Nanofiltration membrane preparation method and system based on artificial intelligence

Abstract

The invention provides a nanofiltration membrane preparation method and system based on artificial intelligence, and relates to the technical field of intelligent manufacturing. The preparation method of the nanofiltration membrane based on artificial intelligence comprises the following steps: controlling curing equipment to pre-treat raw materials and auxiliary oil based on preset curing parameters; according to a first production parameter, obtained in real time, of production equipment, predicting a performance score of the nanofiltration membrane obtained after the production equipment is controlled to process the pretreated raw materials according to the first production parameter; adjusting the first production parameter according to the performance score to obtain a second production parameter; and according to the second production parameter, controlling the production equipment to process the pretreated raw material to obtain the hollow fiber nanofiltration membrane. The preparation efficiency of the nanofiltration membrane can be improved.

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 a nanofiltration membrane based on artificial intelligence. Background Art

[0002] With the advancement of data science, some companies and organizations are increasingly using data analysis within their production environments to improve productivity and equipment maintenance. In the nanofiltration membrane production process, raw materials are typically matured using aging equipment, and the matured materials are then processed using production equipment to produce hollow fiber nanofiltration membranes. 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 a nanofiltration membrane preparation method and system based on artificial intelligence to solve the technical problems of low consistency and low preparation efficiency of hollow fiber nanofiltration membrane products.

[0004] The present application provides an artificial intelligence-based nanofiltration membrane preparation method, which is applied to electronic equipment, wherein the electronic equipment is communicatively connected to a maturation device and a production equipment. The method includes: controlling the maturation equipment to pretreat raw materials and auxiliary oil based on preset maturation parameters; predicting, based on a first production parameter of the production equipment acquired in real time, a performance score of the nanofiltration membrane obtained after controlling the production equipment to process the pretreated raw materials based on the first production parameter; adjusting the first production parameter based on the performance score to obtain a second production parameter; and controlling the production equipment to process the pretreated raw materials based on the second production parameter to obtain a hollow fiber nanofiltration membrane.

[0005] In some embodiments, the method of predicting the performance score of the nanofiltration membrane obtained after controlling the production equipment to process the pretreated raw materials based on the first production parameter of the production equipment obtained in real time includes: determining the target production parameters from the historical production parameters of multiple batches based on the first production parameters; and determining the performance score of the nanofiltration membrane based on a pre-trained evaluation model based on the target production parameters.

[0006] In some embodiments, the training of the evaluation model includes: obtaining historical data and label data of multiple batches; the historical data includes historical production parameters, porosity, tensile strength and energy consumption; the label data is used to indicate the performance score of the nanofiltration membrane corresponding to the historical data of each batch; based on the historical data of any batch, determining the reward value corresponding to the historical data of any batch; inputting the historical production parameters of any batch into a pre-constructed initial evaluation model to obtain the predicted performance score output by the initial evaluation model; based on the reward value, the predicted performance score and the label data, determining the first loss value of the initial evaluation model; updating the initial evaluation model based on the back propagation algorithm until the first loss value meets the preset conditions, stopping updating the initial evaluation model, and obtaining an evaluation model trained to a convergence state.

[0007] In some embodiments, determining the reward value corresponding to any batch of historical data based on the historical data of any batch includes: performing weighted sum calculation on the porosity, tensile strength and energy consumption in the historical data according to a preset weight parameter to obtain the reward value corresponding to the historical data of any batch; the reward value calculation method satisfies the following relationship: Reward = α*porosity + β*tensile strength - γ*energy consumption; wherein, Reward represents the reward value corresponding to any batch of historical data; α represents the first weight in the weight parameter, and the first weight is used to characterize the degree of influence of porosity on the reward value; β represents the second weight in the weight parameter, and the second weight is used to characterize the degree of influence of tensile strength on the reward value; γ represents the third weight in the weight parameter, and the third weight is used to characterize the degree of influence of energy consumption on the reward value.

[0008] In some embodiments, determining a first loss value of the initial evaluation model based on the reward value, the prediction performance score, and the label data includes: Among them, Loss1 represents the first loss value of the initial evaluation model; i represents the index of the batch of historical data, m represents the number of batches of historical data; e represents a natural constant; Reward i represents the reward value corresponding to the historical data of the i-th batch; S i Represents the label data of the i-th batch; S ′ i represents the prediction performance score determined based on the historical data of the i-th batch.

[0009] In some embodiments, adjusting the first production parameter according to the performance score to obtain the second production parameter includes: determining a score mean based on the performance scores corresponding to historical data of multiple batches; and adjusting the first production parameter according to the ratio of the performance score to the score mean to obtain the second production parameter.

[0010] An embodiment of the present application also provides an artificial intelligence-based nanofiltration membrane preparation system, which includes an electronic device, which is communicatively connected to a maturation device and a production device; the electronic device is used to control the maturation device to pretreat raw materials and auxiliary oil based on preset maturation parameters; the electronic device is also used to predict, based on a first production parameter of the production equipment acquired in real time, a performance score of the nanofiltration membrane obtained after controlling the production equipment to process the pretreated raw materials according to the first production parameter; the electronic device is also used to adjust the first production parameter according to the performance score to obtain a second production parameter; the electronic device is also used to control the production equipment to process the pretreated raw materials according to the second production parameter to obtain a hollow fiber nanofiltration membrane.

[0011] In some embodiments, the electronic device is further used to determine target production parameters from historical production parameters of multiple batches based on the first production parameters; the electronic device is further used to determine the performance score of the nanofiltration membrane based on a pre-trained evaluation model based on the target production parameters.

[0012] In some embodiments, the electronic device is also used to obtain historical data and label data of multiple batches; the historical data includes historical production parameters, porosity, tensile strength and energy consumption; the label data is used to indicate the performance score of the nanofiltration membrane corresponding to the historical data of each batch; the electronic device is also used to determine the reward value corresponding to the historical data of any batch based on the historical data of any batch; the electronic device is also used to input the historical production parameters of any batch into a pre-built initial evaluation model to obtain the predicted performance score output by the initial evaluation model; the electronic device is also used to determine the first loss value of the initial evaluation model based on the reward value, the predicted performance score and the label data; the electronic device is also used to update the initial evaluation model based on the back propagation algorithm until the first loss value meets the preset conditions, stop updating the initial evaluation model, and obtain an evaluation model trained to a convergence state.

[0013] In some embodiments, the electronic device is further used to perform weighted summation calculation on the porosity, tensile strength and energy consumption in the historical data according to preset weight parameters to obtain a reward value corresponding to any batch of historical data; the reward value calculation method satisfies the following relationship: Reward = α*porosity + β*tensile strength - γ*energy consumption; wherein, Reward represents the reward value corresponding to any batch of historical data; α represents the first weight in the weight parameter, and the first weight is used to characterize the degree of influence of porosity on the reward value; β represents the second weight in the weight parameter, and the second weight is used to characterize the degree of influence of tensile strength on the reward value; γ represents the third weight in the weight parameter, and the third weight is used to characterize the degree of influence of energy consumption on the reward value.

[0014] It can be seen from the above technical solutions that the embodiment of the present application realizes precise control of the mixed aging process of raw materials and auxiliary oils (for example, polytetrafluoroethylene / oil agent) in the preparation process of nanofiltration membranes through multimodal perception, adaptive decision-making and digital twin verification. Compared with the static aging process, it can improve the aging efficiency and aging uniformity, and can control energy consumption in real time. Multi-scale modeling is also performed through the historical production parameters of the production equipment, and the production parameters obtained in real time are processed according to the evaluation model to achieve efficient closed-loop control, thereby further improving the preparation efficiency of polytetrafluoroethylene hollow fiber nanofiltration membranes. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0016] Figure 2 This is a flow chart of a method for preparing a nanofiltration membrane 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 nanofiltration membrane 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 nanofiltration membrane preparation method, which can be applied to one or more electronic devices. An electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. 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 1The figure shows an application scenario diagram of an artificial intelligence-based nanofiltration membrane preparation method provided in one embodiment of the present application. The artificial intelligence-based nanofiltration membrane preparation method provided in the present application can be applied to an electronic device 100. The electronic device 100 is communicatively connected to the aging device 200 and the production device 300. Specifically, the electronic device 100 is used to control the aging device 200 to pre-treat the raw materials and auxiliary oil based on the preset aging parameters. The electronic device 100 is also used to predict the performance score of the nanofiltration membrane obtained after the production equipment 300 controls the pre-treated raw materials according to the first production parameter acquired in real time. The electronic device 100 is also used to adjust the first production parameter according to the performance score to obtain the second production parameter. The electronic device 100 is also used to control the production equipment to process the pre-treated raw materials according to the second production parameter to obtain a hollow fiber nanofiltration membrane.

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

[0028] S20, controlling the curing equipment to pre-treat the raw materials and auxiliary oil based on preset curing parameters.

[0029] In some embodiments, the raw 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 nanofiltration membrane made of polytetrafluoroethylene is made through a stretching process, so that the membrane filament opening rate of the nanofiltration membrane is higher than that of the membrane made of polyvinylidene fluoride, and the flux of the nanofiltration membrane is higher than that of the membrane made of polyvinylidene fluoride, and the membrane filament 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 one embodiment of the present application, during the preparation of hollow fiber nanofiltration membranes, polytetrafluoroethylene (PTFE) undergoes a aging process to further enhance its performance and stability. The aging equipment is used to mix PTFE with additive oil, thereby aging the PTFE material using the additive oil. By controlling the temperature and time, the crystalline structure and physical properties of the PTFE are altered. This aging process eliminates crystal defects within the PTFE material, improving the orderliness of its crystal structure and thereby enhancing its mechanical properties and durability. Furthermore, aging removes residual solvents and impurities from the PTFE material, ensuring its purity and stability.

[0031] S21 , based on first production parameters of the production equipment acquired in real time, predicting a performance score of a nanofiltration membrane obtained after the pretreated raw materials are processed by controlling the production equipment according to the first production parameters.

[0032] In one embodiment of the present application, a production device is used to process aging-treated polytetrafluoroethylene to obtain a hollow fiber nanofiltration membrane with polytetrafluoroethylene as a support layer. Specifically, the production equipment performs embryo pressing, pushing, degreasing, stretching, shaping, wrapping, sintering, hydrophilic modification, and drying on the aging-treated polytetrafluoroethylene to obtain a hollow fiber nanofiltration membrane with polytetrafluoroethylene as a support layer. Specifically, after the hydrophilic modification, the production equipment also uses a solution containing polyethyleneimine, polyvinyl alcohol, sodium hydroxide, and water to treat the membrane filaments; then, the membrane filaments are raised and excess solution inside the membrane filaments is removed by gravity; finally, before drying, the membrane filaments are solidified using a solution containing succinaldehyde, polyethylene glycol, and diglycidyl ether.

[0033] In some embodiments, the method of predicting the performance score of the nanofiltration membrane obtained after controlling the production equipment to process the pretreated raw materials based on the first production parameter of the production equipment obtained in real time includes: determining the target production parameters from the historical production parameters of multiple batches based on the first production parameters; and determining the performance score of the nanofiltration membrane based on a pre-trained evaluation model based on the target production parameters.

[0034] In some embodiments, the method also includes training the evaluation model, and the training of the evaluation model includes: obtaining historical data and label data of multiple batches; the historical data includes historical production parameters, porosity, tensile strength and energy consumption; the label data is used to indicate the performance score of the nanofiltration membrane corresponding to the historical data of each batch; based on the historical data of any batch, determining the reward value corresponding to the historical data of any batch; inputting the historical production parameters of any batch into a pre-constructed initial evaluation model to obtain the predicted performance score output by the initial evaluation model; based on the reward value, the predicted performance score and the label data, determining the first loss value of the initial evaluation model; updating the initial evaluation model based on the back propagation algorithm until the first loss value meets the preset conditions, stopping updating the initial evaluation model, and obtaining an evaluation model trained to a convergence state.

[0035] In some embodiments, determining the reward value corresponding to any batch of historical data based on the historical data of any batch includes: performing weighted sum calculation on the porosity, tensile strength and energy consumption in the historical data according to a preset weight parameter to obtain the reward value corresponding to the historical data of any batch; the reward value calculation method satisfies the following relationship: Reward = α*porosity + β*tensile strength - γ*energy consumption; wherein, Reward represents the reward value corresponding to any batch of historical data; α represents the first weight in the weight parameter, and the first weight is used to characterize the degree of influence of porosity on the reward value; β represents the second weight in the weight parameter, and the second weight is used to characterize the degree of influence of tensile strength on the reward value; γ represents the third weight in the weight parameter, and the third weight is used to characterize the degree of influence of energy consumption on the reward value.

[0036] In some embodiments, determining a first loss value of the initial evaluation model based on the reward value, the prediction performance score, and the label data includes: Among them, Loss1 represents the first loss value of the initial evaluation model; i represents the index of the batch of historical data, m represents the number of batches of historical data; e represents a natural constant; Reward i represents the reward value corresponding to the historical data of the i-th batch; S i Represents the label data of the i-th batch; S ′ i represents the prediction performance score determined based on the historical data of the i-th batch.

[0037] S22: Adjust the first production parameter according to the performance score to obtain a second production parameter.

[0038] In some embodiments, adjusting the first production parameter according to the performance score to obtain the second production parameter includes: determining a score mean based on the performance scores corresponding to historical data of multiple batches; and adjusting the first production parameter according to the ratio of the performance score to the score mean to obtain the second production parameter.

[0039] S23, controlling the production equipment to process the pretreated raw materials according to the second production parameters to obtain a hollow fiber nanofiltration membrane.

[0040] In some embodiments, the production equipment preforms polytetrafluoroethylene through an extrusion process, forms a microporous structure through biaxial stretching, and sinteres at high temperature to stabilize the crystalline state, thereby ensuring the strength of the hollow fiber nanofiltration membrane.

[0041] It can be seen from the above technical solutions that the embodiment of the present application realizes precise control of the mixed aging process of raw materials and auxiliary oils (for example, polytetrafluoroethylene / oil agent) in the preparation process of nanofiltration membranes through multimodal perception, adaptive decision-making and digital twin verification. Compared with the static aging process, it can improve the aging efficiency and aging uniformity, and can control energy consumption in real time. Multi-scale modeling is also performed through the historical production parameters of the production equipment, and the production parameters obtained in real time are processed according to the evaluation model to achieve efficient closed-loop control, thereby further improving the preparation efficiency of polytetrafluoroethylene hollow fiber nanofiltration membranes.

[0042] See Figure 3 , Figure 3 This is a functional block diagram of an artificial intelligence-based nanofiltration membrane production system according to one embodiment of the present application. The artificial intelligence-based nanofiltration membrane production system 500 includes an electronic device 100, which is communicatively connected to a maturation device 200 and a production device 300. A module / unit as referred to herein refers to a series of computer-readable instruction segments that can be executed by a processor 13 and perform a fixed function, and is stored in a memory 12. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0043] The electronic device 100 is used to control the curing device 200 to pre-treat the raw materials and auxiliary oil based on preset curing parameters.

[0044] The electronic device 100 is further used to predict, based on the first production parameter of the production equipment 300 obtained in real time, a performance score of the nanofiltration membrane obtained after the production equipment 300 is controlled to process the pretreated raw materials according to the first production parameter.

[0045] The electronic device 100 is further configured to adjust the first production parameter according to the performance score to obtain a second production parameter.

[0046] The electronic device 100 is further configured to control the production equipment 300 to process the pretreated raw materials according to the second production parameter to obtain a hollow fiber nanofiltration membrane.

[0047] In some embodiments, the electronic device 100 is further used to determine target production parameters from historical production parameters of multiple batches based on the first production parameters; the electronic device 100 is further used to determine the performance score of the nanofiltration membrane based on a pre-trained evaluation model based on the target production parameters.

[0048] In some embodiments, the electronic device 100 is also used to obtain historical data and label data of multiple batches; the historical data includes historical production parameters, porosity, tensile strength and energy consumption; the label data is used to indicate the performance score of the nanofiltration membrane corresponding to the historical data of each batch; the electronic device 100 is also used to determine the reward value corresponding to the historical data of any batch based on the historical data of any batch; the electronic device 100 is also used to input the historical production parameters of any batch into a pre-built initial evaluation model to obtain the predicted performance score output by the initial evaluation model; the electronic device 100 is also used to determine the first loss value of the initial evaluation model based on the reward value, the predicted performance score and the label data; the electronic device 100 is also used to update the initial evaluation model based on the back propagation algorithm until the first loss value meets the preset conditions, stop updating the initial evaluation model, and obtain an evaluation model trained to a convergence state.

[0049] In some embodiments, the electronic device 100 is further used to perform weighted sum calculation on the porosity, tensile strength and energy consumption in the historical data according to preset weight parameters to obtain a reward value corresponding to any batch of historical data; the calculation method of the reward value satisfies the following relationship: Reward = α*porosity + β*tensile strength - γ*energy consumption; wherein, Reward represents the reward value corresponding to any batch of historical data; α represents the first weight in the weight parameter, and the first weight is used to characterize the degree of influence of porosity on the reward value; β represents the second weight in the weight parameter, and the second weight is used to characterize the degree of influence of tensile strength on the reward value; γ represents the third weight in the weight parameter, and the third weight is used to characterize the degree of influence of energy consumption on the reward value.

[0050] 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 is used to execute the computer-readable instructions stored in the memory to implement the artificial intelligence-based nanofiltration membrane preparation method described in any of the above embodiments.

[0051] 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 nanofiltration membrane preparation program.

[0052] Figure 4 Only 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 limit 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.

[0053] Combine Figure 2 The memory 12 in the electronic device 100 stores a plurality of computer-readable instructions to implement the artificial intelligence-based nanofiltration membrane preparation method, and the processor 13 can execute the plurality of instructions to achieve: based on the preset aging parameters, controlling the aging equipment to pre-treat the raw materials and auxiliary oil; based on the first production parameters of the production equipment acquired in real time, predicting the performance score of the nanofiltration membrane obtained after controlling the production equipment to process the pretreated raw materials according to the first production parameters; adjusting the first production parameters according to the performance score to obtain the second production parameters; and controlling the production equipment to process the pre-treated raw materials according to the second production parameters to obtain a hollow fiber nanofiltration membrane.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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 a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an 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, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a 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 nanofiltration membrane preparation program based on artificial intelligence, but can also be used to temporarily store data that has been output or is to be output.

[0058] 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 nanofiltration membrane preparation program) and calls data stored in the memory 12 to perform various functions of the electronic device 100 and process data.

[0059] 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 nanofiltration membrane preparation method based on artificial intelligence, such as Figure 2 Steps shown.

[0060] 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.

[0061] 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 is stored in a storage medium and 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 a portion of an artificial intelligence-based nanofiltration membrane preparation method described in various embodiments of the present application.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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 nanofiltration membrane preparation method described in any of the above embodiments.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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 nanofiltration membrane based on artificial intelligence, applied to an electronic device, wherein the electronic device is communicatively connected to a maturation device and a production device, characterized in that: The method comprises: Control the curing equipment to pre-treat the raw materials and auxiliary oil based on the preset curing parameters; predicting, based on a first production parameter of a production device acquired in real time, a performance score of a nanofiltration membrane obtained by controlling the production device to process the pretreated raw materials according to the first production parameter; adjusting the first production parameter according to the performance score to obtain a second production parameter; According to the second production parameter, the production equipment is controlled to process the pretreated raw materials to obtain a hollow fiber nanofiltration membrane.

2. The method for preparing a nanofiltration membrane based on artificial intelligence according to claim 1, wherein: The predicting, based on the first production parameter of the production equipment obtained in real time, the performance score of the nanofiltration membrane obtained after the production equipment is controlled according to the first production parameter to process the pretreated raw materials comprises: determining target production parameters from historical production parameters of multiple batches based on the first production parameters; According to the target production parameters, a performance score of the nanofiltration membrane is determined based on a pre-trained evaluation model.

3. The method for preparing a nanofiltration membrane based on artificial intelligence according to claim 2, wherein: The method further includes training the evaluation model, wherein the training the evaluation model includes: Acquire historical data and label data for multiple batches; the historical data includes historical production parameters, porosity, tensile strength, and energy consumption; the label data is used to indicate a performance score of the nanofiltration membrane corresponding to each batch of historical data; Determine, based on any batch of historical data, a reward value corresponding to the historical data of the batch; Inputting historical production parameters of any batch into a pre-built initial evaluation model to obtain a prediction performance score output by the initial evaluation model; determining a first loss value of the initial evaluation model based on the reward value, the prediction performance score, and the label data; The initial evaluation model is updated based on the back propagation algorithm until the first loss value meets a preset condition, and then the updating of the initial evaluation model is stopped to obtain an evaluation model trained to a converged state.

4. The method for preparing a nanofiltration membrane based on artificial intelligence according to claim 3, wherein: The determining, based on any batch of historical data, a reward value corresponding to the any batch of historical data includes: The porosity, tensile strength and energy consumption in the historical data are weighted and summed according to preset weight parameters to obtain a reward value corresponding to any batch of historical data; the reward value calculation method satisfies the following relationship: Reward = α*porosity + β*tensile strength - γ*energy consumption; Wherein, Reward represents the reward value corresponding to the historical data of any batch; α represents the first weight in the weight parameter, and the first weight is used to characterize the influence of porosity on the reward value; β represents the second weight in the weight parameter, and the second weight is used to characterize the influence of tensile strength on the reward value; γ represents the third weight in the weight parameter, and the third weight is used to characterize the influence of energy consumption on the reward value.

5. The method for preparing a nanofiltration membrane based on artificial intelligence according to claim 3, wherein: Determining a first loss value of the initial evaluation model based on the reward value, the prediction performance score, and the label data includes: Among them, Loss1 represents the first loss value of the initial evaluation model; i represents the index of the batch of historical data, m represents the number of batches of historical data; e represents a natural constant; Reward i represents the reward value corresponding to the historical data of the i-th batch; S i Represents the label data of the i-th batch; S ′ i represents the prediction performance score determined based on the historical data of the i-th batch.

6. The method for preparing a nanofiltration membrane based on artificial intelligence according to claim 1, wherein: The adjusting the first production parameter according to the performance score to obtain the second production parameter includes: Determine the average score based on the performance scores corresponding to multiple batches of historical data; The first production parameter is adjusted according to the ratio of the performance score to the score mean to obtain a second production parameter.

7. A nanofiltration membrane preparation system based on artificial intelligence, characterized in that: The system includes an electronic device, the electronic device being communicatively connected to the curing device and the production device; The electronic device is used to control the curing equipment to pre-treat the raw materials and auxiliary oil based on preset curing parameters; The electronic device is further used to predict, based on the first production parameter of the production equipment obtained in real time, a performance score of the nanofiltration membrane obtained after the production equipment is controlled according to the first production parameter to process the pretreated raw materials; The electronic device is further configured to adjust the first production parameter according to the performance score to obtain a second production parameter; The electronic device is further used to control the production equipment to process the pretreated raw materials according to the second production parameter to obtain a hollow fiber nanofiltration membrane.

8. The artificial intelligence-based nanofiltration membrane preparation system according to claim 7, characterized in that: The electronic device is further configured to determine, based on the first production parameter, a target production parameter from historical production parameters of multiple batches; The electronic device is further configured to determine a performance score of the nanofiltration membrane based on the target production parameters and a pre-trained evaluation model.

9. The artificial intelligence-based nanofiltration membrane preparation system according to claim 8, characterized in that: The electronic device is further used to obtain historical data and label data of multiple batches; the historical data includes historical production parameters, porosity, tensile strength and energy consumption; the label data is used to indicate the performance score of the nanofiltration membrane corresponding to the historical data of each batch; The electronic device is further configured to determine, based on any batch of historical data, a reward value corresponding to the any batch of historical data; The electronic device is further used to input historical production parameters of any batch into a pre-built initial evaluation model to obtain a prediction performance score output by the initial evaluation model; The electronic device is further configured to determine a first loss value of the initial evaluation model based on the reward value, the prediction performance score, and the label data; The electronic device is further configured to update the initial evaluation model based on a back propagation algorithm until the first loss value satisfies a preset condition, stop updating the initial evaluation model, and obtain an evaluation model trained to a converged state.

10. The artificial intelligence-based nanofiltration membrane preparation system according to claim 9, characterized in that: The electronic device is further used to perform weighted summation calculation on the porosity, tensile strength and energy consumption in the historical data according to preset weight parameters to obtain a reward value corresponding to any batch of historical data; the calculation method of the reward value satisfies the following relationship: Reward = α*porosity + β*tensile strength - γ*energy consumption; wherein, Reward represents the reward value corresponding to any batch of historical data; α represents the first weight in the weight parameter, and the first weight is used to characterize the degree of influence of porosity on the reward value; β represents the second weight in the weight parameter, and the second weight is used to characterize the degree of influence of tensile strength on the reward value; γ represents the third weight in the weight parameter, and the third weight is used to characterize the degree of influence of energy consumption on the reward value.

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