Artificial intelligence-based curtain membrane element preparation method and system

Abstract

This application proposes a method and system for preparing curtain membrane elements based on artificial intelligence. The method includes: controlling a screening device to screen a material based on screening parameters to obtain a dispersed material; controlling a mixing device to mix the dispersed material and a lubricant based on mixing parameters to obtain a mixed material; determining molding parameters of the mixed material based on a pre-trained first prediction model according to the screening parameters and the mixing parameters; controlling an extrusion device to pre-form the mixed material based on the molding parameters to obtain an extruded hollow fiber membrane; determining sintering parameters of the hollow fiber membrane based on a pre-trained second prediction model according to the molding parameters; and controlling a sintering device to sinter the hollow fiber membrane based on the sintering parameters to obtain a hollow fiber curtain membrane element. This application relates to the field of intelligent manufacturing technology and can improve the efficiency of curtain membrane element preparation.

Description

Technical Field

[0001] The present application relates to the field of intelligent manufacturing technology, and in particular to a method and system for preparing curtain membrane elements based on artificial intelligence. Background Art

[0002] With the development of data science, some companies and organizations are increasingly using data analysis within production environments to improve productivity and equipment maintenance. In the production process of curtain membrane elements, materials are typically pretreated using screening and mixing equipment, and then processed using extrusion equipment to produce hollow fiber curtain membrane elements. To improve production efficiency, digital control technology is often used to assist in developing production strategies. However, this approach is not adaptable enough to the dynamically changing production parameters during the production process, which can lead to reduced production efficiency. Summary of the Invention

[0003] In view of the above, it is necessary to propose an artificial intelligence-based curtain membrane element preparation method and system to solve the technical problems of low finished product consistency and low preparation efficiency of hollow fiber curtain membrane elements.

[0004] The present application provides an artificial intelligence-based curtain membrane element preparation method, which is applied to electronic equipment, wherein the electronic equipment is communicatively connected to a screening device, a mixing device, an extrusion device and a sintering device, and the method comprises: controlling the screening device to screen the material based on preset screening parameters to obtain a dispersed material; controlling the mixing device to mix the dispersed material and a lubricant based on preset mixing parameters to obtain a mixed material; determining the molding parameters of the mixed material based on a pre-trained first prediction model according to the screening parameters and the mixing parameters; controlling the extrusion device to pre-form the mixed material based on the molding parameters to obtain an extruded hollow fiber membrane; determining the sintering parameters of the hollow fiber membrane based on a pre-trained second prediction model according to the molding parameters; controlling the sintering device to sinter the hollow fiber membrane based on the sintering parameters to obtain a hollow fiber curtain membrane element.

[0005] In some embodiments, the method further includes training the first prediction model, and the training of the first prediction model includes: obtaining first production data, second production data and first label data of multiple batches; wherein the first production data includes the screening temperature of the screening device, the mesh aperture of the screening device, the mixing temperature of the mixing device, the aging temperature of the mixing device, and the mixing speed of the mixing device; the second production data includes the extrusion temperature of the extrusion device and the extrusion speed of the extrusion device, and the first label data includes the cone angle, compression ratio, aspect ratio and porosity of the hollow fiber membrane; the first label data is used to indicate the performance of the hollow fiber membrane corresponding to the second production data of each batch; according to the first label data of any batch, determining the first reward value corresponding to the second production data of any batch; inputting the first production data of any batch into a pre-constructed first initial prediction model to obtain the predicted production data output by the first initial prediction model; determining the first loss value of the first initial prediction model based on the first reward value, the predicted production data and the second production data; updating the first prediction model based on the back propagation algorithm until the first loss value meets the preset condition, stopping updating the first prediction model, and obtaining the first prediction model trained to a convergence state.

[0006] In some embodiments, determining the first reward value corresponding to the second production data of any batch based on the first label data of any batch includes: normalizing the first label data of the multiple batches to obtain normalized first label data of any batch; wherein the normalized first label data includes a normalized cone angle, a normalized compression ratio, a normalized aspect ratio and a normalized porosity; determining a first difference between the normalized first label data and a preset first standard data; wherein the first difference includes a cone angle difference, a compression ratio difference, a aspect ratio difference and a porosity difference; and determining the first reward value corresponding to the second production data of any batch based on a preset first weight parameter and the first difference.

[0007] In some embodiments, determining the first reward value corresponding to the second production data of any one batch according to the preset first weight parameter and the first difference includes: Among them, Reward1 represents the first reward value corresponding to the second production data of any one batch; α represents the first weight in the first weight parameter, and the first weight is used to characterize the influence of the cone angle on the first reward value; β represents the second weight in the first weight parameter, and the second weight is used to characterize the influence of the compression ratio on the first reward value; γ represents the third weight in the first weight parameter, and the third weight is used to characterize the influence of the aspect ratio on the first reward value; θ represents the fourth weight in the first weight parameter, and the fourth weight is used to characterize the influence of the porosity on the first reward value.

[0008] In some embodiments, determining the first loss value of the first initial prediction model based on the first reward value, the predicted production data, and the second production data includes: Wherein, Loss1 represents the first loss value of the first prediction model; i represents the index of the batch of the second production data, m represents the number of batches of the second production data; j represents the index of the dimension in the second production data, k represents the number of dimensions of the second production data; e represents a natural constant; represents the first reward value corresponding to the second production data of the i-th batch; A ij Represents the value of the jth dimension in the second production data of the i-th batch; B ij Represents the value of the j-th dimension in the predicted production data of the i-th batch.

[0009] In some embodiments, the method also includes training the second prediction model, and the training of the second prediction model includes: obtaining multiple batches of second production data, historical sintering data and second label data; wherein the second production data includes the extrusion temperature of the extrusion equipment and the extrusion speed of the extrusion equipment, and the historical sintering data includes the sintering temperature and sintering time; the second label data includes the membrane wire diameter, flux, pressure difference, and tensile strength of the hollow fiber curtain membrane element; the second label data is used to indicate the performance of the curtain membrane element corresponding to the second production data of each batch; according to the second label data of any batch, the second reward value corresponding to the second production data of any batch is determined; the second production data of any batch is input into the pre-constructed second initial prediction model to obtain the predicted sintering data output by the second initial prediction model; based on the second reward value, the predicted sintering data and the historical sintering data, the second loss value of the second initial prediction model is determined; the second prediction model is updated based on the back propagation algorithm until the second loss value meets the preset conditions, and the updating of the second prediction model is stopped to obtain the second prediction model trained to a convergence state.

[0010] In some embodiments, determining the second reward value corresponding to the second production data of any batch based on the second label data of any batch includes: normalizing the second label data of the multiple batches to obtain normalized second label data of any batch; wherein the normalized second label data includes normalized membrane filament diameter, normalized flux, normalized pressure difference and normalized tensile strength; determining the second difference between the normalized second label data and the preset second standard data; wherein the second difference includes diameter difference, flux difference, pressure difference difference and tensile strength difference; determining the second reward value corresponding to the second production data of any batch based on the preset second weight parameter and the second difference.

[0011] In some embodiments, determining the second reward value corresponding to the second production data of any one batch according to the preset second weight parameter and the second difference includes: Among them, Reward2 represents the second reward value corresponding to the second production data of any one batch; a represents the first weight in the second weight parameter, which is used to indicate the degree of influence of the membrane filament diameter on the second reward value; b represents the second weight in the second weight parameter, which is used to characterize the degree of influence of the flux on the second reward value; c represents the third weight in the second weight parameter, which is used to characterize the degree of influence of the pressure difference on the second reward value; d represents the fourth weight in the second weight parameter, which is used to characterize the degree of influence of the tensile strength on the second reward value.

[0012] In some embodiments, determining the second loss value of the second initial prediction model based on the second reward value, the predicted sintering data, and the historical sintering data includes: Wherein, Loss2 represents the second loss value of the second prediction model; i represents the index of the batch of the second production data, m represents the number of batches of the second production data; x represents the index of the dimension in the historical sintering data, y represents the number of dimensions of the historical sintering data; e represents a natural constant; represents the second reward value corresponding to the second production data of the i-th batch; U ix Represents the value of the xth dimension in the historical sintering data of the i-th batch; V ix Represents the value of the xth dimension in the predicted sintering data of the i-th batch.

[0013] An embodiment of the present application also provides an artificial intelligence-based curtain membrane element preparation system, which includes an electronic device, which is communicatively connected to a screening device, a mixing device, an extrusion device and a sintering device; the electronic device is used to control the screening device to screen the material based on preset screening parameters to obtain a dispersed material; the electronic device is also used to control the mixing device to mix the dispersed material and the lubricant based on preset mixing parameters to obtain a mixed material; the electronic device is also used to determine the molding parameters of the mixed material based on a pre-trained first prediction model according to the screening parameters and the mixing parameters; the electronic device is also used to control the extrusion device to pre-mold the mixed material based on the molding parameters to obtain an extruded hollow fiber membrane; the electronic device is also used to determine the sintering parameters of the hollow fiber membrane based on a pre-trained second prediction model according to the molding parameters; the electronic device is also used to control the sintering device to sinter the hollow fiber membrane based on the sintering parameters to obtain a hollow fiber curtain membrane element.

[0014] As can be seen from the above technical solutions, the embodiments of the present application use a pre-trained first prediction model to learn the complex nonlinear relationship between screening parameters, mixing parameters, and extrusion molding parameters, and use a pre-trained second prediction model to learn the correlation between extrusion molding parameters and sintering parameters, thereby achieving accurate prediction of process parameters. Based on a large amount of historical data and real-time data, the reliance on human experience and trial and error is reduced, and the accuracy of the parameters is improved. It can ensure that the process parameters remain relatively stable and consistent under different batches and production conditions, thereby reducing performance differences between products and improving product stability and reliability. It can also achieve real-time monitoring and adjustment of the entire preparation process. When the parameters of a certain step fluctuate, the parameters of the subsequent steps can be quickly predicted and adjusted to maintain the stability of the entire process. It can reduce the testing and adjustment process, thereby shortening the production cycle and improving production efficiency. At the same time, due to the improved stability of product performance, the scrap rate and rework rate can be reduced, further reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is an application scenario diagram of an artificial intelligence-based curtain membrane element preparation method provided in one embodiment of the present application.

[0016] Figure 2 This is a flow chart of a method for preparing a curtain membrane element based on artificial intelligence provided in one embodiment of the present application.

[0017] Figure 3 This is a functional module diagram of an artificial intelligence-based curtain membrane element preparation system provided in one embodiment of the present application.

[0018] Figure 4 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to more clearly understand the purpose, features and advantages of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. In the following description, many specific details are set forth to facilitate a full understanding of the present application. The embodiments described are only a part of the embodiments of the present application, rather than all of the embodiments.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the described features. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] An embodiment of the present application provides an artificial intelligence-based method for preparing a curtain membrane element, which can be applied to one or more electronic devices. The electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0023] An electronic device can be any electronic product that can interact with a user, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), game console, Internet Protocol Television (IPTV), smart wearable device, etc.

[0024] The electronic device may also include a network device and / or a client device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0025] The network where the electronic device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0026] like Figure 1 The figure shows an application scenario diagram of an artificial intelligence-based curtain membrane element preparation method provided in an embodiment of the present application. The artificial intelligence-based curtain membrane element preparation method provided in the present application can be applied to an electronic device 100. Among them, the electronic device 100 is communicatively connected to a screening device 200, a mixing device 300, an extrusion device 400 and a sintering device 500. Specifically, the electronic device 100 is used to control the screening device 200 to screen the material based on preset screening parameters to obtain a dispersed material. The electronic device 100 is also used to control the mixing device 300 to mix the dispersed material and the lubricant based on preset mixing parameters to obtain a mixed material. The electronic device 100 is also used to determine the molding parameters of the mixed material based on the screening parameters and the mixing parameters and a pre-trained first prediction model. The electronic device 100 is also used to control the extrusion device 400 to pre-form the mixed material based on the molding parameters to obtain an extruded hollow fiber membrane. The electronic device 100 is further configured to determine sintering parameters for the hollow fiber membrane based on the molding parameters and a pre-trained second prediction model. The electronic device 100 is further configured to control the sintering device 500 to sinter the hollow fiber membrane based on the sintering parameters to produce a hollow fiber curtain membrane element. This ensures that the membrane filaments of the hollow fiber curtain membrane element are neatly arranged, thereby improving the element's resistance to contamination.

[0027] like Figure 2 The figure shows a flow chart of an artificial intelligence-based curtain membrane element manufacturing method according to one embodiment of the present application. The order of the steps in this flow chart may be changed, and some steps may be omitted, depending on different needs. The artificial intelligence-based curtain membrane element manufacturing method according to one embodiment of the present application includes the following steps.

[0028] S20, controlling the screening device to screen the material based on preset screening parameters to obtain dispersed material.

[0029] In some embodiments, the material may be polytetrafluoroethylene, and the auxiliary oil may be paraffin oil. Among them, polytetrafluoroethylene is a polymer with a low friction coefficient, non-adhesiveness, high temperature resistance and corrosion resistance. Specifically, polytetrafluoroethylene can be applied to the treatment process of highly polluting industrial wastewater containing oil, solvents, etc. For example, polytetrafluoroethylene can be used for landfill leachate treatment, and can also be used for water reuse in the steel industry, and can also be used for wastewater treatment in the printing and dyeing and electroplating industries, and can also be used for wastewater treatment in the chemical industry. Among them, polytetrafluoroethylene can solve the problems of membrane bioreactor products made of polyvinylidene fluoride being not oil-resistant and not having sufficient anti-pollution performance, and can improve the efficiency of industrial wastewater treatment. Specifically, compared with polyvinylidene fluoride, polytetrafluoroethylene has more stable hydrophilicity; the curtain membrane element made of polytetrafluoroethylene is made through a stretching process, so that the membrane fiber opening rate of the curtain membrane element is higher than that of the membrane made of polyvinylidene fluoride, so that the flux of the curtain membrane element is higher than that of the membrane made of polyvinylidene fluoride, and the membrane fiber strength is high; and polytetrafluoroethylene can be shut down for dry storage, which is easier to store than polyvinylidene fluoride stored in a wet manner.

[0030] In some embodiments, in the process of preparing hollow fiber curtain membrane elements using polytetrafluoroethylene (PTFE), the polytetrafluoroethylene dispersion resin may form agglomerates or oversized particles during storage and transportation due to factors such as moisture, pressure, or temperature changes. This may affect the fluidity and dispersibility of the material during subsequent processing, thereby affecting the quality of the final product. Therefore, the material can be screened by a screening device based on preset screening parameters to obtain a dispersed material. The screening parameters include the screening temperature and the mesh size of the screening device. For example, the polytetrafluoroethylene dispersion resin can be screened using a mesh with an aperture of approximately 2 mm at a screening temperature of 19 degrees Celsius. This screening process ensures the uniformity and consistency of the resin particles, providing high-quality raw materials for subsequent processing. The dispersed material after screening has better fluidity and dispersibility, making it easier to mix evenly with other additives such as lubricants. The screening process can also remove impurities and unqualified particles from the resin, reducing defects in the final product.

[0031] S21, controlling the mixing equipment to mix the dispersed material and the lubricant based on preset mixing parameters to obtain a mixed material.

[0032] In one embodiment of the present application, in the process of preparing hollow fiber curtain membrane elements using polytetrafluoroethylene (PTFE), in order to improve the uniformity and consistency of the material, it is ensured that the polytetrafluoroethylene resin and other auxiliary materials such as lubricants are fully mixed during the mixing process, and that the material has uniform fluidity and dispersibility in the subsequent processing process. The mixing device can be controlled based on preset mixing parameters to mix the dispersed material and lubricant to obtain a mixed material. Wherein, the mixing parameters include the mixing temperature of the mixing device, the aging temperature of the mixing device, and the mixing speed of the mixing device. For example, the polytetrafluoroethylene resin and the lubricant can be mixed in a certain proportion under the temperature condition of the mixing temperature below 19 degrees Celsius. During mixing, artificial shaking or a double-roll rolling agitator can be used to mix to ensure the uniformity of the mixture. After mixing, in order to promote the penetration of the lubricant, the mixture is placed in an environment with an aging temperature of 35 degrees Celsius for 10 hours, so that the lubricant gradually penetrates into the interior of the polytetrafluoroethylene resin, improves the lubricity between the resin particles, and reduces friction and wear during processing. Through mixing processing, a good combination of PTFE resin and other auxiliary materials such as lubricants can be ensured, thereby improving the quality and performance of the final product.

[0033] In one embodiment of the present application, a lubricant is added during the mixing process to reduce friction and wear between the polytetrafluoroethylene resin and the processing equipment, thereby protecting the equipment from damage. Lubricant types include liquid lubricants and solid lubricants. Liquid lubricants include mineral oil, sodium-based lubricating oil, synthetic oil, etc. They can be used to reduce the coefficient of friction, reduce the wear rate, and protect mechanical equipment. They can penetrate between polytetrafluoroethylene resins, improve the fluidity of the material, and reduce friction and heat generation during processing. Solid lubricants include graphite, molybdenum disulfide, polytetrafluoroethylene (PTFE) micropowder, etc., which are used to fill the micropores on the surface of polytetrafluoroethylene, reduce friction, and improve wear resistance. A lubricating film can be formed on the surface of the polytetrafluoroethylene resin, reducing the friction between the resin particles and improving the surface finish and mechanical properties of the product.

[0034] S22: Determine molding parameters of the mixed material according to the screening parameters and the mixing parameters based on a pre-trained first prediction model.

[0035] In one embodiment of the present application, in order to improve the performance of a hollow fiber membrane obtained by processing a mixed material using an extrusion device, the molding parameters of the mixed material can be determined based on the screening parameters and the mixing parameters based on a pre-trained first prediction model. This can improve the degree of correlation between each process step in the preparation of a curtain membrane element, thereby determining appropriate molding parameters based on the pre-set screening parameters and mixing parameters, thereby improving the performance of the hollow fiber membrane generated by the extrusion device controlled by the molding parameters. The molding parameters include the extrusion temperature of the extrusion device and the extrusion speed of the extrusion device. Specifically, training the first prediction model includes: obtaining multiple batches of first production data, second production data and first label data; wherein the first production data includes the screening temperature of the screening device, the mesh aperture of the screening device, the mixing temperature of the mixing device, the aging temperature of the mixing device, and the mixing speed of the mixing device; the second production data includes the extrusion temperature of the extrusion device and the extrusion speed of the extrusion device, and the first label data includes the cone angle, compression ratio, aspect ratio and porosity of the hollow fiber membrane; the first label data is used to indicate the performance of the hollow fiber membrane corresponding to the second production data of each batch; according to the first label data of any batch, determining the first reward value corresponding to the second production data of any batch; inputting the first production data of any batch into the pre-constructed first initial prediction model to obtain the predicted production data output by the first initial prediction model; determining the first loss value of the first initial prediction model based on the first reward value, the predicted production data and the second production data; updating the first prediction model based on the back propagation algorithm until the first loss value meets the preset condition, stopping updating the first prediction model, and obtaining the first prediction model trained to a convergence state.

[0036] In one embodiment of the present application, determining the first reward value corresponding to the second production data of any batch based on the first label data of any batch includes: normalizing the first label data of the multiple batches to obtain normalized first label data of any batch; wherein the normalized first label data includes a normalized cone angle, a normalized compression ratio, a normalized aspect ratio and a normalized porosity; determining a first difference between the normalized first label data and a preset first standard data; wherein the first difference includes a cone angle difference, a compression ratio difference, a aspect ratio difference and a porosity difference; and determining the first reward value corresponding to the second production data of any batch based on a preset first weight parameter and the first difference.

[0037] In one embodiment of the present application, determining the first reward value corresponding to the second production data of any one batch according to the preset first weight parameter and the first difference includes: Among them, Reward1 represents the first reward value corresponding to the second production data of any one batch; α represents the first weight in the first weight parameter, and the first weight is used to characterize the influence of the cone angle on the first reward value; β represents the second weight in the first weight parameter, and the second weight is used to characterize the influence of the compression ratio on the first reward value; γ represents the third weight in the first weight parameter, and the third weight is used to characterize the influence of the aspect ratio on the first reward value; θ represents the fourth weight in the first weight parameter, and the fourth weight is used to characterize the influence of the porosity on the first reward value.

[0038] In one embodiment of the present application, determining the first loss value of the first initial prediction model based on the first reward value, the predicted production data, and the second production data includes: Wherein, Loss1 represents the first loss value of the first prediction model; i represents the index of the batch of the second production data, m represents the number of batches of the second production data; j represents the index of the dimension in the second production data, k represents the number of dimensions of the second production data; e represents a natural constant; represents the first reward value corresponding to the second production data of the i-th batch; A ij Represents the value of the jth dimension in the second production data of the i-th batch; B ij Represents the value of the j-th dimension in the predicted production data of the i-th batch.

[0039] S23, controlling the extrusion equipment to perform pre-forming on the mixed material according to the forming parameters to obtain an extruded hollow fiber membrane.

[0040] In one embodiment of the present application, in the process of preparing hollow fiber curtain membrane elements using polytetrafluoroethylene (PTFE), preforming the mixture can reduce the volume of the mixed powder, and the blank after preforming has better fluidity and dispersibility, preparing for the subsequent pushing process. During the preforming process, by controlling the speed and pressure of the blanking, the mixture is subjected to uniform shear force in the material cavity of the extrusion equipment, thereby fiberizing the resin particles. The fiberized resin particles have better fluidity and dispersibility. In addition, by controlling the molding parameters in the preforming process (for example, the extrusion temperature of the extrusion equipment, the extrusion speed of the extrusion equipment, etc.), the performance parameters such as the pore size and porosity of the final product can be regulated. Ensure that the product after preforming has better mechanical properties such as tensile strength and elongation at break, as well as higher dimensional accuracy and stability.

[0041] S24, determining the sintering parameters of the hollow fiber membrane according to the molding parameters and based on a pre-trained second prediction model.

[0042] In one embodiment of the present application, to improve the performance of a curtain membrane element produced by processing a hollow fiber membrane using sintering equipment, sintering parameters of the hollow fiber membrane can be determined based on the molding parameters and a pre-trained second prediction model. The sintering parameters include the sintering temperature and sintering time of the sintering equipment. Specifically, training the second prediction model includes: obtaining multiple batches of second production data, historical sintering data and second label data; wherein, the second production data includes the extrusion temperature of the extrusion equipment and the extrusion speed of the extrusion equipment, and the historical sintering data includes the sintering temperature and sintering time; the second label data includes the membrane wire diameter, flux, pressure difference and tensile strength of the hollow fiber curtain membrane element; the second label data is used to indicate the performance of the curtain membrane element corresponding to the second production data of each batch; according to the second label data of any batch, the second reward value corresponding to the second production data of any batch is determined; the second production data of any batch is input into the pre-constructed second initial prediction model to obtain the predicted sintering data output by the second initial prediction model; based on the second reward value, the predicted sintering data and the historical sintering data, the second loss value of the second initial prediction model is determined; based on the back propagation algorithm, the second prediction model is updated until the second loss value meets the preset conditions, and the updating of the second prediction model is stopped to obtain the second prediction model trained to a convergence state.

[0043] In one embodiment of the present application, determining the second reward value corresponding to the second production data of any batch based on the second label data of any batch includes: normalizing the second label data of the multiple batches to obtain normalized second label data of any batch; wherein the normalized second label data includes normalized membrane filament diameter, normalized flux, normalized pressure difference and normalized tensile strength; determining the second difference between the normalized second label data and the preset second standard data; wherein the second difference includes diameter difference, flux difference, pressure difference difference and tensile strength difference; determining the second reward value corresponding to the second production data of any batch based on the preset second weight parameter and the second difference.

[0044] In an embodiment of the present application, determining the second reward value corresponding to the second production data of any one batch according to the preset second weight parameter and the second difference includes: Among them, Reward2 represents the second reward value corresponding to the second production data of any one batch; a represents the first weight in the second weight parameter, which is used to indicate the degree of influence of the membrane filament diameter on the second reward value; b represents the second weight in the second weight parameter, which is used to characterize the degree of influence of the flux on the second reward value; c represents the third weight in the second weight parameter, which is used to characterize the degree of influence of the pressure difference on the second reward value; d represents the fourth weight in the second weight parameter, which is used to characterize the degree of influence of the tensile strength on the second reward value.

[0045] In one embodiment of the present application, determining the second loss value of the second initial prediction model based on the second reward value, the predicted sintering data, and the historical sintering data includes: Wherein, Loss2 represents the second loss value of the second prediction model; i represents the index of the batch of the second production data, m represents the number of batches of the second production data; x represents the index of the dimension in the historical sintering data, y represents the number of dimensions of the historical sintering data; e represents a natural constant; represents the second reward value corresponding to the second production data of the i-th batch; U ix Represents the value of the xth dimension in the historical sintering data of the i-th batch; V ix Represents the value of the xth dimension in the predicted sintering data of the i-th batch.

[0046] S25 , controlling the sintering equipment to sinter the hollow fiber membrane based on the sintering parameters to obtain a hollow fiber curtain membrane element.

[0047] In one embodiment of the present application, in the process of preparing hollow fiber curtain membrane elements using polytetrafluoroethylene (PTFE), sintering and shaping treatment is performed to melt the resin particles in the preformed blank and fuse them into a uniform structure. For example, the preformed blank is heated to above the melting point of polytetrafluoroethylene (327°C) to melt the resin particles. In the molten state, the interface between the resin particles disappears, forming a uniform structure. Through the sintering and shaping treatment, the resin particles in the blank are fully melted and fused, eliminating the boundaries between the particles and forming a dense and uniform structure. This significantly improves the density, hardness and mechanical properties of the product. The sintering and shaping treatment can also eliminate internal stress and defects in the blank and improve the dimensional accuracy and stability of the product. During the sintering process, the blank is subjected to high temperature, and the internal stress and defects are released and repaired. At the same time, by controlling process parameters such as the heating rate, sintering temperature and holding time, it can be ensured that the blank is heated evenly to avoid local overheating or overcooling. After sintering and shaping, the internal stress and defects in the blank are effectively eliminated, and the dimensional accuracy and stability of the product are improved. By controlling the process parameters during the sintering process, performance parameters such as the pore size and porosity of the product can be adjusted. Process parameters such as sintering temperature, holding time, and heating rate have a significant impact on the pore size and porosity of the product. By optimizing these parameters, products with different pore sizes and porosities can be prepared to meet the needs of different application scenarios. By regulating the process parameters during the sintering process, performance parameters such as the pore size and porosity of the product can be precisely controlled. This allows the polytetrafluoroethylene hollow fiber curtain membrane element to be optimized according to different usage requirements, improving its applicability and performance.

[0048] As can be seen from the above technical solutions, the embodiments of the present application use a pre-trained first prediction model to learn the complex nonlinear relationship between screening parameters, mixing parameters, and extrusion molding parameters, and use a pre-trained second prediction model to learn the correlation between extrusion molding parameters and sintering parameters, thereby achieving accurate prediction of process parameters. Based on a large amount of historical data and real-time data, the reliance on human experience and trial and error is reduced, and the accuracy of the parameters is improved. It can ensure that the process parameters remain relatively stable and consistent under different batches and production conditions, thereby reducing performance differences between products and improving product stability and reliability. It can also achieve real-time monitoring and adjustment of the entire preparation process. When the parameters of a certain step fluctuate, the parameters of the subsequent steps can be quickly predicted and adjusted to maintain the stability of the entire process. It can reduce the testing and adjustment process, thereby shortening the production cycle and improving production efficiency. At the same time, due to the improved stability of product performance, the scrap rate and rework rate can be reduced, further reducing costs.

[0049] See Figure 3 , Figure 3This is a functional block diagram of an artificial intelligence-based curtain membrane element production system provided in one embodiment of the present application. The artificial intelligence-based curtain membrane element production system 600 includes an electronic device 100, which is communicatively connected to a screening device 200, a mixing device 300, an extrusion device 400, and a sintering device 500. The modules / units referred to herein are a series of computer-readable instruction segments that can be executed by a processor 13 and perform a fixed function, and are stored in a memory 12. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0050] The electronic device 100 is used to control the screening device 200 to screen the material based on preset screening parameters to obtain dispersed material.

[0051] The electronic device 100 is further configured to control the mixing device 300 to mix the dispersed material and the lubricant based on preset mixing parameters to obtain a mixed material.

[0052] The electronic device 100 is further configured to determine the molding parameters of the mixed material according to the screening parameters and the mixing parameters based on a pre-trained first prediction model.

[0053] The electronic device 100 is further used to control the extrusion device 400 to perform pre-forming processing on the mixed material according to the forming parameters to obtain an extruded hollow fiber membrane.

[0054] The electronic device 100 is further configured to determine the sintering parameters of the hollow fiber membrane according to the molding parameters and based on a pre-trained second prediction model.

[0055] The electronic device 100 is further configured to control the sintering device 500 to sinter the hollow fiber membrane based on the sintering parameters to obtain a hollow fiber curtain membrane element.

[0056] See Figure 4 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Electronic device 100 includes memory 12 and processor 13. Memory 12 is used to store computer-readable instructions, and processor 13 executes the computer-readable instructions stored in the memory to implement the artificial intelligence-based curtain membrane element preparation method described in any of the above embodiments.

[0057] In one embodiment of the present application, the electronic device 100 further includes a bus, a computer program stored in the memory 12 and executable on the processor 13 , such as an artificial intelligence-based curtain membrane element preparation program.

[0058] Figure 4Only the electronic device 100 having the memory 12 and the processor 13 is shown. It can be understood by those skilled in the art that Figure 4 The structure shown does not constitute a limitation on the electronic device 100 , and the electronic device 100 may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0059] Combine Figure 2 The memory 12 in the electronic device 100 stores a plurality of computer-readable instructions to implement the artificial intelligence-based curtain membrane element preparation method, and the processor 13 can execute the plurality of instructions to achieve: based on the preset screening parameters, controlling the screening device to screen the material to obtain a dispersed material; based on the preset mixing parameters, controlling the mixing device to mix the dispersed material and the lubricant to obtain a mixed material; according to the screening parameters and the mixing parameters, based on a pre-trained first prediction model, determining the molding parameters of the mixed material; according to the molding parameters, controlling the extrusion device to pre-form the mixed material to obtain an extruded hollow fiber membrane; according to the molding parameters, based on a pre-trained second prediction model, determining the sintering parameters of the hollow fiber membrane; based on the sintering parameters, controlling the sintering device to sinter the hollow fiber membrane to obtain a hollow fiber curtain membrane element.

[0060] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0061] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation on the electronic device 100. The electronic device 100 may have a bus structure or a star structure. The electronic device 100 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components. For example, the electronic device 100 may also include input and output devices, network access devices, etc.

[0062] It should be noted that the electronic device 100 is only an example. Other existing or future electronic products that are suitable for this application should also be included in the scope of protection of this application and incorporated herein by reference.

[0063] Among them, the memory 12 includes at least one type of readable storage medium, and the readable storage medium can be non-volatile or volatile. The readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 100, such as a mobile hard disk of the electronic device 100. In other embodiments, the memory 12 can also be an external storage device of the electronic device 100, such as a plug-in mobile hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card (Flash Card), etc. equipped on the electronic device 100. The memory 12 can not only be used to store application software and various types of data installed in the electronic device 100, such as the code of a curtain membrane element preparation program based on artificial intelligence, but can also be used to temporarily store data that has been output or is to be output.

[0064] In some embodiments, the processor 13 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 13 is the control core (Control Unit) of the electronic device 100, connecting the various components of the entire electronic device 100 using various interfaces and circuits. It executes or executes programs or modules stored in the memory 12 (for example, executing an artificial intelligence-based curtain membrane element preparation program) and calls data stored in the memory 12 to perform various functions of the electronic device 100 and process data.

[0065] The processor 13 executes the operating system of the electronic device 100 and various installed applications. The processor 13 executes the applications to implement the steps in the above-mentioned embodiments of the method for preparing a curtain membrane element based on artificial intelligence, for example Figure 2 Steps shown.

[0066] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 100.

[0067] The above-mentioned integrated unit implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module stored in a storage medium includes a number of instructions for causing a computer device (which can be a personal computer, computer device, or network device, etc.) or a processor to execute the portion of the artificial intelligence-based curtain membrane element preparation method described in various embodiments of the present application.

[0068] If the modules / units integrated in the electronic device 100 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment methods, and can also instruct the relevant hardware devices to complete them through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments.

[0069] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory, or other memory.

[0070] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0071] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The diagram is represented by only one arrow, but it does not mean that there is only one bus or one type of bus. The bus is configured to implement connection and communication between the memory 12 and at least one processor 13, etc.

[0072] An embodiment of the present application also provides a computer-readable storage medium (not shown), in which computer-readable instructions are stored. The computer-readable instructions are executed by a processor in an electronic device to implement the artificial intelligence-based curtain membrane element preparation method described in any of the above embodiments.

[0073] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0074] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0075] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0076] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices listed in the specification may also be implemented by a single unit or device through software or hardware. Terms such as first and second are used to indicate names and do not imply any particular order.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for preparing a curtain membrane element based on artificial intelligence, applied to an electronic device, wherein the electronic device is communicatively connected to a screening device, a mixing device, an extrusion device, and a sintering device, characterized in that: The method comprises: Controlling the screening equipment to screen the material based on preset screening parameters to obtain dispersed material; Controlling the mixing equipment to mix the dispersed material and the lubricant based on preset mixing parameters to obtain a mixed material; According to the screening parameters and the mixing parameters, the molding parameters of the mixed material are determined based on a pre-trained first prediction model; wherein, training the first prediction model includes: obtaining first production data, second production data and first label data of multiple batches; wherein, the first production data includes the screening temperature of the screening device, the mesh aperture of the screening device, the mixing temperature of the mixing device, the aging temperature of the mixing device, and the mixing speed of the mixing device; the second production data includes the extrusion temperature of the extrusion device and the extrusion speed of the extrusion device, and the first label data includes the cone angle, compression ratio, aspect ratio and porosity of the hollow fiber membrane; the first label data includes the first production data, the second production data and the first label data of the hollow fiber membrane; The method comprises the following steps: determining the performance of the hollow fiber membrane corresponding to the second production data of each batch according to the first label data of any batch; determining the first reward value corresponding to the second production data of any batch according to the first label data of the batch; inputting the first production data of any batch into a pre-built first initial prediction model to obtain the predicted production data output by the first initial prediction model; determining the first loss value of the first initial prediction model based on the first reward value, the predicted production data and the second production data; updating the first prediction model based on the back propagation algorithm until the first loss value meets a preset condition, stopping updating the first prediction model, and obtaining a first prediction model trained to a convergence state; Controlling the extrusion equipment to preform the mixed material according to the molding parameters to obtain an extruded hollow fiber membrane; According to the molding parameters, based on the pre-trained second prediction model, the sintering parameters of the hollow fiber membrane are determined; wherein, training the second prediction model includes: obtaining multiple batches of second production data, historical sintering data and second label data; wherein, the second production data includes the extrusion temperature of the extrusion equipment and the extrusion speed of the extrusion equipment, and the historical sintering data includes the sintering temperature and sintering time; the second label data includes the membrane wire diameter, flux, pressure difference and tensile strength of the hollow fiber curtain membrane element; the second label data is used to indicate the performance of the curtain membrane element corresponding to the second production data of each batch; according to the second label data of any batch, the second reward value corresponding to the second production data of any batch is determined; the second production data of any batch is input into the pre-constructed second initial prediction model to obtain the predicted sintering data output by the second initial prediction model; based on the second reward value, the predicted sintering data and the historical sintering data, the second loss value of the second initial prediction model is determined; the second prediction model is updated based on the back propagation algorithm until the second loss value meets the preset condition, and the updating of the second prediction model is stopped to obtain the second prediction model trained to a convergence state; The sintering equipment is controlled based on the sintering parameters to perform a sintering process on the hollow fiber membrane to obtain a hollow fiber curtain membrane element.

2. The method for preparing a curtain membrane element based on artificial intelligence according to claim 1, wherein: The determining, based on the first label data of any batch, a first reward value corresponding to the second production data of any batch includes: Normalizing the first label data of the plurality of batches to obtain normalized first label data of any batch; wherein the normalized first label data includes a normalized cone angle, a normalized compression ratio, a normalized aspect ratio, and a normalized porosity; Determining a first difference between the normalized first label data and a preset first standard data; wherein the first difference includes a cone angle difference, a compression ratio difference, an aspect ratio difference, and a porosity difference; A first reward value corresponding to the second production data of any one batch is determined according to a preset first weight parameter and the first difference.

3. The method for preparing a curtain membrane element based on artificial intelligence according to claim 2, wherein: The determining, based on the preset first weight parameter and the first difference, the first reward value corresponding to the second production data of any one batch includes: ； Wherein, Reward1 represents the first reward value corresponding to the second production data of any batch; represents a first weight in the first weight parameter, where the first weight is used to characterize the influence of the cone angle on the first reward value; represents a second weight in the first weight parameter, where the second weight is used to characterize the influence of the compression ratio on the first reward value; represents a third weight in the first weight parameter, the third weight being used to characterize the influence of the aspect ratio on the first reward value; represents a fourth weight in the first weight parameter, and the fourth weight is used to characterize the influence of porosity on the first reward value.

4. The method for preparing a curtain membrane element based on artificial intelligence according to claim 1, wherein: The determining, based on the first reward value, the predicted production data, and the second production data, a first loss value of the first initial prediction model includes: ； Wherein, Loss1 represents the first loss value of the first prediction model; i represents the index of the batch of the second production data, m represents the number of batches of the second production data; j represents the index of the dimension in the second production data, k represents the number of dimensions of the second production data; e represents a natural constant; represents the first reward value corresponding to the second production data of the i-th batch; A ij Represents the value of the jth dimension in the second production data of the i-th batch; B ij Represents the value of the j-th dimension in the predicted production data of the i-th batch.

5. The method for preparing a curtain membrane element based on artificial intelligence according to claim 1, wherein: The determining, based on the second label data of any batch, a second reward value corresponding to the second production data of the any batch includes: Normalizing the second label data of the multiple batches to obtain normalized second label data of any batch; wherein the normalized second label data includes normalized membrane filament diameter, normalized flux, normalized pressure difference and normalized tensile strength; Determining a second difference between the normalized second label data and the preset second standard data; wherein the second difference includes a diameter difference, a flux difference, a pressure difference, and a tensile strength difference; A second reward value corresponding to the second production data of any one batch is determined according to a preset second weight parameter and the second difference.

6. The method for preparing a curtain membrane element based on artificial intelligence according to claim 5, characterized in that: The determining, based on the preset second weight parameter and the second difference, the second reward value corresponding to the second production data of any one batch includes: ； Among them, Reward2 represents the second reward value corresponding to the second production data of any one batch; a represents the first weight in the second weight parameter, which is used to indicate the degree of influence of the membrane filament diameter on the second reward value; b represents the second weight in the second weight parameter, which is used to characterize the degree of influence of the flux on the second reward value; c represents the third weight in the second weight parameter, which is used to characterize the degree of influence of the pressure difference on the second reward value; d represents the fourth weight in the second weight parameter, which is used to characterize the degree of influence of the tensile strength on the second reward value.

7. The method for preparing a curtain membrane element based on artificial intelligence according to claim 1, wherein: Determining a second loss value of the second initial prediction model based on the second reward value, the predicted sintering data, and the historical sintering data includes: ； Wherein, Loss2 represents the second loss value of the second prediction model; i represents the index of the batch of the second production data, m represents the number of batches of the second production data; x represents the index of the dimension in the historical sintering data, y represents the number of dimensions of the historical sintering data; e represents a natural constant; represents the second reward value corresponding to the second production data of the i-th batch; U ix Represents the value of the xth dimension in the historical sintering data of the i-th batch; V ix Represents the value of the xth dimension in the predicted sintering data of the i-th batch.

8. An artificial intelligence-based curtain membrane element preparation system, characterized in that: The artificial intelligence-based curtain membrane element preparation system is used to implement the method according to any one of claims 1 to 7, wherein the system includes an electronic device, and the electronic device is communicatively connected to a screening device, a mixing device, an extrusion device, and a sintering device; The electronic device is used to control the screening device to screen the material based on preset screening parameters to obtain dispersed material; The electronic device is further used to control the mixing device to mix the dispersed material and the lubricant based on preset mixing parameters to obtain a mixed material; The electronic device is further configured to determine the molding parameters of the mixed material based on the screening parameters and the mixing parameters and a pre-trained first prediction model; The electronic device is further used to control the extrusion device to perform pre-forming processing on the mixed material according to the molding parameters to obtain an extruded hollow fiber membrane; The electronic device is further configured to determine, according to the molding parameters, a sintering parameter of the hollow fiber membrane based on a pre-trained second prediction model; The electronic device is further used to control the sintering equipment to sinter the hollow fiber membrane based on the sintering parameters to obtain a hollow fiber curtain membrane element.

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