Sleep intervention control method and device, equipment and storage medium

Abstract

The application discloses a sleep intervention control method and device, equipment and storage medium, and relates to the technical field of intelligent monitoring, wherein the sleep intervention control method comprises the following steps: collecting multi-source data signals of a user in a state of wearing a sleep-aiding wearable device; when the multi-source data signals meet a first preset condition, switching a working mode of the sleep-aiding wearable device to a low-intervention monitoring mode; and performing a minimum necessary intervention operation in the low-intervention monitoring mode. According to the scheme, exclusive low-intervention control logic is configured, the user automatically enters the low-intervention monitoring mode when the user's sleep state meets the preset condition, and corresponding sleep intervention operations are performed in the low-intervention monitoring mode, so that sleep disturbance caused by continuous output of a fixed sleep-aiding scheme can be effectively avoided, and the stability of user sleep can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and in particular to a sleep intervention and control method, device, equipment, and storage medium. Background Technology

[0002] Most existing sleep aids, white noise devices, or sleep apps revolve around playing corresponding audio based on the user's state. For example, some solutions play white noise when the user is awake, activate noise reduction when the user is in a light sleep state, and turn off playback when the user is in a deep sleep state; other solutions switch between playing or pausing different audio content based on different stages such as light sleep and deep sleep.

[0003] The above solutions are effective in helping people fall asleep, but their control logic is mostly still based on a one-to-one mapping between sleep state and voice output. The focus is on selecting playback content based on the state, rather than establishing a control mechanism around the process of maintaining the user's stable sleep.

[0004] Therefore, existing devices often continue playing, maintain playback, or simply shut down the system after the user has entered a stable sleep state. They lack low-intervention management logic specifically for the stable sleep stage, which can easily lead to over-intervention and negatively impact the established sleep stability. Summary of the Invention

[0005] The main objective of this invention is to propose a sleep intervention control method, device, equipment, and storage medium, aiming to solve the technical problem that existing sleep aid solutions lack low-intervention control logic for stable sleep stages, and are prone to over-intervention and disruption of sleep stability.

[0006] To achieve the above objectives, this application proposes a sleep intervention and control method, the sleep intervention and control method comprising: Collect multi-source data signals from users while they are wearing sleep aid wearable devices; When the multi-source data signals meet the first preset condition, the working mode of the sleep aid wearable device is switched to the low-intervention monitoring mode; In the low-intervention monitoring mode, the minimum necessary interventions are performed.

[0007] In one embodiment, the multi-source data signal includes at least one or more of ear physiological signals, user body movement signals, and environmental signals.

[0008] In one embodiment, the step of switching the working mode of the sleep aid wearable device to a low-intervention monitoring mode when the multi-source data signal meets a first preset condition includes: Feature extraction is performed on the multi-source data signals to obtain the heart rate stability, respiratory rhythm stability, body movement attenuation degree, and environmental noise changes within a continuous time window; The stable sleep assessment index is calculated based on the heart rate stability, respiratory rhythm stability, body movement attenuation, and changes in environmental noise. When the stable sleep determination index meets the preset threshold, it is determined that the user has entered a stable sleep state, and the working mode of the sleep aid wearable device is switched to the low-intervention monitoring mode.

[0009] In one embodiment, the step of performing the minimum necessary intervention in the low-intervention monitoring mode includes: In the low-intervention monitoring mode, the presence of sleep disturbance events is detected; When the sleep disturbance event is detected, the disturbance recovery window is opened; Within the disturbance recovery window, determine the minimum necessary intervention strategy corresponding to the sleep disturbance event.

[0010] The step of determining the minimum necessary intervention strategy corresponding to the sleep disturbance event within the disturbance recovery window includes: Within the disturbance recovery window, the disturbance type of the sleep disturbance event is identified; The minimum necessary intervention strategy is matched based on the type of disturbance in the sleep disturbance event.

[0011] In one embodiment, the sleep disturbance events include environmental noise disturbance events, physiological fluctuation disturbance events, and minor body movement disturbance events; The step of matching the minimum necessary intervention strategy based on the type of sleep disturbance event includes: When the sleep disturbance event is an environmental noise disturbance event, a short-term masking sound intervention is performed; and / or, When the sleep disturbance event is a physiological fluctuation disturbance event, soothing audio or gentle rhythmic guidance is performed; and / or, When the sleep disturbance event is a minor body movement disturbance event, maintain the current low-intervention monitoring mode and output a preset basic wake-up sound or maintain the current voice output intensity.

[0012] In one embodiment, after the step of performing the corresponding minimum necessary intervention operation when the disturbance event number meets the second preset condition, the method further includes: Within the disturbance recovery window, multi-source data signals are continuously acquired, and it is determined whether the multi-source data signals meet the first preset condition. If so, terminate the current minimum necessary intervention operation, exit the disturbance recovery window, and revert to the low intervention monitoring mode; If not, adjust the intervention parameters and / or upgrade the intervention strategy within the disturbance recovery window.

[0013] Furthermore, to achieve the above objectives, this application also proposes a control device for a sleep aid wearable device, characterized in that the control device comprises: The signal monitoring module is used to collect multi-source data signals when the user is wearing the sleep aid wearable device; The signal processing module is used to switch the working mode of the sleep aid wearable device to a low-intervention monitoring mode when the multi-source data signal meets the first preset condition. An intervention execution module is used to perform the minimum necessary intervention operations in the low-intervention monitoring mode.

[0014] In addition, to achieve the above objectives, this application also proposes a sleep aid 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 sleep intervention control method as described above.

[0015] 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 sleep intervention control method described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the sleep intervention control method described above.

[0017] In the technical solution of this invention, multi-source data signals are collected when the user is wearing a sleep aid wearable device; when the multi-source data signals meet a first preset condition, the working mode of the sleep aid wearable device is switched to a low-intervention monitoring mode; in the low-intervention monitoring mode, the minimum necessary intervention operation is performed. This solution, by configuring dedicated low-intervention control logic, automatically enters the low-intervention monitoring mode when the user's sleep state meets the preset conditions, and performs corresponding sleep intervention operations in this low-intervention monitoring mode. This effectively avoids sleep disturbances caused by the continuous output of a fixed sleep aid program, effectively improving the stability of the user's sleep. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the sleep intervention and control method provided by the present invention; Figure 2 This is a flowchart illustrating the second embodiment of the sleep intervention and control method provided by the present invention; Figure 3 This is a flowchart illustrating the third embodiment of the sleep intervention and control method provided by the present invention. Figure 4 This is a flowchart illustrating the fourth embodiment of the sleep intervention and control method provided by the present invention. Figure 5 This is a flowchart illustrating the fifth embodiment of the sleep intervention and control method provided by the present invention; Figure 6 This is a flowchart illustrating the sixth embodiment of the sleep intervention and control method provided by the present invention. Figure 7 This is a schematic diagram of the module structure of the sleep intervention control device according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the hardware operating environment involved in the sleep intervention control method in this embodiment of the invention.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] 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.

[0022] 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.

[0023] The main solution of this application embodiment is: to collect multi-source data signals when the user is wearing a sleep aid wearable device; when the multi-source data signals meet a first preset condition, to switch the working mode of the sleep aid wearable device to a low-intervention monitoring mode; and to perform the minimum necessary intervention operation in the low-intervention monitoring mode.

[0024] In this embodiment, for ease of description, the following description uses a sleep aid wearable device as the subject of the explanation.

[0025] Because existing devices often continue playing, maintain playback, or simply shut down the system after the user has entered a stable sleep state, they lack low-intervention management logic specifically for the stable sleep stage, which can easily lead to over-intervention and negatively impact the established sleep stability.

[0026] This application provides a solution that comprehensively determines a user's sleep state by collecting multi-source data signals while the user is wearing a sleep aid wearable device, combined with multi-dimensional data on the user's physiology, body movement, and environment. Once the multi-source data signals meet preset stable sleep criteria and the user enters a stable sleep state, the system automatically switches to a dedicated low-intervention monitoring mode, stopping the output of conventional sleep aid strategies and reducing passive interference with stable sleep. Simultaneously, this solution monitors various sleep disturbance events in real time in low-intervention monitoring mode. Upon detecting a disturbance, a disturbance recovery window is opened, and the minimum necessary intervention method is matched for refined intervention based on the type, intensity, and duration of the disturbance, avoiding fixed, high-intensity continuous stimulation. During the intervention process, the user's sleep recovery state is assessed in real time, and intervention parameters can be adaptively adjusted according to the user's state. Once the user returns to stable sleep, the system quickly reverts to low-intervention monitoring mode, achieving dynamic, graded, and minimal control of sleep intervention. This application effectively solves the technical shortcomings of traditional sleep aid devices, such as excessive sleep intervention and a single control method, maximizing the continuity and stability of the user's sleep, and significantly improving the sleep aid adaptability and user experience of wearable devices.

[0027] In the technical solution of this invention, when the user wears the main body of the device, the sleep aid wearable device enters the wearing state. In this wearing state, the voice output module continuously outputs voice information to the user to assist the user in falling asleep. Simultaneously, during this stage, the signal monitoring module continuously collects multi-source data signals, enabling the information processing module to determine the user's sleep status based on the multi-source data signals. Once the information processing module determines that the user has entered stable sleep based on the multi-source data signals, it controls the voice output module to adjust the intensity of its output voice information or turn off voice information output to reduce secondary interference to the user's stable sleep. With this configuration, the sleep aid wearable device can adaptively adjust its intervention strategy for different stages of the user's sleep, ensuring the user falls asleep smoothly while avoiding excessive interference during stable sleep stages, effectively matching the actual needs of the user's sleep cycle and improving the user's overall sleep quality.

[0028] It should also be noted that when the user is in a stable sleep state, the signal monitoring module continues to collect multi-source data signals from the user to monitor the user's sleep state at all times. During the user's stable sleep, if a sleep disturbance event occurs, the multi-source data signals detected by the signal monitoring module will also change. At this time, if the multi-source data signals exceed a threshold, the information processing module determines that it is necessary to intervene in the user's sleep state and controls the voice output module to adjust the output voice intensity and / or voice type. According to the specific type of sleep disturbance event, targeted intervention is carried out to help the user quickly recover from the impact of sleep disturbance and enable the user to quickly return to a stable sleep state. At the same time, after the user returns to a stable sleep state, the voice output module will reduce or turn off its voice information output to ensure the continuity and stability of the user's sleep process. No manual adjustment is required from the user. The whole process is completed adaptively, and the user experience is smoother and more natural.

[0029] Understandably, sleep disturbances can take many different forms, such as environmental noise exceeding a disturbance threshold, a sudden increase in body movement intensity within a unit of time, a deviation of heart rate or respiratory rhythm from the stable sleep range, or a tendency for short-term awakenings. For different types of sleep disturbances, the voice output module can be controlled to adjust its output voice information accordingly, thereby achieving targeted intervention.

[0030] In one embodiment of the present invention, when the ambient noise exceeds the disturbance threshold, the voice output module is controlled to perform short-term masking tone intervention, that is, by outputting masking voice that matches the frequency band of the ambient noise for a short time, the influence of external ambient noise on the user's sleep is offset, helping the user to quickly restore stable sleep, and without causing additional interference due to continuous output of voice information.

[0031] In another embodiment of the present invention, when heart rate fluctuations or respiratory rhythms deviate from the stable range, the voice output module is controlled to execute soothing audio or mild rhythmic guidance. That is, by using soothing low-intensity white noise or fixed low-frequency guiding rhythm audio, the user can be helped to calm physiological fluctuations and return to a stable sleep rhythm, thus preventing physiological fluctuations from developing into complete wakefulness.

[0032] In another embodiment of the present invention, if slight body movements occur during the user's sleep, it is determined that the disturbance will not affect the user's current sleep stability, and the voice output module is controlled to maintain the current voice output state unchanged, so as to avoid unnecessary intervention that may interrupt the user's sleep.

[0033] In other embodiments of the present invention, targeted interventions are performed for other types of disturbances. In actual settings, the appropriate intervention can be selected according to the requirements, which will not be elaborated here.

[0034] It should be further explained that when the sleep aid wearable device performs intervention for disturbance events, in order to avoid excessive intervention intensity affecting the user's sleep quality, the sleep aid wearable device first performs only the minimum necessary intervention operation. The minimum necessary intervention operation is to select the intervention method with the least stimulation and shortest duration but sufficient to help the user restore stable sleep from the preset intervention strategy based on the type, intensity and duration of the disturbance event.

[0035] If the current intervention method and intensity cannot help the user quickly alleviate the effects of sleep disturbances, the wearable sleep aid device can gradually increase the intervention intensity and duration. That is, it controls the voice output module to gradually increase the voice output intensity and duration until the signal monitoring module detects that the multi-source data signal once again meets the conditions for stable sleep for the user. Then, it immediately reduces or shuts down the voice signal output by the voice output module. In this way, it can effectively intervene in sleep disturbances while minimizing the impact of intervention on stable sleep.

[0036] Furthermore, when the intensity of the disturbance exceeds the intervention range of the sleep aid wearable device, such as when the user is in a fully awake state for more than ten minutes, if the sleep aid wearable device is still in the wearing state, the sleep aid wearable device will automatically switch its mode to re-execute the sleep aid program for the sleep-on stage to adapt to the user's need to fall back asleep.

[0037] Based on this, the embodiments of this application provide a sleep intervention and control method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the sleep intervention and control method of this application.

[0038] In this embodiment, the sleep intervention and control method includes steps S10 to S30: S10. Collect multi-source data signals from the user while wearing the device; When a user wears the sleep aid wearable device, the device enters the wearing state. In the wearing state, it continuously outputs voice information to the user to help the user fall asleep. At the same time, during this stage, it continuously uses the multi-source data signals so that the user's sleep status can be determined later based on the multi-source data signals.

[0039] The wearable sleep aid device can be a headband device, a neckband device, or an in-ear device. It can also be a smart bracelet, smartwatch, or other device that can collect physiological signals by being worn on the wrist. As long as it can meet the functional requirements of wearing and using, collecting multi-source data, and outputting sleep aid information, it is acceptable.

[0040] The multi-source data includes at least one or more of the following: ear physiological signals, such as heart rate, heart rate variability, respiratory rhythm, and blood oxygenation signals; user body movement signals, such as head micro-movements, turning over movements, and changes in wearing stability; and environmental signals, such as environmental noise intensity, sudden noise events, and continuous background noise characteristics.

[0041] Through the above steps, the user's sleep state can be comprehensively judged from multiple dimensions such as physiology, movement, and environment, effectively reducing the probability of error in judging a single signal, making the judgment of whether the user has entered a stable sleep more accurate and reliable, and ensuring the accuracy of the triggering time of the low-intervention mode.

[0042] S20. When the multi-source data signal meets the first preset condition, the working mode of the sleep aid wearable device is switched to the low-intervention monitoring mode. In this embodiment, when the multi-source data signal meets the first preset condition, it indicates that the user has reached a stable sleep. At this time, in order to avoid the sleep aid information output by the sleep aid wearable device from assisting the user to fall asleep from affecting the user's stable sleep, the working mode of the sleep aid wearable device is switched to a low-intervention monitoring mode to reduce the impact of the sleep aid wearable device on the user's stable sleep.

[0043] Through the above steps, the device's working mode can be dynamically switched according to the user's actual sleep state. Once the user enters a stable sleep state, the high-intensity sleep aid output state can be promptly exited, avoiding secondary sleep disturbances caused by the continuous output of traditional fixed sleep aid logic. This enables on-demand exit of sleep aid intervention and ensures the stable continuation of the user's sleep state.

[0044] Step S30: In the low-intervention monitoring mode, perform the minimum necessary intervention.

[0045] In this embodiment, under the low-intervention monitoring mode, the headphones stop the regular sleep aid voice output, or only maintain a background with extremely low stimulation and extremely low volume, and no longer continuously execute the regular sleep aid strategies for the sleep-onset stage, so as to reduce secondary interference with stable sleep.

[0046] Meanwhile, it continuously collects multi-source data signals and monitors various sleep disturbance events such as environmental disturbances, user physiological fluctuations, and changes in body movement in real time. When a disturbance risk is detected, a disturbance recovery window of preset duration is opened, and the intervention method with the lowest stimulation and shortest duration is matched according to the specific type, intensity, and duration of the disturbance for targeted repair, so as to avoid uniform and high-intensity intervention operations from disrupting the user's sleep state.

[0047] Through the above steps, refined repair of sleep disturbances can be achieved in a low-intervention monitoring mode. This avoids the problem of excessive intervention caused by indiscriminate continuous sleep aid, and can also provide timely and gentle repair when sleep is disturbed, taking into account both low sleep disturbance and sleep stability, and effectively improving the overall sleep aid effect.

[0048] The method described herein collects multi-source data signals from a user while wearing a sleep aid wearable device. When the multi-source data signals meet a first preset condition, the device's operating mode is switched to a low-intervention monitoring mode. In this low-intervention monitoring mode, minimal necessary intervention operations are performed. This solution, by configuring dedicated low-intervention control logic, automatically enters the low-intervention monitoring mode when the user's sleep state meets preset conditions, and performs corresponding sleep intervention operations within this mode. This effectively avoids sleep disturbances caused by the continuous output of a fixed sleep aid program, and effectively improves the stability of the user's sleep.

[0049] Please see Figure 2 In the second embodiment of the present invention, step S20 includes: S21. Perform feature extraction on the multi-source data signal to obtain the heart rate stability, respiratory rhythm stability, body movement attenuation degree and environmental noise change within a continuous time window; In this embodiment, the multi-source data signals specifically include user vital sign data and human body movement data collected by the sleep aid wearable device, as well as environmental noise data collected by the supporting environmental monitoring module. Before feature extraction, the original multi-source data signals are preprocessed, specifically including data filtering, denoising, outlier removal, and time-series alignment. Denoising is achieved by using an adaptive Kalman filter algorithm to eliminate high-frequency current noise and transient jitter noise generated during the sleep aid wearable device's data acquisition process. Data filtering is used to remove invalid and abnormal data caused by temporary user movements or slight device displacement, ensuring the authenticity and validity of subsequent feature data.

[0050] After data preprocessing, features are extracted from the normalized time-series data in units of preset continuous time windows. A 30-second sliding window is preferred to ensure data continuity and real-time performance. Corresponding algorithms are used to extract the user's real-time heart rate stability, respiratory rhythm stability, body movement attenuation, and real-time environmental noise variation characteristics. Heart rate stability characterizes the fluctuation range of the user's heart rate; respiratory rhythm stability reflects the stability of the user's breathing frequency and depth; body movement attenuation reflects the frequency and amplitude changes of limb movements during sleep; and environmental noise variation monitors the intensity and fluctuation of external noise in the sleep environment, providing multi-dimensional feature data for subsequent determination of stable sleep state.

[0051] S22. Calculate stable sleep criteria based on the heart rate stability, respiratory rhythm stability, body movement attenuation, and environmental noise changes. In this embodiment, unlike the traditional approach that uses a single parameter, this step integrates four core dimensions: heart rate, respiration, body movement, and environmental noise. A multi-factor weighted calculation model is constructed, and a quantitative stable sleep assessment index is obtained by comprehensively calculating the heart rate stability, respiratory rhythm stability, body movement attenuation, and environmental noise change data within a continuous time window.

[0052] S23. When the stable sleep determination index meets the preset threshold, it is determined that the user has entered a stable sleep state, and the working mode of the sleep aid wearable device is switched to the low-intervention monitoring mode.

[0053] In this embodiment, a preset threshold for stable sleep is obtained in advance through training and calibration using a large amount of user sleep sample data. The stable sleep determination index calculated in real time is compared with the preset threshold. When the determination index is continuously greater than or equal to the preset threshold within multiple consecutive sliding time windows, it is determined that the user has completely left the sleep transition period and entered a deep and stable sleep state.

[0054] For example, a monitoring window of a preset duration can be set, such as a 30-second monitoring window. When the user's heart rate fluctuation amplitude and respiratory rhythm fluctuation amplitude are both within a preset stable range, and the user does not turn over significantly or the frequency of body movements decreases significantly, and there is no sudden high-intensity noise interference in the environment, it is determined that the multi-source data signals meet the first preset condition, confirming that the user has entered a deep and stable sleep state. At this time, the device automatically turns off the regular sleep aid voice output, or only retains a very low volume of background maintenance sound effect, completing the switching of working modes. Through multi-dimensional and continuous time-series comprehensive judgment, misjudgments caused by instantaneous data fluctuations are effectively avoided, ensuring the accuracy and rationality of mode switching.

[0055] By integrating the temporal characteristics of four dimensions—heart rate, respiration, body movement, and environmental noise—the method comprehensively determines whether the user has entered a stable sleep state. Only when multiple signals meet the conditions can the mode switch be triggered, transitioning from the working mode to the low-intervention monitoring mode. This effectively avoids misjudgment caused by judging a single signal, ensuring that the mode switch timing accurately matches the user's actual sleep state. It neither interrupts the user's incomplete sleep process nor continuously outputs unnecessary sleep aid voices after the user has entered a stable sleep state, thus balancing the sleep aid effect with sleep maintenance quality and more closely reflecting the user's actual sleep change patterns.

[0056] Please see Figure 3 In the third embodiment of the present invention, step S30 includes: S31. In the low-intervention monitoring mode, detect whether there are sleep disturbance events; It should be noted that the sleep disturbance events include, but are not limited to: environmental noise exceeding the disturbance threshold, a sudden increase in body movement intensity per unit time, heart rate or respiratory rhythm deviating from the stable sleep range, and the user showing a short-term awakening trend.

[0057] In this embodiment, after the device enters the low-intervention monitoring mode, it continues to collect and monitor multi-source data at high frequency in real time. It no longer outputs conventional sleep-aid intervention content, but only relies on multi-dimensional data to identify various sleep disturbance events in real time, accurately capture abnormal signals that can break the user's stable sleep state, and provide data support for subsequent precise intervention.

[0058] S32. When the disturbance event is detected, the disturbance recovery window is opened; It should be noted that the disturbance recovery window refers to a fixed-duration intervention monitoring period preset for a single sleep disturbance event, used to limit the effective processing time of disturbance repair and avoid unlimited continuous intervention; within this window, the device can continuously monitor changes in the user's sleep state and perform targeted intervention operations, and the disturbance repair process is determined to be terminated when the window ends.

[0059] In this embodiment, once a sleep disturbance event that meets the judgment criteria is identified, the disturbance recovery window is immediately triggered and opened, locking the repair processing cycle of this disturbance to avoid problems such as intervention delays and incorrect intervention timing, and to ensure the timeliness and standardization of disturbance repair.

[0060] Step S33: Within the disturbance recovery window, determine the minimum necessary intervention strategy corresponding to the sleep disturbance event.

[0061] In this embodiment, within the opened disturbance recovery window, based on the specific type, intensity, and duration of the current disturbance event, the minimum necessary intervention strategy is selected from the device's preset multi-level intervention strategies. This minimum necessary intervention strategy has the lowest stimulation level, shortest duration, and can effectively counteract sleep disturbances. Through differentiated and lightweight targeted intervention, sleep disturbances caused by external or physiological factors can be effectively counteracted, while strictly avoiding secondary sleep disturbances caused by excessive intervention.

[0062] Through the methods described above, in low-intervention monitoring mode, the presence of sleep disturbance events is detected. When a disturbance event is detected, a disturbance recovery window is opened. Within this window, the minimum necessary intervention strategy corresponding to the sleep disturbance event is determined. This reduces the impact of sleep disturbance events on the user, helping them quickly return to a stable sleep state. This setup avoids unnecessary intervention when there is no disturbance or only slight disturbance, preventing disruption to the user's original stable sleep. Simultaneously, it allows for timely intervention when disturbance events affecting sleep stability occur, helping the user quickly calm the disturbance, restore stable sleep, effectively maintain the user's complete sleep cycle, and improve sleep maintenance.

[0063] Please see Figure 4 In the fourth embodiment of the present invention, step S33 includes: S331. Within the disturbance recovery window, identify the disturbance type of the sleep disturbance event; It should be noted that the sleep disturbance events include environmental noise disturbance events, physiological fluctuation disturbance events, and mild body movement disturbance events. In this way, different types of sleep disturbance events are categorized so that disturbances of different degrees and types can be accurately identified, avoiding misjudging mild disturbances as disturbances requiring intervention, and also avoiding omitting disturbance types that require intervention, thereby further improving the accuracy of sleep disturbance identification and making intervention triggers more reasonable.

[0064] In this embodiment, a multi-dimensional feature matching algorithm is used to classify and identify disturbances within the disturbance recovery window. The specific judgment logic is as follows: if environmental noise suddenly exceeds the standard and vital signs and body movements are normal, it is judged as an environmental noise disturbance event; if heart rate and respiratory rhythm fluctuate significantly and environmental and body movements are normal, it is judged as a physiological fluctuation disturbance event; if only small body movements occur or the frequency of body movements increases briefly, while the environment and core physiological indicators remain stable, it is judged as a mild body movement disturbance event. This accurate classification provides a basis for subsequent matching of specific intervention strategies.

[0065] S332. Match the minimum necessary intervention strategy based on the disturbance type of the sleep disturbance event.

[0066] It should be noted that the minimum necessary intervention strategy is a lightweight, adaptive intervention scheme pre-calibrated based on the type, intensity, and duration of the disturbance.

[0067] In this embodiment, after identifying the type of sleep disturbance, the minimum necessary intervention strategy that matches the current disturbance type is quickly matched according to the pre-stored strategy mapping relationship, so as to achieve a precise correspondence between the disturbance event and the intervention plan, and provide an execution basis for subsequent precise and lightweight sleep-aid intervention.

[0068] The methods described above accurately identify various sleep disturbance events through multi-dimensional data features, effectively avoiding the problems of missed or false judgments caused by single signal determination, and ensuring that all disturbance events requiring intervention can be effectively captured. Simultaneously, intervention strategies are matched on demand based on disturbance type, simplifying the determination process and shortening response time, ensuring the timeliness of intervention triggering. This prevents delays caused by multiple signal determinations from missing the optimal intervention opportunity, allowing intervention actions to quickly respond to sleep disturbances, improving the effectiveness of intervention and better maintaining the stability of the user's sleep.

[0069] Please see Figure 5 In the fifth embodiment of the present invention, step S332 includes: S332a. When the sleep disturbance event is an environmental noise disturbance event, a short-term masking sound intervention is performed.

[0070] In this embodiment, if the sleep disturbance event is an environmental noise disturbance event, short-term masking sound intervention is performed first. For example, short-term masking sound can mask sudden environmental noise from the outside world, block the noise from stimulating the user's sleep, prevent the noise from waking the user, maintain the user's current sleep stability, and the short-term masking sound will not create new continuous interference. While achieving noise masking, it avoids generating additional sleep effects.

[0071] S332b: When the sleep disturbance event meets the physiological fluctuation disturbance event, it is determined that a physiological fluctuation disturbance has occurred, and a soothing audio or a mild rhythmic voice is output.

[0072] In this embodiment, for sleep disturbances caused by physiological fluctuations, soothing audio or gentle rhythmic guidance is prioritized. For example, soothing voice information can calm the user's fluctuating heart rate and breathing, guide the user's physiological indicators back to the stable sleep range, prevent the physiological fluctuations from continuing to intensify and causing the user to wake up completely, help the user quickly return to a stable state at the physiological level, and maintain the continuity of sleep.

[0073] S332c. When the sleep disturbance event is a minor body movement disturbance event, maintain the current low-intervention monitoring mode and output a preset basic wake-up sound or maintain the current voice output intensity.

[0074] In this embodiment, if the current disturbance event is identified as a minor body movement disturbance, it is determined that the user is only unconsciously making slight movements such as turning over, shifting limbs, or making minor adjustments to body posture. These types of body movements are normal physiological behaviors during sleep and will not directly cause the user to wake up, having a very low impact on overall sleep stability. Therefore, this solution does not require additional adjustment of the sleep aid intervention level or addition of audio output stimulation, continuously maintaining the device's current low-intervention monitoring mode and keeping the preset basic wake-up sound or the original voice output intensity unchanged, avoiding unnecessary intervention actions that could disturb the user's originally stable sleep state.

[0075] Through the methods described above, corresponding minimum necessary intervention strategies are matched for different types of disturbance events. It has an identification mechanism for independent events, achieving precise response. It avoids over-intervention in minor and ineffective disturbances, and can accurately respond to different types of disturbances that require intervention. This enables precise and differentiated sleep maintenance intervention, further improving the rationality of intervention in the stable sleep stage and better ensuring the continuity and stability of users' sleep.

[0076] Please see Figure 6 In the sixth embodiment of the present invention, after step S332, the method further includes: step A1, continuously collecting multi-source data signals within the disturbance recovery window, and determining whether the multi-source data signals meet the first preset condition; In this embodiment, the recovery result is evaluated within the disturbance recovery window. Multi-source data signals are continuously acquired, and it is determined whether the multi-source data signals meet the first preset condition again, that is, whether the user is still in a stable sleep state.

[0077] If step A2 is correct, then terminate the current minimum necessary intervention operation, exit the disturbance recovery window, and revert to the low intervention monitoring mode; In this embodiment, if the user's multi-source data signal is detected to meet the first preset condition again within the disturbance recovery window, it is determined that the user's sleep state has stabilized. At this time, the currently executing minimum necessary intervention operation is immediately terminated, the disturbance recovery window is exited in time, and the system automatically returns to the low intervention monitoring mode to avoid continuing to perform intervention operations after the user's sleep has stabilized, thus preventing additional audio stimulation from interfering with the user's sleep.

[0078] Step A3: If not, adjust the intervention parameters and / or upgrade the intervention strategy within the disturbance recovery window.

[0079] In this embodiment, if the user's multi-source data signals consistently fail to meet the first preset condition within the disturbance recovery window, it is determined that the current minimum necessary intervention intensity or intervention method cannot effectively calm sleep disturbances and restore the user's sleep state. At this time, within the limited disturbance recovery window, the intervention output parameters are adaptively fine-tuned, or the intervention strategy is progressively adjusted to appropriately enhance the intervention adaptability, thereby improving the disturbance repair effect and helping the user's sleep state recover.

[0080] Furthermore, if the user's sleep state remains unstable within the disturbance recovery window, a tiered progressive adjustment process is implemented; once the user's sleep state is successfully restored, a rapid rollback process is implemented. The rapid rollback process immediately retracts all intervention strategies after the user achieves stable sleep, preventing the device from continuously maintaining strong intervention stimuli and causing secondary interference to the user's stable sleep. The tiered progressive adjustment process gradually and incrementally increases the intensity of intervention within the disturbance recovery window when the minimum necessary intervention is ineffective, avoiding unlimited and prolonged continuous stimulation, thus balancing the effectiveness of sleep restoration with low sleep disturbance.

[0081] Through the methods described in the above embodiments, multi-source data signals are continuously collected within the disturbance recovery window, and it is determined whether the multi-source data signals meet the first preset condition. If yes, the current minimum necessary intervention operation is terminated, the disturbance recovery window is exited, and the system reverts to the low-intervention monitoring mode. If no, the intervention parameters and / or intervention strategies are adjusted within the disturbance recovery window. This allows for timely removal of intervention when the user quickly recovers stable sleep, ensuring a low-interference sleep environment. Furthermore, it allows for moderate progressive adjustment within a controllable range when a single lightweight intervention is ineffective, improving the success rate of disturbance repair. Simultaneously, the window time limit constraint and rapid rollback mechanism completely avoid the problems of excessive and continuous intervention, further enhancing the intelligence and precision of the overall sleep aid control.

[0082] This application also provides a sleep intervention control device, please refer to... Figure 7 The sleep intervention control device includes: Signal monitoring module 10 is used to collect multi-source data signals when the user is wearing the sleep aid wearable device; The signal processing module 20 is used to switch the working mode of the sleep aid wearable device to a low-intervention monitoring mode when the multi-source data signal meets the first preset condition. Intervention execution module 30 is used to perform the minimum necessary intervention operations in the low intervention monitoring mode.

[0083] The following is for reference. Figure 8The diagram illustrates a structural schematic suitable for implementing the sleep aid wearable device in the embodiments of this application. The sleep aid wearable device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The sleep aid wearable device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0084] like Figure 8 As shown, the sleep aid wearable device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, 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 sleep aid wearable 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 touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the sleep aid wearable device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show sleep aid wearable devices with various systems, it should be understood that implementing or having all of the systems shown is not required. More or fewer systems may be implemented alternatively.

[0085] 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.

[0086] The sleep aid wearable device provided in this application, employing the sleep intervention control method described in the above embodiments, can solve the technical problem that existing sleep aid solutions lack low-intervention control logic for stable sleep stages, easily leading to over-intervention and disruption of sleep stability. Compared with the prior art, the beneficial effects of the sleep aid wearable device provided in this application are the same as those of the sleep intervention control method provided in the above embodiments, and other technical features of this sleep aid wearable device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0087] 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.

[0088] 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 scope of the technology 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.

[0089] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the sleep intervention control method in the above embodiments.

[0090] 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 or 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 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.

[0091] The aforementioned computer-readable storage medium may be included in the sleep aid wearable device; or it may exist independently and not assembled into the sleep aid wearable device.

[0092] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the sleep aid wearable device, cause the sleep aid wearable device to: collect multi-source data signals from the user while wearing the sleep aid wearable device; when the multi-source data signals meet a first preset condition, switch the working mode of the sleep aid wearable device to a low-intervention monitoring mode; and in the low-intervention monitoring mode, perform the minimum necessary intervention operation.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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 sleep intervention control method. This addresses the technical problem that existing sleep aid solutions lack low-intervention control logic for stable sleep stages, easily leading to over-intervention and disruption of sleep stability. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the sleep intervention control method provided in the above embodiments, and will not be elaborated upon here.

[0097] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the sleep intervention control method described above.

[0098] The computer program product provided in this application can solve the technical problem that existing sleep aid solutions lack low-intervention control logic for stable sleep stages, which easily leads to over-intervention and disruption of sleep stability. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the sleep intervention control method provided in the above embodiments, and will not be repeated here.

[0099] 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 sleep intervention and control method, characterized in that, The sleep intervention and control methods include: Collect multi-source data signals from users while they are wearing sleep aid wearable devices; When the multi-source data signals meet the first preset condition, the working mode of the sleep aid wearable device is switched to the low-intervention monitoring mode; In the low-intervention monitoring mode, the minimum necessary interventions are performed.

2. The sleep intervention and control method as described in claim 1, characterized in that, The multi-source data signals include at least one or more of the following: ear physiological signals, user body movement signals, and environmental signals.

3. The sleep intervention and control method as described in claim 1, characterized in that, The step of switching the working mode of the sleep aid wearable device to a low-intervention monitoring mode when the multi-source data signals meet the first preset condition includes: Feature extraction is performed on the multi-source data signals to obtain the heart rate stability, respiratory rhythm stability, body movement attenuation degree, and environmental noise changes within a continuous time window; The stable sleep assessment index is calculated based on the heart rate stability, respiratory rhythm stability, body movement attenuation, and changes in environmental noise. When the stable sleep determination index meets the preset threshold, it is determined that the user has entered a stable sleep state, and the working mode of the sleep aid wearable device is switched to the low-intervention monitoring mode.

4. The sleep intervention and control method as described in claim 1, characterized in that, The steps for performing the minimum necessary intervention under the low-intervention monitoring mode include: In the low-intervention monitoring mode, the presence of sleep disturbance events is detected; When the sleep disturbance event is detected, the disturbance recovery window is opened; Within the disturbance recovery window, determine the minimum necessary intervention strategy corresponding to the sleep disturbance event.

5. The sleep intervention and control method as described in claim 4, characterized in that, The step of determining the minimum necessary intervention strategy corresponding to the sleep disturbance event within the disturbance recovery window includes: Within the disturbance recovery window, the disturbance type of the sleep disturbance event is identified; The minimum necessary intervention strategy is matched based on the type of disturbance in the sleep disturbance event.

6. The sleep intervention and control method as described in claim 5, characterized in that, The types of sleep disturbance events include environmental noise disturbance events, physiological fluctuation disturbance events, and minor body movement disturbance events; The step of matching the minimum necessary intervention strategy based on the type of sleep disturbance event includes: When the sleep disturbance event is an environmental noise disturbance event, a short-term masking sound intervention is performed; And / or, When the sleep disturbance event is a physiological fluctuation disturbance event, soothing audio or gentle rhythmic guidance is performed; and / or, When the sleep disturbance event is a minor body movement disturbance event, maintain the current low-intervention monitoring mode and output a preset basic wake-up sound or maintain the current voice output intensity.

7. The sleep intervention and control method as described in claim 4, characterized in that, After the step of determining the minimum necessary intervention strategy corresponding to the sleep disturbance event within the disturbance recovery window, the method further includes: Within the disturbance recovery window, multi-source data signals are continuously acquired, and it is determined whether the multi-source data signals meet the first preset condition. If so, terminate the current minimum necessary intervention operation, exit the disturbance recovery window, and revert to the low intervention monitoring mode; If not, adjust the intervention parameters and / or upgrade the intervention strategy within the disturbance recovery window.

8. A sleep intervention control device, characterized in that, The sleep intervention control device includes: The signal monitoring module is used to collect multi-source data signals when the user is wearing the sleep aid wearable device; The signal processing module is used to switch the working mode of the sleep aid wearable device to a low-intervention monitoring mode when the multi-source data signal meets the first preset condition. An intervention execution module is used to perform the minimum necessary intervention operations in the low-intervention monitoring mode.

9. A wearable sleep aid device, characterized in that, The wearable sleep aid 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 sleep intervention control method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, 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 sleep intervention control method as described in any one of claims 1 to 7.

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