A signal optimization method, system, medium and product for liquid crystal display touch screen

Abstract

A signal optimization method, system, medium and product for a liquid crystal display touch screen, which relates to the field of optical modulation technology. The method includes: obtaining a real-time touch signal of the touch screen and extracting characteristic parameters of the touch signal; comparing the signal strength and signal fluctuation range with a preset characteristic range to identify whether the current touch mode is a glove touch mode or a direct touch mode; determining the touch threshold and filter parameters of the touch screen based on the current touch mode; calculating the touch signal change rate of the touch signal characteristic parameters within N consecutive sampling periods, and when the touch signal change rate exceeds the preset change threshold, updating the filter parameters to filter optimization parameters; filtering the real-time touch signal based on the filter optimization parameters to obtain a filtered touch signal; and determining the touch determination result based on the filtered touch signal, the touch threshold and the signal duration. The implementation of this application can enable the display touch screen to adapt to different touch media and ensure the accuracy of the diagnostic operation.

Description

Technical Field

[0001] The present application relates to the field of optical modulation technology, and in particular to a signal optimization method, system, medium and product for a liquid crystal display touch screen. Background Art

[0002] With the development of intelligent medical diagnostic equipment, touch screens have become an important way of human-computer interaction. During the medical diagnosis process, medical staff need to accurately control the equipment through touch screens, which places strict requirements on the accuracy of touch signal recognition. Especially in medical environments, electromagnetic interference generated by various diagnostic equipment, the use of medical gloves, and the need for fast operation all pose challenges to the accurate recognition of touch signals.

[0003] At present, medical touch devices mainly use the method of adding a physical shielding layer and digital filtering to process touch signals. In specific implementation, a conductive shielding layer is added to the touch screen to block external electromagnetic interference, and a digital filtering algorithm with fixed parameters is used to reduce the noise of the collected touch signals. In terms of touch sensitivity, a fixed detection threshold is pre-set to distinguish between valid touch and interference signals.

[0004] However, in actual applications, when doctors need to quickly switch between glove operation and direct touch, the preset touch parameters often lead to unstable signal recognition, affecting the accuracy of diagnostic operations. Summary of the invention

[0005] The present application provides a signal optimization method, system, medium and product for a liquid crystal display touch screen, which are used to adapt the display touch screen to different touch media and ensure the accuracy of diagnostic operations.

[0006] In the first aspect, the present application provides a signal optimization method for a liquid crystal display touch screen, which is applied to a signal optimization system, and the method includes: obtaining a real-time touch signal of the touch screen, and extracting touch signal characteristic parameters including signal strength, signal fluctuation range and signal duration; comparing the signal strength and signal fluctuation range with a preset characteristic range, and identifying that the current touch mode is a glove touch mode or a direct touch mode; based on the current touch mode, determining the touch threshold and filter parameters of the touch screen; calculating the touch signal change rate of the touch signal characteristic parameters within N consecutive sampling periods, and when the touch signal change rate exceeds the preset change threshold, updating the filter parameters to filter optimization parameters; filtering the real-time touch signal based on the filter optimization parameters to obtain a filtered touch signal; and determining a touch judgment result based on the filtered touch signal, the touch threshold and the signal duration.

[0007] In the above embodiment, the signal optimization system acquires the characteristic parameters of the touch signal in real time, dynamically identifies the touch mode and adjusts the touch threshold and filtering parameters, and performs filtering optimization in combination with the touch signal change rate, thereby achieving accurate recognition and signal optimization of the touch signal. It can adapt to the characteristics of different touch media, ensure stable signal recognition effects in both glove touch and direct touch modes, and improve the accuracy of medical device operation.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the signal strength and the signal fluctuation range are compared with a preset characteristic range to identify the current touch mode as a glove touch mode or a direct touch mode, specifically including: obtaining multiple groups of signal strength samples and multiple groups of signal fluctuation range samples within a preset time window; normalizing the multiple groups of signal strength samples and the multiple groups of signal fluctuation range samples to obtain normalized signal strength and normalized fluctuation range; when the normalized signal strength is less than a preset strength threshold and the normalized fluctuation range is greater than a preset fluctuation threshold, identifying the current touch mode as a glove touch mode; when the normalized signal strength is greater than the preset strength threshold and the normalized fluctuation range is less than the preset fluctuation threshold, identifying the current touch mode as a direct touch mode.

[0009] In the above embodiment, the signal optimization system normalizes the signal strength and fluctuation range samples, accurately distinguishes between glove touch and direct touch modes based on preset feature thresholds, so that the system can quickly adapt to the needs of medical staff to switch touch modes during diagnosis and treatment, thereby improving the accuracy and response speed of touch mode recognition.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the touch judgment result based on the filtered touch signal, the touch threshold and the signal duration specifically includes: determining the corresponding judgment parameter set based on the touch threshold; the judgment parameter set includes the minimum effective touch duration, the maximum touch interval duration and the touch response delay; making a judgment based on the signal duration and the minimum effective touch duration, and when it is detected that the interval duration between two consecutive touch signals is less than the maximum touch interval duration, merging the two touch signals into a touch event; and adjusting the timing of the touch event based on the touch response delay to generate a touch judgment result.

[0011] In the above embodiment, the signal optimization system sets a determination parameter set to perform timing merging and adjustment on the touch signal, thereby effectively avoiding misjudgment due to too short a touch interval, achieving accurate recognition of continuous touch operations, and improving the reliability of touch event processing.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the steps of adjusting the timing of touch events according to the touch response delay and generating touch determination results specifically include: obtaining the start time point and the end time point of the touch event, and calculating the actual duration of the touch event; based on the touch response delay, compensating and correcting the start time point and the end time point to obtain the corrected time point; establishing a time window sequence of the touch event according to the corrected time point; within the time window sequence, detecting the amplitude change trend of the filtered touch signal, and identifying the valid signal segment and the transition signal segment of the touch event; based on the signal characteristics of the valid signal segment, interpolating the transition signal segment to generate a continuous touch trajectory; and generating a touch determination result according to the touch trajectory and the touch threshold.

[0013] In the above embodiment, the signal optimization system achieves accurate reconstruction of continuous touch tracks by performing timing compensation and signal interpolation processing on touch events, thereby ensuring the smoothness and accuracy of touch operations and effectively improving the quality of touch response.

[0014] In combination with some embodiments of the first aspect, in some embodiments, before the step of acquiring the real-time touch signal of the touch screen and extracting touch signal characteristic parameters including signal strength, signal fluctuation range and signal duration, the method also includes: collecting multiple groups of reference signal samples of different touch modes under a standard experimental environment; performing statistical analysis on the reference signal samples to construct a signal characteristic parameter database; and determining the initial characteristic threshold, initial touch threshold and initial filter parameters based on the signal characteristic parameter database.

[0015] In the above embodiment, the signal optimization system establishes a reference signal sample library and predetermines initial parameters, thereby providing a reliable parameter basis for the system and ensuring that the system can maintain a good touch recognition effect at the initial startup.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after calculating the touch signal change rate of the touch signal characteristic parameters within N consecutive sampling periods, and when the touch signal change rate exceeds a preset change threshold, updating the filter parameters to filter optimization parameters, the method also includes: constructing a touch quality evaluation model based on a signal-to-noise ratio index, a stability index, and a response delay index; calculating a signal quality score of the filter optimization parameters based on the touch quality evaluation model; and adjusting the filter optimization parameters when the signal quality score is lower than a preset standard threshold.

[0017] In the above embodiment, the signal optimization system realizes dynamic optimization of touch signal quality by constructing a touch quality evaluation model, monitoring and optimizing filtering parameters in real time, and ensuring the stability of the system in various usage environments.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the steps of constructing a touch quality evaluation model based on the signal-to-noise ratio index, stability index and response delay index specifically include: collecting multiple groups of touch signal samples under different environments, and extracting the signal-to-noise ratio, signal stability and response delay of each group of touch signal samples; normalizing the signal-to-noise ratio, signal stability and response delay to construct a feature vector space; constructing a touch quality evaluation model based on the feature vector space; establishing a filter parameter optimization rule library based on the evaluation results of the touch quality evaluation model; when abnormal touch signal quality is detected, determining the filter parameter combination with the highest priority from the filter parameter optimization rule library.

[0019] In the above embodiment, the signal optimization system realizes parameter adjustment for different touch control scenarios by establishing a filter parameter optimization rule base, thereby improving the system's ability to handle various touch control abnormalities.

[0020] In a second aspect, an embodiment of the present application provides a signal optimization system, which 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 includes computer instructions, and the one or more processors call the computer instructions to enable the signal optimization system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the above-mentioned computer program product runs on a signal optimization system, the above-mentioned signal optimization system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the instructions are executed on a signal optimization system, the signal optimization system executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] It can be understood that the signal optimization system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. The method adopts a method of dynamically identifying the touch mode by acquiring the characteristic parameters such as the intensity, fluctuation range and duration of the touch signal in real time, combining the preset characteristic range, and adaptively adjusting the touch threshold and filter parameters according to the touch mode. When it is detected that the change rate of the touch signal exceeds the preset threshold, the filter parameters will be updated in time to optimize the touch signal. This effectively solves the problem that the fixed parameters in the prior art cannot adapt to different touch modes, thereby realizing the accurate identification and signal optimization of the touch signal, and ensuring the operational stability and accuracy of the medical device under different touch conditions.

[0026] 2. The method of obtaining the time point of the touch event, calculating the actual duration, and performing time compensation correction based on the touch response delay is adopted. By establishing a time window sequence, identifying the effective signal segment and the transition signal segment, and generating a continuous touch trajectory through interpolation processing, the problems of discontinuous touch response and inaccurate trajectory reconstruction in the prior art are effectively solved, thereby achieving accurate identification and smooth transition of touch events, and ensuring the continuity and accuracy of medical diagnosis operations.

[0027] 3. The touch quality evaluation model is constructed based on indicators such as signal-to-noise ratio, stability and response delay, and the signal quality score of the filter optimization parameters is calculated in real time, and the parameter adjustment method is performed when the score is lower than the preset standard. This effectively solves the problems of large fluctuations in touch signal quality and poor anti-interference ability in the existing technology, thereby achieving continuous optimization of touch signal quality and ensuring the reliable operation of medical equipment in complex electromagnetic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of a signal optimization method for a liquid crystal display touch screen in an embodiment of the present application;

[0029] Figure 2 is another flow chart of the signal optimization method for a liquid crystal display touch screen in an embodiment of the present application;

[0030] Figure 3 It is a schematic diagram of a physical device structure of a signal optimization system in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0033] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0034] In the operating room of a certain hospital, doctors need to operate the medical imaging system through a touch screen to view the patient's CT images. During the operation, doctors often need to switch between wearing gloves and not wearing gloves: they can directly use their fingers to obtain precise control when viewing the image, and they need to wear surgical gloves for touch when performing the operation. However, the electromagnetic interference generated by various medical equipment in the operating room is strong, and liquids such as saline may interfere with the touch interface during the operation. When doctors quickly switch touch modes, touch failure or accidental touch often occurs, affecting the continuity of the operation process. Especially when the surgical plan needs to be adjusted quickly, the instability of touch recognition may cause delays in viewing key information.

[0035] The following describes a scenario in which a signal optimization method for a liquid crystal display touch screen in the related art is used.

[0036] A medical device manufacturer uses a traditional touch recognition solution to solve the above problems: using a digital filtering algorithm with fixed parameters to process touch signals and setting a unified touch detection threshold. In actual applications, when doctors operate with surgical gloves, the system often fails to recognize touch due to weak signal strength; when quickly switching to operation without gloves, repeated touches or false touches occur due to the previous parameter settings. In addition, the electromagnetic environment in the operating room changes with the switching of various devices, and the fixed filtering parameters cannot adapt to this dynamic change, resulting in unstable touch recognition.

[0037] The signal optimization method for the liquid crystal display touch screen in the embodiment of the present application is used to realize accurate recognition and signal optimization of the touch signal by real-time analysis of the touch signal characteristics, construction of the quality assessment model, and dynamic adjustment of the filtering parameters, which not only improves the accuracy of touch recognition, but also ensures the real-time response of the system. The following introduces the scenario in which the signal optimization method for the liquid crystal display touch screen in the present application is used.

[0038] The medical display system using this solution can intelligently identify the doctor's touch method: when the doctor touches the device while wearing surgical gloves, the system automatically switches to glove touch mode and adjusts the touch threshold and filter parameters by analyzing the intensity and fluctuation characteristics of the touch signal in real time; when the doctor takes off the gloves and touches the device directly, the system can quickly adjust to the corresponding parameter configuration. Even when the electromagnetic environment in the operating room changes, the system can dynamically optimize parameters through the touch quality assessment model to ensure the stability of touch recognition. For example, during a heart operation, the doctor needs to frequently switch gloves to operate different medical equipment, and the system always maintains accurate touch response, effectively improving the efficiency of the operation.

[0039] It can be seen that the signal optimization method of the liquid crystal display touch screen in the embodiment of the present application can not only realize accurate recognition of touch signals, but also effectively solve the recognition instability problem caused by environmental interference and touch mode switching, thereby realizing the continuity and reliability of touch operation of medical equipment.

[0040] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a signal optimization method for a liquid crystal display touch screen in an embodiment of the present application.

[0041] S101, acquiring a real-time touch signal of the touch screen, and extracting characteristic parameters of the touch signal including signal strength, signal fluctuation range, and signal duration.

[0042] Among them, the real-time touch signal represents the original capacitance signal data collected by the touch screen at the current moment; the signal strength refers to the capacitance change amplitude at the touch point; the signal fluctuation range represents the difference between the maximum and minimum values ​​of the touch signal within the sampling window; the signal duration is used to represent the time span from the start to the end of the touch; the touch signal characteristic parameters refer to various quantitative indicators used to characterize the characteristics of the touch signal, including but not limited to signal strength, fluctuation range and duration.

[0043] After the touch screen is started and enters the working state, the signal optimization system needs to continuously monitor the touch signal to identify effective touch operations. Specifically, the system first collects real-time capacitance data through the touch sensor array of the touch screen, preprocesses and reduces noise on the raw data, and calculates the signal strength value of each sampling point. Then, the fluctuation of the signal is counted within a fixed-size time window to determine the signal fluctuation range. At the same time, the start and end timestamps of the touch signal are recorded to calculate the signal duration. Finally, these feature parameters are organized into feature vectors for subsequent touch pattern recognition.

[0044] S102: Compare the signal strength and the signal fluctuation range with a preset characteristic range to identify whether the current touch mode is a glove touch mode or a direct touch mode.

[0045] Among them, the preset feature range represents the standard interval of signal characteristics under different touch modes; the glove touch mode refers to the state in which medical staff wear medical gloves to perform touch operations; the direct touch mode is used to indicate the state in which touch operations are performed directly with fingers.

[0046] After obtaining the characteristic parameters of the touch signal, the signal optimization system needs to accurately identify the current touch mode to optimize the processing strategy. Specifically, the system first reads the pre-calibrated characteristic range parameters of glove touch and direct touch, including the upper and lower limits of the signal strength and fluctuation range. Then the measured signal strength value is compared with the strength range, and the fluctuation range value is compared with the preset fluctuation range. Based on the comparison results, it is determined whether the signal characteristics fall within the characteristic range of a certain touch mode, thereby identifying the current touch mode.

[0047] In medical display touch screens, the signal optimization system identifies the touch mode by analyzing two key features of the touch signal: signal strength and fluctuation range. When medical staff operate wearing medical gloves, the touch signal will show the characteristics of "low intensity and large fluctuation" due to the barrier effect of the glove material. This is because the gloves reduce the capacitive coupling effect between the fingers and the touch screen, resulting in a weaker signal strength; at the same time, the contact between the gloves and the touch screen surface is not stable, which will produce large signal fluctuations.

[0048] On the contrary, when touching directly with fingers, good capacitive coupling is formed between the skin and the touch screen, generating a signal characteristic of "high intensity and small fluctuation". The system compares the signal characteristics collected in real time with the pre-calibrated characteristic range: if the signal strength is lower than 70% of the nominal value and the fluctuation range exceeds 30% of the nominal value, it is determined to be a glove touch mode; if the signal strength exceeds 90% of the nominal value and the fluctuation range is less than 15% of the nominal value, it is determined to be a direct touch mode.

[0049] This recognition mechanism based on dual feature thresholds can accurately distinguish different touch modes and provide the correct parameter configuration basis for subsequent signal processing. By monitoring the changes in these features in real time, the system can quickly respond to the touch mode switching of medical staff during the operation to ensure the continuity of diagnostic operations.

[0050] In some embodiments, the recognition of touch mode can be achieved in a variety of ways: Optionally, a threshold judgment method is used: set thresholds for signal strength and fluctuation range, and when the signal strength is lower than the threshold and the fluctuation range is greater than the threshold, it is judged as glove touch; when the signal strength is higher than the threshold and the fluctuation range is less than the threshold, it is judged as direct touch; the recognition reliability is improved by double threshold judgment. Optionally, a feature space classification method is used: construct a two-dimensional feature space with signal strength and fluctuation range as coordinate axes, and divide the feature areas of glove touch and direct touch; map the measured feature points into the feature space, and determine the touch mode according to the area to which they belong. It can be understood that machine learning and other methods can also be used to achieve intelligent recognition of touch modes, such as support vector machines, neural networks, etc., which are not limited here.

[0051] S103: Determine a touch threshold and filter parameters of the touch screen based on the current touch mode.

[0052] The touch threshold refers to the signal strength threshold for determining effective touch; the filter parameter refers to various parameter configurations used for signal noise reduction processing.

[0053] After the signal optimization system identifies the current touch mode, it needs to adjust the signal processing parameters accordingly to obtain the best effect. Specifically, the system first reads the reference parameter group corresponding to the current touch mode from the parameter database. Then, the parameters are fine-tuned according to the real-time signal characteristics, including adjusting the sensitivity of the touch judgment threshold and optimizing the filter's cutoff frequency, order and other key parameters. Finally, the adjusted parameters are applied to the subsequent signal processing process.

[0054] Among them, the signal optimization system uses a multi-stage cascade filter structure for signal processing. The filter parameters of the system include cutoff frequency (fc), passband ripple (δp), stopband attenuation (δs) and filter order (N). For the direct touch mode, the initial parameters are set to fc=1kHz, δp=0.1dB, δs=40dB, N=4; for the glove touch mode, the parameters are adjusted to fc=500Hz, δp=0.2dB, δs=35dB, N=6. The system uses a double second-order section to construct an IIR filter, and converts the analog filter coefficients into digital filter coefficients through the bilinear transformation method. The differential equation of the filter is:

[0055] y(n)=b0x(n)+b1x(n-1)+b2x(n-2)-a1y(n-1)-a2y(n-2), where the coefficients are calculated using the Butterworth prototype filter.

[0056] S104 , calculating the touch signal change rate of the touch signal characteristic parameters within N consecutive sampling periods, and when the touch signal change rate exceeds a preset change threshold, updating the filter parameters to filter optimization parameters.

[0057] Among them, the sampling period represents the time interval for collecting touch signals; the touch signal change rate refers to the speed at which the signal characteristic parameters change over time; the preset change threshold is used to represent the allowable range of touch changes; and the filtering optimization parameter represents the optimized and adjusted filter configuration.

[0058] During normal operation, the signal optimization system needs to continuously monitor the change trend of the touch signal to maintain the processing effect. Specifically, the system collects the signal characteristic parameters of N sampling points within a fixed-length time window and calculates the change rate between adjacent sampling points. When the change rate is detected to exceed the preset threshold, it means that the touch state has changed significantly. At this time, the filter parameters need to be updated to adapt to the new signal characteristics to ensure the optimal filtering effect.

[0059] Among them, the signal optimization system uses the sliding window method to calculate the dynamic change characteristics of the touch signal. Within N sampling cycles (usually N=10), the system uses the weighted average method to calculate the instantaneous change rate of the signal characteristic parameters, that is, the touch signal change rate. For the signal characteristic value S(t) at time t, its change rate R(t) is calculated in the following way: first calculate the difference value d(i)=S(t-i+1)-S(ti) of adjacent sampling points, then use the exponential decay weight w(i)=exp(-i / τ) to weight the difference value, and finally obtain the weighted average change rate R(t)=Σ(w(i)×d(i)) / Σw(i), where τ is the characteristic time constant, which is used to control the influence of historical data.

[0060] S105 , filtering the real-time touch signal based on the filtering optimization parameter to obtain a filtered touch signal.

[0061] Among them, filtering processing refers to the process of suppressing noise on the signal; filtering optimization parameters refer to the filter configuration parameters that have been dynamically optimized; the real-time touch signal is used to represent the original touch data currently collected; the filtered touch signal refers to the optimized signal after filtering processing; signal quality refers to the signal-to-noise ratio, stability and other characteristic indicators of the signal after filtering.

[0062] After obtaining the optimized filtering parameters, the signal optimization system needs to filter the touch signal in real time to improve the signal quality. Specifically, the system first configures the digital filter according to the filtering optimization parameters, including setting the filter type, order, bandwidth and other parameters. Then the real-time collected touch signal is input into the filter for processing to suppress high-frequency noise and interference components. Finally, the filtered touch signal with noise reduction optimization is output for subsequent touch judgment.

[0063] In some embodiments, signal filtering can be achieved in a variety of ways: Optionally, a multi-stage filtering method is used: median filtering is performed in sequence to remove pulse noise, low-pass filtering is performed to suppress high-frequency interference, and notch filtering is performed to eliminate power frequency interference; parameters of each level of filters are adjusted; and filtering effects are evaluated. Optionally, an adaptive filtering method is used: the filter structure is dynamically adjusted according to signal characteristics; filter coefficients are updated in real time; filtering performance indicators are monitored; and filtering effects are optimized. It is understandable that other filtering algorithms can also be used to achieve signal optimization, such as wavelet denoising, empirical mode decomposition, etc., which are not limited here.

[0064] S106 : Determine a touch determination result according to the filtered touch signal, the touch threshold and the signal duration.

[0065] The touch determination result indicates the final determination of the touch validity; the touch threshold refers to the signal strength threshold used to determine the touch validity; and the signal duration is used to indicate the duration of the touch.

[0066] After completing signal filtering, the signal optimization system needs to determine the effectiveness of the touch based on multiple judgment criteria. Specifically, the system first compares the strength of the filtered signal with the touch threshold to determine whether it meets the strength requirements for effective touch. At the same time, it checks whether the signal duration meets the minimum touch duration requirements and filters out instantaneous interference. Finally, the spatial distribution characteristics of the touch are comprehensively considered to output the final touch judgment result.

[0067] In some embodiments, touch determination can be implemented in a variety of ways: Optionally, a multi-threshold determination method is used: set the main threshold and auxiliary threshold of signal strength, and make multiple determinations in combination with the signal duration; establish a touch effectiveness scoring mechanism; and determine the determination result based on the score. Optionally, a pattern recognition method is used: extract the time domain and frequency domain features of the touch signal; build a touch pattern recognition model; calculate the touch credibility; and output the determination result. It is understandable that other determination methods can also be used to implement touch recognition, such as fuzzy logic, probability statistics, etc., which are not limited here.

[0068] It should be noted that the touch pattern recognition model inputs signal strength samples and fluctuation range samples in two modes, glove touch and direct touch, during training. A standardized feature space is established through normalization processing, and the training standard is that the pattern recognition accuracy and response time meet the medical operation requirements. The touch pattern recognition model includes three main modules: feature extraction, pattern classification, and result output. Rapid recognition of touch patterns is achieved through a dual threshold judgment mechanism. By inputting the characteristic parameters of real-time touch signals, the touch pattern recognition model can output the current touch pattern type and trigger the corresponding parameter configuration update.

[0069] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the signal optimization method for a liquid crystal display touch screen in an embodiment of the present application.

[0070] S201 : Acquire a real-time touch signal of the touch screen, and extract characteristic parameters of the touch signal including signal strength, signal fluctuation range, and signal duration.

[0071] Referring to step S101 , the signal optimization system determines characteristic parameters of the touch signal.

[0072] In some embodiments, the signal optimization system first collects multiple groups of reference signal samples of different touch modes in a standard experimental environment; performs statistical analysis on the reference signal samples to build a signal characteristic parameter database; and determines the initial characteristic threshold, initial touch threshold and initial filter parameters based on the signal characteristic parameter database.

[0073] Among them, the standard experimental environment refers to a test site with controllable temperature, humidity and electromagnetic interference level; the benchmark signal sample refers to the touch signal data collected under standard conditions; and the initial parameter configuration refers to the basic parameter settings when the system starts.

[0074] When the signal optimization system is deployed for the first time or important parameters are updated, a reliable parameter foundation needs to be established. Specifically, the system first collects touch signal samples of different methods, including direct touch and glove touch, in a standard laboratory with controllable temperature and humidity and a stable electromagnetic environment. Then, mathematical and statistical analysis is performed on the collected samples to extract the distribution law of characteristic parameters and establish a feature-parameter mapping relationship. Finally, according to the statistical results, the initial parameters required for system operation are set.

[0075] In some embodiments, parameter initialization configuration can be implemented in a variety of ways: Optionally, a stratified sampling method is used: design a stratified sampling scheme, perform multiple rounds of data collection, and statistically analyze feature distribution; establish a parameter mapping model; and generate an initial configuration. Optionally, a cross-validation method is used: divide the samples into a training set and a validation set, train the parameter model, and verify the validity of the parameters; optimize the parameter configuration; and output the optimal initial value. It is understandable that other methods can also be used to implement parameter initialization, such as machine learning, data mining, etc., which are not limited here.

[0076] S202: Compare the signal strength and the signal fluctuation range with a preset characteristic range to identify whether the current touch mode is a glove touch mode or a direct touch mode.

[0077] Referring to step S102 , the signal optimization system identifies the current touch mode.

[0078] In some embodiments, the signal optimization system obtains multiple groups of signal strength samples and multiple groups of signal fluctuation range samples within a preset time window; normalizes the multiple groups of signal strength samples and multiple groups of signal fluctuation range samples to obtain normalized signal strength and normalized fluctuation range; when the normalized signal strength is less than a preset strength threshold and the normalized fluctuation range is greater than a preset fluctuation threshold, identifies the current touch mode as a glove touch mode; when the normalized signal strength is greater than the preset strength threshold and the normalized fluctuation range is less than the preset fluctuation threshold, identifies the current touch mode as a direct touch mode.

[0079] Among them, the preset time window represents a fixed time period for signal analysis; the signal strength sample refers to the collected touch signal amplitude data; the signal fluctuation range sample is used to represent the statistical value of the signal change amplitude; the normalization processing refers to the process of mapping data of different dimensions to a unified interval; the feature threshold refers to the critical value used to distinguish different touch modes.

[0080] When performing real-time touch recognition, the signal optimization system needs to accurately distinguish different touch modes. Specifically, the system first collects multiple sets of touch signal data within a fixed time window and extracts the signal strength and fluctuation range characteristics. Then, these feature data are normalized to eliminate dimensional differences. Finally, by comparing with the preset feature threshold, the current touch mode is determined based on the combined characteristics of signal strength and fluctuation range.

[0081] Among them, the signal optimization system uses an adaptive normalization method based on statistical features to process signal features. The system first collects 200 sampling points within a preset time window (usually 1 second) and calculates the mean (μ) and standard deviation (σ) of the signal strength and fluctuation range. Then the Z-score standardization method is used for normalization: for each sample value x, its normalized value z = (x-μ) / σ is calculated. In order to avoid the influence of outliers, the system sets a truncation range of ±3σ, and values ​​outside the range will be limited to the boundary value. Finally, the normalized value is mapped to the [0, 1] interval through linear mapping: y = (z + 3) / (6). This method can adapt to the dynamic range changes of the signal and ensure the reliability of feature comparison.

[0082] S203: Determine a touch threshold and filter parameters of the touch screen based on the current touch mode.

[0083] Referring to step S103 , the signal optimization system determines the touch threshold and filter parameters.

[0084] S204 , calculating the touch signal change rate of the touch signal characteristic parameters within N consecutive sampling periods, and when the touch signal change rate exceeds a preset change threshold, updating the filter parameters to filter optimization parameters.

[0085] Referring to step S104 , the signal optimization system updates the filter parameters.

[0086] S205: construct a touch quality evaluation model based on the signal-to-noise ratio index, the stability index, and the response delay index.

[0087] Among them, the signal-to-noise ratio index represents the ratio of the effective signal to the noise in the touch signal; the stability index refers to the evaluation value of the degree of signal fluctuation; the response delay index is used to indicate the response speed of the touch system; the touch quality evaluation model represents the mathematical model used to comprehensively evaluate the quality of the touch signal; the indicator weight refers to the importance of each evaluation indicator in the model.

[0088] When implementing signal optimization processing, the signal optimization system needs to establish a scientific quality assessment mechanism. Specifically, the system first collects the signal-to-noise ratio data of the touch signal, calculates the signal stability parameters, and measures the system response delay. Then, the evaluation criteria and weight coefficients of each indicator are determined, and a multi-dimensional evaluation indicator system is constructed. Finally, mathematical modeling is used to integrate various indicators into a unified quality assessment model to guide subsequent parameter optimization.

[0089] It should be noted that the touch quality assessment model inputs touch signal samples collected under multiple sets of environmental conditions during training, and extracts characteristic parameters such as signal-to-noise ratio, signal stability and response delay. After normalizing these parameters, a feature vector space is constructed. The training criteria include that the signal quality score needs to be greater than a preset threshold, and the model's recognition accuracy for different touch modes meets the specified requirements. The touch quality assessment model adopts a multi-layer evaluation architecture, including a feature extraction layer, a weight calculation layer, and a decision output layer. The model maps the normalized feature parameters to the quality score space, and obtains a comprehensive evaluation result through weighted calculation. By inputting the characteristic parameters of the touch signal in real time, the touch quality assessment model can output a signal quality score and optimization suggestions. When the quality score is lower than the threshold, the parameter optimization mechanism is triggered.

[0090] In some embodiments, the signal optimization system collects multiple groups of touch signal samples under different environments, extracts the signal-to-noise ratio, signal stability and response delay of each group of touch signal samples; normalizes the signal-to-noise ratio, signal stability and response delay to construct a feature vector space; based on the feature vector space, constructs a touch quality evaluation model; according to the evaluation results of the touch quality evaluation model, establishes a filter parameter optimization rule library; when abnormal touch signal quality is detected, determines the filter parameter combination with the highest priority from the filter parameter optimization rule library.

[0091] Among them, the signal-to-noise ratio refers to the ratio of effective signal to noise; signal stability refers to the evaluation of the degree of signal fluctuation; response delay is used to represent the time characteristics of system response; feature vector space represents the mathematical space composed of multidimensional feature parameters; and optimization rule base refers to the data set that stores parameter optimization strategies.

[0092] When implementing adaptive optimization, the signal optimization system needs to establish a complete quality evaluation system. Specifically, the system first collects touch signal samples under different environmental conditions and extracts key indicators such as signal-to-noise ratio, stability, and latency. These indicators are then normalized and a feature vector space is established to construct a quality evaluation model. Finally, optimization rules are formulated based on the evaluation results. When abnormal signal quality is detected, the system selects the most suitable parameter configuration from the rule base.

[0093] S206: Calculate the signal quality score of the filtering optimization parameter based on the touch quality evaluation model.

[0094] Among them, the signal quality score represents the quantitative evaluation value of the touch signal quality; the evaluation indicator weight refers to the importance coefficient of each indicator when calculating the score; the indicator scoring rule is used to represent the scoring standard of each indicator; the evaluation process represents the calculation process from the original data to the final score.

[0095] After the signal optimization system completes the construction of the quality assessment model, it needs to score the current signal processing effect. Specifically, the system first collects the performance index data under the current filtering parameters, including signal-to-noise ratio, stability, and response delay. Then these index data are input into the evaluation model and calculated according to the preset scoring rules. Finally, a comprehensive signal quality score is obtained to judge the pros and cons of the current parameter configuration.

[0096] Among them, the signal optimization system uses a multi-dimensional weighted scoring method to calculate the touch quality, that is, the preset scoring rules for calculating the signal quality score include: the system first divides the measured signal-to-noise ratio by the reference signal-to-noise ratio and multiplies it by 40 to get the signal-to-noise ratio score, divides the measured volatility by the maximum allowable volatility, subtracts the value from 1 and multiplies it by 35 to get the stability score, and divides the measured delay by the maximum allowable delay, subtracts the value from 1 and multiplies it by 25 to get the delay score. Then, these three scores are multiplied by the corresponding weight coefficients (the sum of the weights is 1) and added together to get the final touch quality evaluation score. For example, when the measured signal-to-noise ratio is 15dB (reference value 20dB), the volatility is 8% (maximum allowable 15%), and the delay is 30ms (maximum allowable 50ms), the signal-to-noise ratio score is 30 points, the stability score is 24.5 points, and the delay score is 20 points. If the weights are 0.4, 0.35, and 0.25 respectively, the final score is 25.575 points.

[0097] S207: When the signal quality score is lower than a preset standard threshold, adjust the filtering optimization parameters.

[0098] Among them, the preset standard threshold represents the minimum acceptable signal quality level; the filtering optimization parameter refers to the set of adjustable parameters used for signal processing; the parameter adjustment strategy is used to represent the specific method of optimizing the parameters; and the parameter optimization range represents the allowable interval for parameter adjustment.

[0099] After obtaining the signal quality score, the signal optimization system needs to optimize the processing parameters according to the score result. Specifically, the system first compares the current signal quality score with the preset standard threshold to confirm whether parameter adjustment is required. When the score is lower than the threshold, the system will analyze the shortcomings of the current parameter configuration, determine the optimization direction based on the feedback of the quality assessment model, and then update the filter parameter configuration according to the preset adjustment strategy.

[0100] Among them, the signal optimization system adopts a progressive parameter optimization strategy to dynamically adjust the filter configuration. The specific strategy includes: the system first optimizes the signal quality by adjusting the filter bandwidth. The bandwidth adjustment step is 10% of the current value, and the adjustment direction is determined by the change trend of the signal-to-noise ratio score. If the signal quality has not improved after three consecutive bandwidth adjustments, the system will gradually increase the filter order and recalculate the filter coefficient each time. If the effect is still not ideal after the order reaches the upper limit, the system will try to switch to different types of filter structures and try them in turn according to the differences in frequency response characteristics. After each parameter adjustment, the system maintains an observation period of 50 milliseconds and evaluates the optimization effect by calculating the signal quality improvement rate before and after the adjustment. For example, when the system detects that the signal quality score is 65 points, it first adjusts the bandwidth from 2kHz to 1.8kHz. After observation, it is found that the score has increased to 72 points. The optimization continues until the score reaches the target value or the upper limit of the adjustment is reached.

[0101] The signal optimization system also uses a multi-layer protection mechanism to deal with the continuous deterioration of signal quality. When it is detected that the signal quality score has dropped three times in a row and the drop exceeds 20%, the system starts the emergency handling process: first, it quickly switches to the backup parameter group, which is the most recent set of stable operating parameters saved by the system during normal operation. At the same time, the parallel signal processing channel is started, and the input signal is processed simultaneously using two different sets of parameters, and the output result with the higher signal quality score is selected. If the backup parameters still cannot improve the situation, the system will trigger the automatic reset mechanism, restore to the factory calibration parameters, and start the adaptive parameter learning process in the background. Throughout the emergency handling process, the system will lower the touch sensitivity threshold to ensure that the basic functions of key operations are available, and send signal quality warnings to upper-level applications to prompt users to adjust their operating methods.

[0102] S208 , filtering the real-time touch signal based on the filtering optimization parameter to obtain a filtered touch signal.

[0103] Referring to step S105 , the signal optimization system determines the filtered touch signal.

[0104] S209: Determine a corresponding determination parameter set according to the touch threshold.

[0105] Among them, the judgment parameter set represents the key parameter combination used for touch judgment; the minimum effective touch duration refers to the shortest time that a valid touch signal needs to last; the maximum touch interval duration is used to indicate the maximum time interval that can be identified as continuous touch; the touch response delay represents the time difference from the occurrence of touch to the system response.

[0106] The signal optimization system needs to set reasonable judgment criteria when making touch judgments. Specifically, the system first selects the corresponding judgment parameter configuration based on the current touch threshold, including setting the minimum effective touch duration to filter interference signals, determining the maximum touch interval duration to identify continuous operations, and configuring the touch response delay to ensure the real-time performance of the system. These parameters together constitute a complete judgment parameter set for subsequent touch event recognition.

[0107] The signal optimization system dynamically adjusts the judgment parameters based on the physical properties of the touch medium and the requirements of human-computer interaction. That is, the steps for determining the numerical range of the judgment parameter set include: the system calculates the minimum effective touch duration based on the current touch threshold. This value increases linearly with the touch threshold, and the baseline value is 50 milliseconds for direct touch. The maximum touch interval is set to 4 times the minimum effective touch duration to accommodate natural pauses in continuous operations. The touch response delay is determined by a nonlinear mapping relationship based on the strength of the touch signal. The weaker the signal, the larger the delay setting to ensure the integrity of the signal acquisition. When a glove touch mode is detected, the system will automatically amplify these parameter values ​​by a factor of 2 to accommodate the increased signal conduction delay during glove touch.

[0108] The signal optimization system establishes a mapping relationship between the touch threshold and the minimum effective touch duration based on the physical model and experimental data. Assuming the touch threshold is Vth and the reference touch threshold is Vref (usually the nominal value in direct touch mode), the calculation formula for the minimum effective touch duration Tmin is: Tmin=T0+k×(Vth / Vref-1), where T0 is the reference duration (usually 50ms) and k is the proportional coefficient (usually 100ms). The physical basis of this linear relationship is that the higher the touch threshold, the longer it takes for the capacitor charging and discharging process to reach a stable state, so the minimum effective touch duration needs to be extended accordingly. For example, when the touch threshold is 1.5 times the reference value, the minimum effective touch duration will increase to 100ms. The system dynamically adjusts the proportional coefficient k by monitoring the rise time and stabilization time of the touch signal in real time to adapt to changes in touch characteristics under different usage environments.

[0109] S210: judging according to the signal duration and the minimum effective touch duration, when it is detected that the interval between two consecutive touch signals is less than the maximum touch interval duration, combining the two touch signals into a touch event.

[0110] Among them, continuous touch determination refers to identifying whether multiple touches belong to the same operation; touch events are used to represent the complete touch operation process; and time correlation represents the correlation degree of touch signals in the time dimension.

[0111] The signal optimization system needs to correctly handle continuous touch operations when performing touch recognition. Specifically, the system first checks whether the duration of each touch signal exceeds the minimum effective touch duration to confirm the validity of the touch. Then it analyzes the time interval between adjacent touch signals. When the interval is less than the maximum touch interval duration, these touch signals are regarded as the same touch event. Finally, the merged touch events are feature extracted and marked.

[0112] In some embodiments, the merging of touch signals can be achieved in a variety of ways: Optionally, a timing analysis method is used: establish a touch signal timing relationship diagram, identify signal time correlation, perform signal merging; extract event features; generate event descriptions. Optionally, a pattern matching method is used: build a touch pattern library, match touch sequence patterns, determine merging strategies; optimize merging results; output touch events. It is understandable that other methods can also be used to achieve signal merging, such as state machines, timing logic, etc., which are not limited here.

[0113] S211 . Adjust the timing of touch events according to the touch response delay to generate a touch determination result.

[0114] Among them, timing adjustment refers to the correction processing of the time characteristics of touch events; touch judgment result refers to the system's final judgment on the effectiveness of touch; response timing compensation is used to represent the correction mechanism for system delay; judgment reliability refers to the accuracy of the touch judgment result.

[0115] After completing the touch event recognition, the signal optimization system needs to perform the final timing optimization. Specifically, the system first calculates the deviation between the actual touch occurrence time and the system response time based on the preset touch response delay parameters. Then, the timestamp of the touch event is compensated and corrected to eliminate the impact of the system delay. Finally, based on the corrected timing information and combined with the touch characteristics, the final touch determination result is output.

[0116] In some embodiments, the timing adjustment of touch events can be achieved in a variety of ways: Optionally, a delay compensation method is used: measuring the system response delay, calculating the compensation value, performing time correction; verifying the compensation effect; and outputting the adjustment result. Optionally, a prediction correction method is used: establishing a touch behavior prediction model, estimating the actual touch moment, and performing timing optimization; evaluating the prediction accuracy; and generating a final judgment. It is understandable that other methods can also be used to achieve timing adjustment, such as Kalman filtering, time series analysis, etc., which are not limited here.

[0117] In some embodiments, the signal optimization system obtains the start time point and the end time point of the touch event, and calculates the actual duration of the touch event; based on the touch response delay, the start time point and the end time point are compensated and corrected to obtain the corrected time point; according to the corrected time point, a time window sequence of the touch event is established; within the time window sequence, the amplitude change trend of the filtered touch signal is detected, and the valid signal segment and transition signal segment of the touch event are identified; based on the signal characteristics of the valid signal segment, the transition signal segment is interpolated to generate a continuous touch trajectory; according to the touch trajectory and the touch threshold, a touch judgment result is generated.

[0118] Among them, the time point represents the start and end time of the touch event; the actual duration refers to the real duration of the touch operation; the compensation correction is used to represent the correction of system delay; the time window sequence represents the timing feature set of the touch event; the effective signal segment and the transition signal segment refer to different characteristic intervals of the touch signal.

[0119] When processing touch events, the signal optimization system needs to accurately grasp the timing characteristics of touch. Specifically, the system first records the start and end time of the touch event and calculates the actual duration. Then, it performs time compensation based on the system's response delay and establishes an accurate time window sequence. Finally, it analyzes the signal characteristics within these time windows, identifies valid signal segments and transition signal segments, and generates a continuous touch trajectory through interpolation processing.

[0120] Among them, the signal optimization system uses a cubic spline interpolation algorithm to achieve smooth reconstruction of the transition signal segment, that is, the interpolation processing steps of the transition signal segment include: the system first extracts the position coordinates and derivative values ​​at the endpoints of adjacent valid signal segments to construct a cubic polynomial interpolation function. The polynomial contains four undetermined coefficients, and a four-dimensional linear equation system can be constructed through the position and derivative constraints at the endpoints. The system uses Cramer's rule to solve the equation system to obtain the polynomial coefficients, and then uses the polynomial to generate a continuous and smooth signal curve in the transition segment. For example, when there is a 20ms transition interval between two valid signal segments, the system will generate 100 interpolation points in this interval to ensure the continuity and smoothness of the signal trajectory.

[0121] In the embodiment of the present application, due to the use of touch pattern recognition technology based on multi-dimensional feature analysis, adaptive parameter optimization mechanism and touch quality assessment model, it is possible to analyze touch signal characteristics in real time, dynamically adjust processing parameters, and optimize signal quality, effectively solving the problems of unstable touch recognition, fixed parameter configuration, and poor environmental adaptability in the prior art, thereby achieving stable response, accurate recognition and continuous operation of the touch interface of medical equipment in different usage scenarios. By constructing a touch quality assessment model and a parameter optimization rule library, the system realizes the intelligence and adaptability of touch signal processing, can adapt to various interference factors and operation requirements in the medical environment, and improves the operating efficiency and reliability of medical equipment.

[0122] The signal optimization system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3 , which is a schematic diagram of a physical device structure of a signal optimization system in an embodiment of the present application.

[0123] It should be noted that Figure 3 The structure of the signal optimization system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0124] like Figure 3 As shown, the signal optimization system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0125] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0126] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0127] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0129] Specifically, the signal optimization system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the signal optimization method for the liquid crystal display touch screen provided in the above embodiment is implemented.

[0130] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the signal optimization system described in the above embodiment; or may exist independently without being assembled into the signal optimization system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the signal optimization system, the signal optimization system implements the signal optimization method for the liquid crystal display touch screen provided in the above embodiment.

[0131] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0132] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0133] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A signal optimization method for a liquid crystal display touch screen, characterized in that: Applied to a signal optimization system, the method comprises: Acquire the real-time touch signal of the touch screen and extract the characteristic parameters of the touch signal including signal strength, signal fluctuation range and signal duration; Comparing the signal strength and the signal fluctuation range with a preset characteristic range, and identifying whether the current touch mode is a glove touch mode or a direct touch mode; Determining a touch threshold and filter parameters of the touch screen based on the current touch mode; Calculating the touch signal change rate of the touch signal characteristic parameter within N consecutive sampling periods, and when the touch signal change rate exceeds a preset change threshold, updating the filter parameter to a filter optimization parameter; the touch signal change rate refers to the speed at which the touch signal characteristic parameter changes over time; Based on the signal-to-noise ratio, signal stability and response delay, a touch quality evaluation model is constructed, including: collecting multiple groups of touch signal samples under different environments, extracting the signal-to-noise ratio, signal stability and response delay of each group of touch signal samples; normalizing the signal-to-noise ratio, the signal stability and the response delay to construct a feature vector space; and constructing a touch quality evaluation model based on the feature vector space; Establishing a filter parameter optimization rule base according to the evaluation result of the touch quality evaluation model; When abnormal quality of the touch signal is detected, determining a filter parameter combination with the highest priority from the filter parameter optimization rule library as a filter optimization parameter; Based on the touch quality evaluation model, calculating the signal quality score of the filtering optimization parameter; When the signal quality score is lower than a preset standard threshold, adjusting the filtering optimization parameter; Filtering the real-time touch signal based on the filtering optimization parameter to obtain a filtered touch signal; A touch determination result is determined according to the filtered touch signal, the touch threshold and the signal duration.

2. The method according to claim 1, characterized in that The step of comparing the signal strength and the signal fluctuation range with a preset characteristic range to identify the current touch mode as a glove touch mode or a direct touch mode specifically includes: Obtain multiple groups of signal strength samples and multiple groups of signal fluctuation range samples within a preset time window; Normalizing the multiple groups of signal strength samples and the multiple groups of signal fluctuation range samples to obtain normalized signal strength and normalized fluctuation range; When the normalized signal strength is less than a preset strength threshold and the normalized fluctuation range is greater than a preset fluctuation threshold, identifying the current touch mode as a glove touch mode; When the normalized signal strength is greater than the preset strength threshold and the normalized fluctuation range is less than the preset fluctuation threshold, the current touch mode is identified as the direct touch mode.

3. The method according to claim 1, characterized in that The step of determining the touch determination result according to the filtered touch signal, the touch threshold and the signal duration specifically includes: Determine a corresponding determination parameter set according to the touch threshold; the determination parameter set includes a minimum effective touch duration, a maximum touch interval duration, and a touch response delay; Judging according to the signal duration and the minimum effective touch duration, when it is detected that the interval between two consecutive touch signals is less than the maximum touch interval duration, merging the two touch signals into a touch event; The touch event is time-adjusted according to the touch response delay to generate a touch determination result.

4. The method according to claim 3, characterized in that The step of adjusting the timing of the touch event according to the touch response delay to generate a touch determination result specifically includes: Obtaining the start time point and the end time point of the touch event, and calculating the actual duration of the touch event; Based on the touch response delay, compensating and correcting the start time point and the end time point to obtain a corrected time point; Establishing a time window sequence of touch events according to the correction time points; In the time window sequence, detecting the amplitude variation trend of the filtered touch signal, and identifying the effective signal segment and the transition signal segment of the touch event; Based on the signal characteristics of the effective signal segment, interpolating the transition signal segment to generate a continuous touch track; A touch determination result is generated according to the touch track and the touch threshold.

5. The method according to claim 1, characterized in that Before the step of acquiring the real-time touch signal of the touch screen and extracting the touch signal characteristic parameters including signal strength, signal fluctuation range and signal duration, the method further includes: In a standard experimental environment, collect multiple sets of reference signal samples of different touch modes; Performing statistical analysis on the reference signal samples to construct a signal characteristic parameter database; Based on the signal characteristic parameter database, an initial characteristic threshold, an initial touch threshold and an initial filter parameter are determined.

6. A signal optimization system, characterized in that: The signal optimization system comprises: 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 comprises computer instructions, and the one or more processors call the computer instructions so that the signal optimization system executes the method as described in any one of claims 1-5.

7. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a signal optimization system, the signal optimization system is caused to execute the method according to any one of claims 1 to 5.

8. A computer program product, characterized in that When the computer program product runs on a signal optimization system, the signal optimization system is enabled to perform the method according to any one of claims 1 to 5.

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