Touch screen control method and device and electronic equipment

Abstract

The invention relates to the technical field of man-machine interaction, and discloses a touch screen control method and device and electronic equipment. The method comprises the steps that S1, it is detected that a user generates a touch starting event in a screen edge area, touch track data are collected, and the track data comprise touch position coordinates, pressure values and corresponding timestamps; s2, constructing the touch track data into a track function with continuous time, and obtaining speed information, acceleration information and track steering change characteristics of a track; and S3, according to the speed information, the acceleration information and the track steering change characteristics, calculating a group of track dynamic characteristic indexes representing track naturalness. By adopting a combined judgment technology of track naturalness and structural complexity, the naturalness and the complexity of the touch track can be accurately recognized, and a high-precision gesture recognition effect is achieved. Compared with the scheme depending on the touch position and speed in the prior art, the method can effectively distinguish mistaken touch and effective touch, and the problem of frequent misoperation is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of human-computer interaction, and specifically to a touch screen control method, device, and electronic device. Background Art

[0002] In existing touch control technologies, most gesture recognition methods rely on touch positions and sliding speeds for judgment. These methods often neglect the naturalness and complexity of touch trajectories, resulting in unsatisfactory performance of the system under complex trajectories or variable gestures. Due to the lack of in-depth analysis of trajectory details, false touches or misjudgments are likely to occur, bringing a poor experience to users.

[0003] Existing technologies usually identify touch trajectories through simple rules and rely on static judgment models. For changes in speed and acceleration, existing systems often fail to handle them adequately or simplify them too much, resulting in slow or incorrect system responses when facing gestures with large speed changes and being unable to accurately distinguish different types of gesture actions.

[0004] In traditional gesture recognition technologies, the complexity evaluation of trajectories mostly stays at basic shape recognition. Many methods use static analysis to process trajectory data but often neglect the dynamic changes and complexity of trajectories. In this way, the system cannot effectively handle irregular gestures, leading to unclear recognition of some complex operations and affecting the operation fluency of users.

[0005] In existing technologies, a high false touch rate is a common problem. Many touch control devices adopt fixed touch sensitivity settings and cannot be adjusted according to different operating environments or user habits. In this way, false touches often occur during ordinary touches or edge operations, reducing the stability and reliability of the device. Especially in complex environments, the false touch problem of the system is more obvious, seriously affecting the user experience. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a touch screen control method, device, and electronic device, which solve the problems of low recognition accuracy of edge gestures, high false touch rate, and poor complex gesture processing ability in the existing technology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A touch screen control method, including the following steps; S1. When a touch start event is detected in the edge area of the screen by the user, start collecting touch trajectory data, where the trajectory data includes touch position coordinates, pressure values, and corresponding times; S2. Construct the touch trajectory data into a time-continuous trajectory function to obtain the speed information, acceleration information, and trajectory turning change characteristics of the trajectory; S3. Calculate a set of trajectory dynamic characteristic indicators representing the naturalness of the trajectory based on the speed information, acceleration information, and trajectory turning change characteristics; S4. Encode the trajectory data into a symbolic state sequence, statistically calculate the distribution probability of each state in the trajectory, and calculate the entropy value of the trajectory to measure the structural complexity of the trajectory; S5. Construct a joint discriminant value based on the trajectory naturalness index and the structural complexity information; S6. Compare the joint discriminant value with a preset determination threshold to determine whether the touch is a valid edge gesture; S7. If it is determined to be a valid edge gesture, trigger a system function response operation associated with the edge area. If it is determined to be an invalid touch, suppress the response.

[0008] Preferably, the construction of the trajectory function includes performing first-order and second-order derivative operations on the trajectory point sequence to obtain the instantaneous velocity vector and acceleration vector of the trajectory.

[0009] Preferably, the construction of the trajectory function includes the following steps: Perform interpolation processing on the collected touch trajectory data to ensure that the time intervals of the trajectory points are uniform; Perform a first-order derivative operation on the processed trajectory data to obtain the instantaneous velocity vector of the trajectory; Perform a second-order derivative operation on the instantaneous velocity vector to obtain the acceleration vector of the trajectory.

[0010] Preferably, the trajectory naturalness index includes the speed stability of the trajectory, the acceleration change amplitude, and the path turning rate, and the path turning rate is measured by the change frequency of the trajectory turning angle.

[0011] Preferably, the entropy value of the structural complexity is calculated by the Shannon entropy formula, and the formula is; ; where, is the entropy of the random variable , is the probability of the symbol appearing in the symbol sequence, is the symbol in the symbol sequence, is the logarithm of, and is the total number of symbols in the symbol sequence.

[0012] Preferably, the construction of the joint discriminant value includes the following steps: Based on the trajectory naturalness index and the structural complexity information, perform normalization processing on both of them respectively; According to the normalized naturalness index and structural complexity information, calculate the weighted sum to obtain the joint discriminant value; The weighting coefficient is adaptively adjusted by a pre-trained machine learning model according to historical trajectory data.

[0013] Preferably, the formula for weighted summation is: ; Where is the joint discrimination value, is the normalized trajectory naturalness index, is the normalized structural complexity information, and are the weight coefficients automatically generated by the machine learning model.

[0014] Preferably, the step of comparing the joint discrimination value with a preset determination threshold includes: Using a weighted fusion model to generate a joint discrimination value, combining multiple trajectory dynamic features and the structural complexity entropy value according to a preset weight to form a fusion vector; Performing a one-dimensional mapping on the fusion vector to obtain a normalized discrimination value; Comparing the normalized discrimination value with the preset determination threshold to output a validity determination result.

[0015] A touch control device, including; A trajectory acquisition module, configured to collect a sequence of touch points when an initial touch event is detected in the screen edge area, the point sequence including touch position coordinates, pressure values, and timestamps, and send the sequence of touch points to the trajectory modeling module; A trajectory modeling module, configured to receive the sequence of touch points from the trajectory acquisition module, construct it into a time-continuous trajectory function, calculate the speed and acceleration information of the function, and send the processing result to the feature analysis module; A feature analysis module, configured to receive the speed, acceleration, and trajectory direction information output by the trajectory modeling module, analyze and generate a trajectory naturalness index, and send the index to the discrimination module and the state encoding module; A state encoding module, configured to receive the sequence of trajectory points from the trajectory modeling module, encode it into a symbol state sequence, calculate the corresponding probability distribution and entropy value, and send it to the discrimination module; A discrimination module, configured to receive the trajectory naturalness index output by the feature analysis module and the entropy value output by the state encoding module, perform a weighted process on the two, generate a joint discrimination value, and compare it with a preset threshold to output a determination result of whether it is a valid edge gesture; A response control module, configured to receive the determination result from the discrimination module, if it is determined to be a valid edge gesture, trigger a corresponding edge function response operation; if it is determined to be an invalid gesture, do not perform any system functions.

[0016] An electronic device, comprising: A processor; A memory; A touch display screen; The processor executes a computer program stored in the memory, so that the electronic device executes a touch screen control method.

[0017] The present invention provides a touch screen control method, device and electronic device. It has the following beneficial effects: 1. By adopting the combined discrimination technology of trajectory naturalness and structural complexity, the present invention can accurately identify the naturalness and complexity of the touch trajectory, achieving a high-precision gesture recognition effect. Compared with the existing technology that relies on touch position and speed, the present invention can effectively distinguish accidental touches from valid touches, solving the problem of frequent accidental operations.

[0018] 2. By analyzing the dynamic characteristics of the touch trajectory, especially the changes in speed and acceleration, the present invention can automatically adjust the response sensitivity to gestures. Compared with the simple trajectory judgment method in the existing technology, the present invention can better handle the changes of complex gestures, avoiding common misjudgment problems.

[0019] 3. By combining entropy value to calculate the structural complexity of the trajectory and evaluating the trajectory characteristics through the probability distribution of the symbol state sequence, the present invention greatly improves the robustness of trajectory recognition. Compared with the simple static analysis of the trajectory in the traditional method, the present invention can adapt to various irregular touch operations, solving the problem that the traditional method is not clear about the recognition of complex trajectories.

[0020] 4. By adopting the method of real-time calculation and dynamic adjustment, the present invention solves the problem of high accidental touch rate in the existing technology. Through precise trajectory analysis and complexity evaluation, the present invention greatly improves the ability to suppress accidental touches, making it difficult for users to have accidental triggers during use, and greatly improving the stability and fluency of interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the method of the present invention.

[0022] Figure 2 It is a system framework diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] Please refer to the appendix Figure 1 , the embodiment of the present invention provides a touch screen control method, including the following steps; S1. Detect that the user generates a touch start event in the screen edge area, and start collecting touch trajectory data. The trajectory data includes touch position coordinates, pressure values, and corresponding times; Specifically, in this embodiment, step S1 is the starting step of touch data collection. The key lies in accurately identifying and collecting the touch start event of the user in the screen edge area. The smooth execution of this step is crucial for the accuracy of subsequent trajectory analysis. In order to achieve precise identification of the edge area, the system needs to continuously monitor each touch point on the screen with the help of a touch sensor.

[0025] In this step, first, the touch sensor triggers the collection of the touch start event by detecting the action of the user's finger touching the screen edge. The occurrence of the touch start event marks the initial contact between the user's finger and the screen, and determines that the touch belongs to the touch within the edge area. In order to ensure high-precision collection, the touch sensor of the device has the ability to accurately locate the edge area, and an effective detection area will be delimited in the edge area of the screen, and this area will be specifically used to process the touch data related to the edge.

[0026] The collection of touch points includes but is not limited to obtaining information such as the coordinate position, pressure value, and timestamp of the touch. These information provide the basis for further constructing trajectory data. The touch coordinate position can be represented by the two-dimensional coordinate system of the device screen, the pressure value reflects the intensity of the touch, and the timestamp is used to mark the chronological order of the touch points. Through these information, the system can accurately track the sliding path of the finger on the screen edge and provide a reliable data source for trajectory analysis.

[0027] The detection accuracy of the touch sensor in the screen edge area is relatively high. Its working principle can be based on capacitive or piezoelectric touch technology, and these technologies can maintain stable responses under different touch pressures, ensuring that subtle touch changes can be captured in real time. In addition, the sensor can continuously monitor the touch event and record the information of each touch point in real time. Whenever the user's finger slides on the edge area, the device will continuously update and record the data of the touch point.

[0028] Based on these data, the system will assign a unique identifier to each touch point and store these data in chronological order. In this way, as the touch process progresses, all the collected data forms a continuous trajectory.

[0029] In terms of technical implementation, the touch position coordinates describe the two-dimensional coordinates of the touch point at time ; and the pressure value represents the touch intensity. Timestamp is used to identify the time point of data acquisition. The formula is as follows; ; Where: and these two variables represent the abscissa and ordinate of the touch point at time respectively; represents the pressure value of the touch point at time i.e., the value measured by the pressure sensor of the touch screen; represents the timestamp of collecting touch data at time which refers to the specific time when the data is recorded.

[0030] The key to this step is to ensure the accurate capture of the user's touch in the edge area and record the data of the touch point at a high frequency, so as to provide a high-quality data basis for subsequent trajectory construction, speed and acceleration analysis.

[0031] S2. Construct the touch trajectory data into a time-continuous trajectory function to obtain the speed information, acceleration information and trajectory turning change characteristics of the trajectory; Specifically, in this embodiment, in step S1, the system has successfully collected and stored the touch data, and these data provide key information such as the touch position, pressure and timestamp of the user's finger on the screen edge. After entering step S2, the system needs to perform deeper processing and analysis on the collected touch data. Specifically, this step mainly involves calculating the speed and acceleration of the touch data to provide more accurate information for subsequent trajectory smoothness and gesture recognition.

[0032] After the touch data is collected, the system will perform data analysis based on the position information of the touch point and the timestamp. In this process, the system first calculates the speed of the touch trajectory, and then infers the acceleration information according to the speed data, so as to obtain the dynamic characteristics of the user's operation gesture.

[0033] The speed of the touch data can be calculated by taking the difference of the position information of adjacent touch points. Specifically, the speed vector can be calculated from the position information of adjacent touch points and the time interval. The formula is as follows: ; Where: is the speed vector at time ; and are the touch positions at times and respectively; is the time interval between two touch data acquisitions.

[0034] Through this speed formula, the system can estimate the moving speed of the user's finger on the screen. This process is crucial for subsequent judgment of the smoothness of the gesture and the naturalness of the trajectory.

[0035] Specifically, the calculation of acceleration is based on the rate of change of speed. The speed at time and the speed at time . The formula is: ; where: is the acceleration at time ; and are the velocity vectors at times and respectively.

[0036] Through this formula, the system can calculate the acceleration of the touch data, and then understand whether the movement of the user's finger is stable. If the acceleration changes significantly, it may be that the user is performing a relatively intense operation, or the trajectory is unstable.

[0037] In this implementation, in some embodiments, digital filtering technology is used for the calculation of speed and acceleration to reduce calculation fluctuations caused by environmental interference or sensor errors. A low-pass filter can be used to smooth the speed and acceleration to ensure the stability and reliability of the calculation results.

[0038] Through the precise calculation of touch data, the system can effectively evaluate the smoothness of the trajectory and judge the naturalness of the gesture, providing reliable data support for subsequent trajectory analysis and gesture recognition. By combining the dynamic analysis of speed and acceleration, the system can improve the accuracy of touch recognition and ensure that the user's operation intention is accurately feedback.

[0039] S3. Calculate a set of trajectory dynamic characteristic indexes representing the naturalness of the trajectory according to the speed information, acceleration information and trajectory turning change characteristics; Specifically, in this embodiment, in step S2, the system has completed the analysis of the speed and acceleration of the touch data, providing necessary information for subsequent trajectory judgment and gesture recognition. After entering step S3, the system will further analyze the smoothness and naturalness of the touch trajectory based on the dynamic features obtained in the previous steps. Specifically, the core task of this step is to extract the dynamic features of the trajectory and further evaluate the stability of the trajectory, especially in-depth analysis of aspects such as speed, acceleration, and path turning.

[0040] Generally, after obtaining the basic information of speed and acceleration, the system needs to quantify these dynamic features to determine whether the touch operation conforms to the expected edge gesture. At this time, the system mainly focuses on the following dynamic features: the speed stability of the trajectory, the amplitude of acceleration change, and the turning rate of the trajectory.

[0041] As an option, the speed stability can be evaluated by calculating the degree of speed fluctuation in the touch trajectory. Specifically, the system will calculate the average speed of the trajectory and the standard deviation of speed , and measure the stability of the trajectory through the following formula: ; ; Where: is the speed at the th time point in the trajectory; is the total number of touch points in the trajectory; is the average speed of the trajectory; is the standard deviation of speed.

[0042] Specifically, the smaller the speed stability , the smoother the trajectory and the more natural the gesture. If the speed fluctuates greatly, the system can determine that the user's operation is not smooth enough, which may be a mis-trigger or a gesture with unclear intention.

[0043] In a possible implementation, the amplitude of acceleration change is also an important judgment criterion. The amplitude of acceleration change can be determined by calculating the difference between the maximum value and the minimum value of acceleration. The specific calculation formula is as follows: ; Where: represents the acceleration at time , that is, the degree of acceleration of the object at that moment; Represents the change amplitude of acceleration, which is calculated as the difference between the maximum and minimum values of acceleration within this time period; Represents the maximum value of acceleration within the time period; Represents the minimum value of acceleration within the time period.

[0044] The change amplitude of velocity The larger it is, the more intense the actions in the touch trajectory are, which may indicate that the user is performing relatively fast gestures or edge operations with large changes. And if is smaller, it means the trajectory is relatively smooth and the gestures are more natural.

[0045] In addition, in some embodiments, the trajectory turning rate is also a key indicator for evaluating the stability and naturalness of the trajectory. The trajectory turning rate reflects the frequency of changes in the trajectory during the finger movement process, and can be achieved by calculating the angular changes between adjacent points in the trajectory. If the angular change between adjacent points exceeds a certain threshold, it can be considered that the trajectory has turned. The turning rate TTT of the trajectory can be defined by the following formula: ; Where: is the turning angle between the and the points in the trajectory; is the total number of touch points in the trajectory.

[0046] The higher the turning rate, the more intense the changes in the trajectory are, usually corresponding to more complex gesture actions. For edge gestures, a higher turning rate may be an expected feature, especially when the user performs operations such as swiping to switch or split screen.

[0047] By analyzing these dynamic features, the system can quantitatively evaluate the smoothness and stability of the trajectory, and further confirm whether it meets the requirements of valid gestures. If the velocity stability, acceleration change amplitude, and turning rate in the trajectory are all within the normal range, it can be considered that the trajectory is valid and conforms to the characteristics of edge gestures.

[0048] In step S3, by extracting dynamic features such as the velocity stability, acceleration change amplitude, and turning rate of the trajectory, the system can comprehensively evaluate the naturalness and smoothness of the touch trajectory. The quantitative analysis of these features provides an important basis for subsequent gesture recognition and response, ensuring that the system can accurately judge the user's intention and make appropriate responses.

[0049] S4. Encode the trajectory data into a symbol state sequence, count the distribution probability of each state in the trajectory, and calculate the entropy value of the trajectory to measure the structural complexity of the trajectory. Specifically, in this embodiment, in step S3, the system has already calculated a set of dynamic feature indicators representing the naturalness of the trajectory based on the speed, acceleration information of the touch trajectory and the trajectory turning change characteristics. These dynamic features provide an important reference for the subsequent determination of gesture validity. After entering step S4, the system will further analyze the structural complexity of the trajectory. Specifically, the main task of this step is to encode the touch trajectory data into a symbol state sequence and calculate the entropy value of the trajectory, so as to measure the structural complexity of the trajectory.

[0050] Generally, the goal of step S4 is to calculate the entropy value of the trajectory by encoding the touch trajectory data into a symbol state sequence. As an important indicator to measure the structural complexity of the trajectory, the entropy value can reflect the randomness and regularity of the trajectory. If the entropy value of the trajectory is high, it indicates that the trajectory has a high structural complexity and may represent complex or irregular gesture operations; on the contrary, a low entropy value means that the trajectory is relatively simple and may be a simple gesture or a smooth sliding operation.

[0051] The process of encoding the trajectory data first maps each touch data point to a symbol state. The selection of symbols is based on the characteristic values of the trajectory data, such as the change range of the touch position, the touch pressure value, and the fluctuations of the speed or acceleration. These characteristics are discretized within a certain interval to generate a symbol state sequence. Specifically, the system will discretize these data into symbols according to the touch position coordinates , speed , acceleration and other information through discretization techniques.

[0052] Specifically, for each symbol , its state is based on the interval division of the characteristic values of the trajectory points within a time window, for example: , where is the number of symbol types; Each symbol represents a specific characteristic of the trajectory within a certain time interval, such as position change, speed range, etc.

[0053] In a possible implementation, the entropy value of the trajectory can be calculated by the Shannon entropy formula. The Shannon entropy formula is: ; where: is the entropy of the random variable . is the symbol the occurrence probability in the symbol sequence; is the symbol in the symbol sequence; is the logarithm of; is the total number of symbols in the symbol sequence.

[0054] As an option, the entropy value reflects the structural complexity of the trajectory. When the symbols in the trajectory are evenly distributed, the entropy value is high, meaning the trajectory has a high complexity; while when some symbols appear with a high frequency, the entropy value of the system is low, indicating that the trajectory is relatively simple and regular.

[0055] Specifically, the calculation of the trajectory entropy value can effectively distinguish different types of gesture operations. For simple gesture operations, such as linear sliding, the symbol state sequence is relatively single and the entropy value is low; while for complex gestures, such as circular or multi-point operations, the symbol state sequence is relatively rich and the entropy value is high.

[0056] The trajectory encoding and entropy value calculation in step S4 are to quantify the structural complexity of the trajectory and provide a basis for further feature analysis. By calculating the entropy value of the symbol state sequence, the system can evaluate the complexity of the trajectory and provide important information for subsequent validity determination.

[0057] S5. Construct a joint discriminant value based on the trajectory naturalness index and structural complexity information; Specifically, in this embodiment, in step S4, the system encodes the touch trajectory data into a symbol state sequence and calculates the entropy value to evaluate the structural complexity of the trajectory. Step S5 is a stage of further constructing a joint discriminant value based on the trajectory naturalness index and structural complexity information calculated in the foregoing steps. The core purpose of this stage is to combine the naturalness and complexity of the trajectory to generate a comprehensive discriminant value for accurately judging whether the touch is an effective edge gesture.

[0058] Generally, in this step, a joint discriminant value is obtained by weighting the trajectory naturalness index (such as speed stability, acceleration change amplitude, path turning rate, etc.) and structural complexity information (such as entropy value). This joint discriminant value, as a comprehensive index, reflects the comprehensive characteristics of the trajectory and can effectively help the system identify whether it is an effective gesture.

[0059] As an option, in this embodiment, first, the system normalizes the naturalness index and the structural complexity information of the trajectory to ensure that the eigenvalue of different dimensions is within the same range. This is because there may be large differences in the magnitudes of the naturalness index and the structural complexity information, and the normalization process can eliminate these differences to ensure their equivalence in subsequent calculations. The normalized trajectory naturalness index and the structural complexity information are processed respectively through the following formulas: ; ; where: is the naturalness index of the trajectory; is the structural complexity information of the trajectory; and are respectively the minimum and maximum values of the naturalness index; and are respectively the minimum and maximum values of the structural complexity information; and are respectively the normalized naturalness index and the structural complexity information.

[0060] Specifically, the normalized indexes and will be weighted and summed according to the predetermined weighting coefficients to obtain the final joint discrimination value , and the calculation formula of the joint discrimination value is as follows: ; where: and are respectively the weight coefficients of the naturalness index and the structural complexity information; and are the normalized naturalness index and the structural complexity information.

[0061] In a possible implementation manner, these weighting coefficients and are adaptively adjusted by a pre-trained machine learning model according to the historical trajectory data. This means that the setting of the weight coefficients is not fixed, but is optimized according to a large amount of historical data to improve the accuracy and sensitivity of discrimination. The machine learning model can analyze the feature differences of different gestures and automatically adjust the weights to achieve the best gesture recognition effect.

[0062] The main task of step S5 is to comprehensively process the naturalness and complexity information of the trajectory to generate a combined discriminant value, providing a basis for determining valid edge gestures. Through the normalization and weighting of the naturalness index and complexity information, the system can flexibly evaluate the characteristics of the touch trajectory, thereby achieving accurate gesture recognition. S6. Compare the combined discriminant value with a preset determination threshold to determine whether the touch is a valid edge gesture; Specifically, in the foregoing steps of this embodiment, the system has obtained the combined discriminant value through weighted summation of the naturalness index and structural complexity information of the trajectory. In the next step S6, it involves comparing this combined discriminant value with the preset determination threshold to determine whether the touch is a valid edge gesture. This step is an important decision-making link in the entire touch recognition process and will directly affect the response behavior of the system.

[0063] Generally, the purpose of this step is to determine whether the touch operation is a valid edge gesture by comparing the combined discriminant value with the preset determination threshold. If the combined discriminant value is greater than or equal to the preset determination threshold , it is considered that the touch is a valid edge gesture, triggering the corresponding system function response; if it is less than the preset determination threshold , it is determined that the touch is invalid and the system will not respond.

[0064] In this embodiment, the combined discriminant value is obtained by the weighted summation calculation in the foregoing step S5. The weighted summation formula is; ; where: is the combined discriminant value; is the normalized trajectory naturalness index; is the normalized structural complexity information; and are weight coefficients automatically generated by the machine learning model.

[0065] Specifically, the determination process can be represented by the following formula: ; where: represents the current distance value or a specific metric value. The specific meaning may be related to the system settings of touch recognition or gesture detection; Represents a preset threshold value, which is usually set by historical data or environmental factors. This value is used to determine the current whether a certain standard is met.

[0066] The key to step S6 lies in comparing the combined discrimination value calculated through the aforementioned step S5 with the preset determination threshold to make a comparison. Through this determination process, the system can determine whether the touch operation is a valid edge gesture and make corresponding responses. Combining with the adaptive adjustment of the machine learning model, the weighting coefficients and provide flexibility and adaptability for the calculation of the combined discrimination value, thereby further improving the accuracy and reliability of touch recognition.

[0067] S7. If it is determined to be a valid edge gesture, trigger the system function response operation associated with the edge area; if it is determined to be an invalid touch, suppress the response; Specifically, in step S6, the system constructs the combined discrimination value based on the previous steps , and compares it with the preset determination threshold to complete the judgment logic of whether the touch is a valid edge gesture. As the final module in the entire method process, step S7 is the function call or control operation process executed by the system after completing the edge gesture determination. Its goal is to control whether to respond to the touch operation according to the determination result to ensure that the system can intelligently suppress false touches and ensure that valid gestures are correctly responded to during actual operation.

[0068] Generally, when the determination result indicates that the touch operation is a valid edge gesture, the system will trigger the execution of subsequent function modules according to the configuration logic, including but not limited to interface response, function activation, animation feedback, etc.; when the determination result is an invalid touch, the system will directly ignore the touch event and not generate any further response.

[0069] In this embodiment, step S7 is specifically used to control whether to respond to the edge touch operation according to the result of comparing the combined discrimination value with the determination threshold in step S6.

[0070] Specifically, the system will compare the combined discrimination value obtained in the previous steps with the determination threshold to make a judgment. If the condition is met, the system determines it as a valid gesture. At this time, the following operation process will be executed: Send a valid touch signal to the event scheduling module; Call the registered function response module; Update the status of interface elements, such as activating the sidebar, returning to the previous page, or launching the multitasking view, etc.

[0071] In a possible implementation, the system can represent the final gesture processing control through the following control logic function: ; Where: Control instruction, boolean value. 1 represents response, 0 represents inhibition; Control function, which determines whether to respond; Unit step function. When it is 1, otherwise it is 0; Currently measured input value, such as gesture features; Threshold set by the system.

[0072] As an option, multiple types of system operation response modes can be preset in the response module, corresponding to different categories of edge gestures one by one. For example: Sliding leftward along the edge corresponds to the return operation; Quickly sliding upward along the edge corresponds to the activation of the task manager; Staying in the edge area for a long time may correspond to functions such as mute switching and brightness adjustment.

[0073] In this embodiment, to enhance the adaptability of the system to different devices or screen sizes, the response module also has a policy adaptation mechanism. That is, it automatically adjusts the response area and response method of edge gestures according to factors such as device type, geometric information of the screen boundary area, and user personalized configuration, so as to have good response consistency in different terminal environments.

[0074] The control response module involved in step S7 not only completes the function activation after touch determination, but also provides strong decision-making and execution support for the entire system. By introducing response control functions, preset policy adaptation, low-latency response mechanisms, and multi-finger parallel judgment strategies, this step ensures the accuracy and stability of the gesture recognition system during actual operation.

[0075] The touch screen control device described below can be correspondingly referred to the touch screen control method described above.

[0076] Please refer to the appendix Figure 2 , the present invention also provides a touch screen control device, including: A trace acquisition module, which is configured to acquire a sequence of touch points when an initial touch event is detected in the edge area of the screen. The point sequence includes touch position coordinates, pressure values, and timestamps, and sends the sequence of touch points to the trajectory modeling module; A trajectory modeling module, which is configured to receive the sequence of touch points from the trajectory acquisition module, construct it into a time-continuous trajectory function, calculate the speed and acceleration information of the function, and send the processing result to the feature analysis module; A feature analysis module, which is configured to receive the speed, acceleration, and trajectory direction information output by the trajectory modeling module, analyze and generate a trajectory naturalness index, and send the index to the discrimination module and the state encoding module; A state encoding module, which is configured to receive the sequence of trajectory points from the trajectory modeling module, encode it into a symbol state sequence, calculate the corresponding probability distribution and entropy value, and then send it to the discrimination module; A discrimination module, which is configured to receive the trajectory naturalness index output by the feature analysis module and the entropy value output by the state encoding module, perform weighted processing on the two, generate a combined discrimination value, compare it with a preset threshold, and output a determination result of whether it is a valid edge gesture; A response control module, which is configured to receive the determination result from the discrimination module. If it is determined to be a valid edge gesture, it triggers a corresponding edge function response operation; if it is determined to be an invalid gesture, it does not perform any system functions.

[0077] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Touch screen control method, characterized in that, Including the following steps; S1. When a touch start event is detected in the screen edge area, start collecting touch trajectory data, where the trajectory data includes touch position coordinates, pressure values, and corresponding timestamps; S2. Construct the touch trajectory data into a time - continuous trajectory function, and obtain the speed information, acceleration information, and trajectory turning change characteristics of the trajectory; S3. According to the speed information, acceleration information, and trajectory turning change characteristics, calculate a set of trajectory dynamic characteristic indicators representing the naturalness of the trajectory; S4. Encode the trajectory data into a symbol state sequence, statistically calculate the distribution probability of each state in the trajectory, and calculate the entropy value of the trajectory to measure the structural complexity of the trajectory; S5. Construct a joint discrimination value based on the trajectory naturalness index and structural complexity information; S6. Compare the joint discrimination value with a preset determination threshold to determine whether the touch is a valid edge gesture; S7. If it is determined to be a valid edge gesture, trigger the system function response operation associated with the edge area. If it is determined to be an invalid touch, suppress the response.

2. The touch screen control method according to claim 1, characterized in that, The construction of the trajectory function includes performing first - order and second - order derivative operations on the trajectory point sequence to obtain the instantaneous velocity vector and acceleration vector of the trajectory.

3. The touch screen control method according to claim 1, wherein The construction of the trajectory function includes the following steps: Perform interpolation processing on the collected touch trajectory data to ensure uniform time intervals of the trajectory points; Perform a first - order derivative operation on the processed trajectory data to obtain the instantaneous velocity vector of the trajectory; Perform a second - order derivative operation on the instantaneous velocity vector to obtain the acceleration vector of the trajectory.

4. The touch screen control method according to claim 1, wherein The trajectory naturalness index includes the speed stability of the trajectory, the acceleration change amplitude, and the path turning rate, and the path turning rate is measured by the change frequency of the trajectory turning angle.

5. The touch screen control method according to claim 1, characterized in that The entropy value of the structural complexity is calculated by the Shannon entropy formula, and the formula is; ; Among them, is the entropy of the random variable , is the probability of occurrence of the symbol in the symbol sequence, is the symbol in the symbol sequence, is the logarithm of and is the total number of symbols in the symbol sequence.

6. The touch screen control method according to claim 1, characterized in that The construction of the joint discrimination value includes the following steps: Based on the trajectory naturalness index and structural complexity information, perform normalization processing on both of them respectively; According to the normalized naturalness index and structural complexity information, calculate the weighted sum to obtain the joint discrimination value; The weighting coefficient is adaptively adjusted by a pre - trained machine learning model according to historical trajectory data.

7. The touch screen control method according to claim 6, characterized in that, The formula for the weighted summation is as follows: ; Among them, is the combined discrimination value, is the normalized trajectory naturalness index, is the normalized structural complexity information, and are the weight coefficients automatically generated by the machine learning model.

8. The touch screen control method according to claim 1, wherein The step of comparing the joint discrimination value with the preset determination threshold includes: Use a weighted fusion model to generate the joint discrimination value, and form a fusion vector by combining multiple trajectory dynamic characteristics and the structural complexity entropy value according to preset weights; Perform a one - dimensional mapping on the fusion vector to obtain a normalized discrimination value; Perform a single - value comparison between the normalized discrimination value and the preset determination threshold to output the validity determination result.

9. A touch screen control device, according to the touch screen control method described in any one of claims 1-8, characterized in that Including; A trajectory acquisition module, which is used to collect a touch point sequence when an initial touch event is detected in the screen edge area. The point sequence includes touch position coordinates, pressure values, and timestamps, and send the touch point sequence to the trajectory modeling module; A trajectory modeling module, which is used to receive the touch point sequence from the trajectory acquisition module, construct it into a time - continuous trajectory function, calculate the speed and acceleration information of the function at the same time, and send the processing result to the feature analysis module; A feature analysis module, which is used to receive the speed, acceleration, and trajectory direction information output by the trajectory modeling module, analyze and generate the trajectory naturalness index, and send the index to the discrimination module and the state encoding module; A status encoding module, configured to receive a sequence of trajectory points from a trajectory modeling module, encode the sequence into a sequence of symbol states, calculate the corresponding probability distribution and entropy value, and then send them to a discrimination module; A discrimination module, configured to receive the trajectory naturalness index output by a feature analysis module and the entropy value output by the status encoding module, perform weighted processing on the two to generate a combined discrimination value, compare the combined discrimination value with a preset threshold, and output a determination result as to whether it is a valid edge gesture; A response control module, configured to receive the determination result from the discrimination module, and if it is determined to be a valid edge gesture, trigger a corresponding edge function response operation; If it is determined to be an invalid gesture, no system function is executed.

10. An electronic device, characterized in that, including; a processor; a memory; a touch display screen; The processor executes a computer program stored in the memory, so that the electronic device executes the touch screen control method according to any one of claims 1 to 8.

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