Working scenario determination method and apparatus, and wearable device and storage medium

Abstract

Disclosed in the present application are a working scenario determination method and apparatus, and a wearable device and a computer-readable storage medium. The working scenario determination method comprises: acquiring an acoustic time-domain signal, which is detected by an acoustic perception sensor, and a vibration time-domain signal, which is detected by a vibration perception sensor; preprocessing the acoustic time-domain signal and the vibration time-domain signal to obtain a target acoustic signal and a target vibration signal, and comparing signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information; and if the signal parameter deviation information is within a preset parameter deviation range, starting a preset working module of a wearable device, and entering an operating state.

Description

Methods, devices, wearable devices, and storage media for determining work scenarios

[0001] Related applications

[0002] This application claims priority to Chinese patent application No. 202410563471.7, filed on May 8, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of wearable device technology, and in particular to a method, apparatus, wearable device and storage medium for determining a work scenario. Background Technology

[0004] Currently, mainstream wearable devices, such as TWS (True Wireless Stereo) Bluetooth earphones and wired headphones, use various sensors to sense the operating environment of the device and activate the corresponding working modules. For example, acoustic sensors can acquire the user's acoustic signals to determine the device's operating environment and then activate the corresponding working modules.

[0005] Existing technologies use acoustic sensors to acquire acoustic signals to determine the working environment. However, due to the limited judgment criteria, misjudgments are prone to occur. The system is also susceptible to interference from the sounds of others in the surrounding environment, which can lead to incorrect judgments. Consequently, these misjudgments trigger the corresponding working modules, increasing the power consumption of the device. Therefore, reducing the misjudgment rate of the device in determining the working environment is a problem that urgently needs to be solved.

[0006] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0007] The main purpose of this application is to provide a method, apparatus, wearable device and storage medium for determining work scenarios, aiming to solve the technical problem of how to reduce the misjudgment of work scenarios by the device.

[0008] To achieve the above objectives, this application proposes a method for determining a work scenario, the method comprising:

[0009] Acquire the acoustic time-domain signal detected by the acoustic sensing sensor and the vibration time-domain signal detected by the vibration sensing sensor;

[0010] The acoustic time-domain signal and the vibration time-domain signal are preprocessed respectively to obtain the target acoustic signal and the target vibration signal. The signal parameters of the target acoustic signal and the target vibration signal are compared to obtain signal parameter deviation information.

[0011] If the signal parameter deviation information is within the preset parameter deviation range, the preset working module of the wearable device is activated and enters the running state.

[0012] Receive externally input acoustic and vibration signals, and preprocess the acoustic and vibration signals;

[0013] The preprocessed acoustic signal and vibration signal are compared to obtain the maximum amplitude difference;

[0014] If the maximum amplitude difference is within the preset amplitude difference range, the preset working module is started; if the maximum amplitude difference is not within the preset amplitude difference range, the preset sleep mode is returned.

[0015] In one embodiment, the step of comparing the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information further includes:

[0016] Acquire the first amplitude data of the target acoustic signal in the frequency domain, and the second amplitude data of the target vibration signal in the frequency domain;

[0017] Based on the first amplitude data, the first amplitude value of the target acoustic signal corresponding to the preset characteristic frequency point is determined;

[0018] Based on the second amplitude data, the second amplitude value of the target vibration signal corresponding to the preset characteristic frequency point is determined;

[0019] Based on the ratio between the first amplitude and the second amplitude, signal parameter deviation information is obtained.

[0020] In one embodiment, the step of preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively to obtain the target acoustic signal and the target vibration signal includes:

[0021] The acoustic time-domain signal and the vibration time-domain signal are converted into frequency domain signals to obtain acoustic frequency domain signals and vibration frequency domain signals.

[0022] The acoustic frequency domain signal and the vibration frequency domain signal are subjected to signal filtering and echo cancellation processing to obtain effective acoustic signal and effective vibration signal;

[0023] Based on the effective acoustic signal, a target acoustic signal is obtained, and based on the effective vibration signal, a target vibration signal is obtained.

[0024] In one embodiment, the steps of determining the target acoustic signal based on the effective acoustic signal and determining the target vibration signal based on the effective vibration signal include:

[0025] The effective acoustic signal is amplified by a preset first amplification factor to obtain the target acoustic signal;

[0026] The effective vibration signal is amplified by a preset second amplification factor to obtain the target vibration signal.

[0027] In one embodiment, the method further includes, before the step of preprocessing the acoustic signal and the vibration signal respectively:

[0028] Determine the intensity of the first signal corresponding to the vibration time-domain signal;

[0029] Determine whether the intensity of the first signal is greater than a preset vibration intensity threshold;

[0030] If the intensity of the first signal is greater than the preset vibration intensity threshold, then the step of preprocessing the acoustic signal and the vibration signal respectively is triggered.

[0031] In one embodiment, the step of preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively further includes:

[0032] Determine the first signal intensity corresponding to the vibration time-domain signal and the second signal intensity corresponding to the acoustic time-domain signal;

[0033] Determine whether the first signal strength is greater than a preset vibration intensity threshold, and whether the second signal strength is greater than a preset acoustic intensity threshold;

[0034] If the intensity of the first signal is greater than a preset vibration intensity threshold and the intensity of the second signal is greater than a preset acoustic intensity threshold, then the step of preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively is triggered.

[0035] In one embodiment, the step of obtaining signal parameter deviation information further includes:

[0036] If the signal parameter deviation information is not within the preset parameter deviation range, then the device enters sleep mode. The sleep mode is a mode in which the wearable device can normally receive the acoustic time domain signal and the vibration time domain signal, and the preset working module is in a dormant state.

[0037] Furthermore, to achieve the above objectives, this application also proposes a work scene determination device, which includes:

[0038] The signal processing module is used to acquire the acoustic time-domain signal detected by the acoustic sensing sensor and the vibration time-domain signal detected by the vibration sensing sensor, and to preprocess the acoustic time-domain signal and the vibration time-domain signal respectively to obtain the target acoustic signal and the target vibration signal.

[0039] The signal comparison module is used to compare the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information;

[0040] If the signal parameter deviation information is within the preset parameter deviation range, the preset working module of the wearable device is started and enters the running state.

[0041] In addition, to achieve the above objectives, this application also proposes a wearable device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the work scene determination method described above.

[0042] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the work scenario determination method described above.

[0043] One or more technical solutions proposed in this application have at least the following technical effects:

[0044] This application provides a method for determining a working scenario: acquiring an acoustic time-domain signal detected by an acoustic sensing sensor and a vibration time-domain signal detected by a vibration sensing sensor; preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively to obtain a target acoustic signal and a target vibration signal; comparing the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information; if the signal parameter deviation information is within a preset parameter deviation range, then activating the preset working module of the wearable device and entering the running state.

[0045] In the above scheme, the acoustic time-domain signal and vibration time-domain signal, before preprocessing, are more difficult to process in subsequent steps due to environmental interference. The preprocessed target acoustic signal and target vibration signal reduce the algorithm difficulty in subsequent steps, thereby reducing the power consumption of the method implemented in this application. Moreover, the signal parameter deviation information is obtained by comparing the acoustic signal and vibration signal, which can reflect the difference between the acoustic signal and vibration signal. The working scene is determined by comparing the two signals. Since the wearable device can obtain the difference between the sound emitted by the wearer and the two signals, and it is smaller than the difference between the external interference signal and the two signals, the preset parameter deviation range can distinguish the sound emitted by the wearer and the external interference signal, reducing the misjudgment rate of the working scene determination.

[0046] In summary, related technologies that determine the working scene using a single method are prone to misjudgment due to interference from other signals. However, the method of this application reduces the misjudgment rate of the device when determining the working scene by combining acoustic signals and vibration signals for comparison, thereby reducing the probability of opening the working mode due to misjudgment and thus reducing the overall power consumption of the device. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 is a flowchart of the first embodiment of the working scenario determination method of this application;

[0050] Figure 2 is a schematic diagram of the functional modules provided in the first embodiment of the working scenario determination method of this application;

[0051] Figure 3 is a flowchart of one embodiment of the second embodiment of the working scenario determination method of this application;

[0052] Figure 4 is a flowchart illustrating another embodiment of the second embodiment of the working scenario determination method of this application;

[0053] Figure 5 is a simplified flowchart of the working scenario method provided in Embodiment 2 of this application;

[0054] Figure 6 is a schematic diagram of the module structure of the working scene determination device according to an embodiment of this application;

[0055] Figure 7 is a schematic diagram of the hardware operating environment involved in the working scene determination method in this application embodiment.

[0056] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0058] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0059] The main solution of this application embodiment is: to acquire the acoustic time-domain signal detected by the acoustic sensing sensor and the vibration time-domain signal detected by the vibration sensing sensor; to preprocess the acoustic time-domain signal and the vibration time-domain signal respectively to obtain the target acoustic signal and the target vibration signal, and to compare the signal parameters of the target acoustic signal and the target vibration signal to obtain the signal parameter deviation information; if the signal parameter deviation information is within the preset parameter deviation range, the preset working module of the wearable device is started and enters the running state.

[0060] Currently, the mainstream approach uses a single sensor to determine the working environment of wearable devices. However, relying on a single sensor has poor ability to filter out external interference signals, making it prone to false alarms—that is, activating the working module incorrectly when the user does not intend to do so. For example, using a vibration sensor to acquire vibration signals and compare them with a preset vibration threshold to determine the working environment, while avoiding interference from external sounds while wearing the device, can still lead to false alarms and activation of the working module if the wearable device is placed on a table due to vibrations from the table, increasing the device's power consumption. Similarly, while using an acoustic sensor can avoid interference from vibrations from contacting objects, it can still lead to false alarms and activation of the working module due to sounds from other people in the environment. Therefore, reducing the false alarm rate of wearable devices in determining the working environment is a pressing issue that needs to be addressed.

[0061] This application combines vibration sensing sensors and acoustic sensing sensors, and comprehensively determines the working scene by comparing acoustic signals and vibration signals. This overcomes the misjudgment caused by the single judgment condition in mainstream solutions, and reduces the misjudgment rate of wearable devices in determining the working scene.

[0062] Wearable devices mainly refer to intelligent devices capable of receiving and emitting acoustic signals, and capable of receiving vibration signals when a user issues commands. Examples include TWS earphones, wired earphones, or over-ear Bluetooth earphones. It should be noted that the subject of this application can be TWS earphones, or other wearable devices capable of receiving and emitting acoustic signals, such as Mixed Reality (MR) devices (e.g., MR glasses or MR helmets), Augmented Reality (AR) devices (e.g., AR glasses or AR helmets), Virtual Reality (VR) devices (e.g., VR glasses or VR helmets), Extended Reality (XR) devices, or some combination thereof. This embodiment does not specifically limit this. The following describes this embodiment and the following embodiments using wearable devices as the subject of execution.

[0063] Based on this, the present application provides a method for determining a work scenario. Referring to Figure 1, Figure 1 is a flowchart of the first embodiment of the work scenario determination method of the present application.

[0064] In this embodiment, the work scene determination method includes steps S10 to S30:

[0065] Step S10: Acquire the acoustic time-domain signal detected by the acoustic sensing sensor and the vibration time-domain signal detected by the vibration sensing sensor.

[0066] It should be noted that acoustic sensing sensors refer to devices used to detect and measure sound waves. They use sound waves as input signals and convert them into electrical signals. Acoustic sensing sensors typically include the following components:

[0067] Microphones: Microphones are the core component of acoustic sensing sensors. They are used to capture sound waves from the surrounding environment, such as the voice of a user or people around them, and convert them into electrical signals, i.e., acoustic time-domain signals.

[0068] Audio processing chip: The audio processing chip receives the electrical signal output from the microphone and processes it by amplification, filtering, and digital conversion. These chips typically contain analog-to-digital converters and digital-to-analog converters.

[0069] Signal processing unit: This is the core of the acoustic sensing sensor, used to perform tasks such as signal analysis, feature extraction, and pattern recognition. The signal processing unit can be a microcontroller, a digital signal processor, or a dedicated sound processing chip.

[0070] Interface: Acoustic sensing sensors typically have interfaces for communicating with other devices, such as serial interfaces or analog output interfaces.

[0071] Similar to acoustic sensors, vibration sensors are devices used to detect and measure the vibration of objects. They can sense and record information such as the vibration frequency, amplitude, and direction of an object, and convert this information into electrical or digital signals for further processing and analysis.

[0072] Vibration sensing sensors typically include the following components:

[0073] Accelerometer: An accelerometer is the core component of a vibration sensing sensor. They measure changes in an object's acceleration along three axes. By integrating the acceleration signal, the object's velocity and displacement information can be obtained.

[0074] Vibration sensor chip: The vibration sensor chip receives the electrical signal output from the accelerometer and processes it by amplification, filtering, and digital conversion. These chips typically contain analog-to-digital converters and digital-to-analog converters.

[0075] Signal processing unit: Similar to acoustic sensors, vibration sensors also require a signal processing unit to perform tasks such as signal analysis, feature extraction, and pattern recognition. This can be a microcontroller, a digital signal processor, or a dedicated vibration processing chip.

[0076] Interface: Vibration sensing sensors typically have an interface for communicating with other devices, such as a serial interface or an analog output interface.

[0077] Additionally, it should be noted that acoustic time-domain signals refer to the electrical signals in the time domain converted by acoustic sensing sensors from external sound waves. Typically, the amplitude of an acoustic time-domain signal can be expressed in units of voltage or decibels to represent the loudness of the input external sound wave.

[0078] Similar to acoustic time-domain signals, vibration time-domain signals refer to the electrical signals in the time domain converted by vibration sensing sensors from external vibrations. The amplitude of vibration time-domain signals can usually be identified by voltage or decibels to represent the intensity of the input external vibration.

[0079] It is understandable that the time domain in acoustic time domain signals and vibration time domain signals is to limit the acquired electrical signals to the time domain. Acoustic time domain signals acquired by sensors belong to acoustic signals, which include acoustic time domain signals acquired by sensors as well as signals after various processing of the acoustic time domain signals.

[0080] Similarly, vibration signals include vibration time-domain signals obtained through sensors and signals obtained after various processing of subsequent vibration time-domain signals.

[0081] For example, the acoustic sensing sensor is an air conduction sound sensor, and the vibration sensing sensor is a bone conduction sound sensor.

[0082] In one embodiment, an acoustic sensing sensor converts external sound waves into acoustic time-domain signals, and a vibration sensing sensor converts external vibrations into vibration time-domain signals.

[0083] It is understandable that since the subsequent steps of this application are all based on the acoustic time-domain signal and vibration time-domain signal obtained in step S10, performing step S10 is a necessary step to execute the subsequent steps. Furthermore, since the acoustic sensing sensor and vibration sensing sensor have high reliability, the data of externally input sound and vibration obtained through the acoustic sensing sensor and vibration sensing sensor can accurately reflect the physical properties of the externally input sound and vibration, thereby ensuring the reliability of the subsequent steps and reducing the false judgment rate.

[0084] The above is only one feasible implementation of step S10 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S10 for obtaining acoustic time domain signals and vibration time domain signals.

[0085] Step S20: Preprocess the acoustic time-domain signal and the vibration time-domain signal respectively to obtain the target acoustic signal and the target vibration signal, and compare the signal parameters of the target acoustic signal and the target vibration signal to obtain the signal parameter deviation information;

[0086] It should be noted that preprocessing is for extracting useful information from the signal and preparing data for further analysis and application, or it can be for improving the signal quality. Preprocessing steps can vary depending on the specific application requirements. For example, when analyzing timbre, frequency domain transformation is performed to analyze the signal's frequency information; when signal strength is insufficient, an amplifier is used to amplify the signal's amplitude range; and when signal quality needs improvement, frequency bands are adjusted to reduce signal glitches.

[0087] Furthermore, signal quality refers to the accuracy and clarity of an acoustic signal in maintaining its original information during transmission, processing, and amplification. The following are some factors that affect signal quality:

[0088] Noise level: Noise refers to unwanted additional sounds present in a signal. Noise degrades signal quality, making sounds muffled or chaotic. Therefore, reducing noise is crucial for improving signal quality.

[0089] Distortion: Distortion refers to changes in the shape, frequency response, or phase of a signal during transmission or processing. Distortion causes sound to sound distorted, unnatural, or distorted. Reducing distortion is crucial for maintaining signal quality.

[0090] Dynamic range: Dynamic range refers to the difference between the strongest and weakest parts of a signal. A larger dynamic range can provide more audio detail and emotional expression. If the dynamic range is limited or excessively compressed, the signal quality will degrade.

[0091] Frequency response: Frequency response describes the transmission and amplification characteristics of a signal at different frequencies. The frequency response of a signal should be as flat as possible to ensure that all frequency components are accurately transmitted and amplified.

[0092] Signal-to-noise ratio (SNR): The SNR is the ratio between the signal strength and the background noise strength. A higher SNR means the signal is more clearly distinguishable relative to the noise. Improving the SNR can improve signal quality.

[0093] In summary, improving signal quality requires attention to reducing noise, reducing distortion, maintaining a large dynamic range, achieving a flat frequency response, and increasing the signal-to-noise ratio. These factors all need to be considered and optimized during acoustic signal preprocessing.

[0094] It is understandable that the acoustic time-domain signal, after preprocessing, becomes the target acoustic signal, and the vibration time-domain signal, after preprocessing, becomes the target vibration signal.

[0095] Furthermore, signal parameter deviation information refers to the deviation information obtained after comparing the target acoustic signal and the target vibration signal. It represents the difference between the target acoustic signal and the target vibration signal. It is a ratio type of data, such as decibel difference, or it can be a difference type of data, such as voltage difference.

[0096] It is understandable that the aforementioned preprocessing is implemented through the central processing unit of the wearable device in related technologies. Although the central processing unit has strong computing power, the excess power consumption generated by the strong computing power will also increase the overall power consumption of the wearable device. Based on this, in one example, this embodiment proposes a signal processing module and places the signal processing module in the signal processing unit of the vibration sensing module in step S10, as shown in Figure 2. In Figure 2, the acoustic sensing module is the aforementioned acoustic sensing sensor, the vibration sensing module is the aforementioned vibration sensing sensor, the signal processing module refers to the algorithm module that can realize the preprocessing in the subsequent steps, the signal processing entry refers to the interface for inputting the signal into the signal processing module, when the signal processing module is placed in the vibration sensing module, the signal processing entry specifically refers to the interface for inputting the acoustic time domain signal into the signal processing module, the acoustic interface refers to the interface for connecting the acoustic sensing sensor to the wearable device, the signal processing interface refers to the interface for connecting the signal processing module to the wearable device, and the vibration interface refers to the interface for connecting the vibration sensing sensor to the wearable device.

[0097] In another feasible embodiment, the signal processing module may be placed within the signal processing unit of the acoustic sensing module.

[0098] By placing the signal processing module within the acoustic sensing module or the vibration sensing module, the workload of the central processing unit is reduced, thereby lowering the power consumption of the wearable device when using the working scene determination method of this application. Of course, the signal processing module can also be set up separately from the acoustic sensing module and the vibration sensing module, and this embodiment does not specifically limit this.

[0099] In one feasible implementation, step S20, which preprocesses the acoustic time-domain signal and the vibration time-domain signal to obtain the target acoustic signal and the target vibration signal, may include steps A11 to A13:

[0100] Step A11: Perform frequency domain conversion on the acoustic time domain signal and the vibration time domain signal to obtain the acoustic frequency domain signal and the vibration frequency domain signal;

[0101] It should be noted that frequency domain conversion refers to converting information of a signal in the time domain into information in the frequency domain, which can be achieved through methods such as Fourier transform, fast Fourier transform, and wavelet transform.

[0102] Furthermore, acoustic frequency domain signals refer to electrical signals in the frequency domain, which can reflect the intensity of sound waves at each frequency in the frequency domain. Acoustic time domain signals are converted to acoustic frequency domain signals through frequency domain transformation. Vibration frequency domain signals refer to electrical signals in the frequency domain, which can reflect the intensity of external vibrations at each frequency in the frequency domain. The amplitude units of acoustic frequency domain signals and vibration frequency domain signals can be voltage or decibels, and the amplitude value can reflect the intensity of the signal at the frequency corresponding to the amplitude value.

[0103] It is understandable that intensity refers to the energy of an acoustic frequency domain signal, while amplitude can reflect intensity. Therefore, the amplitude at a specific frequency point can reflect the energy level of the signal at that specific frequency point. Since different signals have different energy distributions in the frequency domain, frequency domain analysis can be used to distinguish signals. In this embodiment, by performing frequency domain conversion, acoustic frequency domain signals and vibration frequency domain signals are obtained, which can make subsequent comparison and judgment more accurate. Compared with related technologies that use intensity in the time domain for judgment, this method has advantages, thereby further reducing the misjudgment rate when judging working scenarios.

[0104] Step A12: Perform signal filtering and echo cancellation processing on the acoustic frequency domain signal and the vibration frequency domain signal to obtain effective acoustic signal and effective vibration signal;

[0105] It should be noted that filtering and echo cancellation are common preprocessing methods. Filtering refers to methods used to remove or alter certain frequency components in a signal. Filtering can remove or change noise components at the frequencies of noise in acoustic and vibration frequency domain signals. This can be achieved using FIR filters (Finite Impulse Response Filters): FIR filters are non-recursive filters with a finite-length impulse response. FIR filters calculate the output by weighted summation of past and current values ​​of the input signal. Common FIR filter design methods include window function methods, frequency sampling methods, and optimization methods, or statistical filters: statistical filters use statistical methods to filter signals and are often used to remove random noise. Common statistical filters include median filters and mean filters.

[0106] Echo cancellation can be achieved using adaptive filters. Algorithms such as LMS (Least Mean Square) or NLMS (Normalized Least Mean Square) can be used to adjust the adaptive filter to eliminate echoes.

[0107] Furthermore, the effective acoustic signal and effective vibration signal are the effective signals retained after signal filtering and echo cancellation processing. The effective signal refers to the processable signal retained after noise and echo signals are eliminated. Because the effective acoustic signal and effective vibration signal have filtered out noise and echoes, they have a high signal-to-noise ratio.

[0108] Furthermore, the signal-to-noise ratio (SNR) is a measure of the relative strength between a signal and noise. It represents the ratio between the power or energy of the signal and the power or energy of the noise. The unit of SNR is usually expressed in decibels (dB). In the fields of communications and signal processing, a higher SNR generally indicates better signal quality because the signal has more energy relative to the noise, making the signal easier to receive or interpret correctly.

[0109] It is understood that the above description is only an explanation of signal filtering and echo cancellation processing. This application does not limit the methods of signal filtering and echo cancellation processing. The purpose of using signal filtering and echo cancellation processing is to reduce noise and echo, obtain effective acoustic signals and effective vibration signals with higher signal-to-noise ratios, and improve signal-to-noise ratios, which can improve signal quality. Therefore, the signal quality of effective acoustic signals and effective vibration signals is higher than that of acoustic frequency domain signals and vibration frequency domain signals. By improving signal quality, the accuracy of subsequent signal parameter comparison can be improved, thereby indirectly reducing the misjudgment rate of working scene determination.

[0110] Step A13: Based on the effective acoustic signal, obtain the target acoustic signal, and based on the effective vibration signal, obtain the target vibration signal.

[0111] In this embodiment, the effective acoustic signal, after being filtered and echo-cancelled, can be used as the target acoustic signal, and the effective vibration signal, after being filtered and echo-cancelled, can be used as the target vibration signal.

[0112] It is understandable that the target acoustic signal and target vibration signal after filtering and echo cancellation are of better quality than the effective acoustic signal and effective vibration signal. This can lead to more accurate results in subsequent signal processing steps, thereby reducing the misjudgment rate when determining the working scene.

[0113] In another feasible implementation, step A13 may include steps B11 to B12:

[0114] Step B11: Amplify the effective acoustic signal by a preset first amplification factor to obtain the target acoustic signal;

[0115] It should be noted that amplification at a preset magnification factor is a preprocessing method that increases signal strength and amplifies the difference between noise and effective signal. Signal strength refers to the voltage intensity of the signal acquired by the sensor, which can be expressed as voltage, power, or decibels.

[0116] It is understandable that while amplification can increase signal strength, signal quality will decrease as the amplification factor increases. However, if the amplification factor is not limited, the signal will be distorted when the amplification factor reaches a certain threshold. The distorted signal will lose its meaning for analysis and processing because it can no longer truly reflect the physical properties of the signal itself. The aforementioned threshold is an assumed value. When the signal acquisition method and amplification structure are different, the value of this threshold will not be uniform. This application does not limit the value of the threshold.

[0117] Furthermore, this application sets a preset amplification factor. When amplified at the preset amplification factor, it can be ensured that the normally acquired acoustic and vibration signals will not be distorted after amplification, and that the difference between the acoustic and vibration signals will not be amplified.

[0118] It is understood that this application does not limit the value of the preset amplification factor. When using different devices to complete the working scenario determination method of this application, the preset amplification factor will be different. When the purpose of setting the amplification factor is to increase the signal strength and amplify the difference between noise and effective signal, it is considered to be the same as the method of setting the amplification factor in this application.

[0119] In this embodiment, the effective acoustic signal is amplified by a preset amplification factor, which can further increase the difference between the noise and the effective signal in the effective acoustic signal after filtering and echo cancellation processing, thereby further improving the signal quality of the target acoustic signal, further improving the accuracy of subsequent signal parameter comparison, and thus indirectly reducing the misjudgment rate.

[0120] Step B12: Amplify the effective vibration signal by a preset second amplification factor to obtain the target vibration signal;

[0121] It is understandable that amplifying the effective vibration signal by a preset amplification factor serves a similar purpose to the amplification process in step B11, and the preset amplification factor is the same.

[0122] The second magnification factor can be the same as or different from the first magnification factor. Those skilled in the art can set it according to the actual situation. This embodiment does not make specific limitations.

[0123] In this embodiment, the effective vibration signal is amplified by a preset amplification factor, which can further increase the difference between noise and effective signal in the effective vibration signal after filtering and echo cancellation, thereby further improving the signal quality of the target vibration signal, further improving the accuracy of subsequent signal parameter comparison, and thus indirectly reducing the misjudgment rate.

[0124] This embodiment provides a method for determining the working scene. In the above embodiment, the acoustic time-domain signal and vibration time-domain signal are increased in difficulty in subsequent steps due to environmental interference before preprocessing. The preprocessed target acoustic signal and target vibration signal reduce the algorithm difficulty in subsequent steps, thereby reducing the power consumption of the method implemented in this application. Moreover, the signal parameter deviation information is obtained by comparing the acoustic signal and the vibration signal. The working scene is determined by comparing the two signals. Since the wearable device can obtain the difference between the sound emitted by the wearer and the two signals, but the difference is smaller than the difference between the external interference signal and the two signals, the sound emitted by the wearer and the external interference signal can be distinguished by the preset parameter deviation range, thereby reducing the misjudgment rate of the working scene.

[0125] In summary, existing technologies that determine the working scene using a single method are prone to misjudgment due to interference from other signals. The method of this application reduces the misjudgment rate of the device when determining the working scene by combining acoustic signals and vibration signals for comparison, thereby reducing the probability of activating the working mode due to misjudgment and thus reducing the overall power consumption of the device.

[0126] It is understood that the first implementation of the step S20 above, which preprocesses the acoustic time-domain signal and the vibration time-domain signal to obtain the target acoustic signal and the target vibration signal, is simpler than the second implementation. Therefore, the first implementation has a higher efficiency in determining the target acoustic signal and the target vibration signal. Since the second implementation performs amplification processing compared to the first implementation, the second implementation has a higher accuracy in obtaining the target acoustic signal and the target vibration signal.

[0127] The above are only two feasible implementations of the step S20 in this embodiment, which involves preprocessing the acoustic time-domain signal and the vibration time-domain signal to obtain the target acoustic signal and the target vibration signal. This embodiment does not specifically limit the specific implementation of the step S20, which involves preprocessing the acoustic time-domain signal and the vibration time-domain signal to obtain the target acoustic signal and the target vibration signal.

[0128] In one feasible implementation, step S20, which compares the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information, may include steps C11 to C14:

[0129] Step C11: Obtain the first amplitude data of the target acoustic signal in the frequency domain and the second amplitude data of the target vibration signal in the frequency domain;

[0130] It should be noted that the first amplitude data refers to all amplitude data of the target acoustic signal in the full frequency domain, that is, it includes the amplitude of the target acoustic signal at each frequency. The second amplitude data refers to all amplitude data of the target vibration signal in the full frequency domain, that is, it includes the amplitude of the target vibration signal at each frequency.

[0131] It is understandable that by acquiring the first amplitude data and the second amplitude data, the comparison process in subsequent steps can be simplified, thereby optimizing the algorithm, reducing the computational load of the algorithm, and reducing the power consumption of the operation method in the working scenario of this application.

[0132] Step C12: Based on the first amplitude data, determine the first amplitude value of the target acoustic signal at the preset characteristic frequency point;

[0133] Step C13: Based on the second amplitude data, determine the second amplitude value of the target vibration signal at the preset characteristic frequency point;

[0134] In this embodiment, the preset characteristic frequency point refers to a frequency point that is preset in the full frequency domain. The amplitude of the target acoustic signal and the target vibration signal at the preset characteristic frequency point can reflect the unique characteristics of the target acoustic signal and the target vibration signal in the frequency domain. The unique characteristics refer to the characteristics that can be distinguished from other signals.

[0135] It should be noted that the algorithm for determining the first and second amplitudes includes:

[0136] Directly read amplitude values: In the spectrum representation, the first and second amplitude values ​​of preset characteristic frequency points can be read directly.

[0137] Interpolation or smoothing: When the target frequency is not at discrete frequency points, interpolation is needed to estimate the amplitude of that frequency. Interpolation methods can include linear interpolation, spline interpolation, etc. Additionally, to obtain a smoother spectral curve, smoothing filters, such as moving average filtering or Gaussian smoothing filtering, can be applied.

[0138] Window-weighted averaging: In frequency domain analysis, to reduce the impact of spectral leakage, time-domain signals are typically weighted using a window function. The function of a window function is to weight the signal within a certain window range in the time domain, thereby reducing the influence of spectral leakage. With window-weighted averaging, multiple frequency components near the target frequency can be weighted to obtain a more accurate amplitude estimate.

[0139] It is understood that the above embodiments do not limit the type of algorithm for determining the first amplitude and the second amplitude. When the algorithm used is the same as that in this embodiment for obtaining the amplitude at a frequency point, it is considered to be equivalent to the preset algorithm mentioned in this embodiment.

[0140] The first amplitude and the second amplitude refer to the amplitudes of the target acoustic signal and the target vibration signal at preset characteristic frequency points obtained through a preset algorithm. The first amplitude can reflect the unique characteristics of the target acoustic signal, and the second amplitude can reflect the unique characteristics of the target vibration signal.

[0141] Step C14: Based on the ratio between the first amplitude and the second amplitude, obtain the signal parameter deviation information.

[0142] It should be noted that signal parameter deviation information can represent the difference in amplitude at independent frequency points in the frequency domain between the target acoustic signal and the target vibration signal. Typically, the unit of amplitude in the frequency domain can be voltage or decibels.

[0143] As those skilled in the art will recognize, a ratio can reflect the degree of difference between the data used in the ratio calculation. By calculating the ratio of the first amplitude and the second amplitude, the resulting ratio can reflect the difference between the first amplitude and the second amplitude. This is because the first amplitude reflects the unique characteristics of the target acoustic signal, and the second amplitude reflects the unique characteristics of the target vibration signal. Therefore, the resulting ratio can reflect the difference between the target acoustic signal and the target vibration signal. Furthermore, the resulting ratio can represent the difference in amplitude between the target acoustic signal and the target vibration signal at characteristic frequency domain points, i.e., signal parameter deviation information.

[0144] In this embodiment, the ratio between the first amplitude and the second amplitude is not limited to the ratio of the first amplitude to the second amplitude, but can also be the ratio of the second amplitude to the first amplitude. The order of the ratio calculation will not affect the result of the subsequent determination.

[0145] It is understandable that calculating signal parameter deviation information through characteristic frequency points is a common implementation method because it involves less computation and effectively reduces power consumption. Other methods can also be used to obtain and calculate this information. In one embodiment, a full-frequency domain algorithm calculates the ratio of the amplitude of the target acoustic signal to that of the target vibration signal at each frequency point in the full-frequency domain, forming a ratio data set, which is then used as the signal parameter deviation information. While the full-frequency domain algorithm can more accurately distinguish whether the target acoustic signal and the target vibration signal originate from the same signal source, not all devices can perform full-frequency domain algorithms, and the power consumption of the full-frequency domain algorithm is far greater than that of calculating signal parameter deviation information through characteristic frequency points, making it not a preferred solution.

[0146] The above are only two implementation methods of the step S20 in this embodiment, which compares the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information. This embodiment does not specifically limit the specific implementation method of the step S20, which compares the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information.

[0147] After step S20, step S30 is executed. If the signal parameter deviation information is within the preset parameter deviation range, the preset working module of the wearable device is started and enters the running state.

[0148] It should be noted that the preset parameter deviation range refers to the acceptable range of signal parameter deviation information. If it exceeds the preset parameter deviation range, it is considered that the target acoustic signal and the target vibration signal do not come from the same signal source, that is, the user has not output the command to open the preset working scene. The running state refers to the working state after the working module is started. In the running state, the device will execute the function corresponding to the working module.

[0149] In one embodiment, the preset parameter deviation range can be set to -20 dB to 20 dB. The preset working module refers to a functional module that can realize the work scenario required by the user. For ease of understanding, in one embodiment, when the work scenario requires voice dialogue, the preset working module is a voice module. In another embodiment, when the work scenario requires playing music, the preset working module is a music playback module. In yet another embodiment, when the work scenario requires turning on noise reduction, the preset working module is a noise reduction module.

[0150] It is understandable that setting the parameter deviation range will affect the accuracy of the working scene determination method of this application, but it will not affect the implementation of the working scene determination method of this application. By setting the parameter deviation range, the signal parameter deviation information is determined, and then the working scene of the device is determined. Because the signal parameter deviation information contains the unique characteristics of acoustic signals and vibration signals, by combining the two signals to determine the working scene, the misjudgment situation when judging a single signal is avoided, and the misjudgment rate of working scene determination is reduced.

[0151] The above is only one feasible implementation of step S30 provided in this embodiment. This embodiment does not specifically limit the setting of the preset parameter deviation range in step S30.

[0152] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, please refer to Figure 3. In a feasible embodiment, before step S20, the working scene determination method further includes steps S01 to S03:

[0153] Step S01: Determine the intensity of the first signal corresponding to the vibration time-domain signal;

[0154] It should be noted that the first signal intensity refers to the amplitude of the vibration time-domain signal at each time point in the time domain. The intensity unit is voltage or decibel, which represents the vibration intensity of the vibration time-domain signal at each time point.

[0155] It is understandable that determining the first signal strength is for the purpose of making the judgment in step S02. Since the vibration time-domain signal is acquired by the vibration sensing sensor, the acquired vibration time-domain signal already contains the first signal strength. Without calculation, the first signal strength corresponding to each time point of the vibration time-domain signal can be directly obtained, which indirectly reduces the power consumption of the working scene determination method of this application.

[0156] Step S02: Determine whether the intensity of the first signal is greater than a preset vibration intensity threshold.

[0157] Step S03: If the intensity of the first signal is greater than the preset vibration intensity threshold, then the following steps are triggered: preprocessing the acoustic signal and the vibration signal respectively.

[0158] It should be noted that the preset vibration intensity threshold in step S02 refers to the minimum voltage value or minimum decibel value required for the vibration time domain signal to execute subsequent steps. Step S20 will only be triggered when the intensity of the first signal is greater than the vibration intensity threshold.

[0159] It is understandable that when a user issues a command to activate a work scene, the wearable device, through physical contact with the user, uses a vibration sensor to detect the vibration of the command and converts it into a time-domain vibration signal. This time-domain signal is an electrical signal, and its voltage intensity is the first signal intensity, representing the vibration intensity of the command. Since there is direct physical contact between the user and the wearable device when the command is issued, the detected first signal intensity is higher than the intensity of external interference signals. A preset vibration intensity threshold is used to determine whether to run the work scene determination method of this application. Because the intensity of external interference signals is less than the preset threshold, this avoids interference even when the user has not issued a command. Interference signals trigger the working scene determination method of this application. However, under certain circumstances, the working scene determination method of this application may still be erroneously triggered. For example, when a wearable device is placed on a table, the table will vibrate when it is hit. At this time, the intensity of the first signal obtained is greater than the vibration intensity threshold, triggering the working scene determination method of this application. However, it is not the user who issues the command to trigger the working scene, but the vibration generated by the impact of the table that triggers the working scene determination method of this application. By judging the intensity of the first signal by the preset vibration intensity threshold, some cases of erroneous execution of the working scene determination method of this application due to external signal interference can be avoided. However, judging only the vibration signal can prevent the external sound from erroneously executing the working scene determination method of this application, but it cannot prevent the erroneous execution of the working scene determination method of this application due to the vibration of the contact object.

[0160] Therefore, in order to further reduce the power consumption of the method of this application, in another feasible embodiment, as shown in FIG4, before step S20, the working scenario determination method further includes steps S011 to S031:

[0161] Step S011: Determine the first signal intensity corresponding to the vibration time-domain signal and the second signal intensity corresponding to the acoustic time-domain signal;

[0162] It should be noted that the second signal intensity refers to the amplitude of the acoustic time-domain signal at each time point in the time domain. The intensity unit is voltage or decibel, which represents the sound intensity of the acoustic time-domain signal at the corresponding time point. Sound intensity can directly reflect the loudness of the sound.

[0163] It is understandable that determining the second signal strength is for the purpose of making the judgment in step S021. Since the acoustic time-domain signal is acquired by the acoustic sensing sensor, the acquired acoustic time-domain signal already contains the second signal strength. Without calculation, the second signal strength corresponding to each time point of the acoustic time-domain signal can be directly obtained, which indirectly reduces the power consumption of the working scene determination method of this application.

[0164] Step S021: Determine whether the intensity of the first signal is greater than a preset vibration intensity threshold and whether the intensity of the second signal is greater than a preset acoustic intensity threshold.

[0165] Step S031: If the intensity of the first signal is greater than the preset vibration intensity threshold and the intensity of the second signal is greater than the preset acoustic intensity threshold, then the following steps are triggered: preprocessing the acoustic time domain signal and the vibration time domain signal respectively.

[0166] It should be noted that the preset acoustic intensity threshold in step S021 refers to the minimum voltage value or minimum decibel value required for the acoustic time domain signal to execute subsequent steps. Step S20 will only be triggered when the second signal intensity is greater than the acoustic intensity threshold and the first signal intensity is greater than the vibration intensity threshold.

[0167] In one embodiment, when a user issues a command to activate a work scene, the wearable device acquires the sound wave of the command via an acoustic sensing sensor and converts it into an acoustic time-domain signal. The acoustic time-domain signal is an electrical signal, and its voltage intensity is the second signal intensity. This second signal intensity refers to the acoustic intensity of the sound wave of the command. Since the acquired second signal intensity is higher than the intensity of external interference signals when the user issues the command, a preset acoustic intensity threshold is used to determine whether to run the work scene determination method of this application. Because the intensity of external interference signals is less than the preset vibration intensity threshold, this method can avoid triggering the work scene determination method when the user has not issued a command. The proposed method for determining the working scene can still erroneously activate under certain circumstances. For example, when people around are chatting and the volume of their conversation exceeds the acoustic intensity threshold, the resulting second signal strength also exceeds the acoustic intensity threshold, thus activating the proposed method for determining the working scene. However, this is not due to a user issuing a command to activate the working scene, but rather the sound waves from the surrounding conversation that activate the proposed method. By judging the second signal strength using a preset acoustic intensity threshold, some instances of erroneous execution of the proposed method due to external signal interference can be avoided. However, judging only the acoustic signal can prevent erroneous execution of the proposed method due to external vibrations, but it cannot prevent erroneous execution of the proposed method due to external sound waves.

[0168] It is understood that this embodiment requires not only that the second signal intensity is greater than the acoustic intensity threshold, but also that the first signal intensity is greater than the vibration intensity threshold before step S20 is executed. This embodiment can not only avoid the subsequent steps being executed incorrectly due to external sound waves, but also avoid the subsequent steps being executed incorrectly due to contact with an object and the object vibrating. Compared with the method implemented by steps S01 to S03, it can avoid more erroneous execution situations and further reduce the power consumption of the method of this application.

[0169] Based on the first and / or second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to the first and second embodiments described above can be referred to the above description and will not be repeated hereafter. Following the step of obtaining the signal parameter deviation information, the method further includes:

[0170] Step S301: If the signal parameter deviation information is not within the preset parameter deviation range, then enter sleep mode. The sleep mode is a mode in which the wearable device can normally receive acoustic time-domain signals and vibration time-domain signals, and the preset working module is in a dormant state.

[0171] It should be noted that sleep mode is a standby mode for wearable devices. In this mode, the wearable device can only receive external acoustic time-domain signals and vibration time-domain signals, and determine whether to perform preprocessing according to the steps in Embodiment 2. In sleep mode, the power consumption of the wearable device will be further reduced. The preset working module being in a dormant state means that the working module is not in use at this time.

[0172] It is understandable that although sleep mode is a standby state, the steps in the first embodiment and / or the second embodiment described above can still be performed. By setting sleep mode, the problem of additional power consumption caused by long-term use of the working module is solved.

[0173] For example, to help understand the implementation flow of the work scene determination method obtained by combining the above embodiments, please refer to Figure 5. Figure 5 provides a simplified flowchart of a work scene determination method, specifically:

[0174] First, when the acoustic and vibration sensing signals in the wearable device have not yet detected acoustic and vibration time-domain signals, it will be in sleep mode. Once acoustic and vibration time-domain signals are detected, the first and second signal intensities of the vibration and acoustic time-domain signals are acquired. Based on preset vibration and acoustic intensity thresholds, it is determined whether the first signal intensity is greater than the vibration intensity threshold and whether the second signal intensity is greater than the acoustic intensity threshold. If the above conditions are met, the acoustic and vibration time-domain signals are preprocessed to obtain target acoustic and target vibration signals. By calculating the ratio of the target acoustic and target vibration signals, signal parameter deviation information is obtained. Then, it is determined whether the signal parameter deviation information is within the preset parameter deviation range. If it is within the parameter deviation range, the working module is started and the device enters the running state. If it is not within the parameter deviation range, the device returns to sleep mode until the next time the acoustic and vibration sensing signals in the wearable device have not detected acoustic and vibration time-domain signals, at which point the above steps are repeated.

[0175] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for determining the working scenario of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0176] This application also provides a work scene determination device. Please refer to Figure 6. The work scene determination device includes:

[0177] The signal processing module 10 is used to acquire the acoustic time-domain signal detected by the acoustic sensing sensor and the vibration time-domain signal detected by the vibration sensing sensor, and to preprocess the acoustic time-domain signal and the vibration time-domain signal respectively to obtain the target acoustic signal and the target vibration signal.

[0178] The signal comparison module 20 is used to compare the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information;

[0179] If the signal parameter deviation information is within the preset parameter deviation range, the working module 30 starts the preset working module of the wearable device and enters the running state.

[0180] In one embodiment, the signal comparison module 20 is further configured to:

[0181] Acquire the first amplitude data of the target acoustic signal in the frequency domain, and the second amplitude data of the target vibration signal in the frequency domain;

[0182] Based on the first amplitude data, determine the first amplitude value of the target acoustic signal at the preset characteristic frequency point;

[0183] Based on the second amplitude data, determine the second amplitude value of the target vibration signal at the preset characteristic frequency point;

[0184] Based on the ratio between the first amplitude and the second amplitude, signal parameter deviation information is obtained.

[0185] In one embodiment, the signal processing module 10 is further configured to:

[0186] The acoustic time-domain signal and the vibration time-domain signal are converted into the frequency domain to obtain the acoustic frequency domain signal and the vibration frequency domain signal.

[0187] Signal filtering and echo cancellation are performed on acoustic frequency domain signals and vibration frequency domain signals to obtain effective acoustic signals and effective vibration signals;

[0188] The target acoustic signal is obtained based on the effective acoustic signal, and the target vibration signal is obtained based on the effective vibration signal.

[0189] In one embodiment, the signal processing module 10 is further configured to:

[0190] The effective acoustic signal is amplified by a preset first amplification factor to obtain the target acoustic signal;

[0191] The effective vibration signal is amplified by a preset second amplification factor to obtain the target vibration signal.

[0192] In one embodiment, the signal comparison module 20 is further configured to:

[0193] Determine the intensity of the first signal corresponding to the vibration time-domain signal;

[0194] Determine whether the intensity of the first signal is greater than a preset vibration intensity threshold;

[0195] If the intensity of the first signal is greater than the preset vibration intensity threshold, the following steps are triggered: preprocessing the acoustic signal and the vibration signal respectively.

[0196] In one embodiment, the signal comparison module 20 is further configured to:

[0197] Determine the first signal intensity corresponding to the vibration time-domain signal and the second signal intensity corresponding to the acoustic time-domain signal;

[0198] Determine whether the intensity of the first signal is greater than a preset vibration intensity threshold, and whether the intensity of the second signal is greater than a preset acoustic intensity threshold;

[0199] If the intensity of the first signal is greater than the preset vibration intensity threshold and the intensity of the second signal is greater than the preset acoustic intensity threshold, then the following steps are triggered: preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively.

[0200] In one embodiment, the working module initiation module 30 is further configured to:

[0201] If the signal parameter deviation information is not within the preset parameter deviation range, the device enters sleep mode. Sleep mode is a mode in which the wearable device can normally receive acoustic time-domain signals and vibration time-domain signals, and the preset working module is in a dormant state.

[0202] The work scene determination device provided in this application, employing the work scene determination method described in the above embodiments, can solve the technical problem of work scene determination. Compared with the prior art, the beneficial effects of the work scene determination device provided in this application are the same as those of the work scene determination method described in the above embodiments, and other technical features in the work scene determination device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0203] This application provides a wearable device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the working scenario determination method in Embodiment 1 above.

[0204] Referring to Figure 7 below, a schematic diagram of a wearable device suitable for implementing embodiments of this application is shown. The wearable device in this application embodiment may include, but is not limited to, TWS earphones, wired earphones, or over-ear Bluetooth earphones, or mixed reality (MR) devices (e.g., MR glasses or MR helmets), augmented reality (AR) devices (e.g., AR glasses or AR helmets), virtual reality (VR) devices (e.g., VR glasses or VR helmets), extended reality (XR) devices, or some combination thereof, etc. This embodiment does not specifically limit the specific type of wearable device. The wearable device shown in Figure 7 is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0205] As shown in Figure 7, the wearable device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchpad, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the wearable device to exchange data with other devices wirelessly or via wired communication. While the diagrams show wearable devices with various systems, it should be understood that implementation or possession of all of the systems shown is not required. More or fewer systems may be implemented alternatively.

[0206] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0207] The wearable device provided in this application, employing the work scene determination method described in the above embodiments, can solve the technical problem of work scene determination. Compared with the prior art, the beneficial effects of the work scene determination device provided in this application are the same as those of the work scene determination method described in the above embodiments, and other technical features of the work scene determination device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0208] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0209] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0210] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the working scene determination method in the above embodiments.

[0211] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0212] The aforementioned computer-readable storage medium may be included in the wearable device; or it may exist independently and not assembled into the wearable device.

[0213] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the wearable device, cause the wearable device to: acquire acoustic time-domain signals detected by acoustic sensing sensors and vibration time-domain signals detected by vibration sensing sensors;

[0214] The acoustic time-domain signal and the vibration time-domain signal are preprocessed separately to obtain the target acoustic signal and the target vibration signal. The signal parameters of the target acoustic signal and the target vibration signal are compared to obtain the signal parameter deviation information.

[0215] If the signal parameter deviation information is within the preset parameter deviation range, the preset working module of the wearable device will be activated and enter the running state.

[0216] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0217] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0218] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0219] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described work scenario determination method, and is able to solve the technical problem of work scenario determination. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the work scenario determination method provided in the above embodiments, and will not be repeated here.

[0220] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for determining a work scenario, wherein, The work scene determination method is applied to a wearable device, which is equipped with an acoustic sensing sensor and a vibration sensing sensor. The work scene determination method includes: Acquire the acoustic time-domain signal detected by the acoustic sensing sensor and the vibration time-domain signal detected by the vibration sensing sensor; The acoustic time-domain signal and the vibration time-domain signal are preprocessed respectively to obtain the target acoustic signal and the target vibration signal. The signal parameters of the target acoustic signal and the target vibration signal are compared to obtain signal parameter deviation information. If the signal parameter deviation information is within the preset parameter deviation range, the preset working module of the wearable device is activated and enters the running state.

2. The method as described in claim 1, wherein, The step of comparing the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information further includes: Acquire the first amplitude data of the target acoustic signal in the frequency domain, and the second amplitude data of the target vibration signal in the frequency domain; Based on the first amplitude data, the first amplitude value of the target acoustic signal corresponding to the preset characteristic frequency point is determined; Based on the second amplitude data, the second amplitude value of the target vibration signal corresponding to the preset characteristic frequency point is determined; Based on the ratio between the first amplitude and the second amplitude, signal parameter deviation information is obtained.

3. The method as described in claim 1, wherein, The step of preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively to obtain the target acoustic signal and the target vibration signal includes: The acoustic time-domain signal and the vibration time-domain signal are converted into frequency domain signals to obtain acoustic frequency domain signals and vibration frequency domain signals. The acoustic frequency domain signal and the vibration frequency domain signal are subjected to signal filtering and echo cancellation processing to obtain effective acoustic signal and effective vibration signal; Based on the effective acoustic signal, a target acoustic signal is obtained, and based on the effective vibration signal, a target vibration signal is obtained.

4. The method of claim 3, wherein, The steps of determining the target acoustic signal based on the effective acoustic signal and determining the target vibration signal based on the effective vibration signal include: The effective acoustic signal is amplified by a preset first amplification factor to obtain the target acoustic signal; The effective vibration signal is amplified by a preset second amplification factor to obtain the target vibration signal.

5. The method of claim 1, wherein, Before the step of preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively, the method further includes: Determine the intensity of the first signal corresponding to the vibration time-domain signal; Determine whether the intensity of the first signal is greater than a preset vibration intensity threshold; If the intensity of the first signal is greater than the preset vibration intensity threshold, then the step of preprocessing the acoustic signal and the vibration signal respectively is triggered.

6. The method of claim 1, wherein, Before the step of preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively, the method further includes: Determine the first signal intensity corresponding to the vibration time-domain signal and the second signal intensity corresponding to the acoustic time-domain signal; Determine whether the first signal strength is greater than a preset vibration intensity threshold, and whether the second signal strength is greater than a preset acoustic intensity threshold; If the intensity of the first signal is greater than a preset vibration intensity threshold and the intensity of the second signal is greater than a preset acoustic intensity threshold, then the step of preprocessing the acoustic time-domain signal and the vibration time-domain signal respectively is triggered.

7. The method of claim 1, wherein, Following the step of obtaining the signal parameter deviation information, the method further includes: If the signal parameter deviation information is not within the preset parameter deviation range, then the device enters sleep mode. The sleep mode is a mode in which the wearable device can normally receive the acoustic time domain signal and the vibration time domain signal, and the preset working module is in a dormant state.

8. A work scene determination device, wherein, The device includes: The signal processing module is used to acquire the acoustic time-domain signal detected by the acoustic sensing sensor and the vibration time-domain signal detected by the vibration sensing sensor, and to preprocess the acoustic time-domain signal and the vibration time-domain signal respectively to obtain the target acoustic signal and the target vibration signal. The signal comparison module is used to compare the signal parameters of the target acoustic signal and the target vibration signal to obtain signal parameter deviation information; If the signal parameter deviation information is within the preset parameter deviation range, the preset working module of the wearable device is started and enters the running state.

9. A wearable device, wherein, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the working scene determination method as described in any one of claims 1 to 7.

10. A storage medium, wherein, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the working scene determination method as described in any one of claims 1 to 7.

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