A water purifier touch screen interactive system

Abstract

This invention discloses a water purifier touchscreen interaction system, belonging to the field of human-computer interaction technology. It includes a raw signal acquisition unit, a touch-deformation preliminary separation unit, a deformation field forward prediction unit, a signal iteration purification unit, a touch intent precise analysis unit, and an iteration process control unit. The raw signal acquisition unit generates a raw mixed signal spectrum. This invention makes it possible to decipher the true intent from a single signal source contaminated by its own physical consequences, significantly improving the interaction accuracy and reliability of flexible screen devices in complex scenarios. It fully meets the real-time requirements of user interaction without perceptible delay. By achieving robust support for highly dynamic and high-pressure gestures, it greatly enriches the interaction dimensions on flexible screen devices, paving the way for the development of more complex and intuitive applications and interfaces.

Description

Technical Field

[0001] This invention relates to the field of human-computer interaction technology, specifically to a water purifier touchscreen interaction system. Background Technology

[0002] In recent years, with the popularization of the smart home concept, traditional home appliances are developing towards intelligence, high-end and integration. Taking water purifiers as an example, in order to pursue a more beautiful industrial design to integrate into the modern home environment and provide a richer and more intuitive human-computer interaction experience, their operation panels are increasingly adopting flexible or curved capacitive touch screens that are integrally molded with the curved surface of the body.

[0003] These new water purifiers abandon traditional mechanical buttons and instead support more complex dynamic interactions. For example, users can press and drag a virtual slider on the screen to achieve precise and rapid adjustment of water temperature or water flow. While this interaction method is intuitive, it presents challenges to the underlying touch technology that are difficult to overcome with existing technology.

[0004] The core challenge stems from the physical flexibility of touchscreens. When a user performs a dynamic gesture with a significant normal pressure component on a flexible screen, the pressure from the fingertip instantly induces a localized, high-curvature micro-deformation on the flexible substrate around the touch point, much like ripples created by throwing a stone into water. This deformation ripple has two characteristics that are extremely detrimental to capacitive sensing: its propagation speed far exceeds the overall macroscopic bending change of the screen, rendering conventional low-speed deformation compensation mechanisms completely ineffective; the deformation directly causes an instantaneous physical change in the interlayer spacing between the upper and lower sensing electrodes of the capacitive sensor, which, according to the principle of capacitance, directly modulates the sensor's output capacitance value. The end result is that at the source of the touch signal, a deformation artifact signal that is physically and signalally highly coupled with the actual touch intent has already been mixed in. This phenomenon raises a fundamental technical dilemma, namely the chicken-and-egg decoupling paradox. The core of this paradox is that if the system wants to accurately calculate the user's touch intent from the mixed signal, it needs to know the shape and intensity of the deformation artifact caused by the touch itself in order to separate it from the original signal. However, to accurately predict this deformation artifact, it is necessary to know the precise touch parameters first.

[0005] Existing technologies, such as compensation schemes that measure overall deformation by deploying low-speed strain gauges, are completely ineffective in addressing this problem because they cannot capture such high-speed, microscopic deformation dynamics in terms of both time response and spatial resolution. This transforms the technical challenge from a simple signal denoising problem into a core challenge: how to reverse-engineer, in real time, the pure user input intent and the deformation field causing the contamination from a single sensor reading that is contaminated in real time by its own physical consequences. If this problem cannot be solved, the so-called advanced interactive functions of devices such as smart water dispensers will become useless due to inaccurate operation and malfunctioning response, severely impacting the user experience.

[0006] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a water purifier touch screen interactive system to solve the problems mentioned in the background art.

[0008] The technical solution of the present invention includes: a raw signal acquisition unit, a touch-deformation preliminary separation unit, a deformation field forward prediction unit, a signal iterative purification unit, a touch intent precise analysis unit, and an iterative process control unit;

[0009] The original signal acquisition unit is used to generate an original mixed signal spectrum;

[0010] The touch-deformation preliminary separation unit is used to receive the original mixed signal spectrum and generate preliminary touch estimation parameters;

[0011] The deformation field forward prediction unit is used to receive the preliminary touch estimation parameters or a refined touch parameter set, and generate a predicted deformation artifact map.

[0012] The signal iteration purification unit is used to receive the original mixed signal spectrum and the predicted deformation artifact spectrum, and to purify the original mixed signal spectrum to generate a purified touch signal spectrum.

[0013] The touch intent precision analysis unit is used to receive the purified touch signal spectrum and generate the refined touch parameter set;

[0014] The iterative process control unit is used to receive the refined touch parameter set and perform a convergence determination;

[0015] When the convergence determination result is convergent, the iterative process control unit is used to output the current refined touch parameter set;

[0016] When the convergence determination result is non-convergence, the iterative process control unit is used to send the current refined touch parameter set to the deformation field forward prediction unit to start a new round of prediction.

[0017] Preferably, the touch-deformation preliminary separation unit is used to perform the following steps:

[0018] S11. Based on a touch signal morphology feature model, perform pattern matching on the original mixed signal spectrum;

[0019] The touch signal morphology feature model is established by statistical analysis and feature extraction of the capacitance signal data generated by standard touch operation under no deformation interference.

[0020] S12. Based on the pattern matching results, identify the signal area that matches the touch characteristics;

[0021] S13. Based on the signal area, estimate the initial position of a contact and an approximate pressure;

[0022] S14. Combine the initial position with the approximate pressure to generate the preliminary touch estimation parameters.

[0023] Preferably, the deformation field forward prediction unit includes a substrate dynamic response feature library;

[0024] The deformation field forward prediction unit is used for:

[0025] The received preliminary touch estimation parameters or the refined touch parameter set are used as indexes;

[0026] The query or interpolation calculation is performed in the substrate dynamic response feature library;

[0027] Generate the predicted deformation artifact map.

[0028] Preferably, the substrate dynamic response feature library is established through a preliminary experimental calibration process, which includes the following steps:

[0029] S21. Using a high-precision force sensor, apply a force with known parameters to the flexible substrate;

[0030] The parameters include the position, pressure, area of ​​action, and dynamic changes of the force;

[0031] S22. Synchronously record the signal response generated by the capacitive sensor array under the action of the force;

[0032] S23. Associate the known input force with the signal response to form an input-output data pair;

[0033] S24. The input-output data pairs are processed and summarized to construct the dynamic response feature library of the substrate.

[0034] Preferably, the signal iterative purification unit is used to perform the following steps:

[0035] S31. Obtain the capacitance value of each sensor node in the original mixed signal spectrum;

[0036] S32. Obtain the artifact intensity value corresponding to the sensor node in the predicted deformation artifact map;

[0037] S33. Subtract the artifact intensity value from the capacitance value to obtain a purified capacitance value;

[0038] S34. Combine the purified capacitance values ​​of all sensor nodes to generate the purified touch signal spectrum.

[0039] Preferably, the touch intent precise parsing unit is used to perform the following steps:

[0040] S41. Apply a touch point positioning algorithm to analyze the cleaned touch signal spectrum to calculate a precise touch point position and a precise touch point profile.

[0041] S42. Apply a pressure analysis algorithm to analyze the cleaned touch signal spectrum to calculate a precise movement trajectory and a precise normal pressure.

[0042] S43. By combining the precise touch point position, the precise touch point contour, the precise movement trajectory, and the precise normal pressure, the refined touch parameter set is generated.

[0043] Preferably, the iterative process control unit performs the convergence determination as follows:

[0044] Maintain the refined touch parameter set generated in the previous iteration;

[0045] Receive the refined touch parameter set generated in the current round;

[0046] Calculate the change range between the current round's refined touch parameter set and the previous round's refined touch parameter set;

[0047] The magnitude of the change is compared with a dynamically stable threshold.

[0048] The dynamic stability threshold is set to balance calculation accuracy and system response speed, and is optimized based on user experience testing and empirical data.

[0049] If the change magnitude is lower than the dynamic stability threshold, a convergence determination result is generated.

[0050] If the magnitude of the change is not lower than the dynamic stability threshold, a determination result indicating non-convergence is generated.

[0051] This invention provides an improved touchscreen interactive system for water purifiers, which has the following improvements and advantages compared to the prior art:

[0052] 1. Through an innovative prediction-iteration closed-loop architecture, it breaks the chicken-and-egg dilemma of decoupling, making it possible to decipher the true intent from a single signal source that is contaminated by its own physical consequences.

[0053] 2. It can accurately process user input when using gestures such as heavy pressure dragging and rapid pinching, which used to cause serious signal distortion, significantly improving the interaction accuracy and reliability of flexible screen devices in complex scenarios;

[0054] 3. The entire solution process is based on high-speed signal processing and model query, which can complete multiple iterations and converge in a very short time, fully meeting the real-time requirements of user interaction, and will not produce perceptible delay;

[0055] 4. Different hardware can be adapted through a one-time, well-defined offline calibration, without the need to add additional strain gauges or other physical sensors, thus not increasing hardware costs or design complexity;

[0056] 5. By achieving robust support for highly dynamic and high-pressure gestures, the interactive dimensions on flexible screen devices are greatly enriched, paving the way for the development of more complex and intuitive applications and interfaces. Attached Figure Description

[0057] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0058] Figure 1 This is a flowchart of a water purifier touch screen interactive system according to the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0060] Example 1:

[0061] Please see Figure 1 The present invention provides a technical solution for a water purifier touch screen interactive system, comprising: a raw signal acquisition unit, a touch-deformation preliminary separation unit, a deformation field forward prediction unit, a signal iterative purification unit, a touch intent precise analysis unit, and an iterative process control unit;

[0062] The raw signal acquisition unit is used to generate a raw mixed signal spectrum;

[0063] The touch-deformation preliminary separation unit is used to receive the original mixed signal spectrum and generate preliminary touch estimation parameters;

[0064] The deformation field forward prediction unit is used to receive preliminary touch estimation parameters or a refined set of touch parameters and generate a predicted deformation artifact map.

[0065] The signal iteration purification unit is used to receive the original mixed signal spectrum and the predicted deformation artifact spectrum, and to purify the original mixed signal spectrum to generate a purified touch signal spectrum.

[0066] The touch intent precision analysis unit is used to receive the purified touch signal spectrum and generate a refined touch parameter set;

[0067] The iterative process control unit receives the refined touch parameter set and performs a convergence determination.

[0068] When the convergence determination result is convergence, the iterative process control unit is used to output the current refined touch parameter set;

[0069] When the convergence determination result is non-convergence, the iterative process control unit is used to send the current set of refined touch parameters to the deformation field forward prediction unit to start a new round of prediction.

[0070] This technical solution aims to address the signal contamination problem caused by substrate micro-deformation under high-pressure dynamic gestures in flexible screens, thereby achieving high-fidelity gesture tracking. Unlike existing technologies that rely on pure signal filtering or adding extra hardware sensors, the core idea of ​​this solution is to confirm that touch behavior and substrate deformation are physically coupled and mutually causal. A forceful touch will cause deformation, and the deformation, in turn, will contaminate the capacitive signal used to locate the touch. This is a nonlinear, dynamic coupling problem that is difficult to solve using traditional methods. This system innovatively constructs a touch-deformation feedforward prediction and iterative feedback coupled solution mechanism. The non-obviousness of this mechanism lies in the fact that it does not attempt... Figure 1Instead of separating the signal in one step, this method uses a self-calibrating closed-loop system to transform the decoupling process into a fast-converging numerical calculation process. Based on a noisy signal, the main noise pattern is predicted, and then the predicted noise is subtracted from the original signal to obtain a cleaner signal. More accurate predictions are made based on this cleaner signal, and this process is repeated until the touch parameters stabilize and converge. This proactive prediction and artifact elimination method breaks the lock between touch and deformation, achieving accurate calculation of the user's true intentions without adding any additional physical sensors, thus ensuring the low cost and high applicability of the solution.

[0071] Example 2

[0072] The touch-deformation preliminary separation unit is used to perform the following steps:

[0073] S11. Based on a touch signal morphology feature model, perform pattern matching on the original mixed signal spectrum;

[0074] Among them, the touch signal morphology feature model is established by statistical analysis and feature extraction of the capacitance signal data generated by standard touch operation under no deformation interference;

[0075] S12. Based on the pattern matching results, identify the signal area that matches the touch characteristics;

[0076] S13. Based on the signal area, estimate the initial position of a contact and an approximate pressure;

[0077] S14. Combine the initial position and approximate pressure to generate preliminary touch estimation parameters.

[0078] In this embodiment, to ensure that those skilled in the art can implement it, the touch signal morphology feature model is described in detail. This model is not a general model, but a database established through offline calibration for a specific flexible screen product. The establishment process includes: under conditions without deformation interference, for example, when the screen is laid flat on a rigid surface, using a standard-sized conductive pen with different slight pressures to perform touch and slow smooth sliding operations.

[0079] Standard-sized conductive pens can be made with a diameter that conforms to industry-standard testing specifications. or The standard test refers to different slight pressures, the range of which can be... Newton to Between Newtons, to cover the typical force range of a user from a light touch to an initial press;

[0080] It records the two-dimensional spatial distribution data of the capacitive signal; by statistically analyzing thousands of such standard gesture data, it extracts key features, such as the gradient, aspect ratio, and peak-to-average ratio of signal clusters, thereby constructing a feature vector library that can characterize the shape of standard touch signals; in step S11, the pattern matching process compares the features of the real-time signal spectrum with the feature vectors in this library to find the matching item with the closest Euclidean distance or cosine similarity, which is much more efficient than complex image recognition algorithms, thus meeting the speed requirements of the initial separation unit.

[0081] Example 3

[0082] The deformation field positive prediction unit includes a substrate dynamic response feature library;

[0083] The deformation field forward prediction unit is used for:

[0084] Indexed by the received preliminary touch estimation parameters or refined touch parameter set;

[0085] Perform queries or interpolation calculations in the substrate dynamic response feature library;

[0086] Generate a map of predicted deformation artifacts.

[0087] The substrate dynamic response feature library was established through a preliminary experimental calibration process, which included the following steps:

[0088] S21. Using a high-precision force sensor, apply a force with known parameters to the flexible substrate;

[0089] The parameters include the location, pressure, area of ​​action, and dynamic changes of the force.

[0090] S22. Synchronously record the signal response generated by the capacitive sensor array under the action of force;

[0091] S23. Correlate the known input force with the signal response to form an input-output data pair;

[0092] S24. Process and summarize the input-output data pairs to construct a dynamic response feature library for the substrate.

[0093] To enable those skilled in the art to implement this, at least two feasible processing and inductive schemes are provided here: Firstly, all known input force parameters, such as the position coordinates of the force... ,pressure Area of ​​action As multiple dimensions, a discretized multidimensional grid is constructed. During the experimental calibration process in S21-S23, the parameter combination at each grid point is measured, and the signal response generated by the corresponding capacitive sensor array recorded in S22 is stored as the value of that grid point, thus forming a high-dimensional lookup table. When the deformation field forward prediction unit receives a set of touch parameters, if the parameter happens to fall on a grid point, the corresponding artifact map is directly called. If the parameter falls between grid points, one or more standard interpolation algorithms are used, such as trilinear interpolation or higher-dimensional multilinear interpolation, to calculate the predicted deformation artifact map under the current parameters based on the data at neighboring grid points.

[0094] Secondly, the input-output data pairs formed in S23 are used as training samples. The known input force parameters, such as position, pressure, and area, constitute the input feature vector of the model, while the synchronously recorded signal response map serves as the output label of the model. A suitable machine learning regression model, such as a multilayer perceptron or convolutional neural network, is selected to train these training samples. After training, the model itself constitutes the substrate dynamic response feature library. During system operation, the deformation field forward prediction unit inputs the received touch parameters into this trained model, and the model will directly infer and calculate to generate a complete predicted deformation artifact map.

[0095] In this embodiment, the deformation field forward prediction unit is the core technology for solving the decoupling problem of the interdependence between touch and deformation. Through the rigorous physical calibration described above, the system obtains a priori knowledge: that is, touch input with specific physical characteristics will cause what shape and intensity of capacitance change artifacts on the substrate. Therefore, when touch parameters are received, the unit does not perform signal processing, but performs a forward rendering based on the physical model to directly calculate the corresponding contamination signal shape. This forward prediction capability based on physical calibration is the fundamental prerequisite for the accurate execution of subsequent signal purification steps, providing solid physical model support for the effectiveness of the entire decoupling process.

[0096] Example 4

[0097] The signal iterative purification unit is used to perform the following steps:

[0098] S31. Obtain the capacitance value of each sensor node in the original mixed signal spectrum;

[0099] S32. Obtain the artifact intensity value corresponding to the sensor node in the predicted deformation artifact map;

[0100] S33. Subtract the artifact intensity value from the capacitance value to obtain a purified capacitance value;

[0101] S34. Combine the purified capacitance values ​​of all sensor nodes to generate a purified touch signal spectrum.

[0102] This unit is the direct actuator for signal separation; the processing logic is a highly efficient model-guided signal subtraction operation. The effectiveness of this operation directly depends on the accuracy of the prediction provided by the deformation field forward prediction unit. In each iteration, as the touch parameters input to the prediction model become more refined, the output artifact spectrum increasingly approximates the real interference signal, thereby improving the signal-to-noise ratio of the purified touch signal spectrum round by round, and making the morphological characteristics of the real touch signal more prominent. This unit constitutes a key bridge connecting prediction and analysis, directly translating the model's prediction effect into an improvement in signal quality.

[0103] Example 5

[0104] The touch intent precise parsing unit is used to perform the following steps:

[0105] S41. Apply a touch point positioning algorithm to analyze the cleaned touch signal spectrum to calculate a precise touch point position and a precise touch point profile.

[0106] S42. Apply a pressure analysis algorithm to analyze the cleaned touch signal spectrum to calculate a precise movement trajectory and a precise normal pressure.

[0107] S43. By combining precise touch point position, precise touch point contour, precise movement trajectory and precise normal pressure, a refined touch parameter set is generated.

[0108] The goal of this unit is to extract high-precision parameters based on a signal spectrum where interference has been significantly suppressed. Since the signal-to-noise ratio of the input signal has been greatly improved by the cleansing unit, this unit can effectively apply more complex and precise analytical algorithms than those used in the initial separation stage. For example, using a Gaussian mixture model instead of a simple centroid method can more accurately handle fingertip contact with tilted or irregular shapes. The pressure analysis algorithm, also based on pre-calibrated data, establishes a nonlinear mapping relationship between the total signal energy and normal pressure, thereby achieving accurate pressure sensing. The analytical results of this unit transform the cleaned signal data into meaningful and operable control parameters for upper-level applications, completing a leap from signal processing to intent understanding.

[0109] Example 6

[0110] The steps used by the iterative process control unit to perform convergence determination are as follows:

[0111] Maintain the refined touch parameter set generated in the previous iteration ;

[0112] Receive the refined touch parameter set generated in the current round ;

[0113] Calculate the refined touch parameter set for the current round Compared to the previous round of refined touch parameter set The range of change between The magnitude of this change is a dimensionless comprehensive quantitative indicator, calculated as follows:

[0114] First, calculate the normalized changes in the position and pressure parameters respectively:

[0115] Location normalization change ;

[0116] Normalized change in pressure ;

[0117] in, It is the norm of the difference between the contact point position vectors obtained in the current iteration and the previous iteration; As a positional reference, its function is to make positional changes dimensionless, for example, it can be the length of the screen diagonal or the physical size of a pixel; This represents the absolute value of the difference between the normal pressures obtained from the two iterations. As a pressure reference, its function is to make pressure changes dimensionless, for example, by taking the maximum pressure value that the system can perceive or the typical pressure range observed during the calibration process.

[0118] Calculate comprehensive quantitative indicators :

[0119] ;

[0120] in, and These are preset dimensionless weighting coefficients used to adjust the relative importance of position and pressure changes in convergence assessment; their sum is generally recommended to be 1. For example, if position and pressure changes are considered equally important, a weighting of 1 can be set. and .

[0121] The dimensionless range of change With a preset dimensionless stability threshold Compare them.

[0122] Wherein, the stability threshold The settings are designed to balance computational accuracy and system response speed. Their specific values ​​are optimized based on extensive user experience testing and empirical data from different gestures. For example, they can be set to... .

[0123] like Then a convergence determination result is generated;

[0124] like If so, a result indicating non-convergence is generated;

[0125] The control unit in this iterative process is the core of the decision-making and control logic of the entire adaptive closed-loop system; when the determination result is convergence, the control unit terminates the iterative loop and updates the latest refined touch parameter set. As the final valid output, it is transmitted to the operating system or upper-level application; conversely, if it is determined to be non-converged, the control unit will send this latest and most accurate set of refined touch parameters to date. As input for the next round of prediction, the reverse-drive deformation field forward prediction unit is used. Through this closed-loop feedback and convergence control, the system achieves self-optimization from coarse estimation to precise solution until the preset accuracy standard is reached, thus providing highly reliable touch data while ensuring response speed.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A water purifier touchscreen interactive system, characterized in that, include: The system includes a raw signal acquisition unit, a touch-deformation preliminary separation unit, a deformation field forward prediction unit, a signal iterative purification unit, a touch intent precise analysis unit, and an iterative process control unit. The original signal acquisition unit is used to generate an original mixed signal spectrum; The touch-deformation preliminary separation unit is used to receive the original mixed signal spectrum and generate preliminary touch estimation parameters; The deformation field forward prediction unit is used to receive the preliminary touch estimation parameters or a refined touch parameter set, and generate a predicted deformation artifact map. The signal iteration purification unit is used to receive the original mixed signal spectrum and the predicted deformation artifact spectrum, and to purify the original mixed signal spectrum to generate a purified touch signal spectrum. The touch intent precision analysis unit is used to receive the purified touch signal spectrum and generate the refined touch parameter set; The iterative process control unit is used to receive the refined touch parameter set and perform a convergence determination; When the convergence determination result is convergent, the iterative process control unit is used to output the current refined touch parameter set; When the convergence determination result is non-convergence, the iterative process control unit is used to send the current refined touch parameter set to the deformation field forward prediction unit to start a new round of prediction. The deformation field positive prediction unit includes a substrate dynamic response feature library; The deformation field forward prediction unit is used for: The received preliminary touch estimation parameters or the refined touch parameter set are used as indexes; The query or interpolation calculation is performed in the substrate dynamic response feature library; Generate the predicted deformation artifact map.

2. The water purifier touchscreen interactive system according to claim 1, characterized in that, The touch-deformation preliminary separation unit is used to perform the following steps: S11. Based on a touch signal morphology feature model, perform pattern matching on the original mixed signal spectrum; The touch signal morphology feature model is established by statistical analysis and feature extraction of the capacitance signal data generated by standard touch operation under no deformation interference. S12. Based on the pattern matching results, identify the signal area that matches the touch characteristics; S13. Based on the signal area, estimate the initial position of a contact and an approximate pressure; S14. Combine the initial position with the approximate pressure to generate the preliminary touch estimation parameters.

3. The water purifier touchscreen interactive system according to claim 1, characterized in that, The substrate dynamic response feature library was established through a preliminary experimental calibration process, which includes the following steps: S21. Using a high-precision force sensor, apply a force with known parameters to the flexible substrate; The parameters include the position, pressure, area of ​​action, and dynamic changes of the force; S22. Synchronously record the signal response generated by the capacitive sensor array under the action of the force; S23. Associate the known input force with the signal response to form an input-output data pair; S24. The input-output data pairs are processed and summarized to construct the dynamic response feature library of the substrate.

4. The water purifier touchscreen interactive system according to claim 1, characterized in that, The signal iterative purification unit is used to perform the following steps: S31. Obtain the capacitance value of each sensor node in the original mixed signal spectrum; S32. Obtain the artifact intensity value corresponding to the sensor node in the predicted deformation artifact map; S33. Subtract the artifact intensity value from the capacitance value to obtain a purified capacitance value; S34. Combine the purified capacitance values ​​of all sensor nodes to generate the purified touch signal spectrum.

5. The water purifier touchscreen interactive system according to claim 1, characterized in that, The touch intent precise parsing unit is used to perform the following steps: S41. Apply a touch point positioning algorithm to analyze the cleaned touch signal spectrum to calculate a precise touch point position and a precise touch point profile. S42. Apply a pressure analysis algorithm to analyze the cleaned touch signal spectrum to calculate a precise movement trajectory and a precise normal pressure. S43. By combining the precise touch point position, the precise touch point contour, the precise movement trajectory, and the precise normal pressure, the refined touch parameter set is generated.

6. The water purifier touchscreen interactive system according to claim 1, characterized in that, The steps for the convergence determination performed by the iterative process control unit are as follows: Maintain the refined touch parameter set generated in the previous iteration; Receive the refined touch parameter set generated in the current round; Calculate the change range between the current round's refined touch parameter set and the previous round's refined touch parameter set; The magnitude of the change is compared with a dynamically stable threshold. The dynamic stability threshold is set to balance calculation accuracy and system response speed, and is optimized based on user experience testing and empirical data. If the change magnitude is lower than the dynamic stability threshold, a convergence determination result is generated. If the magnitude of the change is not lower than the dynamic stability threshold, a determination result indicating non-convergence is generated.

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