Intelligent dispersion system and method for preparing high-performance nano photocatalytic water-based paint

Abstract

The invention provides an intelligent dispersion system for preparing a high-performance nano photocatalytic water-based coating and an application method of the intelligent dispersion system. The intelligent dispersion system for preparing the high-performance nano photocatalytic water-based paint comprises an ultrasonic dispersion machine, a micro-jet homogenizer, an online laser particle analyzer, a conductivity sensor, a PH sensor, an electric valve, an intelligent predictive control and process supervision system and a low-temperature control device. Through ultrasonic-microjet synergistic dispersion, online monitoring and AI dynamic regulation and control technologies, the problem that the performance of a photocatalyst is reduced in coating production is solved, and efficient, low-consumption and stable nano dispersion is realized. The system has multi-catalyst adaptability and can be widely applied to production of industrial coatings.

Description

Technical Field

[0001] The present invention relates to the technical field of the preparation of photocatalytic waterborne coatings and the improvement of catalytic performance, and particularly to an intelligent photocatalytic nano-dispersion system that combines ultrasonic and microfluidic synergistic dispersion with real-time monitoring and intelligent regulation. Background Art

[0002] Common photocatalysts such as TiO2, BiVO4, ZnO, etc. are widely used in the fields of coatings, environmental protection, and energy due to their excellent photocatalytic performance. By compounding photocatalysts with coatings, the coatings can be made to have the function of degrading NOx. Photocatalysts generate electron-hole pairs through photoexcitation, and further generate reactive oxygen species, which can efficiently oxidize NOx into harmless products such as nitrate (NO3 - ). Such photocatalytic coatings can be applied to the surfaces of building facades, road guardrails, etc., continuously purifying the environment under light irradiation and effectively alleviating urban air pollution. With the tightening of global environmental protection regulations and the promotion of the "dual-carbon" goal, the development of highly efficient photocatalytic coatings has become a key technology for realizing green buildings and improving air quality, and has an urgent significance for promoting sustainable urban development.

[0003] During the coating production process, photocatalysts are prone to agglomeration due to high surface energy, electrostatic adsorption, and mechanical shear force, resulting in insufficient exposure of active sites and a significant decrease in photocatalytic efficiency; the agglomerated catalysts also lead to poor coating stability and a significant shortening of the storage period, further causing an increase in cost due to the addition of extra catalysts to compensate for efficiency losses during coating production and the waste of catalyst raw materials. Existing dispersion technologies such as mechanical stirring and single ultrasonic treatment have problems such as high energy consumption, uneven dispersion, and lack of real-time regulation, and are difficult to meet the requirements of industrial-scale production. Therefore, it is necessary to develop a nano-dispersion system for waterborne coatings with catalytic functions to better solve the problem of performance degradation caused by agglomeration and other reasons after the addition of photocatalysts to coatings, improve the dispersion efficiency and coating performance, save production raw materials, and reduce production costs. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent photocatalytic nano-dispersion system that combines ultrasonic and microfluidic synergistic dispersion with real-time monitoring and intelligent regulation, which is used to solve the problem of the degradation of catalyst performance of photocatalysts in coating production, improve the photocatalytic efficiency and coating stability, realize online monitoring and dynamic intelligent regulation of the catalytic performance of catalysts in coatings, and can maintain the stability and controllability of catalyst performance in coating production through ultrasonic-microfluidic synergistic dispersion.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions: An intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating, comprising a material processing unit, an online monitoring unit, an execution control unit, a computer, and a pipeline system; the computer is communicatively connected to the material processing unit, the online monitoring unit, and the execution unit; The material processing unit includes an ultrasonic disperser and a microfluidic homogenizer; The online monitoring unit includes an online laser particle size analyzer, a conductivity sensor, and a pH sensor; The execution control unit includes two three-way valves with the same structure: a first electric valve and a second electric valve. The first electric valve is used to switch between the internal circulation and discharge modes; the second electric valve is used to switch between the internal circulation and introduction modes; The computer includes an intelligent predictive control and process supervision system for real-time data acquisition, processing, and adjustment; The pipeline system includes a first delivery pipe, a second delivery pipe, a third delivery pipe, and a fourth delivery pipe; The second electric valve is connected to the ultrasonic disperser through the first delivery pipe. The ultrasonic disperser is connected to the microfluidic homogenizer through the second delivery pipe. The microfluidic homogenizer is connected in series with the conductivity sensor, the pH sensor, and the online laser particle size analyzer through the third delivery pipe to the first electric valve. The first electric valve is connected to the second electric valve through the fourth delivery pipe.

[0006] Preferably, the intelligent predictive control and process supervision system includes a real-time data acquisition module, a parameter identification module, a communication module, and a security protection mechanism, and is connected to the material processing unit, the online monitoring unit, and the execution unit through an industrial communication protocol to achieve closed-loop control; The specific implementation method of the intelligent predictive control and process supervision system is as follows: The real-time data acquisition module synchronously acquires the particle size distribution (including D10 / D50 / D90 values), conductivity, and pH value data of the slurry through the online laser particle size analyzer, the conductivity sensor, and the pH sensor at a frequency of ≥1 Hz; the data is transmitted to the computer in real time through the Modbus-RTU / TCP industrial bus protocol, and the transmission delay ≤20 ms; the collected original data is preprocessed by sliding window mean filtering and z-score standardization to eliminate noise and dimension differences; The AI prediction model is a time series prediction model constructed based on the long short-term memory network (LSTM); the input layer of the model receives the time series process parameters in the standardized dataset, including particle size distribution, conductivity, pH value, and the current operating parameters of the ultrasonic disperser and the microfluidic homogenizer; the hidden layer adopts a double-layer LSTM structure to capture the dynamic correlation relationship between process parameters; the output layer generates optimal control instructions for the ultrasonic frequency adjustment amount, the microfluidic pressure adjustment amount, and the opening degree of the electric valve; The AI prediction model is trained by a supervised learning method. The training data set is sourced from more than 10,000 valid data records accumulated during the historical production process. The records need to cover the dispersion process parameters and their corresponding performance indicators of various typical nanophotocatalysts such as TiO2, BiVO4, ZnO, and their composites. During the training process, the mean squared error is used as the loss function, and the Adam optimizer is used for parameter optimization. After the AI prediction model is trained, it needs to pass five-fold cross-validation to ensure that its prediction accuracy on the independent test set is not less than 93% before it can be deployed and applied. The anomaly detection module of the AI prediction model constructs a parameter fluctuation warning interval by setting the 3σ principle. When the sampling values exceed the interval three times in a row, the reflux mechanism is triggered. The Web interaction interface supports engineers to manually set parameter thresholds, view real-time production curves, and export historical process reports, and has a multi-level permission management function to achieve hierarchical control of administrators and operators. The safety protection mechanism includes a multi-level interlock protection module, a hardware redundancy module, an emergency power supply module, and a physical isolation module. The startup method of the multi-level interlock protection module is as follows: When the online laser particle size analyzer (14) detects that D90 > 300nm is continuously exceeded, the first-level protection is triggered, and the micro-jet pressure is automatically reduced to the reference value of 500 bar. When the conductivity sensor (15) detects that the fluctuation > 15% and the PH exceeds the range of 6.5 - 9.0, the second-level protection is triggered, and the feed valve is immediately closed and the internal circulation reflux is started. When the temperature sensor detects that the material temperature ≥ 32°C, the third-level protection is triggered, and the low-temperature control device is forced to start cooling and the ultrasonic output is suspended. The hardware redundancy module includes that the conductivity sensor (15), the PH sensor (12), and the online laser particle size analyzer (14) all adopt a dual-probe redundancy design. When the data difference between the master and slave probes > 5%, it automatically switches to the standby signal. The electric valve is equipped with a dual-channel control signal, and when the main control signal fails, it automatically switches to the 4 - 20mA analog backup channel. The emergency power supply module includes configuring a UPS uninterruptible power supply to maintain the system running at the lowest power consumption ≥ 30 minutes when the main power supply is interrupted, and giving priority to ensuring the storage of sensor data and the safe position switching of the valve. The physical isolation module includes isolating the strong electrical control loop and the weak electrical signal loop through an optocoupler to prevent misoperation caused by electromagnetic interference. The control instruction transmission uses CRC-16 check, and when the error rate > 1‰, it automatically retransmits.

[0007] Preferably, the intelligent predictive control includes an AI prediction model, an anomaly detection module, and a Web interaction interface. The conductivity sensor, pH sensor and on-line laser particle size analyzer collect the particle size distribution, pH value and conductivity parameters of the waterborne coating in real time. After data cleaning, they are input into a pre-trained AI prediction model. The AI prediction model generates and executes dynamic adjustment of ultrasonic frequency, micro-jet pressure and valve opening based on historical data, and controls the opening of the discharge when the parameters are qualified and automatically generates a process report. The process report is a PDF format document, which automatically records the following data: the time-axis curve of the dispersion process of the dispersion quality parameters, the process timing parameters, and provides downloads through a Web interface. The dispersion quality parameters include: the particle size distribution is measured in real time by the on-line laser particle size analyzer; the conductivity is measured by the conductivity sensor; the pH value is measured by the pH sensor.

[0008] The process timing parameters include: the time-axis curve is generated by the real-time database of the computer, and the valve action log is derived from the 4-20 mA valve position feedback signal of the electric valve. Equipment operation parameters: the number of cycles is obtained through the OPC-UA interface of the micro-jet homogenizer. The process supervision system includes a preset basic anomaly detection module in the AI prediction model. When the collected parameters deviate from the threshold, an alarm is triggered and the system switches to a preset safe mode for operation. The preset safe mode in the system is: when any dispersion quality parameter exceeds the threshold range three times in a row, the system automatically closes the feed valve, switches to internal circulation reflux, and triggers an audible and visual alarm to reset the equipment parameters to the reference value. The threshold range refers to that the on-line laser particle size analyzer detects D90>300 nm, the conductivity sensor detects a conductivity fluctuation>15%, and the pH sensor detects pH<6.5 or>9.0. The above parameter ranges can be adjusted according to the actual situation. The reference value refers to that the ultrasonic disperser outputs ultrasonic waves at 20 kHz, and the micro-jet homogenizer is set to 500 bar of micro-jet. The Web interaction interface is used to support manual intervention.

[0009] Preferably, the ultrasonic disperser includes an ultrasonic generator, an ultrasonic probe and a sealed liquid storage tank; the ultrasonic generator is connected to the sealed liquid storage tank through the ultrasonic probe; the ultrasonic generator is connected to a computer, and the intelligent prediction control and process supervision system of the computer dynamically adjusts the ultrasonic frequency of the ultrasonic generator based on the particle size distribution monitored by the on-line laser particle size analyzer through an algorithm.

[0010] Preferably, a low-temperature control device is configured in the ultrasonic disperser and the micro-jet homogenizer to control the temperature of the material during the treatment process below 30 °C to inhibit the attenuation of catalyst activity caused by heat generation of ultrasonic waves and micro-jets.

[0011] Preferably, the inner walls of the pipeline system are coated with superhydrophobic coatings to reduce the adhesion loss of the catalyst.

[0012] Preferably, both three-way valves include a first interface, a second interface, and a third interface; The first interface of the second electric valve is connected to the second interface of the first electric valve through a fourth delivery pipe, and the first interface of the first electric valve is connected to a third delivery pipe; The second interface of the second electric valve is connected to the feed port of the sealed liquid storage tank of the ultrasonic disperser through a first delivery pipe; The third interface of the second electric valve is connected to the feed of the mixture of the catalyst and the water-based coating through a sixth delivery pipe, and the third interface of the first electric valve discharges through a fifth delivery pipe.

[0013] The present invention also provides an intelligent dispersion method for preparing a high-performance nano-photocatalytic water-based coating, including the following steps: Step S1: Premixing: Add nano-photocatalyst to the water-based coating and mix by mechanical stirring to form a preliminary catalyst coating mixture; Step S2: Dispersion: The catalyst coating mixture enters the second interface of the second electric valve through a sixth delivery pipe, is introduced into the sealed liquid storage tank of the ultrasonic disperser through a first delivery pipe, and the ultrasonic probe works at a preset frequency to initially break up the agglomerates to obtain the dispersed coating; Step S3: Real-time monitoring: Enter the microfluidic homogenizer through a second delivery pipe, refine the particles, and successively monitor the particle size distribution of the dispersed coating, the PH value and conductivity data of the conductivity sensor and the PH sensor in the online laser particle size analyzer in real time; and synchronously collect the data and transmit it to the computer; Step S4: Intelligent evaluation and adjustment: After data cleaning, input it into the AI prediction model of the pre-trained computer. The AI prediction model generates and executes dynamic adjustment of the ultrasonic frequency, microfluidic pressure, and valve opening based on historical data to optimize the dispersion effect; Step S5: When the parameters of the material are qualified, the computer controls the first electric valve to open the third interface for discharging and automatically generates a process report; And control to close the first interface of the second electric valve, open the third interface and the second interface of the second electric valve, receive the catalyst coating mixture through a sixth delivery pipe, and introduce it into the sealed liquid storage tank of the ultrasonic disperser through the second interface of the second electric valve through a first delivery pipe to continue the subsequent treatment; When the parameters of the material are unqualified, the computer controls to open the second interface of the first electric valve and enter the first interface of the second electric valve through the fourth conveying pipe, close the sixth conveying pipe of the second electric valve, stop receiving the new catalyst coating mixture, open the second interface of the second electric valve, receive the unqualified material through the first conveying pipe and introduce it into the sealed liquid storage tank of the ultrasonic disperser, and return it to the system for reprocessing.

[0014] Preferably, step S2 adopts multi-stage dispersion: the preliminary catalyst coating mixture is sequentially subjected to ultrasonic dispersion and microfluidic homogenization treatment.

[0015] Preferably, in step S3, the real-time monitoring includes monitoring the particle size of the catalyst and the coating particles in the coating and monitoring the coating conductivity to judge the agglomeration trend.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Through the synergistic effect of ultrasonic and microfluidics and combined with AI dynamic regulation, the present invention significantly improves the catalyst dispersion efficiency, avoids the agglomeration and performance degradation problems caused by traditional methods, and realizes efficient and stable dispersion.

[0017] 2. Low-temperature control protects the catalyst activity: the system of the present invention adopts low-temperature treatment below 30°C, effectively inhibits the influence of thermal effects on the catalyst activity, and prolongs the service life.

[0018] 3. The AI prediction model of the present invention adjusts the process parameters in real time to adapt to different catalyst types, improves the production flexibility and stability, and realizes intelligent adaptive optimization.

[0019] 4. The online monitoring + automatic reflux mechanism of the present invention ensures the reprocessing of unqualified materials, improves the yield rate, reduces waste, and realizes closed-loop quality control.

[0020] 5. The system of the present invention supports Modbus communication, high-precision valve control and Web interaction, is easy to integrate into the existing production line, and is suitable for large-scale coating production.

[0021] In summary, the present invention solves the problem of performance degradation of photocatalysts in coating production through ultrasonic-microfluidic synergistic dispersion, online monitoring and AI dynamic regulation technology, and realizes efficient, low-consumption and stable nano-dispersion. The system has multi-catalyst adaptability and can be widely applied to industrial-grade coating production. Description of the Drawings

[0022] Figure 1 It is a schematic structural diagram of an intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating provided by an embodiment of the present invention;

[0023] Figure 2Schematic structural diagram of an electric valve in an intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating provided by an embodiment of the present invention;

[0024] Figure 3 Flow schematic diagram of an application method of an intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating provided by an embodiment of the present invention.

[0025] The serial numbers in the figure are as follows:

[0026] No. 1 conveying pipe; 2. Ultrasonic disperser; 3. No. 2 conveying pipe; 4. Microfluidic homogenizer; 5. No. 3 conveying pipe; 6. No. 1 electric valve; 7. No. 4 conveying pipe; 8. Computer; 9. No. 5 conveying pipe; 10. Sampling pipe of online laser particle size analyzer; 11. No. 6 conveying pipe; 12. PH sensor; 13. No. 2 electric valve; 14. Online laser particle size analyzer; 15. Conductivity sensor; 16. Sealed liquid storage tank; 17. Ultrasonic generator; 18. Ultrasonic probe; 19. No. 1 interface; 20. No. 2 interface; 21. No. 3 interface. Specific embodiments

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0028] As Figure 1 and Figure 2 shown, an intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating provided by this embodiment includes a No. 1 conveying pipe 1, an ultrasonic disperser 2, a No. 2 conveying pipe 3, a microfluidic homogenizer 4, a No. 3 conveying pipe 5, a No. 1 electric valve 6, a No. 4 conveying pipe 7, a computer 8, a No. 5 conveying pipe 9, a sampling pipe 10 of an online laser particle size analyzer, a No. 6 conveying pipe 11, a PH sensor 12, a No. 2 electric valve 13, an online laser particle size analyzer 14, a conductivity sensor 15, a sealed liquid storage tank 16, an ultrasonic generator 17, and an ultrasonic probe 18.

[0029] Among them, the No. 1 conveying pipe 1, the ultrasonic disperser 2, the No. 2 conveying pipe 3, the microfluidic homogenizer 4, the No. 3 conveying pipe 5, the conductivity sensor 15, the PH sensor 12, the No. 1 electric valve 6, the No. 4 conveying pipe 7, and the No. 2 electric valve 13 are connected in sequence.

[0030] The computer 8 is respectively connected to the ultrasonic disperser 2, the microfluidic homogenizer 4, the first electric valve 6, the online laser particle size analyzer 14, the conductivity sensor 15, the pH sensor 12, and the second electric valve 13. The first electric valve 6 is respectively connected to the third delivery pipe 5, the fourth delivery pipe 7, and the fifth delivery pipe 9. The second electric valve 13 is respectively connected to the first delivery pipe 1, the fourth delivery pipe 7, and the sixth delivery pipe 11.

[0031] The microfluidic homogenizer 4 is connected to the computer 8 by a shielded cable with a digital signal transmitter. Its parameters such as pressure, flow rate, number of cycles, and temperature are all regulated by the output signal of the computer and are monitored in real time by the above intelligent prediction control and process supervision system.

[0032] The third delivery pipe 5 is connected to the inlet of the online laser particle size analyzer 14 and is used to collect a small amount of processed coating samples for the online laser particle size analyzer 14 to detect the particle size distribution of the catalyst in the slurry in real time.

[0033] The conductivity sensor 15 receives computer instructions through a digital signal or bus protocol, is connected to the intelligent prediction control and process supervision system, and transmits data related to the dynamic particle conductivity for warning the agglomeration trend.

[0034] The intelligent prediction control and process supervision system includes a real-time data acquisition module, a parameter identification module, a communication module, and a security protection mechanism, and connects the material processing unit, the online monitoring unit, and the execution unit through an industrial communication protocol to achieve closed-loop control.

[0035] The specific implementation method of the intelligent prediction control and process supervision system is as follows: The real-time data acquisition module synchronously acquires the particle size distribution (including D10 / D50 / D90 values), conductivity, and pH value data of the slurry at a frequency of ≥1Hz through the online laser particle size analyzer 14, the conductivity sensor 15, and the pH sensor 12. The data is transmitted to the computer in real time through the Modbus-RTU / TCP industrial bus protocol, and the transmission delay ≤20ms. The collected raw data is preprocessed by moving window mean filtering and z-score standardization to eliminate noise and dimensional differences;

[0036] Furthermore, in this embodiment, the security protection mechanism includes a multi-level interlock protection module, a hardware redundancy module, an emergency power supply module, and a physical isolation module; The startup method of the multi-level interlock protection module is as follows: When the online laser particle size analyzer 14 detects that D90>300nm is continuously exceeded, the first-level protection is triggered, and the microfluidic pressure is automatically reduced to the reference value of 500bar; When the conductivity sensor 15 detects a fluctuation > 15% and the PH exceeds the range of 6.5 - 9.0, the secondary protection is triggered, the feed valve is immediately closed, and the internal circulation reflux is started; When the temperature sensor detects that the material temperature ≥ 32 °C, the tertiary protection is triggered, the low-temperature control device is forced to start cooling, and the ultrasonic output is suspended; The hardware redundancy module includes the conductivity sensor 15, the PH sensor 12, and the online laser particle size analyzer 14, all of which adopt a dual-probe redundancy design. When the data difference between the master and slave probes > 5%, it automatically switches to the standby signal; The electric valve is equipped with a dual-channel control signal. When the main control signal fails, it automatically switches to the 4 - 20 mA analog backup channel; The emergency power supply module includes a configured UPS uninterruptible power supply, which maintains the system to operate at the minimum power consumption ≥ 30 minutes when the main power supply is interrupted, and preferentially ensures the storage of sensor data and the safety position switching of the valve; The physical isolation module includes isolating the strong electrical control loop and the weak electrical signal loop through an optocoupler to prevent misoperation caused by electromagnetic interference; the control instruction transmission adopts CRC-16 checksum, and when the error rate > 1‰, it automatically retransmits.

[0037] Intelligent predictive control includes an AI prediction model, an anomaly detection module, and a Web interaction interface.

[0038] The AI prediction model is a time series prediction model constructed based on the long short-term memory network (LSTM). The input layer of this model receives the time series process parameters in the standardized dataset, including particle size distribution, conductivity, PH value, and the current operating parameters of the ultrasonic disperser and the microfluidic homogenizer 4. The hidden layer adopts a double-layer LSTM structure to capture the dynamic correlation relationship between process parameters. The output layer generates the optimal control instructions for the ultrasonic frequency adjustment amount, the microfluidic pressure adjustment amount, and the opening degree of the electric valve.

[0039] The model is trained through a supervised learning method. The training dataset comes from more than 10,000 groups of effective data records accumulated during the historical production process. The records need to cover the dispersion process parameters and their corresponding performance indicators of various typical nanophotocatalysts such as TiO2, BiVO4, ZnO, and their composites. The mean square error is used as the loss function during the training process, and the Adam optimizer is used for parameter optimization. After the model training is completed, it needs to pass a five-fold cross-validation to ensure that its prediction accuracy on the independent test set is not less than 93% before it can be deployed and applied.

[0040] The anomaly detection module of the model constructs a parameter fluctuation warning range by setting the 3σ principle, and triggers the reflux mechanism when the sampling values exceed the range for three consecutive times; the Web interaction interface supports engineers to manually set parameter thresholds, view real-time production curves and export historical process reports, and has a multi-level permission management function to achieve hierarchical control of administrators and operators.

[0041] Furthermore, in this embodiment, the model output layer of the AI prediction model is mapped to control instructions through a fully connected layer: ultrasonic frequency adjustment amount (±5kHz step), micro-jet pressure adjustment amount (±50bar step), valve opening (0-100% linear adjustment), and the instructions are sent to the corresponding devices in real time through the Modbus-RTU protocol.

[0042] The on-line laser particle size analyzer 14 includes a sample injection system and an analysis system; the sample injection system of the on-line laser particle size analyzer 14, the PH sensor 12 and the conductivity sensor 15 are all installed on the third conveying pipe 5. They are respectively used to collect the particle size distribution, PH value and conductivity parameters of the water-based coating in real time. After data cleaning, they are input into the pre-trained AI prediction model. The AI prediction model generates and executes dynamic adjustment of ultrasonic frequency, micro-jet pressure and valve opening based on historical data, and controls the opening of the discharge when the parameters are qualified and automatically generates a process report.

[0043] In this embodiment, the process report is a PDF format document, which automatically records the following data: the time-axis curve of the dispersion process of the dispersion quality parameters, the process timing parameters, and provides downloads through the Web interface; The dispersion quality parameters include: the particle size distribution is measured in real time by the on-line laser particle size analyzer; the conductivity fluctuation is measured by the conductivity sensor 15; the PH value is measured by the PH sensor 12.

[0044] The process timing parameters include: the time-axis curve is generated by the real-time database of the computer, and the valve action log is derived from the 4-20mA valve position feedback signal of the electric valve; Equipment operation parameters: The number of cycles is obtained through the OPC-UA interface of the micro-jet homogenizer 4.

[0045] The process supervision system includes a preset basic anomaly detection module of the AI prediction model, which triggers an alarm and switches to a preset safe mode when the collected parameters deviate from the threshold.

[0046] The preset safe mode in the system is: when any dispersion quality parameter exceeds the threshold range three consecutive times, the system automatically closes the feed valve, switches to the internal circulation reflux, and triggers an audible and visual alarm to reset the equipment parameters to the reference value.

[0047] The above threshold range means that the online laser particle size analyzer 14 detects D90>300nm, the conductivity sensor 15 detects conductivity fluctuation>15%, and the pH sensor 12 detects pH<6.5 or>9.0. The above parameter ranges can be adjusted according to actual conditions.

[0048] The above reference values refer to that the ultrasonic disperser 2 outputs ultrasonic waves at 20 kHz and the microjet homogenizer 4 is set to have a microjet pressure of 500 bar. The above parameter ranges can be adjusted according to actual conditions.

[0049] The sampling system of the online laser particle size analyzer 14 can be configured with three sets of sampling in turn to achieve the effect of multi-point sampling. The sampling drive mechanism is simple and stable and reliable. The clean diluent in the dilution component can be used to reversely clean the sampling pipeline to ensure that no residual sample will be taken in the next sampling.

[0050] Further, the online laser particle size analyzer 14 is configured to support a dual-mode communication interface of Modbus-RTU serial protocol or Ethernet protocol, and retains a 4-20mA analog quantity standby channel; Furthermore, the data packets transmitted by the online laser particle size analyzer 14 to the computer 8 include: D10, D50, D90 cumulative particle size distribution data and interval particle size distribution data, and the test cycle can be configured to 1-5 minutes according to production needs.

[0051] The trained AI prediction model includes using the pre-trained artificial intelligence model to identify the parameter characteristics of the coating in production and obtain the coating monitoring and identification results, including: First, use a pre-trained artificial intelligence recognition model to identify the parameter characteristics of the coating in production according to the preset catalyst characteristics; the recognition results include the particle size distribution of the coating, the electrical conductivity of the coating, and the pH value of the coating.

[0052] When it is identified that the paint particle size distribution, conductivity and pH value are within the preset qualified range, the electric valve is controlled to open the discharge port, and the artificial intelligence model automatically outputs the process analysis report according to the preset analysis template.

[0053] The process supervision system includes a basic anomaly detection module preset in the AI prediction model. When the collected parameters deviate from the threshold, an alarm is triggered and the system switches to the preset safety mode. The Web interactive interface is used to support manual intervention.

[0054] The ultrasonic disperser 2 includes an ultrasonic generator 17, an ultrasonic probe 18 and a sealed liquid storage tank 16; the ultrasonic generator 17 is connected to the sealed liquid storage tank 16 through the ultrasonic probe 18; the bottom of the sealed liquid storage tank 16 is conical, which is convenient for the coating to be output from the bottom infusion port.

[0055] Further, a temperature sensor is provided on the inner wall of the sealed liquid storage tank 16, and a temperature display electrically connected to the temperature sensor is provided on the outer wall of the sealed liquid storage tank. The temperature sensor and the temperature display cooperate to measure the temperature inside the acoustic wave disperser. The models and types of the temperature sensor and the temperature display are the models and types commonly used by those skilled in the art, and it is only necessary to realize real-time temperature measurement and display.

[0056] The ultrasonic generator 17 is connected to the computer 8 through a shielded cable with a digital signal transmitter. The intelligent prediction control and process supervision system of the computer 8 dynamically adjusts the ultrasonic frequency of the ultrasonic generator 17 through an algorithm based on the particle size distribution monitored by the online laser particle size analyzer 14.

[0057] Further, the ultrasonic disperser 2 is configured to: based on the Modbus RTU / TCP protocol, real-time feedback the monitoring data of power, frequency, and amplitude through the RS-485 or Ethernet digital interface, and support adjusting the dispersion parameters by writing instructions through function codes. The device can use commercially available mature products.

[0058] Further, the microfluidic homogenizer 4 is configured to: based on the Modbus RTU / TCP protocol, real-time feedback the monitoring data of pressure, flow rate, and temperature through the RS-485 or Ethernet digital interface, and support adjusting the dispersion parameters by writing instructions through function codes. The device can use commercially available mature products.

[0059] Further, the online laser particle size analyzer 14 is configured to transmit data through the RS-485 or Ethernet digital interface, and the sampling frequency is not less than 1 Hz. The device can use commercially available mature products.

[0060] Further, the electric valve supports PWM or analog input to adjust the opening degree, and has a valve position feedback signal. The device can use commercially available mature products.

[0061] Further, the electric valve is configured to receive control instructions through a 4-20 mA analog signal and feedback the real-time opening degree. The opening degree adjustment resolution ≤ 0.5%, and it supports positioning at full open (100%), full close (0%), and any intermediate position; and automatically resets to the preset safety position when the signal is interrupted.

[0062] The intelligent prediction control and process supervision system calculates and evaluates the catalytic performance of the paint after dispersion treatment; the evaluation results include: the degree of catalyst dispersion and the activity of the catalyst.

[0063] The ultrasonic disperser 2 and the microfluidic homogenizer 4 are equipped with a low-temperature control device to control the temperature of the material during the treatment process below 30 °C to inhibit the attenuation of the catalyst activity caused by heat generation of ultrasonic waves and microfluidics.

[0064] The inner walls of the conveying pipes are all coated with superhydrophobic coatings to reduce the adhesion loss of the catalyst. Further, the types and models of the conveying pipes are the types and models commonly used by those skilled in the art, as long as they can meet the production requirements. The types of superhydrophobic coatings are the types commonly used by those skilled in the art, as long as they can reduce the adhesion loss of the catalyst.

[0065] Both of the two three-way valves include a first interface 19, a second interface 20, and a third interface 21; both receive instructions from the computer 8 through digital signals or bus protocols; The first interface 19 of the second electric valve 13 is connected to the second interface 20 of the first electric valve 6 through the fourth conveying pipe 7, and the first interface 19 of the first electric valve 6 is connected to the third conveying pipe 5; The second interface 20 of the second electric valve 13 is connected to the feed port of the sealed liquid storage tank 16 of the ultrasonic disperser 2 through the first conveying pipe 1; The third interface 21 of the second electric valve 13 is connected to the feed of the mixture of the catalyst and the water-based coating through the sixth conveying pipe 11, and the third interface 21 of the first electric valve 6 discharges through the fifth conveying pipe 9.

[0066] The present invention also provides an intelligent dispersion method for preparing a high-performance nano-photocatalytic water-based coating, including the following steps: Step S0: Preset data: The intelligent prediction control and process supervision system in the computer 8 calls preset parameters according to the catalyst type, and selects suitable parameters such as ultrasonic frequency, micro-jet pressure, flow rate, cooling temperature, and number of cycles. Input to the ultrasonic disperser 2 and the micro-jet homogenizer 4. And control the opening of the third interface 21 and the second interface 20 of the second electric valve 13.

[0067] Step S1: Premixing: Add the nano-photocatalyst to the water-based coating and mix it by mechanical stirring to form a preliminary catalyst coating mixture; Step S2: Multi-stage dispersion: The catalyst coating mixture enters the second interface 20 of the second electric valve 13 through the sixth conveying pipe 11, and is introduced into the sealed liquid storage tank 16 of the ultrasonic disperser 2 through the first conveying pipe 1. The computer 8 sends the preset frequency parameter to the ultrasonic generator 17, and the ultrasonic probe 18 emits ultrasonic waves to break up the catalyst agglomerates, and the preliminary catalyst coating mixture is sequentially subjected to ultrasonic dispersion and micro-jet homogenization treatment by the micro-jet homogenizer 4 to initially break up the agglomerates to obtain the dispersed coating; after the ultrasonic treatment of the coating is completed, it discharges from the bottom of the sealed liquid storage tank 16. In addition, in this embodiment, the dispersion of the catalyst coating mixture should include: ultrasonic dispersion of the catalyst agglomerates and step-by-step mixing of the catalyst and the coating particles. In this embodiment, through preset multi-stage dispersion parameter combinations, for different catalyst usage conditions, one-key calling of the required parameters of each instrument in the system is realized.

[0068] Step S3: Real-time monitoring: After discharging, it enters the microfluidic homogenizer 4 through the second conveying pipe 3, and the catalyst particles are refined to the nanoscale by the shear force generated by the microfluidics; The conductivity sensor 15, pH sensor 12, and online laser particle size analyzer 14 installed on the third conveying pipe 5 respectively sample and analyze from the sampling port installation location; sequentially, the online laser particle size analyzer 14 monitors the particle size distribution of the dispersed coating in real time, and the pH value and conductivity data of the conductivity sensor 15 and pH sensor 12; and synchronously collects data and transmits it to the computer 8; Among them, real-time monitoring should include monitoring the catalyst and coating particle size in the coating and monitoring the coating conductivity to judge the agglomeration trend; Step S4: Intelligent evaluation and adjustment: After data cleaning, it is input into the AI prediction model of the pre-trained computer. The AI prediction model generates and executes dynamic adjustment of the ultrasonic frequency, microfluidic pressure, and valve opening based on historical data to optimize the dispersion effect; dynamically optimizes production parameters such as ultrasonic power, microfluidic flow rate, and dispersant addition amount.

[0069] Among them, the reference control parameter of the stirring speed is output to the electric valve, ultrasonic disperser 2, and microfluidic homogenizer 4 in the form of a 4-20 mA analog signal.

[0070] Step S5: When the parameters of the material are qualified, the computer 8 controls the first electric valve 6 to open the third interface 21 to discharge the material, and automatically generates a process report; And controls to close the first interface 19 of the second electric valve 13, open the third interface 21 and the second interface 20 of the second electric valve 13, receive the catalyst coating mixture through the sixth conveying pipe 11, and introduce it into the sealed liquid storage tank 16 of the ultrasonic disperser 2 through the second interface 20 of the second electric valve 13 through the first conveying pipe 1 for subsequent processing; When the parameters of the material are unqualified, the computer 8 controls to open the second interface 20 of the first electric valve 6 to enter the first interface 19 of the second electric valve 13 through the fourth conveying pipe 7, close the sixth conveying pipe 11 of the second electric valve 13 to stop receiving the new catalyst coating mixture, open the second interface 20 of the second electric valve 13, receive the unqualified material through the first conveying pipe 1 and introduce it into the sealed liquid storage tank 16 of the ultrasonic disperser 2, and return it to the system for reprocessing.

[0071] This embodiment solves the problem of the performance decline of photocatalysts in coating production through ultrasonic-microfluidic collaborative dispersion, online monitoring, and AI dynamic regulation technology, and realizes efficient, low-consumption, and stable nano-dispersion. The system has multi-catalyst adaptability and can be widely applied to industrial-grade coating production.

[0072] The following is a detailed description in combination with specific parameters: Example 1 Taking the TiO2 photocatalyst as an example, this embodiment specifically illustrates the implementation mode of the intelligent dispersion system of the present invention. First, select the preset parameters of the TiO2 catalyst in the intelligent formula library of the computer 8, including an ultrasonic frequency of 20 kHz, a microjet pressure of 1000 bar, three circulation times, and a cooling temperature of 25 °C. After mixing the TiO2 catalyst and the water-based coating in a mass ratio of 1:100 in advance, it enters the sealed liquid storage tank 16 of the ultrasonic disperser 2 through the sixth delivery pipe 11. The ultrasonic probe 18 works at the preset frequency for 10 minutes to initially break up the TiO2 aggregates. Subsequently, the slurry enters the microjet homogenizer 4 through the second delivery pipe 3 to refine the particles to D50 ≤ 100 nm. The online laser particle size analyzer 14 monitors the particle size distribution in real time, and the conductivity sensor 15 and the PH sensor 12 synchronously collect data and transmit it to the computer 8. The AI prediction model dynamically adjusts the microjet pressure to 1100 bar to optimize the dispersion effect. When D90 ≤ 200 nm and the conductivity is stable, the first electric valve 6 opens the 3rd interface for discharging, otherwise it returns to the system through the 2nd interface for reprocessing.

[0073] Example 2 Taking the TiO2 photocatalyst as an example, this embodiment specifically illustrates the implementation mode of the intelligent dispersion system of the present invention. First, select the preset parameters of the TiO2 catalyst in the intelligent formula library of the computer 8, including an ultrasonic frequency of 20 kHz, a microjet pressure of 1000 bar, 3 circulation times, and a cooling temperature of 25 °C. After mixing the TiO2 catalyst and the water-based coating in a mass ratio of 1:100 in advance, it enters the sealed liquid storage tank 16 of the ultrasonic disperser 2 through the sixth delivery pipe 11. The ultrasonic probe 18 works at the preset frequency for ten minutes to initially break up the TiO2 aggregates. Subsequently, the slurry enters the microjet homogenizer 4 through the second delivery pipe 3 to refine the particles to D50 ≤ 100 nm. The online laser particle size analyzer 14 monitors the particle size distribution in real time, and the conductivity sensor 15 and the PH sensor 12 synchronously collect data and transmit it to the computer 8. The AI prediction model dynamically adjusts the microjet pressure of the microjet homogenizer 4 to 1100 bar to optimize the dispersion effect. When D90 ≤ 200 nm and the conductivity is stable, the first electric valve 6 opens its 3rd interface 21 for discharging, otherwise it returns to the system through the 2nd interface 20 of the first electric valve 6 for reprocessing.

[0074] Example 3 In this embodiment, the composite photocatalyst BiVO4 / ZnO is taken as an example to demonstrate the adaptability of the system to multi-component catalysts. The cooperative dispersion parameters of BiVO4 / ZnO are pre-loaded in the intelligent formula library: dual-frequency alternation of ultrasonic frequencies 25 kHz and 40 kHz, micro-jet pressure 800 bar, and number of cycles 5 times. BiVO4 and ZnO are premixed at a mass ratio of 1:2 and then added to the water-based coating, and enter the ultrasonic disperser 2 through the first conveying pipe 1. The dual-frequency ultrasonic waves act alternately for 15 minutes to overcome the sedimentation caused by the density difference of the components. When the slurry passes through the micro-jet homogenizer 4, the online laser particle size analyzer 14 detects that the D50 of BiVO4 particles is 120 nm and that of ZnO is 80 nm. The AI prediction model automatically increases the number of cycles to 7 times and adjusts the PH to 8.5 to balance the dispersion efficiency. When the final slurry has D90 ≤ 180 nm and the conductivity fluctuation < 5%, the first electric valve 6 opens the qualified product outlet, and at the same time the system generates an optimization report including particle size distribution, energy consumption, and process parameters.

[0075] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present invention.

[0076] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0077] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered by the protection scope of the present invention.

Claims

1. An intelligent dispersion system for preparing high-performance nano-photocatalytic waterborne coatings, characterized in that, It includes a material handling unit, an on-line monitoring unit, an execution control unit, a computer (8) and a pipeline system; the computer (8) is communicatively connected to the material handling unit, the on-line monitoring unit and the execution unit; The material handling unit includes an ultrasonic disperser (2) and a microfluidic homogenizer (4); The on-line monitoring unit includes an on-line laser particle size analyzer (14), a conductivity sensor (15) and a pH sensor (12); The execution control unit includes two three-way valves with the same structure: a first electric valve (6) and a second electric valve (13). The first electric valve (6) is used to switch between the internal circulation and discharge modes; the second electric valve (13) is used to switch between the internal circulation and introduction modes; The computer (8) includes an intelligent predictive control and process supervision system for real-time data acquisition, processing and adjustment; The pipeline system includes a first delivery pipe (1), a second delivery pipe (3), a third delivery pipe (5) and a fourth delivery pipe (7); The second electric valve (13) is connected to the ultrasonic disperser (2) through the first delivery pipe (1). The ultrasonic disperser (2) is connected to the microfluidic homogenizer (4) through the second delivery pipe (3). The microfluidic homogenizer (4) is connected in series with the conductivity sensor (15), the pH sensor (12) and the on-line laser particle size analyzer (14) to the first electric valve (6) through the third delivery pipe (5). The first electric valve (6) is connected to the second electric valve (13) through the fourth delivery pipe (7).

2. The intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating according to claim 1, wherein The intelligent predictive control and process supervision system includes a real-time data acquisition module, a parameter identification module, a communication module and a safety protection mechanism, and connects the material handling unit, the on-line monitoring unit and the execution unit through an industrial communication protocol to achieve closed-loop control; The specific implementation method of the intelligent predictive control and process supervision system is as follows: The real-time data acquisition module synchronously acquires the particle size distribution, conductivity and pH value data of the slurry through the on-line laser particle size analyzer (14), the conductivity sensor (15) and the pH sensor (12) at a frequency of ≥1 Hz; the data is transmitted to the computer in real time through the Modbus-RTU / TCP industrial bus protocol, and the transmission delay ≤20 ms; the collected original data is preprocessed by moving window mean filtering and z-score standardization to eliminate noise and dimensional differences; The safety protection mechanism includes a multi-level interlock protection module, a hardware redundancy module, an emergency power supply module and a physical isolation module; The starting method of the multi-level interlock protection module is as follows: When the on-line laser particle size analyzer (14) detects that D90>300 nm is continuously exceeded, the first-level protection is triggered, and the microfluidic pressure is automatically reduced to the reference value of 500 bar; When the conductivity sensor (15) detects a fluctuation >15% and the pH exceeds the range of 6.5-9.0, the second-level protection is triggered, and the feed valve is immediately closed and the internal circulation reflux is started; When the temperature sensor detects that the material temperature ≥32 °C, the third-level protection is triggered, and the low-temperature control device is forced to start cooling and the ultrasonic output is suspended; The hardware redundancy module includes a conductivity sensor (15), a pH sensor (12), and an online laser particle size analyzer (14), all of which adopt a dual-probe redundancy design. When the data difference between the master and slave probes is >5%, it automatically switches to the backup signal; The electric valve is equipped with dual-channel control signals and automatically switches to the 4-20 mA analog backup channel when the main control signal fails; The emergency power supply module includes a configured UPS uninterruptible power supply, which maintains the system running at the lowest power consumption for ≥30 minutes when the main power supply is interrupted, and gives priority to ensuring the storage of sensor data and the safety position switching of the valve; The physical isolation module includes isolating the strong electrical control circuit and the weak electrical signal circuit through an optocoupler to prevent misoperation caused by electromagnetic interference; the control instruction transmission adopts CRC-16 verification and automatically retransmits when the error rate >1‰.

3. The intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating according to claim 2, characterized in that, The intelligent predictive control includes an AI prediction model, an anomaly detection module, and a Web interaction interface; the conductivity sensor (15), pH sensor (12), and online laser particle size analyzer (14) collect the particle size distribution, pH value, and conductivity parameters of the water-based coating in real time. After data cleaning, they are input into the pre-trained AI prediction model. The AI prediction model generates dynamic adjustments to the ultrasonic frequency, micro-jet pressure, and valve opening based on historical data, and controls the opening of the discharge and automatically generates a process report when the parameters are qualified; The process report is a PDF format document that automatically records the following data: the time-axis curve of the dispersion process of the dispersion quality parameters, the process timing parameters, and provides downloads through the Web interface; The dispersion quality parameters include: the particle size distribution is measured in real time by the online laser particle size analyzer (14); the conductivity fluctuation is measured by the conductivity sensor (15); the pH value is measured by the pH sensor (12); The process timing parameters include: the time-axis curve is generated by the real-time database of the computer (8), and the valve action log is derived from the 4-20 mA valve position feedback signal of the electric valve; Equipment operation parameters: the number of cycles is obtained through the OPC-UA interface of the micro-jet homogenizer (4); The AI prediction model is a time series prediction model based on the long short-term memory network (LSTM); the input layer of the model receives the time series process parameters in the standardized dataset, including the particle size distribution, conductivity, pH value, and the current operation parameters of the ultrasonic disperser and the micro-jet homogenizer; the hidden layer adopts a double-layer LSTM structure to capture the dynamic correlation relationship between the process parameters; the output layer generates the optimal control instructions for the ultrasonic frequency adjustment amount, micro-jet pressure adjustment amount, and electric valve opening; The AI prediction model is trained through a supervised learning method. The training dataset comes from more than 10,000 groups of data records accumulated during the historical production process. The records need to cover TiO2, BiVO4, ZnO, and their composites, as well as the dispersion process parameters and their corresponding performance indicators of the nano-photocatalyst; the mean square error is used as the loss function during the training process, and the Adam optimizer is used for parameter optimization; after the AI prediction model is trained, it needs to pass five-fold cross-validation to ensure that its prediction accuracy on the independent test set is not less than 93%; The anomaly detection module of the AI prediction model constructs a parameter fluctuation warning interval by setting the 3σ principle, and triggers the reflux mechanism when the sampling values exceed the interval for three consecutive times; the Web interaction interface supports engineers to manually set parameter thresholds, view real-time production curves and export historical process reports, and has a multi-level permission management function to achieve hierarchical control of administrators and operators; The process supervision system includes a preset basic anomaly detection module of the AI prediction model, which triggers an alarm and switches to a preset safe mode when the collected parameters deviate from the threshold; The safe mode preset in the system is: when any discrete quality parameter exceeds the threshold interval three consecutive times, the system automatically closes the feed valve, switches to the internal circulation reflux, and triggers an audible and visual alarm to reset the equipment parameters to the reference value; The threshold interval means that the online laser particle size analyzer (14) detects that D90>300nm, the conductivity sensor (15) detects that the conductivity fluctuation>15%, and the PH sensor (12) detects that PH<6.5 or>9.0; The reference value means that the ultrasonic disperser (2) outputs ultrasonic waves at 20kH, and the microfluidic homogenizer (4) is set at 500bar for microfluidics; The Web interaction interface is used to support manual intervention.

4. An intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating according to claim 2, characterized in that, The ultrasonic disperser (2) includes an ultrasonic generator (17), an ultrasonic probe (18) and a sealed liquid storage tank (16); the ultrasonic generator (17) is connected to the sealed liquid storage tank (16) through the ultrasonic probe (18); the ultrasonic generator (17) is connected to the computer (8), and the intelligent prediction control and process supervision system of the computer (8) dynamically adjusts the ultrasonic frequency of the ultrasonic generator (17) based on the particle size distribution monitored by the online laser particle size analyzer (14).

5. An intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating according to claim 1, characterized in that, The ultrasonic disperser (2) and the microfluidic homogenizer (4) are equipped with a low-temperature control device to control the material temperature during the treatment process below 30°C to inhibit the attenuation of catalyst activity caused by heat generation of ultrasonic waves and microfluidics.

6. The intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating according to claim 1, characterized in that, The inner walls of the pipeline system are all coated with a superhydrophobic coating to reduce catalyst adhesion loss.

7. An intelligent dispersion system for preparing a high-performance nano-photocatalytic waterborne coating according to claim 1, characterized in that, Both three-way valves include a first interface (19), a second interface (20) and a third interface (21); The first interface (19) of the second electric valve (13) is connected to the second interface (20) of the first electric valve (6) through the fourth delivery pipe (7), and the first interface (19) of the first electric valve (6) is connected to the third delivery pipe (5); The second interface (20) of the second electric valve (13) is connected to the feed port of the sealed liquid storage tank (16) of the ultrasonic disperser (2) through the first delivery pipe (1); The third interface (21) of the second electric valve (13) is connected to the feed of the mixture of catalyst and water-based coating through the sixth delivery pipe (11), and the third interface (21) of the first electric valve (6) discharges through the fifth delivery pipe (9).

8. A method for an intelligent dispersion system of preparing a high-performance nano-photocatalytic waterborne coating according to any one of claims 1-7, characterized in that, It includes the following steps: Step S1: Premixing: Add nano-photocatalyst to the water-based coating and mix it by mechanical stirring to form a preliminary catalyst coating mixture; Step S2: Dispersion: The catalyst coating mixture enters the second interface (20) of the second electric valve (13) through the sixth delivery pipe (11), and is introduced into the sealed liquid storage tank (16) of the ultrasonic disperser (2) through the first delivery pipe (1). The ultrasonic probe (18) operates at a preset frequency to initially break up the agglomerates to obtain the dispersed coating; Step S3: Real-time monitoring: It enters the microfluidic homogenizer (4) through the second delivery pipe (3) to refine the particles, and the particle size distribution of the dispersed coating, the conductivity data of the conductivity sensor (15) and the pH value of the pH sensor (12) are monitored in real time in the online laser particle size analyzer (14) in sequence; and the data is synchronously collected and transmitted to the computer (8); Step S4: Intelligent evaluation and adjustment: After data cleaning, it is input into the AI prediction model of the pre-trained computer (8). The AI prediction model generates and executes dynamic adjustment of the ultrasonic frequency, microfluidic pressure and valve opening based on historical data to optimize the dispersion effect; Step S5: When the parameters of the material are qualified, the computer (8) controls the first electric valve (6) to open the third interface (21) for discharging, and automatically generates a process report; And control to close the first interface (19) of the second electric valve (13), open the third interface (21) and the second interface (20) of the second electric valve (13), receive the catalyst coating mixture through the sixth delivery pipe (11), and introduce it into the sealed liquid storage tank (16) of the ultrasonic disperser (2) through the second interface (20) of the second electric valve (13) via the first delivery pipe (1) to continue the subsequent processing; When the parameters of the material are unqualified, the computer (8) controls to open the second interface (20) of the first electric valve (6) to enter the first interface (19) of the second electric valve (13) through the fourth delivery pipe (7), close the sixth delivery pipe (11) of the second electric valve (13) to stop receiving new catalyst coating mixture, open the second interface (20) of the second electric valve (13), receive the unqualified material through the first delivery pipe (1) and introduce it into the sealed liquid storage tank (16) of the ultrasonic disperser (2), and return it to the system for reprocessing.

9. A method for an intelligent dispersion system of a high-performance nano-photocatalytic waterborne coating according to claim 8, characterized in that Step S2 adopts multi-stage dispersion: The preliminary catalyst coating mixture is sequentially subjected to ultrasonic dispersion and microfluidic homogenizer treatment.

10. A method for an intelligent dispersion system of a high-performance nano-photocatalytic waterborne coating according to claim 8, characterized in that, In Step S3, the real-time monitoring includes monitoring the catalyst and coating particle size in the coating and monitoring the coating conductivity to judge the agglomeration trend.

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