Self-learning anti-mistouch method, device and computer-readable storage medium

Abstract

The present invention discloses a self-learning method, device and computer-readable storage medium for preventing false touches, wherein the method comprises: recording the pressing area information corresponding to each touch event, wherein the pressing area information comprises the pressing area and the area shape; setting a determination range including the mean value; in the application stage, determining the current area shape of the current touch event in any current sub-area, and determining whether the current pressing area of ​​the current touch event is within the current sub-area and the determination range corresponding to the current area shape; if not, determining that the current touch event is a false touch event with a shielded response. A humanized self-learning solution for preventing false touches is implemented, so that the false touch mechanism of the device itself can be adaptively set according to the hardware and software environment and the operating environment, thereby improving the detection efficiency and accuracy of false touch events and enhancing the user experience.

Description

Technical Field

[0001] The present invention relates to the field of mobile communications, and in particular to a self-learning anti-mistouch method, device, and computer-readable storage medium. Background Art

[0002] In the prior art, with the continuous development of smart terminal devices, large screens and narrow bezels have become the mainstream design of various display devices. Under this structural design, the probability of users' accidental touches is also greatly increased.

[0003] In the existing technology, accidental touch detection or identification is generally performed by setting an accidental touch shielding area. However, this setting scheme is relatively fixed and cannot be adaptively adjusted according to the user's usage environment. The anti-accidental touch effect is poor and will to some extent affect the integrity and practicality of the touch function within the touch area. The user experience needs to be improved. Summary of the Invention

[0004] In order to solve the above technical defects in the prior art, the present invention proposes a self-learning method for preventing false touches, which includes:

[0005] In the learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is acquired, and the touch area is divided into at least two sub-areas according to the environmental information.

[0006] A first number of touch events are monitored in the two sub-areas respectively, and pressing area information corresponding to each touch event is recorded, wherein the pressing area information includes a pressing area and an area shape.

[0007] The average value of the pressed areas in the same sub-region and the same type of region shape is obtained by counting and obtaining the average value through a preset error tolerance rate, and a determination range including the average value is set.

[0008] During the application stage, the current area shape of the current touch event in any current sub-area is determined, and whether the current pressing area of ​​the current touch event is within the current sub-area and the judgment range corresponding to the current area shape is determined. If not, the current touch event is determined to be a false touch event with a shielded response.

[0009] Optionally, in the learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is obtained, and the touch area is divided into at least two sub-areas according to the environmental information, including:

[0010] Preset hand shape information related to palm size and finger length, preset application information related to application type and operation type, and preset holding information related to horizontal and vertical screen states.

[0011] A first manipulation feature corresponding to the hand shape information, a second manipulation feature corresponding to the application information, and a third manipulation feature corresponding to the holding information are preset respectively.

[0012] Optionally, in the learning phase, obtaining environmental information consisting of one or more of hand shape information, application information, and grip information, and dividing the touch area into at least two sub-areas according to the environmental information, further includes:

[0013] A corresponding manipulation type zone or a manipulation function zone is determined according to one or more of the first manipulation feature, the second manipulation feature, and the third manipulation feature.

[0014] The touch area is divided into at least two sub-areas according to the manipulation type partition or the manipulation function partition.

[0015] Optionally, monitoring a first number of touch events in the two sub-areas respectively and recording pressed area information corresponding to each touch event, wherein the pressed area information includes a pressed area and an area shape, including:

[0016] When the touch events detected in any of the sub-regions reach the first quantity, a first quantity of the pressed areas and the region shapes is recorded.

[0017] When all the sub-regions have completed recording of the first number of pressed areas and the region shapes, monitoring of the touch event is suspended.

[0018] Optionally, the obtaining of a mean value of the pressed areas of the same sub-region and the same type of region shape by counting based on a preset error tolerance, and setting a determination range including the mean value, includes:

[0019] The error tolerance rate corresponding to the sub-region division state is acquired, and the first number of pressed areas and the region shapes in each of the sub-regions are counted in combination with the error tolerance rate.

[0020] An average value of the pressed areas in the same sub-region and belonging to the same type of region shape is obtained, and the average value is set as a reference value.

[0021] Optionally, the obtaining of the average value of the pressed areas of the same sub-region and the same type of the region shape by counting and using a preset error tolerance, and setting a determination range including the average value, further includes:

[0022] The touch event is continuously monitored with reference to the benchmark value, and the monitoring of the touch event is stopped when the second number of pressed areas and area shapes have been recorded in all sub-regions.

[0023] The reference value is calibrated based on the second number of pressed areas and the region shape, and the determination range including a calibrated median value is set.

[0024] Optionally, during the application phase, determining the current area shape of a current touch event within any current sub-area, and determining whether the current pressed area of ​​the current touch event is within the current sub-area and within a determination range corresponding to the current area shape; if not, determining that the current touch event is a false touch event with a shielded response includes:

[0025] The current sub-region, the current region shape, and the current pressed area corresponding to the current touch event are determined.

[0026] The determination range corresponding to both the current sub-region and the current region shape is acquired.

[0027] Optionally, during the application phase, determining the current area shape of a current touch event within any current sub-area, and determining whether the current pressed area of ​​the current touch event is within the current sub-area and within a determination range corresponding to the current area shape; if not, determining that the current touch event is a false touch event for which a response is shielded, further includes:

[0028] If the current pressed area is not within the determination range, the current touch event is determined to be a false touch event with a shielded response.

[0029] Within the preset time range of generating the false touch event, if the pressing area of ​​another touch event received is within the determination range corresponding to the current sub-area or the shape of the current area, the other touch event is determined to be a touch event of normal response.

[0030] The present invention also proposes a self-learning anti-false touch device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the computer program is executed by the processor, the steps of the self-learning anti-false touch method as described in any one of the above items are implemented.

[0031] The present invention also proposes a computer-readable storage medium, which stores a self-learning anti-false touch program. When the self-learning anti-false touch program is executed by a processor, the steps of the self-learning anti-false touch method as described in any one of the above are implemented.

[0032] The self-learning anti-false touch method, device and computer-readable storage medium of the present invention are implemented by obtaining environmental information composed of one or more of hand shape information, application information and holding information in the learning stage, and dividing the touch area into at least two sub-areas according to the environmental information; monitoring a first number of touch events in the two sub-areas respectively, and recording the pressing area information corresponding to each touch event, wherein the pressing area information includes the pressing area and the area shape; statistically obtaining the average of the pressing areas of the same sub-area and the same type of area shape through a preset fault tolerance rate, and setting a judgment range including the average; in the application stage, determining the current area shape of the current touch event in any current sub-area, and determining whether the current pressing area of ​​the current touch event is in the current sub-area and within the judgment range corresponding to the current area shape; if not, determining that the current touch event is a false touch event with a shielded response. A humanized self-learning anti-false touch solution has been implemented, which allows the device's false touch mechanism to be adaptively set according to the software and hardware environment and operating environment, improving the detection efficiency and accuracy of false touch events and enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0034] Figure 1 This is a hardware structure diagram of a mobile terminal according to the present invention;

[0035] Figure 2 This is a diagram of a communication network system architecture provided by an embodiment of the present invention;

[0036] Figure 3 is a flow chart of the first embodiment of the self-learning anti-false touch method of the present invention;

[0037] Figure 4 is a flow chart of a second embodiment of the self-learning method for preventing false touches according to the present invention;

[0038] Figure 5 is a flow chart of a third embodiment of the self-learning method for preventing false touches according to the present invention;

[0039] Figure 6 is a flow chart of a fourth embodiment of the self-learning method for preventing false touches according to the present invention;

[0040] Figure 7 is a flow chart of a fifth embodiment of the self-learning method for preventing false touches according to the present invention;

[0041] Figure 8 is a flow chart of a sixth embodiment of the self-learning method for preventing false touches according to the present invention;

[0042] Figure 9is a flow chart of a seventh embodiment of the self-learning method for preventing false touches according to the present invention;

[0043] Figure 10 is a flow chart of an eighth embodiment of the self-learning method for preventing false touches according to the present invention;

[0044] Figure 11 This is an event record table of the first embodiment of the self-learning method for preventing false touches of the present invention. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] In the subsequent description, suffixes such as "module," "component," or "unit" used to represent elements are only used to facilitate the description of the present invention and have no specific meaning. Therefore, "module," "component," or "unit" can be used interchangeably.

[0047] The terminal can be implemented in various forms. For example, the terminal described in the present invention may include mobile terminals such as mobile phones, tablet computers, laptop computers, PDAs, portable media players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, etc., as well as fixed terminals such as digital TVs and desktop computers.

[0048] The following description will be made by taking a mobile terminal as an example. It will be understood by those skilled in the art that, in addition to components specifically used for mobile purposes, the configuration according to the embodiments of the present invention can also be applied to fixed type terminals.

[0049] See also Figure 1 , which is a schematic diagram of the hardware structure of a mobile terminal for implementing various embodiments of the present invention. The mobile terminal 100 may include: an RF (Radio Frequency) unit 101, a WiFi module 102, an audio output unit 103, an A / V (audio / video) input unit 104, a sensor 105, a display unit 106, a user input unit 107, an interface unit 108, a memory 109, a processor 110, and a power supply 111. Those skilled in the art will understand that Figure 1 The structure of the mobile terminal shown in the figure does not constitute a limitation to the mobile terminal. The mobile terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0050] The following combination Figure 1 A detailed introduction to the various components of the mobile terminal:

[0051] The RF unit 101 can be used to send and receive information or receive signals during calls. Specifically, it receives downlink information from the base station and transmits it to the processor 110 for processing. It also transmits uplink data to the base station. Typically, the RF unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and more. Furthermore, the RF unit 101 can communicate with the network and other devices via wireless communication. The above-mentioned wireless communications may use any communication standard or protocol, including but not limited to GSM (Global System of Mobile communication), GPRS (General Packet Radio Service), CDMA2000 (Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access), FDD-LTE (Frequency Division Duplexing-Long Term Evolution) and TDD-LTE (Time Division Duplexing-Long Term Evolution), etc.

[0052] WiFi is a short-range wireless transmission technology. Mobile terminals can help users send and receive emails, browse web pages, and access streaming media through the WiFi module 102. It provides users with wireless broadband Internet access. Figure 1 The WiFi module 102 is shown, but it is understandable that it is not an essential component of the mobile terminal and can be omitted as needed without changing the essence of the invention.

[0053] The audio output unit 103 can convert audio data received by the RF unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output it as sound when the mobile terminal 100 is in a call signal reception mode, a talk mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Furthermore, the audio output unit 103 can also provide audio output related to a specific function performed by the mobile terminal 100 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, or the like.

[0054] The A / V input unit 104 is used to receive audio or video signals. The A / V input unit 104 may include a graphics processing unit (GPU) 1041 and a microphone 1042. The GPU 1041 processes image data of still images or videos captured by an image capture device (e.g., a camera) in video capture mode or image capture mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the GPU 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the RF unit 101 or the WiFi module 102. The microphone 1042 may receive sound (audio data) in operating modes such as a phone call mode, a recording mode, and a voice recognition mode, and may process such sound into audio data. In the phone call mode, the processed audio (voice) data may be converted into a format that can be transmitted to a mobile communication base station via the RF unit 101. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to eliminate (or suppress) noise or interference generated during the reception and transmission of audio signals.

[0055] The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 1061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 1061 and / or the backlight when the mobile terminal 100 is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that can be configured in the mobile phone, such as fingerprint sensors, pressure sensors, iris sensors, molecular sensors, gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be described here.

[0056] The display unit 106 is used to display information input by the user or information provided to the user. The display unit 106 may include a display panel 1061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0057] The user input unit 107 can be used to receive input digital or character information, and generate key signal input related to user settings and function control of the mobile terminal. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also known as a touch screen, can collect user touch operations on or near it (such as operations performed by the user using a finger, stylus, or any other suitable object or accessory on or near the touch panel 1071) and drive the corresponding connection device according to a pre-set program. The touch panel 1071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction and detects the signal caused by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 110. It can also receive commands sent by the processor 110 and execute them. In addition, the touch panel 1071 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 1071, the user input unit 107 may further include other input devices 1072. Specifically, the other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, power keys, etc.), a trackball, a mouse, a joystick, etc., and are not specifically limited here.

[0058] Furthermore, the touch panel 1071 may cover the display panel 1061. When the touch panel 1071 detects a touch operation on or near it, it transmits the information to the processor 110 to determine the type of touch event. Subsequently, the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of touch event. Figure 1 In the embodiment, the touch panel 1071 and the display panel 1061 are two independent components to realize the input and output functions of the mobile terminal. However, in some embodiments, the touch panel 1071 and the display panel 1061 can be integrated to realize the input and output functions of the mobile terminal, which is not limited here.

[0059] The interface unit 108 serves as an interface through which at least one external device can be connected to the mobile terminal 100. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and an external device.

[0060] Memory 109 can be used to store software programs and various data. Memory 109 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, memory 109 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0061] Processor 110 is the control center of the mobile terminal, connecting all components of the mobile terminal using various interfaces and circuits. By running or executing software programs and / or modules stored in memory 109 and accessing data stored in memory 109, it executes various functions of the mobile terminal and processes data, thereby providing overall monitoring of the mobile terminal. Processor 110 may include one or more processing units; preferably, processor 110 may integrate an application processor and a modem processor. The application processor primarily handles the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 110.

[0062] The mobile terminal 100 may also include a power supply 111 (such as a battery) for supplying power to various components. Preferably, the power supply 111 may be logically connected to the processor 110 through a power management system, thereby managing functions such as charging, discharging, and power consumption through the power management system.

[0063] although Figure 1 Not shown, the mobile terminal 100 may further include a Bluetooth module, etc., which will not be described in detail here.

[0064] To facilitate understanding of the embodiments of the present invention, the communication network system on which the mobile terminal of the present invention is based is described below.

[0065] See also Figure 2 , Figure 2 A communication network system architecture diagram is provided for an embodiment of the present invention. The communication network system is an LTE system of universal mobile communication technology. The LTE system includes a UE (User Equipment) 201, an Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) 202, an Evolved Packet Core (EPC) 203, and an operator's IP service 204, which are sequentially connected in communication.

[0066] Specifically, UE201 may be the above-mentioned terminal 100, which will not be described in detail here.

[0067] E-UTRAN 202 includes eNodeB 2021 and other eNodeBs 2022 , etc. Among them, eNodeB 2021 can be connected to other eNodeBs 2022 via a backhaul (eg, an X2 interface), and eNodeB 2021 is connected to EPC 203 , and eNodeB 2021 can provide UE 201 with access to EPC 203 .

[0068] EPC 203 may include MME (Mobility Management Entity) 2031, HSS (Home Subscriber Server) 2032, other MMEs 2033, SGW (Serving Gate Way) 2034, PGW (PDN Gate Way) 2035, and PCRF (Policy and Charging Rules Function) 2036. MME 2031 is the control node that handles signaling between UE 201 and EPC 203, providing bearer and connection management. HSS 2032 provides registers for managing functions such as the Home Location Register (not shown) and stores user-specific information such as service features and data rates. All user data can be sent through SGW2034, PGW2035 can provide IP address allocation and other functions for UE 201, PCRF2036 is the policy and charging control policy decision point for service data flow and IP bearer resources, and it selects and provides available policy and charging control decisions for the policy and charging execution function unit (not shown in the figure).

[0069] The IP service 204 may include the Internet, an intranet, an IMS (IP Multimedia Subsystem), or other IP services.

[0070] Although the above description is based on the LTE system as an example, those skilled in the art should know that the present invention is not only applicable to the LTE system, but also to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, and future new network systems, and is not limited here.

[0071] Based on the above-mentioned mobile terminal hardware structure and communication network system, various embodiments of the method of the present invention are proposed.

[0072] Example 1

[0073] Figure 3 Flowchart of the first embodiment of the self-learning method for preventing accidental touches according to the present invention. A self-learning method for preventing accidental touches, comprising:

[0074] S1. In a learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is obtained, and a touch area is divided into at least two sub-areas according to the environmental information.

[0075] S2. Monitor a first number of touch events in the two sub-areas respectively, and record pressed area information corresponding to each touch event, wherein the pressed area information includes a pressed area and an area shape.

[0076] S3. Obtain the average value of the pressed areas of the same sub-region and the same type of region shape by counting and using a preset error tolerance rate, and set a determination range including the average value.

[0077] S4. In the application stage, determine the current area shape of the current touch event in any current sub-area, and determine whether the current pressing area of ​​the current touch event is within the current sub-area and the judgment range corresponding to the current area shape. If not, determine that the current touch event is a false touch event with a shielded response.

[0078] Optionally, in this embodiment, first, in the learning stage, environmental information consisting of one or more of hand shape information, application information, and holding information is obtained, and the touch area is divided into at least two sub-areas according to the environmental information; then, a first number of touch events are monitored in the two sub-areas respectively, and the pressing area information corresponding to each touch event is recorded, wherein the pressing area information includes the pressing area and the area shape; then, the average of the pressing areas of the same sub-area and the same type of area shape is obtained by counting and obtaining the average value of the pressing area through the preset fault tolerance rate, and a judgment range including the average value is set; finally, in the application stage, the current area shape of the current touch event in any current sub-area is determined, and it is determined whether the current pressing area of ​​the current touch event is within the current sub-area and the judgment range corresponding to the current area shape. If not, it is determined that the current touch event is a false touch event with a shielded response. For example, in the data collection stage of the learning process, the mobile phone background performs statistics on the touch area, such as clicks, slides and other touch events, and counts the ACTION_MAJOR of each touch response area through the event number, please refer to Figure 11 In the event record table shown, ACTION_MAJOR is used to indicate the size or upper and lower limits of the finger pressing the screen, as well as shape recognition, and is used as benchmark data. When the phone screen recognizes an area much larger than the current fingertip or fingertip, such as an irregular palm contacting the screen, it can be determined that a false touch has occurred.

[0079] Optionally, in this embodiment, the event numbering is as follows: the mobile phone is divided into four parts according to the shape of a field, and the actual reporting events of each partition are recorded and numbered from 000...019 respectively.

[0080] Optionally, in this embodiment, the benchmark data is to take the average of the ACTION_MAJOR sizes of the user's first 20 click or slide events as the benchmark, and the error tolerance can be ±5%, which can improve the user experience; as the data increases, data such as 100 times, 1000 times, etc. can be taken for calculation, which is more accurate.

[0081] Optionally, in this embodiment, the judgment method is that when the user is operating, if the event response in one of the partitions is greater than the AVERAGE (ACTION_MAJOR=1, 0, 1..19), it is determined to be a false touch gesture.

[0082] Optionally, in this embodiment, the user's real-time operation interface is continuously acquired through the terminal process, and the horizontal and vertical screen states are acquired through the gravity sensor.

[0083] Optionally, in this embodiment, for example, when it is determined to be in a vertical screen state, the interface is a settings or elimination game interface, etc., and feature 1 is used to identify the mis-touch position when the screen is held vertically. Data for a period of time can be collected here, and after obtaining the user's continuous holding hand shape, the area where the user has multiple mis-touched is depicted and processed as non-responsive reporting; when the hand shape is smaller (that is, the hand shape that is more prone to mis-touching), the user's mis-touch area can be normally learned and identified based on feature 1, and the area is not responded to.

[0084] Optionally, in this embodiment, for example, when it is determined to be in a horizontal screen state, and the program running at this time is the King of Glory game program; based on feature 1, the touch operation within the user operation time AB is captured, abnormal touch events and normal click events are identified, and the anti-mistouch function is removed in the normal user operation area (it can be pointed out that the anti-mistouch function itself has a delay effect on the game experience), and the area with the mistouch is designated as a non-responsive area.

[0085] The beneficial effects of this embodiment are as follows: during the learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is obtained, and the touch area is divided into at least two sub-areas based on the environmental information; a first number of touch events are monitored in each of the two sub-areas, and the pressing area information corresponding to each touch event is recorded, wherein the pressing area information includes a pressing area and an area shape; the pressing area information is calculated and averaged for the pressing areas of the same sub-area and the same type of area shape using a preset tolerance rate, and a determination range including the average is set; during the application phase, the current area shape of the current touch event in any current sub-area is determined, and whether the current pressing area of ​​the current touch event is within the current sub-area and within the determination range corresponding to the current area shape; if not, the current touch event is determined to be a false touch event with a shielded response. A user-friendly self-learning anti-false touch solution is implemented, so that the false touch mechanism of the device itself can be adaptively set according to the hardware and software environment and the operating environment, thereby improving the detection efficiency and accuracy of false touch events and enhancing the user experience.

[0086] Example 2

[0087] Figure 4 This is a flow chart of a second embodiment of the self-learning method for preventing false touches according to the present invention. Based on the above embodiment, during the learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is obtained, and the touch area is divided into at least two sub-areas based on the environmental information, including:

[0088] S11. Preset hand shape information related to palm size and finger length, preset application information related to application type and operation type, and preset holding information related to horizontal and vertical screen states.

[0089] S12. Preset a first manipulation feature corresponding to the hand shape information, a second manipulation feature corresponding to the application information, and a third manipulation feature corresponding to the holding information.

[0090] Optionally, in this embodiment, hand shape information related to palm size and finger length is preset, application information related to application type and operation type is preset, and holding information related to horizontal and vertical screen states is preset. The above preset information is provided by the device manufacturer or third-party data provider to obtain complete data.

[0091] Optionally, in this embodiment, locally on the device, touch operations with higher probabilities are counted and analyzed based on different hand shapes, different application information, or different holding information in the learning stage, so as to preset a first control feature corresponding to the hand shape information, a second control feature corresponding to the application information, and a third control feature corresponding to the holding information.

[0092] The beneficial effect of this embodiment is that by presetting hand shape information related to palm size and finger length, presetting application information related to application type and operation type, and presetting grip information related to horizontal and vertical screen states, respectively presetting a first manipulation feature corresponding to the hand shape information, a second manipulation feature corresponding to the application information, and a third manipulation feature corresponding to the grip information, a humanized self-learning anti-false touch solution is provided with a correlation basis for manipulation features, so that the false touch mechanism of the device itself can be adaptively set according to the hardware and software environment and the operating environment, thereby improving the detection efficiency and accuracy of false touch events and enhancing the user experience.

[0093] Example 3

[0094] Figure 5 This is a flow chart of a third embodiment of the self-learning method for preventing false touches according to the present invention. Based on the above embodiment, in the learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is obtained, and the touch area is divided into at least two sub-areas based on the environmental information. The method also includes:

[0095] S13: Determine a corresponding manipulation type zone or a manipulation function zone according to one or more of the first manipulation feature, the second manipulation feature, and the third manipulation feature.

[0096] S14: Divide the touch area into at least two sub-areas according to the manipulation type partition or the manipulation function partition.

[0097] Optionally, in this embodiment, a corresponding manipulation type partition or manipulation function partition is determined based on one or more of the first manipulation feature, the second manipulation feature, and the third manipulation feature, wherein the corresponding, high-probability manipulation type partition or manipulation function partition is divided according to the association basis of the above-mentioned manipulation features.

[0098] Optionally, in this embodiment, the touch area is divided into at least two sub-areas based on the control type or control function. For example, in a shooting game, the touch area is divided into two large upper and lower sub-areas, and the lower large sub-area is further divided into two smaller left and right sub-areas. For another example, in a chess game, the touch area is divided into four or eight rectangular areas.

[0099] The beneficial effect of this embodiment is that, by determining the corresponding manipulation type partition or manipulation function partition based on one or more of the first manipulation feature, the second manipulation feature, and the third manipulation feature, and dividing the touch area into at least two sub-areas based on the manipulation type partition or the manipulation function partition, a humanized self-learning anti-false touch solution is provided, providing a method for dividing sub-areas associated with manipulation features. This allows the device's false touch mechanism to be adaptively configured based on the hardware and software environment and the operating environment, thereby improving the efficiency and accuracy of false touch event detection and enhancing the user experience.

[0100] Example 4

[0101] Figure 6 This is a flow chart of a fourth embodiment of the self-learning method for preventing false touches according to the present invention. Based on the above embodiment, the method monitors a first number of touch events in the two sub-areas, and records the pressed area information corresponding to each touch event. The pressed area information includes the pressed area and the shape of the area, including:

[0102] S21: When the touch events detected in any of the sub-regions reach the first quantity, record a first quantity of the pressed areas and the region shapes.

[0103] S22: When all the sub-regions have completed recording the first number of pressed areas and the region shapes, suspend monitoring the touch event.

[0104] Optionally, in this embodiment, when the touch events detected in any of the sub-areas reach the first number, the first number of pressing areas and the area shapes are recorded, wherein the area shapes are classified, for example, as circular, elliptical, bar-like, etc.

[0105] Optionally, in this embodiment, when all the sub-areas have completed recording the first number of the pressed areas and the area shapes, monitoring of the touch event is suspended, wherein after the above-mentioned area shapes are abstracted and classified, the pressed areas belonging to each area shape are counted.

[0106] The beneficial effect of this embodiment is that, by recording the first number of pressed areas and shapes when the touch events detected in any sub-area reach the first number, and pausing the monitoring of touch events when the first number of pressed areas and shapes has been recorded for all sub-areas, a statistical method for touch events is provided to implement a user-friendly self-learning anti-false touch solution, allowing the device's false touch mechanism to be adaptively configured according to the hardware and software environment and operating environment, thereby improving the efficiency and accuracy of false touch event detection and enhancing the user experience.

[0107] Example 5

[0108] Figure 7 This is a flowchart of the fifth embodiment of the self-learning anti-false touch method of the present invention. Based on the above embodiment, the method of obtaining the average value of the pressed areas of the same sub-region and the same type of region shape by using a preset error tolerance rate and setting a determination range including the average value includes:

[0109] S31. Obtain the error tolerance rate corresponding to the sub-region division state, and count the first number of pressed areas and the region shapes in each of the sub-regions in combination with the error tolerance rate.

[0110] S32: Obtain an average value of the pressed areas in the same sub-region and belonging to the same type of region shape, and set the average value as a reference value.

[0111] Optionally, in this embodiment, the fault tolerance rate corresponding to the sub-region division state is obtained, and the first number of pressing areas and the area shapes in each sub-region are counted in combination with the fault tolerance rate, wherein, after abstracting and classifying the above-mentioned area shapes, the pressing areas belonging to each area shape are counted, and the data of excessively large pressing areas and excessively small pressing areas corresponding to the probability are eliminated in combination with the above-mentioned fault tolerance rate, and then the pressing area data of the remaining middle segment is retained.

[0112] Optionally, in this embodiment, the average of the pressed areas of the same sub-region and belonging to the same type of regional shape is obtained, and the average is set as the benchmark value, that is, each benchmark value is a benchmark value of the same sub-region and belonging to the same type of regional shape.

[0113] The beneficial effect of this embodiment is that by obtaining the fault tolerance rate corresponding to the sub-region division state and combining the fault tolerance rate to count the first number of pressed areas and the area shapes in each sub-region, the average of the pressed areas in the same sub-region and belonging to the same type of area shape is obtained and set as the reference value. This provides a method for setting a reference value to implement a user-friendly self-learning anti-false touch solution, allowing the device's false touch mechanism to be adaptively set according to the software and hardware environment and operating environment, thereby improving the detection efficiency and accuracy of false touch events and enhancing the user experience.

[0114] Example 6

[0115] Figure 8 This is a flow chart of a sixth embodiment of the self-learning method for preventing false touches according to the present invention. Based on the above embodiment, the method includes obtaining the average value of the pressed areas of the same sub-region and the same type of region shape by using a preset error tolerance rate, and setting a determination range that includes the average value. The method also includes:

[0116] S33: Continue to monitor the touch event with reference to the benchmark value, and stop monitoring the touch event when all sub-regions have completed recording a second number of pressed areas and region shapes.

[0117] S34: Calibrate the reference value according to the second number of pressed areas and the region shape, and set the determination range including the calibrated median value.

[0118] Optionally, in this embodiment, the touch event monitoring continues with the reference value as a reference, and the touch event monitoring ceases when the sub-regions have completed recording the second number of pressed areas and regional shapes. To improve accuracy, the touch event detection continues for a second number of touch events that is a multiple of the first number. During this process, the reference value is calibrated based on the second number of pressed areas and regional shapes, and a determination range is set that includes the calibrated median value. It will be understood that each determination range corresponds to both a sub-region and the regional shape of the sub-region.

[0119] The beneficial effect of this embodiment is that, by continuously monitoring the touch event with reference to the baseline value, monitoring the touch event ceases when the sub-regions have completed recording the second number of pressed areas and area shapes; the baseline value is calibrated based on the second number of pressed areas and area shapes, and the determination range is set to include the calibrated median value. This provides a more accurate determination range for implementing a user-friendly self-learning anti-false touch solution, allowing the device's false touch mechanism to be adaptively configured based on the hardware and software environment and operating environment, improving the efficiency and accuracy of false touch event detection and enhancing the user experience.

[0120] Example 7

[0121] Figure 9 This is a flow chart of a seventh embodiment of the self-learning false touch prevention method of the present invention. Based on the above embodiment, in the application phase, determining the current area shape of a current touch event within any current sub-area, and determining whether the current pressed area of ​​the current touch event is within the current sub-area and within the determination range corresponding to the current area shape; if not, determining that the current touch event is a false touch event for which a response is shielded, includes:

[0122] S41: Determine the current sub-region, the current region shape, and the current pressed area corresponding to the current touch event.

[0123] S42: Acquire the determination range corresponding to both the current sub-region and the shape of the current region.

[0124] Optionally, in this embodiment, when the current touch event is obtained, the sub-area division method corresponding to the environmental information, the sub-area under the division method, the shape of the area under the sub-area, and the judgment range under the shape of the area can be reversely inferred based on any one of the above-mentioned environmental information.

[0125] Optionally, in this embodiment, the current pressing area corresponding to the current touch event is substituted into the above determination range to perform range determination.

[0126] The beneficial effect of this embodiment is that by determining the current sub-region, the current region shape, and the current pressed area corresponding to the current touch event, and obtaining the judgment range corresponding to both the current sub-region and the current region shape, a method for obtaining judgment data in the application phase is provided to implement a user-friendly self-learning anti-false touch solution, allowing the device's false touch mechanism to be adaptively configured according to the software and hardware environment and operating environment, thereby improving the detection efficiency and accuracy of false touch events and enhancing the user experience.

[0127] Example 8

[0128] Figure 10 This is a flow chart of an eighth embodiment of the self-learning false touch prevention method of the present invention. Based on the above embodiment, in the application phase, the current area shape of the current touch event in any current sub-area is determined, and whether the current pressed area of ​​the current touch event is within the current sub-area and within the determination range corresponding to the current area shape is determined. If not, the current touch event is determined to be a false touch event with a shielded response, and further includes:

[0129] S43: If the current pressed area is not within the determination range, determine that the current touch event is a false touch event with a shielded response.

[0130] S44. Within the preset time range of generating the false touch event, if the pressing area of ​​another touch event received is within the determination range corresponding to the current sub-area or the shape of the current area, then the other touch event is determined to be a touch event of normal response.

[0131] Optionally, in this embodiment, considering that the above-mentioned judgment ranges strictly correspond to the sub-area and the area shape of the sub-area at the same time, the judgment conditions for misidentification may be too strict. In actual application scenarios, when a normal touch of a user is detected as a false touch, the same or similar touch will generally be performed again. Therefore, in this embodiment, the judgment range corresponding to the current sub-area or any of the current area shapes is selected as the judgment basis, so as to appropriately adjust the judgment basis and promptly solve the problem of unavoidable misjudgment.

[0132] The beneficial effect of this embodiment is that, by identifying that if the current pressed area is not within the determination range, the current touch event is determined to be a false touch event with a blocked response; and within the preset time range of generating the false touch event, if the pressed area of ​​another touch event is received within the determination range corresponding to the current sub-region or the shape of the current region, the other touch event is determined to be a touch event with a normal response. This provides a user-friendly self-learning anti-false touch solution with an immediate error correction method based on false judgment, allowing the device's false touch mechanism to be adaptively configured based on the hardware and software environment and operating environment, improving the detection efficiency and accuracy of false touch events and enhancing the user experience.

[0133] Embodiment 9

[0134] Based on the above embodiments, the present invention also proposes a self-learning anti-false touch device, which includes a memory, a processor, and a computer program stored on the memory and runnable on the processor. When the computer program is executed by the processor, the steps of the self-learning anti-false touch method as described in any one of the above items are implemented.

[0135] It should be noted that the above-mentioned device embodiment and method embodiment belong to the same concept, and their specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are applicable to the device embodiment, which will not be repeated here.

[0136] Example 10

[0137] Based on the above embodiments, the present invention also proposes a computer-readable storage medium, which stores a self-learning anti-false touch program. When the self-learning anti-false touch program is executed by a processor, the steps of the self-learning anti-false touch method as described in any one of the above items are implemented.

[0138] It should be noted that the above-mentioned medium embodiment and method embodiment belong to the same concept, and their specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are applicable to the medium embodiment, which will not be repeated here.

[0139] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0140] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0141] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0142] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A self-learning method for preventing accidental touch, characterized in that: The method comprises: In the learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is obtained, and the touch area is divided into at least two sub-areas according to the environmental information; monitoring a first number of touch events in each of the two sub-areas, and recording pressed area information corresponding to each of the touch events, wherein the pressed area information includes a pressed area and an area shape; Obtaining the average value of the pressed areas of the same sub-region and the same type of region shape by counting and calculating the preset error tolerance, and setting a determination range including the average value; During the application stage, the current area shape of the current touch event in any current sub-area is determined, and whether the current pressing area of ​​the current touch event is within the current sub-area and the judgment range corresponding to the current area shape is determined. If not, the current touch event is determined to be a false touch event with a shielded response.

2. The self-learning anti-false touch method according to claim 1, characterized in that: In the learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is obtained, and the touch area is divided into at least two sub-areas according to the environmental information, including: Preset hand shape information related to palm size and finger length, preset application information related to application type and operation type, and preset grip information related to horizontal and vertical screen states; A first manipulation feature corresponding to the hand shape information, a second manipulation feature corresponding to the application information, and a third manipulation feature corresponding to the holding information are preset respectively.

3. The self-learning anti-false touch method according to claim 2, characterized in that: In the learning phase, environmental information consisting of one or more of hand shape information, application information, and grip information is obtained, and the touch area is divided into at least two sub-areas according to the environmental information, further comprising: determining a corresponding manipulation type zone or a manipulation function zone according to one or more of the first manipulation feature, the second manipulation feature, and the third manipulation feature; The touch area is divided into at least two sub-areas according to the manipulation type partition or the manipulation function partition.

4. The self-learning anti-false touch method according to claim 3, characterized in that: The first number of touch events are monitored in the two sub-areas respectively, and the pressing area information corresponding to each touch event is recorded, wherein the pressing area information includes the pressing area and the area shape, including: When the touch events detected in any of the sub-areas reach the first quantity, recording a first quantity of the pressed areas and the area shapes; When all the sub-regions have completed recording of the first number of pressed areas and the region shapes, monitoring of the touch event is suspended.

5. The self-learning anti-false touch method according to claim 4, characterized in that: The obtaining of the average value of the pressed areas of the same sub-region and the same type of region shape by counting and using a preset error tolerance, and setting a determination range including the average value, includes: Acquire the error tolerance rate corresponding to the sub-region division state, and calculate the first number of pressed areas and the region shapes in each of the sub-regions based on the error tolerance rate; An average value of the pressed areas in the same sub-region and belonging to the same type of region shape is obtained, and the average value is set as a reference value.

6. The self-learning anti-false touch method according to claim 5, characterized in that: The method of obtaining the average value of the pressed areas of the same sub-region and the same type of region shape by counting and using a preset error tolerance rate, and setting a determination range including the average value, further includes: continuing to monitor the touch event with reference to the benchmark value, and stopping monitoring the touch event when all sub-regions have completed recording a second number of pressed areas and region shapes; The reference value is calibrated based on the second number of pressed areas and the region shape, and the determination range including a calibrated median value is set.

7. The self-learning anti-false touch method according to claim 6, characterized in that: In the application phase, determining a current area shape of a current touch event in any current sub-area, and determining whether a current pressed area of ​​the current touch event is within the current sub-area and within a determination range corresponding to the current area shape; if not, determining that the current touch event is a false touch event with a shielded response, includes: Determining the current sub-region, the current region shape, and the current pressed area corresponding to the current touch event; The determination range corresponding to both the current sub-region and the current region shape is acquired.

8. The self-learning anti-false touch method according to claim 7, characterized in that: In the application phase, determining the current area shape of a current touch event in any current sub-area, and determining whether the current pressed area of ​​the current touch event is within the current sub-area and within a determination range corresponding to the current area shape; if not, determining that the current touch event is a false touch event with a shielded response, further comprising: If the current pressed area is not within the determination range, determining that the current touch event is a false touch event with a shielded response; Within the preset time range of generating the false touch event, if the pressing area of ​​another touch event received is within the determination range corresponding to the current sub-area or the shape of the current area, the other touch event is determined to be a touch event of normal response.

9. A self-learning anti-mistouch device, characterized in that: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the self-learning anti-false touch method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a self-learning anti-false touch program, which, when executed by a processor, implements the steps of the self-learning anti-false touch method according to any one of claims 1 to 8.

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