Processing method and device of sleep data, computer device, program and medium

Abstract

A sleep data processing method, device, equipment, program and medium, wherein the sleep data processing method comprises: acquiring sleep data collected by a sleep monitoring device (101); extracting sleep features in the sleep data (102); comparing the similarity of standard features of a user with the sleep features to obtain a comprehensive feature similarity (103); and in the case where the comprehensive feature similarity meets the similarity requirement, taking the sleep features as target sleep features of the user (104). By comparing the sleep features in the sleep data collected by the sleep monitoring device with the standard features of the user, and only when the comprehensive similarity of the comparison meets the similarity requirement, the sleep features are attributed to the user, so that the attribution user of the sleep data can be accurately determined without relying on the binding relationship between the sleep monitoring device and the user.

Description

Technical Field

[0001] This disclosure belongs to the field of computer technology, and specifically relates to a method, apparatus, computer equipment, program, and medium for processing sleep data. Background Technology

[0002] Sleep monitoring is a method that uses sleep monitoring devices to monitor events that reflect a user's sleep status, such as breathing and heartbeat, during sleep. The monitored data is then analyzed and processed to assess the user's sleep status, helping users understand and improve their sleep quality. Summary of the Invention

[0003] This disclosure provides a method, apparatus, computer device, program, and medium for processing sleep data, aiming to solve, as far as possible, the problem in related technologies where the attribution of sleep data depends on the binding relationship between the sleep monitoring device and the user, which reduces the accuracy of sleep data attribution.

[0004] This disclosure provides a method for processing sleep data, the method comprising:

[0005] Acquire sleep data collected by sleep monitoring devices;

[0006] Extract sleep features from the sleep data;

[0007] The user's standard features are compared with the sleep features to obtain a comprehensive feature similarity.

[0008] If the comprehensive feature similarity meets the similarity requirements, the sleep feature will be used as the user's target sleep feature.

[0009] Optionally, extracting sleep features from the sleep data includes:

[0010] By using the sleep algorithms corresponding to each sleep stage, the sleep datasets that match each sleep stage and the sleep cycle time sequence of the sleep data are obtained respectively.

[0011] Sleep features are obtained based on the sleep cycle time sequence and the sleep dataset.

[0012] Optionally, the sleep characteristics include at least: breathing characteristics and heart rate characteristics; the step of comparing the user's standard characteristics with the sleep characteristics to obtain a comprehensive feature similarity includes:

[0013] The respiratory and heart rate characteristics are divided according to the sleep cycle sequence to obtain the characteristic set corresponding to each sleep stage;

[0014] Each of the aforementioned stage feature sets is compared with the standard features to obtain the feature similarity corresponding to each of the aforementioned sleep stages;

[0015] The feature similarity is integrated by combining the weight values ​​corresponding to each sleep stage to obtain the comprehensive feature similarity.

[0016] Optionally, before extracting sleep features from the sleep data, the method further includes:

[0017] Filter the sleep data to find data that meets the invalid data requirements, wherein the invalid data requirements include at least one of the following: invalid data format requirements and invalid data value requirements.

[0018] Optionally, the sleep features include at least: sleep quality; obtaining sleep features based on the sleep cycle time series and the sleep dataset includes:

[0019] Based on the sleep dataset and the sleep cycle sequence, obtain the respiratory disturbance index, number of awakenings, sleep onset time, sleep duration, and sleep efficiency;

[0020] The sleep quality is obtained by integrating the respiratory disorder index, the number of awakenings, the time to fall asleep, the sleep duration, and the sleep efficiency.

[0021] Optionally, acquiring the sleep data collected by the sleep monitoring device includes:

[0022] Receive heartbeat messages periodically reported by sleep monitoring devices;

[0023] Extract the device status from the heartbeat message;

[0024] When the device is in the running state, a data acquisition request is sent to the sleep monitoring device;

[0025] Receive sleep data sent by the sleep monitoring device based on the data acquisition request.

[0026] Optionally, before receiving the heartbeat messages periodically reported by the sleep monitoring device, the method further includes:

[0027] Obtain the current time from the time calibration server to synchronize the clock with the sleep monitoring device.

[0028] Optionally, after setting the sleep feature as the user's target sleep feature, the method further includes:

[0029] Extract target sleep suggestions that match the target sleep features and user information from the sleep suggestion information database, and generate a sleep view based on the target sleep features;

[0030] The sleep report, consisting of the sleep view and the target sleep suggestion information, is sent to the client so that the client can display the sleep report.

[0031] Optionally, prior to the sleep report comprising the sleep view and the target sleep recommendation information, the following steps are included:

[0032] The sleep view within the preset time period is combined with the target sleep suggestion information to obtain a sleep report corresponding to the preset time period.

[0033] Optionally, combining the sleep view within a preset time period with the target sleep suggestion information to obtain a sleep report corresponding to the preset time period includes:

[0034] Based on the operating parameters of the sleep monitoring device, generate operating indicator information;

[0035] The sleep view, target sleep suggestion information, and performance indicator information within a preset time period are combined to obtain a sleep report corresponding to the preset time period.

[0036] Optionally, the step of extracting target sleep suggestion information that matches the target sleep characteristics and user information from the sleep suggestion information database includes:

[0037] Extract sleep suggestions that match the target sleep characteristics and user information from the sleep suggestion information database.

[0038] Extract target sleep suggestion information that matches the user configuration type from the sleep suggestion information, wherein the user configuration type includes at least one of the following: audio type, video type, and text type.

[0039] Some embodiments of this disclosure also provide a sleep data processing apparatus, the apparatus comprising:

[0040] The receiving module is configured to acquire sleep data collected by the sleep monitoring device;

[0041] The extraction module is configured to extract sleep features from the sleep data;

[0042] The comparison module is configured to compare the user's standard features with the sleep features to obtain a comprehensive feature similarity.

[0043] The aggregation module is configured to use the sleep feature as the user's target sleep feature if the comprehensive feature similarity meets the similarity requirement.

[0044] Optionally, the extraction module is further configured to:

[0045] By using the sleep algorithms corresponding to each sleep stage, the sleep datasets that match each sleep stage and the sleep cycle time sequence of the sleep data are obtained respectively.

[0046] Sleep features are obtained based on the sleep cycle time sequence and the sleep dataset.

[0047] Optionally, the sleep characteristics include at least: breathing characteristics and heart rate characteristics; the comparison module is further configured to:

[0048] The respiratory and heart rate characteristics are divided according to the sleep cycle sequence to obtain the characteristic set corresponding to each sleep stage;

[0049] Each of the aforementioned stage feature sets is compared with the standard features to obtain the feature similarity corresponding to each of the aforementioned sleep stages;

[0050] The feature similarity is integrated by combining the weight values ​​corresponding to each sleep stage to obtain the comprehensive feature similarity.

[0051] Optionally, the extraction module is further configured to:

[0052] Filter the sleep data to find data that meets the invalid data requirements, wherein the invalid data requirements include at least one of the following: invalid data format requirements and invalid data value requirements.

[0053] Optionally, the sleep characteristics include at least: sleep quality; the comparison module is further configured to:

[0054] Based on the sleep dataset and the sleep cycle sequence, obtain the respiratory disturbance index, number of awakenings, sleep onset time, sleep duration, and sleep efficiency;

[0055] The sleep quality is obtained by integrating the respiratory disorder index, the number of awakenings, the time to fall asleep, the sleep duration, and the sleep efficiency.

[0056] Optionally, the receiving module is further configured to:

[0057] Receive heartbeat messages periodically reported by sleep monitoring devices;

[0058] Extract the device status from the heartbeat message;

[0059] When the device is in the running state, a data acquisition request is sent to the sleep monitoring device;

[0060] Receive sleep data sent by the sleep monitoring device based on the data acquisition request.

[0061] Optionally, the receiving module is further configured to:

[0062] Obtain the current time from the time calibration server to synchronize the clock with the sleep monitoring device.

[0063] Optionally, the device further includes: an output module configured to:

[0064] Extract target sleep suggestions that match the target sleep features and user information from the sleep suggestion information database, and generate a sleep view based on the target sleep features;

[0065] The sleep report, consisting of the sleep view and the target sleep suggestion information, is sent to the client so that the client can display the sleep report.

[0066] Optionally, the output module is further configured to:

[0067] The sleep view within the preset time period is combined with the target sleep suggestion information to obtain a sleep report corresponding to the preset time period.

[0068] Optionally, the output module is further configured to:

[0069] Based on the operating parameters of the sleep monitoring device, generate operating indicator information;

[0070] The sleep view, target sleep suggestion information, and performance indicator information within a preset time period are combined to obtain a sleep report corresponding to the preset time period.

[0071] Optionally, the output module is further configured to:

[0072] Extract sleep suggestions that match the target sleep characteristics and user information from the sleep suggestion information database.

[0073] Extract target sleep suggestion information that matches the user configuration type from the sleep suggestion information, wherein the user configuration type includes at least one of the following: audio type, video type, and text type.

[0074] Some embodiments of this disclosure also provide a computing processing device, including:

[0075] Memory containing computer-readable code;

[0076] One or more processors, when the computer-readable code is executed by the one or more processors, the computing processing device performs the sleep data processing method as described above.

[0077] Some embodiments of this disclosure also provide a computer program, including computer-readable code, which, when run on a computing processing device, causes the computing processing device to perform the sleep data processing method described above.

[0078] Some embodiments of this disclosure also provide a computer-readable medium storing a computer program for processing sleep data as described above.

[0079] The sleep data processing method, apparatus, computer equipment, program, and medium disclosed herein compare sleep features in sleep data collected by a sleep monitoring device with standard features of a user. Only when the overall similarity of the two matches the similarity requirement is the sleep feature attributed to the user. This method can accurately determine the user to whom the sleep data belongs without relying on the binding relationship between the sleep monitoring device and the user.

[0080] The above description is merely an overview of the technical solution disclosed herein. In order to better understand the technical means of this disclosure and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this disclosure more apparent and understandable, specific embodiments of this disclosure are described below. Attached Figure Description

[0081] To more clearly illustrate the technical solutions in the embodiments of this disclosure 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 some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0082] Figure 1 The schematic diagram illustrates a flowchart of a sleep data processing method provided in some embodiments of this disclosure.

[0083] Figure 2 The illustration schematically shows a logical diagram of a firmware update method for a sleep monitoring device provided in some embodiments of this disclosure.

[0084] Figure 3 The schematic diagram illustrates a flowchart of another firmware update method for a sleep monitoring device provided in some embodiments of this disclosure.

[0085] Figure 4 The schematic diagram illustrates the principle of a sleep staging method provided by some embodiments of this disclosure;

[0086] Figure 5The illustration schematically shows a sleep view provided by some embodiments of the present disclosure.

[0087] Figure 6 The schematic diagram illustrates a flowchart of a method for obtaining sleep quality provided by some embodiments of the present disclosure.

[0088] Figure 7 The schematic diagram illustrates a flowchart of a method for generating a sleep report provided by some embodiments of this disclosure.

[0089] Figure 8 The schematic diagram illustrates a flowchart of a method for obtaining sleep advice information provided in some embodiments of this disclosure.

[0090] Figure 9 The diagram illustrates a logical schematic of a sleep data processing method provided in some embodiments of this disclosure.

[0091] Figure 10 The schematic diagram illustrates the structure of a sleep data processing apparatus provided in some embodiments of this disclosure.

[0092] Figure 11 A block diagram of a computing processing apparatus for performing the method according to the present disclosure is shown schematically.

[0093] Figure 12 A storage unit for holding or carrying program code that implements the method according to this disclosure is illustrated schematically. Detailed Implementation

[0094] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0095] In related technologies, sleep monitoring devices are typically pre-linked to users, thus attributing the collected sleep data to that user. However, when the same sleep monitoring device is used by multiple users, the binding between the device and the user may not be changed in a timely manner. This can cause sleep data belonging to one user to be attributed to another, severely affecting the accuracy of sleep data attribution.

[0096] Figure 1 The schematic diagram illustrates a flowchart of a sleep data processing method provided in some embodiments of this disclosure, the method comprising:

[0097] Step 101: Obtain sleep data collected by the sleep monitoring device.

[0098] In this embodiment, the sleep monitoring device refers to a device that generates sleep monitoring signals. This device can connect to the Internet of Things (IoT) via a wireless network to enable data interaction between the device and a server in the IoT. The sleep monitoring device can monitor user sleep behavior using non-contact radar waves, eliminating the need for the user to wear a physical device. Alternatively, the device can be wearable, and all are applicable to the sleep data processing methods provided in some embodiments of this disclosure, as long as the device can connect to the server via a network. Specific settings can be configured according to actual needs and are not limited here. When activated, the sleep monitoring device automatically starts monitoring at preset start and end times, or users can customize settings via a mobile terminal. Users can configure settings for the sleep monitoring device via their mobile terminal, including network information, sleep mode, and breathing light status. The operating status of the sleep monitoring device is synchronized to the user's mobile terminal in real time, allowing users to view sleep monitor parameters and related preferences.

[0099] In practical applications, sleep monitoring devices can use built-in pressure sensors, sound sensors, etc., to monitor a user's breathing, heart rate, body movement, and other behaviors that reflect sleep patterns, generating continuous raw signals of sleep monitoring parameters. The device then uses a built-in information processing module to perform digital-to-analog conversion and data assembly on this raw information, generating formatted data in a specific programming language as sleep data. The sleep monitoring device can also update its various functional modules via pluggable firmware or through remote interaction with a server. It may also include a local storage unit as a temporary database for storing sleep data before data interaction with the server. Furthermore, the device may include an interface transmission request module responsible for network transmission and information interaction with the server, assembling data according to agreed-upon interface protocols and remotely transmitting local area network data to the server. It is worth noting that due to their limited size, sleep monitoring devices have limited hardware configurations, resulting in lower storage, data transmission, and data processing capabilities. A server paired with the sleep monitoring device can be set up to handle the storage, processing, and transmission of sleep data. This server can communicate with the sleep monitoring device via Bluetooth, wireless networks, or mobile networks, enabling real-time data interaction between the two devices and avoiding the risk of data loss due to insufficient storage resources on the sleep monitoring device.

[0100] For example, sleep monitoring devices can send the collected sleep data to a server, which then forwards the data to a remote server for further processing. This remote server can be a distributed server cluster. Upon receiving the sleep data from the server, the system can use distributed task scheduling to select idle or lightly loaded distributed servers to process the data, thus improving both server resource utilization and processing efficiency.

[0101] For example, refer to Figure 2 This application also provides a firmware update method for a sleep monitoring device, wherein the control terminal refers to a terminal responsible for controlling the firmware update, the application server refers to a server that publishes firmware information, the file server refers to a server that stores firmware information, and the sleep monitoring terminal refers to the sleep monitoring device. The method includes:

[0102] Step S1: The control terminal calls the API interface of the application server to obtain the latest firmware details;

[0103] Step S2: The control terminal receives the latest firmware details information sent by the application server;

[0104] Step S3: The control terminal sends an update command to the sleep monitor terminal;

[0105] Step S4: The sleep monitor terminal calls the firmware update function to obtain the resource address according to the update instruction;

[0106] Step S5: The sleep monitor terminal obtains the required firmware update package from the file server;

[0107] Step S6: The sleep monitor terminal installs the received firmware update package;

[0108] Step S7: The sleep monitor terminal is upgraded according to the firmware.

[0109] Step S8: The sleep monitor terminal sends the updated firmware information to the application server, so that the application server updates the device firmware information corresponding to the sleep monitor terminal.

[0110] Step S9: The control terminal obtains the updated device firmware information from the application server and displays the updated device firmware information.

[0111] Of course, this is only an exemplary description of the firmware update method in sleep monitoring devices. In addition, other firmware update methods can be used, such as replacing the pluggable firmware or updating it on-site by maintenance personnel. The specific method can be set according to actual needs and is not limited here.

[0112] Step 102: Extract sleep features from the sleep data.

[0113] In this embodiment, sleep features refer to indicators that reflect a user's sleep status, such as sleep efficiency, sleep quality score, and sleep duration. These sleep features can be raw data directly extracted from sleep data, or indicators obtained after secondary processing of the sleep data. It is understood that sleep data may contain interfering data unrelated to the user's sleep status, such as conversations or footsteps of other users in the same room, or heartbeat or breathing data before the user fell asleep. Therefore, selective extraction from sleep data is necessary. Specifically, a preset sleep algorithm can be used to identify specific indicators in the sleep data, and then that portion of the sleep data can be extracted as sleep features. For example, a heartbeat recognition algorithm or a heart rate algorithm can be set to identify heartbeat data in the sleep data, etc. Specific sleep features can be set according to actual needs by setting different sleep algorithms, and are not limited here.

[0114] Step 103: Compare the user's standard features with the sleep features to obtain a comprehensive feature similarity.

[0115] In this embodiment of the disclosure, standard features refer to feature information that can reflect the sleep status of an individual user. These standard features can be obtained by feature extraction from the sleep data of an individual user. It is understood that since sleep monitoring devices may be used continuously by multiple users, it is difficult to determine which sleep data belongs to which user, resulting in inaccurate attribution of sleep data.

[0116] This embodiment of the disclosure extracts standard features from each user's sleep data in advance as a reference, and establishes and stores the association between user identity information and standard features. When a user actually uses the sleep monitoring device, the remote server queries the associated standard features based on the user's identity information and compares them with the sleep features in the received sleep data to identify which user the sleep data belongs to. Specifically, by calculating the similarity between each standard feature and the sleep feature, the similarity of each dimension of the standard features and sleep features can be compared separately to obtain the similarity of each dimension. Then, the similarity of each dimension of the features is integrated to obtain a comprehensive feature similarity that reflects the overall similarity of the features.

[0117] Step 104: If the comprehensive feature similarity meets the similarity requirement, the sleep feature is taken as the user's target sleep feature.

[0118] In this embodiment, the similarity requirement refers to the value requirement that the comprehensive feature similarity must meet when the sleep feature belongs to a user associated with a standard feature. This can be that the comprehensive feature similarity is greater than a specific similarity threshold, or that the comprehensive feature similarity is within a specific similarity range. Furthermore, the similarity requirement can be manually preset or automatically configured by a remote server for user information. For example, a larger similarity threshold can be set when the number of users associated with the standard feature is large, while a smaller similarity threshold can be set when the number of users associated with the standard feature is small. Of course, the specific similarity requirement can be set according to actual needs and is not limited here.

[0119] In practical applications, if the comprehensive feature similarity meets the similarity requirements, it can be confirmed that the user's sleep status reflected by the sleep feature is consistent with the standard feature. Therefore, the sleep feature can be attributed to the target sleep feature of the user associated with the standard feature.

[0120] In this embodiment of the disclosure, the sleep features in the sleep data collected by the sleep monitoring device are compared with the user's standard features. Only when the overall similarity between the two meets the similarity requirements will the sleep feature be attributed to the user. This method can accurately determine the user to whom the sleep data belongs without relying on the binding relationship between the sleep monitoring device and the user.

[0121] Figure 3 The schematic diagram illustrates a flowchart of another sleep data processing method provided in some embodiments of this disclosure, the method comprising:

[0122] Step 201: Obtain the current time from the time calibration server to synchronize the clock with the sleep monitoring device.

[0123] In this embodiment, the Network Time Protocol (NTP) is a server that provides high-precision time information to enable time correction for connected devices. The remote server and the server to which the sleep monitoring device is connected can connect to this NTP, allowing for periodic information exchange. This enables the local current time to be calibrated using the standard time provided by the NTP, ensuring time synchronization between the sleep monitoring device and the remote server and preventing data transmission delays due to time errors.

[0124] Step 202: Receive heartbeat messages periodically reported by the sleep monitoring device.

[0125] In this embodiment of the disclosure, the heartbeat message is a data message that can reflect the operating status of the sleep monitoring device. The heartbeat message may include device configuration information such as device operating status, network information, sleep mode, monitoring time, sleep aid mode, smart wake-up, and report playback.

[0126] The sleep monitoring device periodically and proactively sends heartbeat messages to a remote server. The remote server responds to these messages by verifying the device and receiving sleep data. It then standardizes the sleep data through format conversion and stores it in a database for later processing. Alternatively, the server connected to the sleep monitoring device can also send information such as the device's operating status and network information from the heartbeat messages to an application client on the user's mobile phone for display, allowing the user to monitor the device's operation in real time.

[0127] Step 203: Extract the device status from the heartbeat message.

[0128] In this embodiment of the disclosure, the device status refers to the operating status of the sleep monitoring device. The device status can be running status, standby status, power off status, etc., and can be set according to actual needs. No limitation is made here.

[0129] Step 204: If the device is in the running state, send a data acquisition request to the sleep monitoring device.

[0130] In this embodiment of the disclosure, when the remote server detects that the device status in the heartbeat message is running, it will actively send a data acquisition request to the server connected to the sleep monitoring device, thereby timely acquiring the sleep data collected by the sleep monitoring device.

[0131] Step 205: Receive sleep data sent by the sleep monitoring device according to the data acquisition request.

[0132] In this embodiment, after detecting a data acquisition request sent by a remote server, the server connected to the sleep monitoring device retrieves sleep data from the temporary storage module and sends it to the remote server. After sending, the server can delete the sent sleep data to ensure sufficient local storage resources. Specifically, the server of the sleep monitoring device can send sleep data to the remote server by calling the remote server's API (Application Programming Interface).

[0133] This embodiment of the invention uses periodic heartbeat message exchanges between the sleep monitoring device and the remote server to determine whether the current network transmission link is smooth, ensuring that the sleep data collected by the sleep monitoring device can be sent to the remote server in a timely manner, thus avoiding the risk of data loss caused by untimely data transmission.

[0134] Step 206: Filter the sleep data that meets the invalid data requirements, wherein the invalid data requirements include at least one of the following: invalid data format requirements and invalid data value requirements.

[0135] In this embodiment, invalid data requirements refer to data that fails to reflect the user's true sleep patterns or that negatively impacts sleep analysis. It is understood that during sleep monitoring, the device may collect irrelevant data due to external interference, or some data may be damaged during transmission, rendering it unusable. Since this invalid data has specific formats and values, a remote server can filter it by setting invalid data format and value requirements. This avoids interference from invalid data in subsequent data processing and improves the accuracy of the obtained sleep characteristics.

[0136] Step 207: Using the sleep algorithms corresponding to each sleep stage, obtain the sleep datasets that match each sleep stage in the sleep data, as well as the sleep cycle time sequence of the sleep data.

[0137] In this embodiment of the disclosure, sleep staging refers to the time segmentation of different states during a user's sleep cycle, for example: referring to Figure 4The entire sleep cycle can be divided into the sleep initiation period, implantation period, sleep stage initiation period, sleep entry period, sleep stage end period, wake-up period, and end monitoring period. Sleep monitoring begins at the start of the sleep initiation period; implantation begins at the start of the implantation period; sleep stage initiation begins at the start of the sleep stage initiation period; light sleep begins at the start of the sleep entry period; sleep stage end at the start of the sleep stage end period; wake-up begins at the start of the wake-up period; and sleep monitoring ends at the end of the wake-up period. Sleep stage refers to the period before and after sleep, and therefore, a sleep stage can be equal to the end of the sleep stage initiation period minus the end of the sleep stage initiation period. The sleep clock is a timer for the user's sleep process, and therefore, the sleep clock can be equal to the sleep initiation period plus the sleep stage end period. The sleep onset period refers to the period from wakefulness to sleep, and therefore, the sleep onset period can be equal to the start of the sleep entry period minus the start of the sleep stage initiation period. Since the sleep period refers to the time from when a user is awake to when they fall asleep to when they wake up, this sleep period can be equal to the end time of the installment period minus the start time of the installment period.

[0138] This refers to the period from when a user lies down in bed to when they fall asleep. Light sleep stages refer to the period when the user is in light sleep, and deep sleep stages refer to the period when the user is in deep sleep. This is just an example. The specific sleep stage division method can be set according to actual needs and is not limited here.

[0139] Specifically, sleep data in different sleep stages can be identified by setting corresponding sleep algorithms for different sleep stages, thereby obtaining the sleep cycle sequence that reflects the time period of different sleep stages. For example, by arranging the data of a complete sleep cycle by minute, a sleep cycle sequence such as [1,3,3,3,3,3,3,3,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,3,3,3...] can be obtained, where 1 represents the awake stage, 2 represents the eye movement stage, 3 represents the light sleep stage, 4 represents the deep sleep stage, and 5 represents the inactive stage.

[0140] Furthermore, sleep data from different sleep stages can be aggregated based on the sleep cycle time sequence to obtain sleep datasets for each sleep stage. For example, sleep data at times 1, 2, 3, 4, and 5 in the sleep cycle time sequence shown above can be aggregated into separate sleep datasets, resulting in five sleep datasets matching the five sleep stages.

[0141] Step 208: Obtain sleep features based on the sleep cycle time sequence and the sleep dataset.

[0142] In this embodiment of the disclosure, the proportion and time point of different sleep stages in the entire sleep cycle can be determined based on the sleep cycle time sequence, and the sleep dataset can provide sleep data in each sleep stage. By using these data to calculate according to various sleep index algorithms or by directly providing sleep data in a specific sleep stage, sleep characteristics that can reflect the user's sleep status can be obtained.

[0143] Step 209: Divide the breathing characteristics and heartbeat characteristics according to the sleep cycle sequence to obtain the characteristic set corresponding to each sleep stage.

[0144] In this embodiment, respiratory features are data that reflect the user's breathing frequency, and heartbeat features are features that reflect the user's heartbeat frequency. The remote server extracts respiratory and heartbeat features from the sleep data, and merges the heartbeat and respiratory features according to each sleep stage to obtain the stage feature set corresponding to each sleep stage. For example, the heartbeat features (heartRateList) and respiratory features (breathRateList) of the entire monitoring period are found to their respective sleep stages according to the time sequence in the sleep cycle, and placed into the corresponding sleep stage list to generate the corresponding stage datasets heartRateWakeList[], heartRateEyeList[], heartRateLightList[], heartRateDeepList[], heartRateOffList[], breathRateWakeList[], breathRateEyeList[], breathRateLightList[], breathRateDeepList[], and breathRateOffList[].

[0145] Step 210: Compare each of the staged feature sets with the standard features to obtain the feature similarity corresponding to each of the sleep stages.

[0146] In this embodiment of the disclosure, multiple stage feature sets and multiple standard features can exist in step 209, each corresponding to a different sleep stage. By comparing the standard features of the sleep data sets corresponding to different sleep stages, the feature similarity corresponding to each sleep stage can be obtained. The calculation method for this feature similarity can refer to the similarity calculation method in related technologies, and will not be elaborated here.

[0147] Step 211: Integrate the feature similarity by using the weight values ​​corresponding to each sleep stage to obtain a comprehensive feature similarity.

[0148] In this embodiment of the application, a corresponding weight value is pre-set for each sleep stage. The weight value can be set with reference to the contribution of each sleep stage to the user's sleep, or it can be set on average. The specific weight value can be determined according to actual needs and is not limited here.

[0149] By weighted summing of the feature similarities corresponding to each sleep stage, a comprehensive feature similarity that reflects the entire sleep cycle can be obtained.

[0150] By setting weights for five sleep stages—w1 (awake), w2 (eye movement), w3 (light sleep), w4 (deep sleep), and w5 (inactive sleep)—the comprehensive feature similarity is calculated based on the following formula (1):

[0151] sim=∑p i w i (1)

[0153] Where sim represents the comprehensive feature similarity, and p i For the i-th installment dataset, w i Let be the weight values ​​of the i-th phased dataset.

[0154] Step 212: If the comprehensive feature similarity meets the similarity requirement, the sleep feature is taken as the user's target sleep feature.

[0155] This step can be referred to in the detailed description of step 104, and will not be repeated here.

[0156] Step 213: Extract target sleep suggestion information that matches the target sleep features and user information from the sleep suggestion information database, and generate a sleep view based on the target sleep features.

[0157] In this embodiment, the sleep suggestion information database stores the correlation between different target sleep characteristics and sleep suggestion information. This sleep suggestion information is pre-defined based on practical experience to improve the sleep of users with different sleep characteristics. It can include sleep improvement course videos, sleep improvement news, etc. The format of the target sleep suggestion information can be set according to actual needs and is not limited here. The sleep view is a visualization of indicator data in various dimensions of the target sleep characteristics, such as a dimensional polygon graph. This involves setting the number of edges of a polygon based on the dimensions of the indicator data, and using the distance from each vertex to the center of the polygon to represent the value of the indicator data. Other formats include radar charts, bar charts, pie charts, and scatter plots. For example: Refer to... Figure 5Here, S represents sleep efficiency, A represents sleep onset time, B represents sleep duration, C represents wakefulness, and D represents sleep breathing quality. A five-dimensional radar chart is generated based on these five sleep characteristics. The larger the area of ​​the shaded region near a certain dimension's vertex, the higher the index value of the sleep characteristic corresponding to that dimension's vertex. Of course, this is just an example description; it is not limited to anything that allows users to intuitively understand their own sleep status through this sleep view.

[0158] Step 214: Combine the sleep view in the preset time period with the target sleep suggestion information to obtain a sleep report corresponding to the preset time period.

[0159] In this embodiment, the preset time period can be daily, weekly, monthly, etc. Furthermore, by combining the obtained sleep view and target sleep suggestion information according to a preset layout template, a comprehensive sleep report reflecting the user's sleep status can be obtained, including data summaries, daily sleep reports, weekly sleep reports, and monthly sleep reports.

[0160] Step 215: Send the sleep report, consisting of the sleep view and the target sleep suggestion information, to the client so that the client can display the sleep report.

[0161] In this embodiment of the disclosure, the remote server can send the sleep report to the client on the user's mobile phone, tablet, smartwatch or other terminal device, so that the user can conveniently view the sleep report and understand his / her sleep status through the client.

[0162] Optionally, the sleep characteristics include at least: sleep quality.

[0163] Reference Figure 6 Step 208 includes:

[0164] Sub-step 2081: Based on the sleep dataset and the sleep cycle sequence, obtain the respiratory disturbance index, number of awakenings, sleep onset time, sleep duration, and sleep efficiency.

[0165] In this embodiment of the disclosure, the sleep disturbance index (AHI, Apnea-Hypopnea Index) refers to the user's apnea and hypopnea index during sleep per hour; the number of awakenings refers to the frequency of compliant awakenings between the first deep sleep stage and the last deep sleep stage in a defined sleep cycle, ultimately presenting the number of awakening intervals in the sleep stage map; the sleep onset time refers to the duration between the start of sleep stage and the first light sleep; and sleep efficiency refers to the ratio of the difference between the user's sleep duration and sleep onset time to the time spent in bed.

[0166] Furthermore, sleep datasets may also include the following:

[0167] Sleep breathing can include the sleep breathing quality index, the number of low-quality breaths, the average low-quality breath duration, and the longest low-quality breath duration. The breathing status of a complete sleep cycle is represented by a two-dimensional array such as [[4572,16,95279,95631],[4571,15,97049,97369],[4701,17,99708,100065]]. The first and second elements (X1 and X2) of the inner array represent the apnea and low-quality breath durations, respectively. The number of non-zero X1 values ​​in the two-dimensional array is the number of apnea (N1), and the number of non-zero X2 values ​​is the number of low-quality breaths (N2). SUM(N1,N2) is the sleep breathing quality index, with -1 indicating an invalid state. MAX(X2) is the longest low-quality breath duration, and AVER(X2) is the average low-quality breath duration.

[0168] Deep sleep duration refers to the length of time a person is in deep sleep during the entire sleep cycle, according to the sleep stage calculation logic. Real-time heart rate and real-time respiratory rate are obtained from the sleep monitoring device, and are stored in the heart rate data list `heartRateList` and the respiratory rate data list `breathRateList` respectively, in minutes and in chronological order. Body movement data is obtained from the sleep monitoring device, and is stored in the body movement data list in minutes and in chronological order, such as [0.0, 1.0, 2.0, 1.0], where 0.0 represents quietness, 1.0 represents minimal movement, and 2.0 represents significant movement. The snoring and sleep talking files are stored locally on the sleep monitor. The remote server stores and represents them in the following format: {"snore":["Sleep-1571760959-26"],"somniloquy":["Sleep-1571771248-3"]}, where snore represents the list of snoring files and somniloquy represents the list of sleep talking files. If it is necessary to play the snoring and sleep talking files, the files are retrieved from the sleep monitoring device's local storage through the file list returned by the interface for display and playback.

[0169] Sub-step 2082 integrates the respiratory disorder index, the number of awakenings, the sleep onset time, the sleep duration, and the sleep efficiency to obtain sleep quality.

[0170] In this embodiment, the factor values ​​f() of the respiratory disturbance index (AHI), number of awakenings (wakeN), sleep onset time (T1), sleep duration (T2), and sleep efficiency (X) are first obtained, and can be specifically obtained by the following formulas (2) to (6):

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] Then, the values ​​of each factor are integrated using the following formula (7):

[0177] Y=f(AHI)+f(wakeN)+f(T1)+f(T2)+f(X)(7)

[0178] Where Y is the value of the comprehensive factor.

[0179] Finally, the value of this comprehensive factor is input into the following formula (8) to obtain the user's sleep quality:

[0180]

[0181] SQI stands for Sleep Quality.

[0182] Optionally, refer to Figure 7 Step 214 includes:

[0183] Sub-step 2141: Generate operating indicator information based on the operating parameters of the sleep monitoring device.

[0184] In this embodiment, operational parameters can be extracted from the heartbeat messages sent from the server of the sleep monitoring device to the remote server. These operational parameters can reflect the operating mode and abnormal conditions of the sleep monitoring device during operation. By visualizing these operational parameters, operational indicator information reflecting the operating status of the sleep monitoring device can be obtained. For example, the values ​​of specific parameters in the operational parameters can be monitored within a certain range. If the values ​​exceed a certain range, an early warning message can be generated as operational indicator information. Alternatively, corresponding icons can be generated based on the operating status as operational indicator information.

[0185] Sub-step 2142 combines the sleep view, the target sleep suggestion information, and the operation indicator information within the preset time period to obtain a sleep report corresponding to the preset time period.

[0186] In this embodiment of the disclosure, the sleep report provided to the user may also include the operating index information of the sleep monitoring device in a specific time period, so that the user can also conveniently understand the operating status of the sleep monitoring device through the sleep report.

[0187] Optionally, refer to Figure 8 Step 213 includes:

[0188] Sub-step 2131: Extract sleep suggestion information that matches the target sleep characteristics and user information from the sleep suggestion information database.

[0189] In this embodiment, the sleep suggestion information stored in the sleep suggestion information database can be associated with sleep characteristics and user information. This user information can include personal details such as user gender, age, and occupation. This allows for setting sleep suggestion information associated with different sleep characteristics based on different user information, achieving customized sleep suggestions tailored to individual user information and making the provided sleep suggestion information more suitable for the user's actual situation.

[0190] Sub-step 2132: Extract target sleep suggestion information that matches the user configuration type from the sleep suggestion information, wherein the user configuration type includes at least one of the following: audio type, video type, and text type.

[0191] Figure 9 This schematically illustrates a logical diagram of a method for collecting sleep data according to some embodiments of the present disclosure, including:

[0192] Sleep monitoring devices collect sleep data by performing non-contact sleep monitoring on users.

[0193] The sleep aid device terminal transmits data in accordance with the Internet of Things protocol via SmartConfig (one-click network configuration mode);

[0194] The sleep aid device terminal can interact with a remote distributed application interaction server to send its operating status, real-time data, and heartbeat messages to the remote distributed interaction server, which then stores the operating status, real-time data, and heartbeat messages in a distributed manner.

[0195] When the sleep monitoring device terminal monitors a user's sleep, it first collects the raw signal values ​​of the sleep monitoring parameters, then processes them through digital-to-analog conversion and data assembly to obtain standard formatted sleep data, then stores the sleep data in the local terminal database for temporary storage, and finally stores the sleep data in a distributed manner through the interface transmission request module.

[0196] The remote distributed application interaction server processes the stored sleep data through the data processing module. The sleep data is then processed sequentially through a preset processing algorithm for indicator generation, sleep stage discrimination, and logical time sequence processing before being handed over to the data collection module.

[0197] The data collection module of the remote distributed application interaction server extracts the required sleep features for the sleep scenario from the sleep data, and then collects the sleep features through boundary similarity calculation to determine the user to which the sleep features belong.

[0198] The remote distributed application interaction server extracts sleep monitoring indicators from sleep characteristics, as well as comprehensive improvement suggestions and sleep quality assessments that match the sleep characteristics. Then, it pushes the sleep monitoring indicators, comprehensive improvement suggestions, and sleep quality assessments to users so that they can view them through the client.

[0199] In this embodiment, the user configuration type refers to the type of sleep suggestion information set by the user. This user configuration type can be audio, video, audio-visual, text, etc. For example, information that helps improve the user's sleep quality, such as relevant news, online courses, and sleep improvement services, can also be recommended based on the user configuration type. Of course, this is merely an illustrative example; the specific settings can be customized according to actual needs and are not limited here.

[0200] This disclosure applies to user information and user settings to recommend customized sleep advice information, making the sleep advice information obtained by the user more in line with the user's actual situation and improving the accuracy of the sleep advice information recommendation.

[0201] Figure 10 The schematic diagram illustrates the structure of a sleep data processing apparatus 30 provided in some embodiments of the present disclosure, the apparatus comprising:

[0202] The receiving module 301 is configured to acquire sleep data collected by the sleep monitoring device;

[0203] Extraction module 302 is configured to extract sleep features from the sleep data;

[0204] The comparison module 303 is configured to compare the user's standard features with the sleep features to obtain a comprehensive feature similarity.

[0205] The aggregation module 304 is configured to use the sleep feature as the user's target sleep feature when the comprehensive feature similarity meets the similarity requirement.

[0206] Optionally, the extraction module 302 is further configured to:

[0207] By using the sleep algorithms corresponding to each sleep stage, the sleep datasets that match each sleep stage and the sleep cycle time sequence of the sleep data are obtained respectively.

[0208] Sleep features are obtained based on the sleep cycle time sequence and the sleep dataset.

[0209] Optionally, the sleep characteristics include at least: breathing characteristics and heart rate characteristics;

[0210] The comparison module 303 is further configured to:

[0211] The respiratory and heart rate characteristics are divided according to the sleep cycle sequence to obtain the characteristic set corresponding to each sleep stage;

[0212] Each of the aforementioned stage feature sets is compared with the standard features to obtain the feature similarity corresponding to each of the aforementioned sleep stages;

[0213] The feature similarity is integrated by combining the weight values ​​corresponding to each sleep stage to obtain the comprehensive feature similarity.

[0214] Optionally, the extraction module 302 is further configured to:

[0215] Filter the sleep data to find data that meets the invalid data requirements, wherein the invalid data requirements include at least one of the following: invalid data format requirements and invalid data value requirements.

[0216] Optionally, the sleep characteristics include at least: sleep quality; the comparison module 303 is further configured to:

[0217] Based on the sleep dataset and the sleep cycle sequence, obtain the respiratory disturbance index, number of awakenings, sleep onset time, sleep duration, and sleep efficiency;

[0218] The sleep quality is obtained by integrating the respiratory disorder index, the number of awakenings, the time to fall asleep, the sleep duration, and the sleep efficiency.

[0219] Optionally, the receiving module 301 is further configured to:

[0220] Receive heartbeat messages periodically reported by sleep monitoring devices;

[0221] Extract the device status from the heartbeat message;

[0222] When the device is in the running state, a data acquisition request is sent to the sleep monitoring device;

[0223] Receive sleep data sent by the sleep monitoring device based on the data acquisition request.

[0224] Optionally, the receiving module 301 is further configured to:

[0225] Obtain the current time from the time calibration server to synchronize the clock with the sleep monitoring device.

[0226] Optionally, the device further includes: an output module configured to:

[0227] Extract target sleep suggestions that match the target sleep features and user information from the sleep suggestion information database, and generate a sleep view based on the target sleep features;

[0228] The sleep report, consisting of the sleep view and the target sleep suggestion information, is sent to the client so that the client can display the sleep report.

[0229] Optionally, the output module is further configured to:

[0230] The sleep view within the preset time period is combined with the target sleep suggestion information to obtain a sleep report corresponding to the preset time period.

[0231] Optionally, the output module is further configured to:

[0232] Based on the operating parameters of the sleep monitoring device, generate operating indicator information;

[0233] The sleep view, target sleep suggestion information, and performance indicator information within a preset time period are combined to obtain a sleep report corresponding to the preset time period.

[0234] Optionally, the output module is further configured to:

[0235] Extract sleep suggestions that match the target sleep characteristics and user information from the sleep suggestion information database.

[0236] Extract target sleep suggestion information that matches the user configuration type from the sleep suggestion information, wherein the user configuration type includes at least one of the following: audio type, video type, and text type.

[0237] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0238] The various component embodiments of this disclosure can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the computing processing device according to embodiments of this disclosure. This disclosure can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing some or all of the methods described herein. Such an implementation of this disclosure can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0239] For example, Figure 11 A computing processing apparatus is shown that can implement the methods according to this disclosure. The computing processing apparatus conventionally includes a processor 410 and a computer program product or computer-readable medium in the form of a memory 420. The memory 420 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. The memory 420 has a storage space 430 for program code 431 for performing any of the method steps described above. For example, the storage space 430 for program code may include various program codes 431 respectively for implementing the various steps in the methods described above. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. Such computer program products are typically as described in the references. Figure 12 The portable or fixed storage unit is described above. This storage unit may have the same characteristics as... Figure 11 The memory 420 in the computing processing device is similarly arranged as storage segments, storage spaces, etc. Program code can be compressed, for example, in an appropriate form. Typically, the storage unit includes computer-readable code 431', that is, code that can be read by a processor such as 410, which, when run by the computing processing device, causes the computing processing device to perform the various steps in the methods described above.

[0240] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0241] The terms "an embodiment," "embodiment," or "one or more embodiments" as used herein mean that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of this disclosure. Furthermore, please note that the examples of the phrase "in one embodiment" do not necessarily all refer to the same embodiment.

[0242] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this disclosure may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0243] In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This disclosure can be implemented by means of hardware comprising a plurality of different elements and by means of a suitably programmed computer. In a unit claim enumerating a plurality of means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

[0244] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for processing sleep data, characterized in that, The method includes: Acquiring sleep data collected by a sleep monitoring device includes: obtaining the current time from a time calibration server to synchronize the clock with the sleep monitoring device; receiving heartbeat messages periodically reported by the sleep monitoring device; extracting the device status from the heartbeat messages; sending a data acquisition request to the sleep monitoring device when the device status is running; and receiving sleep data sent by the sleep monitoring device according to the data acquisition request. The heartbeat messages include: device running status, network information, sleep mode, monitoring time, sleep aid mode, smart wake-up, and report playback device configuration information. Extracting sleep features from the sleep data includes: using sleep algorithms corresponding to each sleep stage to obtain sleep datasets that match each sleep stage, as well as the sleep cycle sequence of the sleep data; obtaining respiratory disturbance index, number of awakenings, sleep onset time, sleep duration, and sleep efficiency based on the sleep datasets and the sleep cycle sequence; integrating the respiratory disturbance index, number of awakenings, sleep onset time, sleep duration, and sleep efficiency to obtain sleep quality; the sleep features include at least: respiratory features, heart rate features, and sleep quality; The breathing and heart rate features are divided according to the sleep cycle sequence to obtain a feature set corresponding to each sleep stage. Each feature set is compared with a standard feature to obtain the feature similarity corresponding to each sleep stage. The feature similarities are integrated using the weight values ​​corresponding to each sleep stage to obtain a comprehensive feature similarity. The standard feature is used to reflect the sleep information of a single user. If the comprehensive feature similarity meets the similarity requirements, the sleep feature is used as the user's target sleep feature. Extract target sleep suggestions that match the target sleep characteristics and user information from the sleep suggestion information database, and generate a sleep view based on the target sleep characteristics; generate operation index information based on the operation parameters of the sleep monitoring device; combine the sleep view, the target sleep suggestion information, and the operation index information in a preset time period to obtain a sleep report corresponding to the preset time period.

2. The method according to claim 1, characterized in that, Prior to extracting sleep features from the sleep data, the method further includes: Filter the sleep data to find data that meets the invalid data requirements, wherein the invalid data requirements include at least one of the following: invalid data format requirements and invalid data value requirements.

3. The method according to claim 1, characterized in that, After setting the sleep feature as the user's target sleep feature, the method further includes: The sleep report, consisting of the sleep view and the target sleep suggestion information, is sent to the client so that the client can display the sleep report.

4. The method according to claim 1, characterized in that, The step of extracting target sleep suggestion information that matches the target sleep characteristics and user information from the sleep suggestion information database includes: Extract sleep suggestion information that matches the target sleep characteristics and user information from the sleep suggestion information database; Extract target sleep suggestion information that matches the user configuration type from the sleep suggestion information, wherein the user configuration type includes at least one of the following: audio type, video type, and text type.

5. A sleep data processing device, characterized in that, The device includes: The receiving module is configured to acquire sleep data collected by the sleep monitoring device, including: acquiring the current time from a time calibration server to synchronize the clock with the sleep monitoring device; receiving heartbeat messages periodically reported by the sleep monitoring device; extracting the device status from the heartbeat messages; sending a data acquisition request to the sleep monitoring device when the device status is running; and receiving sleep data sent by the sleep monitoring device according to the data acquisition request. The heartbeat messages include device configuration information such as device running status, network information, sleep mode, monitoring time, sleep aid mode, smart wake-up, and report playback. The extraction module is configured to extract sleep features from the sleep data, including: obtaining sleep datasets that match each sleep stage and the sleep cycle sequence of the sleep data respectively using sleep algorithms corresponding to each sleep stage; obtaining respiratory disturbance index, number of awakenings, sleep onset time, sleep duration, and sleep efficiency based on the sleep datasets and the sleep cycle sequence; integrating the respiratory disturbance index, number of awakenings, sleep onset time, sleep duration, and sleep efficiency to obtain sleep quality; the sleep features include at least: respiratory features, heart rate features, and sleep quality; The comparison module is configured to divide the breathing features and heart rate features according to the sleep cycle sequence to obtain a feature set corresponding to each sleep stage; compare each feature set with a standard feature to obtain the feature similarity corresponding to each sleep stage; and integrate the feature similarities by using the weight values ​​corresponding to each sleep stage to obtain a comprehensive feature similarity; the standard feature is used to reflect the feature information of a single user's sleep status. The aggregation module is configured to, when the comprehensive feature similarity meets the similarity requirement, use the sleep feature as the user's target sleep feature; extract target sleep suggestion information that matches the target sleep feature and user information from the sleep suggestion information database, and generate a sleep view based on the target sleep feature; generate operating indicator information based on the operating parameters of the sleep monitoring device; and combine the sleep view, the target sleep suggestion information, and the operating indicator information in a preset time period to obtain a sleep report corresponding to the preset time period.

6. A computing processing device, characterized in that, include: Memory containing computer-readable code; One or more processors, when the computer-readable code is executed by the one or more processors, the computing processing device performs the method of processing sleep data as described in any one of claims 1-4.

7. A computer program, characterized in that, Includes computer-readable code that, when executed on a computing processing device, causes the computing processing device to perform a method for processing sleep data according to any one of claims 1-4.

8. A computer-readable medium, characterized in that, It contains a computer program that stores a method for processing sleep data as described in any one of claims 1-4.

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