Trigger detection method, electronic equipment, chip and readable storage medium

Abstract

The invention relates to the technical field of terminals, in particular to a trigger detection method, electronic equipment, a chip and a readable storage medium. The trigger detection method is applied to the electronic equipment, and the electronic equipment can dynamically determine a de-trigger threshold value according to stably changing detection data collected by a sensor in a trigger state, so as to judge whether the electronic equipment is in the trigger state or not at the next moment according to the de-trigger threshold value. Therefore, the de-triggering threshold value of the electronic equipment is dynamically changed according to the data acquired by the sensor of the electronic equipment, and the triggering state of the electronic equipment can be accurately judged under the condition that the sensor is interfered.

Description

Technical Field

[0001] The present application relates to the field of terminal technology, and in particular to a trigger detection method, an electronic device, a chip and a readable storage medium. Background Art

[0002] At present, some electronic devices can determine whether the electronic device is in a trigger state through sensors. For example, some wearable devices such as smart watches and headphones can determine whether the wearable device is in a wearing state through the detection data of corresponding sensors such as capacitance and resistance.

[0003] However, sensors of electronic devices are easily affected by environmental factors, resulting in a high error rate in determining whether the electronic device is in a trigger state. Summary of the invention

[0004] The embodiments of the present application provide a trigger detection method, an electronic device, a chip, and a readable storage medium, so as to improve the accuracy of the electronic device in determining whether it is in a trigger state when there are interference factors in the electronic device causing fluctuations in data detected by the sensor.

[0005] In a first aspect, an embodiment of the present application provides a trigger detection method, which is applied to an electronic device, wherein the electronic device includes a trigger detection device; the method includes: in a triggered state of the electronic device, obtaining multiple first detection data detected by the trigger detection device at multiple different moments in a current time period; judging whether the multiple first detection data are steadily changing data; if the multiple first detection data are steadily changing data, determining a de-triggering threshold of the electronic device based on at least part of the multiple first detection data, and judging whether the electronic device is in a triggered state at the next moment in the current time period based on the de-triggering threshold.

[0006] In some embodiments of the present application, the electronic device can set a de-trigger threshold of the electronic device based on the first detection data that changes steadily, so as to determine whether it is in a triggered state at the next moment through the de-trigger threshold. Therefore, the de-trigger threshold changes dynamically according to the detection data collected by the electronic device. When the electronic device receives interference and the collected detection data fluctuates, the de-trigger threshold can also be used to accurately determine whether it is in a triggered state at the next moment, thereby improving the accuracy of the judgment of the electronic device.

[0007] In a possible implementation of the first aspect, the determining whether the plurality of first detection data are stable data includes: calculating a standard deviation of the plurality of first detection data, and if the standard deviation is less than a first preset value, determining that the plurality of first detection data are stable changing data.

[0008] For example, in some embodiments of the present application, the size of the standard deviation can be used to determine whether the first detection data is data that changes steadily. In some embodiments, the first preset value can also be referred to as a steady-state threshold. When the standard deviation of the first detection data is less than the steady-state threshold, it can be determined that the first detection data is data that changes steadily.

[0009] In a possible implementation of the first aspect, determining the de-trigger threshold of the electronic device based on at least part of the multiple first detection data includes: taking the product of the average of the multiple first detection data and a preset coefficient as the de-trigger threshold.

[0010] Exemplarily, in some embodiments of the present application, the product of the mean of the first detection data and a preset coefficient can be used as a de-trigger threshold. The preset coefficient can be, for example, 0% to 100%. Preferably, the preset coefficient can be set to 20% to 80%.

[0011] In a possible implementation of the first aspect, the plurality of first detection data are data collected by the trigger detection device at preset time intervals.

[0012] In a possible implementation of the first aspect, the preset time interval is determined according to a detection scenario that triggers the detection.

[0013] For example, in some embodiments of the present application, the preset time interval can be determined according to the scene in which the electronic device is detected. For example, in a touch scene, the preset time interval can be 20ms to 50ms. In a wearing scene, the preset time interval can be 100ms to 200ms. In a ranging scene, the preset time interval can be 100ms to 400ms.

[0014] In a possible implementation of the first aspect, the amount of the first detection data is determined according to a detection scenario that triggers the detection.

[0015] For example, in some embodiments of the present application, the number of first detection data may be determined according to the scene in which the electronic device performs detection. For example, in a touch scene, the number of first detection data may be 5 to 10 consecutive data. In a wearing scene, the number of first detection data may be 20 to 40 consecutive data. In a ranging scene, the number of first detection data may be 15 to 30 consecutive data.

[0016] In a possible implementation of the first aspect, before acquiring a plurality of first detection data detected by the trigger detection device at a plurality of different moments in the current time period, the method further includes: determining that the electronic device is in a trigger state.

[0017] For example, in some embodiments of the present application, since the first detection data is obtained based on the electronic device being in a triggered state, it is also necessary to first determine whether the electronic device is in a triggered state, so as to obtain the corresponding first detection data after the trigger state.

[0018] In a possible implementation of the first aspect above, the above determination that the electronic device is in a triggered state includes: acquiring third detection data detected by the trigger detection device at a moment before the current time period; and determining that the electronic device is in a triggered state based on the third detection data being greater than a preset threshold.

[0019] For example, in some embodiments of the present application, the electronic device also pre-stores a preset threshold value, and when the de-triggering threshold value has not been updated, it can be determined whether the electronic device is in a triggered state according to the preset threshold value. For example, when the electronic device is not in a triggered state, it can be determined whether the electronic device is in a triggered state according to the preset threshold value and the third detection data collected by the electronic device.

[0020] In a possible implementation of the first aspect, if the plurality of first detection data are data with non-steady changes, it is determined whether the electronic device is in a triggered state at the next moment of the current time period based on the current de-triggering threshold.

[0021] Exemplarily, in some embodiments of the present application, if the first detection data of the current time period is not data that changes steadily, it is possible to determine whether the electronic device is in a triggered state at the next moment based on the current de-triggering threshold, that is, the de-triggering threshold at the next moment is not updated and is the same as the de-triggering threshold at the current moment. It can be understood that if the first detection data fluctuates greatly, it means that the trigger state of the electronic device may change. Therefore, the de-triggering threshold no longer changes with the change of the first detection data, avoiding the electronic device always judging that the detection data is greater than (or less than) the de-triggering threshold, so as to improve the accuracy of the electronic device in judging whether it is in a triggered state.

[0022] In a possible implementation of the first aspect, the plurality of first detection data are temperature compensated data.

[0023] Exemplarily, in some embodiments of the present application, sensor data collected by the electronic device can be compensated. For example, when the temperature of the sensor changes, the first detection data of the electronic device in the same state collected by the sensor may be different. Therefore, the first detection data needs to be compensated according to the temperature to reduce the impact of temperature changes on the first detection data collected by the sensor of the electronic device, and further improve the effectiveness of the de-triggering threshold determined by the electronic device based on the first detection data, so that the result of the electronic device judging whether it is in a triggered state based on the de-triggering is more accurate.

[0024] In a second aspect, the present application provides a chip, the chip comprising a processor and a data interface, the processor reading instructions stored in a memory through the data interface to execute the method provided in the first aspect and any possible implementation of the first aspect. The beneficial effects that can be achieved in the second aspect can refer to the beneficial effects of the method provided in any implementation of the first aspect, and will not be repeated here.

[0025] In a third aspect, the present application provides an electronic device, the electronic device comprising: a memory for storing instructions; and at least one processor for executing instructions so that the device implements the first aspect and any possible implementation method of the first aspect. The beneficial effects that can be achieved in the third aspect can refer to the beneficial effects of the method provided in any implementation of the first aspect, and will not be repeated here.

[0026] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions are stored, and when the instructions are executed by a device, the computer implements the method provided in the first aspect and any possible implementation of the first aspect. The beneficial effects that can be achieved in the fourth aspect can refer to the beneficial effects of the method provided in any implementation of the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1A A schematic diagram showing a wearable device 100 in a triggered state in an embodiment of the present application is shown;

[0028] Figure 1B A schematic diagram showing a wearable device 100 in a de-triggering state in an embodiment of the present application is shown;

[0029] Figure 1C A schematic diagram showing a mobile phone 200 in a triggered state in an embodiment of the present application is shown.

[0030] Figure 2 According to some embodiments of the present application, a schematic diagram of an electronic device is shown;

[0031] Figure 3 According to some embodiments of the present application, a flowchart of an implementation of an electronic device determining whether it is in a trigger state is shown;

[0032] Figure 4 According to some embodiments of the present application, a schematic diagram showing a trigger detection device of an electronic device acquiring detection data at preset time intervals is shown;

[0033] Figure 5 According to some embodiments of the present application, a flowchart of an implementation of a de-triggering threshold update process is shown;

[0034] Figure 6According to some embodiments of the present application, a simulation flow chart for determining whether an electronic device is in a trigger state is shown;

[0035] Fig. 7A According to some embodiments of the present application, a line graph 1 showing changes in detection data of a motion scene according to time is shown;

[0036] Figure 7B The local detection data ( Fig. 7A A schematic diagram of the detection data of the K1 part of FIG.

[0037] Figure 7C According to some embodiments of the present application, a broken line showing the change of detection data of a motion scene according to time is shown. Figure 2 ;

[0038] Fig.7D The local detection data ( Figure 7C A schematic diagram of the detection data of the K2 part of FIG.

[0039] Fig. 7E According to some embodiments of the present application, a line graph showing changes in detection data of a temperature drift scenario according to time is shown;

[0040] Figure 7F The local detection data ( Fig. 7E A schematic diagram of the detection data of the K3 part of FIG.

[0041] Figure 7G According to some embodiments of the present application, a line graph showing changes in detection data of a water sweat scene according to time is shown;

[0042] Figure 7H The local detection data ( Figure 7G A schematic diagram of the detection data of the K4 part of FIG.

[0043] Figure 8 According to some embodiments of the present application, a structural schematic diagram of a wearable device 100 is shown. DETAILED DESCRIPTION

[0044] Illustrative embodiments of the present application include, but are not limited to, a trigger detection method, an electronic device, a chip, and a readable storage medium.

[0045] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0046] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0048] As mentioned above, the sensors of electronic devices are easily affected by environmental factors, resulting in a high error rate when the electronic device determines whether the corresponding device is in a trigger state through a fixed threshold.

[0049] For example, Figures 1A to 1C An exemplary application scenario of the embodiment of the present application is shown. Figure 1A A schematic diagram showing a wearable device 100 in a triggered state in an embodiment of the present application is shown. Figure 1B A schematic diagram showing a wearable device 100 in a de-triggered state in an embodiment of the present application is shown.

[0050] It should be noted that the present application does not limit the specific form of the electronic device. The electronic device can be Figure 1C The devices shown include mobile phones, laptops, tablets, large-screen devices, wearable devices (e.g., watches, smart glasses, helmets), desktop computers, augmented reality (AR) / virtual reality (VR) devices, personal digital assistants (PDA), etc.

[0051] This application does not limit the specific form of the trigger state of the electronic device.

[0052] In some embodiments, the trigger state of the electronic device may be a state in which the touch screen of the electronic device is activated. When the electronic device is in the trigger state, the touch screen may receive a user's touch operation; when the electronic device is in the non-trigger state, the touch screen may not respond to the user's touch operation.

[0053] In other embodiments, the trigger state of the electronic device may be a state in which a key (e.g., a switch key, a keyboard key, etc.) of the electronic device is activated. When the electronic device is in the trigger state, the electronic device may respond to the key pressing operation of the user; when the electronic device is in the non-trigger state, the electronic device may not respond to the key pressing operation of the user.

[0054] In other embodiments, the trigger state of the electronic device may be a state in which the user approaches the electronic device. When the distance between a specific part of the user (e.g., a hand) and the electronic device is below a set threshold, the electronic device is in a trigger state, so that the user can operate the electronic device normally; when the distance between the specific part of the user and the electronic device is greater than the set threshold, the electronic device is in a non-trigger state, and the electronic device may not respond to the user's operation.

[0055] In other embodiments, the trigger state of the electronic device may be a state in which the electronic device is worn by a user. When the electronic device is worn by a user, the electronic device is in a trigger state; when the electronic device is not worn by a user, the electronic device is in a non-trigger state.

[0056] In the following, a wearable device is taken as an example of an electronic device, and a wearing state of the wearable device is taken as an example of a trigger state. However, it is understood that the present application is not limited thereto.

[0057] For example, in some embodiments of the present application, the wearable device 100 may determine its operating mode according to the wearing state. Figure 1A When the user wears the wearable device 100 (ie, the wearable device 100 is in a triggered state), the wearable device 100 may be in a high power consumption mode, for example, the screen is turned on, heart rate monitoring is turned on, etc. Figure 1B When the user takes off the wearable device 100 (ie, the wearable device 100 is in a non-triggered state), the wearable device 100 may be in a low power consumption mode, for example, turning off the screen, shutting down some sensors, and the like.

[0058] In some other embodiments, a schematic diagram of an electronic device in a triggering state is described by taking a mobile phone 200 as an example. Figure 1C , when the mobile phone 200 is in the screen-off state, the mobile phone 200 can enter the screen-off display (or screen-off wake-up) state (as an example of a trigger state) when the mobile phone 200 detects that the user is approaching. In this way, the mobile phone 200 does not need to enter the screen-on state completely, and the user can check information such as the time through the mobile phone 200, thereby saving power consumption of the mobile phone 200.

[0059] Next, the process of a wearable device determining whether it is in a trigger state is introduced.

[0060] For example, Figure 2 According to some embodiments of the present application, a schematic diagram of an electronic device is shown.

[0061] refer to Figure 2, the electronic device may include a trigger detection device 101 and a chip 102. In the embodiments of the present application, the electronic device is a wearable device 100 and the trigger detection device 101 is a capacitance detection sensor. In some embodiments, the chip 102 can judge the wearing state of the wearable device 100 through the data collected by the capacitance detection sensor. For example, when the wearable device 100 is in a wearing state (trigger state), the user's skin or clothes will contact the capacitance detection sensor of the wearable device 100, thereby changing the electric field distribution between the electrodes of the capacitance detection sensor, thereby causing the capacitance value of the capacitance detection sensor to change. The capacitance detection sensor can send the detection data corresponding to the capacitance value (for example, it can be capacitance value data or other data generated based on the capacitance value data) to the chip 102, and the chip 102 performs subsequent processing and judgment. For example, the chip 102 stores the corresponding trigger threshold value, and after the chip 102 receives the detection data of the capacitance detection sensor, it can judge whether the detection data exceeds the trigger threshold value. Exemplarily, in the embodiments of the present application, the trigger threshold value is, for example, 20000. For example, if the detection data received by the chip 102 is greater than the trigger threshold, for example, the detection data is 100000, it can be determined that the wearable device 100 is in a wearing state, and the wearable device 100 maintains a high power consumption mode. If the detection data received by the chip 102 is less than the trigger threshold, for example, the detection data is 10000, it can be determined that the wearable device 100 is not worn, and the wearable device 100 enters a low power consumption mode.

[0062] However, the trigger detection device 101 of the electronic device is usually affected by interference factors, resulting in fluctuations in the detection data obtained by the trigger detection device 101. Interference factors may include user factors, such as interference of user body temperature, sweat, etc. on the detection data, and may also include environmental factors, such as interference of ambient temperature, humidity, power, radiation, light, etc. on the detection data, etc., which are not limited in this application. The following is an example.

[0063] For example, continue to use the wearable device 100 and the capacitance detection sensor as an example for explanation. In some cases, after the user wears the wearable device 100, the temperature of the wearable device 100 rises due to the user's body temperature. Therefore, the detection data detected by the capacitance detection sensor is too large, for example, the detection data is 110000. When the wearable device 100 is in the wearing state, it may not have much impact on the wearing detection device. However, if the user releases the wearing state, the temperature of the wearable device 100 has not dropped. Since the temperature of the wearable device 100 is high, the detection data obtained by the capacitance detection sensor 101 is 21000. Therefore, the detection data 21000 obtained by the chip 102 is greater than the trigger threshold 20000, and the chip 102 still determines that the wearable device 100 is in the wearing state, resulting in the chip 102 making an incorrect judgment on the state of the wearable device 100, and the wearable device 100 is still in high power consumption mode.

[0064] For example, in other cases, after the user wears the wearable device 100 for exercise, he may sweat a lot, resulting in the presence of sweat on the surface of the capacitance detection sensor of the wearable device 100, which will also affect the detection result of the capacitance detection sensor 101. For example, the detection data detected after the presence of sweat on the surface of the capacitance detection sensor is too large. After the user releases the wearing state of the wearable device 100, the detection data of the capacitance detection sensor 101 is 22000. The chip 102 determines that the wearable device 100 is still in the wearing state based on the detection data.

[0065] In summary, in the presence of interference factors such as temperature, water and sweat, the detection data detected by the trigger detection device of the electronic device fluctuates, resulting in an erroneous judgment result of whether the chip of the electronic device is in a triggered state.

[0066] It is understandable that the above-mentioned capacitance detection sensor is affected by temperature, water, sweat, etc., resulting in errors in the detection result of the wearing state of the wearable device is only an example. In other embodiments, there may be other interference factors (for example, humidity, light, power of the electronic device, etc.) that cause the data detected by the trigger detection device of the electronic device to fluctuate, thereby making the judgment of the trigger state of the electronic device erroneous. The embodiments of the present application do not limit the electronic device, the trigger detection device, and the interference factors. In some embodiments, the trigger detection device can also be other sensors, such as resistive detection, light detection, etc., and the present application does not limit the trigger detection device.

[0067] In order to solve the above problems, the present application provides a trigger detection method for an electronic device. When the electronic device is in a triggered state, multiple first detection data detected by a trigger detection device at multiple different moments in a current time period are obtained; it is determined whether the multiple first detection data are steadily changing data; if the multiple first detection data are steadily changing data, a de-triggering threshold of the electronic device is determined based on at least part of the multiple first detection data, and the de-triggering threshold is used to determine whether the electronic device is in a triggered state at the next moment after the current time period.

[0068] Through this solution, the de-trigger threshold of the electronic device is determined based on the detection data of the electronic device in the triggered state. Therefore, the de-trigger threshold can change with the fluctuation of the trigger detection device under the interference of different interference factors, so that the electronic device can more accurately judge whether it is in the triggered state based on the de-trigger threshold, thereby improving the accuracy of the electronic device's judgment.

[0069] In some embodiments of the present application, it is possible to determine whether the multiple first detection data are steadily changing data based on the size of the standard deviation of the multiple first detection data. For example, a steady-state threshold (as an example of a first preset value) is stored in the electronic device, and the steady-state threshold is used to determine whether the multiple first detection data are steadily changing data. For example, when the standard deviation of the multiple first detection data is less than or equal to the steady-state threshold, it can be determined that the multiple first detection data are steadily changing data. When the standard deviation of the multiple first detection data is greater than the steady-state threshold, it can be determined that the multiple first detection data are not steadily changing data.

[0070] In some embodiments of the present application, the de-triggering threshold may be determined based on the average of multiple first detection data. For example, the product of a preset coefficient and the average of multiple first detection data may be used as the size of the de-triggering threshold. The preset coefficient makes the value 20% to 80%. In addition, the size of the preset coefficient may be obtained by simulation based on historical detection data.

[0071] In some embodiments, if the first detection data is in a state of stable change, the electronic device determines whether it is in a triggered state according to the de-triggering threshold. If the first detection data is not data in a stable change, the electronic device can use the original threshold (as an example of a preset threshold) as the de-triggering threshold and determine whether it is in a triggered state, where the original threshold is a fixed threshold initially set in the electronic device.

[0072] The following describes the process of determining whether the electronic device in the present application is in a trigger state.

[0073] For example, Figure 3 According to some embodiments of the present application, a flowchart for implementing a method of an electronic device to determine whether it is in a trigger state is shown.

[0074] like Figure 3 As shown, the process includes:

[0075] S301, in a triggered state, obtaining a plurality of first detection data detected by a trigger detection device at a plurality of different moments in a current time period.

[0076] Exemplarily, in some embodiments of the present application, when the electronic device is in a triggered state, the trigger detection device (e.g., a capacitive detection sensor) may collect multiple data detected by the sensor at a preset time interval, and use the multiple data collected by the sensor as the first detection data. The preset time interval may be, for example, 100ms to 200ms. The duration of the current time period is related to the number of the first detection data. In some embodiments of the present application, the first detection data may include, for example, 20 to 40 detection data, that is, the duration of the current time period is 2s to 8s.

[0077] For example, taking a user wearing a wearable device (wearing scenario) as an example, in the process of the user wearing the wearable device, the capacitance detection sensor can obtain detection data every 100ms, and within 2s, the capacitance detection sensor can obtain 20 sets of detection data, and the electronic device can use the 20 sets of detection data as the first detection data. It can be understood that in other embodiments, the number of first detection data can be 20 to 40.

[0078] Exemplarily, the duration of the preset time interval is different for the corresponding electronic device in different detection scenarios, that is, the preset time interval is a time interval determined according to the detection scenario of the trigger detection. In addition, the duration of the current time period of the electronic device can also be determined according to the detection scenario of the trigger detection. For example, in some touch scenarios (for example, when the user performs corresponding operations on the electronic device through a touch screen, etc.), the sensor of the electronic device can obtain detection data once every 20ms to 50ms and other time periods. The number of the first detection data can be 5 to 10, that is, the duration of the current time period is 100ms to 5000ms. In some ranging scenarios (for example, capturing the user's movements, gestures, positions, etc.), the sensor of the electronic device can obtain detection data once every 100ms to 400ms and other time periods, and the number of the first detection data can be 15 to 30, that is, the duration of the current time period is 1500ms to 12000ms.

[0079] In some embodiments, the first detection data may also be detection data that has been compensated for interference. For example, taking a wearable device as an example, if the capacitance detection sensor is affected by the user's body temperature, resulting in the detected first detection data being larger, the first detection data may be compensated based on temperature (for example, reducing the size of the first detection data according to the temperature change, etc.) to reduce the impact of temperature on the first detection data.

[0080] S302, determining whether the plurality of first detection data are data with stable changes.

[0081] Exemplarily, in some embodiments of the present application, it is possible to determine whether multiple first detection data are steadily changing data based on the size of the standard deviation of the multiple first detection data. For example, a steady-state threshold (as an example of a first preset value) is stored in the electronic device, and the steady-state threshold is used to determine whether multiple first detection data are steadily changing data. For example, when the standard deviation of multiple first detection data is less than or equal to the steady-state threshold, it can be determined that the multiple first detection data are steadily changing data. When the standard deviation of multiple first detection data is greater than the steady-state threshold, it can be determined that the multiple first detection data are not steadily changing data. The size of the steady-state threshold can be obtained based on simulation of historical sensor detection data, and the process of obtaining steady-state data is described below.

[0082] If the judgment result is yes, then execute S303, determine the de-triggering threshold of the electronic device according to at least part of the plurality of first detection data.

[0083] If the judgment result is no, then S305 is executed to judge whether the next moment of the current time period is in a triggered state based on the current de-triggering threshold.

[0084] It is understandable that in other embodiments, other methods may be used to determine whether the first detection data is data with a steady change, such as range, variance, mean difference, etc. The embodiment of the present application does not limit the method for determining whether the first detection data is data with a steady change.

[0085] S303: Determine a de-triggering threshold of the electronic device according to at least part of the plurality of first detection data.

[0086] Illustratively, in some embodiments of the present application, after the electronic device determines that a plurality of first detection data are data with steady changes, the contact triggering threshold value may be determined according to the plurality of first detection data.

[0087] For example, in some embodiments of the present application, the product of the average of the plurality of first detection data and the preset coefficient may be used as the de-triggering threshold. The size of the preset coefficient may be obtained by simulating the historical sensor detection data, and the process of obtaining the preset coefficient is described below.

[0088] It is understood that in other embodiments, the de-triggering threshold may be determined based on other values ​​in the plurality of first detection data, such as the median, mode, etc. The embodiments of the present application do not limit the method for determining the de-triggering threshold.

[0089] In some other embodiments, the de-triggering threshold may also be determined based on part of the plurality of first detection data. For example, the latest data (ie, the latest collected data) among the plurality of first detection data is used as the de-triggering threshold.

[0090] S304: Determine whether the trigger state is in the next moment of the current period based on the de-triggering threshold.

[0091] For example, in some embodiments of the present application, after the electronic device determines the de-triggering threshold, it can determine whether it is in a triggered state according to the de-triggering threshold and the detection data obtained by the sensor at the next moment.

[0092] Exemplarily, the next moment is the moment after the preset time interval of the current time period after the electronic device obtains the de-triggering threshold value based on the multiple first detection data. At this moment, the trigger detection device of the electronic device obtains the corresponding detection data (as an example of the second detection data), and the electronic device can compare the second detection data with the de-triggering threshold value, and determine whether the electronic device is in a triggered state at the next moment of the current time period according to the comparison result.

[0093] If the judgment result is yes, enter the trigger state mode.

[0094] If the judgment result is no, enter the non-trigger state mode.

[0095] S305: Determine whether the next moment of the current time period is in a triggered state based on the current de-triggering threshold.

[0096] Exemplarily, after the electronic device determines that the plurality of first detection data are not steadily changing data, it can determine whether the electronic device is in a triggered state at the next moment after the current period based on the de-triggering threshold of the current period. That is, the de-triggering threshold will not change according to the first detection data of the current period.

[0097] It can be understood that if the first detection data fluctuates greatly, it means that the trigger state of the electronic device may change. Therefore, the de-trigger threshold no longer changes with the change of the first detection data, so as to avoid the electronic device always judging that the detection data is greater than (or less than) the de-trigger threshold, so as to improve the accuracy of the electronic device in judging whether it is in a triggered state.

[0098] If the judgment result is yes, the trigger state mode is entered.

[0099] If the judgment result is no, enter the non-trigger state mode.

[0100] For example, in some embodiments of the present application, the electronic device determines that it is in a trigger state based on the detection result of the sensor, and the electronic device can operate according to the mode of the trigger state. For example, taking a wearable device as an example, after the wearable device determines that it is in a wearing state, the wearable device can operate in a high power consumption state.

[0101] For example, in some embodiments of the present application, if the electronic device determines that it is not in a trigger state based on the detection result of the sensor, the electronic device can operate according to the non-trigger state mode. For example, taking a wearable device as an example, after the wearable device determines that it is not in a wearing state, the wearable device can operate in a low-power state, thereby reducing the energy consumption of the wearable device.

[0102] In summary, the electronic device can determine whether to adjust the contact trigger threshold according to whether the multiple first detection data are stable and changing data. Therefore, when the sensor of the electronic device is disturbed by interference factors and the detection result fluctuates, the de-trigger threshold of the electronic device can change according to the change of the detection data of the sensor. This enables the electronic device to more accurately determine whether it is in a triggered state based on the de-trigger threshold to execute the function corresponding to the corresponding state.

[0103] Next, a process of the electronic device determining whether a plurality of first detection data are data with steady changes is introduced.

[0104] For example, Figure 4 According to some embodiments of the present application, a schematic diagram is shown in which a trigger detection device of an electronic device acquires detection data at preset time intervals.

[0105] like Figure 4 As shown, the trigger detection device of the electronic device obtains the detection data of the sensor once every preset time interval T, wherein the sliding window can obtain the detection data in the current time period for obtaining the de-triggering threshold. For example, in the current time period A, the electronic device can determine whether the m detection data (detection data 1 to detection data m, as an example of the first detection data) in the current time period A are data that change steadily. It can be understood that from time T1 to time Tm, the electronic device can determine whether the detection data 1 to detection data m are in a triggering state one by one based on the original threshold value. When all the detection data 1 to detection data m are in a triggering state, the de-triggering threshold of the electronic device can be updated according to the detection data of the current time period A.

[0106] The detection data within sliding window A is N(m) = {det_diff[1], det_diff[2], …, det_diff[m]}, where det_diff[1] to det_diff[m] are the detection data from detection data 1 to detection data m. The standard deviation of the detection data within sliding window A for the current period A can be determined by formulas (1) and (2), where formula (1) is used to determine the mean value of the detection data within sliding window A:

[0107]

[0108] Among them, det_diff_ave is the mean value of the detection data within sliding window A, m is the number of detection data within sliding window A, det_diff(i) is the i-th detection data in sliding window A, and 0 < i ≤ m.

[0109] Then the calculation process of the standard deviation within sliding window A can refer to formula (2):

[0110]

[0111] Among them, σ is the standard deviation of the detection data within sliding window A, det_diff_ave is the mean value of the detection data within sliding window A, m is the number of detection data within sliding window A, det_diff(i) is the i-th detection data in sliding window A, and 0 < i ≤ m.

[0112] When the standard deviation σ of the detection data within sliding window A for the current period A < TH (steady-state threshold), it is considered that the detection data within sliding window A is data with a stable change. Then the solution trigger threshold can be determined by formula (3):

[0113] Dynamic_th = update_rate × det_diff_ave (3)

[0114] Among them, Dynamic_th is the solution trigger threshold for the current period A, update_rate is a preset coefficient, and the value range of the preset coefficient is 20% to 80%.

[0115] It can be understood that continue to refer to Figure 4, after the de-triggering threshold of the current time period A is determined, Tm+1 is the next moment A. The electronic device can determine whether the electronic device is in a triggered state based on Dynamic_th and the detection data m+1 of A at the next moment. If the judgment result is yes, the sliding window A can be moved by a preset time interval, and then the sliding window A becomes the sliding window B, and the data in the sliding window B is updated to the detection data 2 to the detection data m+1. The electronic device can continue to determine whether the detection data of the current time period B is a steadily changing data based on the data in the sliding window B, and determine whether the moment Tm+2 (the next moment B) is in a triggered state based on the de-triggering threshold updated based on the detection data of the current time period B. If the detection data of the current time period B is not a steadily changing data, the electronic device can still determine whether B is in a triggered state at the next moment based on Dynamic_th, that is, the de-triggering threshold has not changed.

[0116] Next, the updating process of the de-triggering threshold is introduced.

[0117] For example, Figure 5 According to some embodiments of the present application, a flowchart for implementing a de-triggering threshold update process is shown.

[0118] Exemplarily, the execution entities of the following implementation processes are all electronic devices in the embodiments of the present application. When introducing each process, there is no limitation on the execution entity of each process.

[0119] like Figure 5 As shown, the process includes:

[0120] S501, importing detection data.

[0121] Illustratively, in some embodiments of the present application, a trigger detection device of an electronic device may obtain detection data collected by a corresponding sensor, and import the detection data into a chip of the electronic device, which processes the imported detection data to determine whether the electronic device is in a triggered state.

[0122] For example, the capacitive sensor of the wearable device 100 can detect changes in capacitance and then send the detection data to the chip 102. The chip 102 can determine whether the wearable device 100 is in a wearing state based on the detection data.

[0123] S502, determining whether the current detection data exceeds the de-triggering threshold.

[0124] For example, in some embodiments of the present application, when the electronic device is in an untriggered state (as an example of a moment before the current period), the original threshold value pre-stored in the chip of the electronic device can be used as the de-triggering threshold value. That is, when the electronic device is in an untriggered state, the chip of the electronic device can determine whether the electronic device is in a triggered state based on the original threshold value when receiving the detection data (as an example of the third detection data).

[0125] It can be understood that in some embodiments, if the electronic device is already in a triggered state, the chip of the electronic device can determine whether the electronic device is in a triggered state at the moment when it receives the detection data by using the updated de-triggering threshold.

[0126] If the judgment result is yes, then S503 is executed to enter the trigger state.

[0127] If the judgment result is no, then S508 is executed to enter the de-trigger state and clear the detection data in the sliding window.

[0128] S503, entering the trigger state.

[0129] Illustratively, in some embodiments of the present application, after the chip of the electronic device determines that the electronic device is in a triggered state, the electronic device may enter a triggered state mode.

[0130] For example, taking a wearable device as an example, after the wearable device is determined to be in a triggered state, it can enter a high power consumption mode, such as lighting up the screen, turning on heart rate monitoring, etc.

[0131] S504, filling the detection data in the sliding window.

[0132] Exemplarily, in some embodiments of the present application, the de-triggering threshold in the chip of the electronic device can be determined based on multiple first detection data collected in the trigger state. For example, in some embodiments of the present application, the multiple first detection data are detection data within a sliding window. After the chip of the electronic device determines that the current detection data corresponds to being in the trigger state, the current detection data can be filled into the sliding window so that the subsequent process determines the de-triggering threshold of the current period.

[0133] S505: Determine whether the detection data in the sliding window meets the number threshold.

[0134] For example, in some embodiments of the present application, the electronic device may determine whether to update the de-triggering threshold at the next moment based on the data in the sliding window. The number of detection data in the sliding window is predetermined, so before determining whether to update the de-triggering threshold based on the detection data in the sliding window, it is also necessary to determine whether the detection data in the sliding window meets the number threshold.

[0135] The number threshold is Figure 4 The m in the embodiment, or, is the number of first detection data.

[0136] If the judgment result is yes, then S506 is executed to judge whether the standard deviation of the detection data in the sliding window is less than the steady-state threshold.

[0137] If the judgment result is no, then S509 is executed to use the preset threshold as the de-triggering threshold.

[0138] S506, determining whether the standard deviation of the detection data in the sliding window is less than a steady-state threshold.

[0139] For example, in some embodiments of the present application, when the detection data in the sliding window meets the number threshold, the detection data in the sliding window can be used as the first detection data, and it is determined whether the standard deviation of the detection data in the sliding window (i.e., the first detection data) is less than the steady-state threshold. For example, the process of determining whether the standard deviation of the detection data in the sliding window is less than the steady-state threshold can refer to the process of determining whether the multiple first detection data are steadily changing data in S302.

[0140] If the judgment result is yes, then execute S507 to update the solution triggering threshold.

[0141] If the judgment result is no, S510 is executed to determine whether there is detection data at the next moment.

[0142] S507, updating the solution triggering threshold.

[0143] Exemplarily, in some embodiments of the present application, if the standard deviation of the detection data in the sliding window is less than the steady-state threshold, the de-triggering threshold can be updated according to the detection data in the sliding window, and the updating process of the de-triggering threshold can refer to the process of S303.

[0144] S508, entering the de-triggering state and clearing the detection data in the sliding window.

[0145] For example, in some embodiments of the present application, if the chip of the electronic device determines that the current detection data does not exceed the de-triggering threshold, the electronic device can enter the de-triggering state and clear the detection data in the sliding window. It can be understood that since the electronic device is not in the triggered state, the detection data in the sliding window cannot be used to determine the de-triggering threshold, and the detection data in the sliding window needs to be cleared so that the electronic device can fill the sliding window with new detection data after entering the triggered state again.

[0146] For example, taking a wearable device as an example, after the wearable device is released from being worn (corresponding to entering a de-triggering state), the wearable device can enter a low power consumption mode, such as turning off the screen, turning off heart rate monitoring, etc., thereby reducing the energy consumption of the wearable device.

[0147] S509: Using the preset threshold as the de-triggering threshold.

[0148] Exemplarily, in some embodiments of the present application, when the electronic device detects that the detection data in the sliding window does not meet the target threshold, or the electronic device determines that the standard deviation of the detection data in the sliding window is less than the steady-state threshold, the original trigger threshold can be used as the de-trigger threshold to determine whether the detection data at the next moment corresponds to a non-trigger state.

[0149] S510, determining whether there is detection data for the next moment.

[0150] For example, in some embodiments of the present application, after determining the trigger threshold, the electronic device can determine whether there is detection data for the next moment.

[0151] If yes, execute S511 to obtain the detection data at the next moment.

[0152] If it does not exist, the process ends.

[0153] S511, obtaining the detection data at the next moment.

[0154] Illustratively, in some embodiments of the present application, if the electronic device determines that there is detection data at the next moment, it obtains the detection data at the next moment, and enters S502 to determine whether the current detection data exceeds the de-triggering threshold.

[0155] In summary, when the electronic device is in a triggered state, the de-triggering threshold can be updated according to multiple first detection data (detection data in the sliding window) in the current period in the triggered state, so the de-triggering threshold changes dynamically with the detection data acquired by the electronic device. Therefore, when the sensor of the electronic device is interfered, the electronic device can more accurately determine whether it is in a triggered state according to the detection data through the dynamically changing de-triggering threshold, thereby improving the accuracy of the electronic device in determining the trigger state.

[0156] Next, the process of determining the preset coefficient and the steady-state threshold is introduced.

[0157] For example, Figure 6 According to some embodiments of the present application, a simulation flow chart for determining whether an electronic device is in a trigger state is shown.

[0158] In this embodiment, a wearable device is taken as an example to introduce a process of determining whether the wearable device is in a trigger state in different environments.

[0159] like Figure 6 As shown, the process includes:

[0160] S610, the simulation device collects test data.

[0161] For example, in some embodiments of the present application, the simulation device can collect test data of different users wearing the wearable device in different usage scenarios. The test data mainly includes sensor data used to determine whether the user wears the wearable device, and the actual result of whether the user wears the wearable device.

[0162] Exemplarily, in some embodiments of the present application, the test data may include, for example, motion scene data, temperature drift scene data (data in which the sensor changes due to temperature), and water and sweat scene data.

[0163] For example, the wearable device can be brought close to the user (to make the wearable device in a wearing state), and the simulation device can collect sensor data of the capacitive detection sensor in typical scenarios such as the user's movements while wearing the wearable device, temperature drift caused by wearing the wearable device for a long time (the temperature of the wearable device rises), and wearing the wearable device after sweat is applied to the surface of the wearable device. Then the real result of whether the user removes the wearable device is recorded.

[0164] S620, the simulation device establishes a trigger judgment algorithm.

[0165] For example, in some embodiments of the present application, the simulation device may establish a trigger judgment algorithm. The judgment trigger algorithm determines whether the user is wearing the wearable device based on the sensor data collected by the detection device in the electronic device. For example, the execution process of the judgment trigger algorithm can refer to Figure 3 The parameters in the triggering algorithm may include, for example, a preset coefficient and a steady-state threshold.

[0166] S630: The simulation device performs simulation verification on the trigger judgment algorithm based on the test data.

[0167] Illustratively, in some embodiments of the present application, after the simulation device has established the trigger judgment algorithm, it can perform simulation verification on the trigger judgment algorithm based on test data.

[0168] For example, the simulation device can determine the wearing state of the corresponding electronic device based on the sensor data in the test data, and then detect the trigger judgment algorithm based on the actual result of whether the user wears the wearable device in the test data, and then optimize it to improve the accuracy of the trigger judgment algorithm. For example, in the embodiment of the present application, the preset coefficient and the steady-state threshold in the trigger judgment algorithm can be adjusted to adjust the size of the de-triggering threshold, so that the steady-state threshold and the preset coefficient can more accurately determine whether the wearable device is in a wearing state in different application scenarios.

[0169] S640, the simulation device writes the trigger judgment algorithm into the chip of the wearable device.

[0170] For example, in some embodiments of the present application, after adjusting the trigger judgment algorithm, the simulation device can write the trigger judgment algorithm into the chip of the electronic device, so that the electronic device can judge whether it is in the wearing state based on the trigger judgment algorithm.

[0171] It can be understood that, through the above simulation process, a corresponding trigger judgment algorithm can be obtained, and the trigger judgment algorithm can be implanted into a corresponding electronic device, so that the electronic device can determine whether it is in a trigger state based on the trigger judgment algorithm.

[0172] Next, the comparison results of determining the trigger status of electronic devices in different environments by using a dynamically set de-triggering threshold and a fixed threshold in the embodiments of the present application are introduced.

[0173] For example, 7A to 7H Schematic diagrams showing detection data of different scenarios in some embodiments of the present application. Fig. 7A According to some embodiments of the present application, a line graph 1 showing the change of detection data of a motion scene according to time is shown. Figure 7B The local detection data ( Fig. 7A The schematic diagram of the detection data of the K1 part is shown in Figure 1. Figure 7C According to some embodiments of the present application, a broken line showing the change of detection data of a motion scene according to time is shown. Figure 2 , Fig.7D The local detection data ( Figure 7C The schematic diagram of the detection data of the K2 part is shown in FIG. Fig. 7E According to some embodiments of the present application, a line graph showing the change of detection data of a temperature drift scenario according to time is shown. Figure 7F The local detection data ( Fig. 7E The schematic diagram of the detection data of the K3 part of Figure 7G According to some embodiments of the present application, a line graph showing the change of detection data of a water sweat scene according to time is shown. Figure 7H The local detection data ( Figure 7G Schematic diagram of the detection data of the K4 part).

[0174] refer to Fig. 7A and Figure 7B In the detection data of a sports scene, when the fixed threshold is 20000, the sensor data may still be greater than the fixed threshold of 20000 after the release action occurs, resulting in the wearable device judging that the wearing state is not released. When the fixed threshold is configured to 30000 or 50000, the release function is normal. The solution of dynamically updating the release trigger threshold allows the wearable device to judge that the wearing function is normal.

[0175] refer to Figure 7C and Fig.7D In the detection data of another sports scene, when the fixed threshold is 20000, the sensor data is still greater than the fixed threshold of 20000 after the release action occurs, resulting in the wearable device judging that the wearing state is not released. When the fixed threshold is configured to 30000 or 50000, the release function is normal. However, when the fixed threshold is 50000, there is a misinterpretation of wearing in advance (abnormal judgment). There is no obvious abnormality in the solution of dynamically updating the release trigger threshold.

[0176] refer to Fig. 7E and Figure 7F In this type of scenario, a fixed threshold of 20,000 is used. After the wearable device is released, there is a problem in judging that the wearable device has not been released. The rest is normal.

[0177] refer to Figure 7G and Figure 7H In this scenario, when the threshold is fixed at 50,000, the wearable device can barely correctly judge the release state. When the threshold is fixed at 20,000 or 30,000, the release judgment error occurs. The solution of dynamically updating the release trigger threshold allows the wearable device to judge that the wearing function is normal.

[0178] In summary, in the above scenarios, the fixed threshold solutions all had the situation that the detection data could not fall below the set fixed threshold after the wearable device was released from the wearing state due to temperature rise or residual sweat. It can be understood that no matter which fixed threshold is used, it cannot cover the above test scenarios.

[0179] By dynamically updating the de-triggering threshold, the de-triggering threshold can be updated according to the actual status during the entire wearing period, and the wearable device can finally recognize the normal de-wearing action. The solution of dynamically updating the de-triggering threshold improves the success rate of normal device function in the scenarios of temperature drift and water sweat, which has a great improvement effect on the application in such scenarios.

[0180] The following uses a wearable device as an example to introduce the electronic devices involved in some embodiments of the present invention in detail.

[0181] like Figure 8 As shown, the wearable device 100 may include a processor 110, a wireless communication module 120, a mobile communication module 130, a power module 140, an audio module 150, an interface module 160, a camera 170, a memory 180, a sensor module 190, a button 11, and a display screen 12, etc.

[0182] It is understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the wearable device 100. In other embodiments of the present application, the wearable device 100 may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0183] The processor 110 may include one or more processing units, for example, a processing module or processing circuit including a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor DSP, a microprocessor (MCU), an artificial intelligence (AI) processor, or a programmable logic device (Field Programmable Gate Array, FPGA). Among them, different processing units can be independent devices or integrated in one or more processors. A storage unit may be set in the processor 110 for storing instructions and data. In some embodiments, the storage unit in the processor 110 is a cache memory 180. It can be understood that the sound effect debugging method in the embodiment of the present application can be executed by the processor 110 of the electronic device.

[0184] The wireless communication module 120 may include an antenna, and transmit and receive electromagnetic waves via the antenna. The wireless communication module 120 may provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication technology (NFC), infrared technology (IR), etc., which are applied to the wearable device 100. The wearable device 100 can communicate with the network and other devices through wireless communication technology. In some embodiments, the mobile communication module 130 and the wireless communication module 120 of the wearable device 100 may also be located in the same module.

[0185] The mobile communication module 130 may include, but is not limited to, an antenna, a power amplifier, a filter, a low noise amplifier (LNA), etc. The mobile communication module 130 may provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the wearable device 100. The mobile communication module 130 may receive electromagnetic waves by an antenna, filter, amplify, and process the received electromagnetic waves, and transmit them to a modulation and demodulation processor for demodulation. The mobile communication module 130 may also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna. In some embodiments, at least some of the functional modules of the mobile communication module 130 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 130 may be disposed in the same device as at least some of the modules of the processor 110.

[0186] The power module 140 may include a power source, a power management component, etc. The power source may be a battery. The power management component is used to manage the charging of the power source and the power supply of the power source to other modules. In some embodiments, the power management component includes a charging management module and a power management module. The charging management module is used to receive charging input from the charger; the power management module is used to connect the power source, the charging management module and the processor 110. The power management module receives input from the power source and / or the charging management module, and supplies power to the processor 110, the display screen 12, the camera 170, and the wireless communication module 120, etc.

[0187] The audio module 150 is used to convert digital audio information into analog audio signal output, or convert analog audio input into digital audio signal. The audio module 150 can also be used to encode and decode audio signals. In some embodiments, the audio module 150 can be arranged in the processor 110, or some functional modules of the audio module 150 can be arranged in the processor 110. In some embodiments, the audio module 150 can include a speaker, an earpiece, a microphone, and a headphone interface. For example, in the present embodiment, the audio module 150 converts the digital audio information in the copied sound effect algorithm module into an analog audio signal output, thereby adjusting the data in the sound effect algorithm module according to the sound effect of the output analog audio.

[0188] The interface module 160 includes an external memory interface, a universal serial bus (USB) interface, and a subscriber identification module (SIM) card interface. The external memory interface can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the wearable device 100. The external memory card communicates with the processor 110 through the external memory interface to implement a data storage function. The universal serial bus interface is used for the wearable device 100 to communicate with other electronic devices. The subscriber identification module card interface is used to communicate with a SIM card installed in the wearable device 100, for example, to read a phone number stored in the SIM card, or to write a phone number into the SIM card.

[0189] The camera 170 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal to the image signal processing (Image Signal Processing, ISP) to convert it into a digital image signal. The wearable device 100 can achieve the shooting function through the ISP, the camera 170, the video codec, the graphics processing unit (Graphic Processing Unit, GPU), the display screen 12 and the application processor.

[0190] The sensor module 190 may include a proximity light sensor, a pressure sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a distance sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor, a bone conduction sensor, and the like.

[0191] In some embodiments, the wearable device 100 further includes a button 11, a motor, and an indicator. The button 11 may include a volume button, an on / off button, etc. The motor is used to make the wearable device 100 vibrate, for example, vibrate when the wearable device 100 of the user is called, so as to prompt the user to answer the call of the wearable device 100. The indicator may include a laser indicator, a radio frequency indicator, an LED indicator, etc.

[0192] The display screen 12 is used to display a human-computer interaction interface, images, videos, etc. The display screen 12 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), MiniLED, MicroLED, Micro-oLED, quantum dot light emitting diodes (QLED), etc.

[0193] An embodiment of the present application also provides a chip, which can implement the methods provided in the aforementioned embodiments.

[0194] An embodiment of the present application further provides a readable storage medium, in which one or more programs are stored. When the one or more programs are executed by an electronic device, the electronic device implements the methods provided in the aforementioned embodiments.

[0195] In the accompanying drawings, some structural or method features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be required. Instead, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. In addition, the inclusion of structural or method features in a particular figure does not mean that such features are required in all embodiments, and in some embodiments, these features may not be included or may be combined with other features.

[0196] It should be noted that in the examples and description of this patent, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0197] Although the present application has been illustrated and described with reference to certain preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the present application.

Claims

1. A trigger detection method, applied to electronic equipment, characterized in that: The electronic device comprises a trigger detection device; the method comprises: In the triggered state of the electronic device, acquiring a plurality of first detection data detected by the trigger detection device at a plurality of different moments in a current time period; Determining whether the plurality of first detection data are data with stable changes; If the plurality of first detection data are data that change steadily, determining a de-triggering threshold of the electronic device according to at least part of the plurality of first detection data; Based on the de-triggering threshold, it is determined whether the electronic device is in a triggered state at a next moment in the current time period.

2. The method according to claim 1, characterized in that The determining whether the plurality of first detection data are stable data comprises: The standard deviation of the plurality of first detection data is calculated, and if the standard deviation is less than a first preset value, it is determined that the plurality of first detection data is data with a stable change.

3. The method according to claim 1, characterized in that The step of determining a de-triggering threshold of the electronic device according to at least part of the plurality of first detection data comprises: The product of the average value of the plurality of first detection data and a preset coefficient is used as the de-triggering threshold.

4. The method according to claim 1, characterized in that: The plurality of first detection data are data collected by the trigger detection device at preset time intervals.

5. The method according to claim 4, characterized in that The preset time interval is determined according to the detection scenario of the trigger detection.

6. The method according to claim 1, characterized in that The amount of the first detection data is determined according to the detection scenario that triggers the detection.

7. The method according to claim 1, characterized in that Before acquiring a plurality of first detection data detected by the trigger detection device at a plurality of different moments in the current time period, the method further comprises: It is determined that the electronic device is in a triggering state.

8. The method according to claim 7, characterized in that The determining that the electronic device is in a triggering state includes: Acquire third detection data detected by the trigger detection device at a time before the current time period; Based on the third detection data being greater than a preset threshold, it is determined that the electronic device is in a triggering state.

9. The method according to claim 8, characterized in that If the plurality of first detection data are data with non-steady changes, it is determined whether the electronic device is in a triggered state at the next moment of the current time period based on the current de-triggering threshold.

10. The method according to claim 9, characterized in that The plurality of first detection data are temperature compensated data.

11. A chip, characterized in that: The chip includes a processor and a data interface, and the processor reads instructions stored in a memory through the data interface to execute the method according to any one of claims 1 to 10.

12. An electronic device, characterized in that: Comprising the chip according to claim 11.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 10.

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