An intelligent washing and care device control method based on dynamic pulse width modulation

Abstract

The present invention relates to the technical field of pulse width modulation, and particularly to an intelligent washing and care device control method based on dynamic pulse width modulation. The method includes the following steps: collecting real-time water flow monitoring parameters, performing real-time water flow output distribution calculation, and constructing a real-time water flow distribution map at the nozzle orifice; performing multi-frequency wavelet transform decomposition on the real-time water flow distribution map at the nozzle orifice, and performing water flow output pulse width waveform calculation to obtain a timing water flow pulse width curve; detecting abnormal water flow fluctuations based on the timing water flow pulse width curve, and predicting the difference in water pressure perception in the user area to generate predicted data on the user's water pressure perception difference state; performing dynamic pulse width duty ratio adjustment and asynchronous coordination adjustment of multiple nozzle orifices according to the predicted data on the user's water pressure perception difference state, and constructing a coordinated adjustment curve for multiple nozzle orifices; The present invention automatically adjusts the pulse width of the water flow output to improve the user's perception of water pressure uniformity.

Description

Technical Field

[0001] The invention relates to the technical field of pulse width modulation, and in particular to an intelligent washing and care equipment control method based on dynamic pulse width modulation. Background Art

[0002] With the continuous advancement of science and technology, intelligent shampooing equipment is becoming more and more popular among consumers in daily life, especially in homes, hotels, beauty salons and other places. Such equipment is gradually replacing the traditional manual shampooing method due to its high efficiency, comfort and personalization. Intelligent shampooing equipment can not only achieve precise water flow regulation through advanced water flow control technology, but also sense the user's head characteristics through intelligent sensors, thereby providing a customized washing and care experience. Especially in terms of personalized customization, improved comfort, energy saving and environmental protection, intelligent shampooing equipment shows great potential.

[0003] Traditional smart shampooing equipment usually relies on a single water pump system to provide a stable water flow. Although this solution can meet basic needs in terms of performance, it often has certain limitations in terms of water flow uniformity, intensity adjustment, and multi-zone coordination. As consumers' requirements for washing and care effects continue to increase, the single water pump control method can no longer meet the challenges of modern smart shampooing equipment for precise water flow adjustment, comfort, and multi-zone customization.

[0004] In order to meet this challenge, in recent years, the pulse width control method of intelligent shampooing equipment based on multi-pump linkage has gradually become a research focus. Through the coordinated work of multiple water pumps, the distribution, intensity and time of water flow can be accurately adjusted according to the needs of different areas of the user's head, thereby providing a more accurate and comfortable washing experience. Through multi-pump linkage technology, the system can distribute water flow between different nozzles, so that each nozzle can provide appropriate water flow intensity according to user needs, avoiding uneven water flow or discomfort caused by water supply from a single water pump.

[0005] However, although the application prospects of multi-pump linkage technology in intelligent shampooing equipment are very broad, its implementation process also faces some challenges, especially in the application of pulse width modulation technology. Pulse width modulation (PWM), as a common control method, plays a vital role in regulating the water flow of water pumps. However, how to achieve the coordinated work of multiple water pumps to ensure that the water flow at different time points and in different areas can be accurately controlled, how to avoid interference between multiple water pumps, and maintain system stability and accuracy are still urgent problems to be solved in current technology. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes an intelligent washing equipment control method based on dynamic pulse width modulation to solve at least one of the above technical problems.

[0007] To achieve the above object, the present invention provides a control method for an intelligent washing and care device based on dynamic pulse width modulation. The intelligent washing and care device includes a shampoo cylinder body and three micro water pumps. The shampoo cylinder body is internally provided with a nozzle port array, and the method includes the following steps:

[0008] Step S1: Collect real-time water flow monitoring parameters, perform real-time water flow output distribution calculation, and construct a real-time water flow distribution map of the nozzle ports;

[0009] Step S2: Perform multi-frequency wavelet transform decomposition on the real-time water flow distribution map of the nozzle ports, and perform water flow output pulse width waveform calculation to obtain a time-sequence water flow pulse width curve;

[0010] Step S3: Detect abnormal water flow fluctuations according to the time-sequence water flow pulse width curve, and predict the difference in water pressure perception in the user area to generate predicted data on the difference state of the user's water pressure perception;

[0011] Step S4: Adjust the dynamic pulse width duty ratio and perform asynchronous coordination adjustment of multiple nozzle ports according to the predicted data on the difference state of the user's water pressure perception, and construct a coordinated adjustment curve for multiple nozzle ports;

[0012] Step S5: Perform water flow pulse width simulation based on the coordinated adjustment curve of multiple nozzle ports, and predict the potential water loss instability trend to obtain the water loss instability prediction situation;

[0013] Step S6: Perform local pulse width parameter pre-adjustment based on the water loss instability prediction situation, and perform dynamic current compensation calculation to execute the intelligent pulse width modulation operation of the washing and care device.

[0014] By collecting water flow monitoring parameters in real time, the present invention can accurately understand the water flow state during the operation of the washing and care device, avoiding adverse effects on the user experience caused by excessive or too small water flow. According to the requirements of different washing and care tasks, the system can adjust the water flow output in real time, achieve flexible water flow distribution, and ensure that the required washing and care modes of users are accurately executed. By constructing a real-time water flow distribution map at the nozzle orifice, the system can intelligently adjust the water flow output at the nozzle orifice according to the actual situation of the user's head, providing a more personalized washing and care service. Multi-frequency wavelet transform decomposition can accurately capture different frequency characteristics of water flow changes, thus more precisely describing the instantaneous changes and fluctuations of water flow. This helps to effectively identify tiny water flow disturbances and avoid uneven water flow affecting the washing and care effect. Calculating the water flow output pulse width waveform (i.e., the pulse width modulation signal) can achieve precise control of the water flow, ensuring that the water flow and water pressure in each time period meet the preset requirements, improving the comfort level and reducing energy waste. Through the detection of abnormal fluctuations in the time-sequence water flow pulse width curve, the system can identify irregular fluctuations or instabilities in the water flow in real time and make adjustments in a timely manner to avoid discomfort or damage caused by too strong or too weak water flow. There may be differences in the perception of water flow in different regions of the user's head. By predicting the differences in water pressure perception in the user's regions, the system can adjust the water flow intensity and pressure in different regions to ensure that the comfort level of the entire scalp reaches the best balance. Combining the water pressure perception data of the user's scalp region, the device can automatically adjust the water flow output according to the needs of each user, providing a customized washing and care experience to meet the needs of different users. Adjusting the dynamic pulse width duty cycle can effectively control the intensity and duration of the water flow, avoiding uneven washing and care effects caused by overly concentrated or dispersed water flow, and ensuring balanced care of the scalp and hair strands. The asynchronous cooperative adjustment of multiple nozzle orifices ensures that the water flow in different regions can be accurately distributed while avoiding interference between multiple nozzle orifices. This can effectively improve the overall washing and care efficiency and enhance the user's comfort experience. Through the cooperative adjustment of multiple nozzle orifices, the washing and care device can dynamically adjust the output of each nozzle orifice according to the user's scalp condition, improving the accuracy and comfort of the water flow and avoiding water flow imbalance caused by too large or too small local water pressure. Through the simulation analysis of the water flow pulse width curve, the system can predict the possible trend of water flow instability, take measures in advance for adjustment, and avoid a decline in the washing and care effect or device damage caused by excessive water flow fluctuations. By predicting the water flow instability situation, the system can optimize the water flow pulse width in real time, ensure water flow stability, reduce the unstable factors of the system, and improve the reliability of the device and the stability of long-term operation. Based on the predicted water flow instability situation, the system can pre-adjust the pulse width parameters in advance to prevent sudden water flow instability phenomena and ensure that the water flow quality during the washing and care process always remains in the best state. When performing pulse width adjustment, the system calculates the current compensation value in real time according to the current monitoring data to ensure that while the device adjusts the water flow, the current fluctuation is effectively controlled, avoiding overload or failure of the device caused by current changes.Dynamic current compensation not only helps to maintain the stable operation of the system, but also optimizes energy usage efficiency, reduces unnecessary energy consumption, and enhances the economy and environmental friendliness of the equipment. Through intelligent pulse width modulation operation, the equipment can automatically adjust the pulse width of the water flow output to adapt to different user needs without manual intervention, improving the intelligence level of the washing and care equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flow chart of the steps of a control method for an intelligent washing and care equipment based on dynamic pulse width modulation according to the present invention;

[0016] Figure 2 It is a schematic detailed implementation step flow chart of step S1;

[0017] Figure 3 It is a schematic detailed implementation step flow chart of step S2. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] The embodiments of the present application provide a control method for an intelligent washing and care equipment based on dynamic pulse width modulation. The execution subjects of the control method for the intelligent washing and care equipment based on dynamic pulse width modulation include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that can be regarded as general computing nodes of the present application. The data processing platform includes but is not limited to at least one of: audio and image management systems, information management systems, and cloud data management systems.

[0020] Please refer to Figures 1 to 3 , the present invention provides a control method for an intelligent washing and care equipment based on dynamic pulse width modulation. The control method for the intelligent washing and care equipment based on dynamic pulse width modulation includes the following steps:

[0021] Step S1: Collect real-time water flow monitoring parameters, perform real-time water flow output distribution calculation, and construct a real-time water flow distribution map at the nozzle orifice;

[0022] Step S2: Perform multi-frequency wavelet transform decomposition on the real-time water flow distribution map at the nozzle orifice, and perform water flow output pulse width waveform calculation to obtain a time-series water flow pulse width curve;

[0023] Step S3: Detect abnormal water flow fluctuations based on the time-series water flow pulse width curve, and predict the difference in water pressure perception in the user area to generate predicted data on the difference state of the user's water pressure perception;

[0024] Step S4: Adjust the dynamic pulse width duty ratio and perform asynchronous collaborative adjustment of multiple nozzle orifices according to the predicted data on the difference state of the user's water pressure perception, and construct a collaborative adjustment curve for multiple nozzle orifices;

[0025] Step S5: Conduct water flow pulse width simulation based on the multi-nozzle collaborative adjustment curve, and predict the potential water loss instability trend to obtain the water loss instability prediction situation;

[0026] Step S6: Conduct local pulse width parameter pre-adjustment based on the water loss instability prediction situation, and perform dynamic current compensation calculation to execute the intelligent pulse width adjustment operation of the washing and care device.

[0027] By collecting water flow monitoring parameters in real time, the present invention can accurately understand the water flow state during the operation of the washing and care device, avoiding adverse effects on the user experience caused by excessive or too small water flow. According to the requirements of different washing and care tasks, the system can adjust the water flow output in real time, realizing flexible water flow distribution and ensuring the accurate execution of the washing and care modes required by users. By constructing a real-time water flow distribution map at the nozzle orifice, the system can intelligently adjust the water flow output at the nozzle orifice according to the actual situation of the user's head, providing a more personalized washing and care service. Multi-frequency wavelet transform decomposition can accurately capture different frequency characteristics of water flow changes, thus more precisely describing the instantaneous changes and fluctuations of the water flow. This helps to effectively identify minor water flow disturbances and avoid uneven water flow affecting the washing and care effect. Calculating the water flow output pulse width waveform (i.e., the pulse width modulation signal) can achieve precise control of the water flow, ensuring that the water flow and water pressure within each time period meet the preset requirements, enhancing the comfort level and reducing energy waste. Through the detection of abnormal fluctuations in the time-sequence water flow pulse width curve, the system can identify irregular fluctuations or instabilities in the water flow in real time and make timely adjustments to avoid discomfort or damage caused by too strong or too weak water flow. There may be differences in the perception of water flow in different regions of the user's head. By predicting the differences in water pressure perception in the user's regions, the system can adjust the water flow intensity and pressure in different regions to ensure the best balance of comfort across the entire scalp. Combining the water pressure perception data of the user's scalp region, the device can automatically adjust the water flow output according to the needs of each user, providing a customized washing and care experience to meet the needs of different users. Adjusting the dynamic pulse width duty cycle can effectively control the intensity and duration of the water flow, avoiding uneven washing and care effects caused by overly concentrated or dispersed water flow and ensuring balanced care of the scalp and hair strands. The asynchronous collaborative adjustment of multiple nozzle orifices ensures the accurate distribution of water flow in different regions while avoiding interference between multiple nozzle orifices. This can effectively improve the overall washing and care efficiency and enhance the user's comfort experience. Through the collaborative adjustment of multiple nozzle orifices, the washing and care device can dynamically adjust the output of each nozzle orifice according to the user's scalp condition, improving the accuracy and comfort of the water flow and avoiding water flow imbalance caused by excessive or too small local water pressure. Through the simulation analysis of the water flow pulse width curve, the system can predict possible water flow instability trends and take measures in advance for adjustment to avoid a decline in the washing and care effect or device damage caused by excessive water flow fluctuations. By predicting the water flow instability situation, the system can optimize the water flow pulse width in real time to ensure water flow stability, reduce unstable factors in the system, and improve the reliability of the device and the stability of long-term operation. Based on the predicted water flow instability situation, the system can pre-adjust the pulse width parameters in advance to prevent sudden water flow instability phenomena and ensure that the water flow quality during the washing and care process always remains in the best state. When performing pulse width adjustment, the system calculates the current compensation value in real time according to the current monitoring data to ensure that while the device adjusts the water flow, the current fluctuation is effectively controlled, avoiding overload or failure of the device caused by current changes.Dynamic current compensation not only helps to maintain the stable operation of the system, but also optimizes energy usage efficiency, reduces unnecessary energy consumption, and enhances the economy and environmental friendliness of the equipment. Through intelligent pulse width modulation operation, the equipment can automatically adjust the pulse width of the water flow output to adapt to different user needs without manual intervention, improving the intelligence level of the washing and care equipment.

[0028] In the embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step flow of a control method for an intelligent washing and care equipment based on dynamic pulse width modulation. In this example, the steps of the control method for the intelligent washing and care equipment based on dynamic pulse width modulation include:

[0029] Step S1: Collect real-time water flow monitoring parameters, perform real-time water flow output distribution calculation, and construct a real-time water flow distribution map of the nozzle ports.

[0030] In this embodiment, the collection of real-time water flow monitoring parameters is achieved through flow sensors installed at each nozzle port. These sensors can detect the water flow in real time and transmit the data to the central control system. Set the collection frequency to collect water flow data once per second to ensure that instantaneous changes can be captured. Install flow sensors at each nozzle port. Assume that the nozzle port array contains five nozzles, labeled as nozzle A, B, C, D, and E respectively. The flow sensors of each nozzle are connected to the control system. Start the flow monitoring system and set to collect water flow data once per second. Assume that in a test cycle, the recorded water flow data is: nozzle A: 150 ml / min, nozzle B: 200 ml / min, nozzle C: 180 ml / min, nozzle D: 220 ml / min, nozzle E: 210 ml / min. The purpose of the real-time water flow output distribution calculation is to summarize the water flow data of each nozzle port to form an overall water flow distribution map. Through calculation, the output situation of each nozzle and the uniformity of the overall flow can be intuitively understood. Set the calculation method to calculate the proportion of the flow of each nozzle port in the total flow and generate a distribution map based on this. Calculate the total water flow of the current nozzle port array. According to the data in the previous step, calculate the total flow: total flow = 150 + 200 + 180 + 220 + 210 = 1160 ml / min. The purpose of constructing the real-time water flow distribution map of the nozzle ports is to visually display the water flow situation of each nozzle in a visual way. This distribution map will help users quickly identify and analyze the distribution state of the water flow. Select a suitable graphical display method, such as a bar chart or a pie chart, to clearly present the flow proportion of each nozzle. According to the flow proportion of each nozzle port calculated previously, use a drawing tool to generate the water flow distribution map of the nozzle ports. If a bar chart is used, each nozzle corresponds to a column, and the height of the column represents the flow proportion of the nozzle.

[0031] Step S2: Perform multi-frequency wavelet transform decomposition on the real-time water flow distribution map at the nozzle orifice, and calculate the water flow output pulse width waveform to obtain the time-series water flow pulse width curve;

[0032] In this embodiment, multi-frequency wavelet transform is an effective method for analyzing the frequency components of a signal, which can decompose the water flow signal into different frequency components, thereby identifying the instantaneous changes and long-term trends of the water flow. This process is crucial for understanding the dynamic characteristics of the water flow. Select a suitable wavelet basis, such as the Haar wavelet or Gaussian wavelet, for decomposition. Set the decomposition level, such as 4 levels, to capture a wide range of frequency components. Collect the data of the real-time water flow distribution map at the nozzle orifice. Assume that the recorded water flow data is: [150, 200, 180, 220, 210] ml / min, representing the flow rates of each nozzle at different time points. Perform wavelet transform on these data. Assume that the Haar wavelet is selected for decomposition. In the control system, set the parameters of the wavelet transform, including the wavelet basis selection and decomposition level. Run the wavelet transform to obtain multiple frequency components of the water flow signal. The first-level decomposition may obtain high-frequency components (instantaneous changes) and low-frequency components (long-term trends), corresponding to the rapid fluctuations and overall stability of the water flow respectively. Record the results of each level of decomposition in a data table to ensure that each frequency component and its corresponding amplitude and phase information are saved for subsequent analysis. The calculation of the water flow output pulse width waveform aims to generate a pulse width waveform by analyzing the frequency components obtained from the wavelet transform. This waveform will be used to control the water flow output at the nozzle orifice to achieve the purpose of optimizing the washing and care effect. Set the calculation criterion that the pulse width is proportional to the flow rate, and determine the pulse width according to the amplitude of the wavelet transform result. According to the results of the wavelet transform, calculate the contribution of each frequency component to the pulse width. Assume that among the amplitudes of the frequency components, the high-frequency components with large fluctuations correspond to the rapid changes in the pulse width, while the low-frequency components correspond to the relatively stable pulse width. Assume that the amplitude of the high-frequency component in the first level is 5, representing the rapid fluctuation of the water flow, and set its pulse width to 80 ms; while the amplitude of the low-frequency component is 1, representing the base flow rate, and set the pulse width to 60 ms. Based on the calculation of each frequency component, generate the time-series water flow pulse width curve. Assume that the generated pulse width changes with time as: [80, 75, 70, 65, 60] ms. Plot the time-series water flow pulse width curve as a graph to show the change trend of the pulse width over time for subsequent monitoring and adjustment. Ensure that the graph is clear and intuitive, and can accurately reflect the dynamic changes of the water flow.

[0033] Step S3: Detect abnormal water flow fluctuations based on the time-series water flow pulse width curve, and predict the difference in water pressure perception in the user area to generate the predicted data of the user water pressure perception difference state;

[0034] In this embodiment, the abnormal water flow rate fluctuation detection aims to identify abnormal changes in the pulse width curve, which may affect the user's water pressure perception and the overall washing and care effect. Through this step, potential problems can be detected in a timely manner and adjusted. Set the detection standard to define the normal range of the pulse width. If the pulse width exceeds this range, it is marked as an abnormal fluctuation. The normal range can be determined based on the average value and standard deviation of historical data. Collect the time-series water flow pulse width curve data. Assume that the pulse width data recorded over a period of time is: [80 ms, 75 ms, 90 ms, 100 ms, 50 ms, 70 ms, 110 ms]. Calculate the average value and standard deviation of the pulse width data. If the average pulse width is 80 ms and the standard deviation is 15 ms, the normal range is set to [65 ms, 95 ms]. Check the pulse width data one by one and find that the pulse widths of 50 ms and 110 ms exceed the normal range. Therefore, these two data points are marked as abnormal fluctuations. Record the timestamps and specific pulse width values of all abnormal fluctuations for subsequent analysis and adjustment to form an abnormal fluctuation report. The prediction of water pressure perception differences in the user area aims to evaluate the possible water pressure differences that users may feel in different areas based on abnormal water flow rate fluctuations. This prediction will help optimize the working state of the nozzle and improve the user experience. Set the prediction standard and establish a relationship model between water pressure perception and pulse width based on the change of pulse width and the user's historical feedback data. Collect the historical water pressure perception data in the user area. Assume that under normal circumstances, the water pressure perceptions of the user in different areas are: [Pressure A: 3.5 bar, Pressure B: 4.0 bar, Pressure C: 2.5 bar]. At the same time, record the user's feedback on the water flow rate. Establish a water pressure perception prediction model and analyze the relationship between the pulse width and the user's perception through linear regression. Assume that the obtained model is: Water pressure perception = k·Pulse width + b, where k and b are regression coefficients. Use the pulse width values of abnormal fluctuations for prediction analysis. For the abnormal pulse width of 50 ms, assume that k is 0.05 and b is 2, then the predicted water pressure perception is: Water pressure perception = 0.05×50 + 2 = 4.5 bar.

[0035] Step S4: Perform dynamic pulse width duty cycle adjustment and asynchronous coordination adjustment of multiple nozzle orifices according to the predicted data of the user's water pressure perception difference state, and construct a coordinated adjustment curve of multiple nozzle orifices;

[0036] In this embodiment, the dynamic pulse width duty ratio adjustment aims to predict data based on the difference state of the user's water pressure perception, optimize the working state of each nozzle, and ensure the uniformity of the overall water flow and user satisfaction. By adjusting the duty ratio, fine control of each nozzle can be achieved, improving the water pressure perception. Set the adjustment criteria, and define the target duty ratio for each nozzle according to the user feedback data and predicted water pressure perception. Collect the predicted data of the difference state of the user's water pressure perception. Suppose the predicted result shows that the water pressure perception of nozzle A is relatively low (3.0 bar), nozzle B is normal (4.0 bar), and nozzle C is relatively high (4.5 bar). Set the target duty ratio for each nozzle. Suppose the current pulse width of nozzle A is 70 ms, nozzle B is 80 ms, and nozzle C is 90 ms. The adjustment of the target duty ratio can be as follows: nozzle A: increase to 80 ms (increase the water flow to improve the water pressure perception), nozzle B: maintain at 80 ms, nozzle C: decrease to 70 ms (reduce the water flow to reduce the water pressure perception). Record the adjusted duty ratio and pulse width values, generate an adjustment report, and ensure that the system can update the working parameters of each nozzle in real time according to the feedback. The asynchronous collaborative adjustment of multiple nozzle orifices aims to ensure that different nozzles can work in coordination after adjustment, optimize the overall water flow distribution, and ensure uniform water pressure perception in different areas for users. Set the collaborative adjustment criteria, and use the weighted average method to integrate the duty ratios of each nozzle to achieve the balance of the overall flow rate. Calculate the flow rate of each nozzle according to the target duty ratio and pulse width of each nozzle. Suppose the flow rate of nozzle A (80 ms) is 250 ml / min, nozzle B (80 ms) is 300 ml / min, and nozzle C (70 ms) is 220 ml / min. Calculate the total flow rate: total flow rate = 250 + 300 + 220 = 770 ml / min. The flow rate ratio = nozzle flow rate / total flow rate × 100%. Record these flow rate ratios in the collaborative adjustment report, and fine-tune the flow rate of each nozzle to ensure that the overall flow rate remains within the target range.

[0037] Step S5: Perform water flow pulse width simulation based on the multi-nozzle orifice collaborative adjustment curve, and predict the potential water loss instability trend to obtain the water loss instability prediction situation;

[0038] In this embodiment, the water flow pulse width simulation aims to generate the water flow pulse widths of each nozzle by using the multi-nozzle orifice collaborative adjustment curve to evaluate the overall water flow state. This process can help predict the water flow performance under different working conditions and provide a basis for subsequent water flow control. Set the simulation parameters, determine the duty cycle and working frequency of each nozzle, so as to calculate the corresponding pulse width. According to the multi-nozzle orifice collaborative adjustment curve, assume that the target duty cycles of nozzles A, B, and C are 80%, 80%, and 70% respectively. Set the working frequency to 1 Hz. Calculate the pulse width of each nozzle using the formula: Pulse width = Duty cycle × Period, where the period is 1 second. The specific calculations are as follows: The pulse width of nozzle A is: 80% × 1 s = 0.8 s = 800 ms, the pulse width of nozzle B is: 80% × 1 s = 0.8 s = 800 ms, and the pulse width of nozzle C is: 70% × 1 s = 0.7 s = 700 ms. Use these pulse width values for water flow simulation. Assume the flow rates of nozzles A and B are 300 ml / min, and nozzle C is 250 ml / min. Record these data to form a preliminary water flow simulation data set. The prediction of potential water loss instability trend aims to identify the factors that may cause water flow instability by analyzing the results of the water flow pulse width simulation, so as to formulate corresponding countermeasures. This prediction can help detect problems in advance and make adjustments during actual operation. Set up a prediction model, using linear regression or time series analysis, to analyze the relationship between the pulse width and the water flow rate, and predict the future change trend. Collect stage water flow data. Assume the recorded pulse widths and corresponding flow rates are: Time point 1: Pulse width 800 ms, flow rate 300 ml / min, Time point 2: Pulse width 800 ms, flow rate 300 ml / min, Time point 3: Pulse width 700 ms, flow rate 250 ml / min. Through linear regression analysis, establish a relationship model between the pulse width and the flow rate. Assume the model is: Flow rate = k · Pulse width + b, where k is the regression coefficient and b is a constant. Use the existing data to fit the model to obtain the values of k and b. Assume that through regression analysis, k = -0.1 and b = 350, and the prediction formula is: Flow rate = -0.1 · Pulse width + 350. According to the possible future change in the pulse width (assuming it may decrease to 650 ms), predict the flow rate: Flow rate = -0.1 ⋅ 650 + 350 = 285 ml / min. Record the prediction results and generate a water loss instability prediction situation report to clarify the potential instability trend and its possible impacts, ensuring that the working state of the nozzle can be adjusted in a timely manner during subsequent operations to avoid instability.

[0039] Step S6: Based on the water loss instability prediction situation, perform local pulse width parameter pre-adjustment and dynamic current compensation calculation to execute the intelligent pulse width adjustment operation of the washing and care device.

[0040] In this embodiment, the purpose of pre-adjusting the local pulse width parameters is to adjust the pulse width parameters of each nozzle according to the predicted situation of water flow instability to improve the stability and uniformity of the water flow. This process can take measures before potential instability occurs through pre-adjustment, thereby ensuring the normal operation of the washing and care equipment. Set the pre-adjustment standard, and formulate corresponding pulse width adjustment strategies for the flow data and instability prediction results of different nozzles. The goal here is to adjust the pulse width to a safe range that can avoid instability. According to the water flow instability prediction situation in the previous step, it is assumed that the current pulse widths of nozzles A, B, and C are 80 ms, 75 ms, and 90 ms, respectively. The prediction results show that nozzle A is at risk of instability, and it is recommended that the pulse width be adjusted to 70 ms. For nozzles B and C, considering their flow stability, the pulse width of nozzle B can be maintained at 75 ms, while nozzle C can be slightly increased to 95 ms to enhance the flow output. Record the adjusted pulse width parameters and generate a pre-adjustment report to ensure that all adjustments have a basis and facilitate subsequent monitoring and evaluation. Assume that the pre-adjusted pulse width parameters are: Nozzle A: 70 ms, Nozzle B: 75 ms, Nozzle C: 95 ms. After setting these new pulse width parameters, conduct a comprehensive test to ensure that the output flow of each nozzle is within an acceptable range and meets the user's washing and care needs. Dynamic current compensation calculation is used to evaluate the change in power demand of the washing and care equipment after the new pulse width parameter adjustment, and perform corresponding current compensation to ensure the stable operation and energy efficiency of the equipment. Set the calculation standard, and dynamically adjust the current output to match the new pulse width setting based on the linear relationship between pulse width and current. Collect the current parameters, assuming that the average current of the device is 2.5 A before adjustment. Set the relationship ratio between pulse width and current, and set the current to increase by 0.02 A for every 1 ms increase in pulse width. Calculate the new current demand based on the adjusted pulse width parameters. Assume that the pulse width of nozzle A is adjusted to 70 ms, nozzle B to 75 ms, and nozzle C to 95 ms. Convert these pulse widths to current requirements: The current requirement of nozzle A is: Current A = 2.5 + (80-70) × 0.02 = 2.5 + 0.2 = 2.7 A, the current requirement of nozzle B is: Current B = 2.5 + (80-75) × 0.02 = 2.5 + 0.1 = 2.6 A, and the current requirement of nozzle C is: Current C = 2.5 + (90-95) × 0.02 = 2.5-0.1 = 2.4 A. Summarize these current requirements and calculate the total current requirement of the equipment to ensure that it is within the rated range of the equipment. Record the calculation results and generate a current compensation report for current adjustment. Finally, perform intelligent pulse width modulation operations on the washing and care equipment, monitor the current and water flow status in real time, and ensure that the equipment operates smoothly under the new working parameters.

[0041] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0042] Obtain the instruction stream of the user input device; identify the washing and care control mode of the instruction stream;

[0043] Call the preset output parameter group of the micro water pump according to the washing and care control mode, control the micro water pump to discharge water, and collect the real-time water flow monitoring parameters;

[0044] Calculate the spatial coordinates of each nozzle orifice of the nozzle orifice array;

[0045] Conduct nozzle orifice spatial layout analysis based on the nozzle orifice spatial coordinates to obtain the nozzle orifice array spatial distribution map;

[0046] Conduct real-time water flow output distribution calculation on the nozzle orifice array spatial distribution map based on the real-time water flow monitoring parameters to construct the nozzle orifice real-time water flow distribution map.

[0047] In this embodiment, based on the control terminal of the washing and care device, a user instruction stream is obtained. The instruction stream includes parameters such as the washing and care mode, water output, and working cycle. The instruction stream may contain "Mode: Deep cleaning; Water flow: 500 ml / min; Working time: 10 minutes". Implement a data receiving module in the control unit to obtain the instruction stream input by the user in real time, and parse and verify it to ensure the validity and accuracy of the instructions. If the instruction stream does not meet the preset standards, an error message is feedback. According to the parsed instruction stream, identify the washing and care control mode selected by the user. This process requires matching the user instructions with the preset modes to determine the corresponding operation parameters. Set the matching criteria, for example, identify the mode through a string matching algorithm. Use conditional judgment statements to parse the instruction stream input by the user. If the instruction stream contains "Deep cleaning", mark this mode as the current control mode. Convert the identified control mode into a specific set of operation parameters, including the output water flow setting of the micro water pump, the nozzle working time, and other relevant parameters. In the deep cleaning mode, the water flow is set to 500 ml / min, and the nozzle working time is 10 minutes. Generate a confirmation message for the control mode and feedback it to the user, such as "It has been set to the deep cleaning mode, water flow 500 ml / min, working time 10 minutes". According to the identified control mode, call the corresponding output parameter group of the micro water pump from the preset parameter library to ensure that the water pump can work according to the user's requirements. Set the parameter library standards. The preset library should include the water flow, water output time, and nozzle control parameters for each mode. Access the preset parameter library and retrieve the water pump parameter group corresponding to the current control mode. Assume that the parameter group for the deep cleaning mode is "Water flow: 500 ml / min; Working time: 10 minutes". Send an output instruction to the micro water pump control module to start the water pump and set the output parameters. According to the experimental parameters, the response time of the micro water pump is set to 2 seconds to ensure the stable output of the water flow. Monitor the working state of the water pump in real time to ensure that it discharges water according to the set parameters, and record the water output time for subsequent water flow monitoring. Install a water flow sensor at the water outlet of the micro water pump to monitor the water flow in real time. This sensor can accurately measure the flow velocity and flow rate of the water flow and feedback the data to the control system. Set the monitoring standards. The flow sensor collects data once per second and records the current flow rate. Start the water flow sensor and establish a data connection with the control unit to ensure that real-time data can be transmitted to the main control system. Assume the accuracy of the sensor is ±1%. During the operation of the water pump, record the water flow data in real time. During the 10-minute deep cleaning process, monitor and record the water flow rate every second to obtain a series of data points. Store the collected data in a data recording system for subsequent analysis and processing. If the measured average water flow rate within 10 minutes shows 480 ml / min, record this data for subsequent evaluation. The calculation of the spatial coordinates of the nozzle outlet needs to consider the nozzle layout design and relative positions.Set the arrangement of the nozzle orifices, such as linear arrangement or matrix arrangement. Set up a coordinate system, for example, with the center point of the nozzle orifice array as the origin, and determine the coordinates of each nozzle relative to the center. Determine the coordinates of each nozzle according to the layout design of the nozzle orifices. If the nozzle orifices are arranged in a matrix, the spacing between each nozzle is 10 cm. Suppose there is a nozzle array with 3 rows and 4 columns. Calculate the coordinates of each nozzle. The coordinates of the first nozzle are (0, 0), the second nozzle is (10, 0), and so on until the coordinates of all nozzles are calculated. Store the coordinates of all nozzles in the database of the control system for subsequent spatial layout analysis. The spatial layout analysis of the nozzle orifices aims to evaluate the impact of the nozzle orifice layout on the water flow distribution. By analyzing the spatial coordinates of the nozzle orifices, the nozzle arrangement can be optimized to improve the washing and care effect. Set the analysis criteria, considering the coverage area and overlap degree of each nozzle to optimize the working efficiency of the nozzle. According to the spatial coordinates of the nozzles, draw the spatial distribution map of the nozzle orifice array. Use visualization tools to mark the coordinate points of the nozzle orifices in the coordinate system to form a clear layout diagram. Evaluate the coverage area of each nozzle. Assume that the effective spraying range of each nozzle is a radius of 15 cm, then calculate the coverage area of each nozzle and identify the possible overlapping areas. Analyze the arrangement effect of the nozzle orifices. If it is found that the overlap rate between the nozzle orifices exceeds 30%, the nozzle layout needs to be adjusted to reduce water flow waste and improve the washing and care efficiency. Utilize the real-time water flow monitoring parameters to calculate the water flow output distribution of each nozzle in the nozzle orifice array to ensure the working efficiency of each nozzle. Set the distribution calculation criteria, and evaluate the contribution of each nozzle to the overall washing and care effect based on the flow rate and coverage area of each nozzle. Collect the real-time monitored water flow data and combine it with the spatial coordinates of the nozzles. If the flow rate of the first nozzle is 60 ml / min, the second is 50 ml / min, and so on. Calculate the flow output distribution of each nozzle, combine it with the coverage area to form the overall water flow distribution map. If the flow coverage area of nozzle A overlaps with that of nozzle B, calculate the total combined flow rate. Generate the real-time water flow distribution map of the nozzle orifices, clearly identifying the flow output of each nozzle and its impact on the washing and care area. This graphical result will help optimize subsequent nozzle regulation and resource allocation.

[0048] In this embodiment, the specific operation of controlling the micro water pump to discharge water is as follows:

[0049] The three micro water pumps are respectively: the first washing liquid micro water pump, the second washing liquid micro water pump, and the disinfection water micro water pump;

[0050] The washing and care control modes include the speed washing mode, the normal mode, the overtime mode, and the maintenance mode;

[0051] Call the preset micro water pump output parameter group according to the washing and care control mode;

[0052] Output the first round of water flow from the nozzle orifice array based on the preset output parameter group of the micro water pump;

[0053] Perform shampoo cleaning based on the first shampoo micro water pump and the second shampoo micro water pump;

[0054] Repeat the above steps according to the instruction stream;

[0055] Perform one-key disinfection treatment based on the disinfectant micro water pump.

[0056] In this embodiment, a parameter group for the first washing and care liquid micro-pump is set. In the "normal mode", its output flow rate is 300 ml / min and the working time is 5 minutes; while in the "quick wash mode", the flow rate increases to 500 ml / min and the working time is shortened to 3 minutes. Similar parameters are set for the second washing and care liquid micro-pump, and its flow rate and working time are adjusted according to different modes. Suppose in the "maintenance mode", the flow rate is set to 200 ml / min and the working time is set to 10 minutes. The disinfection water micro-pump is set to have an output flow rate of 400 ml / min and a duration of 2 minutes in the one-key disinfection mode to ensure that all nozzles can be quickly and effectively covered when using disinfection water. According to the selected washing and care control mode by the user, the system automatically calls the corresponding parameter group of the micro-pump to facilitate controlling the working state of the pump and ensuring that its output meets the preset standards. When the user selects a certain washing and care mode, the system parses the instruction stream, identifies the selected mode, and calls the corresponding pump parameters from the parameter library. If the user selects the "extended time mode", the system calls the parameter group of the first washing and care liquid micro-pump, with an output flow rate of 350 ml / min and a working time of 7 minutes. Maintenance mode: D_base = 55% Kp = 0.12 τ = 0.8s, strong mode: D_base = 80% Kp = 0.18 τ = 0.4s. Through the control module, these parameters are passed to the micro-pump to ensure that it starts working according to the set flow rate and time. The system monitors the working state of the pump in real time during this process to ensure that it outputs according to the preset parameters. According to the called output parameter group of the micro-pump, the first round of water flow output is executed to provide the necessary washing and care liquid or disinfectant for the nozzle orifice array. Start the first washing and care liquid micro-pump and start outputting water flow according to the set flow rate and working time. Suppose in the "normal mode", this pump starts to output at a speed of 300 ml / min for 5 minutes. During the operation of the pump, the water flow rate of the nozzle orifice array is monitored in real time to ensure that each nozzle can receive the washing and care liquid evenly. If the flow rate of a certain nozzle is detected to be lower than expected, the system will make adjustments to increase the output of the pump. Record the flow rate data during the output for subsequent analysis and optimization. After completing the first round of water flow output, combine the output of the first washing and care liquid and the second washing and care liquid to achieve a comprehensive cleaning effect. Start the first washing and care liquid micro-pump to output shampoo, with a set flow rate of 300 ml / min and a working time of 5 minutes. At the same time, start the second washing and care liquid micro-pump, with a set flow rate of 250 ml / min and a working time of 4 minutes, to ensure the effective combination of different washing and care liquids. During the shampoo cleaning process, monitor the working conditions of the nozzles in real time to ensure that the washing and care liquid is evenly distributed in the washing and care area. If it is found that the flow rate of a certain nozzle is insufficient, the system can adjust the working state of other pumps to maintain the overall flow balance. Record the flow rate and time data during the cleaning process to evaluate the cleaning effect and provide a basis for subsequent optimization.According to the user's instruction stream, the system can repeat the above cleaning steps for multiple rounds of washing and care according to personal needs. Monitor the user's instruction stream. If the user chooses to continue cleaning, the system will re-call the preset output parameter group of the micro water pump and execute the cleaning steps in the corresponding mode. After the user completes the first cleaning and selects the "extended time mode" again, the system will reconfigure the parameters of the micro water pump according to this mode and output water flow again. During each repetition, record the flow rate and time of each round of cleaning, analyze its effect, so as to optimize the next washing and care process. After the cleaning is completed, the user can select the one-key disinfection function to start the disinfection water micro water pump for disinfection treatment. Once the user selects one-key disinfection, the system immediately calls the parameter group of the disinfection water micro water pump, sets the flow rate to 400 ml / min and the duration to 2 minutes to ensure that the disinfectant can effectively cover all nozzles and the washing and care area. During the disinfection process, the system will monitor the output of the disinfectant in real time and ensure that the disinfection effect meets the expectations. If the flow rate of a certain nozzle is insufficient, the system will adjust the output of the disinfection water pump. Record the flow rate and time data of the disinfection process and generate a disinfection treatment report for the user to view and confirm the disinfection effect.

[0057] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of the said step S2 include:

[0058] Perform multi-time window sampling on the real-time water flow rate distribution map of the nozzle orifice to obtain a multi-time window water flow rate distribution map;

[0059] Calculate the instantaneous water flow rate vector of the said multi-time window water flow rate distribution map and fit the instantaneous water flow rate vector field;

[0060] Perform multi-frequency wavelet transform decomposition on the instantaneous water flow rate vector field to obtain water flow rate frequency domain maps of multiple frequency components;

[0061] Calculate the frequency component density of the water flow rate frequency domain maps of multiple frequency components to obtain the water flow rate frequency domain density value of each frequency;

[0062] Perform water flow output pulse width waveform calculation on the said water flow rate frequency domain density value to obtain a time-sequence water flow pulse width curve.

[0063] In this embodiment, multi-time window sampling aims to extract flow rate data within different time periods from the real-time water flow rate distribution map at the nozzle outlet for further analysis. This method can capture the instantaneous changes in water flow rate and help understand the dynamic characteristics of the water flow. Set the length of the time window, and select three time windows of 1 minute, 5 minutes, and 10 minutes for uniform sampling to obtain water flow rate distribution data for multiple time windows. In the real-time water flow rate monitoring system at the nozzle outlet, set a regular sampling mechanism to regularly record water flow rate data according to the set time window. Within the 1-minute window, record the water flow rate every 10 seconds to obtain 6 data points. Assume that within the 1-minute window, the recorded water flow rate data are: 80, 85, 90, 95, 100, 75 ml / min. Store these data in the database. For the 5-minute and 10-minute time windows, perform sampling in the same way to ensure obtaining the change trend of water flow rate within different time periods. Record the average flow rate and fluctuation of each time window for subsequent analysis. The calculation of the instantaneous water flow rate vector aims to convert the water flow rate data within each time window into a vector form for subsequent spatial analysis and fitting. This process requires combining time and the corresponding flow rate to form a flow rate vector. Determine the representation method of the vector, and use a two-dimensional vector representation method, where one dimension is time and the other dimension is the water flow rate. According to the water flow rate data of each time window, construct the instantaneous water flow rate vector. Within the 1-minute window, the formed vector can be expressed as: V1 = (0, 80), V2 = (10, 85), V3 = (20, 90), and so on until V6 = (50, 75). Calculate the average flow rate of each time window. Assume that the average flow rate of the 1-minute window is 85 ml / min, and the formed instantaneous water flow rate vector is expressed as: V_avg = (0, 85). Organize the instantaneous water flow rate vectors of all time windows into a vector field for subsequent fitting analysis. The fitting of the instantaneous water flow rate vector field aims to model the collected vector data through a mathematical model to obtain the overall dynamic characteristics of the water flow. This fitting can help understand the distribution and change trend of the water flow. Select a suitable fitting model, such as using polynomial fitting or Gaussian process regression model. Use the collected instantaneous water flow rate vector data and apply the fitting algorithm. Use the polynomial regression method and set the order of the polynomial to 2 to capture the non-linear characteristics of the flow rate change. Input the instantaneous water flow rate vector data and run the fitting algorithm to obtain the mathematical expression of the flow rate vector field. Assume that the final obtained fitting formula is: F(t) = at^2 + bt + c, where a, b, and c are model parameters. Visualize the fitting result to generate a water flow rate vector field diagram, clearly showing the change trend of water flow rate within different time windows. Multi-frequency wavelet transform is used to analyze the frequency domain characteristics of water flow rate data and can effectively decompose different frequency components in the instantaneous water flow rate vector field. This process helps identify the periodic characteristics of water flow changes.Select a suitable wavelet basis function, such as the Haar wavelet or wavelet packet, for frequency-domain analysis. Apply wavelet transform to the fitted instantaneous water flow vector field and set the parameters of the wavelet transform, including the wavelet basis function and the decomposition level. Assume the Haar wavelet is selected and the decomposition level is set to 4. Run the wavelet transform to obtain the frequency-domain diagrams of water flow with multiple frequency components, and record the amplitude and phase information of each frequency component. Generate the visualization results of the frequency-domain diagrams to facilitate the analysis of the frequency characteristics of water flow, identify the main frequency components and their corresponding water flow change patterns. The frequency component density calculation aims to quantify the energy distribution of different frequency components and evaluate the contribution of each frequency to the overall water flow. This calculation can help optimize the control strategy of water flow. Set the calculation formula, such as using the spectral density function to calculate the energy of each frequency component. According to the wavelet transform results, extract the amplitude information of each frequency component and calculate its corresponding spectral density. Assume the amplitude of the first frequency component is 10, the second is 5, and the third is 8, then calculate its frequency component density. Use the formula: , calculate the spectral density to obtain the density value of each frequency. Record the calculated frequency component density values in a data table and generate a frequency component density diagram for subsequent analysis and comparison. The water flow output pulse width waveform calculation is used to evaluate the dynamic control of water flow. By analyzing the frequency-domain density values, the corresponding pulse width waveform is generated. This waveform will be used for the control strategy of dynamic pulse width modulation. Set the calculation standard, define the relationship between the pulse width and the water flow output, and map the frequency-domain density values to the pulse width waveform. According to the frequency component density values, calculate the pulse width corresponding to each frequency. Set the density value of a certain frequency component to 2, which is mapped to a pulse width of 200 ms. Perform this operation on all frequency components to generate the complete water flow pulse width waveform data. Assume the obtained pulse width array is: [150 ms, 200 ms, 250 ms]. Generate a time-series water flow pulse width curve through a visualization tool to show the dynamic change trend of water flow output and help optimize the water flow control strategy of the washing and care equipment.

[0064] In this embodiment, the specific steps of step S3 are as follows:

[0065] Calculate the average pulse width frequency range for the time-series water flow pulse width curve to obtain the reference pulse width frequency range;

[0066] Detect abnormal water flow fluctuations according to the reference pulse width frequency range and mark the abnormal water flow fluctuation points;

[0067] Extract the abnormal timestamps and abnormal water flow spatial positions based on the abnormal water flow fluctuation points;

[0068] Based on the abnormal timestamps and abnormal water flow spatial positions, perform abnormal nozzle traceability positioning on the spatial distribution diagram of the nozzle orifice array and mark the abnormal water flow nozzle orifices;

[0069] Perceive the water flow difference of adjacent nozzle orifices from the abnormal water flow nozzle orifice to obtain the water flow difference perception feature;

[0070] Predict the difference in water pressure perception in the user area based on the water flow difference perception feature, and generate the predicted data of the user water pressure perception difference state.

[0071] In this embodiment, the calculation of the average pulse width frequency range aims to extract the average pulse width from the water flow pulse width curve and determine its frequency range. This range will serve as the benchmark for subsequent anomaly detection. Set the calculation criteria. By averaging the sequential pulse width values, the average pulse width is obtained, and the frequency range is determined based on the variation amplitude of the pulse width. Collect the sequential water flow pulse width curve data. Assume the recorded pulse width data is: [150 ms, 200 ms, 250 ms, 180 ms, 220 ms]. Calculate the average value of these pulse widths: Average pulse width = (150 + 200 + 250 + 180 + 220) / 5 = 200 ms. Determine the frequency range based on the standard deviation of the pulse width. Assume the calculated standard deviation is 30 ms. Then the reference pulse width frequency range can be set as: Frequency range = [170 ms, 230 ms]. Record the reference pulse width frequency range in the system to provide a reference standard for subsequent anomaly detection. Anomaly detection of water flow fluctuations is achieved by comparing the real-time water flow pulse width data with the reference pulse width frequency range to identify the fluctuation points that do not meet the standard. Set the detection criteria. If a certain pulse width value exceeds the reference frequency range, it is marked as an anomaly. Continuously monitor the real-time water flow pulse width data. Assume the currently monitored pulse width data is: [160 ms, 240 ms, 300 ms, 190 ms, 220 ms]. Compare these pulse width values with the reference frequency range one by one. It is found that 240 ms and 300 ms exceed the range of [170 ms, 230 ms]. Therefore, these two data points are marked as anomaly points of water flow fluctuations. Record all the marked anomaly fluctuation points and their timestamps for subsequent analysis and tracking. Extract the anomaly timestamps and the spatial positions of the anomalous water flow for further root cause analysis. The timestamps help determine the specific time when the anomaly occurred, while the spatial positions point to specific nozzle outlets or areas. Set the extraction criteria. Record the time and the corresponding nozzle outlet numbers of all anomaly fluctuation points. According to the marked anomaly points of water flow fluctuations, extract their corresponding timestamps. Assume 240 ms appears at time t1 and 300 ms appears at time t2. Determine the spatial positions of the anomalous water flow. Assume the nozzle corresponding to 240 ms is nozzle A and the nozzle corresponding to 300 ms is nozzle B. Record the anomaly timestamps and spatial positions in the database to form an anomaly event record table for subsequent analysis and location. Traceability and location of the anomalous nozzle are achieved by analyzing the anomaly timestamps and the nozzle outlet positions to identify the specific nozzle that caused the anomaly. This step helps quickly locate the source of the problem. Set the traceability criteria. Select all nozzle outlets during the anomaly time period for analysis. Combine the anomaly event records and analyze the correspondence between the timestamps and the nozzle positions. Assume that at times t1 and t2, nozzles A and B respectively show anomalies. Use the spatial distribution map of the nozzle array to mark the positions of nozzles A and B and analyze their impact on the entire nozzle array. If the flow rate difference between nozzle A and other nozzles is relatively large, it may cause overall water flow fluctuations.Record the traceability results, generate an abnormal nozzle traceability report, clearly identify the abnormal water flow nozzle orifice, and provide suggestions for subsequent processing. The perception of water flow difference at adjacent nozzle orifices aims to analyze the water flow difference between the abnormal nozzle and its adjacent nozzles to further understand the cause of the abnormality. Set the perception standard, calculate the difference value by comparing the water flow of the abnormal nozzle with that of the surrounding nozzles. Measure the flow rate at the adjacent nozzle orifices of the abnormal nozzle (such as nozzle A). Assume that the flow rates of its adjacent nozzles B and C are 220 ml / min and 210 ml / min respectively. The prediction of the difference in water pressure perception in the user area evaluates the possible change in water pressure that the user may feel during the washing and grooming process by analyzing the characteristics of water flow difference perception. This process helps to optimize the user experience. Set the prediction model, calculate the regional water pressure change based on the flow difference, and compare it with the user's expected value. Use the calculated water flow difference characteristics to apply the model to predict the water pressure perception in the user area. Assume that the flow difference of nozzle A causes a 5% decrease in the water pressure felt by the user. Combine the user's feedback data to establish a prediction model for the difference in water pressure perception, analyze the impact of water pressure change on the user experience. Generate the prediction data for the state of the difference in the user's water pressure perception, record the prediction results for subsequent analysis, and provide suggestions for the optimization of the device. Calculate the flow difference between nozzle A (assuming a flow rate of 300 ml / min) and its adjacent nozzles: Difference B = ∣300 - 220∣ = 80 ml / min, Difference C = ∣300 - 210∣ = 90 ml / min. Record these differences in the perception report, analyze their impact on the overall water flow, and provide a basis for subsequent processing.

[0072] In this embodiment, step S4 includes the following steps:

[0073] Calculate the fluctuation amplitude of the water flow pulse transient peak at the abnormal water flow fluctuation point, and extract the pulse peak fluctuation amplitude;

[0074] Quantify the degree of water flow pulse disturbance according to the pulse peak fluctuation amplitude to obtain the pulse disturbance degree value;

[0075] Adjust the dynamic pulse width duty cycle of the abnormal nozzle orifice based on the pulse disturbance degree value to obtain the dynamic duty cycle adjustment curve;

[0076] Perform asynchronous collaborative adjustment of the dynamic duty cycle adjustment curve for multiple nozzle orifices according to the prediction data of the difference in the user's water pressure perception state to construct a multi-nozzle orifice collaborative adjustment curve.

[0077] In this embodiment, the calculation of the transient peak fluctuation amplitude of the water flow pulse aims to extract the pulse peak from the abnormal water flow fluctuation points to evaluate the instantaneous change of the water flow. This process helps to understand the dynamic characteristics of the water flow and its impact on the user experience. Set the calculation standard, and select the highest value of the abnormal fluctuation point minus the baseline flow value as the fluctuation amplitude. Collect data on abnormal water flow fluctuation points. Assume that in a certain measurement, the water flow time series data is: [320 ml / min, 250 ml / min, 300 ml / min, 400 ml / min, 350 ml / min]. Determine the baseline flow. Assume that the baseline flow is 250 ml / min. Calculate the pulse peak fluctuation amplitude: Peak = max(320, 250, 300, 400, 350) - baseline flow = 400 - 250 = 150 ml / min. The purpose of quantifying the degree of water flow pulse disturbance is to evaluate the severity of the water flow fluctuation so as to take appropriate adjustment measures. This quantification will help to understand the stability of the flow and the user's water pressure perception. Apply the standardized formula to convert the peak fluctuation amplitude into the disturbance degree value. Adopt the disturbance degree quantification formula, for example: Disturbance degree = Peak fluctuation amplitude / Baseline flow × 100%. Use the peak fluctuation amplitude of 150 ml / min obtained in the previous step to calculate the disturbance degree: Disturbance degree = 150 / 250 × 100% = 60%. The dynamic pulse width duty cycle adjustment aims to optimize the working state of the nozzle orifice according to the pulse disturbance degree value to reduce the water flow fluctuation and improve the user experience. Set the duty cycle adjustment standard, and adjust the pulse width duty cycle according to the size of the disturbance degree to ensure the stable output of the nozzle. Set the initial duty cycle. The duty cycle in the normal working state is 50%. Adjust the duty cycle according to the disturbance degree. Assume a linear adjustment strategy: New duty cycle = 50% + Disturbance degree / 2. For a disturbance degree of 60%, calculate the new dynamic duty cycle: New duty cycle = 50% + 60 / 2 = 80%. Record the new dynamic duty cycle and generate an adjustment curve to show the duty cycle changes under different disturbance degrees for subsequent monitoring and optimization. The asynchronous collaborative adjustment of multiple nozzle orifices aims to optimize the dynamic duty cycles of multiple nozzles according to the predicted data of the user's water pressure perception differences to achieve the balance of the overall water flow. Set the collaborative adjustment standard, and adjust the duty cycle of each nozzle based on the user feedback and predicted data to make the overall water flow output more uniform. Collect the predicted data on the user's water pressure perception differences. Assume that the prediction results show that the user's perception of nozzle A is low, nozzle B is normal, and nozzle C is high. Set the adjustment strategy, increase the duty cycle of nozzle A to 90%, maintain nozzle B at 80%, and reduce nozzle C to 70%. Record the new duty cycle of each nozzle and generate a multi-nozzle orifice collaborative adjustment curve to show the duty cycle changes of different nozzles at the same time for real-time monitoring and subsequent analysis.

[0078] In this embodiment, the specific steps for constructing the multi-nozzle synchronous collaborative adjustment curve by performing asynchronous collaborative adjustment on the dynamic duty cycle adjustment curve according to the predicted data of the user's water pressure perception difference state are as follows:

[0079] Identify the nozzle port association relationship of the nozzle port array;

[0080] Perform asynchronous and synchronous control nozzle identification according to the nozzle port association relationship, and extract asynchronous associated nozzle ports and synchronous associated nozzle ports;

[0081] Mine the water flow kinetic energy distribution based on the asynchronous associated nozzle ports and synchronous associated nozzle ports, and generate the water flow kinetic energy distribution characteristics of the asynchronous and synchronous nozzle ports;

[0082] Adjust the water flow distribution uniformity of the water flow kinetic energy distribution characteristics of the asynchronous and synchronous nozzle ports according to the predicted data of the user's water pressure perception difference state, and obtain the water flow pressure distribution uniformity adjustment parameter;

[0083] Perform local difference collaborative adjustment on the dynamic duty cycle adjustment curve based on the water flow pressure distribution uniformity adjustment parameter, and construct the multi-nozzle synchronous collaborative adjustment curve.

[0084] In this embodiment, the identification of the nozzle orifice association relationship is used to determine the working relationship between nozzles, such as working simultaneously or alternately at the same time. This process helps to understand the collaborative working mechanism of the nozzle orifices. Set the association relationship standard. If two nozzles work simultaneously under the same control mode, they are regarded as synchronously associated; if they work in different time periods, they are regarded as asynchronously associated. Collect the working data of the nozzle orifices. Assume that the nozzle orifice array contains five nozzles, and record the working status of each nozzle. Nozzle A and nozzle B work at the same time, while nozzle C and nozzle D work at different times. By analyzing the working status data, identify the association relationship between the nozzle orifices. Assume that the association status between nozzle A and nozzle B is synchronous, the status between nozzle C and nozzle D is asynchronous, and nozzle E has no association with other nozzles. Record the identified nozzle orifice association relationship in the database for subsequent analysis and control strategy design. Based on the identified nozzle orifice association relationship, extract the asynchronously associated nozzle orifices and synchronously associated nozzle orifices for subsequent analysis of the water flow kinetic energy distribution. Determine the identification criteria. All synchronous nozzle orifices will be classified into one category, and asynchronous nozzle orifices will be classified into another category. According to the identification result of the first step, mark nozzle A and nozzle B as synchronously associated nozzles, and nozzle C, D, and E as asynchronously associated nozzles. For the asynchronously associated nozzle orifices, further analyze their independence during the working process and their influence on the water flow rate. Monitor the flow rate of nozzle C as 200 ml / min and the flow rate of nozzle D as 150 ml / min. Record the identification results of the asynchronously associated nozzle orifices and synchronously associated nozzle orifices in the system for subsequent mining of the water flow kinetic energy distribution characteristics. The water flow kinetic energy distribution mining aims to analyze the water flow rate and pressure of different nozzle orifices and generate the water flow kinetic energy distribution characteristics. This will help to optimize the working status of the nozzle orifices and improve the overall washing and care effect. Set the mining criteria and calculate the ratio of the flow rate of each nozzle to the total flow rate of the nozzle orifice array. Collect the water flow rate data of the asynchronous and synchronous nozzle orifices. Assume that the flow rates of nozzle A and nozzle B are 300 ml / min and 250 ml / min respectively, while the flow rates of nozzle C, D, and E are 200 ml / min, 150 ml / min, and 100 ml / min respectively. Calculate the kinetic energy distribution characteristics of each nozzle using the formula: kinetic energy = nozzle flow rate / total flow rate. The total flow rate of nozzle A is 300 + 250 + 200 + 150 + 100 = 1000 ml / min, so the kinetic energy characteristic of nozzle A is: kinetic energy A = 300 / 1000×100% = 30%. Record the water flow kinetic energy distribution characteristics of all nozzles in the database and generate a water flow kinetic energy distribution map for subsequent analysis. The water flow distribution uniformity adjustment aims to adjust the water flow kinetic energy distribution of the asynchronously associated nozzle orifices according to the predicted data of the user's water pressure perception difference to ensure the overall water flow uniformity. Set the adjustment criteria. If the user feedbacks that the water pressure in a certain area is insufficient, it is necessary to increase the flow rate of the nozzles in that area.Collect the predicted data on the difference in the user's water pressure perception. Assume that the user feedback shows that the water pressure perception of nozzle C is low, while that of nozzle D is normal and that of nozzle E is high. According to the feedback information, adjust the water flow rate at the asynchronous nozzle orifice. The flow rate of nozzle C can be increased from 200 ml / min to 250 ml / min, that of nozzle D remains unchanged, and that of nozzle E can be reduced to 80 ml / min. Record the adjusted water flow rate, generate the adjustment parameters for the uniformity of the water flow distribution, and apply them to the control system to optimize the working state of the nozzle orifice. The coordinated adjustment of the local differences in the duty cycle adjustment curve aims to optimize the dynamic duty cycles of multiple nozzles according to the adjustment parameters for the uniformity of the water flow pressure distribution to achieve better water flow control. Set the adjustment criteria and adjust the duty cycle of each nozzle according to the flow rate change of each nozzle to achieve uniform output of the flow rate. Calculate the new dynamic duty cycle of each nozzle according to the adjustment parameters for the uniformity of the water flow pressure just now. Assume that the new duty cycle of nozzle C is adjusted to 70%, that of nozzle D is 80%, and that of nozzle E is 60%. Record the new dynamic duty cycles of each nozzle and generate the coordinated adjustment curve of local differences to show the duty cycle changes of different nozzles at the same time. Display the adjustment results through a visualization tool for real-time monitoring and subsequent optimization.

[0085] In this embodiment, the specific steps of step S5 are as follows:

[0086] Perform a water flow pulse width simulation based on the coordinated adjustment curve of multiple nozzle orifices to generate pulse width water flow simulation data;

[0087] Calculate the water flow deviation after modulating the real-time water flow monitoring parameters based on the pulse width water flow simulation data to obtain the water flow deviation value;

[0088] Analyze the water flow change trajectory of the pulse width water flow simulation data to obtain the water flow change trajectory;

[0089] Predict the potential water loss steady trend of the water flow change trajectory based on the water flow deviation value to obtain the water loss steady prediction situation.

[0090] In this embodiment, the water flow pulse width simulation aims to generate corresponding pulse width water flow simulation data according to the multi-nozzle collaborative adjustment curve. This simulation provides basic data for subsequent water flow monitoring and adjustment. Set the simulation parameters, including the working frequency, duty cycle, and pulse width of the nozzles, to ensure that the simulation reflects the real water flow state. Collect data from the multi-nozzle collaborative adjustment curve. Assume that within a certain period of time, the duty cycles of nozzles A, B, and C are 70%, 80%, and 90% respectively. Calculate the pulse width of each nozzle according to the duty cycle and working frequency. Assume the working frequency is 1 Hz, then the pulse width can be calculated as: Pulse width = Duty cycle × Period = Duty cycle × 1 / Frequency. The calculated pulse widths are 70 ms for nozzle A, 80 ms for nozzle B, and 90 ms for nozzle C. Generate corresponding water flow data based on these pulse width simulations. Assume the flow rates of nozzles A, B, and C are 300 ml / min, 350 ml / min, and 400 ml / min respectively. Water flow deviation calculation is used to evaluate the difference between the actual water flow and the simulated water flow. This calculation can help identify potential flow problems and make corresponding adjustments. Set the deviation calculation formula, using the difference between the actual flow rate and the simulated flow rate to represent the deviation. Collect real-time water flow monitoring parameters. Assume the actually monitored water flow data is: [290 ml / min, 360 ml / min, 380 ml / min], corresponding to nozzles A, B, and C. Calculate the water flow deviation of each nozzle using the formula: Deviation = Actual flow rate - Simulated flow rate. For nozzle A, calculate the deviation as: Deviation A = 290 - 300 = -10 ml / min. Perform the same calculation for nozzles B and C, and obtain Deviation B = 10 ml / min and Deviation C = -20 ml / min respectively. Record all deviation values in the system for subsequent analysis. Water flow change trajectory analysis aims to extract the trend of flow rate changes from the pulse width water flow simulation data to evaluate the stability and change pattern of the water flow. Set the analysis criteria, and visualize the change of water flow by plotting a time series graph. Use the generated pulse width water flow simulation data to establish a time series. Assume the water flow data within the time period is: [300, 290, 350, 370, 400] ml / min. Plot the water flow change curve, observe the change trend of water flow over time, and analyze its fluctuation amplitude and change pattern. Record the flow rate changes at each time point and calculate the change rate of the water flow. If the change from the first time point to the second time point is: Change rate = (290 - 300) / 300 × 100% = -3.33%, record all change trajectories and the corresponding change rates in the analysis report to provide data support for subsequent prediction of water flow instability. Water flow instability trend prediction aims to analyze the potential instability risk of the flow rate based on the water flow deviation values. This process can help formulate countermeasures to reduce the instability of the water flow. Set the prediction method, and adopt a linear regression or time series prediction model to analyze the impact of deviation values on the flow rate change.Collect the water flow rate deviation values obtained in the previous step. Assume the deviation data is: [-10, 10, -20] ml / min. Use a linear regression model to analyze the impact of these deviation values on the change in water flow rate, and establish the model: Y = aX + b, where Y is the water flow rate, X is the deviation value, and a and b are regression coefficients. Through the regression results obtained from the analysis, predict the possible instability trend of the water flow rate in the future time period. If the prediction shows that the future flow rate will decrease by 5%, then the working state of the nozzle needs to be adjusted to cope with this change. Record the prediction results, generate a water flow instability prediction situation report, and provide a basis for subsequent water flow control and adjustment.

[0091] In this embodiment, the specific steps of step S6 are as follows:

[0092] Based on the water flow instability prediction situation, perform local pulse width parameter pre-adjustment on the multi-nozzle collaborative modulation curve to obtain the instability pre-adjusted local pulse width parameters;

[0093] Perform global linkage optimization on the instability pre-adjusted local pulse width parameters to construct a globally linked optimized pulse width curve;

[0094] Calculate the current parameters of the current washing and care device;

[0095] According to the globally linked optimized pulse width curve, perform dynamic current compensation calculation on the current parameters to obtain a dynamic current compensation value;

[0096] Execute the intelligent pulse width modulation operation of the washing and care device according to the dynamic current compensation value.

[0097] In this embodiment, the pre-adjustment of local pulse width parameters aims to adjust the pulse width parameters of multiple nozzle orifices according to the water loss stability prediction situation, so as to improve the stability of the system. In this way, preventive measures can be taken before potential instability to ensure the normal operation of the washing and care equipment. Set the pre-adjustment standard, and adjust the pulse width according to the flow deviation of different nozzles to ensure that the flow rate is within an acceptable range. According to the water loss stability prediction situation obtained from the previous steps, identify which nozzles have instability risks. Suppose the pulse width of nozzle A is 80 ms, nozzle B is 90 ms, nozzle C is 70 ms, and the deviation of nozzle A shows a greater instability risk. For nozzle A, consider adjusting the pulse width parameter to 75 ms to reduce the flow rate fluctuation. For nozzle B, keep it unchanged, and the pulse width of nozzle C can be optimized to 65 ms to enhance the stability of its flow rate output. Record the pre-adjustment results of these local pulse width parameters and generate a pre-adjustment report to ensure that all adjustments are well-documented for subsequent monitoring and evaluation. The global linkage optimization aims to integrate the pre-adjustment results of local pulse width parameters to achieve the coordination and balance of the overall water flow. Through global optimization, the synergistic effect of each nozzle can be fully exerted to improve the overall washing and care effect. Set the optimization standard, and use the weighted average method to integrate the pulse width parameters of each nozzle, and make dynamic adjustments according to the flow rate demand and instability risk. According to the local pre-adjustment results, collect the new pulse width parameters of each nozzle. Suppose nozzle A is 75 ms, nozzle B is 90 ms, and nozzle C is 65 ms. Calculate the global pulse width parameter, using weighted average: global pulse width = (75 + 90 + 65) / 3 = 76.67 ms. Through global linkage optimization, adjust the pulse width of each nozzle so that they work within the range of the global pulse width parameter, while considering the balance and stability of the flow rate. Generate the global linkage optimization pulse width curve and record the optimized parameters for subsequent implementation and monitoring. The calculation of the current parameters of the current washing and care equipment is used to evaluate the power consumption of the equipment in the operating state. This calculation can help optimize power management and improve the energy efficiency of the equipment. Set the calculation method, and obtain the current data in real time by monitoring the current sensor and calculate its average value. In the current monitoring system of the washing and care equipment, collect the current data in real time. Suppose the current data measured in a working cycle is: [2.5 A, 2.7 A, 2.6 A, 2.4 A, 2.8 A]. Calculate the average value of these current parameters: average current = (2.5 + 2.7 + 2.6 + 2.4 + 2.8) / 5 = 2.6 A, and record the calculation result in the database for querying and analyzing the power consumption data and providing basic information for subsequent dynamic current compensation. The dynamic current compensation calculation aims to adjust the current parameters according to the change of the global linkage optimization pulse width curve to ensure that the power supply of the equipment remains stable under different working conditions. Set the compensation standard and use the proportional relationship between the pulse width and the current for compensation calculation.In the global linkage optimization of the pulse width curve, assume that the global pulse width is 76.67 ms and the set current compensation ratio is 0.8 A / ms. Calculate the dynamic current compensation value based on the global pulse width: Dynamic current compensation = global pulse width × 0.8 = 76.67 × 0.8 = 61.34 A. Compare the calculated dynamic current compensation value with the current average current to determine whether adjustment is needed. Record the compensation calculation results and generate a compensation strategy report to ensure the transparency and traceability of current adjustment. The implementation of the intelligent pulse width modulation operation aims to adjust the working state of the washing and care equipment according to the dynamic current compensation value to ensure that the equipment operates in an efficient mode while meeting the user's water flow requirements. Set the execution standard and adjust the pulse width setting of the nozzle according to the compensation value to achieve the best water flow output. Adjust the pulse width setting of the washing and care equipment according to the calculated dynamic current compensation value. If the dynamic current compensation value is 61.34 A, adjust the pulse width to 80 ms in combination with the actual requirements. Start the washing and care equipment and monitor the current and water flow status of the equipment in real time to ensure that the equipment operates smoothly under the new settings. Record the changes in current and water flow during the operation of the equipment and generate an operation report for subsequent analysis and optimization.

[0098] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0099] As described above, these are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for an intelligent washing and care device based on dynamic pulse width modulation, characterized in that, The intelligent washing and care device includes a shampoo tub body and three micro water pumps. The shampoo tub body is internally provided with a nozzle orifice array, and the method includes the following steps: Step S1: Collect real-time water flow monitoring parameters, perform real-time water flow output distribution calculation, and construct a real-time water flow distribution map of the nozzle orifices; Step S2: Perform multi-frequency wavelet transform decomposition on the real-time water flow distribution map of the nozzle orifices, and perform water flow output pulse width waveform calculation to obtain a time-sequence water flow pulse width curve; Step S3: Detect abnormal water flow fluctuations according to the time-sequence water flow pulse width curve, and predict the difference in water pressure felt by the user in different regions, and generate predicted data on the difference state of the user's water pressure feeling; The water pressure feeling difference prediction is to evaluate the water pressure difference felt by the user in different regions according to the abnormal water flow fluctuations; Step S4: Perform dynamic pulse width duty ratio adjustment and asynchronous coordination adjustment of multiple nozzle orifices according to the predicted data on the difference state of the user's water pressure feeling, and construct an asynchronous coordination adjustment curve of multiple nozzle orifices; Step S5: Perform water flow pulse width simulation based on the asynchronous coordination adjustment curve of multiple nozzle orifices, and predict the potential water loss instability trend to obtain a water loss instability prediction situation; Step S6: Perform local pulse width parameter pre-adjustment on the asynchronous coordination adjustment curve of multiple nozzle orifices based on the water loss instability prediction situation, and perform dynamic current compensation calculation to execute the intelligent pulse width adjustment operation of the washing and care device; Among them, the specific steps of Step S4 are: Calculate the fluctuation amplitude of the transient peak value of the water flow pulse at the abnormal water flow fluctuation point, and extract the fluctuation amplitude of the pulse peak value; Quantify the degree of water flow pulse disturbance according to the fluctuation amplitude of the pulse peak value to obtain a pulse disturbance degree value; Perform dynamic pulse width duty ratio adjustment of the abnormal nozzle orifice based on the pulse disturbance degree value to obtain a dynamic duty ratio adjustment curve; Perform asynchronous coordination adjustment of multiple nozzle orifices on the dynamic duty ratio adjustment curve according to the predicted data on the difference state of the user's water pressure feeling, and construct an asynchronous coordination adjustment curve of multiple nozzle orifices; Among them, the specific steps of Step S5 are: Perform water flow pulse width simulation based on the asynchronous coordination adjustment curve of multiple nozzle orifices to generate pulse width water flow simulation data; Perform modulated water flow deviation calculation on the real-time water flow monitoring parameters based on the pulse width water flow simulation data to obtain a water flow deviation value; Analyze the water flow change trajectory of the pulse width water flow simulation data to obtain a water flow change trajectory; Predict the potential water loss instability trend of the water flow change trajectory based on the water flow deviation value to obtain a water loss instability prediction situation.

2. The control method of the intelligent washing and care device based on dynamic pulse width modulation according to claim 1, wherein, The specific steps of Step S1 are: Obtain the instruction stream input by the user to the device; identify the washing and care control mode of the instruction stream; Call a preset output parameter group of the micro water pump according to the washing and care control mode, control the micro water pump to discharge water, and collect real-time water flow monitoring parameters; Calculate the spatial coordinates of each nozzle orifice of the nozzle orifice array; Perform nozzle orifice spatial layout analysis based on the nozzle orifice spatial coordinates to obtain a spatial distribution map of the nozzle orifice array; Perform real-time water flow output distribution calculation on the spatial distribution map of the nozzle orifice array based on the real-time water flow monitoring parameters, and construct a real-time water flow distribution map of the nozzle orifices.

3. The control method of the intelligent washing and care device based on dynamic pulse width modulation according to claim 2, wherein, The specific operation of controlling the micro water pump to discharge water is: The three micro water pumps are respectively: a first washing liquid micro water pump, a second washing liquid micro water pump, and a disinfection water micro water pump; The washing and care control modes include a speed washing mode, a normal mode, an overtime mode, and a maintenance mode; Call a preset output parameter group of the micro water pump according to the washing and care control mode; Based on the preset output parameter group of the micro water pump, perform the first round of water flow output on the nozzle orifice array; Perform shampoo cleaning based on the first washing liquid micro water pump and the second washing liquid micro water pump; Repeat the above steps according to the instruction stream; Perform one-key disinfection treatment based on the disinfection water micro water pump.

4. The control method of the intelligent washing and care device based on dynamic pulse width modulation according to claim 1, characterized in that, The specific steps of step S2 are as follows: Perform multi-time window sampling on the real-time water flow distribution map of the nozzle orifice to obtain a multi-time window water flow distribution map; Calculate the instantaneous water flow vector of the multi-time window water flow distribution map and fit the instantaneous water flow vector field; Perform multi-frequency wavelet transform decomposition on the instantaneous water flow vector field to obtain water flow frequency domain maps of multiple frequency components; Perform frequency component density calculation on the water flow frequency domain maps of multiple frequency components to obtain the water flow frequency domain density value of each frequency; Perform water flow output pulse width waveform calculation on the water flow frequency domain density value to obtain a timing water flow pulse width curve.

5. The intelligent washing and care device control method based on dynamic pulse width modulation according to claim 1, characterized in that, The specific steps of step S3 are as follows: Perform average pulse width frequency range calculation on the timing water flow pulse width curve to obtain a reference pulse width frequency range; Perform abnormal water flow fluctuation detection according to the reference pulse width frequency range and mark the abnormal water flow fluctuation points; Extract the abnormal time stamp and the abnormal water flow spatial position based on the abnormal water flow fluctuation points; Perform abnormal nozzle traceability positioning on the nozzle orifice array spatial distribution map based on the abnormal time stamp and the abnormal water flow spatial position, and mark the nozzle orifices with abnormal water flow; Perform adjacent nozzle orifice water flow difference perception on the nozzle orifices with abnormal water flow to obtain water flow difference perception features; Perform user area water pressure feeling difference prediction based on the water flow difference perception features to generate user water pressure feeling difference state prediction data.

6. The control method of the intelligent washing and care device based on dynamic pulse width modulation according to claim 1, wherein, The specific steps of constructing a multi-nozzle orifice collaborative adjustment curve by performing multi-nozzle orifice asynchronous collaborative adjustment on the dynamic duty ratio adjustment curve according to the user water pressure feeling difference state prediction data are as follows: Identify the nozzle orifice association relationship of the nozzle orifice array; Perform asynchronous and synchronous control nozzle identification according to the nozzle orifice association relationship, and extract asynchronous associated nozzle orifices and synchronous associated nozzle orifices; Perform water flow kinetic energy distribution mining according to the asynchronous associated nozzle orifices and the synchronous associated nozzle orifices to generate the water flow kinetic energy distribution features of the asynchronous and synchronous nozzle orifices; Perform water flow distribution uniformity adjustment on the water flow kinetic energy distribution features of the asynchronous and synchronous nozzle orifices according to the user water pressure feeling difference state prediction data to obtain water flow pressure distribution uniformity adjustment parameters; Perform local difference collaborative adjustment on the dynamic duty ratio adjustment curve based on the water flow pressure distribution uniformity adjustment parameters to construct a multi-nozzle orifice collaborative adjustment curve.

7. The control method of the intelligent washing and care device based on dynamic pulse width modulation according to claim 1, wherein, The specific steps of step S6 are as follows: Perform local pulse width parameter pre-adjustment on the multi-nozzle orifice collaborative modulation curve based on the water loss instability prediction situation to obtain instability pre-adjusted local pulse width parameters; Perform global linkage optimization on the instability pre-adjusted local pulse width parameters to construct a global linkage optimization pulse width curve; Calculate the current parameters of the current washing and care equipment; Perform dynamic current compensation calculation on the current parameters according to the global linkage optimization pulse width curve to obtain a dynamic current compensation value; Execute the intelligent pulse width modulation operation of the washing and care device according to the dynamic current compensation value.

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