Screen detection method and electronic device

Abstract

The application provides a screen detection method and an electronic device, relates to the technical field of display, and can accurately determine whether the screen is abnormal, so that the user can repair or replace the screen in time, and the influence on the user is reduced. The method can be applied to an electronic device including a screen, and the screen includes a detection area. In the method, the electronic device receives a first operation of a user, the first operation includes a sliding operation, and a sliding track of the sliding operation passes through the detection area. The electronic device obtains a data set corresponding to the sliding track, the data set includes data of track points on the sliding track, and the data of the track points includes capacitance data, resistance data or inductance data. The electronic device determines the state of the screen according to the data set.

Description

Technical Field

[0001] This application relates to the field of display technology, and in particular to a screen detection method and electronic device. Background Technology

[0002] During use, the flexible screen of foldable devices is frequently bent. Due to factors such as structure and materials, after a period of use, the flexible screen is prone to problems such as detachment and dents. These issues can affect the display of content on the flexible screen, causing inconvenience to users and thus impacting the user experience. Summary of the Invention

[0003] This application provides a screen detection method and electronic device, which can accurately determine whether there is an abnormality in the screen, allowing users to repair or replace the abnormal screen in a timely manner, reducing the impact on users.

[0004] Firstly, a screen detection method is provided. This method can be applied to electronic devices including a screen, wherein the screen includes a detection area. In this method, the electronic device receives a first operation from a user, wherein the first operation includes a swipe operation, and the swipe trajectory passes through the detection area. The electronic device acquires a dataset corresponding to the swipe trajectory, wherein the dataset includes data of trajectory points on the swipe trajectory, and the data of the trajectory points includes capacitance data, resistance data, or inductance data. The electronic device determines the state of the screen based on the dataset.

[0005] In the above method, the user can simply perform a swipe operation to detect the screen status. Furthermore, the screen status is determined based on the data obtained during the user's operation, which can obtain relatively accurate detection results. This allows the user to understand the screen status and promptly repair or replace any problematic screens, reducing the impact on the user.

[0006] In one possible implementation of the first aspect, during the process of determining the screen state based on a dataset, the electronic device first obtains predicted data for target trajectory points based on the dataset, wherein the target trajectory points include trajectory points corresponding to the minimum data in the dataset. If the deviation between the data corresponding to the target trajectory point in the dataset and the predicted data is within a preset deviation range, the electronic device determines that the screen state is normal; if the deviation between the data corresponding to the target trajectory point in the dataset and the predicted data is outside the preset deviation range, the electronic device determines that the screen state is abnormal. The dataset includes capacitance data or inductance data.

[0007] In the above implementation, the data corresponding to the target trajectory point in the dataset can represent the actual data detected at the target trajectory point. Comparing the actual data and the predicted data of the target trajectory point, if the deviation between the two is within a preset deviation range, it indicates that the predicted data and the actual data of the target trajectory point are not significantly different, or there may be an error. In this case, the difference between the two can be ignored, and the electronic device can determine that the screen state is normal. If the deviation between the actual data and the predicted data of the target trajectory point is outside the preset deviation range, it indicates that the predicted data and the actual data of the target trajectory point differ significantly. In this case, the difference between the two cannot be ignored, and the electronic device can determine that the screen state is abnormal.

[0008] In one possible implementation of the first aspect, the screen is a flexible screen, and the detection area includes a first region and a second region. The first region is located within the second region, and the axis of the bendable portion of the flexible screen passes through both the first and second regions. The dataset includes a first dataset and a second dataset. The first dataset includes data on trajectory points located within the first region on the sliding trajectory, and the second dataset includes data on trajectory points located within the second region on the sliding trajectory. In the process of obtaining predicted data for the target trajectory point based on the datasets, the electronic device determines the target trajectory point corresponding to the minimum value data in the first dataset and obtains the predicted data for the target trajectory point based on the maximum value data in the second dataset. The dataset includes capacitance data or inductance data.

[0009] In the above implementation method, the electronic device can also detect whether there are any abnormalities in the bendable part of the flexible screen.

[0010] In one possible implementation of the first aspect, during the process of determining the screen state based on a dataset, the electronic device first obtains predicted resistance data for target trajectory points, whereby the target trajectory points include trajectory points corresponding to the maximum resistance data in the dataset. If the deviation between the resistance data corresponding to the target trajectory point in the dataset and the predicted resistance data is within a preset deviation range, the electronic device determines that the screen state is normal; if the deviation between the resistance data corresponding to the target trajectory point in the dataset and the predicted resistance data is outside the preset deviation range, the electronic device determines that the screen state is abnormal. The dataset includes resistance data.

[0011] In the above implementation, the trajectory point corresponding to the maximum resistance data can represent the most severe or relatively severe location of screen abnormalities within the detection area; the trajectory point corresponding to the minimum resistance data can represent the least severe location or no abnormality within the detection area. When the screen surface is uneven or the screen has poor contact with the electronic device's body, the data corresponding to the sliding operation detected by the electronic device will change. Therefore, in the above implementation, the electronic device can more accurately detect whether there are abnormalities such as delamination, dents, creases, and cracks on the screen based on the trajectory point data.

[0012] In one possible implementation of the first aspect, the screen is a flexible screen, and the detection area includes a first region and a second region. The first region is located within the second region, and the axis of the bendable portion of the flexible screen passes through both the first and second regions. The dataset includes a first dataset and a second dataset. The first dataset includes data on trajectory points on the sliding trajectory located within the first region, and the second dataset includes data on trajectory points on the sliding trajectory located within the second region. In the process of obtaining predicted data for the target trajectory point based on the datasets, the electronic device determines the target trajectory point corresponding to the maximum value data in the first dataset and obtains the predicted data for the target trajectory point based on the minimum value data in the second dataset. The dataset includes resistance data.

[0013] In the above implementation method, the electronic device can also detect whether there are any abnormalities in the bendable part of the flexible screen.

[0014] In one possible implementation of the first aspect, when the dataset includes capacitance data or inductance data, the electronic device, in the process of obtaining the predicted data of the target trajectory point based on the dataset, performs interpolation processing on the target trajectory point based on the maximum value data in the dataset to obtain the predicted data of the target trajectory point.

[0015] In one possible implementation of the first aspect, when the dataset includes resistance data, the electronic device, in the process of obtaining predicted data of the target trajectory point based on the dataset, interpolates the target trajectory point based on the minimum value data in the dataset to obtain the predicted data of the target trajectory point.

[0016] In one possible implementation of the first aspect, during the process of determining the screen state based on the dataset, the electronic device acquires the dataset corresponding to the sliding trajectory for each of the multiple executed first operations. For each dataset in the multiple datasets, the electronic device acquires the screen state corresponding to that dataset, wherein the screen state is determined based on the dataset. Then, if the proportion of abnormal datasets in the multiple datasets is within a preset ratio range, the electronic device determines that the screen state is normal, wherein the screen state corresponding to the abnormal dataset is abnormal. If the proportion of abnormal datasets in the multiple datasets is outside the preset ratio range, the electronic device determines that the screen state is abnormal.

[0017] In the above implementation, the electronic device can combine the screen states corresponding to multiple datasets to determine the final screen state, which can reduce the impact of misjudgment on state detection and improve the accuracy of state detection.

[0018] In one possible implementation of the first aspect, the electronic device also collects sound data corresponding to the aforementioned sliding trajectory. This allows for further detection of the screen's state based on the sound data.

[0019] In one possible implementation of the first aspect, if the deviation between the data corresponding to the target trajectory point in the dataset and the predicted data is outside a preset deviation range, the electronic device obtains a first result for screen detection based on the sound data corresponding to the sliding trajectory. If the first result indicates that the screen state is abnormal, the electronic device determines that the screen state is abnormal. For example, the electronic device determines the screen state based on the spectral characteristics of the sound data, thus obtaining the first result.

[0020] In the above implementation method, the screen state is determined by combining data and sound, which can eliminate the possibility of misjudging the state by relying on the dataset and improve the accuracy of state detection.

[0021] In one possible implementation of the first aspect, during the process of determining the screen state based on the spectral characteristics of the sound data, the electronic device acquires a first sound set corresponding to an abnormal screen state and a second sound set corresponding to a normal screen state. If, based on the spectral characteristics of the first sound set, the electronic device determines that the spectral characteristics of the sound data match an abnormal screen state, the obtained first result indicates an abnormal screen state. If, based on the spectral characteristics of the second sound set, the electronic device determines that the spectral characteristics of the sound data match a normal screen state, the obtained first result indicates a normal screen state.

[0022] In the above implementation, if the screen has abnormalities such as delamination, dents, creases, or cracks, then high-frequency components will appear at specific positions on the spectrum of the sound data corresponding to the first operation. Therefore, the electronic device can determine whether the sound data matches the abnormal screen state based on the spectral characteristics of the sound data, thereby enabling more accurate screen state detection.

[0023] In one possible implementation of the first aspect, during the process of determining the state of the screen based on the dataset, if an indication of an abnormal screen state is obtained based on the sound data corresponding to the sliding trajectory, and / or an indication of an abnormal screen state is obtained based on the dataset, the electronic device ultimately determines that the screen state is abnormal.

[0024] In the above implementation method, the screen state is determined by combining data and sound, thereby further improving the accuracy of state detection.

[0025] In one possible implementation of the first aspect, when the noise in the environment where the electronic device is located is less than a preset noise, and / or when the electronic device does not change its posture under the action of the first operation, the electronic device then determines the state of the screen based on the sound data corresponding to the sliding trajectory.

[0026] In the above implementation, if the noise level of the environment in which the electronic device is located is less than the preset noise level, it means that the environment in which the electronic device is located is relatively quiet and the environmental noise will not have a significant impact on the status detection. In this case, the electronic device can continue to use the sound data.

[0027] Furthermore, if the electronic device does not change its posture under the action of the first operation, it means that the position or posture of the electronic device is relatively stable. In this case, when the user performs the first operation, the electronic device will not collide with other objects (such as a desktop) because of the first operation. Therefore, in this case, apart from the sound generated by the first operation itself, there is a high probability that there will be no other sounds such as collision sounds. Other sounds will not affect the state detection and the state detection can be performed more accurately.

[0028] In one possible implementation of the first aspect, if the sliding speed of the first operation (or sliding operation) is within a preset range, the electronic device acquires the dataset and / or sound data corresponding to the sliding trajectory.

[0029] In the above implementation, if the user's swipe is too slow or too fast, it is likely due to a user error, and the user's true intention may not be to detect the screen's state. Therefore, the electronic device can exclude cases where the swipe is too fast or too slow. Only if the swipe speed is within a preset range will the electronic device begin to acquire the aforementioned dataset and / or audio data, ensuring that data corresponding to the swipe trajectory is obtained when the user intends to detect the screen's state, thus making the data more accurate.

[0030] In one possible implementation of the first aspect, the electronic device receives a second operation from the user and, in response to the second operation, activates a status detection function; or, when the usage of the electronic device meets preset conditions, the electronic device activates the status detection function, wherein meeting the preset conditions includes the usage duration of the electronic device meeting a preset duration or the number of times the electronic device is used meeting a preset number of times.

[0031] In the above implementation, the status detection function of the electronic device can be triggered actively by the user or by the electronic device based on usage. When the user actively triggers the status detection function, the electronic device can promptly detect the screen status, allowing the user to understand the current screen status in a timely manner; when the electronic device automatically triggers the status detection function, it can also allow the user to understand the current screen status in a timely manner.

[0032] In one possible implementation of the first aspect, the method further includes: if the screen is in an abnormal state, the electronic device displays and / or broadcasts an abnormal prompt message. This allows the user to understand the screen's status more intuitively.

[0033] In one possible implementation of the first aspect, the abnormality prompt information includes at least one of the following: a prompt information indicating screen detachment, a prompt information indicating screen dents, a prompt information indicating screen creases, or a prompt information indicating screen cracks.

[0034] In a second aspect, an electronic device is provided, including a screen, the screen including a detection area; the electronic device further includes a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the screen detection method as described in the first aspect and any of its implementable embodiments.

[0035] Thirdly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform the screen detection method as described in the first aspect and any of its implementable embodiments.

[0036] Fourthly, a computer program product is provided that, when run on a computer, causes the computer to perform the screen detection method as described in the first aspect and any of its implementable embodiments.

[0037] The beneficial effects that the electronic device provided in the second aspect, the computer-readable storage medium provided in the third aspect, and the computer program product provided in the fourth aspect can achieve are similar to the beneficial effects that can be achieved in the first aspect and any of its implementations, and will not be repeated here. Attached Figure Description

[0038] Figure 1 Schematic diagram of the structure of the electronic device provided in the embodiments of this application Figure 1 ;

[0039] Figure 2 This is a schematic diagram of the axis of the flexible screen provided in the embodiments of this application;

[0040] Figure 3 Schematic diagram of the structure of the electronic device provided in the embodiments of this application Figure 2 ;

[0041] Figure 4 Schematic diagram of the structure of the electronic device provided in the embodiments of this application Figure 3 ;

[0042] Figure 5 Flowchart of the screen detection method provided in the embodiments of this application Figure 1 ;

[0043] Figure 6 A schematic diagram of operation A provided for the embodiments of this application Figure 1 ;

[0044] Figure 7 A schematic diagram of operation A provided for the embodiments of this application Figure 2 ;

[0045] Figure 8 A schematic diagram of the settings page provided in an embodiment of this application;

[0046] Figure 9 This application provides an illustration of the relationship between data and anomalies in its embodiments. Figure 1 ;

[0047] Figure 10 This application provides an illustration of the relationship between data and anomalies in its embodiments. Figure 2 ;

[0048] Figure 11 This application provides an illustration of the relationship between data and anomalies in its embodiments. Figure 3 ;

[0049] Figure 12 This is a schematic diagram of the abnormal feedback information of the foldable screen mobile phone provided in the embodiments of this application;

[0050] Figure 13 Flowchart of the screen detection method provided in the embodiments of this application Figure 2 ;

[0051] Figure 14 Flowchart of the screen detection method provided in the embodiments of this application Figure 3 ;

[0052] Figure 15 Flowchart of the screen detection method provided in the embodiments of this application Figure 4 ;

[0053] Figure 16 Flowchart of the screen detection method provided in the embodiments of this application Figure 5 ;

[0054] Figure 17 This is a schematic diagram of debonding and creases provided in an embodiment of this application;

[0055] Figure 18 Schematic diagram of the structure of the electronic device provided in the embodiments of this application Figure 4 . Detailed Implementation

[0056] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" are not necessarily different. Meanwhile, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present related concepts in a concrete manner for ease of understanding.

[0057] Furthermore, the business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0058] Flexible screens are a crucial component of foldable devices. Due to factors such as structure and materials, flexible screens are prone to issues like delamination and dents. These problems affect the display of content on the flexible screen, and the foldable device's response to user actions is often unresponsive, thus impacting the user experience.

[0059] Based on the above, this application provides a screen detection method applicable to electronic devices including a screen. The screen includes a detection area. In the screen detection method, the electronic device receives a first user operation, including a sliding operation whose trajectory passes through the detection area. The electronic device then acquires a dataset corresponding to the sliding trajectory, where the dataset includes data of trajectory points on the sliding trajectory, and the trajectory point data includes capacitance data, resistance data, or inductance data. The electronic device then determines the screen state based on the dataset.

[0060] In the above method, the user can simply perform a swipe operation to detect the screen status. Furthermore, the screen status is determined based on the data obtained during the user's operation, which can obtain relatively accurate detection results. This allows the user to understand the screen status and promptly repair or replace any problematic screens, reducing the impact on the user.

[0061] In some embodiments, the structure of the above-described electronic device can be seen as follows: Figure 1 As shown. The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, antenna 1, antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a microphone 170C, a sensor module 180, buttons 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, a magnetic sensor 180D, an accelerometer sensor 180E, a touch sensor 180K, etc.

[0062] The structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 100. In some embodiments of this application, the electronic device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements.

[0063] Processor 110 may include one or more processing units, such as: application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0064] In some embodiments, the electronic device 100 may implement the screen detection method in the embodiments of this application through the processor 110.

[0065] USB port 130 is a USB standard compliant interface, specifically a Mini USB port, Micro USB port, USB Type-C port, etc. USB port 130 can be used to connect a charger to charge electronic device 100, and can also be used for data transfer between electronic device 100 and peripheral devices. It can also be used to connect headphones for audio playback. This interface can also be used to connect other electronic devices, such as AR devices.

[0066] The charging management module 140 receives charging input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 receives charging input from the wired charger via a USB interface 130. In some wireless charging embodiments, the charging management module 140 receives wireless charging input via the wireless charging coil of the electronic device 100. While charging the battery 142, the charging management module 140 can also supply power to the electronic device 100 via the power management module 141.

[0067] The power management module 141 connects the battery 142, the charging management module 140, and the processor 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, providing power to the processor 110, internal memory 121, external memory, display 194, camera 193, and wireless communication module 160, etc. The power management module 141 can also monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage current, impedance). In some embodiments, the power management module 141 may also be located within the processor 110. In some embodiments, the power management module 141 and the charging management module 140 may also be located in the same device.

[0068] The wireless communication function of electronic device 100 can be realized through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor and baseband processor, etc.

[0069] Electronic device 100 implements display functions through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. Processor 110 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0070] Display screen 194 is used to display images, videos, etc. In some examples, the display screen (or screen) in electronic device 100 may be a flexible screen, and the unfolding and folding of electronic device 100 depends on the bendable portion on the flexible screen.

[0071] In some examples, the bendable portion of the flexible screen corresponds to a bending axis, around which the flexible screen bends, for example... Figure 2 (a) and Figure 2 As shown in (b) of the diagram.

[0072] The detection area of ​​the electronic device 100 can be any area on the screen or the entire screen. When the screen is a flexible screen, the axis can be within the detection area or not.

[0073] In some embodiments, if an abnormality or problem is found on the screen after the screen is detected, the screen may also display a prompt message to alert the user.

[0074] Electronic device 100 can perform shooting functions through ISP, camera 193, video codec, GPU, display 194 and application processor.

[0075] The ISP is used to process the data fed back by the camera 193. In some embodiments, the ISP may be located in the camera 193.

[0076] Camera 193 is used to capture still images or videos. In some embodiments, electronic device 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0077] The external storage interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 through the external storage interface 120 to perform data storage functions. For example, music, video, and other files can be saved on the external memory card.

[0078] Internal memory 121 can be used to store computer executable program code, which includes instructions. Processor 110 executes various functional applications and data processing of electronic device 100 by running the instructions stored in internal memory 121. Internal memory 121 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback, image playback, etc.), etc. The data storage area may store data created during the use of electronic device 100 (such as audio data, phonebook, etc.). Furthermore, internal memory 121 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0079] Electronic device 100 can implement audio functions such as music playback and recording through audio module 170, speaker 170A, microphone 170C, and application processor.

[0080] The audio module 170 is used to convert digital audio information into analog audio signals for output, and also to convert analog audio input into digital audio signals. The audio module 170 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 170 may be located in the processor 110, or some functional modules of the audio module 170 may be located in the processor 110.

[0081] The speaker 170A, also known as a "loudspeaker," is used to convert audio electrical signals into sound signals. The electronic device 100 can listen to music or make hands-free calls through the speaker 170A.

[0082] In some embodiments, if an abnormality or problem is found on the screen after the screen is detected, the speaker 170A may also broadcast a prompt message to alert the user.

[0083] Microphone 170C, also known as a "microphone" or "voice transducer," is used to convert sound signals into electrical signals. When making a phone call or sending a voice message, the user can speak by bringing their mouth close to microphone 170C, inputting the sound signal into microphone 170C. Electronic device 100 may be equipped with at least one microphone 170C.

[0084] In some embodiments, the electronic device 100 can acquire sound data corresponding to a swipe operation on the screen via the microphone 170C, so that the electronic device 100 can detect the screen based on the sound.

[0085] In some embodiments, the electronic device 100 may also acquire sound data of the environment in which the electronic device 100 is located via the microphone 170C in order to determine whether the ambient noise is too loud, thereby determining whether to detect the screen based on the sound.

[0086] Pressure sensor 180A is used to sense pressure signals and convert them into electrical signals. In some embodiments, pressure sensor 180A may be disposed on display screen 194. There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, and capacitive pressure sensors. A capacitive pressure sensor may include at least two parallel plates with conductive material. When a force is applied to pressure sensor 180A, the capacitance between the electrodes changes. Electronic device 100 determines the pressure intensity based on the change in capacitance. When a touch operation is applied to display screen 194, electronic device 100 detects the intensity of the touch operation based on pressure sensor 180A. Electronic device 100 may also calculate the touch position based on the detection signal from pressure sensor 180A.

[0087] In some embodiments, the electronic device 100 can detect sliding operations via a pressure sensor 180A. The electronic device 100 can also obtain capacitance, resistance, or inductance data corresponding to the sliding trajectory of the sliding operation on the screen via the pressure sensor 180A.

[0088] The gyroscope sensor 180B can be used to determine the motion attitude of the electronic device 100. In some embodiments, the angular velocity of the electronic device 100 about three axes (i.e., the x, y, and z axes) can be determined by the gyroscope sensor 180B.

[0089] In some embodiments, the electronic device 100 may detect sliding operations, the speed of sliding operations, the acceleration of sliding operations, etc., through a gyroscope sensor 180B.

[0090] In some embodiments, the electronic device 100 can also detect the attitude change of the electronic device 100 under the action of the sliding operation by the gyroscope sensor 180B.

[0091] The magnetic sensor 180D includes a Hall sensor. The electronic device 100 can use the magnetic sensor 180D to detect the opening and closing of the flip cover.

[0092] In some embodiments, the electronic device 100 can detect sliding operations, the speed of sliding operations, the acceleration of sliding operations, etc., through a magnetic sensor 180D.

[0093] In some embodiments, the electronic device 100 can also detect the change in attitude of the electronic device 100 under the action of the sliding operation by the magnetic sensor 180D.

[0094] The 180E accelerometer can detect the magnitude of acceleration of electronic device 100 in various directions (typically three axes). When electronic device 100 is stationary, it can detect the magnitude and direction of gravity. It can also be used to identify the posture of electronic devices and applied to applications such as screen orientation switching and pedometers.

[0095] In some embodiments, the electronic device 100 may detect sliding operations, the speed of sliding operations, the acceleration of sliding operations, etc., through the accelerometer 180E.

[0096] In some embodiments, the electronic device 100 can also detect the attitude change of the electronic device 100 under the action of the sliding operation by the accelerometer 180E.

[0097] Touch sensor 180K, also known as a "touch panel," can be located on display screen 194. The touch sensor 180K and display screen 194 together form a touchscreen, also known as a "touchscreen." Touch sensor 180K detects touch operations applied to or near it. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through display screen 194. In some embodiments, touch sensor 180K may also be located on the surface of electronic device 100, in a different position than display screen 194.

[0098] In some embodiments, the electronic device 100 can detect a sliding operation via a touch sensor 180K.

[0099] Buttons 190 include a power button, volume buttons, etc. Motor 191 can generate vibration alerts. Motor 191 can be used for incoming call vibration alerts or for touch vibration feedback. Indicator 192 can be an indicator light, used to indicate charging status, battery level changes, or to indicate messages, missed calls, notifications, etc. SIM card interface 195 is used to connect a SIM card.

[0100] The electronic device provided in this application embodiment can run an operating system (OS). This operating system can be various operating systems used in the industry, such as an operating system based on OpenHarmony, like HarmonyOS; or other operating systems such as Android. TM An operating system can refer to the iOS mobile operating system; it can also refer to various open-source operating systems or their derivatives, such as Linux OS and other embedded operating systems; or it can refer to future new operating systems, such as AI operating systems based on artificial intelligence. An operating system is a set of interconnected system software programs that manage and control the operation of electronic devices, utilize and run hardware and software resources, and provide public services to organize user interactions. In electronic devices, the operating system connects downwards to the physical devices at the hardware layer and upwards to provide a runtime environment for application software.

[0101] An operating system typically includes a kernel layer, a middleware layer, and an application layer. The application layer includes applications, which can include system applications and third-party applications. The middleware layer includes a suite of software providing various services to application developers, or frameworks providing services such as databases, multimedia, and graphics, or capabilities such as distributed scheduling and system scaling. For example, the middleware layer may include a framework layer and / or a system service layer. The framework layer provides application programming interfaces (APIs) and programming frameworks for applications in the application layer. The system service layer includes the system's core capabilities, providing services to applications through the framework layer. The kernel layer is the layer between hardware and software. The kernel layer may include hardware drivers and the operating system kernel. In addition to providing hardware drivers, the kernel layer also supports functions such as memory management and system process management.

[0102] The electronic devices we use in our daily lives come in various types and forms, and are applied in a wide range of scenarios. Therefore, based on the different forms and functions of electronic devices, different application scenarios, and different user needs, the operating systems used in these devices may also differ. The basic functions implemented by the electronic devices provided in this application embodiment can be implemented using a general-purpose operating system or a dedicated operating system. To more clearly illustrate the implementation of this application embodiment under a specific operating system, the architecture of HarmonyOS is shown below. Those skilled in the art can deduce the implementation of this application embodiment under other specific operating systems, such as Android. TM Implementation under operating systems, etc.

[0103] The software architecture of an electronic device can be divided into several layers. In some embodiments, see [link to documentation]. Figure 3As shown, from bottom to top, the layers are: kernel layer, system service layer, framework layer, and application layer. Layers communicate with each other through software interfaces. System functions can be tailored, added, or combined at the subsystem level depending on the deployment scenario of different device types, and each subsystem can also be tailored, added, or combined at the functional level.

[0104] The kernel layer includes the kernel abstraction layer, the kernel subsystem, and the driver subsystem.

[0105] The kernel abstract layer (KAL) provides basic kernel capabilities to upper layers by shielding the differences between multiple kernels, including but not limited to process / thread management, memory management, file system, network management, and peripheral device management.

[0106] Kernel Subsystem: Supports the selection of a suitable OS kernel for different resource-constrained devices, including but not limited to Linux kernel, HarmonyOS kernel, LiteOS (Lite Operating System), etc.

[0107] Driver Subsystem: The driver framework is the foundation for the open system hardware ecosystem, providing unified peripheral access capabilities and a framework for driver development and management. The driver framework includes: display drivers, camera drivers, audio drivers, Bluetooth drivers, sensor drivers, etc.

[0108] The system service layer comprises the core capabilities of the system, providing services to applications through the framework layer. This layer includes, but is not limited to, the following subsystems:

[0109] The system's basic capability subsystem set provides fundamental capabilities for the operation, scheduling, and migration of distributed applications across multiple devices. This set may include distributed soft bus, distributed data management, distributed task scheduling, and Ark multi-language runtime; it may also include multi-modal input subsystem, graphics subsystem, security subsystem, and AI business subsystem.

[0110] Basic software service subsystem set: provides public and general software services; the basic software service subsystem set may include event notification subsystem, telephone service subsystem, multimedia subsystem, etc.

[0111] Enhanced software service subsystem suite: Provides differentiated enhanced software services for different devices; the enhanced software service subsystem suite may include smart screen proprietary business subsystem, wearable proprietary business subsystem, IoT proprietary business subsystem, etc.

[0112] Hardware service subsystem set: Provides hardware services; the hardware service subsystem set may include location service subsystem, unified identity and access management (IAM) subsystem, wearable proprietary hardware service subsystem, biometric identification, IoT proprietary hardware service subsystem, etc.

[0113] Distributed task scheduling enables distributed service management (discovery, synchronization, registration, and invocation), supporting remote startup, remote invocation, remote connection, and migration of applications across devices.

[0114] Distributed data management enables data synchronization, data storage, data sharing, and data access across all scenarios and devices.

[0115] The distributed soft bus provides communication-related capabilities for seamless interconnection between multiple devices, including: WLAN service capabilities, Bluetooth service capabilities, soft bus, inter-process communication RPC (Remote Procedure Call), and StarFlash communication capabilities.

[0116] Ark Multilingual Runtime is a unified compilation runtime platform designed to support the joint compilation and execution of multiple programming languages ​​and multiple chip platforms.

[0117] The framework layer provides application programming interfaces (APIs) and programming frameworks for applications in the application layer. The framework layer includes: the ArkUI framework (which provides a complete infrastructure for UI development of system applications, including UI functions such as components, layouts, animations, and interactive events, as well as a real-time interface preview tool), the user application framework, and the Ability framework (an Ability is a lightweight application; the Ability framework schedules and manages the operation and lifecycle of Abilities). Different devices may have different operating systems, and the APIs they support may also differ.

[0118] The HarmonyOS API is a series of open capabilities provided to support HarmonyOS application development. The HarmonyOS API can be set at the framework layer or independently of the framework layer. The HarmonyOS API includes the Audio API (audio service), Push API (push service), and Account API (account service), among others.

[0119] Applications in the application layer can include system applications and extended / third-party applications. System applications can include the desktop, control bar, settings, contacts, phone, camera, etc., while extended / third-party applications can include social applications, travel applications, etc.

[0120] In some embodiments, the structure of the electronic device may also be referred to Figure 4 As shown in the figure. The electronic device may include a touch unit, a storage unit, a computing unit, a speaker, a display unit, and a microphone.

[0121] The touch unit receives swiping operations performed on the screen and collects capacitance signals (or capacitance data), inductance signals (or inductance data), and resistance signals (or resistance data) corresponding to the swiping trajectory as it passes through the screen detection area. In some examples, the touch unit can be the multi-modal input subsystem in HarmonyOS. The multi-modal input subsystem integrates input from multiple dimensions. Specifically, it receives device input events, such as those from keyboards, mice, touchscreens, and touchpads, based on the kernel subsystem and driver framework. After normalizing and standardizing the input events, it distributes them to the ArkUI framework. The ArkUI framework then encapsulates the events and forwards them to the application, or distributes the events to the application through other interfaces.

[0122] The storage unit is used to buffer or store sound collected by the microphone, capacitance signals (or capacitance data) collected by the touch unit, and detection results from the screen.

[0123] The display unit is used to display prompts when there are problems or abnormalities on the screen. The prompts can be text, images, or other content.

[0124] The computing unit is used to detect the screen using signals (or data) collected by the touch unit and sound data collected by the microphone, and to determine whether there are any abnormalities on the screen, such as delamination, dents, creases, cracks, etc.

[0125] A speaker is used to broadcast prompts when there are problems or abnormalities on the screen.

[0126] A microphone is used to collect sound data of the environment in which the electronic device is located, and / or to collect sound data corresponding to the vertical swipe of a finger across the detection area of ​​the screen.

[0127] When the electronic device detects the screen status, the user can simply perform a swipe operation. Furthermore, the electronic device determines the screen status based on the data obtained during the user's operation, which can obtain relatively accurate detection results. This allows the user to understand the screen's status and promptly repair or replace any problematic screens, reducing the impact on the user.

[0128] The electronic devices in this application can be mobile phones, tablets, smartwatches, in-vehicle terminals, handheld computers, laptops, personal computers (PCs), ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), augmented reality (AR) / virtual reality (VR) devices, smart home devices (such as smart TVs, smart screen devices, and smart central control devices), and other devices with a display screen or screen. When the screen is flexible, the electronic device can also be a foldable phone, foldable computer, foldable watch, or other terminal device with a flexible screen. This application does not impose any special limitations on the specific form of the electronic device.

[0129] The following description uses the application of the above-described screen detection method to a foldable screen phone as an example to illustrate the screen detection method provided in this application. The foldable screen phone includes a flexible screen.

[0130] In some embodiments, see Figure 5 As shown, the screen detection method described above may include the following steps S501-S503.

[0131] S501, Foldable screen phone receives user operation A.

[0132] The foldable phone's screen includes a detection area. For example, this detection area can be any region of the screen, or the entire screen, allowing the foldable phone to detect any anomalies on the screen.

[0133] In some examples, when testing flexible screens, the bendable portion of the screen is frequently used and prone to anomalies. Therefore, the axis of the bendable portion can be within the detection area, allowing the foldable phone to detect anomalies in the area near the axis. For instance, when the axis is horizontal, the two lateral edges of the detection area can each be 8mm from the axis, meaning the vertical length of the detection area can be 16mm, with the axis dividing the detection area evenly. As another example, the lateral length of the detection area can be equal to the screen width of the foldable phone.

[0134] Operation A can be the first operation in the foregoing embodiments. The first operation can include a sliding operation, the sliding trajectory of which passes through the detection area. For example, the sliding trajectory passing through the detection area can be longitudinal, such as... Figure 6 The sliding motion shown in (a) from bottom to top can also be, for example... Figure 6The sliding motion shown in (b) is from top to bottom; or, the sliding trajectory may also have a vertical component, for example... Figure 7 The slide to the upper right shown in (a) can also be, for example... Figure 7 The sliding trajectory shown in (b) is a curve.

[0135] For another example, the sliding trajectory can cross the detection area laterally, such as sliding from left to right, or sliding from right to left; or the sliding trajectory can also have a component in the horizontal direction, such as sliding to the upper right.

[0136] In some possible implementations, the screen detection method described above can be performed after the foldable phone has its status detection function enabled. There are several ways to enable the status detection function.

[0137] In some examples, foldable phones can passively activate state detection functionality in response to user actions. Specifically, the foldable phone can receive user action B and, in response to action B, activate the state detection function.

[0138] Operation B can be a second operation, which may include clicking the switch control, selecting the detection function, etc.

[0139] For example, see Figure 8 As shown, after the foldable phone displays the settings page, the settings page includes a detection function switch 801. The user's second action can be to click the detection function switch 801. After performing the second action, the detection function switch 801 turns on, and the foldable phone enables the status detection function. Alternatively, after performing the second action, the detection function switch 801 turns off, and the foldable phone disables the status detection function.

[0140] In some examples, foldable phones can proactively activate the status detection function when current usage conditions meet preset criteria. These preset criteria may include, but are not limited to, usage duration meeting a preset time limit or usage frequency meeting a preset number of times. In other words, a foldable phone can activate the status detection function when the usage duration meets a preset time limit (e.g., 3 months, 6 months, etc.) or when the usage frequency meets a preset number of times (e.g., 500 times, 1000 times, etc.).

[0141] In the above implementation, the status detection function of the foldable screen phone can be triggered actively by the user or by the foldable screen phone itself based on usage. After the user actively triggers the status detection function, the foldable screen phone can promptly detect the screen's status, allowing the user to understand the current state of the screen; the foldable screen phone can also promptly understand the current state of the screen by automatically triggering the status detection function.

[0142] In some examples, the aforementioned state detection function or screen detection method can be executed by a process in the foldable phone.

[0143] S502, Foldable screen phone obtains the dataset corresponding to the sliding trajectory of operation A.

[0144] The dataset includes data on trajectory points along the sliding trajectory, and the data on these trajectory points can include capacitance data, resistance data, or inductance data.

[0145] In some possible implementations, after receiving operation A, the foldable phone can also detect the speed of operation A, that is, detect the sliding speed of the swipe operation. If the sliding speed of operation A is within a preset range, it means that the sliding speed is neither too fast nor too slow, and thus the foldable phone can obtain the dataset corresponding to the sliding trajectory.

[0146] In some examples, the above preset range can be [0.16m / s, 1.6m / s]. That is, if the sliding speed is less than 0.16m / s, it means that the sliding operation is too slow, and if the sliding speed is greater than 1.6m / s, it means that the sliding operation is too fast.

[0147] In the above implementation, a swipe operation that is too slow or too fast is likely due to user error, and the user's true intention may not be to perform an operation related to screen state detection. Alternatively, in some cases, a swipe operation that is too fast may prevent the foldable phone from detecting data (such as capacitance data), thus affecting the state detection results. Therefore, foldable phones can exclude situations where the swipe operation is too fast or too slow. If the swipe speed is within a preset range, the foldable phone will begin acquiring the aforementioned dataset, ensuring that data corresponding to the swipe trajectory is obtained when the user intends to perform screen state detection, making the data more accurate.

[0148] The S503 foldable phone determines the screen state based on a dataset.

[0149] The screen's state can be either normal or abnormal. For example, an abnormal screen state can include at least one of the following: screen detachment, screen dent, screen crease, or screen crack.

[0150] In some possible implementations, if the screen is in a normal state, the trajectory points in the dataset will not change, or the change will be insignificant. If the screen is in an abnormal state, the trajectory points in the dataset will change significantly.

[0151] In some possible implementations, the trajectory points on the aforementioned sliding trajectory may include target trajectory points. For example, the target trajectory points may include trajectory points corresponding to the maximum value data in the aforementioned dataset, or trajectory points corresponding to the minimum value data in the aforementioned dataset. The foldable phone can obtain predicted data for the target trajectory points based on the acquired dataset. And based on the deviation between the data corresponding to the target trajectory points in the dataset and the predicted data, the screen state is determined.

[0152] When the target trajectory point includes the trajectory point corresponding to the maximum value in the above dataset, the foldable screen phone can interpolate the target trajectory point based on the minimum value in the dataset to obtain the predicted data of the target trajectory point; when the target trajectory point includes the trajectory point corresponding to the minimum value in the above dataset, the foldable screen phone can interpolate the target trajectory point based on the maximum value in the dataset to obtain the predicted data of the target trajectory point.

[0153] Specifically, if the deviation between the target trajectory point's corresponding data in the dataset and the predicted data is within a preset deviation range, the foldable phone can determine that the screen is functioning normally. If the deviation between the target trajectory point's corresponding data in the dataset and the predicted data is outside the preset deviation range, the foldable phone can determine that the screen is functioning abnormally.

[0154] In some possible implementations, if the data (or dataset) of the aforementioned trajectory points includes capacitance or inductance data, then the target trajectory points include the trajectory points corresponding to the minimum values ​​in the dataset. If there are anomalies in the positions traversed by the sliding trajectory, the data value of the trajectory point corresponding to that position will decrease. For example, see [link to relevant documentation] for details on data changes. Figure 9 The trend of the line shown in (a) is shown in the figure.

[0155] Taking capacitance data as an example, when a foldable phone determines the screen state based on a dataset, it can obtain the minimum and maximum capacitance data from the dataset. The foldable phone then identifies the trajectory point corresponding to the minimum capacitance data as the target trajectory point. Next, the foldable phone uses the maximum capacitance data to interpolate the target trajectory point, obtaining predicted data for the target trajectory point. Finally, the foldable phone determines whether the deviation between the target trajectory point data (i.e., the minimum capacitance data) and the predicted data is within a preset deviation range, thereby determining the screen state.

[0156] For example, there can be one minimum capacitance data point, and at least two maximum capacitance data points, if the sliding trajectory is as follows: Figure 9 As shown in (b), if the sliding operation passes through the detection area, there are two maximum capacitance data. The maximum capacitance data can represent the capacitance data corresponding to the sliding operation received when no abnormality occurs, which is located within or near the detection area. For example, the trajectory points corresponding to the maximum capacitance data are located near the position where the sliding trajectory enters the detection area and near the position where the sliding trajectory slides out of the detection area.

[0157] The trajectory points corresponding to the minimum data (e.g., minimum capacitance data) can represent the most severe or relatively severe location of screen abnormalities within the detection area; the trajectory points corresponding to the maximum data (e.g., maximum capacitance data) can represent the least severe location or no location of screen abnormalities within the detection area. Taking screen detachment as an example, the trajectory points corresponding to the minimum data can represent the most severe detachment location, and the trajectory points corresponding to the maximum data can represent the edge points of detachment.

[0158] For example, see Figure 9 As shown in (b) in the figure, the dashed area on the screen can represent the debonding area; Figure 9 On the line shown in (a), point a is the trajectory point corresponding to the minimum value data (i.e., the target trajectory point). In other words, point a corresponds to... Figure 9 Point A is the most severely affected location within the debonding area shown in (b); points b and c on the line are the trajectory points corresponding to the maximum value data, that is, points b and c correspond to... Figure 9 Points B and C are shown at the edge of the debonded area in (b).

[0159] After interpolating the maximum value data, foldable screen phones can obtain the predicted data (or the predicted data when no anomaly occurs at the trajectory point) corresponding to the minimum value data (i.e., the target trajectory point). For example, foldable screen phones can predict the data of the location where the screen delamination is most severe and compare the predicted data with the actual data (minimum value data) of that point to determine the state of the screen.

[0160] In the embodiments of this application, the following is adopted: Figure 9 The state of the screen can be determined by the two maximum capacitance (or inductance) values ​​and one minimum capacitance (or inductance) value shown in (b).

[0161] In some examples, a single swipe operation can cross the detection area multiple times, and the swipe trajectory can pass through abnormal positions multiple times. In such cases, a foldable phone can obtain two maximum values ​​and one minimum value for each swipe that crosses the detection area. For example... Figure 9As shown in (c), trajectory points a, b, and c are the trajectory points corresponding to a sliding process across the detection area. Trajectory point a corresponds to the minimum capacitance data during the sliding process, and trajectory points b and c correspond to the maximum capacitance data during the sliding process. Trajectory points a, b, and c can respectively correspond to... Figure 9 Points A, B, and C are shown in (d) within the debonding area. Trajectory points a1, b1, and c1 are the trajectory points corresponding to a single sliding process across the detection area. Trajectory point a1 corresponds to the minimum capacitance data during this sliding process, while trajectory points b1 and c1 correspond to the maximum capacitance data. Trajectory points a1, b1, and c1 can respectively correspond to... Figure 9 Points A1, B1, and C1 are shown in (d) within the debonding region.

[0162] For each sliding process across the detection zone, the foldable phone can acquire a minimum capacitance value and two maximum capacitance values. Based on these values, the phone can determine the predicted capacitance value of the target trajectory point corresponding to the minimum capacitance value and whether the deviation between the minimum and predicted capacitance values ​​is within a preset range. This determines the screen state for each sliding process. If the proportion of sliding processes with abnormal screen states is greater than or equal to a third proportion, the phone can definitively determine that the screen state is abnormal. If the proportion is less than the third proportion, the phone can definitively determine that the screen state is normal. The third proportion can be between 30% and 50%, or it can include 30%, 40%, or 50%, etc.

[0163] When the screen surface is uneven or the screen does not make good contact with the body of the foldable phone, the data corresponding to the sliding operation detected by the foldable phone will change. Therefore, in the above implementation method, the foldable phone can more accurately detect whether there are abnormalities such as delamination, dents, creases, and cracks on the screen based on the data.

[0164] In some examples, the aforementioned deviations may include absolute deviation, relative deviation, percentage deviation, standard deviation, error, etc. Taking relative deviation or percentage deviation as an example, the preset deviation range may be [0, 5%], [0, 10%], etc.

[0165] The target trajectory point data consists of the actual data detected at that point. Comparing the actual data with the predicted data, if the deviation between the actual and predicted data is within a preset deviation range, it indicates that the predicted and actual data are not significantly different, or there may be some error. In this case, the difference can be ignored, and the foldable phone can determine that the screen is functioning normally. Conversely, if the deviation between the actual and predicted data is outside the preset deviation range, it indicates that the predicted and actual data differ significantly. In this case, the difference cannot be ignored, and the foldable phone can determine that the screen is functioning abnormally.

[0166] In some examples, the aforementioned deviation may also include the ratio between the predicted data and the actual data (minimum value data) of the target trajectory point. In this case, the preset deviation range is a preset threshold. The foldable phone can determine whether the ratio between the two is less than the preset threshold. If it is less than the preset threshold, it indicates that the predicted data and the actual data are not significantly different, or there may be an error. In this case, the difference between the two can be ignored, and the foldable phone can determine that the screen is in a normal state. However, if the ratio between the two is greater than the preset threshold, it indicates that the predicted data and the actual data differ significantly. In this case, the difference between the two cannot be ignored, and the foldable phone can determine that the screen is in an abnormal state.

[0167] In some examples, the preset threshold can be in the range of 90%-95%, or the preset threshold can include 95% or 90%, etc.

[0168] In some examples, there may be multiple minimum capacitance data points. In this case, the foldable phone can select any one of the trajectory points corresponding to the multiple minimum capacitance data points as the target trajectory point. Alternatively, the foldable phone can calculate the aforementioned deviation for each trajectory point corresponding to the minimum capacitance data point and vote on the obtained deviations. For example, for each deviation, it can determine whether it is within a preset deviation range. If the proportion of deviations within the preset deviation range is greater than or equal to a second proportion, then the deviation is ultimately determined to be within the preset deviation range, and the screen is in normal condition. If the proportion of deviations within the preset deviation range is less than the second proportion, then the deviation is ultimately determined to be outside the preset deviation range, and the screen is in abnormal condition. The second proportion can be in the range of 40%-60%, or it can include 40%, 50%, or 60%, etc.

[0169] In the above implementation, the foldable screen phone can further determine the screen state based on the deviation between the predicted data and the actual data (minimum value data). In the process of determining the screen state, some cases with errors will also be eliminated, thereby making the obtained state detection results more accurate.

[0170] In some possible implementations, if the data (or dataset) of the aforementioned trajectory points includes resistance data, then the target trajectory points include the trajectory points corresponding to the minimum values ​​in the dataset. If there are anomalies in the positions traversed by the sliding trajectory, the data value of the trajectory point corresponding to that position will increase. For example, see [link to relevant documentation] for details on data changes. Figure 10 The trend of the line shown in (a) is shown in the figure.

[0171] Taking resistance data as an example, when a foldable phone determines the screen state based on a dataset, it can obtain the minimum and maximum resistance data from the dataset. The foldable phone then identifies the trajectory point corresponding to the maximum resistance data as the target trajectory point. Next, the foldable phone uses the minimum resistance data to interpolate the target trajectory point, obtaining predicted data for the target trajectory point. Finally, the foldable phone determines whether the deviation between the target trajectory point data (i.e., the maximum resistance data) and the predicted data is within a preset deviation range, thereby determining the screen state.

[0172] For example, there can be one maximum resistance data and multiple minimum resistance data. The minimum resistance data can represent the resistance data corresponding to the sliding operation received when no abnormality occurs, located within or near the detection area. For example, the trajectory points corresponding to the minimum resistance data are located near the position where the sliding trajectory enters the detection area and near the position where the sliding trajectory slides out of the detection area.

[0173] The trajectory points corresponding to the maximum value data (e.g., maximum resistance data) can represent the most severe or relatively severe location points within the detection area when screen abnormalities occur; the trajectory points corresponding to the minimum value data (e.g., minimum resistance data) can represent the least severe location points within the detection area when screen abnormalities occur, or locations where no abnormalities occur. Taking screen detachment as an example, the trajectory points corresponding to the maximum value data can represent the location points where detachment is most severe, and the trajectory points corresponding to the minimum value data can represent the edge points of detachment.

[0174] For example, see Figure 10 As shown in (b) in the figure, the dashed area on the screen can represent the debonding area; Figure 10 On the line shown in (a), point d is the trajectory point corresponding to the maximum value data (i.e., the target trajectory point). In other words, point d corresponds to... Figure 10 Point D is the most severe location within the debonding area shown in (b); points e and f on the line are the trajectory points corresponding to the minimum data, that is, points e and f correspond to... Figure 10 The locations E and F at the edge of the debonded area are shown in (b) in the diagram.

[0175] After interpolating the minimum value data, foldable screen phones can obtain the predicted data (or the predicted data when no anomaly occurs at the trajectory point) corresponding to the maximum value data (i.e., the target trajectory point). For example, foldable screen phones can predict the data of the location where the screen delamination is most severe and compare the predicted data with the actual data (maximum value data) of that point to determine the state of the screen.

[0176] In the embodiments of this application, the following is adopted: Figure 10 The state of the screen can be determined by the two minimum resistance values ​​and one maximum resistance value shown in (b).

[0177] In some examples, a single swipe operation can cross the detection area multiple times, and the swipe trajectory can pass through abnormal positions multiple times. In this case, the foldable screen phone can obtain two minimum resistance data and one maximum resistance data for each swipe process that crosses the detection area.

[0178] For each sliding process across the detection zone, the foldable phone can acquire one maximum resistance value and two minimum resistance values. Based on these values, the phone can determine the predicted resistance data for the target trajectory point corresponding to the maximum resistance value and whether the deviation between the maximum and predicted resistance values ​​is within a preset range. This determines the screen state for each sliding process. If the proportion of sliding processes with abnormal screen states is greater than or equal to a third proportion, the phone can definitively determine that the screen state is abnormal. If the proportion is less than the third proportion, the phone can definitively determine that the screen state is normal. The third proportion can be between 30% and 50%, or it can include 30%, 40%, or 50%, etc.

[0179] When the screen surface is uneven or the screen does not make good contact with the body of the foldable phone, the data corresponding to the sliding operation detected by the foldable phone will change. Therefore, in the above implementation method, the foldable phone can more accurately detect whether there are abnormalities such as delamination, dents, creases, and cracks on the screen based on the data.

[0180] In some examples, the aforementioned deviations may include absolute deviation, relative deviation, percentage deviation, standard deviation, error, etc. Taking relative deviation or percentage deviation as an example, the preset deviation range may be [0, 5%], [0, 10%], etc.

[0181] The target trajectory point data consists of the actual data detected at that point. Comparing the actual data with the predicted data, if the deviation between the actual and predicted data is within a preset deviation range, it indicates that the predicted and actual data are not significantly different, or there may be some error. In this case, the difference can be ignored, and the foldable phone can determine that the screen is functioning normally. Conversely, if the deviation between the actual and predicted data is outside the preset deviation range, it indicates that the predicted and actual data differ significantly. In this case, the difference cannot be ignored, and the foldable phone can determine that the screen is functioning abnormally.

[0182] In some examples, the aforementioned deviation may also include the ratio between the predicted data and the actual data (maximum value data) of the target trajectory point. In this case, the preset deviation range is a preset threshold. The foldable phone can determine whether the ratio between the two is less than the preset threshold. If it is less than the preset threshold, it indicates that the predicted data and the actual data are not significantly different, or there may be an error. In this case, the difference between the two can be ignored, and the foldable phone can determine that the screen is in a normal state. However, if the ratio between the two is greater than the preset threshold, it indicates that the predicted data and the actual data differ significantly. In this case, the difference between the two cannot be ignored, and the foldable phone can determine that the screen is in an abnormal state.

[0183] In some examples, the preset threshold can be in the range of 90%-95%, or the preset threshold can include 95% or 90%, etc.

[0184] In some examples, there may be multiple maximum resistance data points. In this case, the foldable phone can select any one of the trajectory points corresponding to the multiple maximum resistance data points as the target trajectory point. Alternatively, the foldable phone can calculate the aforementioned deviation for each trajectory point corresponding to the maximum resistance data point and vote on the obtained deviations. For example, for each deviation, it can determine whether it is within a preset deviation range. If the proportion of deviations within the preset deviation range is greater than or equal to a second proportion, then the deviation is ultimately determined to be within the preset deviation range, and the screen is in normal condition. If the proportion of deviations within the preset deviation range is less than the second proportion, then the deviation is ultimately determined to be outside the preset deviation range, and the screen is in abnormal condition. The second proportion can be in the range of 40%-60%, or it can include 40%, 50%, or 60%, etc.

[0185] In the above implementation, the foldable screen phone can further determine the screen state based on the deviation between the predicted data and the actual data (maximum value data). In the process of determining the screen state, some cases with errors will also be eliminated, thereby making the obtained state detection results more accurate.

[0186] In some possible implementations, when a foldable phone detects the screen, the detection area can be further divided, for example, into at least two nested regions. The foldable phone obtains the maximum and minimum values ​​of the trajectory points in different regions from the dataset, and determines the predicted data for the target trajectory point based on the maximum and minimum values ​​in different regions.

[0187] For example, the detection area includes a first region and a second region, the first region is within the second region, and the dataset includes a first dataset and a second dataset. The first dataset includes data of trajectory points on the sliding trajectory that are located within the first region, and the second dataset includes data of trajectory points on the sliding trajectory that are located within the second region.

[0188] Taking the trajectory point data (or dataset) including capacitance data or inductance data as an example, if the maximum value of the trajectory point in the second region is greater than the maximum value in other regions, then the edge of the second region may be the edge of the debonding region. If the minimum value of the trajectory point in the first region is less than the minimum value in other regions, then the first region may be the region with more severe debonding.

[0189] Then, the foldable phone determines the target trajectory point corresponding to the minimum value data in the first dataset, and obtains the predicted data of the target trajectory point based on the maximum value data in the second dataset.

[0190] Taking the data (or dataset) of trajectory points including resistance data as an example, if the minimum value of the trajectory points on the sliding trajectory in the second region is less than the minimum value in other regions, then the edge of the second region may be the edge of the debonding region. If the maximum value of the trajectory points on the sliding trajectory in the first region is greater than the maximum value in other regions, then the first region may be the region with more severe debonding.

[0191] Then, the foldable phone determines the target trajectory point corresponding to the maximum value in the first dataset, and obtains the predicted data of the target trajectory point based on the minimum value in the second dataset.

[0192] In some examples, where the trajectory point data (or dataset) includes capacitance data, when a foldable phone detects the area near the axis of the flexible screen's bendable portion, it can further divide the detection area into a first region and a second region. Both the first and second regions are smaller than the detection area, and the first region is located within the second region. The axis of the flexible screen's bendable portion passes through both the first and second regions. For example, see [link to example]. Figure 11As shown in (a), the solid line area can represent the detection area, the first area, and the second area. The two lateral edges of the detection area can each be 8 mm from the axis, meaning the longitudinal length of the detection area can be 16 mm, and the axis divides the detection area evenly. The two lateral edges of the first area can each be 2 mm from the axis; the two lateral edges of the second area can each be 6 mm from the axis.

[0193] Next, the foldable phone can determine the target trajectory point corresponding to the minimum capacitance data in the first dataset. Then, based on the maximum capacitance data in the second dataset, the foldable phone obtains the predicted capacitance data for the target trajectory point, for example, by interpolating the maximum capacitance data in the second dataset. Finally, the foldable phone determines the state of the flexible screen based on the ratio of the predicted capacitance data to the actual capacitance data of the target trajectory point (i.e., the minimum capacitance data).

[0194] Taking the abnormal state of a flexible screen as an example of debonding, the trajectory point corresponding to the minimum capacitance data can represent the location where debonding is most severe, and the trajectory point corresponding to the maximum capacitance data can represent the edge point of debonding.

[0195] For example, see Figure 11 As shown in (a) in the figure, the dashed area within the detection area can represent the debonding area; Figure 11 The line shown in (b) in the dataset can represent the changes in capacitance data of trajectory points in the dataset. Figure 11 On line (b) shown in the diagram, point g is the trajectory point corresponding to the minimum capacitance data in the first region. In other words, point g corresponds to... Figure 11 Point G is the location of the most severe debonding in the first region shown in (a); points h and m on the line are the trajectory points corresponding to the maximum capacitance data near the two edges in the second region, that is, points h and m respectively correspond to Figure 11 The locations H and M in the second region shown in (a) are where the debonding is least severe.

[0196] Foldable screen phones can obtain predicted capacitance data at target trajectory points by interpolating the maximum capacitance data. The predicted capacitance data is then compared with the minimum capacitance data at that trajectory point to determine the screen state.

[0197] In some examples, after obtaining the minimum and maximum data mentioned above, foldable screen phones can also normalize the obtained data to complete the conversion of data units and formats, ensuring that the data units and formats are consistent when performing difference processing.

[0198] In some possible implementations, if the screen is in an abnormal state, the foldable phone can display and / or broadcast an error message.

[0199] For example, the abnormal prompt information may include at least one of the following: a prompt information that the screen has detached from the adhesive, a prompt information that the screen has a dent, a prompt information that the screen has a crease, or a prompt information that the screen has a crack.

[0200] Among them, see Figure 12 As shown in (a), the error message may be something like "A possible detachment issue has been detected on the phone screen. Please have it professionally checked promptly." Or, see [link to relevant documentation]. Figure 12 As shown in (b), the abnormal prompt message broadcast by the foldable screen phone can be, for example, "Xiaoyi reminds you that there may be a dent on your phone screen. Please have it repaired in time to avoid affecting your use"; or, the foldable screen phone can also broadcast the abnormal prompt message while displaying it.

[0201] Once users learn that their screen is malfunctioning, they can promptly undergo more professional testing to determine whether the screen needs repair or replacement, thereby reducing issues that could affect user operation, content display, and user experience.

[0202] In some possible implementations, if the screen is in normal condition, the foldable phone can display and / or broadcast normal prompts, such as "The current screen is in normal condition, please use it with confidence."

[0203] In the above embodiments, when detecting the screen state, the user can perform a simple swipe operation. Furthermore, the foldable screen phone can determine the screen state based on the data obtained during the user's operation, thus obtaining a relatively accurate detection result.

[0204] In some embodiments, foldable phones may also use group voting to determine the screen state when detecting the screen state.

[0205] In the group voting process, the user performs multiple actions (A) on the screen. For each action A, the foldable phone obtains a dataset corresponding to a swipe trajectory, effectively grouping the acquired data into sets. The foldable phone then obtains the screen state corresponding to each dataset and determines the final screen state based on this state. For example, if the proportion of abnormal datasets within the multiple datasets is within a preset range, the screen state is determined to be normal, while the screen state corresponding to the abnormal datasets is determined to be abnormal. Conversely, if the proportion of abnormal datasets is outside the preset range, the screen state is determined to be abnormal. This process essentially involves voting on the screen states corresponding to multiple groups.

[0206] The method of obtaining the screen state for each dataset can refer to the relevant scheme in S503 of the aforementioned embodiment. That is, in this embodiment, the foldable screen phone can obtain the dataset multiple times and obtain the screen state multiple times. By combining the multiple obtained screen states, it can more accurately determine whether the screen state is abnormal.

[0207] The preset ratio range can be [100%, 60%], [100%, 50%], [100%, 40%], etc.

[0208] In some examples, when conducting group voting, foldable phones can also determine whether the percentage of abnormal datasets in multiple datasets is a first proportion. If this proportion is greater than the first proportion, the screen is determined to be in a normal state; if the proportion is less than the first proportion, the screen is determined to be in an abnormal state. The first proportion can be in the range of 40%-60%, or the second proportion can include 40%, 50%, or 60%, etc.

[0209] If the proportion of abnormal datasets in multiple datasets is too high, it indicates that there are too many abnormal datasets. In this case, it is very likely that there is a problem with the screen. Therefore, in this situation, the foldable screen phone can be determined to have an abnormal screen status.

[0210] If the proportion of abnormal datasets in multiple datasets is too small, it means that there are not many or few abnormal datasets. In this case, there may be some misjudgment. It is very likely that the screen itself is not problematic. Therefore, in this case, the foldable screen phone can be determined to be in normal condition.

[0211] For example, the specific process of the above-mentioned group voting can be as follows: Figure 13 As shown in step S1301, after the state detection function is enabled, the user performs operation A on the screen. In step S1302, the foldable phone acquires a dataset for operation A. In step S1303, the foldable phone acquires the corresponding screen state based on the dataset. During this process, the user can continue to perform operation A on the screen. In step S1304, if the number of datasets is less than a preset number, the foldable phone continues to acquire the dataset corresponding to operation A, and then continues to execute step S1303 until the number of acquired datasets is greater than or equal to the preset number. In step S1305, if the number of datasets is greater than or equal to the preset number, the foldable phone combines the multiple acquired screen states to determine the final screen state.

[0212] In some examples, the preset number can be in the range of 30 to 50 times, or the preset number can include 30, 40, or 50 times, etc.

[0213] When a foldable phone obtains the screen state based on a single dataset, misjudgments may occur. Therefore, in this embodiment, the foldable phone can combine the screen states corresponding to multiple datasets to determine the final screen state, which can reduce the impact of misjudgments on screen state detection and improve the accuracy of state detection.

[0214] In some embodiments, the foldable phone can also collect sound data corresponding to operation A, or sound data corresponding to the sliding trajectory. When detecting the screen state, the foldable phone can also use a combination of data and sound.

[0215] In the data and sound combination method, if the foldable screen phone has initially determined that the screen state is abnormal based on the acquired dataset, then the foldable screen phone can further determine the screen state based on the sound data corresponding to operation A (or swiping trajectory).

[0216] The method for determining the screen state based on the acquired dataset can refer to the relevant scheme in S503 of the aforementioned embodiments. Alternatively, it can refer to the aforementioned group voting method, where the determined screen state is the result obtained after group voting.

[0217] For example, if the deviation between the target trajectory point's corresponding data and the predicted data in the dataset is outside a preset deviation range, the foldable phone can obtain a first result for screen detection based on the sound data corresponding to the sliding trajectory; this first result can indicate whether the screen's state is normal or abnormal. If the first result indicates that the screen's state is abnormal, the foldable phone ultimately determines that the screen's state is abnormal.

[0218] For example, the above-mentioned combination of data and sound can be achieved in the following ways: Figure 14 As shown in step S1401, after the state detection function is enabled, the user performs operation A on the screen. In step S1402, the foldable phone obtains the dataset and sound data corresponding to the sliding trajectory of operation A. In step S1403, the foldable phone obtains the screen state based on the dataset. In step S1404, if the screen state is normal, the detection ends. In step S1405, if the screen state is abnormal, the foldable phone determines the screen state based on the spectral characteristics of the sound data.

[0219] In this embodiment, if the screen state is determined to be abnormal based on the dataset, the foldable phone can further determine the screen state based on the sound data corresponding to the sliding trajectory. If the screen state is also determined to be abnormal based on the sound data, the foldable phone can finally determine that the screen state is abnormal. However, if the screen state is determined to be normal based on the sound data, it means that the abnormal result determined based on the data corresponding to the sliding trajectory may be a misjudgment. In this case, the foldable phone can finally determine that the screen state is normal.

[0220] In this embodiment, the screen state is determined by combining data and sound, which can eliminate the possibility of misjudging the screen state based on data corresponding to the sliding trajectory and improve the accuracy of state detection.

[0221] In some possible implementations, the foldable phone can also use or acquire the aforementioned sound data if the first condition is met. The first condition may include, but is not limited to: the noise level in the environment where the foldable phone is located is less than a preset noise level, and / or the foldable phone does not undergo a change in posture under operation A. If the foldable phone does not meet the above conditions, the sound data is not considered, and the screen state is still determined based on the dataset corresponding to the sliding trajectory.

[0222] For example, see Figure 15 As shown, in step S1501, after the state detection function is enabled, the user performs operation A on the screen. In step S1502, the foldable phone acquires a dataset for operation A. In step S1503, the foldable phone acquires the screen state based on the dataset. In step S1504, if the screen state is normal, the detection ends. In step S1505, if the screen state is abnormal, the screen state is determined based on the spectral characteristics of the sound data, provided the foldable phone meets the first condition. In step S1506, if the foldable phone does not meet the first condition, the abnormal screen state is output as the result.

[0223] In this implementation, if the ambient noise level of the foldable phone's environment is lower than the preset noise level, it indicates that the environment is relatively quiet and the ambient noise will not significantly affect the state detection results. In this case, the foldable phone can use sound data for detection. However, if the ambient noise level of the foldable phone's environment is greater than or equal to the preset noise level, it indicates that the environment is too noisy and the ambient noise will significantly affect the state detection. In this case, the foldable phone will not continue to use sound data.

[0224] In some examples, the preset noise can be in the range of 30dB-50dB, or the preset noise can include 30dB, 40dB, or 50dB, etc.

[0225] Furthermore, if the foldable phone does not change its posture under operation A, it indicates that the phone's position or posture is relatively stable. In this case, the foldable phone will not collide with other objects (such as a table) when the user performs operation A. Therefore, besides the sound generated by operation A itself, there is a high probability that there will be no other sounds, such as collision sounds. These other sounds will not affect the state detection, and the obtained state detection results will be more accurate. Therefore, in this case, the foldable phone can continue to use sound data for detection. However, if the foldable phone changes its posture under operation A, it indicates that its position or posture is not very stable. In this case, the foldable phone may collide with other objects (such as a table) when the user performs operation A, thus generating other sounds. These other sounds will affect the state detection results. Therefore, in this case, the foldable phone will not continue to use sound data.

[0226] In some possible implementations, when a foldable phone determines the screen state based on sound data, it can do so by considering the spectral characteristics of the sound data. In this process, a first sound set corresponding to abnormal screen states and a second sound set corresponding to normal screen states can be obtained first. The first sound set can include multiple historical sound data points corresponding to abnormal screen states, each with spectral characteristics; the second sound set can include multiple historical sound data points corresponding to normal screen states, each also with spectral characteristics.

[0227] If, based on the spectral characteristics of the first sound set, the foldable phone determines that the spectral characteristics of the currently acquired sound data match the condition of an abnormal screen state, then the foldable phone can determine that the screen state is abnormal. Specifically, the determination can be made by comparing the spectral characteristics of the currently acquired sound data with the spectral characteristics of multiple historical sound data in the first sound set. If the spectral characteristics of the currently acquired sound data are the same as or similar to the spectral characteristics of at least one historical sound data (similarity greater than or equal to a preset similarity), then the foldable phone can determine that the spectral characteristics of the currently acquired sound data match the condition of an abnormal screen state. Alternatively, if the number of spectral characteristics of multiple historical sound data that are the same as (or similar to) the spectral characteristics of the currently acquired sound data is greater than a first preset number, then the foldable phone can determine that the spectral characteristics of the currently acquired sound data match the condition of an abnormal screen state.

[0228] If, based on the spectral characteristics of the second sound set, the foldable phone determines that the spectral characteristics of the currently acquired sound data match the condition of a normal screen state, then the foldable phone can determine that the screen state is normal. Specifically, this determination can be achieved by comparing the spectral characteristics of the currently acquired sound data with the spectral characteristics of multiple historical sound data sets in the second sound set. If the spectral characteristics of the currently acquired sound data are the same as or similar to the spectral characteristics of at least one historical sound data set (with a similarity greater than or equal to a preset similarity), then the foldable phone can determine that the spectral characteristics of the currently acquired sound data match the condition of a normal screen state. Alternatively, if the number of spectral characteristics of multiple historical sound data sets that are the same as (or similar to) the spectral characteristics of the currently acquired sound data is greater than a first preset number, then the foldable phone can determine that the spectral characteristics of the currently acquired sound data match the condition of a normal screen state.

[0229] In the above implementation, if the screen has abnormalities such as delamination, dents, creases, or cracks, then high-frequency components will appear at specific positions on the spectrum of the sound data corresponding to operation A (or sliding trajectory), for example, high-frequency components will appear around 6K. Therefore, foldable screen phones can more accurately determine whether the sound data matches the abnormal screen state based on the spectral characteristics of the sound data, thus enabling more accurate state detection.

[0230] In some possible implementations, foldable phones can also utilize a pre-defined model (such as a sound classification model) to determine whether sound data matches an abnormal screen state. For example, the aforementioned first and second sound sets can be used to train the sound classification model. During this process, the sound classification model can extract spectral features from historical sound data in the first sound set and from historical sound data in the second sound set. The sound classification model can then use machine learning, deep learning, and other algorithms to learn these spectral features, thereby completing the training of the sound classification model.

[0231] Subsequently, when classifying or recognizing the currently acquired sound data, the sound data can be input into the sound classification model. The sound classification model can automatically classify the sound data and output the classification result, such as abnormal sound (i.e., the situation that matches the abnormal screen state) or normal sound (i.e., the situation that matches the normal screen state).

[0232] In some possible implementations, the foldable phone can begin collecting audio data and simultaneously record the timestamps corresponding to the audio data after the status detection function is enabled. Then, if the screen status is determined to be abnormal based on the dataset, or if the foldable phone meets a first condition, the foldable phone retrieves the audio data corresponding to the timestamp of operation A from the collected audio data.

[0233] In some embodiments, when a foldable phone determines the screen state using a combination of data and sound, it can also determine the screen state based on the dataset and sound data separately. Specifically, the method for determining the screen state based on the dataset can refer to the relevant scheme in S503 of the aforementioned embodiments, and the method for determining the screen state based on sound data can refer to the relevant scheme for determining the screen state based on the spectral characteristics of sound data in the aforementioned embodiments.

[0234] Subsequently, if the result indicating an abnormal screen state is obtained based on the sound data, and / or the result indicating an abnormal screen state is obtained based on the dataset, then the foldable phone will ultimately determine that the screen state is abnormal.

[0235] If the result indicating that the screen is in a normal state is obtained based on sound data, and the result indicating that the screen is in a normal state is obtained based on the dataset, then the foldable phone will ultimately determine that the screen is in a normal state. For example... Figure 16 As shown.

[0236] In this embodiment, the screen state is determined by combining data and sound, thereby further improving the accuracy of state detection.

[0237] In some possible implementations, foldable phones can also utilize a pre-defined model (such as a state classification model) to determine the screen state based on data and sound. For example, the state classification model can be trained using fusion features corresponding to normal screen states and fusion features corresponding to abnormal screen states. The fusion features corresponding to normal screen states include data features (e.g., capacitance, resistance, and inductance data features) and spectral features of sound data; similarly, the fusion features corresponding to abnormal screen states also include data features (e.g., capacitance, resistance, and inductance data features) and spectral features of sound data.

[0238] State classification models can use machine learning, deep learning, and other algorithms to learn the aforementioned data features and spectral features, thereby completing the training of the state classification model.

[0239] Then, the currently acquired dataset and audio data can be input into the state classification model. The state classification model can automatically classify the dataset and audio data and output the classification results, such as abnormal screen state or normal screen state.

[0240] In the above implementation, the preset model can be a single model or an integration of multiple models. The multiple models can be trained and classified separately for the dataset and the sound data.

[0241] In all the above embodiments, the screen detection method is implemented using a foldable screen phone as an example to illustrate the method. In other embodiments, the method can also be applied to non-foldable screen phones, tablets, smartwatches, in-vehicle terminals, handheld computers, laptops, PCs, UMPCs, netbooks, PDAs, AR / VR devices, smart home devices (such as smart TVs, smart screen devices, and smart central control devices), foldable screen computers, foldable screen watches, and other terminal devices with displays or screens.

[0242] Furthermore, in all the above embodiments, the method of detecting screen debonding using the aforementioned screen detection method is used as an example to illustrate the method. In other embodiments, the aforementioned screen detection method can also detect other screen anomalies, such as dents, creases, cracks, etc. For example, screen debonding can be detected by... Figure 17 As shown in (a), there may be gaps between the devices during debonding; the creases in the flexible screen can be, for example... Figure 17 As shown in (b) in the figure, the dashed line can represent the axis and the solid line can represent the crease.

[0243] In some solutions, multiple embodiments of this application can be combined, and the combined solution can be implemented. Optionally, some operations in the processes of each method embodiment may be combined, and / or the order of some operations may be changed. Furthermore, the execution order between the steps of each process is merely exemplary and does not constitute a limitation on the execution order between steps; other execution orders are also possible. It is not intended to indicate that the execution order is the only possible order in which these operations can be performed. Those skilled in the art will conceive of various ways to reorder the operations described in the embodiments of this application. In addition, it should be noted that the process details involved in one embodiment of this application are also applicable to other embodiments in a similar manner, or different embodiments may be combined.

[0244] Furthermore, some steps in the method embodiments can be equivalently replaced with other possible steps. Alternatively, some steps in the method embodiments may be optional and can be deleted in certain use cases. Or, other possible steps may be added to the method embodiments.

[0245] Furthermore, the various method embodiments can be implemented individually or in combination.

[0246] It is understood that, in order to achieve the above functions, the aforementioned electronic device includes hardware and / or software modules corresponding to perform each function. Based on the algorithmic steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in conjunction with the embodiments, but such implementation should not be considered beyond the scope of this application.

[0247] This embodiment can divide the electronic device into functional modules according to the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0248] This application also provides an electronic device, such as... Figure 18 As shown, the electronic device may include a screen, the screen including a detection area, and the electronic device may also include one or more processors 1801, memory 1802 and communication interface 1803.

[0249] The memory 1802, communication interface 1803, and processor 1801 are coupled together. For example, the memory 1802, communication interface 1803, and processor 1801 can be coupled together via bus 1804.

[0250] The communication interface 1803 is used for data transmission with other devices. The memory 1802 stores computer program code. The computer program code includes computer instructions, which, when executed by the processor 1801, cause the electronic device to perform the screen detection method described in this embodiment.

[0251] The processor 1801 can be a processor or controller, such as a CPU, a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0252] The bus 1804 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The aforementioned bus 1804 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 18 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0253] This application also provides a computer-readable storage medium that includes computer instructions. When the computer instructions are executed on an electronic device, the electronic device performs the relevant method steps described in the above method embodiments.

[0254] This application also provides a computer program product that, when run on a computer, causes the computer to execute the relevant method steps described in the above method embodiments.

[0255] The electronic devices, computer-readable storage media, or computer program products provided in this application are all used to perform the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0256] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0257] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0258] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0259] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0260] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, in essence, or the part that contributes, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A screen detection method, characterized in that, Applied to an electronic device including the screen, the screen including a detection area, the method includes: Receive a first operation from the user; the first operation includes a swipe operation, the swipe trajectory of which passes through the detection area; Obtain the dataset corresponding to the sliding trajectory; the dataset includes data of trajectory points on the sliding trajectory, and the data of the trajectory points includes capacitance data, resistance data, or inductance data. The state of the screen is determined based on the dataset.

2. The method according to claim 1, characterized in that, Determining the state of the screen based on the dataset includes: Based on the dataset, predicted data for target trajectory points are obtained; the target trajectory points include trajectory points corresponding to the minimum value data in the dataset. If the deviation between the data corresponding to the target trajectory point in the dataset and the predicted data is within a preset deviation range, the screen is determined to be in normal condition. If the deviation between the target trajectory point and the corresponding data in the dataset and the predicted data is outside a preset deviation range, the state of the screen is determined to be abnormal.

3. The method according to claim 2, characterized in that, The dataset includes capacitance data or inductance data.

4. The method according to claim 2 or 3, characterized in that, The screen is a flexible screen, and the detection area includes a first area and a second area. The first area is located within the second area, and the axis of the bendable portion of the flexible screen passes through the first area and the second area. The dataset includes a first dataset and a second dataset. The first dataset includes data of trajectory points on the sliding trajectory located within the first area, and the second dataset includes data of trajectory points on the sliding trajectory located within the second area. The step of obtaining predicted data for the target trajectory points based on the dataset includes: Determine the target trajectory point corresponding to the minimum value data in the first dataset; Based on the maximum value data in the second dataset, the predicted data of the target trajectory point is obtained.

5. The method according to claim 1, characterized in that, The dataset includes resistance data; determining the state of the screen based on the dataset includes: Based on the dataset, predicted resistance data for target trajectory points are obtained; the target trajectory points include trajectory points corresponding to the maximum resistance data in the dataset. If the deviation between the resistance data corresponding to the target trajectory point in the dataset and the predicted resistance data is within a preset deviation range, the screen is determined to be in normal condition. If the deviation between the resistance data corresponding to the target trajectory point in the dataset and the predicted resistance data is outside a preset deviation range, the state of the screen is determined to be abnormal.

6. The method according to claim 5, characterized in that, The screen is a flexible screen, and the detection area includes a first area and a second area. The first area is located within the second area, and the axis of the bendable portion of the flexible screen passes through the first area and the second area. The dataset includes a first dataset and a second dataset. The first dataset includes data of trajectory points on the sliding trajectory located within the first area, and the second dataset includes data of trajectory points on the sliding trajectory located within the second area. Determine the target trajectory point corresponding to the maximum value in the first dataset; Based on the minimum value data in the second dataset, the predicted data of the target trajectory point is obtained.

7. The method according to any one of claims 2-4, characterized in that, The step of obtaining predicted data for the target trajectory points based on the dataset includes: Based on the maximum value data in the dataset, interpolation is performed on the target trajectory point to obtain the predicted data of the target trajectory point.

8. The method according to claim 5 or 6, characterized in that, The step of obtaining predicted data for the target trajectory points based on the dataset includes: Based on the minimum value data in the dataset, interpolation is performed on the target trajectory point to obtain the predicted data of the target trajectory point.

9. The method according to any one of claims 1-8, characterized in that, Determining the state of the screen based on the dataset includes: For each of the multiple executions of the first operation, the dataset corresponding to the sliding trajectory is obtained; For each of the multiple datasets, the state of the screen corresponding to that dataset is obtained; the state of the screen is determined based on the dataset. If the proportion of abnormal datasets in the multiple datasets is within a preset proportion range, the screen is determined to be in a normal state; the screen corresponding to the abnormal dataset is in an abnormal state. If the proportion of abnormal datasets in the multiple datasets is outside the preset proportion range, the screen is determined to be in an abnormal state.

10. The method according to any one of claims 2-4, characterized in that, The method further includes: Collect the sound data corresponding to the sliding trajectory.

11. The method according to claim 10, characterized in that, If the deviation between the target trajectory point and the corresponding data in the dataset and the predicted data is outside a preset deviation range, the screen state is determined to be abnormal, including: If the deviation between the data corresponding to the target trajectory point in the dataset and the predicted data is outside the preset deviation range, a first result for the screen detection is obtained based on the sound data corresponding to the sliding trajectory. If the first result indicates that the screen is in an abnormal state, then the screen is determined to be in an abnormal state.

12. The method according to claim 10, characterized in that, Determining the state of the screen based on the dataset includes: If a result indicating an abnormal state of the screen is obtained based on the sound data corresponding to the sliding trajectory, and / or a result indicating an abnormal state of the screen is obtained based on the dataset, then the abnormal state of the screen is determined to be abnormal.

13. The method according to any one of claims 1-12, characterized in that, The step of obtaining the dataset corresponding to the sliding trajectory includes: If the sliding speed of the sliding operation is within a preset range, obtain the dataset corresponding to the sliding trajectory.

14. The method according to any one of claims 1-13, characterized in that, The method further includes: Receive the user's second operation and respond to the second operation by enabling the status detection function; or, When the usage of the electronic device meets preset conditions, the status detection function is activated; the usage meeting preset conditions includes the usage duration of the electronic device meeting a preset duration or the number of times the electronic device is used meeting a preset number of times.

15. The method according to any one of claims 1-14, characterized in that, The method further includes: If the screen is in an abnormal state, display and / or broadcast an abnormal prompt message.

16. The method according to claim 15, characterized in that, The abnormal prompt information includes at least one of the following: a prompt information indicating that the screen has detached, a prompt information indicating that the screen has a dent, a prompt information indicating that the screen has a crease, or a prompt information indicating that the screen has a crack.

17. An electronic device, characterized in that, The device includes a screen, the screen including a detection area; it also includes a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the screen detection method as described in any one of claims 1-16.

18. A computer-readable storage medium, characterized in that, Includes computer instructions that, when executed on an electronic device, cause the electronic device to perform the screen detection method as described in any one of claims 1-16.

19. A computer program product, characterized in that, When the computer program product is run on a computer, the computer performs the screen detection method as described in any one of claims 1-16.

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