Electrolyte treatment device based on membrane flux prediction and control method thereof

Abstract

The invention belongs to the technical field of non-traditional machining, and particularly relates to an electrolyte treatment device based on membrane flux prediction and a control method thereof. Collecting data in real time through a sensor, and preprocessing the collected data; constructing a membrane flux prediction model; performing parameter optimization decision through the membrane flux prediction model; and the control of the electrolyte treatment device based on membrane flux prediction is realized. The defects that in the prior art, a filtering device needs to be replaced regularly, and detection hysteresis is large are overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of special processing, and in particular relates to an electrolyte treatment device based on membrane flux prediction and a control method thereof. Background Art

[0002] The electrolyte is a liquid medium with specific electrochemical properties in electrolytic machining. The system is the core subsystem of electrolytic machining technology, and is composed of an electrolyte circulation loop, a multi-stage filtration and purification unit, and a temperature control and component control module. Its working principle is that when the electrolyte flows in an electric field, the metal anode dissolves and the cathode precipitates metal, thereby achieving the purpose of material removal and processing. Its function is to ensure processing accuracy and stability, remove electrolytic mud and suspended matter, and control temperature and conductivity by dynamically adjusting flow and pressure. General filtration methods include: multi-stage filtration and purification units (such as coarse filters, plate and frame filter presses, centrifugal separation devices) and vibration filtration technology, which can remove electrolytic mud and suspended matter and improve the reuse rate of electrolyte.

[0003] Existing technology utilizes two parallel filtration circuits: a plate-and-frame filter press in the primary circuit for coarse filtration and a precision filter in the secondary circuit. This design ensures high electrolyte cleanliness while improving reliability through redundant systems. If the primary circuit becomes clogged, the secondary circuit automatically takes over filtration.

[0004] Trace impurities and moisture are adsorbed by molecular sieve columns, followed by secondary impurity removal by a pre-filter, supplemented by steam heating to accelerate filtration efficiency. Activated carbon and quartz sand layer filtration: A three-level structure of glass fiber layer, activated carbon granule layer, and quartz sand layer is used to remove electrolytic mud and suspended solids step by step.

[0005] After electrolytic processing, the salt solution, which contains a large amount of Cr6+ and heavy metal ions, is first coarsely filtered through a centrifugal filter and a ceramic membrane filter, followed by physical adsorption using coconut shell activated carbon. Iron filings powder and alkali are added to adjust the pH value, reducing and removing heavy metal ions. Chemical and physical adsorption is then performed using an alkaline resin. Finally, the solution is neutralized and ready for the next processing step.

[0006] Based on the above existing technologies, the current electrolyte filtration device mainly adopts multi-stage filtration when facing processing needs. The filter element includes activated carbon, ceramic membrane, physical movement such as centrifugation, vibration, etc., and the pH value, temperature and special ions are detected by sensors.

[0007] However, existing osmotic filtration technologies are prone to clogging, and traditional homogenizing membranes, due to their single pore size, lead to rapid accumulation of contaminants, requiring frequent downtime for replacement or cleaning. Detection hysteresis: Feedback from each sensor parameter can only provide a single indicator of electrolyte condition changes, and only passively responds to membrane flux decay, relying heavily on operator experience. Maintenance is cumbersome: Manually cleaning or replacing membrane components is time-consuming and labor-intensive, impacting continuous production in electrolytic processing. Summary of the Invention

[0008] The present invention provides an electrolyte treatment device based on membrane flux prediction and a control method thereof, which are used to overcome the shortcomings and deficiencies of the prior art, such as the need for regular replacement of filtering devices and large detection hysteresis.

[0009] The present invention is achieved through the following technical solutions:

[0010] An electrolyte treatment device based on membrane flux prediction, the electrolyte treatment device comprising a ceramic filtration membrane structure and a multi-parameter sensor network;

[0011] The ceramic filter membrane structure adopts gradient pore size membrane design and preparation membrane structure;

[0012] The multi-parameter sensor network includes a pressure sensor, a turbidity sensor and an electrochemical impedance spectroscopy sensor;

[0013] A front-membrane pressure sensor is arranged on the surface layer of the ceramic filter membrane structure membrane, a rear-membrane pressure sensor is arranged on the bottom layer of the ceramic filter membrane structure membrane, the turbidity sensors are respectively arranged above the surface layer of the ceramic filter membrane structure membrane and below the bottom layer of the ceramic filter membrane structure membrane, and the electrochemical impedance spectroscopy sensor is arranged below the bottom layer of the ceramic filter membrane structure membrane.

[0014] Furthermore, the ceramic filter membrane structure adopts transverse circulation and spiral circulation to perform fluid dynamics optimization.

[0015] Furthermore, the pore size of the ceramic filter membrane structure is distributed from large to small from the surface to the deep layer, that is, the pore size of the surface layer is 50-100 μm, the pore size of the middle layer is 20-50 μm, and the pore size of the bottom layer is 5-20 μm.

[0016] Furthermore, the pressure sensor is used to monitor the pressure difference between the surface layer of the ceramic filter membrane structure membrane and the bottom layer of the ceramic filter membrane structure membrane;

[0017] The turbidity sensor is used to detect the concentration of pollutants and feed back to the control system;

[0018] The electrochemical impedance spectroscopy sensor is used to evaluate the thickness of the membrane fouling layer in real time.

[0019] A control method for an electrolyte treatment device based on membrane flux prediction, the control method comprising:

[0020] Collect data in real time through sensors and pre-process the collected data;

[0021] Construct a membrane flux prediction model;

[0022] Make parameter optimization decisions through membrane flux prediction model;

[0023] Realize the control of electrolyte treatment device based on membrane flux prediction.

[0024] Furthermore, the collected data are pre-processed and the collected data are synchronized: the data of each sensor are collected with seconds as the adjustment unit and period;

[0025] The synchronously collected data is filtered through Kalman or Gaussian filtering to eliminate sampling noise signals for feature extraction and calculate the change rate of key parameters.

[0026] Furthermore, the membrane flux prediction model is constructed by constructing a data set based on the preprocessed data, fusing the sensor detection data, extracting feature data, and establishing a membrane flux prediction model based on XGBoost, with the objective function being membrane flux maximization;

[0027] The model parameters are dynamically updated through the online random forest learning algorithm to adaptively detect changes in electrolyte working conditions.

[0028] Furthermore, the parameter optimization decision-making by the membrane flux prediction model is specifically as follows: the pre-processed real-time data is input into the prediction model, and the optimal operating parameters are output; if the predicted flux is lower than the set threshold, the primary adjustment is triggered to adjust the variable frequency pump pressure and increase the cross flow velocity;

[0029] If the flux does not recover, secondary intervention is performed to start the module in reverse operation or switch to the backup membrane group;

[0030] If the above operations are still ineffective, the ultimate cleaning is triggered and high-pressure pulse cleaning is activated to remove contaminants on the membrane surface.

[0031] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described above is implemented.

[0032] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0033] The beneficial effects of the present invention are:

[0034] The invention has a data prediction model, is automatically cleaned and can be recycled.

[0035] The automatic graded filtration of the present invention can achieve the advantage of multi-level filtration on one ceramic membrane.

[0036] The present invention adopts fluid optimization based on transverse circulation and spiral circulation, which brings the advantages of being more in line with fluid dynamics characteristics and more uniform filtration distribution.

[0037] The present invention utilizes a machine learning algorithm to predict membrane flux, realizes intelligent control cleaning, and improves processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a structural schematic diagram of the present invention.

[0039] Figure 2 It is a schematic diagram of the transverse circulation of the present invention.

[0040] Figure 3 It is a schematic diagram of the spiral circulation of the present invention.

[0041] Figure 4 It is a flow chart of the electrolyte filtration method of the present invention. DETAILED DESCRIPTION

[0042] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0043] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0044] It should also be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0045] The following is a clear and complete description of the technical solutions in the embodiments of this application in conjunction with the drawings in the specification of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0046] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0047] Implementation Method 1

[0048] The embodiment of the present invention provides an electrolyte treatment device based on membrane flux prediction, such as Figure 1-3 As shown, an electrolyte filtration monitoring device is used to monitor the secondary filter and the plate and frame filter press in real time, and the electrolyte circulation filtration system is highly reliable during the electrolytic machining process; an electrolyte component control device monitors the pH value and conductivity of the electrolyte in real time, and an industrial computer controls the on and off of the corresponding solenoid valve of the liquid regulating tank, ensuring the stability of the pH value and conductivity of the electrolyte during the electrolytic machining process; the electrolyte treatment device includes a ceramic filter membrane structure and a multi-parameter sensor network;

[0049] The ceramic filter membrane structure adopts gradient pore size membrane design and preparation membrane structure;

[0050] The multi-parameter sensor network includes a pressure sensor (for differential pressure monitoring), a turbidity sensor (for detecting pollutant concentration), and an electrochemical impedance spectroscopy sensor (EIS);

[0051] A front-membrane pressure sensor is arranged on the surface layer of the ceramic filter membrane structure membrane, a rear-membrane pressure sensor is arranged on the bottom layer of the ceramic filter membrane structure membrane, the turbidity sensors are respectively arranged above the surface layer of the ceramic filter membrane structure membrane and below the bottom layer of the ceramic filter membrane structure membrane, and the electrochemical impedance spectroscopy sensor is arranged below the bottom layer of the ceramic filter membrane structure membrane, and is used to detect the metal ion concentration in the electrolyte after filtering through the ceramic filter membrane structure membrane.

[0052] Furthermore, the ceramic filtration membrane structure utilizes transverse and spiral circulation for fluid dynamics optimization, making the electrolyte more suitable for cross-flow filtration. To control cross-flow velocity, the electrolyte is set to flow tangentially along the membrane surface, and spoilers or corrugated flow channels are designed at the membrane module inlet to increase local turbulence intensity and inject high-speed liquid flow to flush the membrane surface.

[0053] Furthermore, the pore size of the ceramic filtration membrane structure is distributed from large to small from the surface to the deep layer, that is, the surface layer pore size is 50-100μm, the middle layer pore size is 20-50μm, and the bottom layer pore size is 5-20μm; pollutants of different particle sizes are gradually intercepted, large particles are retained on the surface, and small molecules are filtered in the deep layer. The surface layer is made of hydrophobic and corrosion-resistant material, polyvinylidene fluoride PVDF, and the bottom layer is made of a ceramic-based composite membrane with high mechanical strength material. The multi-layer pore structure is prepared by combining phase separation method with gradient deposition to ensure the interlayer bonding strength, and the peeling force is >10N / cm 2 .

[0054] Furthermore, the pressure sensor is used to monitor the pressure difference between the surface layer of the ceramic filter membrane structure membrane and the bottom layer of the ceramic filter membrane structure membrane;

[0055] The turbidity sensor is used to detect the concentration of pollutants and feed back to the control system;

[0056] The electrochemical impedance spectroscopy sensor achieves quantitative analysis of target substances by detecting changes in electrode interface impedance caused by changes in the concentration of ions or molecules in the solution; and is used to evaluate the thickness of the membrane fouling layer in real time.

[0057] The specific electrolyte treatment method is as follows: Figure 4 As shown, the electrolyte enters the electrolyte recovery and treatment tank (temperature, pH, and ion content are adjusted), and the gradient pore size filter membrane installed therein adopts a cross-flow design and flow field optimization to separate the heavy metal ions and enter the working tank of the electrolytic processing electrolyte, and then flows back to the liquid storage tank. The liquid storage tank is equipped with self-cleaning control, and the central controller membrane flux prediction model preventively adjusts and controls the membrane flux. The central controller obtains real-time data information through a multi-parameter sensor network.

[0058] Implementation Method 2

[0059] An embodiment of the present invention provides a control method for an electrolyte treatment device based on membrane flux prediction, the control method comprising:

[0060] Collect data in real time through sensors and pre-process the collected data;

[0061] Construct a membrane flux prediction model;

[0062] Make parameter optimization decisions through membrane flux prediction model;

[0063] Realize the control of electrolyte treatment device based on membrane flux prediction.

[0064] Furthermore, the collected data are pre-processed and the collected data are collected synchronously: the data of each sensor are collected with 1 second (dynamically adjusted according to the processing situation) as the adjustment unit and period;

[0065] The synchronously collected data is filtered through Kalman or Gaussian filtering to eliminate sampling noise signals for feature extraction and calculate the change rate of key parameters.

[0066] Furthermore, in the electrolyte circulation system, corresponding detection temperature, pH value, conductivity, contamination, flow rate detection and corresponding data acquisition card are as follows:

[0067] Temperature sensor (such as PT100 platinum resistance, accuracy ±0.1°C), pH sensor (such as glass electrode type, detection range 0-14, error ±0.05), conductivity sensor (four-electrode type, range 0-1000mS / cm), turbidity / pollutant concentration sensor (laser scattering, detection of particles with a size >0.1μm), flow rate sensor (electromagnetic flowmeter, range 0-10m 3 / h), pre-membrane / post-membrane pressure sensors (pressure differential monitoring), electrochemical impedance spectroscopy (EIS) sensor (real-time evaluation of membrane fouling layer thickness), data acquisition module: transmits sensor data to the central controller MCU or industrial computer via RS485 or Modbus protocol for data acquisition and storage.

[0068] Furthermore, the hydrodynamics of the ceramic filtration membrane structure were optimized based on transverse and spiral circulation, making the electrolyte more suitable for cross-flow filtration. In terms of cross-flow velocity control, the electrolyte is set to flow tangentially along the membrane surface, with a flow rate controlled at 2-5m / s, preventing pollutant deposition through shear force.

[0069] Furthermore, spoilers or corrugated flow channels are designed at the inlet of the membrane module to increase the local turbulence intensity to a Reynolds number of Re>4000. A multi-channel circulation loop is designed, with the main circulation pipeline and the secondary circulation pipeline connected in parallel. The secondary pipeline is equipped with a high-frequency pulse valve, which periodically injects high-speed liquid flow at intervals of 10-30 seconds to flush the membrane surface.

[0070] Furthermore, the membrane flux prediction model is constructed specifically by constructing a data set based on the preprocessed data, extracting characteristic data such as the pressure difference change rate, turbidity accumulation, and impedance phase angle from the data collected by the sensor, and fusing the characteristic data to establish a membrane flux prediction model based on XGBoost, whose objective function is to maximize the membrane flux (target R 2 >0.95);

[0071] The XGBoost membrane flux prediction model is trained using the fused feature data, making the XGBoost algorithm more suitable for the field of membrane flux prediction;

[0072] The model parameters are dynamically updated through the online random forest learning algorithm, the changes in electrolyte working conditions are adaptively detected, and the thickness of the membrane fouling layer, operating conditions and corresponding membrane flux are evaluated in real time for preventive adjustment.

[0073] Preventive adjustment: when the predicted contamination risk is medium, the cross-flow velocity is automatically increased by 10%-20% and the secondary pipeline pulse flushing is triggered. When the predicted contamination risk is high or the pressure difference exceeds the threshold (ΔP>0.5MPa), the following self-cleaning process is started for intelligent cleaning.

[0074] Furthermore, the parameter optimization decision-making through the membrane flux prediction model is specifically as follows: pre-processed real-time data is input into the prediction model, and the optimal operating parameters (transmembrane pressure, flow rate, temperature threshold, etc.) are output; if the predicted flux is lower than the set threshold (such as 80% of the initial flux), the primary adjustment is triggered to adjust the variable frequency pump pressure (±10%) and increase the cross flow velocity (20%-50%);

[0075] If the flux does not recover, perform secondary intervention and start the module in reverse operation (cycle 10-30 minutes) or switch to the standby membrane group;

[0076] If the above operations are still ineffective, the ultimate cleaning is triggered and high-pressure pulse cleaning (pulse frequency 1-5kHz, duration 5-10 seconds) is activated to remove contaminants on the membrane surface.

[0077] Based on the replacement of implementation mode one or two, the type and quantity of sensors can be increased or decreased according to different workpieces to be processed; the cross-flow filtration flow field is re-optimized and improved according to the suspended particles.

[0078] Implementation Method 3

[0079] An embodiment of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The memory is used to store software programs and modules, and the processor executes various functional applications and data processing by executing the software programs and modules stored in the memory. The memory and processor are connected via a bus. Specifically, the processor implements any step of the first embodiment described above by executing the computer program stored in the memory.

[0080] It should be understood that in the embodiments of the present invention, the processor referred to may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0081] The memory may include a read-only memory, a flash memory, and a random access memory, and provides instructions and data to the processor. A portion or all of the memory may also include a non-volatile random access memory.

[0082] It should be understood that if the above-mentioned integrated modules / units 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 invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in source code form, object code form, executable file or some intermediate form. The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the above-mentioned computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0083] The above description of the disclosed embodiments will enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein, but is to be construed in the widest manner consistent with the principles and novel features disclosed herein.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, and will not be repeated here.

[0085] It should be noted that the methods and detailed examples provided in the above embodiments can be combined with the devices and equipment provided in the embodiments, and references can be made to each other, and no further details will be given.

[0086] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0087] In the embodiments provided by the present invention, it should be understood that the disclosed apparatus / terminal equipment and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For example, the division of the modules or units described above is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. An electrolyte treatment device based on membrane flux prediction, characterized in that: The electrolyte treatment device includes a ceramic filter membrane structure and a multi-parameter sensor network; The ceramic filter membrane structure adopts gradient pore size membrane design and preparation membrane structure; The multi-parameter sensor network includes a pressure sensor, a turbidity sensor and an electrochemical impedance spectroscopy sensor; A front-membrane pressure sensor is arranged on the surface layer of the ceramic filter membrane structure membrane, a rear-membrane pressure sensor is arranged on the bottom layer of the ceramic filter membrane structure membrane, the turbidity sensors are respectively arranged above the surface layer of the ceramic filter membrane structure membrane and below the bottom layer of the ceramic filter membrane structure membrane, and the electrochemical impedance spectroscopy sensor is arranged below the bottom layer of the ceramic filter membrane structure membrane.

2. The device according to claim 1, characterized in that The ceramic filter membrane structure adopts transverse circulation and spiral circulation to perform fluid dynamics optimization.

3. The device according to claim 2, characterized in that: The pore sizes of the ceramic filtration membrane structure are distributed from large to small from the surface to the deep layer, that is, the pore size of the surface layer is 50-100 μm, the pore size of the middle layer is 20-50 μm, and the pore size of the bottom layer is 5-20 μm.

4. The device according to claim 1, characterized in that The pressure sensor is used to monitor the pressure difference between the surface layer of the ceramic filter membrane structure membrane and the bottom layer of the ceramic filter membrane structure membrane; The turbidity sensor is used to detect the concentration of pollutants and feed back to the control system; The electrochemical impedance spectroscopy sensor is used to evaluate the thickness of the membrane fouling layer in real time.

5. A control method for an electrolyte treatment device based on membrane flux prediction according to any one of claims 1 to 4, characterized in that: The control method includes: Collect data in real time through sensors and pre-process the collected data; Construct a membrane flux prediction model; Make parameter optimization decisions through membrane flux prediction model; Realize the control of electrolyte treatment device based on membrane flux prediction.

6. The method according to claim 5, characterized in that The collected data are pre-processed and the collected data are collected synchronously: the data of each sensor is collected in seconds as the adjustment unit and period; The synchronously collected data is filtered through Kalman or Gaussian filtering to eliminate sampling noise signals for feature extraction and calculate the change rate of key parameters.

7. The method according to claim 5, characterized in that The membrane flux prediction model is constructed by constructing a data set based on preprocessed data, fusing sensor detection data, extracting feature data, and establishing a membrane flux prediction model based on XGBoost, with the objective function being membrane flux maximization; The model parameters are dynamically updated through the online random forest learning algorithm to adaptively detect changes in electrolyte working conditions.

8. The method according to claim 5, characterized in that The parameter optimization decision-making by the membrane flux prediction model is specifically as follows: pre-processed real-time data is input into the prediction model, and the optimal operating parameters are output; if the predicted flux is lower than the set threshold, the primary adjustment is triggered to adjust the variable frequency pump pressure and increase the cross flow velocity; If the flux does not recover, secondary intervention is performed to start the module in reverse operation or switch to the backup membrane group; If the above operations are still ineffective, the ultimate cleaning is triggered and high-pressure pulse cleaning is activated to remove contaminants on the membrane surface.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 5 to 8 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 5 to 8 is implemented.

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