Touch screen control method, device and electronic equipment

Abstract

The present invention relates to the field of human-computer interaction technology, and discloses a touch screen control method, device and electronic device, the method comprising the following steps: S1, detecting a touch start event generated by a user in the edge area of ​​the screen, and starting to collect touch trajectory data, the trajectory data including touch position coordinates, pressure value and corresponding timestamp; S2, constructing the touch trajectory data into a time-continuous trajectory function, and obtaining the velocity information, acceleration information and trajectory steering change characteristics of the trajectory; S3, calculating a set of trajectory dynamic feature indicators representing the naturalness of the trajectory based on the velocity information, acceleration information and trajectory steering change characteristics. By adopting a joint discrimination technology of trajectory naturalness and structural complexity, the naturalness and complexity of the touch trajectory can be accurately identified, achieving a high-precision gesture recognition effect. Compared with the prior art solution that relies on touch position and velocity, the present invention can effectively distinguish between false touches and valid touches, solving the problem of frequent erroneous operations.

Description

Technical Field

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

[0002] In existing touch technology, most gesture recognition methods rely on touch location and sliding speed for judgment. These methods often ignore the naturalness and complexity of touch trajectories, resulting in unsatisfactory performance with complex trajectories or varied gestures. This lack of in-depth analysis of trajectory details can easily lead to false touches or misjudgments, resulting in a poor user experience.

[0003] Existing technologies typically identify touch trajectories using simple rules, relying on static judgment models. Existing systems often fail to adequately handle or oversimplify changes in speed and acceleration, resulting in slow or erroneous responses to gestures with large speed variations and an inability to accurately distinguish between different gesture types.

[0004] Traditional gesture recognition technology often focuses on evaluating trajectory complexity, focusing on basic shape recognition. Many methods use static analysis to process trajectory data, but often overlook the dynamic changes and complexity of trajectories. This makes the system ineffective in handling irregular gestures, leading to unclear recognition of certain complex operations and affecting user experience.

[0005] High false touch rates are a common problem in existing technologies. Many touch devices use fixed touch sensitivity settings, which cannot be adjusted to suit different operating environments or user habits. This often leads to false touches during normal touch or edge operations, reducing device stability and reliability. The false touch problem is particularly pronounced in complex environments, severely impacting the user experience. Summary of the Invention

[0006] In response to the deficiencies of the prior art, the present invention provides a touch screen control method, device, and electronic device, which solve the problems of low edge gesture recognition accuracy, high false touch rate, and poor complex gesture processing capability in the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a touch screen control method, comprising the following steps:

[0008] S1: Detecting a touch start event generated by the user at the edge of the screen and starting to collect touch trajectory data, the trajectory data includes the touch position coordinates, pressure value and corresponding time;

[0009] S2. Constructing the touch trajectory data into a time-continuous trajectory function to obtain the velocity information, acceleration information, and trajectory steering change characteristics of the trajectory;

[0010] S3. Calculating a set of trajectory dynamic characteristic indices representing the naturalness of the trajectory based on the velocity information, acceleration information, and trajectory turning change characteristics;

[0011] S4. Encode the trajectory data into a sequence of symbolic states, 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;

[0012] S5. Construct a joint discriminant value based on trajectory naturalness index and structural complexity information;

[0013] S6. Compare the joint discrimination value with a preset judgment threshold to determine whether the touch is a valid edge gesture;

[0014] S7. If it is determined to be a valid edge gesture, a system function response operation associated with the edge area is triggered; if it is determined to be an invalid touch, the response is suppressed.

[0015] 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.

[0016] Preferably, the construction of the trajectory function includes the following steps:

[0017] Interpolate the collected touch trajectory data to ensure that the time intervals of the trajectory points are uniform;

[0018] Perform first-order derivative operation on the processed trajectory data to obtain the instantaneous velocity vector of the trajectory;

[0019] Perform the second-order derivative operation on the instantaneous velocity vector to obtain the acceleration vector of the trajectory.

[0020] Preferably, the trajectory naturalness index includes the velocity stability, acceleration change amplitude and path turning rate of the trajectory, and the path turning rate is measured by the frequency of change of the trajectory turning angle.

[0021] Preferably, the entropy value of the structural complexity is calculated using the Shannon entropy formula, which is:

[0022] ;

[0023] in, is a random variable The entropy of It is a symbol The probability of occurrence in the symbol sequence, is a symbol in the symbol sequence, yes The logarithm of , is the total number of symbols in the symbol sequence.

[0024] Preferably, the construction of the joint discriminant value includes the following steps:

[0025] Based on the trajectory naturalness index and structural complexity information, normalizing the two respectively;

[0026] According to the normalized naturalness index and structural complexity information, the weighted sum is calculated to obtain the joint discriminant value;

[0027] The weighting coefficients are adaptively adjusted based on historical trajectory data through a pre-trained machine learning model.

[0028] Preferably, the weighted summation formula is:

[0029] ;

[0030] in, is the joint discriminant value, is the normalized trajectory naturalness index, is the normalized structural complexity information, and is the weight coefficient automatically generated by the machine learning model.

[0031] Preferably, the step of comparing the joint discriminant value with a preset determination threshold comprises:

[0032] A weighted fusion model is used to generate a joint discriminant value, combining multiple trajectory dynamic features and structural complexity entropy values ​​according to preset weights to form a fusion vector;

[0033] Performing one-dimensional mapping on the fusion vector to obtain a normalized discriminant value;

[0034] The normalized discriminant value is compared with the preset judgment threshold value to output the validity judgment result.

[0035] Touchscreen controls including;

[0036] A trajectory acquisition module is used to collect a touch point sequence including touch position coordinates, pressure value and timestamp when an initial touch event is detected in the edge area of ​​the screen, and send the touch point sequence to the trajectory modeling module;

[0037] The trajectory modeling module is used to receive the touch point sequence from the trajectory acquisition module and construct it into a time-continuous trajectory function, while calculating the velocity and acceleration information of the function and sending the processing results to the feature analysis module;

[0038] The feature analysis module 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 state encoding module;

[0039] The state encoding module is used to receive the trajectory point sequence 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;

[0040] The discrimination module is used 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 joint discrimination value, compare it with the preset threshold, and output the judgment result of whether it is a valid edge gesture;

[0041] The response control module is used to receive the judgment result from the judgment module. If it is judged to be a valid edge gesture, the corresponding edge function response operation is triggered; if it is judged to be an invalid gesture, no system function is executed.

[0042] An electronic device comprising:

[0043] processor;

[0044] Memory;

[0045] Touch screen display;

[0046] The processor executes the computer program stored in the memory to enable the electronic device to perform the touch screen control method.

[0047] The present invention provides a touch screen control method, device, and electronic device, which have the following beneficial effects:

[0048] 1. By employing a combined approach to discriminating trajectory naturalness and structural complexity, this invention accurately identifies both the naturalness and complexity of touch trajectories, achieving highly accurate gesture recognition. Compared to existing approaches that rely on touch position and velocity, this invention effectively distinguishes between false and valid touches, addressing the problem of frequent misoperations.

[0049] 2. This invention automatically adjusts gesture sensitivity by analyzing the dynamic characteristics of touch trajectories, particularly changes in velocity and acceleration. Compared to the simple trajectory determination methods used in existing technologies, this invention better handles complex gestures and avoids common misjudgments.

[0050] 3. This invention significantly improves the robustness of trajectory recognition by combining entropy to calculate the structural complexity of trajectories and evaluating trajectory characteristics through the probability distribution of symbolic state sequences. Compared to traditional methods that simply analyze trajectories statically, this invention can adapt to various irregular touch operations, resolving the problem of traditional methods' unclear recognition of complex trajectories.

[0051] 4. This invention addresses the high false-touch rate of gestures in existing technologies by employing real-time calculation and dynamic adjustment. Through precise trajectory analysis and complexity assessment, this invention significantly improves false-touch suppression, making it less likely for users to accidentally trigger gestures during use and significantly enhancing the stability and fluidity of interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Flow chart of the method of the present invention.

[0053] Figure 2 This is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] Please see the attached Figure 1 , an embodiment of the present invention provides a touch screen control method, comprising the following steps;

[0056] S1: Detecting a touch start event generated by the user at the edge of the screen and starting to collect touch trajectory data, the trajectory data includes the touch position coordinates, pressure value and corresponding time;

[0057] Specifically, step S1 in this embodiment is the initial step for touch data collection. The key is to accurately identify and capture the user's touch initiation event at the edge of the screen. The successful execution of this step is crucial for the accuracy of subsequent trajectory analysis. To achieve accurate identification of the edge area, the system needs to continuously monitor every touch point on the screen with the touch sensor.

[0058] In this step, the touch sensor first detects the user's finger touching the edge of the screen, triggering the capture of a touch start event. The occurrence of a touch start event marks the initial contact between the user's finger and the screen and determines that the touch is within the edge area. To ensure high-precision capture, the device's touch sensor is capable of accurately locating the edge area and demarcates an effective detection area at the edge of the screen, which is specifically used to process edge-related touch data.

[0059] Touch point acquisition includes, but is not limited to, obtaining information such as touch coordinates, pressure values, and timestamps. This information provides the foundation for further constructing trajectory data. Touch coordinates are represented by the device screen's two-dimensional coordinate system, pressure values ​​reflect the intensity of the touch, and timestamps mark the temporal sequence of touch points. This information enables the system to accurately track the path of a finger sliding along the screen edge, providing a reliable data source for trajectory analysis.

[0060] The touch sensor achieves high detection accuracy at the edge of the screen. Its operating principle can be based on capacitive or piezoelectric touch technology, which maintains a stable response under varying touch pressures, ensuring that subtle touch changes are captured in real time. Furthermore, by continuously monitoring touch events, the sensor records information about each touch point in real time. Each time a user's finger slides over the edge, the device continuously updates and records the touch point data.

[0061] Using this data, the system assigns a unique identifier to each touch point and stores the data in chronological order, so that as the touch process progresses, all the collected data forms a continuous track.

[0062] In terms of technical implementation, the touch position coordinates Describes the time The two-dimensional coordinates of the touch point at the moment; and the pressure value Indicates the touch intensity. Timestamp It is used to identify the time point of data collection. The formula is as follows;

[0063] ;

[0064] in:

[0065] and These two variables represent the time At the moment, the horizontal and vertical coordinates of the touch point;

[0066] Indicates the touch point at time The pressure value at the moment, that is, the value measured by the pressure sensor of the touch screen;

[0067] Indicates time The timestamp of touch data collection refers to the specific time when the data is recorded.

[0068] The key to this step is to ensure accurate capture of user touches in the edge area and record touch point data at a high frequency to provide a high-quality data foundation for subsequent trajectory construction, velocity, and acceleration analysis.

[0069] S2. Constructing the touch trajectory data into a time-continuous trajectory function to obtain the velocity information, acceleration information, and trajectory steering change characteristics of the trajectory;

[0070] Specifically, in this embodiment, in step S1, the system has successfully collected and stored touch data, which provides 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 further processing and analysis on the collected touch data. Specifically, this step mainly involves calculating the velocity and acceleration of the touch data to provide more accurate information for subsequent trajectory smoothness and gesture recognition.

[0071] After the touch data collection is completed, the system will and timestamp During this process, the system first calculates the speed of the touch track, and then infers the acceleration information based on the speed data, so as to obtain the dynamic characteristics of the user's operation gesture.

[0072] The speed of touch data can be calculated by differentiating the position information of adjacent touch points. Specifically, the speed vector It can be calculated from the position information and time interval of adjacent touch points. The formula is as follows:

[0073] ;

[0074] in:

[0075] For time Velocity vector at time instant;

[0076] and Time and The touch position at the moment;

[0077] The time interval between two touch data collections.

[0078] Using this speed formula, the system can estimate the speed at which the user's finger moves on the screen. This process is crucial for subsequently determining the smoothness of the gesture and whether the trajectory is natural.

[0079] Specifically, acceleration is calculated based on the rate of change of velocity. The speed of time and The speed of time The difference between the two can give the acceleration information The formula is:

[0080] ;

[0081] in:

[0082] For time acceleration of the moment;

[0083] and Time and Velocity vector at time.

[0084] Using this formula, the system can calculate the acceleration of touch data and determine whether the user's finger movement is smooth. If the acceleration changes significantly, it may be that the user is performing a more intense operation or the trajectory is unstable.

[0085] In this implementation, in some embodiments, digital filtering techniques are used in the calculation of velocity and acceleration to reduce fluctuations in the calculation due to environmental interference or sensor errors. A low-pass filter can be used to smooth the velocity and acceleration to ensure the stability and reliability of the calculation results.

[0086] By accurately calculating touch data, the system can effectively assess trajectory smoothness and determine the naturalness of gestures, providing reliable data support for subsequent trajectory analysis and gesture recognition. By combining dynamic analysis of velocity and acceleration, the system can improve touch recognition accuracy and ensure accurate feedback of user operation intentions.

[0087] S3. Calculating a set of trajectory dynamic characteristic indices representing the naturalness of the trajectory based on the velocity information, acceleration information, and trajectory turning change characteristics;

[0088] Specifically, in this embodiment, in step S2, the system has completed the velocity and acceleration analysis of the touch data, providing the necessary information for subsequent trajectory determination 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 acquired 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 conducting an in-depth analysis of speed, acceleration, and path turning points.

[0089] Generally, after acquiring basic velocity and acceleration information, the system needs to quantify these dynamic features to determine whether the touch operation conforms to the expected edge gesture. At this point, the system primarily focuses on the following dynamic features: velocity stability of the trajectory, acceleration change amplitude, and trajectory turning rate.

[0090] Alternatively, speed stability can be evaluated by calculating the degree of speed fluctuation in the touch trajectory. Specifically, the system calculates the average speed of the trajectory. and speed standard deviation , the stability of the trajectory is measured by the following formula:

[0091] ;

[0092] ;

[0093] in:

[0094] It is the first Speed ​​at a point in time;

[0095] is the total number of touch points in the trajectory;

[0096] is the average velocity of the trajectory;

[0097] is the standard deviation of velocity.

[0098] Specifically, speed stability The smaller the speed, 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 false trigger or an unclear gesture.

[0099] In a possible implementation, the magnitude of the acceleration change is also an important criterion. It can be determined by calculating the difference between the maximum and minimum acceleration values. The specific calculation formula is as follows:

[0100] ;

[0101] in:

[0102] Indicates time The acceleration at a moment, that is, the degree of acceleration of the object at that moment;

[0103] Indicates the magnitude of acceleration change, calculated as the difference between the maximum and minimum acceleration values ​​during the time period;

[0104] Indicates the maximum value of acceleration within a time period;

[0105] Indicates the minimum value of acceleration during the time period.

[0106] Speed ​​variation The larger the value, the more intense the movement in the touch track, which may mean that the user is performing a faster gesture or a larger edge operation. A smaller value means a smoother trajectory and a more natural gesture.

[0107] In addition, in some embodiments, the trajectory turning rate is also a key indicator for evaluating trajectory stability and naturalness. The trajectory turning rate reflects the frequency of trajectory changes during finger movement, which can be achieved by calculating the angle change between two adjacent points in the trajectory. If the angle change between adjacent points exceeds a certain threshold, it can be considered that the trajectory has turned. The trajectory turning rate TTT can be defined by the following formula:

[0108] ;

[0109] in:

[0110] The first Hedi Turning angles between points;

[0111] is the total number of touch points in the track.

[0112] A higher turning rate means a more dramatic change in the trajectory, typically corresponding to a more complex gesture. For edge gestures, a higher turning rate may be an expected characteristic, especially when the user performs operations such as swiping and split-screen.

[0113] By analyzing these dynamic characteristics, the system can quantitatively assess the smoothness and stability of the trajectory, further confirming whether it meets the requirements of a valid gesture. If the velocity stability, acceleration change amplitude, and turning rate of the trajectory are all within normal ranges, the trajectory can be considered valid and meets the characteristics of an edge gesture.

[0114] In step S3, the system comprehensively evaluates the naturalness and smoothness of the touch trajectory by extracting dynamic features such as velocity stability, acceleration change amplitude, and turning rate. Quantitative analysis of these features provides an important basis for subsequent gesture recognition and response, ensuring that the system can accurately determine the user's intention and respond appropriately.

[0115] S4. Encode the trajectory data into a sequence of symbolic states, 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;

[0116] Specifically, in step S3 of this embodiment, the system calculates a set of dynamic feature indicators that represent the naturalness of the touch trajectory based on its velocity, acceleration information, and trajectory direction change characteristics. These dynamic features provide an important reference for subsequent gesture validity determination. After entering step S4, the system further analyzes the structural complexity of the trajectory. Specifically, the main task of this step is to encode the touch trajectory data into a sequence of symbolic states and calculate the trajectory's entropy value, thereby measuring the trajectory's structural complexity.

[0117] Generally, the goal of step S4 is to encode the touch trajectory data into a sequence of symbolic states and then calculate the trajectory's entropy. Entropy, as an important indicator of trajectory structural complexity, reflects the trajectory's randomness and regularity. A high entropy indicates a high structural complexity, possibly representing a complex or irregular gesture. Conversely, a low entropy indicates a concise trajectory, likely a simple gesture or smooth sliding operation.

[0118] The process of trajectory data encoding 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 range of touch position changes, touch pressure value, and fluctuations in speed or acceleration. These characteristics are discretized within a certain interval to generate a sequence of symbol states. Specifically, the system will ,speed , acceleration And other information, these data are converted into symbols through discretization technology.

[0119] Specifically, for each symbol , whose state is based on the interval division of the trajectory point feature value within a time window, for example:

[0120] ,in is the number of symbol types;

[0121] Each symbol It represents the specific characteristics of the trajectory within a certain time interval, such as position change, speed range, etc.

[0122] In one possible implementation, the entropy of the trajectory It can be calculated using the Shannon entropy formula. The Shannon entropy formula is:

[0123] ;

[0124] in:

[0125] is a random variable Entropy;

[0126] It is a symbol Probability of occurrence in a symbol sequence;

[0127] is a symbol in a sequence of symbols;

[0128] yes The logarithm of

[0129] is the total number of symbols in the symbol sequence.

[0130] Alternatively, entropy This reflects the structural complexity of the trajectory. When the symbols in the trajectory are evenly distributed, the entropy is high, indicating that the trajectory is more complex. When certain symbols appear more frequently, the entropy of the system is low, indicating that the trajectory is relatively simple and regular.

[0131] Specifically, trajectory entropy calculations can effectively distinguish different types of gestures. Simple gestures, such as linear swipes, have a relatively simple sequence of symbolic states and low entropy, while complex gestures, such as circular or multi-point operations, have a richer sequence of symbolic states and higher entropy.

[0132] The trajectory encoding and entropy calculation in step S4 are designed to quantify the structural complexity of the trajectory and provide a basis for further feature analysis. By calculating the entropy of the symbol state sequence, the system can assess the complexity of the trajectory and provide important information for subsequent validity determination.

[0133] S5. Construct a joint discriminant value based on trajectory naturalness index and structural complexity information;

[0134] Specifically, in step S4 of this embodiment, the system encodes the touch trajectory data into a sequence of symbolic states and calculates an entropy value to assess the trajectory's structural complexity. Step S5 further constructs a joint discriminant value based on the trajectory naturalness index and structural complexity information calculated in the previous steps. The core purpose of this stage is to combine the trajectory's naturalness and complexity to generate a comprehensive discriminant value, enabling accurate determination of whether a touch is a valid edge gesture.

[0135] Generally, this step combines trajectory naturalness metrics (such as velocity stability, acceleration variation, and path turning rate) with structural complexity information (such as entropy) to produce a joint discriminant value. This combined discriminant value, as a comprehensive indicator, reflects the trajectory's overall characteristics and effectively helps the system identify valid gestures.

[0136] As an option, in this embodiment, the system first normalizes the naturalness index and structural complexity information of the trajectory to ensure that the eigenvalues ​​of different dimensions are within the same range. This is because the magnitudes of the naturalness index and structural complexity information may vary greatly, and normalization can eliminate these differences and ensure their equivalence in subsequent calculations. Normalized trajectory naturalness index and structural complexity information The following formulas are used for processing:

[0137] ;

[0138] ;

[0139] in:

[0140] is an indicator of the naturalness of the trajectory;

[0141] is the structural complexity information of the trajectory;

[0142] and are the minimum and maximum values ​​of the naturalness index, respectively;

[0143] and are the minimum and maximum values ​​of the structural complexity information respectively;

[0144] and are the normalized naturalness index and structural complexity information respectively.

[0145] Specifically, the normalized indicators and The weighted sum will be performed according to the predetermined weighting coefficient to obtain the final joint discriminant value , the calculation formula of the joint discriminant value is as follows:

[0146] ;

[0147] in:

[0148] and are the weight coefficients of naturalness index and structural complexity information respectively;

[0149] and is the normalized naturalness index and structural complexity information.

[0150] In one possible implementation, these weighting coefficients and This is achieved through adaptive adjustments based on historical trajectory data using a pre-trained machine learning model. This means that the weight coefficients are not fixed but are optimized based on extensive historical data to improve accuracy and sensitivity. The machine learning model analyzes the characteristic differences between different gestures and automatically adjusts the weights to achieve optimal gesture recognition.

[0151] The main task of step S5 is to comprehensively process the naturalness and complexity information of the trajectory to generate a joint discriminant value, which provides a basis for determining valid edge gestures. By normalizing and weighting the naturalness index and complexity information, the system can flexibly evaluate the characteristics of the touch trajectory, thereby achieving accurate recognition of gestures.

[0152] S6. Compare the joint discrimination value with a preset judgment threshold to determine whether the touch is a valid edge gesture;

[0153] Specifically, in the above steps, the system has obtained the joint discriminant value by weighted summing the naturalness index and structural complexity information of the trajectory. The next step S6 involves the joint discriminant value and preset judgment threshold The comparison is performed 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 system's response behavior.

[0154] Generally, the purpose of this step is to determine whether the touch operation is a valid edge gesture by comparing the joint discrimination value with the preset discrimination threshold. Greater than or equal to the preset judgment threshold , the touch is considered to be a valid edge gesture, triggering the corresponding system function response; if Less than the preset judgment threshold , the touch is determined to be invalid and the system will not respond.

[0155] In this embodiment, the joint discriminant value is obtained by the weighted summation in the aforementioned step S5. The weighted summation formula is:

[0156] ;

[0157] in:

[0158] is the joint discriminant value;

[0159] is the normalized trajectory naturalness index;

[0160] is the normalized structural complexity information;

[0161] and is the weight coefficient automatically generated by the machine learning model.

[0162] Specifically, the judgment process can be expressed by the following formula:

[0163] ;

[0164] in:

[0165] Indicates the current distance value or a specific measurement value. The specific meaning may be related to the system settings of touch recognition or gesture detection;

[0166] Indicates a preset threshold, usually set by historical data or environmental factors. This value is used to determine the current Whether a certain standard is met.

[0167] The key to step S6 is to combine the joint discrimination value calculated in step S5 with the preset judgment threshold. Through this judgment process, the system can determine whether the touch operation is a valid edge gesture and respond accordingly. Combined with the adaptive adjustment of the machine learning model, the weighted coefficient and This provides flexibility and adaptability for the calculation of the joint discriminant value, thereby further improving the accuracy and reliability of touch recognition.

[0168] S7: If the edge gesture is determined to be valid, a system function response operation associated with the edge area is triggered; if the touch is determined to be invalid, the response is suppressed;

[0169] Specifically, in step S6, the system uses the joint discriminant value constructed in the above steps to and compare it with the preset judgment threshold This comparison completes the logic for determining whether the touch is a valid edge gesture. Step S7, the final step in the entire method, is the function call or control operation performed by the system after completing the edge gesture determination. Its goal is to control whether to respond to the touch operation based on the determination result, ensuring that the system can intelligently suppress false touches in actual operation while ensuring that valid gestures are correctly responded to.

[0170] Generally speaking, when the judgment result shows that the touch operation is a valid edge gesture, the system will trigger the execution of subsequent functional modules according to the configuration logic, including but not limited to interface response, function activation, animation feedback, etc.; when the judgment result is an invalid touch, the system will directly ignore the touch event without any further response.

[0171] 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 joint discrimination value with the determination threshold in step S6.

[0172] Specifically, the system will use the joint discriminant value obtained in the previous steps and the judgment threshold Make a judgment, if the conditions are met , the system determines it as a valid gesture. At this time, the following operation process will be executed:

[0173] Sending a valid touch signal to the event dispatch module;

[0174] Call the registered function response module;

[0175] Update the status of interface elements, such as activating the sidebar, returning to the previous page, or starting the multitasking view.

[0176] In one possible implementation, the system can express the final gesture processing control through the following control logic function:

[0177] ;

[0178] in:

[0179] Control instruction, Boolean value. 1 means response, 0 means inhibition;

[0180] Control function, decides whether to respond;

[0181] Unit step function. 1 when it is, otherwise 0;

[0182] The currently measured input value, such as gesture characteristics;

[0183] Threshold set by the system.

[0184] As an option, the response module can preset multiple types of system operation response modes, corresponding to different categories of edge gestures. For example:

[0185] Swiping the left edge corresponds to the return operation;

[0186] A quick swipe up to the edge activates the task manager;

[0187] Staying in the edge area for a long time may correspond to functions such as mute switching and brightness adjustment.

[0188] In this embodiment, to enhance the system's adaptability to different devices and screen sizes, the response module also includes a policy adaptation mechanism. This mechanism automatically adjusts the edge gesture response area and response method based on factors such as device type, screen boundary geometry, and user configuration, ensuring consistent responses across different terminal environments.

[0189] The control response module involved in step S7 not only completes the function activation after touch detection, but also provides strong decision-making and execution support for the entire system. By introducing response control functions, pre-set strategy adaptation, low-latency response mechanisms, and multi-finger parallel judgment strategies, this step ensures the accuracy and stability of the gesture recognition system in actual operation.

[0190] The touch screen control device described below and the touch screen control method described above may refer to each other.

[0191] Please see the attached Figure 2 The present invention also provides a touch screen control device, comprising:

[0192] A trace acquisition module is used to collect a touch point sequence including touch position coordinates, pressure value and timestamp when an initial touch event is detected in the edge area of ​​the screen, and send the touch point sequence to the trajectory modeling module;

[0193] The trajectory modeling module is used to receive the touch point sequence from the trajectory acquisition module and construct it into a time-continuous trajectory function, while calculating the velocity and acceleration information of the function and sending the processing results to the feature analysis module;

[0194] The feature analysis module 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 state encoding module;

[0195] The state encoding module is used to receive the trajectory point sequence 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;

[0196] The discrimination module is used 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 joint discrimination value, compare it with the preset threshold, and output the judgment result of whether it is a valid edge gesture;

[0197] The response control module is used to receive the judgment result from the judgment module. If it is judged to be a valid edge gesture, the corresponding edge function response operation is triggered; if it is judged to be an invalid gesture, no system function is executed.

[0198] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A touch screen control method, characterized in that: The following steps are included: S1: Detecting a touch start event generated by the user at the edge of the screen and starting to collect touch trajectory data, the trajectory data includes touch position coordinates, pressure value and corresponding timestamp; S2. Constructing the touch trajectory data into a time-continuous trajectory function to obtain the velocity information, acceleration information, and trajectory steering change characteristics of the trajectory; S3. Calculating a set of trajectory dynamic characteristic indices representing the naturalness of the trajectory based on the velocity information, acceleration information, and trajectory turning change characteristics; S4. Encode the trajectory data into a sequence of symbolic states, 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; S5. Construct a joint discriminant value based on trajectory naturalness index and structural complexity information; The trajectory naturalness index includes the trajectory speed stability, acceleration change amplitude and path turning rate, and the path turning rate is measured by the frequency of change of the trajectory turning angle; The entropy value of the structural complexity is calculated using the Shannon entropy formula, which is: Among them, H(X) is the entropy of random variable X, P(x i ) is the symbol x i The probability of occurrence in the symbol sequence, x i is a symbol in the symbol sequence, logP(x i ) is P(x i ), where n is the total number of symbols in the symbol sequence; S6. Compare the joint discrimination value with a preset judgment threshold to determine whether the touch is a valid edge gesture; The construction of the joint discriminant value includes the following steps: Based on the trajectory naturalness index and structural complexity information, normalizing the two respectively; According to the normalized naturalness index and structural complexity information, the weighted sum is calculated to obtain the joint discriminant value; The weighting coefficients are adaptively adjusted based on historical trajectory data through a pre-trained machine learning model; S7. If it is determined to be a valid edge gesture, a system function response operation associated with the edge area is triggered; if it is determined to be an invalid touch, the response is suppressed.

2. The touch screen control method according to claim 1, wherein: 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: Interpolate the collected touch trajectory data to ensure that the time intervals of the trajectory points are uniform; Perform first-order derivative operation on the processed trajectory data to obtain the instantaneous velocity vector of the trajectory; Perform the 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 weighted summation formula is: D=w1·N+w2·C; Among them, D is the joint discriminant value, N is the normalized trajectory naturalness index, C is the normalized structural complexity information, and w1 and w2 are weight coefficients automatically generated by the machine learning model.

5. The touch screen control method according to claim 1, wherein: The step of comparing the joint discriminant value with a preset determination threshold comprises: A weighted fusion model is used to generate a joint discriminant value, combining multiple trajectory dynamic features and structural complexity entropy values ​​according to preset weights to form a fusion vector; Performing one-dimensional mapping on the fusion vector to obtain a normalized discriminant value; The normalized discrimination value is compared with the preset judgment threshold value to output the validity judgment result.

6. A touch screen control device according to the touch screen control method according to any one of claims 1 to 5, characterized in that: include; A trajectory acquisition module is used to collect a touch point sequence including touch position coordinates, pressure value and timestamp when an initial touch event is detected in the edge area of ​​the screen, and send the touch point sequence to the trajectory modeling module; The trajectory modeling module is used to receive the touch point sequence from the trajectory acquisition module and construct it into a time-continuous trajectory function, while calculating the velocity and acceleration information of the function and sending the processing results to the feature analysis module; The feature analysis module 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 state encoding module; The state encoding module is used to receive the trajectory point sequence 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; The discrimination module is used 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 joint discrimination value, compare it with the preset threshold, and output the judgment result of whether it is a valid edge gesture; The response control module is used to receive the judgment result from the discrimination module and trigger the corresponding edge function response operation if it is determined to be a valid edge gesture; If the gesture is determined to be invalid, no system function will be executed.

7. An electronic device, characterized in that: include; processor; Memory; Touch screen display; The processor executes the computer program stored in the memory to enable the electronic device to perform the touch screen control method according to any one of claims 1 to 5.

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