Partition cooperative scanning method and system for flexible touch screen

Abstract

The invention discloses a partition cooperative scanning method and system for a flexible touch screen, and relates to the field of display screens. In the method, the touch panel is divided into a plurality of preset partitions; scanning each preset partition by adopting a first scanning mode to obtain an original touch signal of each preset partition; dividing the touch panel into an active zone and an inactive zone comprising a plurality of inactive zones, and selecting the inactive zone in a preset zone from the inactive zone as a noise sampling point; scanning the activity partition by adopting a second scanning mode to obtain an activity signal, and scanning the noise sampling point by adopting the second scanning mode to obtain a local noise sample; constructing a noise reference model to obtain a noise compensation distribution matrix; and denoising the activity signal based on the noise compensation distribution matrix to obtain a target touch signal, and analyzing a touch point coordinate based on the target touch signal. By implementing the technical scheme provided by the invention, the user experience is improved.

Description

Technical Field

[0001] This application relates to the field of displays, specifically to a partitioned collaborative scanning method and system for flexible touch screens. Background Technology

[0002] With the emergence of large-size flexible active-matrix organic light-emitting diode (AMOLED) products such as foldable laptops, their integrated touch technology faces new challenges. The dramatic increase in screen size leads to a significant increase in the traces of the touch sensing electrodes, resulting in a substantial increase in their equivalent resistance and parasitic capacitance (RC). This causes severe attenuation of the touch driving signal during transmission, a decrease in the signal-to-noise ratio, and affects the sensitivity and accuracy of touch. Traditional global scanning methods, which drive and sense the entire touch panel in each scanning cycle, can cover the entire area, but they cause significant energy waste and invalid scanning in most areas where no touch occurs. This results in high overall system power consumption and difficulty in achieving a high reporting rate, failing to meet the stringent requirements of modern portable devices for battery life and a smooth interactive experience.

[0003] To address the high power consumption and low efficiency issues caused by global scanning, the industry has proposed a partitioned scanning technology. This solution typically divides the entire touch panel logically into several fixed sub-regions. Through partitioned management, scanning is performed only on active areas, enabling on-demand resource allocation, effectively reducing the system's average power consumption, and improving the touch detection rate of active areas.

[0004] However, while the aforementioned partitioned scanning technology improves energy efficiency, it fails to adequately address a problem inherent in large-size flexible AMOLED panels. Specifically, display noise in large-size AMOLEDs, such as common-mode noise generated by cathode voltage fluctuations, can globally interfere with the entire touch sensing layer through interlayer coupling capacitance. This problem is particularly pronounced for flexible panels because when the screen is bent or folded, different areas experience uneven mechanical stress. This stress causes localized, minute changes in the physical parameters of each panel layer, resulting in subtle, non-uniform fluctuations in the spatial distribution of the originally relatively uniform global display noise after coupling to the touch layer. Existing partitioned scanning methods, focusing only on scanning active areas, lose awareness of real-time noise levels in inactive areas, leading to a still unsatisfactory touch signal-to-noise ratio in bent screen usage scenarios, thus impacting the user experience. Summary of the Invention

[0005] This application provides a partitioned collaborative scanning method and system for flexible touch screens, which improves the user experience.

[0006] A first aspect of this application provides a partitioned collaborative scanning method for flexible touchscreens. The method includes: dividing the touch panel of a flexible AMOLED display into multiple preset partitions and assigning a corresponding position identifier to each preset partition; scanning each preset partition using a first scanning method to obtain original touch signals for each preset partition; dividing the touch panel into active partitions and inactive areas containing multiple inactive partitions based on the original touch signals, and selecting inactive partitions within a preset area from the inactive areas as noise sampling points; and scanning the active partitions using a second scanning method to obtain data carrying touch information and noise components. The activity signal is analyzed, and the noise sampling points are scanned using the second scanning method to obtain local noise samples. The scanning power of the second scanning method is higher than that of the first scanning method. Noise data of the entire touch panel is collected under a preset driving signal as global display noise. Based on the global display noise and the local noise samples, a noise benchmark model is constructed. The position identifier corresponding to the active partition is input into the noise benchmark model to obtain the noise compensation distribution matrix corresponding to the active partition. The activity signal is denoised based on the noise compensation distribution matrix to obtain the target touch signal, and the touch point coordinates are parsed based on the target touch signal.

[0007] By employing the above technical solution, the touch panel is divided into preset zones and assigned position markers. Different scanning precision methods are used to distinguish between active and inactive areas. Zones within the inactive areas are selected as noise sampling points to construct a noise baseline model of global display noise and local noise samples. Based on this model, noise compensation and denoising are performed on the active zones, ultimately obtaining accurate target touch signals and touch point coordinates. This method fully utilizes the partitioning characteristics of flexible AMOLED displays. Potential touch areas are quickly identified through low-precision scanning, followed by focused high-precision scanning of these areas, improving scanning efficiency while ensuring the quality of touch signal acquisition. Furthermore, the selection of noise sampling points considers the noise distribution characteristics of inactive areas, resulting in a noise baseline model that better reflects actual conditions. Combined with targeted noise compensation strategies, this effectively suppresses noise interference under complex bending conditions, significantly improving the sensitivity and accuracy of touch sensing and enhancing the user experience.

[0008] Optionally, dividing the touch panel into active partitions and inactive areas containing multiple inactive partitions based on the original touch signals specifically includes: performing binarization processing on the original touch signals; determining a first touch signal greater than a preset signal threshold and marking the preset partition corresponding to the first touch signal as a candidate active partition; determining a second touch signal less than or equal to the preset signal threshold and marking the preset partition corresponding to the second touch signal as a candidate inactive partition; merging adjacent candidate active partitions to obtain the active partitions; marking preset partitions not marked as active partitions as candidate inactive partitions and merging adjacent candidate inactive partitions to obtain the inactive areas.

[0009] By employing the above technical solution, the amplitude differences of the original touch signals are utilized, and reasonable signal thresholds are set to initially screen each preset partition. Further optimization of the region boundaries yields a final result that highly matches actual touch behavior, defining active and inactive regions. This adaptive partitioning method reduces unnecessary high-precision scanning, lowers computational resource consumption while ensuring touch detection coverage, and creates favorable conditions for subsequent noise modeling and signal processing. It significantly improves the real-time performance and energy efficiency of touch scanning.

[0010] Optionally, selecting inactive partitions within a preset area from the inactive region as noise sampling points specifically includes: acquiring physical morphological data characterizing the current bending angle, radius of curvature, and stress distribution of the flexible AMOLED display using a stress sensor built into the touch panel; determining one or more key stress regions where the noise gradient change amplitude is greater than or equal to a preset amplitude threshold under the current bending state based on the physical morphological data; and defining all inactive partitions sharing boundaries or vertices with the active partitions and the key stress regions as the preset region.

[0011] By adopting the above technical solution, the physical characteristics and noise distribution patterns of flexible AMOLED displays under bending conditions are fully considered. By acquiring physical morphological data characterizing the bending angle, radius of curvature, and stress distribution, key stress regions with drastic noise gradient changes are identified, and these regions, along with adjacent inactive regions, are included in the preset noise sampling area. This strategy ensures the representativeness and sufficiency of the noise samples, making the constructed noise benchmark model more closely reflect the actual working state of the flexible panel and accurately depicting the noise distribution changes caused by bending. Simultaneously, the size of the preset area is reasonably controlled, avoiding blindly large-scale sampling and reducing computational overhead and power consumption. This solution cleverly utilizes the sensor information of the flexible AMOLED display itself, providing a reliable basis for the optimal selection of noise sampling points, which is of great significance for improving the robustness and adaptability of touch sensing.

[0012] Optionally, the step of collecting noise data of the entire touch panel under a preset driving signal as global display noise, and constructing a noise benchmark model based on the global display noise and the local noise samples, specifically includes: collecting noise data of the flexible AMOLED display in a non-bending state under a preset driving signal as the global display noise; obtaining the global noise sequence of the global display noise within a preset time period, and obtaining the local noise sequence of the local noise samples within the preset time period; extracting the time-domain statistical features and frequency-domain distribution features of the global noise sequence to obtain a global noise vector; extracting the time-frequency statistical features and frequency-domain distribution features of the local noise sequence to obtain a local noise vector; and constructing the noise benchmark model based on the global noise vector and the local noise vector.

[0013] By employing the above technical solution, noise data in the non-bending state is collected under a preset driving signal as global display noise. The statistical characteristics of global and local noise in the time and frequency domains are obtained to form global and local noise vectors, thus constructing a noise baseline model. This combined global and local modeling approach fully utilizes the noise information of the panel in different states. In particular, the global noise in the non-bending state reflects the inherent noise characteristics of the panel, forming a good complement to the local noise samples. Simultaneously, the use of time-frequency domain feature extraction and network fusion effectively captures the multi-scale and cross-domain correlation of the noise signal, giving the noise baseline model stronger representation capabilities and generalization performance. This solution provides a new approach to noise modeling for flexible AMOLED displays, playing a positive role in improving the signal-to-noise ratio of touch sensing and adapting to complex bending conditions.

[0014] Optionally, constructing the noise baseline model based on the global noise vector and the local noise vector specifically includes: using the global noise vector as a static baseline feature characterizing the inherent noise characteristics of the flexible AMOLED display; using the location identifiers of each noise sampling point and the corresponding local noise vectors to form training samples, with the local noise vectors serving as dynamic features characterizing the local noise under the current bending state; using the static baseline feature and the training samples to train a preset machine learning model, configuring the input of the machine learning model as the receiving location identifier, and configuring the output of the machine learning model as predicted noise feature parameters, thereby obtaining the noise baseline model.

[0015] By employing the aforementioned technical solution, a noise baseline model is constructed based on global and local noise vectors, enabling accurate characterization and prediction of the noise characteristics of flexible AMOLED displays under different bending conditions. Specifically, the global noise vector serves as a static baseline feature, characterizing the inherent noise characteristics of the display; the local noise vector, combined with the location identifiers of noise sampling points, constitutes a dynamic feature, reflecting local noise changes under the current bending condition. By training a pre-defined machine learning model with the static and dynamic baseline features, the model can output accurate noise feature parameters using location identifiers as input, thus obtaining a comprehensive and accurate noise baseline model. This technical solution effectively improves the accuracy and dynamic adaptability of noise characteristic prediction, providing reliable support for performance optimization of flexible AMOLED displays under complex bending conditions.

[0016] Optionally, the step of inputting the location identifier corresponding to the active partition into the noise benchmark model to obtain the noise compensation distribution matrix corresponding to the active partition specifically includes: dividing the active partition into multiple noise compensation units, each noise compensation unit containing at least one touch pixel; obtaining the center coordinates of each noise compensation unit as the location identifier of the noise compensation unit; inputting the location identifier of each noise compensation unit into the noise benchmark model to calculate the noise probability distribution parameters of each noise compensation unit; performing mathematical expectation calculation on the noise probability distribution parameters of each noise compensation unit to obtain the noise expectation value, and using the noise expectation value as the target noise compensation value of the corresponding noise compensation unit; and smoothing the target noise compensation value of each noise compensation unit to obtain the noise compensation distribution matrix.

[0017] By adopting the above technical solution, the active area is divided into multiple noise compensation units, each containing at least one touch pixel, and the center coordinates of each unit are obtained as position identifiers. Then, the position identifiers of each noise compensation unit are input into the noise baseline model, and the noise probability distribution parameters of each unit are calculated using the model's prediction function. Next, the expected value of the noise probability distribution parameters of each unit is calculated to obtain the expected noise value as the target noise compensation value for that unit. Finally, the target noise compensation values ​​of all noise compensation units are smoothed to obtain a noise compensation distribution matrix. This regional, parameterized noise compensation scheme fully utilizes the predictive capability of the noise baseline model, adaptively calculating the noise compensation value for each region based on the actual location and noise distribution characteristics of the active area, forming a noise compensation distribution matrix corresponding to the touch panel, thereby achieving regional controllability and adjustable intensity of noise suppression. Simultaneously, the granularity of the noise compensation unit division and the introduction of smoothing effectively balance compensation accuracy and computational efficiency, ensuring the fineness of noise suppression while avoiding overfitting and ringing effects. This scheme complements the aforementioned noise modeling method, further enhancing the environmental adaptability and anti-interference performance of flexible AMOLED touchscreens.

[0018] Optionally, the step of denoising the active signal based on the noise compensation distribution matrix to obtain the target touch signal specifically includes: acquiring the active signal sequence of each touch pixel in the active partition within a preset sampling time period; performing time-frequency transformation on the active signal sequence of each touch pixel to obtain the time-frequency spectrum matrix of the active signal; converting the noise compensation distribution matrix of the active partition into a noise compensation spectrum matrix with the same size as the time-frequency spectrum matrix; performing spectral subtraction on the time-frequency spectrum matrix and the noise compensation spectrum matrix to obtain the noise-suppressed target time-frequency spectrum matrix; performing inverse time-frequency transformation on the target time-frequency spectrum matrix to obtain the denoised target active signal sequence; performing energy normalization processing on the target active signal sequence to obtain a normalized energy value sequence; and performing adaptive threshold decision on the normalized energy value sequence to determine the active signal corresponding to the normalized energy value greater than a preset dynamic threshold as the target touch signal.

[0019] By employing the above technical solution, refined recovery of touch signals is achieved from the time-frequency domain perspective. First, the active signal sequence of each touch pixel within the active zone is acquired within a preset sampling time period and subjected to time-frequency transformation to obtain the time-frequency spectrum matrix of the active signal. Then, the noise compensation distribution matrix of the active zone is converted into a noise compensation spectrum matrix of the same size as the time-frequency spectrum matrix. Next, spectral subtraction is performed on the time-frequency spectrum matrix and the noise compensation spectrum matrix to obtain the noise-suppressed target time-frequency spectrum matrix. Then, an inverse time-frequency transformation is performed on the target time-frequency spectrum matrix to obtain the denoised target active signal sequence. Finally, energy normalization and adaptive threshold decision are applied to the target active signal sequence, identifying active signals with energy values ​​greater than a preset dynamic threshold as the target touch signal. This time-frequency domain denoising method cleverly utilizes the prior information provided by the noise compensation distribution matrix, achieving directional suppression of noise components in the time-frequency domain through spectral subtraction, avoiding signal distortion and detail loss problems associated with traditional time-domain filtering methods. Simultaneously, the introduction of energy normalization and adaptive threshold decision further enhances the detection sensitivity and dynamic response capability of the touch signal, effectively overcoming interference from weak signal overload and false touches. This approach, together with the aforementioned noise modeling and compensation methods, forms a complete solution that significantly improves the touch performance of flexible AMOLED displays from multiple aspects, including noise suppression, signal recovery, and touch decision-making. It has distinctive technical features and practical value.

[0020] Secondly, embodiments of this application provide a partitioned collaborative scanning system for flexible touchscreens. The partitioned collaborative scanning system for flexible touchscreens includes: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the partitioned collaborative scanning system for flexible touchscreens to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a partitioned collaborative scanning system for a flexible touchscreen, cause the partitioned collaborative scanning system for a flexible touchscreen to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a partition collaborative scanning system for a flexible touch screen, causes the partition collaborative scanning system for a flexible touch screen to perform the method described in the first aspect and any possible implementation thereof.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: This touch scanning partition management method logically divides the touch panel of a flexible AMOLED display into multiple preset partitions, dynamically identifies active and inactive areas, performs high-precision scanning only on active partitions, and selects noise sampling points in inactive areas. This achieves on-demand resource allocation, significantly reducing power consumption and invalid scanning. Simultaneously, by combining global display noise acquisition with local noise samples, a dynamic noise benchmark model is constructed, accurately compensating for global display noise and local noise fluctuations caused by screen bending in large-size flexible AMOLED panels. This method employs high-precision scanning and noise reduction technology, significantly improving the signal-to-noise ratio of touch signals and making touch point resolution more sensitive and accurate. It effectively adapts to complex usage scenarios such as screen folding or bending, significantly enhancing the user interaction experience. Furthermore, this solution possesses good robustness and applicability, meeting the technical requirements of future large-size foldable screen devices for low power consumption, high sensitivity, and high reporting rate, providing strong technical support for the development of flexible electronic devices. Attached Figure Description

[0024] Figure 1 This is a schematic flowchart of the partition collaborative scanning method for flexible touch screens disclosed in the embodiments of this application; Figure 2 This is another schematic flowchart of the partition collaborative scanning method for flexible touch screens disclosed in the embodiments of this application; Figure 3 This is a schematic diagram of the partitioned collaborative scanning system for flexible touch screens provided in the embodiments of this application.

[0025] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] This application provides a partitioned collaborative scanning method for flexible touchscreens, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a partitioned collaborative scanning method for flexible touchscreens provided in an embodiment of this application. The method is applied to a system, which can execute a partitioned collaborative scanning program for flexible touchscreens. The method includes steps S101 to S107, as follows: Step S101: Divide the touch panel of the flexible AMOLED display into multiple preset zones and assign a corresponding position identifier to each preset zone.

[0030] In step S101, the touch panel of the flexible AMOLED display refers to the sensor layer integrated on the flexible active-matrix organic light-emitting diode display for sensing user touch operations, whose physical characteristics allow for bending or folding. A preset partition represents a logical division that divides the complete touch panel into multiple non-overlapping rectangular or square blocks at the system level. This division is not a physical cut but rather for ease of management and scanning. For example, a complete touch panel can be logically divided into a 100-row by 200-column grid, with each small grid being a preset partition. A location identifier refers to the unique identification information assigned to each preset partition, typically a coordinate value representing the precise location of that partition on the entire touch panel. For example, a preset partition located at row 10, column 25 could have a location identifier of (10, 25).

[0031] Specifically, during initialization or startup, the system performs logical gridding processing on the entire touch panel of the flexible AMOLED display according to pre-set configuration parameters. The system creates a two-dimensional logical map, the size of which, for example, is M rows and N columns, determining the total number and size of the preset partitions. Subsequently, the system traverses this M x N logical grid, assigning a unique location identifier to each unit, i.e., each preset partition. This location identifier typically uses a row and column index, such as (x, y), where x ranges from 0 to M-1, and y ranges from 0 to N-1. This logical map, containing all preset partitions and their corresponding location identifiers, is stored in the system's memory as the basis for subsequent scanning, partition management, and data positioning.

[0032] Step S102: Scan each preset partition using the first scanning method to obtain the original touch signals of each preset partition.

[0033] In step S102, the first scanning method refers to a fast but low-precision scanning strategy. Its design goal is to quickly cover the entire touch panel, sacrificing the quality of individual point signals in exchange for extremely high scanning speed. For example, this method may be achieved by bundling multiple adjacent sensing lines for a single read, or by reducing the sampling rate and integration time of signal acquisition. The raw touch signal refers to the initial electrical signal values ​​acquired from each preset zone through the first scanning method without any processing. These values ​​roughly reflect the capacitance or voltage changes of the corresponding zone, and may contain a mixture of real touch signals, display noise, and environmental noise.

[0034] Specifically, to quickly detect touch activity, the system instructs the touch controller to operate in a first-scan mode. In this mode, the controller performs a traversal scan of all preset zones with low resource consumption. For each preset zone, the controller performs a coarse signal acquisition operation, obtaining a single signal amplitude. This process is very fast, potentially completing a survey of the entire panel within milliseconds. The system associates the raw touch signals of all preset zones with their respective location markers, forming a preliminary, low-resolution global signal map. This map is crucial for determining which areas on the screen might experience user interaction.

[0035] Step S103: Based on the original touch signal, divide the touch panel into an active partition and an inactive area containing multiple inactive partitions, and select an inactive partition within a preset area from the inactive area as a noise sampling point.

[0036] In step S103, an active zone refers to a preset zone whose original touch signal exceeds a specific threshold, thus being determined by the system as having a current touch event or a high probability of having a touch event. An inactive area refers to a broad region composed of all preset zones not determined to be active, representing a background area where there is currently no touch operation. An inactive zone is a single preset zone constituting the inactive area. Noise sampling points refer to a subset of inactive zones selected from the inactive area according to preset rules; these will be used for subsequent precise noise measurement.

[0037] Specifically, the system analyzes the global signal spectrum acquired in step S102. The system sets a first touch threshold and then compares the original touch signal value of each preset partition with this threshold. If the signal value of a preset partition is greater than this threshold, the system marks it as an active partition. All preset partitions with signal values ​​less than or equal to the threshold are marked as inactive partitions. The set of all inactive partitions constitutes the inactive region. Next, the system needs to select noise sampling points within a preset area of ​​the inactive region.

[0038] In one possible implementation, the touch panel is divided into active zones and inactive areas containing multiple inactive zones based on the original touch signal, specifically including steps S1031-S1034, as follows: Step S1031: Binarize the original touch signal, determine the first touch signal that is greater than the preset signal threshold, and mark the preset partition corresponding to the first touch signal as the candidate active partition.

[0039] In step S1031, binarization processing refers to a method that simplifies continuous signal values ​​into one of two states. Here, it involves classifying the original touch signals into "high" and "low" categories based on a standard. The preset signal threshold represents a pre-defined numerical standard used to distinguish between valid signals that may be actual touches and background noise or invalid signals. For example, in a system with a signal range of 0 to 255, the preset signal threshold might be set to 50. The first touch signal refers to those original touch signals whose values ​​are greater than the preset signal threshold; these are initially considered to be signals generated by touch events. The candidate active partition is used to indicate the preset partition of the original touch signal that corresponds to the first touch signal.

[0040] Specifically, the system acquires the original touch signal map covering all preset zones, collected in step S102. The system iterates through each preset zone in this map, reading its corresponding original touch signal value. Then, the system compares this signal value with preset signal thresholds stored in the configuration. If the signal value of a preset zone is greater than the threshold, the system classifies the signal as the first touch signal and immediately marks this preset zone with a temporary "candidate active zone" label. This process is equivalent to performing a quick filter on the logical map of the entire touch panel, initially identifying all areas where touch is possible.

[0041] Step S1032: Determine a second touch signal that is less than or equal to a preset signal threshold, and mark the preset partition corresponding to the second touch signal as a candidate inactive partition.

[0042] In step S1032, the second touch signal refers to the original touch signal whose value is less than or equal to a preset signal threshold, and is initially considered to be background noise or a signal where no touch has occurred. The candidate inactive partition is used to indicate that its corresponding original touch signal is a preset partition of the second touch signal.

[0043] Specifically, this step is performed synchronously with or immediately following step S1031. When the system traverses all preset partitions and compares their original touch signals with preset signal thresholds, partitions with signal values ​​less than or equal to the threshold are classified as second touch signals. Simultaneously, the system assigns a temporary "candidate inactive partition" label to these preset partitions. After traversing all preset partitions, each preset partition on the entire touch panel will be uniquely labeled as either a "candidate active partition" or a "candidate inactive partition," thus completing the initial binary classification of the entire panel.

[0044] Step S1033: Merge adjacent candidate active partitions to obtain active partitions.

[0045] In step S1033, adjacent candidate active partitions refer to multiple candidate active partitions that are physically adjacent on the logical grid. Adjacency is typically defined as sharing an edge or a vertex. Merging represents a clustering process that combines all interconnected, adjacent candidate active partitions into a single, larger region. An active partition is the final region formed after the merging process; it may consist of a single candidate active partition or be an aggregation of multiple adjacent candidate active partitions, representing a complete and continuous touch event area.

[0046] Specifically, the system analyzes all preset partitions marked as "candidate active partitions." The system uses a connected component search algorithm to perform the merging operation. For example, the system randomly selects an unprocessed candidate active partition, then searches for all directly adjacent candidate active partitions, and then searches for other candidate active partitions adjacent to these newly found partitions, repeating this process until no new adjacent candidate active partitions are found. The set of all candidate active partitions found in this search process is defined by the system as a complete active partition. If there are still unprocessed candidate active partitions at this point, the system repeats the above process to form another independent active partition. This process ensures that multiple adjacent candidate active partitions generated by a large-area touch, such as a palm press, can be correctly identified as a whole, rather than multiple isolated points.

[0047] Step S1034: Mark the preset partitions that are not marked as active partitions as candidate inactive partitions, and merge adjacent candidate inactive partitions to obtain an inactive region.

[0048] In step S1034, the preset partitions not marked as active partitions refer to all preset partitions that do not belong to any of the finally determined active partitions after step S1033 is completed. Candidate inactive partitions here refer to the reconfirmation of the identities of these preset partitions not marked as active partitions; they are the final members constituting the inactive area. Merging here is more of a conceptual aggregation operation, meaning that all these scattered candidate inactive partitions are logically considered as a whole. The inactive area refers to the area composed of all candidate inactive partitions; it represents the sum of all background areas on the current touch panel where no touch events have occurred.

[0049] Specifically, after all active partitions are determined in step S1033, the system has a clear list of active partitions. Next, the system performs a logical reverse selection. The system iterates through all preset partitions of the entire touch panel; any preset partition not included in the aforementioned active partition list is ultimately determined as an inactive partition. The system logically aggregates all these inactive partitions into a large set, which is defined as the inactive region. The inactive region may be a large, continuous area or a discontinuous area separated by one or more active partitions. This ultimately determined inactive region forms the basis for the subsequent selection of noise sampling points.

[0050] In one possible implementation, inactive partitions within a preset area are selected from the inactive region as noise sampling points, specifically including steps S1035-S1037, as follows: Step S1035: Obtain physical morphological data characterizing the current bending angle, radius of curvature, and stress distribution of the flexible AMOLED display using the stress sensor built into the touch panel.

[0051] In step S1035, the stress sensor built into the touch panel refers to a miniature sensing element integrated within the flexible display structure, capable of real-time monitoring and quantification of physical deformation. Physical morphology data represents a comprehensive set of data describing the current physical state of the flexible screen; it is not a single value but a data set. The bending angle represents the angle between two parts of the screen, for example, 180 degrees when fully unfolded and close to 0 degrees when folded. The radius of curvature represents the radius of the arc at the most bent part of the screen; a smaller radius indicates a more severe bending. Stress distribution refers to the magnitude and distribution of internal tensile or compressive forces at different locations on the screen, usually existing as a two-dimensional graph showing which areas experience the greatest stress.

[0052] Specifically, the system periodically, or when it detects a change in screen shape, proactively sends query commands to multiple stress sensors distributed throughout the flexible AMOLED display, especially near the bendable area. These sensors return measured changes in resistance, capacitance, or other physical quantities to the system. Upon receiving this raw sensor data, the system uses a pre-defined conversion algorithm model to convert the raw data into parameters with clear physical meaning. For example, by analyzing the values ​​from sensors at specific locations, the system can calculate the bending angle of the screen; by analyzing the gradient of numerical changes from a series of sensors in the bending area, it can deduce the current radius of curvature; and by integrating the values ​​from all sensors, the system can generate a complete stress distribution map covering the entire screen. Finally, the system packages these key pieces of information—bending angle, radius of curvature, and stress distribution map—into a structured dataset, namely physical morphology data, for use in subsequent steps.

[0053] Step S1036: Based on physical morphology data, identify one or more key stress regions where the noise gradient change amplitude is greater than or equal to a preset amplitude threshold under the current bending state.

[0054] In step S1036, physical morphology data refers to the comprehensive information about the screen bending state obtained in the previous step S1035. The noise gradient change amplitude represents the degree to which the background noise level of the touch signal changes drastically with position within a local area of ​​the screen. A large gradient change amplitude means that the noise values ​​of two adjacent points differ significantly. The preset amplitude threshold is a pre-set judgment standard used to define how large a noise gradient change is considered significant and therefore requires special attention. Key stress regions are used to represent specific areas where the noise characteristics of the touch signal change drastically due to physical bending; these areas are the most unstable and complex in terms of noise.

[0055] Specifically, the system first retrieves the physical morphology data generated in step S1035, particularly the stress distribution map. The system internally stores a noise characteristic model that describes the correspondence between different stress levels and touch signal background noise. Using this model, the system converts the stress distribution map into a predicted noise distribution map. Then, the system performs gradient calculations on this predicted noise distribution map, specifically calculating the difference between the predicted noise values ​​of each preset partition and its adjacent partitions. The magnitude of this difference represents the noise gradient change amplitude. Next, the system compares the calculated noise gradient change amplitude at each location with a pre-calibrated preset amplitude threshold stored in the system. If one or more consecutive preset partitions have a noise gradient change amplitude greater than or equal to this threshold, the system marks these partitions and groups them into one or more key stress regions. These regions typically correspond to the bends or stress concentration edges of the flexible screen.

[0056] Step S1037: Define all inactive partitions and critical stress regions that share boundaries or vertices with active partitions as preset regions.

[0057] In step S1037, the inactive partitions that share a boundary or vertex with the active partition refer to a ring of inactive partitions on the logical grid of the touch panel that are immediately adjacent to the active partition. These inactive partitions either share an edge or a corner with the active partition. The critical stress region refers to the region identified in step S1036 where noise changes drastically due to physical deformation. The preset region represents a specially defined set of regions, composed of the two types of regions mentioned above, and is the target range for the system to select noise sampling points in the next step.

[0058] Specifically, when performing this step, the system already possesses two important pieces of information: one is the list of active partitions determined in step S1033, and the other is the list of critical stress regions determined in step S1036. The system first performs the first task: determining the "halo" region of the active partitions. The system iterates through each preset partition in the active partitions and checks all its directly adjacent neighbor partitions, including those in the top, bottom, left, right, and four diagonal directions. If a neighbor partition belongs to an inactive region, the system selects it. By summing all such selected inactive partitions, a set of inactive partitions sharing boundaries or vertices with the active partitions is obtained. Then, the system performs the second task: region merging. The system merges the "halo" region set obtained in the previous step with the set of critical stress regions determined in step S1036, generating a final set. This final set is formally defined by the system as the preset region. This preset region is carefully selected; it contains the two places where noise is most likely to change: the boundary between the touch signal and background noise, and the area where the screen's physical form is most unstable.

[0059] Step S104: The active area is scanned using a second scanning method to obtain an active signal carrying touch information and noise components, and the noise sampling points are scanned using the second scanning method to obtain local noise samples. The scanning power of the second scanning method is higher than that of the first scanning method.

[0060] In step S104, the second scanning method refers to a high-energy, high-signal-to-noise ratio touch panel scanning mode, characterized by a strong power output of the driving signal. The active zone represents the touch area where touch or hovering activities are detected during the initial scan. The active signal refers to the raw electrical signal acquired within the active zone, which is a mixture of actual touch information and background noise components. Noise sampling points represent specific pixels on the touch panel that are pre-selected or dynamically determined and confirmed to be free of touch, specifically used to measure the noise level of the current environment. Local noise samples represent pure noise signals acquired from noise sampling points that can represent the noise characteristics near the active zone. Scanning power refers to the energy intensity used by the touch controller to drive the transmitting electrode, typically proportional to the amplitude of the driving voltage or current; higher scanning power can generate a stronger sensing signal.

[0061] Specifically, the goal of this step is to acquire a high-quality touch signal for precise analysis. This step is typically performed after a low-power initial scan. Once the system confirms touch activity in a certain area through a low-power first scan—that is, after identifying the active zone—it immediately switches to a second scan mode. Switching to the second scan mode means that the system instructs the touch controller to increase its scan power, for example, by increasing the voltage of the drive signal from 1 volt to 5 volts.

[0062] Next, the system executes two parallel scanning tasks, both using this enhanced second scanning method. The first task is for the system to precisely limit the scanning range to the identified active zone and perform a complete scan of all touch pixels within that zone. The signal acquired in this scan is the activity signal. Due to the high scanning power, the portion of the signal caused by actual touch is significantly enhanced, but it still inevitably contains noise components from the circuitry and the environment.

[0063] The second task is for the system to simultaneously scan the designated noise sampling points using the exact same second scanning method. These points are selected at the screen edges far from the active zone or other areas confirmed to be untouched. Signals acquired from these points, lacking touch input, are considered clean noise signals, i.e., local noise samples. The reason for using the same second scanning method to acquire noise samples is to ensure that the measured noise characteristics are at the same energy level as the noise components mixed into the active signal, thus providing an accurate reference for subsequent precise noise cancellation processing. After this step, the system obtains two sets of key data: one set is a high signal-to-noise ratio mixed signal from the active zone, and the other set is clean noise samples that accurately reflect the current noise level.

[0064] Step S105: Collect noise data of the entire touch panel under a preset driving signal as global display noise, and construct a noise benchmark model based on global display noise and local noise samples.

[0065] In step S105, the system collects noise data of the entire touch panel under a preset driving signal as global display noise, and constructs a noise benchmark model based on global display noise and local noise samples.

[0066] First, the system adjusts the flexible AMOLED display to a flat, non-bent state, ensuring the touch panel is in a physical baseline shape free from mechanical stress. The system applies a preset drive signal to the touch panel and collects the electrical signals output from each preset zone. Since there is no actual touch operation on the touch panel at this time, the collected electrical signals consist entirely of electromagnetic interference from the display itself, crosstalk from the drive circuit, and switching noise from the AMOLED pixel units. The system defines this noise data as global display noise.

[0067] Subsequently, the system extracts features from both global display noise and local noise samples. The system acquires the global noise sequence within a preset time period and extracts its temporal statistical features and frequency domain distribution features to obtain a global noise vector. Simultaneously, the system acquires the local noise sequence of each noise sampling point within a preset time period and extracts its temporal statistical features and frequency domain distribution features to obtain a local noise vector.

[0068] Finally, the system constructs a noise baseline model based on global and local noise vectors. The system uses the global noise vector as a static baseline feature characterizing the inherent noise characteristics of the flexible AMOLED display, and the location identifiers of each noise sampling point and their corresponding local noise vectors constitute training samples. The local noise vectors serve as dynamic features characterizing the local noise under the current bending state. The system uses the static baseline features and training samples to train a pre-defined machine learning model, configuring the model's input as location identifiers and its output as predicted noise feature parameters, thus obtaining the noise baseline model.

[0069] like Figure 2 As shown, in one possible implementation, noise data of the entire touch panel is collected under a preset driving signal as global display noise, and a noise benchmark model is constructed based on the global display noise and local noise samples. Specifically, this includes steps S201-S207, as follows: Step S201: Under a preset driving signal, collect the noise data of the flexible AMOLED display in a non-bending state as the global display noise.

[0070] In step S201, the system collects noise data of the flexible AMOLED display in its non-bending state under a preset driving signal as global display noise. Specifically, the system first places the flexible AMOLED display in a flat, unfolded, non-bending state, where the display is not affected by any mechanical stress and is in its physical baseline state. Subsequently, the system applies a preset driving signal to the touch panel. This preset driving signal is a standardized driving voltage waveform used to excite the sensing electrodes of the touch panel. Under this driving condition, the system synchronously collects the electrical signals output from each preset zone through the sensing channel of the touch panel. Since there is no actual touch operation on the touch panel at this time, the collected electrical signals are entirely composed of electromagnetic interference from the display itself, crosstalk from the driving circuit, and switching noise from the AMOLED pixel units. The system defines this noise data collected in the non-bending baseline state as global display noise, which reflects the inherent noise level of the flexible AMOLED display in its ideal physical state.

[0071] Step S202: Obtain the global noise sequence of the global display noise within the preset time period, and obtain the local noise sequence of the local noise sample within the preset time period.

[0072] In step S202, the system acquires a global noise sequence of global display noise within a preset time period, and acquires a local noise sequence of local noise samples within the same preset time period. Specifically, the system sets a preset time period as the observation window for the noise data; for example, this preset time period can be set to 100 milliseconds. Within this preset time period, the system continuously samples the global display noise at a fixed sampling frequency, arranging the sampled noise data points in chronological order to form a global noise sequence. For example, if the sampling frequency is 1000 Hz and the preset time period is 100 milliseconds, the system can obtain a global noise sequence containing 100 data points. Simultaneously, the system uses the same sampling frequency and preset time period to continuously sample the local noise samples output from each noise sampling point, forming a local noise sequence corresponding to each noise sampling point. Through this processing, the system converts static noise data into a noise sequence with temporal dimension information, laying the data foundation for subsequent feature extraction.

[0073] Step S203: Extract the time-domain statistical features and frequency-domain distribution features of the global noise sequence to obtain the global noise vector.

[0074] In step S203, the system extracts the time-domain statistical features and frequency-domain distribution features of the global noise sequence to obtain a global noise vector. Regarding the extraction of time-domain statistical features, the system calculates the mean, variance, standard deviation, peak value, peak-to-peak value, and root mean square value of the global noise sequence. These statistics characterize the amplitude distribution and fluctuation of the global noise over time. Regarding the extraction of frequency-domain distribution features, the system performs a Fast Fourier Transform on the global noise sequence, converting the time-domain signal into a frequency-domain representation to obtain the spectral data of the global noise. The system further extracts characteristic parameters from the spectral data, such as the dominant frequency component, spectral centroid, spectral bandwidth, spectral flatness, and the energy proportion of each preset frequency band. The system concatenates the aforementioned time-domain statistical features and frequency-domain distribution features in a preset order to form a multi-dimensional global noise vector. This global noise vector comprehensively describes the statistical characteristics and frequency composition of the global display noise in a compact numerical form.

[0075] Step S204: Extract the time-frequency statistical features and frequency domain distribution features of the local noise sequence to obtain the local noise vector.

[0076] In step S204, the system extracts the time-domain statistical features and frequency-domain distribution features of the local noise sequence to obtain a local noise vector. For each noise sampling point, the system uses the same feature extraction method as in step S203 to calculate the time-domain statistical features and frequency-domain distribution features of each local noise sequence. Specifically, the system calculates the mean, variance, standard deviation, peak value, and other time-domain statistics of each local noise sequence, and extracts the dominant frequency component, spectral centroid, and spectral bandwidth of each local noise sequence using Fast Fourier Transform. The system concatenates the time-domain statistical features and frequency-domain distribution features of each local noise sequence to form a local noise vector corresponding to each noise sampling point. Because different noise sampling points are located at different positions on the touch panel and are affected by different degrees of bending, the local noise vectors corresponding to each noise sampling point differ numerically. This difference reflects the differentiated impact of bending deformation on the noise characteristics of different areas of the touch panel.

[0077] Step S205: Use the global noise vector as a static reference feature to characterize the inherent noise characteristics of the flexible AMOLED display.

[0078] In step S205, the system uses the global noise vector as a static reference feature characterizing the inherent noise properties of the flexible AMOLED display. Since the global noise vector is collected in a non-bending state and is unaffected by mechanical deformation, it accurately reflects the background noise level of the flexible AMOLED display under ideal conditions. The system defines the global noise vector as a static reference feature, which serves as the basic reference for the noise benchmark model and is subsequently fused with dynamic features to establish a mapping relationship between location markers and noise characteristics.

[0079] Step S206: The location identifiers of each noise sampling point and the corresponding local noise vectors are used to form training samples, and the local noise vectors are used as dynamic features representing the local noise under the current bending state.

[0080] In step S206, the system constructs training samples by combining the location identifiers of each noise sampling point with the corresponding local noise vectors. The local noise vectors serve as dynamic features characterizing the local noise under the current bending state. Specifically, the system iterates through all noise sampling points, using the location identifier of each noise sampling point as the input feature and the corresponding local noise vector as the output label, thus constructing paired input-output training samples. For example, if the system selects 8 noise sampling points, it can construct 8 sets of training samples, each containing a location identifier and a local noise vector. The system defines the local noise vector as a dynamic feature, which reflects the dynamic law of how the noise characteristics at different locations change with the degree of deformation under the current bending state of the flexible AMOLED display.

[0081] Step S207: Using the static benchmark features and the training samples, train a preset machine learning model, configure the input of the machine learning model as the receiving location identifier, configure the output of the machine learning model as the predicted noise feature parameters, and obtain the noise benchmark model.

[0082] In step S207, the system uses static baseline features and training samples to train a preset machine learning model. The input of the machine learning model is configured as the receiving location identifier, and the output is configured as predicted noise feature parameters, thus obtaining a noise baseline model. Specifically, the system first uses the static baseline features as the global bias parameters or baseline input of the preset machine learning model, enabling the model to perceive the inherent noise level of the flexible AMOLED display. Subsequently, the system inputs the training samples constructed in step S206 into the preset machine learning model for iterative training. By minimizing the error between the predicted noise feature parameters and the actual noise vector, the system optimizes the internal parameters of the preset machine learning model. After training, the system configures the preset machine learning model, setting its input interface to the receiving location identifier and its output interface to the predicted noise feature parameters. After this configuration, the preset machine learning model is transformed into a noise baseline model. This noise baseline model can output the predicted noise feature parameters corresponding to any input location identifier, combining the static baseline features and the learned dynamic feature mapping relationship, thereby providing a basis for subsequent noise compensation.

[0083] Step S106: Input the location identifier corresponding to the active partition into the noise baseline model to obtain the noise compensation distribution matrix corresponding to the active partition.

[0084] In step S106, the noise compensation distribution matrix refers to a data matrix with the same size as the active signal matrix. Each value within the matrix is ​​a predicted noise value for the corresponding sensing unit location within the active partition, calculated using the noise baseline model. Essentially, it is a refined "portrait" of the noise within the active partition.

[0085] Specifically, the system already possesses the location range of the active partition and the high-resolution active signal acquired in step S104. The system iterates through each data point in the active signal matrix, each data point corresponding to a tiny sensing unit on the touch panel and its precise physical coordinates. For the coordinates of each sensing unit, the system uses them as input and calls the noise baseline model constructed in step S105. The noise baseline model calculates or interpolates based on the coordinates, outputting a predicted noise amplitude. The system fills this predicted noise value into the corresponding position in the new matrix. After iterating through all sensing units within the active partition, the system generates a noise compensation distribution matrix that corresponds one-to-one with the active signal matrix.

[0086] In one possible implementation, the location identifier corresponding to the active partition is input into the noise baseline model to obtain the noise compensation distribution matrix corresponding to the active partition, specifically including steps S1061-S1065, as follows: Step S1061: Divide the active partition into multiple noise compensation units, each of the noise compensation units containing at least one touch pixel.

[0087] In step S1061, the active partition refers to the set of preset partitions identified in the previous steps based on the original touch signal where a touch operation exists. The noise compensation unit refers to the smallest noise processing unit obtained after fine-grained division of the active partitions; each noise compensation unit contains at least one touch pixel. A touch pixel is the smallest sensing unit on the touch panel capable of independently sensing touch signals.

[0088] Specifically, the system divides the active partition into multiple noise compensation units based on its spatial range and preset granularity. The system first determines the size parameters of the noise compensation units, which can be configured according to touch accuracy requirements and computational resources. The system uniformly divides the active partition using a row-column grid, ensuring that each noise compensation unit covers a rectangular sub-region within the active partition. Each noise compensation unit contains at least one touch pixel; when the noise compensation unit is large, a single unit can contain multiple touch pixels. Through this division, the system decomposes the active partition into multiple independent noise compensation units, laying the foundation for subsequent noise compensation calculations for each unit separately.

[0089] Step S1062: Obtain the center coordinates of each noise compensation unit as the location identifier of the noise compensation unit.

[0090] In step S1062, the center coordinates refer to the geometric center position of the noise compensation unit in the touch panel coordinate system. The center coordinates serve as the position identifier of the noise compensation unit to uniquely identify the spatial position of the noise compensation unit on the touch panel.

[0091] Specifically, the system iterates through each noise compensation unit and calculates the coordinates of its geometric center for each unit. The system obtains the boundary range of the noise compensation unit in the touch panel coordinate system, including the coordinates of the left, right, upper, and lower boundaries. The system calculates the arithmetic mean of the left and right boundary coordinates to obtain the x-coordinate of the noise compensation unit's center. The system calculates the arithmetic mean of the upper and lower boundary coordinates to obtain the y-coordinate of the noise compensation unit's center. The system combines the x and y coordinates to form the center coordinates and stores these center coordinates as the location identifier for the noise compensation unit. Through this process, the system assigns a unique location identifier to each noise compensation unit, which accurately represents the unit's spatial position on the touch panel.

[0092] Step S1063: Input the location identifier of each noise compensation unit into the noise reference model, and calculate the noise probability distribution parameters of each noise compensation unit.

[0093] In step S1063, the noise probability distribution parameter refers to the set of parameters that describe the statistical distribution characteristics of the noise signal within the noise compensation unit. The noise probability distribution parameter is used to characterize the probability distribution law of the noise at that location.

[0094] Specifically, the system sequentially inputs the location identifiers of each noise compensation unit into the noise baseline model. Based on the input location identifiers, the noise baseline model, combined with static baseline features and the learned dynamic feature mapping relationship, predicts the noise characteristics at that location. The noise baseline model outputs the noise probability distribution parameters corresponding to that noise compensation unit. These parameters include noise mean, noise variance, noise distribution type, and other parameters describing the statistical characteristics of the noise. The system processes each noise compensation unit sequentially until the noise probability distribution parameters of all noise compensation units are obtained. Through this process, the system utilizes the noise baseline model to achieve the mapping calculation from location identifiers to noise probability distribution parameters.

[0095] Step S1064: Perform mathematical expectation calculation on the noise probability distribution parameters of each noise compensation unit to obtain the noise expectation value, and use the noise expectation value as the target noise compensation value of the corresponding noise compensation unit.

[0096] In step S1064, the mathematical expectation calculation refers to calculating the expected value of the noise random variable based on the noise probability distribution parameters and according to the definition of mathematical expectation in probability theory. The noise expectation value refers to the mathematical expectation of the noise probability distribution, and it characterizes the average level of the noise signal within the noise compensation unit. The target noise compensation value refers to the amount of noise compensation used for denoising the active signal.

[0097] Specifically, the system calculates the expected value of the noise probability distribution parameters for each noise compensation unit. Based on the noise distribution type indicated in the noise probability distribution parameters, the system selects the corresponding expected value calculation formula. The system substitutes parameters such as the noise mean and variance from the noise probability distribution parameters into the expected value calculation formula to obtain the expected noise value. The system uses this expected noise value as the target noise compensation value for the corresponding noise compensation unit. Through this process, the system converts the noise probability distribution parameters into deterministic target noise compensation values, which represent the statistical average level of the noise signal within each noise compensation unit.

[0098] Step S1065: Smooth the target noise compensation value of each noise compensation unit to obtain the noise compensation distribution matrix.

[0099] In step S1065, smoothing refers to filtering the target noise compensation values ​​of spatially adjacent noise compensation units to eliminate local abrupt changes and discontinuities. The noise compensation distribution matrix is ​​a two-dimensional matrix formed by arranging the target noise compensation values ​​of each noise compensation unit according to their spatial positions. The noise compensation distribution matrix is ​​used to characterize the spatial distribution of noise compensation amounts within the active zone.

[0100] Specifically, the system arranges the target noise compensation values ​​of each noise compensation unit according to their spatial location within the active partition, forming an initial noise compensation matrix. The system then uses a spatial filtering algorithm to smooth the initial noise compensation matrix. This algorithm performs a weighted average of the target noise compensation value of each noise compensation unit with the target noise compensation values ​​of its adjacent units. This smoothing process eliminates abrupt changes in target noise compensation values ​​between adjacent units, resulting in a smooth and continuous transition in the noise compensation amount across the space. The system outputs the smoothed matrix as the noise compensation distribution matrix. Through this process, the system obtains the noise compensation distribution matrix corresponding to the active partition, where each element corresponds to the final noise compensation amount of a noise compensation unit.

[0101] Step S107: Denoise the active signal based on the noise compensation distribution matrix to obtain the target touch signal, and parse the touch point coordinates based on the target touch signal.

[0102] In step S107, the target touch signal refers to the clean touch signal data obtained by subtracting the predicted noise component from the noisy activity signal. The touch point coordinates refer to the precise physical location of the user's touch point, which is finally resolved by analyzing the target touch signal, and is the final output of the entire touch process.

[0103] Specifically, the system performs a matrix subtraction operation. It subtracts the activity signal matrix obtained in step S104 from the noise compensation distribution matrix generated in step S106 element by element. Since the activity signal is real touch plus noise, while the noise compensation distribution matrix is ​​noise predicted by the model, the subtraction of the two can theoretically eliminate noise components to the greatest extent, preserving the clean signal shape generated by the user's touch. This result is the target touch signal. Finally, the system applies mature coordinate extraction algorithms, such as centroid algorithms or Gaussian surface fitting algorithms, to this clean target touch signal matrix to find the signal peaks or energy centers, thereby calculating the precise touch point coordinates at the sub-pixel level and reporting them to the operating system or application.

[0104] In one possible implementation, the active signal is denoised based on the noise compensation distribution matrix to obtain the target touch signal, specifically including steps S1071-S1077, as follows: Step S1071: Obtain the activity signal sequence of each touch pixel in the active partition within a preset sampling time period.

[0105] In step S1071, the active partition represents the area on the touch panel currently identified as having touch activity. A touch pixel refers to the smallest independent sensing unit that constitutes the touch panel. The preset sampling time period represents a fixed, short time window during which the system continuously collects data, for example, 16 milliseconds. The active signal sequence refers to a continuous string of raw sensor intensity values ​​reported by a single touch pixel within the aforementioned time period.

[0106] Specifically, the system first identifies the active partition that needs noise reduction. Then, for each touch pixel within that partition, the system continuously samples signals at a very high frequency over a preset sampling period. For example, sampling once every millisecond within a 16-millisecond time period will yield a sequence of 16 values ​​for a single touch pixel. This ordered list of continuously sampled values ​​constitutes the active signal sequence for that touch pixel, comprehensively recording the signal fluctuations at that point over a short period, including both signal changes caused by actual touch and ambient or hardware noise.

[0107] Step S1072: Perform time-frequency transformation on the activity signal sequence of each touch pixel to obtain the time-frequency matrix of the activity signal.

[0108] In step S1072, the active signal sequence is the original time-domain signal data acquired in the previous step. Time-frequency transformation (TF-F) is a mathematical analysis method that converts a one-dimensional time-domain signal into a two-dimensional image that simultaneously displays the signal's frequency components and time-varying information. Common algorithms include short-time Fourier transform (SFT) or wavelet transform. The time-frequency matrix represents the two-dimensional data matrix obtained after the TF-F, where the horizontal axis typically represents time and the vertical axis represents frequency. The value of each element in the matrix represents the energy or amplitude of the signal at a specific time and frequency.

[0109] Specifically, the system iterates through each touch pixel within the active partition and extracts its corresponding active signal sequence. Next, the system applies a time-frequency transformation algorithm, such as the short-time Fourier transform, to this one-dimensional time-series data. This algorithm divides the signal sequence into multiple small, overlapping time segments and calculates the frequency components of each segment. Through this process, the original one-dimensional signal is transformed into a two-dimensional time-frequency matrix. This matrix clearly reveals which frequencies of energy appear and disappear during the signal acquisition period, providing a crucial frequency domain perspective for distinguishing useful signals from noise.

[0110] Step S1073: Convert the noise compensation distribution matrix of the active partition into a noise compensation spectrum matrix with the same size as the time spectrum matrix.

[0111] In step S1073, the noise compensation distribution matrix refers to the two-dimensional matrix calculated in the previous step that describes the spatial distribution of noise within the active partition. The time-frequency spectrum matrix is ​​the matrix generated in the previous step that describes the time-frequency characteristics of the signal at a single pixel. The noise compensation spectrum matrix is ​​used to represent a noise estimation matrix that is specifically constructed for spectral subtraction and whose size perfectly matches the time-frequency spectrum matrix.

[0112] Specifically, the system first obtains the noise compensation value corresponding to the current touch pixel location. This value is retrieved from the noise compensation distribution matrix covering the entire active area. Simultaneously, the system also knows the specific dimensions of the time-spectrum matrix generated for this pixel in the previous step, such as 64 frequency levels and 8 time segments. Since we typically assume that background noise is stationary over a short period, its spectral distribution does not change drastically over time, the system creates a new matrix with the exact same dimensions as the time-spectrum matrix and fills all elements of this new matrix with the noise compensation value retrieved from the noise compensation distribution matrix. This newly generated matrix, with matching dimensions and uniform values, is the noise compensation spectrum matrix.

[0113] Step S1074: Perform spectral subtraction on the time spectrum matrix and the noise compensation spectrum matrix to obtain the target time spectrum matrix after noise suppression.

[0114] In step S1074, the time-spectrum matrix represents the spectrum of the original signal containing noise. The noise-compensated spectrum matrix represents the estimate of the noise spectrum. Spectral subtraction is a classic speech and signal enhancement algorithm; its core idea is to subtract the estimated noise spectrum from the spectrum of the noisy signal, thereby suppressing noise. The target time-spectrum matrix is ​​used to represent the estimated clean signal spectrum obtained after spectral subtraction, where the noise components are significantly reduced.

[0115] Specifically, the system aligns the time-frequency spectrum matrix of a specific touch pixel with the noise compensation spectrum matrix generated for it. Then, the system performs spectral subtraction, an element-wise operation. The system iterates through each position in the matrix, subtracting the value of the noise compensation spectrum matrix at that position from the value of the time-frequency spectrum matrix at that position. To ensure physical accuracy—that the signal energy cannot be negative—if the result of the subtraction is less than zero, the system forces it to zero or a very small positive number. After this process, the resulting two-dimensional matrix is ​​the target time-frequency spectrum matrix, representing the purified signal in the time-frequency domain.

[0116] Step S1075: Perform inverse time-frequency transformation on the target time-frequency matrix to obtain the denoised target activity signal sequence.

[0117] In step S1075, the target time-frequency spectrum matrix is ​​the frequency domain representation of the cleaned signal obtained from the spectral subtraction operation in the previous step. The inverse time-frequency transform represents the reverse process of the time-frequency transform; it can reassemble the two-dimensional time-frequency spectrum matrix into a one-dimensional time-domain signal sequence, such as through the inverse short-time Fourier transform. The target active signal sequence is used to represent the clean signal sequence obtained after the inverse transform, which exhibits noise suppression in the time domain.

[0118] Specifically, the system obtains the target time-frequency spectrum matrix calculated for a specific touch pixel in step S1074. Then, the system applies an inverse time-frequency transform algorithm to this matrix, corresponding to the transform used in step S1072. For example, if a short-time Fourier transform was used previously, an inverse short-time Fourier transform will be used here. This inverse transform process recombines the purified frequency and phase information into a one-dimensional waveform that varies over time. This final synthesized signal sequence is the target activity signal sequence, and its background noise has been significantly reduced compared to the original activity signal sequence.

[0119] Step S1076: Perform energy normalization processing on the target activity signal sequence to obtain a normalized energy value sequence.

[0120] In step S1076, the target activity signal sequence is the denoised time-domain signal obtained in the previous step. Energy normalization processing represents a data scaling technique that adjusts the energy or amplitude of the signal to a uniform standard range, such as 0 to 1, to facilitate fair comparison and threshold judgment. The normalized energy value sequence is used to represent a series of numerical sequences representing relative energy intensity obtained after normalization processing.

[0121] Specifically, the system receives a denoised sequence of target activity signals. To ensure accurate subsequent judgments, the system needs to eliminate the influence of the absolute amplitude of the signal and focus instead on its relative intensity. Therefore, the system performs energy normalization on the sequence. One possible approach is to calculate the sum of the squares of all values ​​in the sequence as the total energy, or to find the maximum value in the sequence. Then, each value in the sequence is divided by this total energy or the maximum value. In this way, regardless of the strength of the original signal, the values ​​in the normalized energy value sequence are constrained to a fixed range, such as between 0 and 1, clearly reflecting the relative energy level of the signal at each sampling time.

[0122] Step S1077: Perform adaptive threshold judgment on the normalized energy value sequence, and determine the activity signal corresponding to the normalized energy value that is greater than the preset dynamic threshold as the target touch signal.

[0123] In step S1077, the normalized energy value sequence is the relative energy sequence calculated in the previous step. Adaptive threshold decision represents an intelligent decision-making method that uses a variable, rather than fixed, threshold to determine the validity of a signal. This threshold can be dynamically adjusted based on the current noise level or other environmental factors. The preset dynamic threshold refers to this variable judgment baseline. The target touch signal is used to represent the signal portion that is ultimately confirmed as a genuine and valid touch operation.

[0124] Specifically, the system analyzes the normalized energy value sequence. Simultaneously, the system sets an appropriate preset dynamic threshold based on the environment of the touched pixel, such as whether it is in a curved area of ​​the screen, or based on the recently assessed background noise level. For example, in noisy areas, the threshold might be automatically increased to 0.6, while in clean areas, the threshold might be as low as 0.3. The system then compares each value in the normalized energy value sequence with this dynamic threshold. If the normalized energy value at a given time point is greater than the threshold, the system determines it to be a valid signal triggered by a genuine touch. The system identifies the denoised active signal portion corresponding to this time point and designates it as the target touch signal, then passes it to the operating system for subsequent touch event processing. Signal portions below the threshold are considered residual noise and ignored.

[0125] The following describes the partitioned collaborative scanning system for flexible touchscreens in the embodiments of this invention from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the partitioned collaborative scanning system for flexible touch screens in an embodiment of this application.

[0126] It should be noted that, Figure 3 The illustrated structure of the partitioned collaborative scanning system for flexible touchscreens is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0127] like Figure 3As shown, the partitioned collaborative scanning system for flexible touchscreens includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage portion 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0128] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0129] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0130] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0132] Specifically, the partition collaborative scanning system for flexible touch screens in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the partition collaborative scanning method for flexible touch screens provided in the above embodiment.

[0133] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the partition collaborative scanning system for flexible touch screens described in the above embodiments; or it may exist independently and not assembled into the partition collaborative scanning system for flexible touch screens. The storage medium carries one or more computer programs, which, when executed by a processor of the partition collaborative scanning system for flexible touch screens, cause the partition collaborative scanning system for flexible touch screens to implement the partition collaborative scanning method for flexible touch screens based on IoT data encryption transmission provided in the above embodiments.

Claims

1. A partitioned collaborative scanning method for flexible touchscreens, characterized in that, The method includes: The touch panel of the flexible AMOLED display is divided into multiple preset zones, and a corresponding position identifier is assigned to each preset zone; The first scanning method is used to scan each of the preset partitions to obtain the original touch signals of each preset partition; Based on the original touch signal, the touch panel is divided into an active partition and an inactive area containing multiple inactive partitions, and an inactive partition within a preset area is selected from the inactive area as a noise sampling point; The active area is scanned using a second scanning method to obtain an active signal carrying touch information and noise components, and the noise sampling points are scanned using the second scanning method to obtain local noise samples. The scanning power of the second scanning method is higher than that of the first scanning method. Noise data of the entire touch panel is collected under a preset driving signal as global display noise, and a noise benchmark model is constructed based on the global display noise and the local noise samples. Input the location identifier corresponding to the active partition into the noise baseline model to obtain the noise compensation distribution matrix corresponding to the active partition; The activity signal is denoised based on the noise compensation distribution matrix to obtain the target touch signal, and the touch point coordinates are parsed based on the target touch signal.

2. The method according to claim 1, characterized in that, The process of dividing the touch panel into active zones and inactive areas containing multiple inactive zones based on the original touch signal specifically includes: The original touch signal is binarized to determine a first touch signal that is greater than a preset signal threshold, and the preset partition corresponding to the first touch signal is marked as a candidate active partition. A second touch signal less than or equal to a preset signal threshold is identified, and the preset partition corresponding to the second touch signal is marked as a candidate inactive partition; Adjacent candidate activity partitions are merged to obtain the activity partition; Preset partitions that are not marked as active partitions are marked as candidate inactive partitions, and adjacent candidate inactive partitions are merged to obtain the inactive region.

3. The method according to claim 1, characterized in that, The step of selecting inactive partitions within a preset area from the inactive region as noise sampling points specifically includes: The touch panel uses a built-in stress sensor to acquire physical morphological data characterizing the current bending angle, radius of curvature, and stress distribution of the flexible AMOLED display. Based on the physical morphology data, one or more key stress regions are identified where the noise gradient change amplitude is greater than or equal to a preset amplitude threshold under the current bending state. All inactive partitions that share boundaries or vertices with the active partitions and the critical stress regions are defined as the preset regions.

4. The method according to claim 1, characterized in that, The step of collecting noise data from the entire touch panel under a preset driving signal as global display noise, and constructing a noise baseline model based on the global display noise and the local noise samples, specifically includes: Under a preset driving signal, noise data of the flexible AMOLED display in a non-bending state is collected as the global display noise; Obtain the global noise sequence of the global display noise within the preset time period, and obtain the local noise sequence of the local noise sample within the preset time period; The time-domain statistical features and frequency-domain distribution features of the global noise sequence are extracted to obtain the global noise vector; The time-frequency statistical features and frequency domain distribution features of the local noise sequence are extracted to obtain the local noise vector; The noise baseline model is constructed based on the global noise vector and the local noise vector.

5. The method according to claim 4, characterized in that, The construction of the noise baseline model based on the global noise vector and the local noise vector specifically includes: The global noise vector is used as a static reference feature characterizing the inherent noise properties of the flexible AMOLED display. The location identifiers of each noise sampling point and the corresponding local noise vectors constitute training samples, and the local noise vectors serve as dynamic features characterizing the local noise under the current bending state. Using the static baseline features and the training samples, a preset machine learning model is trained, and the input of the machine learning model is configured as the receiving location identifier, and the output of the machine learning model is configured as the predicted noise feature parameters, to obtain the noise baseline model.

6. The method according to claim 1, characterized in that, The step of inputting the location identifier corresponding to the active partition into the noise baseline model to obtain the noise compensation distribution matrix corresponding to the active partition specifically includes: The active partition is divided into multiple noise compensation units, and each noise compensation unit contains at least one touch pixel. Obtain the center coordinates of each noise compensation unit as the location identifier of the noise compensation unit; The location identifiers of each noise compensation unit are input into the noise reference model to calculate the noise probability distribution parameters of each noise compensation unit. The noise probability distribution parameters of each noise compensation unit are mathematically expected to obtain the noise expectation value, and the noise expectation value is used as the target noise compensation value of the corresponding noise compensation unit. The target noise compensation values ​​of each noise compensation unit are smoothed to obtain the noise compensation distribution matrix.

7. The method according to claim 1, characterized in that, The step of denoising the activity signal based on the noise compensation distribution matrix to obtain the target touch signal specifically includes: Obtain the activity signal sequence of each touch pixel within the active partition during a preset sampling time period; The time-frequency transformation of the activity signal sequence of each of the touch pixels is performed to obtain the time-frequency spectrum matrix of the activity signal; The noise compensation distribution matrix of the active partition is converted into a noise compensation spectrum matrix with the same size as the time spectrum matrix; Perform spectral subtraction on the time-frequency spectrum matrix and the noise compensation spectrum matrix to obtain the target time-frequency spectrum matrix after noise suppression; Perform an inverse time-frequency transform on the target time-spectrum matrix to obtain a denoised target activity signal sequence; The target activity signal sequence is subjected to energy normalization processing to obtain a normalized energy value sequence; An adaptive threshold decision is made on the normalized energy value sequence, and the activity signal corresponding to the normalized energy value that is greater than the preset dynamic threshold is determined as the target touch signal.

8. A partitioned collaborative scanning system for flexible touchscreens, characterized in that, The partition collaborative scanning system for flexible touch screens includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the partition collaborative scanning system for flexible touch screens to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on a partitioned collaborative scanning system for flexible touchscreens, the partitioned collaborative scanning system for flexible touchscreens performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on a partitioned collaborative scanning system for flexible touch screens, the partitioned collaborative scanning system for flexible touch screens performs the method as described in any one of claims 1-7.

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