Touch data processing method and system, and touch screen

Abstract

The present application relates to touch human-computer interaction technical field, especially touch data processing method, system and touch screen. The method comprises the following steps: collecting the capacitance signal of touch surface, extracting the pressure value and contact area parameter of touch point, constructing the touch feature chain with time sequence association, and storing the touch feature chain as user operation data; identifying the pressing action sequence in the touch feature chain, extracting the force variation feature of pressing process, calculating the pressure change rate based on the force variation feature, identifying the force inflection point and the force inflection point, distinguishing physiological fluctuation and random noise, and generating pressure behavior feature; calculating the ratio of pressure change rate and area change rate to form pressure-area response function, determining key pressure interval, calculating function eigenvalue in each interval, and forming tool characteristic response data. The present application greatly improves the accuracy, naturalness and tool modeling ability of touch interaction through touch human-computer interaction technology.

Description

Technical Field

[0001] This invention relates to the field of touch human-computer interaction technology, and in particular to a touch data processing method, system and touch screen. Background Technology

[0002] Traditional touch technologies primarily rely on simple contact point size and capacitive signal feature recognition tools, lacking in-depth analysis of the dynamic relationship between pressure and area, and failing to effectively utilize the temporal correlation information of touch characteristics. This results in a significant decrease in recognition accuracy when multiple tools are used alternately. Existing technologies overemphasize touch position information, ignoring the rich intent information contained in the force change pattern, failing to distinguish between decisive and tentative presses, struggling to perceive the rhythmic characteristics and fine-tuning behavior of user operations, and misinterpreting natural physiological vibrations as intentional operations, seriously affecting the accuracy and naturalness of the touch experience.

[0003] In summary, existing technologies suffer from inaccurate recognition of touch tools and insufficient understanding of touch intent, issues that urgently need to be addressed. Summary of the Invention

[0004] Therefore, it is necessary to provide a touch data processing method, system, and touch screen to solve at least one of the aforementioned technical problems.

[0005] To achieve the above objectives, a touch data processing method includes the following steps: Step S1: Collect the capacitance signal of the touch surface, extract the pressure value and contact area parameters of the touch point, construct a touch feature chain with time-series correlation, and store the touch feature chain as user operation data; Step S2: Identify the pressing action sequence in the touch feature chain, extract the force change features of the pressing process, calculate the pressure change rate based on the force change features, identify the force inflection point and the force release inflection point, distinguish between physiological fluctuations and random noise, and generate pressure behavior features; Step S3: Calculate the ratio of pressure change rate to area change rate to form a pressure-area response function, determine the key pressure range, calculate the function characteristic value in each range, and form tool characteristic response data; Step S4: Combine stress behavior characteristics with tool characteristic response data, and match them with a preset tool type library to determine tool type parameters; Step S5: Dynamically adjust rendering parameters based on tool type parameters and real-time pressure-area relationship to generate a rendering instruction stream that expresses the touch intent.

[0006] This invention provides a comprehensive and dynamic foundation of user operation data by constructing and storing a time-series correlated touch feature chain, accurately recording the continuous evolution and interrelationships of pressure and area parameters. By effectively distinguishing between physiological fluctuations and random noise and adjusting dynamic time windows, it avoids misjudging unintentional jitter and more accurately identifies the purpose of pressing (e.g., decisive / exploratory, click / long press), generating precise pressure behavior characteristics. By calculating the ratio of pressure change rate to area change rate, a pressure-area response function is formed, and key pressure intervals are divided to extract feature values ​​(average value, rate of change, stability). This innovatively quantifies the deformation characteristics of touch tools under different pressure levels, constructs a unique tool elasticity feature vector, effectively captures the physical response differences of different touch tools, and provides a quantitative basis for accurately identifying tool types.

[0007] This invention achieves high-precision tool type parameter recognition by weighted fusion of pressure behavior features and tool characteristic response data, and dynamically adjusting the weights to adapt to operational stability. This multi-dimensional, adaptive matching mechanism overcomes the low accuracy problem of traditional single-feature recognition, significantly improving the recognition accuracy in scenarios where multiple tools are used alternately, and providing quantified confidence. By analyzing the phase relationship between pressure and area and adjusting the rendering response characteristics accordingly, this invention can simulate more realistic physical interaction effects. In particular, it enhances the amplitude of rendering parameter changes at the inflection points of force application / retraction, intuitively expressing the user's strong intention to apply / release force, and greatly improving the naturalness and immersion of touch interaction.

[0008] In summary, this invention achieves accurate identification of touch tools and intentions by constructing a time-series-related touch feature chain, analyzing dynamic changes in pressure-area, and identifying the inflection points of force application / retraction. By introducing pressure-area response functions and pressure behavior feature analysis, this invention can distinguish between physiological fluctuations and random noise, accurately identify user pressing intentions and operating habits, and dynamically adjust rendering parameters, significantly improving the accuracy, naturalness, and tool simulation capabilities of touch interaction.

[0009] Preferably, the present invention also provides a touch data processing system for performing the touch data processing method described above, the touch data processing system comprising: The touch feature acquisition module is used to acquire the capacitance signal of the touch surface, extract the pressure value and contact area parameters of the touch point, construct a touch feature chain with time-series correlation, and store the touch feature chain as user operation data. The pressure behavior analysis module is used to identify the pressing action sequence in the touch feature chain, extract the force change characteristics of the pressing process, calculate the pressure change rate based on the force change characteristics, identify the force inflection point and the force release inflection point, distinguish between physiological fluctuations and random noise, and generate pressure behavior characteristics. The tool characteristic modeling module is used to calculate the ratio of pressure change rate to area change rate to form a pressure-area response function, determine the key pressure range, calculate the function characteristic value in each range, and form tool characteristic response data. The tool type identification module is used to combine stress behavior characteristics and tool characteristic response data, and match them with a preset tool type library to determine the tool type parameters. The rendering instruction generation module is used to dynamically adjust rendering parameters based on tool type parameters and real-time pressure-area relationship to generate a rendering instruction stream that expresses the touch intent.

[0010] This system utilizes high-frequency acquisition and parallel analysis of capacitive signals to ensure real-time, high-precision extraction of pressure values ​​and area parameters. It constructs a time-series correlated touch feature chain, providing a comprehensive and dynamic foundation of user operation data, thus enhancing the system's ability to perceive touch events and improve data accuracy. By calculating the pressure change rate and identifying the inflection point of force application / retraction, the system accurately captures the user's force application intention. It distinguishes between physiological fluctuations and random noise, avoiding misjudgments and accurately determining the purpose of pressure application, generating highly reliable pressure behavior characteristics, greatly improving the system's ability to understand user operation intentions.

[0011] The system calculates the ratio of pressure change rate to area change rate, forming a pressure-area response function, and extracts function feature values ​​based on key pressure intervals. This innovative approach quantifies the deformation response of touch tools under different pressure levels, constructs a unique tool elasticity feature vector, effectively distinguishes the physical characteristics of different tools, and provides a solid quantitative basis for accurate tool type identification. The system combines pressure behavior features with tool characteristic response data, along with a dynamically adjusted weighted fusion mechanism, to achieve high-precision, adaptive identification of touch tool types. This multi-dimensional matching significantly outperforms traditional methods, improving the accuracy and robustness of identification in scenarios involving alternating use of multiple tools, ensuring a personalized interactive experience.

[0012] The system dynamically adjusts rendering parameters based on the identified tool type parameters and real-time pressure-area relationship, generating a rendering command stream that expresses the touch intent, making touch feedback intuitive and expressive. It analyzes the pressure-area phase relationship to adjust rendering response characteristics, simulating more realistic physical interactions. At the force application / retraction inflection points, the system enhances the amplitude of rendering parameter changes, intuitively highlighting the user's force application intent, significantly improving the naturalness, immersion, and user experience of touch interaction.

[0013] Preferably, a touch screen includes: A touch surface; Multiple capacitive sensors are mounted on the touch surface; A memory used to store computer program instructions; A processor electrically connected to a capacitive sensor and a memory; wherein, when executing computer program instructions, the processor is configured to implement the touch data processing method described above. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the steps of a touch data processing method. Figure 2 This is a physical image of the touchscreen component in this invention; Figure 3 This is a characteristic graph of the pressure-area response function in this invention. Detailed Implementation

[0015] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0016] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0017] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a touch data processing method, comprising the following steps: Step S1: Collect the capacitance signal of the touch surface, extract the pressure value and contact area parameters of the touch point, construct a touch feature chain with time-series correlation, and store the touch feature chain as user operation data; In this embodiment of the invention, the touch device continuously acquires the capacitance signal matrix at a frequency of 240 Hz, and simultaneously analyzes and extracts the pressure value. With contact area parameters Each group Data along with high-precision timestamps Encapsulated as a data node The data is appended to the queue in chronological order, and a time-series-associated chain of touch features is constructed and stored as user operation data.

[0019] Step S2: Identify the pressing action sequence in the touch feature chain, extract the force change features of the pressing process, calculate the pressure change rate based on the force change features, identify the force inflection point and the force release inflection point, distinguish between physiological fluctuations and random noise, and generate pressure behavior features; In this embodiment of the invention, the pressure sequence in the touch feature chain is analyzed, and its first-order difference is calculated to obtain the pressure change rate, followed by a second-order difference. Global positive peaks in the second-order difference data are identified as the inflection points of force exertion, and global negative peaks are identified as the inflection points of force reduction. A Fast Fourier Transform is performed on the pressure stability interval data, and physiological fluctuations with specific dominant frequencies are distinguished from random noise with diffused spectra based on spectral characteristics. Finally, the inflection point markers and fluctuation classification results are combined to generate structured pressure behavior features.

[0020] Step S3: Calculate the ratio of pressure change rate to area change rate to form a pressure-area response function, determine the key pressure range, calculate the function characteristic value in each range, and form tool characteristic response data; In this embodiment of the invention, the ratio of pressure change rate to area change rate at each time point is calculated to form a pressure-area response function time series. The pressing process is divided into three key pressure intervals—low, medium, and high—based on the percentage of pressure peak. Within each interval, characteristic values ​​such as the average value, rate of change, and stability of the pressure-area response function are calculated and combined into a feature vector to quantify the deformation response of the touch tool at different pressure stages, forming tool characteristic response data.

[0021] Step S4: Combine stress behavior characteristics with tool characteristic response data, and match them with a preset tool type library to determine tool type parameters; In this embodiment of the invention, pressure behavior features are matched with a pressure behavior sub-library in a preset tool type library to calculate the pressure behavior matching degree. Simultaneously, tool characteristic response data is matched with area behavior and response sub-libraries in the library to calculate the tool characteristic matching degree. The fusion weights of these two matching degrees are dynamically adjusted based on the pressure stability of the current touch operation (the more stable the operation, the higher the tool characteristic matching degree weight), and the tool type with the highest weighted fusion matching degree is determined as the final tool type parameter.

[0022] Step S5: Dynamically adjust rendering parameters based on tool type parameters and real-time pressure-area relationship to generate a rendering instruction stream that expresses the touch intent; In this embodiment of the invention, a basic rendering parameter template is selected based on the determined tool type parameters, and stroke width, texture, etc., are defined. The time difference between the real-time pressure and area sequences reaching their peak values, i.e., the phase relationship, is analyzed, and the dependency weights of the rendering parameters on pressure and area are adjusted according to this phase relationship. During the generation of the rendering command stream, when the inflection points of force application and force reduction are processed, the change amplitude of the rendering parameters is multiplied by an enhancement coefficient to visually highlight the user's intention to change the force.

[0023] Preferably, step S1 includes the following steps: Step S11: Set the sampling frequency to be higher than the screen refresh rate and continuously sample the capacitance signal of the touch surface; Step S12: Parallel analysis of capacitance signals to extract pressure values ​​and contact area parameters; Step S13: Construct a touch feature chain to record the real-time changes in pressure value and area parameters and their interrelationships.

[0024] In this embodiment of the invention, the high-speed capacitive scanning controller continuously scans the capacitive sensor array on the touch surface at a fixed frequency of 1000 Hz, which is higher than the refresh rate of mainstream displays. The controller charges, discharges, and detects the capacitive sensor units through time-division multiplexing or parallel reading, digitizing the original analog capacitance signals obtained from each scan. This enables rapid capture of the capacitance state of all sensors on the touch surface and continuous generation of original digital capacitance data streams.

[0025] The internal processing unit receives the digitized raw capacitance data stream and analyzes the multi-region data in real time using a parallel architecture. The analysis process includes: applying Kalman filtering or moving average filtering to the capacitance value of each sensor unit for noise reduction. Subsequently, a threshold segmentation method is used to identify active sensor units with capacitance values ​​higher than a preset benchmark threshold, constituting the touch area. The contact area parameter is obtained by summing the preset areas of these active sensor units. The pressure value is calculated by summing the capacitance increments of the active sensor units within the touch area and converting it to a dimensionless pressure value from 0 to 1023 using a pre-calibrated mapping curve.

[0026] In each acquisition cycle (1 millisecond), the extracted real-time pressure value and contact area parameters, along with a high-precision timestamp, are encapsulated into a "touch feature frame." The touch feature frame contains the current pressure value, area parameters, and a timestamp accurate to the microsecond level. These continuously generated touch feature frames are stored in a circular buffer queue in timestamp order, forming a temporally correlated "touch feature chain." Each frame in this chain reflects the instantaneous state of pressure and area at a specific moment, and its position reveals the temporal relationship of the touch event, recording the dynamic evolution of the operation. The touch feature chain is continuously updated and stored as raw user operation data.

[0027] Preferably, step S2 includes the following steps: Step S21: Automatically adjust the time window for pressure analysis based on the movement speed of the touch points in the touch feature chain; Step S22: Calculate the pressure rise time and pressure release time during the pressing action to determine the purpose of the pressing action; Step S24: Extract the pressure-area change rate curve during the pressing process to identify the accuracy of the pressing behavior; Step S25: Calculate the pressure change rate to obtain the change rate curve; perform second-order differential calculation on the change rate curve to obtain second-order differential data; identify the positive peak value of the second-order differential data as the inflection point of force exertion and the negative peak value as the inflection point of force retraction. Step S26: Analyze the fluctuation characteristics of the pressure stability range through the rate of change curve, and distinguish between regular touch jitter data and control noise data; generate pressure behavior characteristics based on inflection point markers and fluctuation characteristics.

[0028] In this embodiment of the invention, the touch data processing system continuously monitors the touch point position sequence in the touch feature chain, calculates the average movement distance of the touch point over the past 50 milliseconds every 10 milliseconds, and estimates the real-time speed. If the speed exceeds a preset threshold (e.g., 100 mm / s), the pressure analysis time window is automatically set to 50 milliseconds; otherwise, it is set to 200 milliseconds, dynamically adapting to rapid gestures and fine operations.

[0029] Within the analysis window, the point when the pressure first reaches a preset minimum activation threshold (e.g., 50) is identified as the press start point, and the point when the pressure reaches its peak value is identified as the peak value. The time from the start point to the peak value is the pressure rise time. The point when the pressure falls below the minimum activation threshold again is defined as the release point, and the time from the peak value to the release point is the pressure release time. By analyzing the rise time and release time, the initial purpose of the press action can be determined. For example, a rapid rise and release indicates a light touch, while maintaining the peak value for a long time or a slow release indicates a press or drag intention.

[0030] Extract the pressure value sequence within the current pressing cycle from the touch feature chain. and contact area parameter sequence The pressure change rate is obtained by performing first-order differences on each of them. and area change rate This generates a pressure-area change rate curve. The accuracy of the compression action is identified by evaluating the smoothness, consistency, local slope variations, or second derivative of the curve. A smooth curve with minimal fluctuations indicates high accuracy; a volatile and irregular curve indicates low accuracy.

[0031] For the sequence of pressing pressure values By performing a first-order difference, the rate of change of pressure is obtained. Curve. For The curve is then subjected to first-order difference again to obtain the second-order differential data of pressure. .scanning Positive peak values ​​are marked as the inflection point of force exertion (when the pressure increase is the maximum), and negative peak values ​​are marked as the inflection point of force reduction (when the pressure decreases the maximum).

[0032] The pressure stability interval is defined as the range where the rate of pressure change is below a specific threshold (e.g., 10 units / millisecond) and persists for more than 50 milliseconds. Fast Fourier Transform (FFT) spectral analysis is performed on the pressure data within these intervals to extract the fluctuation frequencies. and amplitude If a fluctuation pattern with a stable frequency (e.g., 8-12 Hz) and regular amplitude is detected, it is classified as regular touch jitter data. If the fluctuation pattern is random and irregular, it is classified as control noise data. Finally, the timing and intensity of the force application inflection point and the force withdrawal inflection point, as well as the features of the touch jitter data (including frequency and amplitude) and the distinguished control noise data, are integrated into a multi-dimensional vector as pressure behavior features.

[0033] Preferably, step S22 includes: Step S221: Calculate the time it takes for the pressure to rise from a preset trigger threshold to the peak value as the pressure rise time; Step S222: Calculate the time it takes for the pressure to drop from its peak value to below a preset trigger threshold as the pressure release time; Step S223: Analyze the ratio of pressure rise time to release time to distinguish between decisive and tentative presses; Step S224: Extract the time interval pattern of multiple consecutive presses and identify the pressing rhythm characteristics of the user's pressing operation; Step S225: Analyze the pressure peak duration of the pressing action by analyzing the pressing rhythm characteristics to determine the purpose of the pressing action.

[0034] In this embodiment of the invention, the touch data processing system continuously monitors the pressure sequence. When the pressure first rises from below a trigger threshold (e.g., 50) and exceeds it, it is marked as the press initiation point. Track to peak pressure. The pressure rise time is and The time difference.

[0035] Identify pressure peaks Afterward, monitor the pressure drop. When the pressure first drops from its peak and falls below the trigger threshold (e.g., 50) again, mark it as the end of the press. The pressure release time is... and The time difference.

[0036] Calculate pressure rise time With pressure release time ratio .like If the pressure is less than a preset threshold (e.g., 0.5), it is considered a decisive press; if... If the value is greater than another threshold (such as 2.0), it is judged as a tentative press.

[0037] Record the start time of multiple presses continuously. For continuous presses (intervals less than 1000 milliseconds), calculate the time interval sequence between presses. Analyze the standard deviation of the interval sequence; if it is below a preset threshold (e.g., 50 milliseconds), a regular press rhythm characteristic is identified.

[0038] Within each press action, the peak pressure duration is first identified, i.e., the time the pressure value remains near the peak (e.g., above 90% of the peak). Combined with the identified press rhythm characteristics, the purpose of the press is comprehensively determined. A short peak duration (e.g., less than 50 milliseconds) with a regular rhythm is identified as a rapid click. A long peak duration (e.g., more than 200 milliseconds) with a discontinuous rhythm is identified as a long press or drag start. A moderate peak duration (e.g., 50-200 milliseconds) with a regular rhythm is identified as a purposeful, regular click.

[0039] Preferably, step S225 includes: Define the time period during which the pressure fluctuation amplitude is less than the preset fluctuation threshold as the peak duration; Analyze the duration of peak duration to distinguish between click, press, and long press operations; Calculate the characteristics of micro-pressure changes during the peak duration to identify the user's adjustment behavior during the pressing process; Extract the ratio of peak duration to rise time to determine the certainty of compression.

[0040] In this embodiment of the invention, in a pressure sequence of a pressing action, the pressure peak is first identified. Then, scan forward and backward to find the longest continuous time period containing all pressure values. satisfy (e.g., 5% or 20 pressure units of the peak value). This longest continuous period is the peak duration. .

[0041] Based on peak duration The system identifies the operation type based on the length of the data. Actions shorter than 80 milliseconds are classified as clicks. Actions between 80 and 450 milliseconds are classified as presses. Actions longer than 450 milliseconds are classified as long presses. These thresholds are based on statistical analysis presets.

[0042] Peak duration The system calculates the standard deviation of all pressure values. This is used to measure minute fluctuations in pressure. If... If the pressure exceeds a preset fine-tuning threshold (e.g., 15 pressure units), it indicates that the user has engaged in fine-tuning behavior; if it is below, it indicates that the pressure remains highly stable.

[0043] Calculate peak duration With time of pressure rise ratio .like If the pressure exceeds a preset deterministic threshold (e.g., 3.0), it is considered a highly deterministic press, indicating rapid application of pressure and sustained pressure over a prolonged period. If the pressure is less than another threshold (such as 0.8), it is judged as uncertainty or tentative pressing, indicating that the pressure rise time is relatively long and the maintenance time is short.

[0044] Preferably, step S26 includes: Frequency domain decomposition is performed on the pressure stability range to extract the frequency and amplitude characteristics of the fluctuations, thus forming the fluctuation characteristics. By matching historical user operation data with fluctuation characteristics, touch jitter patterns can be identified; Based on the frequency stability and amplitude regularity in the touch jitter mode, the fluctuations are classified into touch jitter data and control noise data; Touch jitter data is retained and labeled, and adaptive filtering is applied to smooth control noise data.

[0045] In this embodiment of the invention, a pressure value sequence is extracted from the pressure stability interval of the touch feature chain. After applying Hanning window weighting to this sequence, a Fast Fourier Transform is performed to convert it to the frequency domain. In the frequency domain, the system analyzes the power spectral density, detecting the peak power spectral density within the physiological jitter frequency range of 4 Hz to 12 Hz, and recording its frequency. and amplitude This forms the fluctuation characteristics of this range. .

[0046] The system maintains a touch jitter pattern library containing feature vectors of various typical physiological jitter patterns trained from historical user data. The fluctuation characteristics within the current pressure stability range are also considered. Matching is performed with a pattern library using cosine similarity or Euclidean distance. If... and If the current jitter matches a certain pattern in the library within the preset tolerance range, then the current jitter is identified as belonging to that touch jitter pattern.

[0047] After identifying the touch jitter pattern, the system evaluates its frequency stability and amplitude regularity. Frequency stability is determined by the dominant frequency. Whether the range of variation within the stable interval is less than a preset threshold (e.g., 1 Hz). Amplitude regularity judgment. Whether the fluctuation is persistent and the amplitude is less than a preset threshold (e.g., 10% of the total pressure). If the fluctuation simultaneously meets the requirements of frequency stability, amplitude regularity, and frequency between 4 Hz and 12 Hz, it is classified as regular touch jitter data. Otherwise, it is classified as control noise data.

[0048] For raw pressure values ​​classified as touch jitter data, the system retains them and adds a jitter type label (e.g., "IsPhysiologicalJitter" is true) to indicate that they are physiological fluctuations. For raw pressure values ​​classified as control noise data, the system applies an adaptive Kalman filter method to automatically adjust parameters based on real-time noise covariance and dynamic model to smooth irregular random noise and generate smoothed pressure data.

[0049] Preferably, step S3 includes the following steps: Step S31: Calculate the ratio of the pressure change rate to the area change rate, which is defined as the pressure-area response function; Step S32: By analyzing the changing trend of the pressure-area response function, determine the key pressure range that reflects the characteristics of the tool; Step S33: Calculate the characteristic values ​​of the pressure-area response function within each pressure interval in the key pressure interval, including the average value, rate of change, and stability; Step S34: Combine the eigenvalues ​​of the pressure-area response function with the area response characteristics at the inflection points of force application and force release to form tool characteristic response data.

[0050] In this embodiment of the invention, pressure values ​​are extracted from the touch feature chain. and contact area parameters The system performs a first-order difference between the two sequences to obtain the pressure change rate. and area change rate .like If the absolute value is less than a preset threshold (e.g., 0.01 mm² / ms), the pressure-area response function value... Set to zero or interpolate; otherwise, Defined as .

[0051] The system A moving average filter is applied for smoothing. Then, the graph is plotted. about The curve is used to identify inflection points or plateau regions where the slope changes significantly by calculating the first and second derivatives of the curve. This allows for the determination of key pressure ranges (such as low-pressure, medium-pressure, and high-pressure ranges), which reflect the physical characteristics of the tool at different pressure levels.

[0052] For each defined critical pressure range, the system extracts all data within that range. Numerical analysis is performed. The arithmetic mean is calculated as the average characteristic value; the slope is calculated using linear regression as the rate of change characteristic value; and the standard deviation is calculated as the stability characteristic value. The smaller the standard deviation, the more stable the function is within that interval.

[0053] Finally, the average value, rate of change, and stability of each key pressure range will be analyzed. Eigenvalues, and the instantaneous contact area at the inflection points of force application and force reduction. and Integration. These values ​​are concatenated into a vector or data structure to form tool characteristic response data, which is used for subsequent tool type identification.

[0054] Of particular importance, step S33 includes: The initial response rate of the pressure-area response function is calculated in the low-pressure region to characterize the initial sensitivity of the tool. The linearity coefficient of the pressure-area response function is calculated in the medium-pressure range to characterize the stable control characteristics of the tool. The saturation threshold of the pressure-area response function is calculated in the high-pressure range to characterize the tool's ultimate deformation capacity. Based on the initial sensitivity, stable control characteristics, and ultimate deformation capability, an elastic feature vector of the tool is constructed, and different types of tools form distinguishable clusters in this vector space.

[0055] In this embodiment of the invention, a low-pressure range (e.g., pressure from 50 to 20% of the total peak value) is identified, and extraction is performed. and Values. A linear regression is performed on these data points, and the slope is calculated as the initial response rate, characterizing the initial sensitivity of the tool.

[0056] The system identifies medium-voltage ranges (e.g., 20% to 80% of the total peak value) and extracts... and Values. Perform linear regression on these data points and calculate... The value serves as a linearity coefficient, characterizing the stable control properties of the tool. The closer it is to 1.0, the higher the linearity.

[0057] The system identifies high-pressure ranges (e.g., from 80% of the total peak pressure to the peak pressure) and monitors them. Trend of change. Calculation. Compared to derivative .when When the absolute value of the pressure first decreases and remains below a preset threshold (e.g., 0.005), the corresponding pressure value P is determined as the saturation threshold, which characterizes the tool's ultimate deformation capability.

[0058] The calculated initial response rate, linearity coefficient, and saturation threshold are combined to form a three-dimensional tool elasticity feature vector, such as [initial response rate, linearity coefficient, saturation threshold]. This vector represents the unique elasticity characteristics of the tool in three-dimensional space. By performing cluster analysis on such vectors generated by different tools, the system can discover the inherent differences in these elasticity characteristics among different tool types (such as fingers, styluses, and fingernails), forming distinguishable clusters.

[0059] Preferably, the process of determining the tool type parameter in step S4 includes: Calculate the matching degree between the pressure behavior characteristics and the pressure behavior sub-library in the preset tool type library, and the matching degree between the tool characteristic response data and the area behavior sub-library in the preset tool type library. Calculate the matching degree between the tool characteristic response data and the response sub-library in the preset tool type library; The three matching degrees are weighted and fused together, and the weight coefficients are dynamically adjusted according to the stability of the current touch operation. The tool type with the highest weighted fusion matching degree is selected as the recognition result, and the matching confidence is recorded.

[0060] In this embodiment of the invention, the stress behavior characteristics and tool characteristic response data of the current operation are obtained. The system traverses the tool type library, and for each tool type: the current stress behavior characteristics are compared with the reference feature vectors of the stress behavior sub-library in the library using Euclidean distance or cosine similarity calculation to obtain the stress behavior matching degree. Simultaneously, the area of ​​the force application and withdrawal inflection point in the tool characteristic response data is extracted and compared with the reference area features of the area behavior sub-library in the database to obtain the area behavior matching degree. .

[0061] For each tool type, the system extracts the pre-defined pressure-area response function feature vectors (such as initial response rate, linearity coefficient, and saturation threshold) and the reference values ​​for the area at the force application and retraction inflection points from its response sub-library. The current tool characteristic response data is then matched with these reference feature vectors using methods such as Mahalanobis distance to obtain the response characteristic matching degree M_R.

[0062] The system will and Perform weighted fusion: Weighting coefficients Dynamically adjust based on the stability of the current touch operation. For example, when the operation is stable, Increase (e.g., 0.6). and Lowering the value (e.g., by 0.2) emphasizes physical response characteristics; when operation is fast and unstable, and Enhance the ability to capture fleeting behaviors.

[0063] After calculating the weighted fusion matching degree for all tool types, the system selects the fusion matching degree. The highest-ranking tool type is used as the recognition result, i.e., the tool type parameter. The value is also recorded as the matching confidence level of this identification. The higher the value, the more reliable the result.

[0064] Of particular importance, step S5 includes: Select the basic rendering parameter template based on the tool type parameters; Analyze the phase relationship between pressure and area in the touch feature chain to detect pressure-leading or area-leading operation modes; Adjust the responsiveness of rendering parameters based on phase relationships; Enhance the variation of rendering parameters at the inflection points of force application and release to display the user's intent to change force.

[0065] In this embodiment of the invention, a determined tool type parameter (such as "finger" or "stylus") is received. Based on this parameter, the system loads the corresponding basic rendering parameter template from its internal rendering parameter template library. For example, when identified as a "finger," a template containing a stroke width of 5 to 50 pixels, an opacity of 0.4 to 1.0, a multiply color blending mode, and a blurred edge effect is loaded; when identified as a "stylus," a template with a stroke width of 1 to 10 pixels, an opacity of 0.8 to 1.0, a normal color blending mode, and a disabled blurred edge effect is loaded.

[0066] The system extracts the pressure value sequence of the current pressing process from the real-time touch feature chain. and contact area parameter sequence Through calculation and The cross-correlation function between them is used to find the time delay that maximizes the cross-correlation coefficient. .like If positive, it is judged as "pressure-led" operating mode; if If it is negative, it is judged as the "area leadership" model; if If the value is close to zero, it is judged as a synchronous change mode.

[0067] Based on the detected pressure-area phase relationship, the system dynamically adjusts the responsiveness of the selected base rendering parameter template. In "Pressure-Lead" mode, the sensitivity of rendering parameters to pressure changes is increased; for example, the slope of the brush width pressure mapping function is increased by 20%, while the sensitivity of transparency to area changes is reduced by 10%. In "Area-Lead" mode, the sensitivity of rendering parameters to area changes is increased; for example, the slope of the transparency or texture detail area mapping function is increased by 15%, while the impact of pressure on brush width is slightly reduced.

[0068] The system monitors the pressure inflection points identified in step S2 in real time. When a pressure inflection point is detected (when the pressure increase rate is maximum), the system instantaneously amplifies the changes in stroke width, color saturation, or transparency by a factor of 2 within the current frame and the following 50 milliseconds, creating a visual "impact" effect. Similarly, when a pressure reduction inflection point is detected (when the pressure decrease rate is maximum), the system accelerates the decrease of these rendering parameters by a factor of 2 within the same time frame, creating a visual "finishing" effect. This instantaneous amplification of changes directly expresses the user's strong intention to apply or release pressure.

[0069] Please see Figure 2 This is a picture of the actual touch screen component.

[0070] Please see Figure 3 , is the characteristic graph of the pressure-area response function.

[0071] Graph structure: Horizontal axis: Pressure Vertical axis: Pressure-area response function The three regions are distinguished by different colored backgrounds: blue for low pressure, purple for medium pressure, and orange for high pressure.

[0072] Key annotations: Initial response rate: The slope of the curve, indicated by a red arrow pointing to the beginning of the curve; Linearity: In the medium-pressure range, the relatively straight sections are marked with dashed lines; Saturation threshold: Marked with a vertical line at the starting point of the high-pressure zone as the turning point where the temperature rises sharply.

[0073] Curve characteristics: Low-pressure phase: a slowly rising initial response; Medium pressure range: relatively linear flat response; High-voltage section: a rapidly increasing saturation response.

[0074] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0075] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A touch data processing method, characterized in that, Applied to touch devices, the touch data processing method includes the following steps: Step S1: Collect the capacitance signal of the touch surface, extract the pressure value and contact area parameters of the touch point, construct a touch feature chain with time-series correlation, and store the touch feature chain as user operation data; Step S2: Identify the pressing action sequence in the touch feature chain, extract the force change features of the pressing process, calculate the pressure change rate based on the force change features, identify the force inflection point and the force release inflection point, distinguish between physiological fluctuations and random noise, and generate pressure behavior features; Step S3: Calculate the ratio of pressure change rate to area change rate to form a pressure-area response function, determine the key pressure range, calculate the function characteristic value in each range, and form tool characteristic response data; Step S4: Combine stress behavior characteristics with tool characteristic response data, and match them with a preset tool type library to determine tool type parameters; Step S5: Dynamically adjust rendering parameters based on tool type parameters and real-time pressure-area relationship to generate a rendering instruction stream that expresses the touch intent.

2. The touch data processing method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Set the sampling frequency to be higher than the screen refresh rate and continuously sample the capacitance signal of the touch surface; Step S12: Parallel analysis of capacitance signals to extract pressure values ​​and contact area parameters; Step S13: Construct a touch feature chain to record the real-time changes in pressure value and area parameters and their interrelationships.

3. The touch data processing method according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Automatically adjust the time window for pressure analysis based on the movement speed of the touch points in the touch feature chain; Step S22: Calculate the pressure rise time and pressure release time during the pressing action to determine the purpose of the pressing action; Step S24: Extract the pressure-area change rate curve during the pressing process to identify the accuracy of the pressing behavior; Step S25: Calculate the pressure change rate to obtain the change rate curve; perform second-order differential calculation on the change rate curve to obtain second-order differential data; identify the positive peak value of the second-order differential data as the inflection point of force exertion and the negative peak value as the inflection point of force retraction. Step S26: Analyze the fluctuation characteristics of the pressure stability range through the rate of change curve, and distinguish between regular touch jitter data and control noise data; generate pressure behavior characteristics based on inflection point markers and fluctuation characteristics.

4. The touch data processing method according to claim 3, characterized in that, Step S22 includes: Step S221: Calculate the time it takes for the pressure to rise from a preset trigger threshold to the peak value as the pressure rise time; Step S222: Calculate the time it takes for the pressure to drop from its peak value to below a preset trigger threshold as the pressure release time; Step S223: Analyze the ratio of pressure rise time to release time to distinguish between decisive and tentative presses; Step S224: Extract the time interval pattern of multiple consecutive presses and identify the pressing rhythm characteristics of the user's pressing operation; Step S225: Analyze the pressure peak duration of the pressing action by analyzing the pressing rhythm characteristics to determine the purpose of the pressing action.

5. The touch data processing method according to claim 4, characterized in that, Step S225 includes: Define the time period during which the pressure fluctuation amplitude is less than the preset fluctuation threshold as the peak duration; Analyze the duration of peak duration to distinguish between click, press, and long press operations; Calculate the characteristics of micro-pressure changes during the peak duration to identify the user's adjustment behavior during the pressing process; Extract the ratio of peak duration to rise time to determine the certainty of compression.

6. The touch data processing method according to claim 3, characterized in that, Step S26 includes: Frequency domain decomposition is performed on the pressure stability range to extract the frequency and amplitude characteristics of the fluctuations, thus forming the fluctuation characteristics. By matching historical user operation data with fluctuation characteristics, touch jitter patterns can be identified; Based on the frequency stability and amplitude regularity in the touch jitter mode, the fluctuations are classified into touch jitter data and control noise data; Touch jitter data is retained and labeled, and adaptive filtering is applied to smooth control noise data.

7. The touch data processing method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Calculate the ratio of the pressure change rate to the area change rate, which is defined as the pressure-area response function; Step S32: By analyzing the changing trend of the pressure-area response function, determine the key pressure range that reflects the characteristics of the tool; Step S33: Calculate the characteristic values ​​of the pressure-area response function within each pressure interval in the key pressure interval, including the average value, rate of change, and stability; Step S34: Combine the eigenvalues ​​of the pressure-area response function with the area response characteristics at the inflection points of force application and force release to form tool characteristic response data.

8. The touch data processing method according to claim 7, characterized in that, The process of determining the tool type parameter in step S4 includes: Calculate the matching degree between the pressure behavior characteristics and the pressure behavior sub-library in the preset tool type library, and the matching degree between the tool characteristic response data and the area behavior sub-library in the preset tool type library. Calculate the matching degree between the tool characteristic response data and the response sub-library in the preset tool type library; The three matching degrees are weighted and fused together, and the weight coefficients are dynamically adjusted according to the stability of the current touch operation. The tool type with the highest weighted fusion matching degree is selected as the recognition result, and the matching confidence is recorded.

9. A touch data processing system, characterized in that, The touch data processing system is used to perform the touch data processing method as described in claim 1, and includes: The touch feature acquisition module is used to acquire the capacitance signal of the touch surface, extract the pressure value and contact area parameters of the touch point, construct a touch feature chain with time-series correlation, and store the touch feature chain as user operation data. The pressure behavior analysis module is used to identify the pressing action sequence in the touch feature chain, extract the force change characteristics of the pressing process, calculate the pressure change rate based on the force change characteristics, identify the force inflection point and the force release inflection point, distinguish between physiological fluctuations and random noise, and generate pressure behavior characteristics. The tool characteristic modeling module is used to calculate the ratio of pressure change rate to area change rate to form a pressure-area response function, determine the key pressure range, calculate the function characteristic value in each range, and form tool characteristic response data. The tool type identification module is used to combine stress behavior characteristics and tool characteristic response data, and match them with a preset tool type library to determine the tool type parameters. The rendering instruction generation module is used to dynamically adjust rendering parameters based on tool type parameters and real-time pressure-area relationship to generate a rendering instruction stream that expresses the touch intent.

10. A touch screen, characterized in that, include: A touch surface; Multiple capacitive sensors are mounted on the touch surface; A memory used to store computer program instructions; A processor electrically connected to a capacitive sensor and a memory; wherein, when executing computer program instructions, the processor is configured to implement the touch data processing method as described in any one of claims 1 to 8.

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