameter are input into the dynamic linearization data model of film thickness characteristics, and the film thickness characteristic related features are extracted using the first operating parameter and the second operating parameter of the dynamic linearization data model of film thickness characteristics, and the corresponding film thickness characteristic prediction result is output according to the first operating parameter and the film thickness characteristic related features.

[0101] The slurry property-related features refer to feature data related to the slurry properties mined out based on the correlation between the first operating parameter and the second operating parameter.

[0102] In this embodiment, the first operating parameter and the second operating parameter are input into the slurry characteristic dynamic linearization data model, and the slurry characteristic dynamic linearization data model is used to extract the slurry characteristic related features according to the first operating parameter and the second operating parameter, that is, to mine the deeper characteristic data related to the slurry characteristic. Then, the slurry characteristic dynamic linearization data model is used to output the corresponding slurry characteristic prediction result according to the first operating parameter and the slurry characteristic related features, that is, to output the slurry characteristic prediction result using the first operating parameter that directly controls the slurry characteristic and the deeper characteristic data related to the slurry characteristic.

[0103] The film thickness characteristic-related features refer to feature data related to the film thickness characteristics mined out based on the correlation between the first operating parameter and the second operating parameter.

[0104] In this embodiment, the first operating parameter and the second operating parameter are input into the dynamic linearization data model of film thickness characteristics, and the dynamic linearization data model of film thickness characteristics is used to extract film thickness characteristics-related features according to the first operating parameter and the second operating parameter, that is, to mine deeper feature data related to film thickness characteristics. Then, the dynamic linearization data model of film thickness characteristics is used to output the corresponding film thickness characteristics prediction result according to the second operating parameter and the film thickness characteristics-related features, that is, to output the film thickness characteristics prediction result using the first operating parameter that directly controls the film thickness characteristics and the mined deeper feature data related to the film thickness characteristics.

[0105] In this embodiment, the dynamic linearization data model of slurry characteristics is further utilized to extract the slurry characteristic related features according to the first operating parameter and the second operating parameter, and the dynamic linearization data model of membrane thickness characteristics is further utilized to extract the membrane thickness characteristic related features according to the first operating parameter and the second operating parameter, and the corresponding slurry characteristic prediction results are output according to the first operating parameter and the slurry characteristic related features, and the corresponding membrane thickness characteristic prediction results are output according to the first operating parameter and the membrane thickness characteristic related features, which can improve the accuracy of the slurry characteristic prediction results and the membrane thickness characteristic prediction results.

[0106] On the basis of the above-mentioned embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the operating parameter adjustment value is determined according to the slurry property prediction result and the film thickness property prediction result, and the operating parameter is adjusted by using the operating parameter adjustment value, including:

[0107] Determining a first difference value according to the slurry property prediction result and the slurry target property value;

[0108] determining a second difference value according to the film thickness characteristic prediction result and the film thickness target characteristic value;

[0109] determining an operating parameter adjustment value based on the first difference and the second difference;

[0110] The operating parameters are adjusted using the operating parameter adjustment values.

[0111] Specifically, the first difference between the slurry characteristic prediction result and the slurry target characteristic value is calculated, and the second difference between the membrane thickness characteristic prediction result and the membrane thickness target characteristic value is calculated; the operating parameter adjustment value is determined according to the first difference and the second difference, that is, the current operating parameter is used as the reference value, and the operating parameter adjustment value is adjusted on the basis of the reference value to obtain the final operating parameter.

[0112] Specifically, a correspondence table of the first difference, the second difference and the operating parameter adjustment value may be preset; after the first difference and the second difference are currently calculated, the operating parameter adjustment value corresponding to the first difference and the second difference is searched based on the correspondence table.

[0113] In practical applications, a PID (proportional-integral-differential) controller may be used to adjust parameters, and the operating parameters may be gradually adjusted according to the operating parameter adjustment values.

[0114] According to the method of this embodiment, the operating parameter adjustment value can be determined efficiently and conveniently, and the operating parameter can be adjusted using the operating parameter adjustment value, thereby improving the efficiency and convenience of producing ceramic cast membranes.

[0115] On the basis of the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the first operating parameter and the second operating parameter are input into the slurry characteristic dynamic linearization data model, and the slurry characteristic dynamic linearization data model is used to extract the slurry characteristic related features according to the first operating parameter and the second operating parameter, and the corresponding slurry characteristic prediction results are output according to the first operating parameter and the slurry characteristic related features, including:

[0116] Inputting the first operating parameter and the second operating parameter into a slurry characteristic dynamic linearization data model, using the slurry characteristic dynamic linearization data model to perform data fusion on the first operating parameter and the second operating parameter to obtain slurry characteristic related features, and outputting corresponding slurry characteristic prediction results according to the first operating parameter and the slurry characteristic related features;

[0117] Inputting the first operating parameter and the second operating parameter into the dynamic linearization data model of film thickness characteristics, extracting film thickness characteristic related features using the first operating parameter and the second operating parameter of the dynamic linearization data model of film thickness characteristics, and outputting corresponding film thickness characteristic prediction results according to the first operating parameter and the film thickness characteristic related features, including:

[0118] The first operating parameter and the second operating parameter are input into the dynamic linearization data model of film thickness characteristics, and the dynamic linearization data model of film thickness characteristics is used to perform data fusion on the first operating parameter and the second operating parameter to obtain film thickness characteristic related features, and the corresponding film thickness characteristic prediction result is output according to the first operating parameter and the film thickness characteristic related features.

[0119] In this embodiment, a data fusion algorithm is embedded in the dynamic linearization data model of slurry characteristics. After the first operating parameter and the second operating parameter are input into the dynamic linearization data model of slurry characteristics, the dynamic linearization data model of slurry characteristics is used to perform multi-source information fusion, and each first operating parameter and each second operating parameter are time-synchronized; then, slurry characteristic-related features are extracted based on the first operating parameter and the second operating parameter, and the corresponding slurry characteristic prediction result is output based on the first operating parameter and the slurry characteristic-related features.

[0120] In this embodiment, a data fusion algorithm is embedded in the dynamic linearization data model of film thickness characteristics. After the first operating parameter and the second operating parameter are input into the dynamic linearization data model of film thickness characteristics, the dynamic linearization data model of film thickness characteristics is used to perform multi-source information fusion, and each first operating parameter and each second operating parameter are time-synchronized; then, film thickness characteristic related features are extracted based on the first operating parameter and the second operating parameter, and the corresponding film thickness characteristic prediction result is output based on the first operating parameter and the film thickness characteristic related features.

[0121] It is understandable that data fusion can not only achieve the coordinated control of multiple variables (operating parameters) to ensure that the operating parameters are automatically adjusted to maintain the accuracy of the film thickness when the slurry dispersion changes, but also reduce the redundant information in the operating parameters and optimize the decision-making process. At the same time, data fusion helps to detect slurry anomalies in advance and prevent potential quality problems. By considering the complex relationship between slurry uniformity and thickness accuracy, it is possible to dynamically optimize the control of each link, ultimately achieving global optimization and improving the overall quality and consistency of ceramic cast film.

[0122] In this embodiment, principal component analysis or partial least squares regression is embedded in the dynamic linearization data model of slurry characteristics and the dynamic linearization data model of membrane thickness characteristics to extract slurry characteristic related features according to the first operating parameter and the second operating parameter and to extract membrane thickness characteristic related features according to the first operating parameter and the second operating parameter, respectively. This enables efficient and convenient determination of slurry characteristic related features and membrane thickness characteristic related features.

[0123] Based on the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the operating parameters of the ceramic cast film are determined by using a multi-objective optimization function, including:

[0124] A multi-objective optimization function is constructed according to the characteristics of ceramic cast film;

[0125] The multi-objective optimization function is optimized and solved, and the corresponding operating parameters are calculated.

[0126] In this embodiment, the multi-objective optimization function coordinates the conflicts between different optimization objectives and balances the optimization variables to ensure that the slurry properties and film thickness properties are both optimal, while improving material utilization and production efficiency. For example, the stirring speed, additive dosage and mixing time affect the uniformity and stability of the slurry, while the slurry flow rate and coating pressure are directly related to the accuracy of the film thickness; the temperature and speed during the drying process affect the uniformity of the film thickness.

[0127] The entire optimization process continuously adjusts the optimization variables through a feedback mechanism, ultimately achieving overall optimization of the process and improving the quality and production efficiency of ceramic cast film.

[0128] In a specific embodiment, the optimization variables of the multi-objective optimization function include: stirring speed, additive amount, mixing time, slurry flow rate, coating pressure, drying temperature and speed;

[0129] Setting constraints includes: raw material ratio, stirring time and casting pressure to ensure that the operating parameters are within a reasonable range; the constraints may also include upper and lower limits of equipment operating parameters, which are not limited in this embodiment;

[0130] The objective function is a mathematical expression. The process of setting the objective function includes:

[0131] Determine the optimization goals, which include: slurry uniformity and stability, film thickness uniformity and absolute accuracy, material utilization and production efficiency; that is, ensure the consistency of slurry during the casting process to avoid defects; ensure the consistency of film thickness over the entire surface and achieve the accuracy of design specifications; maximize material utilization and reduce waste; and increase production speed, reduce production cycle time, maximize production efficiency, etc. Use historical operating parameters and corresponding historical prediction results to set the dynamic characteristic model of operating parameters, slurry characteristics and film thickness characteristics. The dynamic characteristic model can describe how operating parameters affect product characteristics; set the objective function according to the dynamic characteristic model and optimization goals.

[0132] Specifically, the multi-objective optimization function can be solved by genetic algorithms, particle swarm optimization, simulated annealing, Pareto optimal solution or weighted sum method, etc., to ensure that the optimal solution is found without violating the constraints, that is, the operating parameters are calculated.

[0133] In practical applications, the target weights of each optimization objective can be dynamically adjusted based on production requirements and product specifications. For example, when the absolute accuracy weight of film thickness is large, the multi-objective optimization function gives priority to ensuring the absolute accuracy of film thickness and calculates the corresponding optimization variables.

[0134] According to the method of this embodiment, precise control of the uniformity and stability of the slurry and the uniformity and absolute accuracy of the film thickness can be achieved, and material utilization and production efficiency can be improved, thereby improving the overall quality and production efficiency of the ceramic cast film.

[0135] Based on the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, a method for determining operating parameters of a ceramic cast film further includes:

[0136] Determine the model influencing factors; model influencing factors include ambient temperature, ambient humidity and machine wear;

[0137] The dynamic linearization data model of slurry characteristics and the dynamic linearization data model of film thickness characteristics are adjusted according to the model influencing factors.

[0138] It is understandable that different ambient temperatures, ambient humidity and machine wear conditions will have a significant impact on the process of producing ceramic cast film.

[0139] It should be noted that the ambient temperature has a significant impact on the rheology of the slurry and the curing process of the ceramic cast film. If the ambient temperature is too high, the coating surface of the ceramic cast film may dry too quickly, forming a hard shell, hindering the volatilization of the internal solvent, and affecting the internal structure and performance of the coating; if the ambient temperature is too low, it will prolong the drying time, reduce production efficiency, and even cause the coating of the ceramic cast film to dry incompletely, affecting the quality of the ceramic cast film.

[0140] Ambient humidity has a significant impact on the drying and uniformity of the ceramic cast film coating. Too high an ambient humidity will lead to poor drying of the ceramic cast film coating, which is prone to defects such as bubbles and wrinkles, and may also increase the adsorption of impurities; while too low an ambient humidity may cause the solvent to evaporate too quickly, affecting the uniformity of the ceramic cast film coating.

[0141] Machine wear refers to the gradual loss of machine parts due to long-term operation during the production process. Machine wear will affect the pressure of the coating head on the base film, the coating speed, etc., and thus affect the uniformity and adhesion of the coating of the ceramic cast film. Appropriate coating pressure can make the slurry more evenly distributed on the base film, improve the flatness and adhesion of the coating, but too much pressure may cause deformation of the base film or even damage the base film; too little pressure cannot ensure sufficient contact between the slurry and the base film, affecting the quality of the coating.

[0142] In this embodiment, the model influencing factors are determined; the model influencing factors include ambient temperature, ambient humidity and machine wear; the dynamic linearization data model of slurry characteristics and the dynamic linearization data model of membrane thickness characteristics are adjusted according to the model influencing factors, and the model parameters in the dynamic linearization data model of slurry characteristics and the dynamic linearization data model of membrane thickness characteristics are modified to obtain updated dynamic linearization data model of slurry characteristics and dynamic linearization data model of membrane thickness characteristics.

[0143] It should also be noted that in practical applications, when slurry characteristics or production requirements change, the control gain and model parameters are adaptively adjusted to ensure the robustness of the control system to uncertainties.

[0144] According to the method of this embodiment, external factors such as ambient temperature, ambient humidity and machine wear are used as model influencing factors, and the dynamic linearization data model of slurry characteristics and the dynamic linearization data model of membrane thickness characteristics are adjusted based on the model influencing factors to improve the robustness of the dynamic linearization data model of slurry characteristics and the dynamic linearization data model of membrane thickness characteristics.

[0145] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0146] It should be noted that the information collection process (such as face image collection process, fingerprint information collection process, etc.) / feature extraction process involved in this application is performed with the user's knowledge and permission, that is, the information collection process / feature extraction process complies with the requirements of laws and regulations and does not constitute an act that harms the public interest.

[0147] Figure 2 FIG. 1 is a schematic diagram of a device for determining operating parameters of a ceramic cast film provided in an embodiment of the present application. Figure 2 As shown, the operating parameter determination device of a ceramic cast film of this embodiment includes an operating parameter determination module, a prediction module, a judgment module and an adjustment module; wherein,

[0148] An operating parameter determination module is used to determine the operating parameters of the ceramic cast film using a multi-objective optimization function; the operating parameters include a first operating parameter for controlling slurry characteristics and a second operating parameter for controlling film thickness characteristics;

[0149] A prediction module, configured to respectively output corresponding slurry property prediction results and film thickness property prediction results based on the operation parameters using the first prediction model and the second prediction model;

[0150] A judgment module is used to determine whether the slurry property prediction result and the film thickness property prediction result reach the slurry target property value and the film thickness target property value respectively; if not, the adjustment module is called;

[0151] The adjustment module is used to determine the operation parameter adjustment value according to the slurry property prediction result and the film thickness property prediction result, and adjust the operation parameter using the operation parameter adjustment value.

[0152] An operating parameter determination device for a ceramic cast membrane provided in an embodiment of the present application has the same beneficial effects as the operating parameter determination method for a ceramic cast membrane described above.

[0153] In one embodiment, the first prediction model is a slurry property dynamic linearization data model, and the second prediction model is a film thickness property dynamic linearization data model.

[0154] In one embodiment, the prediction module includes:

[0155] A first prediction submodule is used to input the first operating parameter and the second operating parameter into the slurry characteristic dynamic linearization data model, extract slurry characteristic related features according to the first operating parameter and the second operating parameter using the slurry characteristic dynamic linearization data model, and output corresponding slurry characteristic prediction results according to the first operating parameter and the slurry characteristic related features;

[0156] The second prediction submodule is used to input the first operating parameter and the second operating parameter into the dynamic linearization data model of film thickness characteristics, extract film thickness characteristic related features using the first operating parameter and the second operating parameter of the dynamic linearization data model of film thickness characteristics, and output the corresponding film thickness characteristic prediction result according to the first operating parameter and the film thickness characteristic related features.

[0157] In one embodiment, the adjustment module includes:

[0158] A first difference determination submodule, used to determine a first difference according to a slurry property prediction result and a slurry target property value;

[0159] A second difference determination submodule, used to determine a second difference according to the film thickness characteristic prediction result and the film thickness target characteristic value;

[0160] An operating parameter adjustment value determination submodule, configured to determine an operating parameter adjustment value according to the first difference and the second difference;

[0161] The operating parameter determination submodule is used to adjust the operating parameter using the operating parameter adjustment value.

[0162] In one embodiment, the first prediction submodule includes:

[0163] A slurry property prediction result prediction unit, used for inputting the first operating parameter and the second operating parameter into the slurry property dynamic linearization data model, using the slurry property dynamic linearization data model to perform data fusion on the first operating parameter and the second operating parameter to obtain slurry property related features, and outputting corresponding slurry property prediction results according to the first operating parameter and the slurry property related features;

[0164] The second prediction submodule includes:

[0165] The film thickness characteristic prediction result prediction unit inputs the first operating parameter and the second operating parameter into the film thickness characteristic dynamic linearization data model, uses the film thickness characteristic dynamic linearization data model to perform data fusion on the first operating parameter and the second operating parameter to obtain film thickness characteristic related features, and outputs the corresponding film thickness characteristic prediction result according to the first operating parameter and the film thickness characteristic related features.

[0166] In one embodiment, the operating parameter determination module includes:

[0167] Function setting submodule, used to construct multi-objective optimization function according to the characteristics of ceramic cast film;

[0168] The optimization solution submodule is used to optimize and solve the multi-objective optimization function and calculate the corresponding operating parameters.

[0169] In one embodiment, a device for determining operating parameters of a ceramic cast membrane further comprises:

[0170] The model influencing factor determination submodule is used to determine the model influencing factors; the model influencing factors include ambient temperature, ambient humidity and machine wear;

[0171] The model adjustment submodule is used to adjust the slurry characteristic dynamic linearization data model and the film thickness characteristic dynamic linearization data model according to the model influencing factors.

[0172] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0173] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0174] Figure 3 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. Figure 3 As shown, the terminal device 300 of this embodiment includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and executable on the processor 302; when the processor 302 executes the computer program 303, the steps in the above-mentioned ceramic cast film operating parameter determination method embodiments are implemented; or when the processor 302 executes the computer program 303, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0175] Exemplarily, the computer program 303 may be divided into one or more modules / units, one or more modules / units are stored in the memory 301, and are executed by the processor 302 to implement the method of the embodiment of the present application. One or more modules / units may be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program 303 in the terminal device 300. For example, the computer program 303 may be divided into an operation parameter determination module, a prediction module, a judgment module, and an adjustment module, and the specific functions of each module are as follows:

[0176] An operating parameter determination module is used to determine the operating parameters of the ceramic cast film using a multi-objective optimization function; the operating parameters include a first operating parameter for controlling slurry characteristics and a second operating parameter for controlling film thickness characteristics;

[0177] A prediction module, configured to respectively output corresponding slurry property prediction results and film thickness property prediction results based on the operation parameters using the first prediction model and the second prediction model;

[0178] A judgment module is used to determine whether the slurry property prediction result and the film thickness property prediction result reach the slurry target property value and the film thickness target property value respectively; if not, the adjustment module is called;

[0179] The adjustment module is used to determine the operation parameter adjustment value according to the slurry property prediction result and the film thickness property prediction result, and adjust the operation parameter using the operation parameter adjustment value.

[0180] In application, the terminal device 300 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 300 may include but is not limited to a memory 301 and a processor 302. Those skilled in the art will appreciate that Figure 3 It is only an example of a terminal device and does not constitute a limitation of the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.; among which, the input and output devices may include cameras, audio acquisition / playback devices, display screens, etc.; the network access device may include a communication module for wireless communication with external devices.

[0181] In applications, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0182] In applications, the memory can be an internal storage unit of a terminal device, such as a hard disk or memory of the terminal device; it can also be an external storage device of the terminal device, such as a plug-in hard disk equipped on the terminal device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.; it can also include both the internal storage unit and the external storage device of the terminal device. The memory is used to store operating systems, applications, boot loaders, data, and other programs, such as program codes of computer programs. The memory can also be used to temporarily store data that has been output or is about to be output.

[0183] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0184] The present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0185] A computer-readable storage medium provided in an embodiment of the present application has the same beneficial effects as the above-mentioned method for determining operating parameters of a ceramic cast membrane.

[0186] An embodiment of the present application also provides a computer program product, including a computer program, which can implement the steps in the above-mentioned method embodiments when executed by a processor.

[0187] A computer program product provided in an embodiment of the present application has the same beneficial effects as the above-mentioned method for determining operating parameters of a ceramic cast membrane.

[0188] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0189] Those of ordinary skill in the art will appreciate that the devices and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0190] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices, which can be electrical, mechanical or other forms.

[0191] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for determining operating parameters of a ceramic cast film, characterized in that: The method comprises: Determining the operating parameters of the ceramic cast film using a multi-objective optimization function; the operating parameters include a first operating parameter for controlling slurry properties and a second operating parameter for controlling film thickness properties; Outputting corresponding slurry property prediction results and film thickness property prediction results respectively based on the operation parameters using the first prediction model and the second prediction model; determining whether the slurry property prediction result and the film thickness property prediction result reach the slurry target property value and the film thickness target property value, respectively; If not, an operation parameter adjustment value is determined according to the slurry property prediction result and the film thickness property prediction result, and the operation parameter is adjusted using the operation parameter adjustment value.

2. The method according to claim 1, characterized in that The first prediction model is a slurry characteristic dynamic linearization data model, and the second prediction model is a film thickness characteristic dynamic linearization data model.

3. The method according to claim 2, characterized in that The method of using the first prediction model and the second prediction model to output the corresponding slurry property prediction results and film thickness property prediction results based on the operation parameters respectively includes: Inputting the first operating parameter and the second operating parameter into the slurry characteristic dynamic linearization data model, extracting slurry characteristic related features according to the first operating parameter and the second operating parameter using the slurry characteristic dynamic linearization data model, and outputting corresponding slurry characteristic prediction results according to the first operating parameter and the slurry characteristic related features; The first operating parameter and the second operating parameter are input into the dynamic linearization data model of the film thickness characteristic, and the first operating parameter and the second operating parameter of the dynamic linearization data model of the film thickness characteristic are used to extract the relevant features of the film thickness characteristic, and the corresponding film thickness characteristic prediction result is output according to the first operating parameter and the relevant features of the film thickness characteristic.

4. The method according to claim 1, characterized in that: The step of determining an operating parameter adjustment value according to the slurry property prediction result and the film thickness property prediction result, and adjusting the operating parameter using the operating parameter adjustment value comprises: Determining a first difference value according to the slurry property prediction result and the slurry target property value; determining a second difference value according to the film thickness characteristic prediction result and the film thickness target characteristic value; determining an operating parameter adjustment value according to the first difference and the second difference; The operating parameter is adjusted using the operating parameter adjustment value.

5. The method according to claim 3, characterized in that: The step of inputting the first operating parameter and the second operating parameter into the slurry characteristic dynamic linearization data model, extracting slurry characteristic related features according to the first operating parameter and the second operating parameter using the slurry characteristic dynamic linearization data model, and outputting corresponding slurry characteristic prediction results according to the first operating parameter and the slurry characteristic related features, comprises: Inputting the first operating parameter and the second operating parameter into the slurry characteristic dynamic linearization data model, using the slurry characteristic dynamic linearization data model to perform data fusion on the first operating parameter and the second operating parameter to obtain slurry characteristic related features, and outputting corresponding slurry characteristic prediction results according to the first operating parameter and the slurry characteristic related features; The step of inputting the first operating parameter and the second operating parameter into the film thickness characteristic dynamic linearization data model, extracting film thickness characteristic related features using the first operating parameter and the second operating parameter of the film thickness characteristic dynamic linearization data model, and outputting corresponding film thickness characteristic prediction results according to the first operating parameter and the film thickness characteristic related features, comprises: The first operating parameter and the second operating parameter are input into the dynamic linearization data model of film thickness characteristics, and the dynamic linearization data model of film thickness characteristics is used to perform data fusion on the first operating parameter and the second operating parameter to obtain film thickness characteristic related features, and the corresponding film thickness characteristic prediction results are output according to the first operating parameter and the film thickness characteristic related features.

6. The method according to claim 1, characterized in that The method of determining the operating parameters of the ceramic cast film by using a multi-objective optimization function comprises: A multi-objective optimization function is constructed according to the characteristics of ceramic cast film; The multi-objective optimization function is optimized and solved to calculate corresponding operating parameters.

7. The method according to claim 3, characterized in that The method further comprises: Determine model influencing factors; the model influencing factors include ambient temperature, ambient humidity and machine wear; The slurry characteristic dynamic linearization data model and the film thickness characteristic dynamic linearization data model are adjusted according to the model influencing factor.

8. A device for determining operating parameters of a ceramic cast film, characterized in that: The device comprises: An operating parameter determination module, used to determine the operating parameters of the ceramic cast film using a multi-objective optimization function; the operating parameters include a first operating parameter for controlling slurry characteristics and a second operating parameter for controlling film thickness characteristics; A prediction module, configured to respectively output corresponding slurry property prediction results and film thickness property prediction results based on the operation parameters using the first prediction model and the second prediction model; A judgment module, used to determine whether the slurry property prediction result and the film thickness property prediction result reach the slurry target property value and the film thickness target property value respectively; if not, calling the adjustment module; The adjustment module is used to determine an operation parameter adjustment value according to the slurry property prediction result and the film thickness property prediction result, and adjust the operation parameter using the operation parameter adjustment value.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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