False electric shock prevention capacitive resistance screen integrated with pressure induction

Abstract

The invention discloses an anti-electric-shock capacitive-resistive screen fused with pressure induction, and relates to the technical field of touch screens, the capacitive-resistive screen comprises a capacitive touch layer and a resistive pressure induction layer, the capacitive touch layer is used for detecting coordinate information of a touch position, the resistive pressure induction layer is used for detecting a pressure signal applied to the screen, and the capacitive touch layer is used for detecting the coordinate information of the touch position. The touch screen further comprises a processing unit, and the processing unit is electrically connected with the capacitance touch layer and the resistance pressure sensing layer and used for executing the following operation. According to the pressure-sensing-fused mistaken-electric-shock-preventing capacitive-resistive screen, accurate analysis of a touch intention is achieved by constructing a dynamic pressure fingerprint discrimination and self-adaptive threshold model. According to the scheme, a multi-level signal processing and decision-making mechanism is adopted, the dynamic click pressure and the static background pressure can be effectively distinguished, the problem that a traditional single pressure threshold criterion is high in misjudgment rate in a composite pressure scene is solved, and therefore the accuracy and reliability of touch operation are improved.

Description

Technical Field

[0001] This invention relates to the field of touch screen technology, specifically to a capacitive resistive touch screen that integrates pressure sensing to prevent accidental touches. Background Technology

[0002] Currently, pressure-sensing touch technology has become an important direction for improving the accuracy of human-computer interaction. By combining the precise coordinate positioning of capacitive touch with the pressure detection capability of resistive structures, the aim is to use pressure information to help determine the operation intention, thereby achieving the function of preventing accidental touches. However, such fusion solutions still face a core bottleneck in practical applications. Their anti-accidental touch logic mostly relies on setting a single and fixed pressure threshold. When the total detected pressure exceeds this threshold, it is judged as a valid touch. This simple judgment mechanism has inherent limitations; it cannot effectively distinguish the source and essential differences of pressure signals. Specifically, in the typical scenario where a user holds a device with one hand, the contact between the palm and the edge or frame of the screen generates a continuous and stable background pressure. At this time, if the user intends to perform a click operation, the instantaneous pressure applied by their fingertip will be superimposed on the existing background pressure. The system ultimately collects a composite pressure signal, the total value of which may far exceed the trigger threshold.

[0003] Because existing technologies lack in-depth analysis of the timing characteristics and dynamic behavior of pressure signals, they can only passively respond to the superimposed total pressure. This can lead to misinterpreting a light touch against background pressure as a heavy press, or unintentional sustained pressure as a long press. Such misinterpretations directly result in unexpected interface responses or omissions of critical operations in scenarios requiring precise control, severely impacting the reliability of interaction and user experience. This invention aims to solve the practical problem of existing pressure-sensing touch technologies failing to effectively distinguish between dynamic click pressure and static background pressure, thus causing inaccurate mistouch judgments. Summary of the Invention

[0004] The purpose of this invention is to provide a pressure-sensing anti-accidental touch capacitive resistive screen to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a capacitive resistive touchscreen with integrated pressure sensing to prevent accidental touches, comprising a capacitive touch layer and a resistive pressure sensing layer, wherein the capacitive touch layer is used to detect the coordinate information of the touch position, and the resistive pressure sensing layer is used to detect the pressure signal applied to the screen. It also includes a processing unit electrically connected to the capacitive touch layer and the resistive pressure sensing layer, for performing the following operations: The original composite electrical signal collected by the resistive pressure sensing layer is preprocessed to extract the temporal dynamic features of the pressure signal, including the pressure change rate and pressure stability index. Based on the temporal dynamic features, the pressure signal is initially separated into dynamic click pressure component and static background pressure component, and the separated pressure components are labeled. The labeled pressure data is subjected to dual correlation verification, including pressure change trajectory screening and spatiotemporal coordinate correlation verification. Pressure change trajectory screening is performed by comparing the pressure events marked as dynamic clicks with the preset valid click fingerprint model to determine the matching degree. Spatiotemporal coordinate correlation verification is performed by comparing the consistency between the touch coordinates reported by the capacitive touch layer and the pressure center coordinates sensed by the resistive pressure sensing layer in real time. The static background pressure baseline model is dynamically updated based on the results of the dual correlation verification. Invalid pressure event data that are screened out during the verification process are fed back to the static background pressure baseline model to optimize the initial separation process of pressure components. For edge stress events that cannot be clearly determined after double correlation verification, a final decision is made by combining historically successfully determined touch sequences and application context information to determine the validity of the stress event; only when the stress event successfully passes through all processing stages is it recognized as a valid user intent input.

[0006] Furthermore, when the processing unit preprocesses the original composite electrical signal, it uses a high-frequency sampling method to capture the instantaneous changes in the pressure signal. The interval of the high-frequency sampling is set to capture the rapid rise and slow release waveforms of the pressure signal. In the extracted time-series dynamic features, the pressure change rate is obtained by calculating the change amplitude of the pressure value per unit time, and the pressure stability index is obtained by analyzing the fluctuation degree of the pressure signal in a short period of time. The dynamic click pressure component is identified as a pulse signal with a high change rate and instability, and the static background pressure component is identified as a baseline signal with a low change rate and high stability. The labeling process assigns an initial type identifier to each pressure data point.

[0007] Furthermore, in the pressure change trajectory screening, the preset effective click fingerprint model includes the complete waveform features of the pressure signal. The complete waveform features require the pressure event to have a continuous pattern of a rapid rise phase, a peak dwell phase, and a slow release phase. The matching degree judgment is achieved by calculating the similarity between the actual pressure event waveform and the effective click fingerprint model. If the similarity is higher than a set threshold, a high-confidence primary trigger command is generated. If the similarity is lower than the set threshold, the pressure event is marked as a pending event.

[0008] Furthermore, the spatiotemporal coordinate association verification requires that the touch coordinates reported by the capacitive touch layer and the pressure center coordinates sensed by the resistive pressure sensing layer be less than a preset tolerance in terms of spatial distance and occur synchronously in terms of time. The spatial distance is calculated using Euclidean distance, and the time synchronization is achieved by comparing timestamp differences. If the coordinates match and the time is synchronized, the pressure event passes the verification; otherwise, no response is given.

[0009] Furthermore, the dynamic update process of the static background pressure baseline model includes: collecting invalid pressure event data that has been filtered out, extracting its pressure characteristics and coordinate information, using these data to adjust the parameters of the baseline model so that the model can adapt to the background pressure distribution under the current grip posture, and the feedback mechanism forms a closed-loop decision loop to continuously optimize the initial separation accuracy of the pressure components.

[0010] Furthermore, the final decision is based on historical behavior patterns, analyzing the sequence of touch events that are successfully determined in a short period of time, and combining application context information to infer the attributes of edge pressure events. The application context information includes the type of the currently running application and the interaction requirements. The historical behavior patterns are constructed by recording the sequence patterns and pressure characteristics of successful touch events.

[0011] Furthermore, the processing unit also includes a signal enhancement module, which is used to filter and denoise the original composite electrical signal before initial separation to enhance the accuracy of extracting time-series dynamic features. The filtering process uses a low-pass filter to remove high-frequency noise, and the denoising process uses a digital signal processing algorithm to reduce environmental interference.

[0012] Furthermore, the effective click fingerprint model supports multi-mode configuration, adjusting the threshold and matching conditions of waveform features according to different application scenarios. The multi-mode configuration is achieved through user settings or automatic learning to adapt to touch requirements in different usage environments.

[0013] Furthermore, the processing unit is connected to the storage module for caching historical stress data and behavioral pattern information. The storage module supports fast read and write operations to assist in real-time decision-making and model updates.

[0014] Furthermore, the screen also includes a user feedback interface for providing tactile or visual cues when a stress event is determined to be invalid. The user feedback interface works in coordination with the processing unit to enhance the user experience.

[0015] This invention provides a capacitive-resistive touchscreen that integrates pressure sensing to prevent accidental touches. It offers the following advantages: This pressure-sensitive capacitive-resistive touchscreen, designed to prevent accidental touches, achieves accurate interpretation of touch intentions by constructing a dynamic pressure fingerprint recognition and adaptive threshold model. The solution employs a multi-level signal processing and decision-making mechanism, effectively distinguishing between dynamic click pressure and static background pressure. This solves the problem of high misjudgment rates in traditional single-pressure threshold criteria under complex pressure scenarios, thereby improving the accuracy and reliability of touch operations.

[0016] This pressure-sensitive capacitive-resistive touchscreen, designed to prevent accidental touches, features an adaptive learning capability that continuously optimizes background pressure recognition accuracy and adapts to the interactive needs of different application scenarios through multi-mode configuration. The introduction of a user feedback mechanism further enhances the intuitiveness of human-computer interaction, achieving a good balance between accuracy in preventing accidental touches and user experience. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the module interaction of a pressure-sensing anti-accidental touch capacitive and resistive screen according to the present invention. Figure 2 This is a flowchart of the pressure signal processing of a pressure-sensing anti-accidental touch capacitive resistive screen according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 and Figure 2 This invention provides a technical solution: a pressure-sensing anti-accidental-touch capacitive-resistive screen, comprising a capacitive touch layer and a resistive pressure sensing layer. The capacitive touch layer is used to detect the coordinate information of the touch position, and the resistive pressure sensing layer is used to detect the pressure signal applied to the screen. It also includes a processing unit, which is electrically connected to the capacitive touch layer and the resistive pressure sensing layer, and is used to perform the following operations: The original composite electrical signal collected by the resistive pressure sensing layer is preprocessed to extract the temporal dynamic features of the pressure signal, including the pressure change rate and pressure stability index. Based on the temporal dynamic features, the pressure signal is initially separated into dynamic click pressure component and static background pressure component, and the separated pressure components are labeled. The labeled pressure data is subjected to dual correlation verification, including pressure change trajectory screening and spatiotemporal coordinate correlation verification. Pressure change trajectory screening is performed by comparing the pressure events marked as dynamic clicks with the preset valid click fingerprint model to determine the matching degree. Spatiotemporal coordinate correlation verification is performed by comparing the consistency between the touch coordinates reported by the capacitive touch layer and the pressure center coordinates sensed by the resistive pressure sensing layer in real time. The static background pressure baseline model is dynamically updated based on the results of the dual correlation verification. Invalid pressure event data that are screened out during the verification process are fed back to the static background pressure baseline model to optimize the initial separation process of pressure components. For edge stress events that cannot be clearly determined after double correlation verification, a final decision is made by combining historically successfully determined touch sequences and application context information to determine the validity of the stress event; only when the stress event successfully passes through all processing stages is it recognized as a valid user intent input.

[0020] It should be further explained that a multi-level pressure signal analysis and decision-making mechanism is implemented through the processing unit. This processing unit is electrically connected to the capacitive touch layer and the resistive pressure sensing layer and performs the following specific operations: First, the original composite electrical signal collected by the resistive pressure sensing layer is preprocessed. This preprocessing process uses a high-frequency sampling method to capture the instantaneous changes of the pressure signal. Through a specific signal processing algorithm, time-series features that can characterize the dynamic characteristics of the pressure are extracted, including the pressure change rate and pressure stability index. Based on these time-series dynamic features, the system initially separates the composite pressure signal into dynamic click pressure component and static background pressure component. The dynamic click pressure is a pulse signal with a high change rate and unstable characteristics, while the static background pressure is a baseline signal with a low change rate and high stability characteristics. The separated pressure data points are then labeled with type identification. The system then performs a dual correlation verification process. The first verification is the screening of pressure change trajectory. This is done by comparing the matching degree of the pressure event waveform labeled as dynamic click with the valid click fingerprint model pre-stored in the system. The valid click fingerprint model defines the complete pressure waveform characteristics including the rapid rise, peak dwell and slow release phases. When the matching degree is higher than the set threshold, a high confidence primary trigger command is generated; otherwise, it is marked as a pending event. The second layer of verification is the spatiotemporal coordinate correlation verification, which compares the touch coordinates reported by the capacitive touch layer with the pressure center coordinates sensed by the resistive pressure sensing layer in real time. The spatial distance between the two is required to be less than the preset tolerance value and the timestamps must be synchronized. Only pressure events that simultaneously meet the requirements of coordinate matching and time synchronization can pass the verification. The system also feeds back invalid pressure event data that was filtered out during the dual correlation verification process to the static background pressure baseline model in real time. By dynamically updating the model parameters, the system can adapt to the background pressure distribution under the current grip posture, forming a closed-loop decision-making cycle that becomes more accurate with use. For edge pressure events that still cannot be clearly determined after the above verification, the system will initiate deep reasoning based on historical behavior patterns. By analyzing the characteristics of touch sequence that has been successfully determined in a short period of time, and combining the interaction requirements context of the currently running application, the system will make a final determination on the attributes of the edge events. The entire technical solution employs a progressive processing flow of signal feature separation, dual correlation verification, model adaptive looping, and context-deep adjudication to ensure that only stress events that successfully pass the rigorous screening process are recognized as valid user intent inputs, thereby fundamentally solving the problem of finely distinguishing between static background stress and dynamic click stress.

[0021] When the processing unit preprocesses the original composite electrical signal, it uses high-frequency sampling to capture the instantaneous changes in the pressure signal. The high-frequency sampling interval is set to capture the rapid rise and slow release waveforms of the pressure signal. Among the extracted time-series dynamic features, the pressure change rate is obtained by calculating the change amplitude of the pressure value per unit time, and the pressure stability index is obtained by analyzing the degree of fluctuation of the pressure signal in a short period of time. The dynamic click pressure component is identified as a pulse signal with a high change rate and instability, and the static background pressure component is identified as a baseline signal with a low change rate and high stability. The labeling process assigns an initial type identifier to each pressure data point.

[0022] It should be further explained that when the processing unit preprocesses the original composite electrical signal, it uses a high-frequency sampling method with a specific frequency to continuously capture the instantaneous change process of the pressure signal. The setting of this sampling interval must ensure that the key morphological features of the fast rising edge and slow releasing edge in the pressure signal waveform can be completely captured. In the time-series dynamic feature extraction stage, the specific calculation of the pressure change rate is achieved by measuring the change amplitude of the pressure value per unit time, that is, performing differential operation on the pressure values ​​of continuous sampling points and calculating its change slope. The pressure stability index is obtained by analyzing the statistical characteristics of the pressure signal fluctuation amplitude within a preset time window, specifically by using the method of calculating the standard deviation of the pressure value within this time period for quantitative evaluation. Based on the above feature extraction results, the system identifies the dynamic click pressure component as an unstable pulse signal with a significant slope and a large standard deviation, and identifies the static background pressure component as a stable baseline signal with a slope close to zero and a small standard deviation. The labeling process assigns a type identifier based on feature threshold judgment to each pressure data point at each sampling time. This identifier is transmitted together with the pressure data as metadata in subsequent processing stages.

[0023] In the pressure change trajectory screening, the preset effective click fingerprint model includes the complete waveform features of the pressure signal. The complete waveform features require the pressure event to have a continuous pattern of a rapid rise phase, a peak dwell phase, and a slow release phase. The matching degree is determined by calculating the similarity between the actual pressure event waveform and the effective click fingerprint model. If the similarity is higher than the set threshold, a high-confidence primary trigger command is generated. If the similarity is lower than the set threshold, the pressure event is marked as a pending event.

[0024] It should be further explained that during the pressure change trajectory screening process, the preset effective click fingerprint model fully defines the waveform morphological characteristics that a qualified pressure event should possess. This model specifically stipulates that the pressure signal must continuously undergo three consecutive morphological change processes: a rapid rise phase, a peak dwell phase, and a slow release phase. During the matching degree judgment, the system calculates the morphological similarity between the actual collected pressure event waveform data and the preset waveform of the effective click fingerprint model through a dynamic time warping algorithm. This algorithm can effectively compensate for individual differences in click speed and force among different users. The minimum cumulative distance is calculated as the similarity evaluation value after nonlinearly aligning the actual waveform with the model waveform. When the calculated similarity evaluation value is higher than the system's preset matching threshold, the pressure event is determined to meet the morphological characteristics of effective touch and a high-confidence primary trigger command is generated; when the similarity evaluation value is lower than the preset matching threshold, the pressure event is marked as a pending event and transferred to the subsequent processing flow; the judgment criteria for the rapid rise phase is that the pressure value increases at a rate that reaches a preset threshold per unit time; the peak dwell phase requires that the pressure value remains relatively stable for a duration that exceeds the minimum time threshold after reaching its maximum value; and the slow release phase requires that the slope of the pressure value decrease process meets the preset release characteristic pattern.

[0025] The spatiotemporal coordinate correlation verification requires that the touch coordinates reported by the capacitive touch layer and the pressure center coordinates sensed by the resistive pressure sensing layer be less than the preset tolerance in terms of spatial distance and occur synchronously in terms of time. The spatial distance is calculated by Euclidean distance, and the time synchronization is achieved by comparing the timestamp differences. If the coordinates match and the time is synchronized, the pressure event passes the verification; otherwise, no response is given.

[0026] It should be further explained that in the process of spatiotemporal coordinate correlation verification, the system first establishes a unified coordinate reference system. Through a predefined coordinate transformation matrix, the touch position coordinates detected by the capacitive touch layer and the pressure center coordinates sensed by the resistive pressure sensing layer are mapped to the same coordinate system for calculation. The specific implementation of spatial distance matching is to calculate the Euclidean distance between two coordinate points and compare the distance value with the spatial tolerance threshold preset by the system. The spatial tolerance threshold is determined comprehensively based on the touch accuracy requirements and the physical characteristics of the screen. Time synchronization verification involves assigning high-precision timestamps to capacitive touch events and pressure-sensitive events, calculating the absolute difference between the two timestamps, and comparing this difference with a preset time window threshold. Only when the calculated spatial distance is less than the spatial tolerance threshold and the timestamp difference is within the time window threshold range is it determined to pass the spatiotemporal coordinate correlation verification. For pressure events that fail verification, the system records the coordinate deviation data and time difference data of the event and feeds this data back to the background pressure model for subsequent calibration and optimization. The entire verification process adopts a parallel processing mechanism, performing spatial coordinate matching analysis and time synchronization verification simultaneously to ensure the real-time performance and accuracy of the judgment results.

[0027] The dynamic update process of the static background pressure baseline model includes: collecting invalid pressure event data that has been filtered out, extracting its pressure characteristics and coordinate information, using these data to adjust the parameters of the baseline model so that the model can adapt to the background pressure distribution under the current grip posture, and the feedback mechanism forms a closed-loop decision loop to continuously optimize the initial separation accuracy of pressure components.

[0028] It should be further explained that the dynamic update process of the static background pressure baseline model is achieved through a continuous learning mechanism. During the double correlation verification process, the system collects data packets of pressure events that are judged to be invalid. Each data packet contains the pressure value sequence, duration, spatial coordinate information, and the reason for being rejected for the event. In the data preprocessing stage, the system extracts key feature parameters from these invalid event data, including pressure mean features, pressure stability index, event duration, and coordinate distribution information. The model parameters are adjusted using an incremental learning approach, which progressively corrects the parameters of the baseline model based on newly collected invalid event feature data. The update of the pressure mean uses an exponentially weighted moving average algorithm, which merges the historical mean with the mean of the newly collected data according to a preset weight. The update of the pressure stability index is dynamically adjusted by a recursive least squares algorithm to adjust the threshold boundary of stability judgment. The system simultaneously establishes a mapping relationship between screen coordinates and background pressure, divides the screen area into several virtual grids, and maintains independent background pressure model parameters for each grid. When invalid event data in a certain grid accumulates to a certain amount, the system automatically triggers the recalibration of the background pressure features in that grid area. The entire feedback mechanism forms a complete closed-loop decision-making cycle, enabling the system to continuously track changes in the user's grip posture and adaptively optimize the accuracy of the initial separation of pressure components. As usage time increases, the system's recognition accuracy for static background pressure shows a gradual improvement trend.

[0029] The final decision is based on historical behavior patterns. It analyzes the sequence of touch events that are successfully detected in a short period of time, and combines the application context information to infer the attributes of edge pressure events. The application context information includes the type of the currently running application and the interaction requirements. The historical behavior patterns are constructed by recording the sequence patterns of successful touch events and pressure characteristics.

[0030] It should be further explained that, in the final adjudication stage, the system conducts in-depth analysis of edge pressure events based on historical behavior patterns. It establishes an advanced behavioral feature database to record and store consecutively successfully adjudicated touch sequences. This database saves multi-dimensional feature data for each touch event, including pressure waveform features, operation time interval, touch coordinate trajectory, and operation force distribution. The system uses a time-series-based pattern recognition method to analyze these historical touch sequences and extracts the rhythmic patterns and force variation patterns of touch operations through a dynamic time warping algorithm to construct a user-specific operation feature model. When an edge pressure event is encountered, the system compares the waveform characteristics of the event with the touch events that have been successfully identified in the current operation sequence. At the same time, it combines the application context information obtained from the application programming interface, including the type identifier of the currently running application, the interactive characteristics declared by the application, and the user's historical operation preferences in the application. The system establishes differentiated adjudication strategies for different types of applications. For example, for game applications that require rapid and continuous touch, the system will appropriately relax the requirements for the integrity of the stress event waveform, while for reading applications that require precise single touch, a more stringent waveform matching standard is adopted. The entire adjudication process adopts a multi-factor weighted decision algorithm, which comprehensively calculates factors such as historical behavior matching degree, application context fit, and the credibility of the stress event's own characteristics according to preset weights, and finally determines the validity judgment result of the edge stress event based on the weighted score.

[0031] The processing unit also includes a signal enhancement module, which filters and denoises the original composite electrical signal before initial separation to enhance the accuracy of extracting time-series dynamic features. The filtering process uses a low-pass filter to remove high-frequency noise, and the denoising process uses digital signal processing algorithms to reduce environmental interference.

[0032] It should be further explained that the signal enhancement module performs multi-stage processing on the original composite electrical signal before initial separation. First, the signal is initially filtered by a low-pass filter with a specific cutoff frequency. This cutoff frequency is determined by the highest effective frequency component of the pressure signal and the sampling theorem, which can effectively filter out high-frequency noise from the screen driving circuit and environmental electromagnetic interference. Then, a wavelet transform-based denoising algorithm is used to perform deep processing on the filtered signal. By selecting appropriate wavelet basis functions and decomposition levels, the signal is decomposed at different scales. The high-frequency coefficients containing noise are thresholded before signal reconstruction, thereby removing random interference while preserving the edge features of the pressure signal. Adaptive filtering technology is also introduced in the digital signal processing. By establishing a noise reference model, the environmental interference characteristics are estimated in real time, and the least mean square algorithm is used to dynamically adjust the filter coefficients to optimize the noise reduction effect. The signal after enhancement processing not only improves the signal-to-noise ratio, but also enhances the edge features and transient characteristics of the pressure signal, providing high-quality input data for subsequent time-series dynamic feature extraction and ensuring the accuracy of the calculation of pressure change rate and stability index.

[0033] The effective click fingerprint model supports multi-mode configuration, adjusting the threshold and matching conditions of waveform features according to different application scenarios. Multi-mode configuration is achieved through user settings or automatic learning to adapt to touch requirements in different usage environments.

[0034] It should be further explained that the multi-mode configuration of the effective click fingerprint model is achieved through a model parameter database. This database stores a variety of waveform feature parameter sets preset for different application scenarios. Each parameter set includes a specific pressure rise slope threshold range, peak residence time requirements, and morphological matching conditions for the pressure release stage. The system monitors application status and obtains the identification information of the currently running foreground application. Based on the preset mapping relationship between application type and model configuration, it automatically selects the corresponding fingerprint model parameters. In automatic learning mode, the system continuously monitors the user's valid operation records in a specific application, identifies the user's typical operation characteristics in that application scenario through clustering analysis algorithms, and dynamically adjusts the waveform matching conditions corresponding to the application based on statistical learning results. The system also provides a user-configurable interactive interface, allowing users to manually select or fine-tune the strictness of the fingerprint model according to their personal operating habits. User settings are bound to specific applications and stored in a local configuration file. When the scene changes, the system loads the corresponding model parameters into the memory of the processing unit in real time to ensure that the pressure trajectory screening process can immediately apply the new matching standard. This multi-mode configuration mechanism allows the system to use strict complete waveform verification in drawing applications that require high-precision touch control, while in game applications that pursue fast response, a simplified matching mode that focuses on the characteristics of the pressure rise phase can be used, thereby achieving an adaptive balance between the accuracy of preventing accidental touches and the responsiveness of operation.

[0035] The processing unit is connected to the storage module to cache historical stress data and behavioral pattern information. The storage module supports fast read and write operations to assist in real-time decision-making and model updates.

[0036] It should be further explained that the storage module adopts a multi-level storage architecture combining non-volatile memory and cache. The non-volatile memory is used to persistently store the characteristic parameters of historical stress data, long-term statistical information of user behavior patterns, and configuration parameters of effective click fingerprint models corresponding to different applications. The cache is used to temporarily store the real-time stress sequence within the current session, the records of the most recently successfully judged touch events, and the background stress baseline model parameters currently in use. The storage module establishes a data transmission channel with the processing unit through a standard bus interface, and uses a block storage management mechanism to organize the stress data in timestamp order, and establishes index information for each data block to support fast query and retrieval. During the decision-making process of the processing unit, the storage module provides data retrieval services in two dimensions: time range and spatial region. When historical behavior pattern analysis is required, it can quickly return touch sequence feature data within a specified time window. The storage module also implements an automatic data update mechanism. When a new touch event is determined to be valid, its pressure characteristics and operation context information are immediately compressed, stored, and updated to the corresponding high-speed cache area. At the same time, important feature parameters are synchronized to non-volatile memory through background tasks. This storage architecture design ensures both the timeliness of historical data access and the preservation of key model parameters and user habit data after system power failure, thus providing complete data support for the system's adaptive learning and real-time decision-making.

[0037] The screen also includes a user feedback interface that provides tactile or visual cues when a stress event is deemed invalid. The user feedback interface works in coordination with the processing unit to enhance the user experience.

[0038] It should be further explained that the user feedback interface includes a haptic feedback unit and a visual indication unit. The haptic feedback unit is implemented by a linear vibration motor integrated into the screen substrate layer, which can generate vibration waveforms of different intensities and durations. The visual indication unit uses an LED indicator array at the edge of the screen display area or directly displays specific visual prompt graphics on the screen interface. When the processing unit determines that a pressure event is an invalid touch, it will immediately send a command packet to the user feedback interface. This command packet contains a feedback type identifier, intensity parameters, and duration parameters. The haptic feedback unit generates a corresponding vibration mode according to the command parameters. For accidental touches caused by static background pressure, a short single vibration is used as a prompt. For invalid operations caused by incomplete pressure waveforms, two consecutive vibrations are used as a prompt. The visual indicator unit synchronously illuminates LEDs of a specific color at the edge of the corresponding area on the screen or displays a transparent prompt icon in the corner of the interface, distinguishing different types of invalid operations through color changes; a two-way communication channel is established between the user feedback interface and the processing unit, which can receive the user's preference for the feedback mode and adjust the feedback strategy accordingly, while recording the user's interaction response data for different feedback modes to optimize the feedback effect. This collaborative working mechanism ensures that users receive timely and clear status feedback during operations, avoiding confusion caused by misoperations and helping users understand the specific reasons for invalid operations through differentiated feedback methods, thereby improving the intuitiveness and friendliness of human-computer interaction.

[0039] By constructing a dynamic pressure fingerprint identification and adaptive threshold model, accurate interpretation of touch intent is achieved. This solution employs a multi-level signal processing and decision-making mechanism, which can effectively distinguish between dynamic click pressure and static background pressure, solving the problem of high misjudgment rate in traditional single pressure threshold criteria under complex pressure scenarios, thereby improving the accuracy and reliability of touch operations.

[0040] The system's adaptive learning capability enables it to continuously optimize background pressure recognition accuracy and adapt to the interaction needs of different application scenarios through multi-mode configuration. The introduction of a user feedback mechanism further enhances the intuitiveness of human-computer interaction, achieving a good balance between accuracy in preventing accidental touches and user experience.

[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A pressure-sensing anti-accidental-touch capacitive-resistive touchscreen, comprising a capacitive touch layer and a resistive pressure-sensing layer, wherein the capacitive touch layer is used to detect the coordinate information of the touch position, and the resistive pressure-sensing layer is used to detect the pressure signal applied to the screen, characterized in that: It also includes a processing unit electrically connected to the capacitive touch layer and the resistive pressure sensing layer, for performing the following operations: The original composite electrical signal collected by the resistive pressure sensing layer is preprocessed to extract the temporal dynamic features of the pressure signal, including the pressure change rate and pressure stability index. Based on the temporal dynamic features, the pressure signal is initially separated into dynamic click pressure component and static background pressure component, and the separated pressure components are labeled. The labeled pressure data is subjected to dual correlation verification, including pressure change trajectory screening and spatiotemporal coordinate correlation verification. Pressure change trajectory screening is performed by comparing the pressure events marked as dynamic clicks with the preset valid click fingerprint model to determine the matching degree. Spatiotemporal coordinate correlation verification is performed by comparing the consistency between the touch coordinates reported by the capacitive touch layer and the pressure center coordinates sensed by the resistive pressure sensing layer in real time. The static background pressure baseline model is dynamically updated based on the results of the dual correlation verification. Invalid pressure event data that are screened out during the verification process are fed back to the static background pressure baseline model to optimize the initial separation process of pressure components. For edge stress events that cannot be clearly determined after double correlation verification, a final decision is made by combining historically successfully determined touch sequences and application context information to determine the validity of the stress event; only when the stress event successfully passes through all processing stages is it recognized as a valid user intent input.

2. The pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 1, characterized in that: When the processing unit preprocesses the original composite electrical signal, it uses a high-frequency sampling method to capture the instantaneous changes in the pressure signal. The interval of the high-frequency sampling is set to capture the rapid rise and slow release waveforms of the pressure signal. In the extracted time-series dynamic features, the pressure change rate is obtained by calculating the change amplitude of the pressure value per unit time, and the pressure stability index is obtained by analyzing the fluctuation degree of the pressure signal in a short period of time. The dynamic click pressure component is identified as a pulse signal with a high change rate and instability, and the static background pressure component is identified as a baseline signal with a low change rate and high stability. The labeling process assigns an initial type identifier to each pressure data point.

3. The pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 2, characterized in that: In the pressure change trajectory screening, the preset effective click fingerprint model includes the complete waveform features of the pressure signal. The complete waveform features require the pressure event to have a continuous pattern of a rapid rise phase, a peak dwell phase, and a slow release phase. The matching degree judgment is achieved by calculating the similarity between the actual pressure event waveform and the effective click fingerprint model. If the similarity is higher than a set threshold, a high-confidence primary trigger command is generated. If the similarity is lower than the set threshold, the pressure event is marked as a pending event.

4. A pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 3, characterized in that: The spatiotemporal coordinate association verification requires that the touch coordinates reported by the capacitive touch layer and the pressure center coordinates sensed by the resistive pressure sensing layer be less than a preset tolerance in terms of spatial distance and occur synchronously in terms of time. The spatial distance is calculated using Euclidean distance, and the time synchronization is achieved by comparing timestamp differences. If the coordinates match and the time is synchronized, the pressure event passes the verification; otherwise, no response is given.

5. A pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 4, characterized in that: The dynamic update process of the static background pressure baseline model includes: collecting invalid pressure event data that has been filtered out, extracting its pressure characteristics and coordinate information, using these data to adjust the parameters of the baseline model so that the model can adapt to the background pressure distribution under the current grip posture, and the feedback mechanism forms a closed-loop decision loop to continuously optimize the initial separation accuracy of pressure components.

6. A pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 5, characterized in that: The final decision is based on historical behavior patterns, analyzing the sequence of touch events that are successfully determined in a short period of time, and combining application context information to infer the attributes of edge pressure events. The application context information includes the type of the currently running application and the interaction requirements. The historical behavior patterns are constructed by recording the sequence patterns and pressure characteristics of successful touch events.

7. A pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 6, characterized in that: The processing unit also includes a signal enhancement module, which is used to filter and denoise the original composite electrical signal before initial separation to enhance the accuracy of extracting time-series dynamic features. The filtering process uses a low-pass filter to remove high-frequency noise, and the denoising process uses a digital signal processing algorithm to reduce environmental interference.

8. A pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 7, characterized in that: The effective click fingerprint model supports multi-mode configuration, adjusting the threshold and matching conditions of waveform features according to different application scenarios. The multi-mode configuration is achieved through user settings or automatic learning to adapt to touch requirements in different usage environments.

9. A pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 8, characterized in that: The processing unit is connected to the storage module to cache historical stress data and behavioral pattern information. The storage module supports fast read and write operations to assist in real-time decision-making and model updates.

10. A pressure-sensing anti-accidental touch capacitive-resistive screen according to claim 9, characterized in that: The screen also includes a user feedback interface for providing tactile or visual cues when a stress event is deemed invalid. The user feedback interface works in coordination with the processing unit to enhance the user experience.

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