A method and system for unattended sleep quality analysis

Abstract

The application discloses a sleep quality analysis method and system for unattended sleep, sleep-related signals are acquired from the surrounding environment of a user through non-contact sensor technology, real-time capture is performed on body movement frequency characteristics and breathing rhythm characteristics, and preliminary sleep behavior data records are obtained; according to the preliminary sleep behavior data records, a signal denoising processing method is used to filter body movement frequency signals and breathing rhythm signals, environmental noise interference is eliminated, and an optimized sleep signal data set is determined; for the optimized sleep signal data set, a pre-established sleep stage classification model is used for feature extraction, regular changes in body movement patterns and breathing regularity are analyzed, and sleep stage distribution of the user is determined; and according to the sleep stage distribution, the sleep quality of the user is graded and evaluated in combination with a pre-set threshold range, so that accurate sleep stage determination and sleep quality evaluation can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of health management, and particularly discloses a sleep quality analysis method and system for unattended care. BACKGROUND

[0002] Sleep quality analysis, as an important field of health management, has an undeniable value in improving individual quality of life and preventing chronic diseases. With the acceleration of life pace and the enhancement of health awareness, more and more people begin to pay attention to their sleep conditions, and related monitoring technologies have become a research hotspot.

[0003] However, the current sleep monitoring methods still have significant limitations in practical application. Traditional monitoring methods often rely on specific environments and devices, requiring users to receive detection in fixed places. This approach not only limits the flexibility of monitoring, but also easily disturbs the natural sleep state of users due to the complexity of device wearing and the unfamiliarity of the environment, thereby affecting the authenticity of the data. In addition, existing methods often require a large amount of manual intervention in data processing, which is inefficient and difficult to meet the needs of large-scale and routine monitoring. Under this background, the core challenges in this field gradually emerge.

[0004] The primary problem is how to break away from the dependence on specific environments and achieve a more free and natural monitoring method, which directly relates to user experience and data reliability. This problem further extends to how to obtain accurate sleep data without affecting user comfort, especially in scenarios where users do not need to wear cumbersome devices or change their daily habits. These two factors are closely related, with the former determining the universality of the monitoring scene and the latter affecting the acceptability of the technology in practical application. If the problems of environmental dependence and comfort cannot be solved, sleep monitoring technology will be difficult to truly integrate into users' daily lives.

[0005] Therefore, how to achieve efficient and accurate sleep data collection without specific environmental restrictions and affecting user comfort has become a key problem that needs to be overcome in this research. SUMMARY

[0006] The present application provides a sleep quality analysis method and system for unattended care, aiming to solve at least one of the defects in the prior art.

[0007] One aspect of the present application relates to a sleep quality analysis method for unattended care, comprising the following steps:

[0008] Obtaining sleep-related signals from the user's surrounding environment through non-contact sensor technology, capturing body movement frequency characteristics and breathing rhythm characteristics in real time to obtain preliminary sleep behavior data records;

[0009] According to the preliminary sleep behavior data record, a signal denoising processing method is used to filter the body motion frequency signal and the breathing rhythm signal, eliminate environmental noise interference, and determine an optimized sleep signal data set;

[0010] For the optimized sleep signal data set, a pre-established sleep stage classification model is used for feature extraction, analysis of the regularity of body motion patterns and breathing regularity, and determination of the sleep stage distribution of the user;

[0011] According to the sleep stage distribution, the sleep quality of the user is graded and evaluated in combination with a pre-set threshold range, the duration of each stage and the conversion frequency are obtained, and the sleep quality evaluation result is obtained;

[0012] For the sleep quality evaluation result, the user's sleep pattern is tracked in the long term through a time series analysis method, the periodic change of the sleep stage distribution is identified, and the user's sleep habit feature is determined;

[0013] According to the sleep habit feature, an adaptive adjustment mechanism is used to dynamically optimize the signal acquisition parameters of the non-contact sensor, and a monitoring configuration scheme conforming to the user's daily habits is obtained;

[0014] For the monitoring configuration scheme, the user's sleep data is uploaded to a remote server for storage and backup through cloud data synchronization technology, and a real-time updated sleep archive record is obtained;

[0015] According to the real-time updated sleep archive record, a sleep quality evaluation report of the user is generated by using data visualization technology, the sleep stage distribution and the sleep quality evaluation result are graphically displayed, and the sleep improvement direction that the user can refer to is determined.

[0016] Further, according to the preliminary sleep behavior data record, a signal denoising processing method is used to filter the body motion frequency signal and the breathing rhythm signal, eliminate environmental noise interference, and determine an optimized sleep signal data set, which comprises the following steps:

[0017] According to the preliminary sleep behavior data record, the original sleep signal containing body motion frequency and breathing rhythm is obtained, the preliminary cleaning sleep signal set is obtained by using a pre-set threshold to preliminarily screen the environmental noise interference in the original sleep signal;

[0018] For the preliminary cleaning sleep signal set, a signal filtering tool is used to denoise the body motion frequency signal and the breathing rhythm signal, and if the environmental noise interference exceeds the pre-set threshold, a frequency domain conversion tool is used to separate the noise component, and a denoised sleep signal combination is obtained;

[0019] According to the denoised sleep signal combination, aiming at the body motion frequency characteristics and the breathing rhythm characteristics, the time domain analysis tool is used for data precision adjustment, whether there is abnormal fluctuation is judged by comparing the normal range value established in advance, and the sleep signal data set after precision adjustment is determined;

[0020] For the sleep signal data set after precision adjustment, the body motion frequency and the breathing rhythm are classified and stored by the data integration tool, the data characteristics related to behavior analysis are obtained according to the real-time recording requirement during storage, and the final optimized sleep signal data set is obtained.

[0021] Further, according to the preliminary sleep behavior data record, the original sleep signal containing body motion frequency and breathing rhythm is obtained, and aiming at the environmental noise interference in the original sleep signal, a preset threshold is used for preliminary screening to obtain the sleep signal set after preliminary cleaning.

[0022]

[0023] Among them, represents the original sleep signal at time , represents the body motion frequency component, represents the breathing rhythm component, represents the environmental noise interference component;

[0024] The determination criterion of environmental noise screening is:

[0025]

[0026] Among them, represents the preset noise screening threshold, represents the mean value of the sleep signal, represents the standard deviation of the sleep signal, represents the threshold coefficient;

[0027] The judgment logic of signal cleaning is:

[0028]

[0029] Among them, represents the sleep signal after preliminary cleaning, represents the original sleep signal, represents the filtering threshold.

[0030] Further, for the optimized sleep signal data set, the sleep stage classification model established in advance is used for feature extraction, the regularity change of body motion mode and breathing law is analyzed, and the sleep stage distribution of the user is judged. The steps include:

[0031] According to the optimized sleep signal data set, a preset feature extraction tool is used for data decomposition according to the body movement mode and breathing regularity therein, a plurality of characteristic values related to sleep stage classification are obtained, and a preliminary extracted feature combination is obtained;

[0032] For the preliminary extracted feature combination, a data integration tool is used for classification processing of the plurality of characteristic values. In the classification, a pre-established stage division standard is combined. If the characteristic value meets the regularity analysis condition of a certain stage, it is classified into the corresponding category, and a classified feature set is determined.

[0033] According to the classified feature set, a time series comparison tool is used for dynamic matching according to the mode change and behavior association therein, a sleep stage distribution judgment basis corresponding to the sleep signal is obtained, and a preliminary mapping result of the sleep stage distribution is obtained.

[0034] For the preliminary mapping result of the sleep stage distribution, a data verification tool is used to perform secondary confirmation on the matching degree of the body movement mode and the breathing regularity. If the matching degree exceeds a preset threshold, the corresponding relationship with the sleep stage classification is confirmed, and the final sleep stage distribution data is determined.

[0035] Further, according to the sleep stage distribution, the sleep quality of the user is graded and evaluated according to a preset threshold range, the duration of each stage and the conversion frequency are obtained, and the steps of obtaining the sleep quality evaluation result include:

[0036] According to the sleep stage distribution, a time series recording tool is used to record the duration and conversion times of each stage according to the stage duration and conversion frequency, and dynamic change data related to sleep quality is obtained from the preliminary time distribution record table.

[0037] According to the preliminary time distribution record table, a data classification tool is used to classify the duration and conversion times of each stage according to a preset threshold range. If the duration or conversion times exceeds the corresponding threshold range, it is classified into an abnormal category, and the classified sleep stage distribution data is determined.

[0038] For the classified sleep stage distribution data, a data comparison tool is used to match the pre-established sleep quality standard according to the abnormal category and behavior mode, and the basis related to the grading evaluation is obtained, and the sleep quality grade distribution of each stage is determined.

[0039] For the sleep quality grade distribution of each stage, a data integration tool is used to aggregate the sleep quality evaluation results according to the distribution, and a final sleep quality evaluation report is obtained, and the overall sleep quality grade of the user is determined.

[0040] Further, for the sleep quality evaluation result, the sleep pattern of the user is tracked for a long time by a time series analysis method, and the step of identifying the periodic change of the sleep stage distribution and determining the sleep habit characteristics of the user includes:

[0041] According to the sleep quality evaluation result, the sleep stage distribution data of the user is collected for a long time by using a time series recording tool, and the starting and ending time points of each stage are labeled to obtain continuous data related to periodic changes, and a complete sleep stage time distribution record table is obtained;

[0042] For the sleep stage time distribution record table, a data comparison tool is used to match the periodic pattern library established in advance, and if the sleep stage distribution data matches the time interval of a certain pattern in the periodic pattern library, it is classified as the corresponding periodic characteristic, and the sleep stage periodic distribution characteristics of the user are determined;

[0043] According to the sleep stage periodic distribution characteristics, a data integration tool is used to associate the periodic change and duration of each stage, obtain the conversion rule between different stages, determine whether there is a regular fluctuation in the sleep pattern, and obtain the long-term trend.

[0044] For the long-term trend, a data classification tool is used to classify the fluctuation amplitude in combination with a preset threshold range, and if the fluctuation amplitude exceeds the threshold range, it is marked as an abnormal pattern, and the sleep habit characteristics of the user are determined.

[0045] Further, according to the sleep habit characteristics, an adaptive adjustment mechanism is used to dynamically optimize the signal acquisition parameters of the non-contact sensor to obtain a monitoring configuration scheme that conforms to the user's daily habits.

[0046] According to the sleep habit characteristics, a data recording tool is used to continuously track the user's daily work and rest time period, obtain sleep-related behavior data, and preliminarily set the acquisition frequency in combination with the environmental adaptation attribute to obtain an initial monitoring scheme that matches the daily habits.

[0047] For the initial monitoring scheme, a data comparison tool is used to match the acquisition frequency with the habit tracking database established in advance, and if the acquisition frequency deviates from the habit data in the habit tracking database by more than a preset threshold range, the signal acquisition parameter adjustment is optimized by an adaptive adjustment mechanism to determine the acquisition rule that conforms to the user's characteristics.

[0048] According to the acquisition rule, a parameter configuration tool is used to dynamically adjust the signal acquisition method of the non-contact sensor in combination with the environmental adaptation attribute to obtain monitoring data consistent with the daily habits and determine the monitoring frequency range suitable for the current state of the user.

[0049] For the monitoring frequency range, the signal acquisition result is associated with the sleep habit data by using a data integration tool, the monitoring configuration is finally calibrated through data matching attributes, and a personalized monitoring configuration scheme compatible with the user's daily habits is obtained.

[0050] Further, for the monitoring configuration scheme, the user's sleep data is uploaded to a remote server for storage and backup through cloud data synchronization technology, and the step of obtaining a real-time updated sleep archive record includes:

[0051] According to the sleep data, the collected content is classified and processed by using a data sorting tool, and the upload priority is set for different categories of data. If the priority exceeds the preset threshold, the data is transmitted to the remote server through the upload mechanism, and a classified data set is obtained.

[0052] For the classified data set, a data synchronization tool is used to synchronize the transmission process at a synchronization frequency. If the transmission is interrupted, the complete transmission log is obtained by reconnecting through a backup path.

[0053] According to the complete transmission log, a data verification tool is used to compare the data uploaded to the remote server, and the consistency is verified in combination with the data integrity attribute. For inconsistent parts, the data content after verification is determined by adjusting the repair tool.

[0054] For the data content after verification, a permission management tool is used to limit the access range in combination with the user's permission, and a copy is generated in the storage space by using a backup tool, and a real-time updated sleep archive record is obtained.

[0055] Further, according to the real-time updated sleep archive record, a sleep quality evaluation report of the user is generated by using data visualization technology, and the sleep stage distribution and sleep quality evaluation result are graphically displayed, and the step of determining the sleep improvement direction that the user can refer to includes:

[0056] According to the sleep archive, a data sorting tool is used to classify and process the updated content, and the sleep stage distribution data set is obtained by hierarchical extraction of the stage distribution data, and the preliminary sorted sleep stage information is obtained.

[0057] For the preliminary sorted sleep stage information, an evaluation standard tool is used to calculate the sleep quality evaluation score. If the score is lower than the preset threshold, the score is recalculated by adjusting the parameters, and the sleep quality evaluation result that meets the standard is determined.

[0058] According to the sleep quality evaluation result, a graphical display tool is used to visually process the stage distribution and evaluation data in combination with the display form, and an intuitive sleep quality evaluation report is generated, and graphical content for presentation is obtained.

[0059] For graphical content, the direction guide tool is used to match the improvement direction, and if the matching result is consistent with the low score item in the sleep quality evaluation report, the relevant suggestions are extracted from the pre-established suggestion library, and the sleep improvement direction that the user can refer to is determined.

[0060] Another aspect of the present application relates to a sleep quality analysis system for unattended care, for executing the above-mentioned sleep quality analysis method for unattended care, comprising:

[0061] A sleep behavior data record acquisition module is used to acquire sleep-related signals from the user's surrounding environment through non-contact sensor technology, and real-time capture is performed on body motion frequency characteristics and breathing rhythm characteristics to obtain preliminary sleep behavior data records.

[0062] A sleep signal data set determination module is used to filter body motion frequency signals and breathing rhythm signals using a signal denoising processing method based on preliminary sleep behavior data records, to eliminate environmental noise interference and determine an optimized sleep signal data set.

[0063] A sleep stage distribution condition judgment module is used to extract features using a pre-established sleep stage classification model based on the optimized sleep signal data set, analyze the regularity of body motion patterns and breathing regularity, and determine the sleep stage distribution condition of the user.

[0064] A sleep quality evaluation result acquisition module is used to grade the sleep quality of the user based on the sleep stage distribution condition and a pre-set threshold range, acquire the duration of each stage and the conversion frequency, and obtain the sleep quality evaluation result.

[0065] A sleep habit feature determination module is used to track the sleep pattern of the user for a long time using a time series analysis method based on the sleep quality evaluation result, identify the periodic changes of the sleep stage distribution, and determine the sleep habit features of the user.

[0066] A monitoring configuration scheme acquisition module is used to dynamically optimize the signal acquisition parameters of the non-contact sensor using an adaptive adjustment mechanism based on the sleep habit features, and acquire a monitoring configuration scheme that conforms to the daily habits of the user.

[0067] A sleep profile record acquisition module is used to upload the sleep data of the user to a remote server for storage and backup through cloud data synchronization technology based on the monitoring configuration scheme, and obtain real-time updated sleep profile records.

[0068] A sleep improvement direction determination module is configured to generate a sleep quality evaluation report of the user by using a data visualization technology according to the real-time updated sleep record, and to determine a sleep improvement direction that can be referenced by the user by graphically displaying the sleep stage distribution and the sleep quality evaluation result.

[0069] The present application has the following beneficial effects:

[0070] The present application provides a sleep quality analysis method and system for unattended care, which acquires sleep-related signals from the user's surrounding environment through non-contact sensor technology, captures body motion frequency characteristics and breathing rhythm characteristics in real time to obtain preliminary sleep behavior data records, filters body motion frequency signals and breathing rhythm signals by using a signal denoising processing method according to the preliminary sleep behavior data records, eliminates environmental noise interference to determine an optimized sleep signal data set, extracts features by using a pre-established sleep stage classification model for the optimized sleep signal data set, analyzes the regularity of body motion patterns and breathing regularity, and determines the sleep stage distribution of the user, grades the sleep quality of the user according to the sleep stage distribution and in combination with a pre-set threshold range, acquires the duration of each stage and the conversion frequency, obtains a sleep quality evaluation result, tracks the sleep pattern of the user in the long term by using a time series analysis method for the sleep quality evaluation result, identifies the periodic changes of the sleep stage distribution, and determines the sleep habit characteristics of the user, dynamically optimizes the signal acquisition parameters of the non-contact sensor by using an adaptive adjustment mechanism according to the sleep habit characteristics, acquires a monitoring configuration scheme that conforms to the daily habits of the user, uploads the sleep data of the user to a remote server for storage and backup by using cloud data synchronization technology for the monitoring configuration scheme, obtains real-time updated sleep record, generates a sleep quality evaluation report of the user by using a data visualization technology according to the real-time updated sleep record, graphically displays the sleep stage distribution and the sleep quality evaluation result, and determines a sleep improvement direction that can be referenced by the user.

[0071] I. Improve monitoring convenience and comfort: The non-contact sensor technology is adopted, and the user does not need to wear any equipment, which avoids the bondage and discomfort caused by contact monitoring, does not interfere with the normal sleep of the user, is especially suitable for natural capture of the sleep state in an unattended care scene, and improves the user acceptance and the continuity of monitoring.

[0072] II. Ensuring the accuracy and reliability of sleep data: By using signal denoising processing methods to filter body movement frequency signals and respiratory rhythm signals, environmental noise interference is effectively eliminated, obtaining an optimized sleep signal dataset, providing a high-quality data basis for subsequent sleep stage analysis and sleep quality evaluation, reducing analysis errors caused by noise.

[0073] III. Precise sleep stage determination and sleep quality evaluation: Using a pre-established sleep stage classification model to extract features from the optimized signals, accurately analyzing the regularity of body movement patterns and respiratory regularity, and accurately determining the sleep stage distribution of users. Combined with the preset threshold for grading evaluation, the duration and conversion frequency of each stage can be clearly presented, allowing users to fully understand their sleep quality.

[0074] IV. Long-term sleep management and habit formation: Through time series analysis, the user's sleep pattern is tracked over a long period of time, identifying periodic changes in sleep stage distribution and determining sleep habit characteristics. Based on this, an adaptive adjustment mechanism is used to optimize the signal acquisition parameters of the non-contact sensor, forming a monitoring configuration scheme that conforms to the user's daily habits, helping users to improve their sleep habits and improve long-term sleep quality.

[0075] V. Ensuring data security and accessibility: With the help of cloud data synchronization technology, sleep data is uploaded to a remote server for storage and backup, forming a real-time updated sleep archive record, ensuring data security and allowing users to view it at any time. At the same time, data visualization technology is used to generate graphical sleep quality evaluation reports, visually displaying sleep stage distribution and sleep quality evaluation results, providing clear and understandable sleep improvement direction guidance for users.

[0076] VI. Suitable for various unattended scenarios: This system can complete sleep monitoring, analysis, evaluation and other processes without human intervention, especially suitable for scenarios such as elderly people living alone, infants, hospitalized patients and other unattended scenarios, providing timely and accurate sleep information for relevant personnel (such as children, parents, medical staff) to better monitor the health status of the monitored person. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 Flowchart of an embodiment of the sleep quality analysis method for unattended scenarios according to the present application;

[0078] Figure 2 Functional block diagram of an embodiment of the sleep quality analysis system for unattended scenarios according to the present application.

[0079] BRIEF DESCRIPTION OF DRAWINGS

[0080] 10. Sleep behavior data recording and acquisition module; 20. Sleep signal dataset determination module; 30. Sleep stage distribution judgment module; 40. Sleep quality assessment result acquisition module; 50. Sleep habit characteristics determination module; 60. Monitoring configuration scheme acquisition module; 70. Sleep record acquisition module; 80. Sleep improvement direction determination module. Detailed Implementation

[0081] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0082] like Figure 1 As shown, the first embodiment of the present invention proposes a method for sleep quality analysis in unattended situations, comprising the following steps:

[0083] Step S100: Acquire sleep-related signals from the user's surrounding environment using non-contact sensor technology, and capture body movement frequency characteristics and breathing rhythm characteristics in real time to obtain preliminary sleep behavior data records.

[0084] The technology and results of collecting sleep-related signals from the user's surrounding environment through non-contact sensor technology without physical contact with the user, and real-time monitoring, capturing and analyzing core physiological and behavioral indicators (focusing on body movement frequency characteristics and respiratory rhythm characteristics), ultimately generating preliminary sleep behavior data records.

[0085] Non-contact sensor technology refers to the technology that collects signals by sensing changes in physical quantities in the surrounding environment (such as air pressure, sound waves, electromagnetic waves, infrared radiation, etc.) without direct contact with the user's body.

[0086] Step S200: Based on the preliminary sleep behavior data recording, the body movement frequency signal and respiratory rhythm signal are filtered using a signal denoising processing method to remove environmental noise interference and determine the optimized sleep signal dataset.

[0087] The optimized sleep signal dataset is based on preliminary sleep behavior data recordings. Targeted signal denoising methods are used to filter and remove interference from body movement frequency and respiratory rhythm signals, resulting in a set of body movement and respiratory rhythm signals with improved signal quality and free from environmental noise interference. This optimized sleep signal dataset represents an upgrade of the original preliminary data, providing a more reliable foundation for subsequent sleep analysis (such as sleep staging and abnormal event identification).

[0088] Step S300: For the optimized sleep signal dataset, feature extraction is performed using a pre-established sleep stage classification model to analyze the regular changes in body movement patterns and breathing patterns, and to determine the distribution of the user's sleep stages.

[0089] With the optimized sleep signal dataset (body motion and breathing rhythm signals after denoising) as input, by means of a pre-constructed sleep stage classification model, by extracting key features in the body motion and breathing rhythm signals, analyzing the regularity of the changes in the time dimension (such as frequency, amplitude, periodic fluctuation of rhythm), and finally outputting the analysis process and results of the time distribution and conversion rule of each stage (such as wakefulness, light sleep, deep sleep, rapid eye movement sleep) in the user's sleep process.

[0090] Step S400, according to the sleep stage distribution, combined with the preset threshold range, the sleep quality of the user is evaluated, the duration of each stage and the conversion frequency are obtained, and the sleep quality evaluation result is obtained.

[0091] With the sleep stage distribution (such as the duration of each stage, the conversion frequency, etc.) as the core basis, combined with the pre-set sleep quality evaluation threshold range (such as the standard of each stage time, the reasonable interval of conversion frequency, etc.), the overall sleep quality of the user is graded (such as excellent, good, medium, and poor), and the comprehensive evaluation process and results of the specific parameters (duration, conversion times and frequency) of each sleep stage are output simultaneously.

[0092] Step S500, according to the sleep quality evaluation result, the sleep mode of the user is tracked for a long time through time series analysis method, the periodic change of sleep stage distribution is identified, and the sleep habit characteristics of the user are determined.

[0093] With the sleep quality evaluation results of the user for a period of time (such as weeks, months) as the core data, the sleep mode of the user is tracked for a long time through time series analysis technology, the periodicity of the sleep stage distribution (such as deep sleep duration, REM sleep proportion, sleep / wake time, etc.) in the time dimension (such as weekly, monthly or seasonal fluctuations) is identified, and the stable and personalized sleep behavior mode of the user is extracted combined with these regular changes, and finally the analysis process and conclusion of the sleep habit characteristics of the user are determined.

[0094] Step S600, according to the sleep habit characteristics, an adaptive adjustment mechanism is adopted to dynamically optimize the signal acquisition parameters of the non-contact sensor, and a monitoring configuration scheme conforming to the user's daily habits is obtained.

[0095] With the sleep habit characteristics of the user as the core basis, through the pre-set adaptive adjustment mechanism, the signal acquisition parameters (such as sampling frequency, sensitivity, monitoring area range, etc.) of the non-contact sensor (such as millimeter wave radar, infrared sensor, sound sensor, etc.) are dynamically adjusted in real time or periodically, and finally the process and results of generating a personalized monitoring configuration scheme that adapts to the user's daily sleep habits and can stably capture high-quality sleep signals are generated.

[0096] Step S700, for the monitoring configuration scheme, the user's sleep data is uploaded to the remote server for storage and backup through the cloud data synchronization technology, and the real-time updated sleep archive record is obtained.

[0097] Based on the personalized monitoring configuration scheme of the non-contact sensor, through the cloud data synchronization technology (such as real-time data transmission protocol, incremental synchronization algorithm, etc.), the sensor collected user sleep original data (such as body movement, breathing rhythm signal) and derived analysis results (such as sleep stage, sleep quality rating) are automatically uploaded to the remote server, after storage, integration and backup, the process and result of forming a complete and traceable personal sleep archive record dynamically updated with the user's sleep process.

[0098] Step S800, according to the real-time updated sleep archive record, the sleep quality evaluation report of the user is generated by using the data visualization technology, the sleep stage distribution and the sleep quality evaluation result are graphically displayed, and the sleep improvement direction that the user can refer to is determined.

[0099] Based on the cloud real-time updated sleep archive record, the data visualization technology (such as line chart, column chart, heat map, time axis, etc.) is used to convert the sleep core indicators (including sleep stage distribution, sleep quality evaluation result, etc.) into intuitive and easy-to-understand graphical reports, and through the analysis of abnormal data or regular characteristics in the report, the process and result of extracting the sleep improvement suggestions for the individual user are extracted.

[0100] Further, the sleep quality analysis method for unattended care provided by the embodiment comprises the following steps:

[0101] Step S110, capturing sleep-related signals from the user's surrounding environment through a non-contact sensor; for body movement frequency characteristics and breathing rhythm characteristics, real-time collection is performed, and a combination of infrared sensors and acoustic sensors is adopted to obtain body movement frequency and breathing rhythm original data when the user is sleeping, and preliminary sleep behavior data record is obtained.

[0102] In the scenario of capturing sleep-related signals through a non-contact sensor, the combination of infrared sensors and acoustic sensors can effectively obtain the body movement frequency and breathing rhythm data of the user.

[0103] The infrared sensor mainly senses body movement by detecting changes in the user's body surface temperature. The principle is that when a person turns over or moves slightly during sleep, the heat distribution between the body and the surrounding environment changes. The sensor captures these changes and converts them into body movement frequency data. Assuming that during a sleep monitoring session, the infrared sensor records 5 body movements per minute of the user, combined with time axis analysis, it can be preliminarily judged whether the user is in a deep sleep or light sleep stage. This method does not require contact with the user's body, avoiding the discomfort that may be caused by traditional contact devices, and helps to improve the user experience.

[0104] The acoustic wave sensor monitors the breathing rhythm by emitting and receiving ultrasonic signals. Its working principle is to use the propagation characteristics of ultrasonic waves in the air to detect the slight distance changes caused by the user's chest and abdomen fluctuations, and thus calculate the breathing frequency. For example, during an 8-hour sleep period, the acoustic wave sensor records that the user breathes an average of 15 times per minute, and the breathing frequency is relatively stable between 2am and 3am, about 12 times per minute, which may indicate that the user is in a deep sleep stage.

[0105] Through this non-invasive way, not only can the respiratory data be accurately captured, but also the sleep disturbance caused by device wearing can be avoided, significantly improving the naturalness and accuracy of monitoring.

[0106] The data collection of the infrared sensor and the acoustic wave sensor is carried out in real time and synchronously to form a more comprehensive sleep behavior record. Assuming that during a certain monitoring, the infrared sensor detects that the user has more frequent body movements at 1am, an average of 8 times per minute, while the acoustic wave sensor records that the breathing frequency suddenly increases to 20 times per minute. Through cross verification of the two sets of data, it can be inferred that the user may have experienced a short awakening or dream stage. This multi-dimensional data fusion method can improve the reliability of sleep analysis, provide a solid basis for subsequent sleep quality evaluation, and also provide more scientific sleep improvement suggestions for users.

[0107] The infrared sensor is arranged at the head of the bed, covering the upper body area of the user, ensuring the sensitivity of heat change capture; while the acoustic wave sensor can be placed at the side of the bed, focusing on the micro-movement detection of the chest and abdomen area. Through this reasonable distribution in space, data interference is reduced and collection accuracy is significantly improved.

[0108] Further, the sleep quality analysis method for unattended care provided by the embodiment comprises the following steps:

[0109] In step S210, according to the preliminary sleep behavior data record, the original sleep signal containing body movement frequency and breathing rhythm is obtained, and a preset threshold is used for preliminary screening of the environmental noise interference in the original sleep signal to obtain a set of sleep signals after preliminary cleaning.

[0110] The original sleep signal is:

[0111]

[0112] In formula (1), represents the original sleep signal at time , the body motion frequency component, , the respiratory rhythm component, , and the environmental noise interference component. Formula (1) describes the basic structure of the original sleep signal composed of body motion frequency, respiratory rhythm, and environmental noise. The determination criterion for environmental noise screening is:

[0113]

[0114] In formula (2),

[0115] represents the preset noise screening threshold, represents the mean of the sleep signal, represents the standard deviation of the sleep signal, represents the threshold coefficient. Formula (2) is used to determine the determination criterion for environmental noise screening, and signals exceeding this threshold are considered as noise interference. The judgment logic for signal cleaning is:

[0116]

[0117] In formula (3),

[0118] represents the sleep signal after preliminary cleaning, represents the original sleep signal, represents the filtering threshold. Formula (3) describes the judgment logic for signal cleaning, when the original signal amplitude does not exceed the filtering threshold, the original signal is retained, and when the threshold is exceeded, the signal is set to zero to remove noise interference. In processing the preliminary sleep behavior data record, for the original sleep signal of body motion frequency and respiratory rhythm, a preliminary screening is performed through a preset threshold to reduce environmental noise interference. Assuming that in a sleep monitoring scene, the original signal is mixed with low-frequency noise from the running air conditioner in the room, which may affect the accuracy of the body motion frequency. By setting a frequency threshold, such as considering signals below 2Hz as noise and removing them, a relatively pure sleep signal set is preliminarily cleaned. This way can effectively reduce irrelevant interference and lay a foundation for subsequent processing.

[0119]

[0120] ​Step S220: For the sleep signal set that has been initially cleaned, a signal filtering tool is used to denoise the body movement frequency signal and breathing rhythm signal. If the environmental noise interference detected exceeds the preset threshold, the noise component is separated by a frequency domain conversion tool to obtain the denoised sleep signal set.

[0121] The sleep signal after filtering is:

[0122]

[0123] In formula (4), This represents the sleep signal after filtering. This represents the original body movement frequency signal and respiratory rhythm signal. Indicates the first The frequency response function of a filter Indicates the first The weighting coefficients of each filter, This indicates the total number of filters. Represents a time variable. The frequency variable is represented by Equation (4). Equation (4) describes the process of denoising the sleep signal using multiple filters.

[0124] For the pre-processed sleep signal set, it is particularly important to use signal filtering tools to denoise the body motion frequency and respiratory rhythm signals. If environmental noise interference is detected to exceed a preset threshold, such as when the noise intensity reaches more than 30% of the original signal during a certain period, the noise component can be separated using frequency domain transformation tools. For example, if continuous vehicle noise is recorded outside the window while the user is sleeping during a monitoring session, the frequency domain transformation tool can separate this high-frequency noise from the respiratory rhythm signal, resulting in a clearer denoised signal combination. This method helps preserve core data characteristics and avoids noise interference in subsequent analysis.

[0125] Step S230: Based on the denoised sleep signal combination, time-domain analysis tools are used to adjust the data precision for the body movement frequency characteristics and breathing rhythm characteristics. By comparing with the pre-established normal range values, it is determined whether there are abnormal fluctuations, and the fine-tuned sleep signal dataset is determined.

[0126] The body motion frequency signal after precision adjustment is:

[0127]

[0128] In formula (5), This represents the body motion frequency signal after precision adjustment. This represents the original body motion frequency signal. Indicates time the body motion amplitude feature, the mean value of the body motion amplitude, the standard deviation of the body motion amplitude, the precision adjustment coefficient. Formula (5) adjusts the precision of the original signal by the body motion amplitude deviation.

[0129] When performing time domain analysis according to the combination of the denoised sleep signal, the precision adjustment is performed for the body motion frequency feature and the breathing rhythm feature, and the abnormal fluctuation is judged by comparing the normal range value established in advance. Assuming that the normal breathing frequency range is 12 to 20 times per minute, if the user's breathing frequency is found to suddenly rise to 25 times per minute in a certain monitoring period, and the duration is short, combined with the body motion frequency analysis, it may be an abnormal fluctuation caused by a short-term awakening. Through the time domain analysis tool, the data is fine-tuned to remove abnormal points, and a more actual sleep signal data set is obtained. This adjustment method can improve the reliability of the data and provide a more accurate basis for subsequent analysis.

[0130] Step S240, for the fine-tuned sleep signal data set, the body motion frequency and the breathing rhythm are classified and stored by the data integration tool, and the data features related to behavior analysis are obtained according to the real-time recording requirements during storage, and the final optimized sleep signal data set is obtained.

[0131] The optimized sleep signal data set is:

[0132]

[0133] In formula (6), represents the optimized sleep signal data set, represents the total number of data samples, represents the weight coefficient of the th sample, represents the th body motion frequency feature value, represents the th breathing rhythm data feature value, represents the classification weight parameter of the body motion frequency, represents the classification weight parameter of the breathing rhythm data.

[0134] For the fine-tuned sleep signal dataset, when storing the body motion frequency and respiratory rhythm using the data integration tool, the features related to behavior analysis are extracted according to real-time recording requirements. Assuming that in an 8-hour sleep monitoring, the system stores the body motion frequency according to the frequency change per hour, and at the same time, the respiratory rhythm data is classified and saved according to the deep sleep and light sleep stages. Through this classification method, the system can quickly extract the behavior characteristics of a specific period, such as the respiratory stable data from 2am to 3am, thereby providing support for sleep quality evaluation. This storage method not only improves the efficiency of data management, but also facilitates subsequent targeted analysis.

[0135] Preferably, the sleep quality analysis method for unattended use provided by the embodiment comprises the following steps:

[0136] Step S310, according to the optimized sleep signal dataset, the body motion pattern and the breathing rule are used to extract features from the data using the preset feature extraction tool, and a plurality of characteristic values related to sleep stage classification are obtained, and a preliminary feature combination is obtained.

[0137] For example, when analyzing based on the optimized sleep signal dataset, the feature extraction of body motion pattern and breathing rule is performed by decomposing the data using the preset tool to obtain characteristic values related to sleep stage classification. The body motion pattern is manifested as the frequency and amplitude of the user turning over in sleep, and the breathing rule is manifested as the number of breaths per minute and the change in depth. Assuming that in a night monitoring, the system records that the user turns over 3 times per hour in a certain period of time, and the breathing frequency is stable at 15 times per minute, through the feature extraction tool, these data can be decomposed into characteristic values related to the light sleep stage, forming a preliminary feature combination. This decomposition method helps to convert complex signals into analyzable indicators, providing a basis for subsequent classification.

[0138] Step S320, for the preliminary feature combination, a data integration tool is used to classify and process a plurality of characteristic values, and in the classification, a pre-established stage division standard is combined, if the characteristic value meets the rule analysis condition of a certain stage, it is classified into the corresponding category, and the classified feature set is determined.

[0139] When classifying the preliminary feature combination, the characteristic values are classified into the corresponding category in combination with the pre-established stage division standard. Assuming that the standard stipulates that the respiratory frequency is 12 to 18 times per minute and the body motion frequency is low, it belongs to the deep sleep stage, then in a monitoring, if the respiratory frequency of a certain data is 14 times per minute and the body motion frequency is 1 time per hour, the system will classify it into the deep sleep feature set. This classification method can integrate scattered characteristic values into meaningful categories, facilitating further analysis of the distribution of sleep structure.

[0140] Step S330, according to the classified feature set, the mode change and behavior association are matched dynamically by using the time series comparison tool to obtain the sleep stage distribution judgment basis corresponding to the sleep signal, and the preliminary mapping result of the sleep stage distribution is obtained.

[0141] When dynamic matching is performed according to the classified feature set, the time series comparison tool is used to help identify the association between body movement mode and breathing rhythm and sleep stage. Assuming that in a certain 8-hour sleep monitoring, the system finds that the user's body movement frequency suddenly increases to 5 times per hour between 1 am and 2 am, and the breathing frequency fluctuates to 20 times per minute, through the time series comparison tool, it can be initially judged that this period may correspond to a short wake-up or light sleep stage, and the mapping result of the stage distribution is obtained. This dynamic matching method can capture the pattern changes in the sleep process and provide more detailed basis for stage division.

[0142] Step S340, for the preliminary mapping result of the sleep stage distribution, the matching degree of the body movement mode and the breathing rhythm is confirmed again by using the data verification tool, if the matching degree exceeds the preset threshold, the corresponding relationship with the sleep stage classification is confirmed, and the final sleep stage distribution data is determined.

[0143] The final sleep stage classification result is:

[0144]

[0145] In formula (7), represents the final sleep stage classification result of the th period, represents the preliminary mapping sleep stage, represents the corrected sleep stage, represents the verification confidence of the th period, represents the confidence determination threshold.

[0146] When the preliminary mapping result of the sleep stage distribution is confirmed again, the data verification tool evaluates the matching degree to ensure the accuracy of the classification. Assuming that the preset matching degree threshold is 80%, in a monitoring, the body movement frequency feature and the breathing rhythm feature of a certain data reach 85% matching degree with the light sleep stage, the system will confirm that it is classified as light sleep, and finally determine the sleep stage distribution data. Otherwise, if the matching degree is only 60%, the data needs to be analyzed again. This secondary confirmation mechanism can improve the credibility of the classification and provide reliable support for subsequent sleep quality evaluation. Through the above multi-level processing method, the system can gradually build a structured sleep analysis framework from the original data to the final stage distribution.

[0147] Furthermore, in the sleep quality analysis method for unattended sleep provided in this embodiment, step S400 includes:

[0148] Step S410: Based on the distribution of sleep stages, and taking into account the duration and frequency of transitions of each stage, use a time series recording tool to record the duration and number of transitions of each stage in detail, thereby obtaining dynamic change data related to sleep quality and obtaining a preliminary time distribution record table.

[0149] When analyzing the distribution of sleep stages, time-series recording tools can capture the dynamic changes in a user's nighttime sleep, recording the duration and frequency of each stage. The basic principle of time-series recording tools is to mark the start and end times of each sleep stage, forming continuous timeline data. Specifically, assuming an 8-hour nighttime sleep monitoring session, the system records 2 hours of deep sleep, 4 hours of light sleep, 1.5 hours of REM sleep, and 0.5 hours of wakefulness, with 5 stage transitions. This data is compiled into a preliminary time distribution record table, laying the foundation for subsequent analysis.

[0150] Step S420: Based on the preliminary time distribution record table and the preset threshold range, use a data classification tool to classify the duration and number of transitions of each stage. If the duration or number of transitions exceeds the corresponding threshold range, it is classified into an abnormal category, and the classified sleep stage distribution data is determined.

[0151] The anomaly detection results for the number of transitions in each stage are as follows:

[0152]

[0153] In formula (8), Indicates the first Anomaly detection results for the number of transitions in each stage. Indicates the first Number of transitions in each stage This represents the mean of the number of conversions. The standard deviation of the number of transformations. This represents the standardized threshold for anomaly detection. An anomaly is identified when the standardized deviation exceeds the threshold.

[0154] For example, when classifying the preliminary time distribution record, the data classification tool can make an abnormality judgment on the duration and the number of transitions in combination with the preset threshold range. Assuming that the preset normal range of deep sleep duration is 1.5 to 3 hours, and the normal range of the number of transitions is 3 to 6 times, if the deep sleep in a certain monitoring lasts only for 1 hour, and the number of transitions is as high as 8 times, the system will classify it into the abnormal category. This classification method helps to quickly screen out key data points that may affect sleep quality.

[0155] Step S430, for the classified sleep stage distribution data, in combination with the abnormal category and the behavior pattern, the data comparison tool is used to match the pre-established sleep quality standard to obtain the basis related to the grading evaluation, and the sleep quality level distribution of each stage is judged.

[0156] The sleep quality level score of a single sleep stage is:

[0157]

[0158] In formula (9), represents the sleep quality level score of a single sleep stage, represents the total number of abnormal behaviors detected in the stage, represents the weight coefficient of the th abnormal behavior represents the actual detection value of the th abnormal behavior, represents the corresponding sleep quality standard threshold, the function represents the matching degree calculation function of the abnormal behavior and the standard.

[0159] The comprehensive evaluation value of the sleep quality level distribution is:

[0160]

[0161] In formula (10), represents the comprehensive evaluation value of the sleep quality level distribution, represents the total number of time periods analyzed, represents the normal behavior pattern intensity in the th time period, represents the abnormal behavior pattern intensity in the th time period, represents the contribution coefficient of the normal behavior, represents the penalty coefficient of the abnormal behavior.

[0162] For example, when matching the sleep quality level based on the classified sleep stage distribution data, the data comparison tool will compare the abnormal categories and behavior patterns with the pre-established sleep quality standards. Assuming that the standard stipulates that the sleep quality level is low when the deep sleep duration is less than 1.2 hours and the conversion times exceed 7 times, then in a certain monitoring, if the user's deep sleep lasts for 1 hour and the conversion times are 8 times, the system will judge that the sleep quality level of this stage is low. This matching process can evaluate the performance of each stage from multiple dimensions and provide the basis for the overall sleep quality grading.

[0163] Step S440, for the sleep quality level distribution of each stage, the data integration tool is used to summarize the sleep quality evaluation results in combination with the distribution, and the final sleep quality evaluation report is obtained to determine the overall sleep quality grading of the user.

[0164] When the sleep quality level distribution of each stage is summarized, the data integration tool integrates the sleep quality evaluation results of all stages into the final sleep quality evaluation report. Assuming that in a certain monitoring, the deep sleep quality level is low, and the light sleep and rapid eye movement sleep are medium, the system will comprehensively calculate that the overall sleep quality grading is medium. This summary method can directly reflect the user's sleep condition and facilitate subsequent targeted adjustment. Through the above multi-level analysis and processing, from the stage duration to the final overall sleep quality grading, the system can build a complete evaluation framework and provide valuable reference information for the user.

[0165] Further, the sleep quality analysis method for unattended care provided by the embodiment comprises the following steps:

[0166] Step S510, according to the sleep quality evaluation results, the sleep stage distribution data of the user is collected for a long time by using the time series recording tool, and the starting and ending time points of each stage are marked to obtain continuous data related to periodic changes and obtain a complete sleep stage time distribution record table.

[0167] When the sleep stage distribution data of the user is collected for a long time by using the time series recording tool, the portable sleep monitoring device continuously records the changes of each stage of the user's night sleep. The principle of the time series recording tool is to accurately mark the starting and ending time points of each stage in the sleep process, such as deep sleep, light sleep and rapid eye movement sleep, to form a continuous time axis data stream. Assuming that in a 30-day monitoring, the device records that the user's deep sleep starts at 23:00 and ends at 01:00, lasting for 2 hours, and the light sleep is concentrated in 01:00-04:00. This long-term collection method can capture the subtle changes of the user's sleep stage and provide detailed data basis for subsequent analysis.

[0168] Step S520, for the sleep stage time distribution record table, use the data comparison tool to match with the pre-established periodic pattern library, if the sleep stage distribution data is consistent with the time interval of a certain pattern in the periodic pattern library, it is classified as the corresponding periodic feature, and the periodic distribution feature of the user's sleep stage is determined.

[0169] The periodic feature of the user's sleep is determined by the weighted voting mechanism:

[0170]

[0171] In formula (11), represents the determined user sleep stage periodic distribution feature category, represents the total number of periodic feature categories, represents the number of sleep stage indicators, represents the weight of the th sleep stage indicator, represents the time interval of the user's th sleep stage, represents the th stage standard time interval of the th periodic feature, is an indicator function, represents the allowed time interval deviation threshold.

[0172] For the matching process of the sleep stage time distribution record table and the periodic pattern library, the automatic analysis is realized by the data comparison tool. It is assumed that the periodic pattern library stores a variety of common sleep cycle features, such as the alternating pattern of deep sleep and rapid eye movement sleep every 90 minutes. In one analysis, if the sleep data of a user in a week shows that the alternating time interval of deep sleep and rapid eye movement sleep is stable between 85 and 95 minutes, the system will classify it as close to the standard periodic feature. This matching method helps to identify the user's sleep regularity and provides a basis for personalized adjustment.

[0173] Step S530, according to the sleep stage periodic distribution feature, use the data integration tool to associate the periodic change and duration of each stage, get the conversion rule between different stages, judge whether there is regular fluctuation in the sleep pattern, and get the long-term change trend.

[0174] For example, when performing correlation processing based on the periodic distribution characteristics of sleep stages, data integration tools combine the periodic changes and duration of each stage for analysis to explore potential patterns in stage transitions. For instance, if long-term monitoring reveals that a user typically enters REM sleep directly after deep sleep, with the transition time consistently around 1:00 AM, the system would determine that their sleep pattern exhibits certain regular fluctuations. This analysis can help reveal the stability of a user's sleep structure, thus providing a reference for improving sleep habits.

[0175] Step S540: For long-term trends, use data classification tools combined with preset threshold ranges to classify the fluctuation amplitude. If the fluctuation amplitude exceeds the threshold range, it is marked as an abnormal pattern to determine the user's sleep habit characteristics.

[0176] The fluctuation range is calculated using the following formula:

[0177]

[0178] In formula (12), Indicates a point in time The volatility index Indicates the length of the time window. Indicates the first Sleep data values ​​at different times. Indicates the first Sleep data values ​​at any given time. Formula (12) quantifies the degree of fluctuation by calculating the average absolute difference of sleep data at consecutive time points.

[0179] For example, the data classification tool categorizes fluctuations in long-term trends and uses preset threshold ranges to mark anomalies. Assuming normal fluctuation is defined as a change in deep sleep duration of no more than 0.5 hours, if monitoring in a given week shows a user's deep sleep duration suddenly dropping from 2 hours to 1 hour, exceeding the threshold, the system will mark it as an abnormal pattern. This categorization method can quickly identify abnormal situations that may affect sleep quality, providing data support for subsequent interventions. Through this multi-dimensional analysis and processing, the system can extract users' sleep habit characteristics from long-term data, providing valuable guidance for optimizing the sleep environment and adjusting behaviors.

[0180] Furthermore, in the sleep quality analysis method for unattended sleep provided in this embodiment, step S600 includes:

[0181] Step S610: Based on sleep habit characteristics, use data recording tools to continuously track the user's daily routine time periods, obtain sleep-related behavioral data, and combine environmental adaptation attributes to initially set the collection frequency to obtain an initial monitoring plan that matches daily habits.

[0182] Describing long-term trend and periodic component of sleep pattern is:

[0183]

[0184] In formula (13), represents the long-term trend value of the th sleep cycle, represents the linear trend coefficient, represents the baseline level, represents the amplitude of periodic fluctuation, represents the cycle length, represents the random error term, represents the serial number of the sleep cycle.

[0185] When the daily work and rest period of the user is continuously tracked by using the data recording tool, the behavior data related to sleep is obtained by recording each time node of the user from getting up to falling asleep through the portable device. Assuming that the daily habit of a user is to get up at 7:00 in the morning and fall asleep at 11:00 at night, the system will initially set the collection frequency to record the key behavior data once an hour, and at the same time, combine the environmental adaptation attribute, such as the user being in a quiet indoor environment, to appropriately reduce the frequency to reduce interference, to obtain an initial monitoring scheme.

[0186] In step S620, for the initial monitoring scheme, the data comparison tool is used to match the collection frequency with the habit tracking database established in advance. If the deviation of the collection frequency from the habit data in the habit tracking database exceeds the preset threshold range, the parameter adjustment of the signal collection is optimized through the adaptive adjustment mechanism to determine the collection rule conforming to the user characteristics.

[0187] The relative deviation degree of the actual collection frequency from the frequency in the habit tracking database established in advance is calculated by the following formula:

[0188]

[0189] In formula (14), represents the deviation percentage of the collection frequency from the habit data in the habit tracking database, represents the current collection frequency value, represents the habit tracking frequency reference value established in advance in the habit tracking database.

[0190] For the matching process of the initial monitoring scheme and the habit tracking database, the rationality of the collection frequency is analyzed through the data comparison tool. Assuming that the typical habit data stored in the habit tracking database suggests a collection frequency of every 2 hours for the night rest period, and the user's initial scheme is every hour, the deviation exceeds the preset threshold range, the system will adjust the frequency to every 1.5 hours through the adaptive adjustment mechanism, ensuring that the collection rule is more in line with the user's characteristics. This adjustment can effectively balance data accuracy and user comfort.

[0191] Step S630, according to the collection rule, the signal collection mode of the non-contact sensor is dynamically adjusted by the parameter configuration tool combined with the environmental adaptation attribute, and the monitoring data consistent with the daily habit is obtained, and the monitoring frequency range suitable for the current state of the user is determined.

[0192] When dynamically adjusting the signal collection mode of the non-contact sensor according to the collection rule, the parameter configuration tool is optimized and set in combination with the environmental adaptation attribute. Assuming that the user's night sleep environment is relatively stable, the sensor can be adjusted to a low-power mode, collecting heart rate and body motion frequency every 2 hours, and determining that the monitoring frequency range suitable for the current state is between 1.5 and 2.5 hours. This way can not only obtain consistent monitoring data, but also avoid potential interference on the user caused by frequent collection.

[0193] Step S640, for the monitoring frequency range, the signal collection result is associated with the sleep habit data by using the data integration tool, and the monitoring configuration is finally calibrated through the data matching attribute, to obtain a personalized monitoring configuration scheme consistent with the user's daily habits.

[0194] For the association processing of the monitoring frequency range and the sleep habit data, the signal collection result is compared with the user's daily habits through the data integration tool, and the monitoring configuration is finally calibrated. Assuming that the user's long-term data shows that the period from 11:00 at night to 1:00 in the morning is the key period for falling asleep, the system will preferentially increase the collection frequency of this period to every 30 minutes, while keeping the collection frequency of other periods at 2 hours, forming a personalized monitoring configuration scheme. This calibration can more accurately capture the user's sleep behavior characteristics and provide a reliable basis for subsequent optimization.

[0195] Further, the sleep quality analysis method for unattended care provided by the embodiment includes the following steps:

[0196] Step S710, according to the sleep data, the data collection is classified and processed by using the data arrangement tool, and the upload priority is set for different categories of data. If the priority exceeds the preset threshold, it is transmitted to the remote server through the upload mechanism to obtain a classified data set.

[0197] The upload priority value of the data is calculated by the following formula:

[0198]

[0199] In formula (15), represents the upload priority value of the first data, represents a data importance weight factor, represents the importance score of the first data, represents a maximum importance score, represents a time sensitivity weight factor, represents a current timestamp, represents a data generation timestamp, represents a time window length, represents a sleep quality weight factor, represents the sleep quality evaluation value of the first data.

[0200] In processing sleep data, using a data sorting tool to classify the collected content is a key link. For different categories of data, such as heart rate, respiratory rate and body movement information, different upload priorities are set. Suppose the heart rate data is set as high priority because its change directly reflects the user's sleep quality, and the body movement frequency has a lower priority. If the priority of the heart rate data exceeds the preset threshold, such as setting the threshold to 80, and the actual score is 85, the system will transmit it to the remote server through the upload mechanism to form a classified data set. This way can ensure that important data is processed and analyzed in time.

[0201] Step S720, for the classified data set, a data synchronization tool is used to synchronize the frequency of the transmission process, if the transmission is interrupted, the complete transmission log is obtained through the backup path reconnection.

[0202] For the classified data set, the data synchronization tool synchronizes the frequency of the transmission process, which is particularly important. Suppose the synchronization frequency is set to detect once every 30 minutes, if transmission interruption is found in a certain detection, the system will automatically switch to the backup path to reconnect, and generate a complete transmission log. This mechanism can effectively avoid data loss and ensure the continuity of transmission.

[0203] Step S730, according to the complete transmission log, a data verification tool is used to compare the data uploaded to the remote server, and the consistency is verified by combining the data integrity attribute, and the inconsistent part is adjusted by the repair tool to determine the verified data content.

[0204] In data verification according to the transmission log, the data verification tool compares the data uploaded to the remote server, verifies the consistency in combination with the data integrity attribute. Assuming that some heart rate data is found to be missing after transmission, the system will adjust the inconsistent part through the repair tool, such as extracting the missing data from the local cache to complete, and finally determine the content of the verified data. This verification process can ensure the accuracy and reliability of the data, providing a solid foundation for subsequent analysis.

[0205] Step S740, for the verified data content, the permission management tool limits the access range in combination with the user permission, generates a copy in the storage space through the backup tool, and obtains the real-time updated sleep archive record.

[0206] For the verified data content, the permission management tool limits the access range in combination with the user permission, which is an important security measure. Assuming that a user is only authorized to view his own heart rate data, the system will limit his access to other types of data. At the same time, by generating a copy in the storage space through the backup tool, it ensures that the data is updated in real time, forming an archive record. Assuming that the backup frequency is once a day, the system will automatically generate a copy at dawn to prevent accidental data loss. This permission management and backup mechanism can effectively protect user privacy while improving data traceability.

[0207] Further, the sleep quality analysis method for unattended care provided by the embodiment comprises the following steps:

[0208] Step S810, according to the sleep archive, the data arrangement tool classifies and processes the updated record, and extracts the data in layers according to the stage distribution to obtain the classified sleep stage distribution data set, and obtains the preliminarily arranged sleep stage information.

[0209] In processing the sleep archive, the data arrangement tool classifies and processes the updated record, which is an important link. For the data of sleep stage distribution, the data is divided into different stages such as light sleep, deep sleep and rapid eye movement sleep through hierarchical extraction. Assuming that a user's sleep archive shows that the total night length is 8 hours, of which light sleep accounts for 40%, deep sleep accounts for 30%, and rapid eye movement sleep accounts for 30%, the data arrangement tool will extract these information in layers to form the classified sleep stage distribution data set. This classification method can provide clear stage information for subsequent analysis, facilitating further exploration of potential problems.

[0210] Step S820, for the preliminarily arranged sleep stage information, the evaluation standard tool calculates the score of the sleep quality evaluation content, and if the score is lower than the preset threshold, the sleep quality evaluation result meeting the standard is determined by recalculating the parameters.

[0211] The adjusted evaluation parameter value is:

[0212]

[0213] In formula (16), represents the adjusted evaluation parameter value, represents the original evaluation parameter value, represents the parameter adjustment coefficient, represents the deviation from the standard value. Formula (16) is used for dynamic adjustment of the parameter when the evaluation score is below the threshold.

[0214] The compliance determination of the sleep quality evaluation result is achieved by the following formula:

[0215]

[0216] In formula (17), represents the compliance determination of the sleep quality evaluation result, represents the current calculated sleep quality evaluation value, represents the preset minimum threshold. When the score reaches or exceeds the threshold, it is determined to meet the standard, otherwise the parameter calculation needs to be adjusted again.

[0217] For the preliminarily sorted sleep stage information, the sleep quality evaluation standard tool will calculate the score. Assuming that the evaluation standard includes sleep continuity, deep sleep proportion, and stage transition frequency, the system will assign weights to each indicator and calculate the total score. If the preset threshold is 75 points, and the sleep quality evaluation result is only 60 points, the system will adjust the parameters, such as increasing the weight of deep sleep proportion, to recalculate the score until the sleep quality evaluation result meets the standard. This dynamic adjustment mechanism helps to more accurately reflect the actual situation of sleep quality.

[0218] In step S830, according to the sleep quality evaluation result, the graphical display tool is used to visualize the stage distribution and evaluation data in combination with the display form, and an intuitive sleep quality evaluation report is generated, obtaining graphical content for presentation.

[0219] When generating the sleep quality evaluation report, the graphical display tool will visualize the stage distribution and evaluation data. Assuming that the system displays the proportion of each stage duration in a column chart, and presents the trend of sleep quality score change in a line chart, the user can directly see that the low deep sleep time may be the reason for the low total score. Such graphical content can help users quickly understand their own sleep status and provide intuitive basis for subsequent improvement.

[0220] Step S840, for graphical content, the direction guide tool is used to match the improvement direction, if the matching result is consistent with the low score item in the sleep quality evaluation report, the related suggestions are extracted from the pre-established suggestion library, and it is judged that the user can refer to the sleep improvement direction.

[0221] The matching degree of the graphical content and the low score item in the sleep quality evaluation report is calculated by the following formula:

[0222]

[0223] In formula (18), the graphical content is matched with the low score item in the sleep quality evaluation report, the matching degree, the total number of matching features, the weight coefficient of the th feature, the value of the graphical content on the th feature, the value of the low score item in the sleep quality evaluation report on the th feature, the similarity function of the two feature values.

[0224] The sleep improvement suggestion score extracted from the suggestion library is:

[0225]

[0226] In formula (19), the sleep improvement suggestion score extracted from the suggestion library, the pre-established suggestion library set, the total number of suggestions in the suggestion library, the priority weight of the suggestion, the correlation coefficient of the suggestion for a specific problem type. The optimal sleep improvement direction is:

[0227]

[0228] In formula (20),

[0229] the optimal sleep improvement direction, the set of all possible improvement directions, the sleep quality evaluation value of the improvement direction, ​​​​feasibility evaluation value, indicates the improvement direction user suitability evaluation value, , , respectively represent the weight coefficients of the sleep quality, feasibility and suitability of the improvement direction.

[0230] For graphical content, the direction guide tool provides the user with an improvement direction through matching processing. Assuming that the sleep quality evaluation report shows that the deep sleep proportion is insufficient, the system extracts relevant suggestions from the pre-established suggestion library, such as adjusting the pre-sleep activity or optimizing the sleep environment. Specifically, if the user is used to using electronic devices before sleeping, the suggestions may include reducing the screen time to 1 hour before sleeping, and trying to use a warm light lamp to create a relaxing atmosphere. This matching mechanism can provide personalized guidance for the user.

[0231] See Figure 2The embodiment also provides a sleep quality analysis system for unattended care, which is used for executing the sleep quality analysis method for unattended care, and comprises a sleep behavior data record acquisition module 10, a sleep signal data set determination module 20, a sleep stage distribution condition judgment module 30, a sleep quality evaluation result acquisition module 40, a sleep habit feature determination module 50, a monitoring configuration scheme acquisition module 60, a sleep record acquisition module 70 and a sleep improvement direction determination module 80. The sleep behavior data record acquisition module 10 is used for acquiring sleep-related signals from the surrounding environment of a user by a non-contact sensor technology, capturing body movement frequency features and breathing rhythm features in real time, and obtaining preliminary sleep behavior data records. The sleep signal data set determination module 20 is used for filtering body movement frequency signals and breathing rhythm signals by a signal denoising processing method according to the preliminary sleep behavior data records, eliminating environmental noise interference, and determining an optimized sleep signal data set. The sleep stage distribution condition judgment module 30 is used for extracting features by using a pre-established sleep stage classification model for the optimized sleep signal data set, analyzing regular changes of body movement patterns and breathing rules, and judging sleep stage distribution conditions of the user. The sleep quality evaluation result acquisition module 40 is used for grading and evaluating sleep quality of the user according to the sleep stage distribution conditions and in combination with a preset threshold range, acquiring duration and conversion frequency of each stage, and obtaining sleep quality evaluation results. The sleep habit feature determination module 50 is used for long-term tracking of a sleep pattern of the user by a time series analysis method for the sleep quality evaluation results, identifying periodic changes of sleep stage distribution, and determining sleep habit features of the user. The monitoring configuration scheme acquisition module 60 is used for dynamically optimizing signal acquisition parameters of the non-contact sensor by an adaptive adjustment mechanism according to the sleep habit features, and acquiring a monitoring configuration scheme conforming to daily habits of the user. The sleep record acquisition module 70 is used for uploading sleep data of the user to a remote server for storage and backup by cloud data synchronization technology for the monitoring configuration scheme, and obtaining real-time updated sleep record. The sleep improvement direction determination module 80 is used for generating a sleep quality evaluation report of the user by using data visualization technology according to the real-time updated sleep record, graphically displaying sleep stage distribution and sleep quality evaluation results, and determining a sleep improvement direction that can be referred by the user.

[0232] The sleep quality analysis method and system for unattended care have the following beneficial effects.

[0233] 1. Improve the convenience and comfort of monitoring: Adopt non-contact sensor technology, without the need for users to wear any equipment, avoiding the restraint and discomfort brought by contact monitoring, not interfering with normal sleep, especially suitable for natural capture of sleep state in unattended scenarios, improving user acceptance and monitoring continuity.

[0234] 2. Ensure the accuracy and reliability of sleep data: Through signal denoising processing method to filter body motion frequency signal and respiratory rhythm signal, effectively eliminate environmental noise interference, get optimized sleep signal data set, provide high quality data basis for subsequent sleep stage analysis and sleep quality evaluation, reduce analysis error caused by noise.

[0235] 3. Realize accurate sleep stage judgment and sleep quality evaluation: Use the pre-established sleep stage classification model to extract the features of the optimized signal, accurately analyze the regularity of body motion pattern and breathing rhythm, and accurately judge the sleep stage distribution of the user. Combined with the preset threshold for grading evaluation, the duration and conversion frequency of each stage can be clearly presented, so that the user can fully understand their own sleep quality.

[0236] 4. Help long-term sleep management and habit formation: Through time series analysis to track the user's sleep pattern for a long time, identify the periodic changes of sleep stage distribution, and determine the sleep habit characteristics. Based on this, use adaptive adjustment mechanism to optimize the signal acquisition parameters of non-contact sensor, form a monitoring configuration scheme that meets the user's daily habits, which helps users to improve their sleep habits and improve long-term sleep quality.

[0237] 5. Ensure data security and accessibility: Use cloud data synchronization technology to upload sleep data to remote server storage and backup, form real-time updated sleep archive record, not only ensure the security of data, but also convenient for users to check at any time. At the same time, use data visualization technology to generate graphical sleep quality evaluation report, intuitively display sleep stage distribution and sleep quality evaluation results, provide clear and understandable sleep improvement direction guide for users.

[0238] 6. Suitable for various unattended scenarios: This system can complete a series of processes such as sleep monitoring, analysis and evaluation without human intervention, especially suitable for unattended scenarios such as elderly people, infants, hospitalized patients, etc. It can provide timely and accurate sleep information for relevant personnel (such as children, parents, medical staff), so as to better pay attention to the health status of the monitored person.

[0239] While the preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the present disclosure without departing from the spirit and scope of the application. Accordingly, it is intended that the appended claims include all such variations and modifications as fall within the scope of the present application. It is apparent that those skilled in the art can modify and adapt the present application in various ways without departing from the spirit and scope of the present application. It is therefore intended that the present application encompass all such modifications and variations as fall within the scope of the claims and their equivalents.

Claims

1. A method for sleep quality analysis for unattended sleep, characterized in that, The method comprises the following steps: acquiring sleep-related signals from the user's surrounding environment through non-contact sensor technology, capturing body motion frequency characteristics and breathing rhythm characteristics in real time to obtain preliminary sleep behavior data records; According to the preliminary sleep behavior data records, the signal denoising processing method is used to filter the body motion frequency signal and the breathing rhythm signal, eliminate environmental noise interference, and determine the optimized sleep signal data set; According to the optimized sleep signal data set, the pre-established sleep stage classification model is used for feature extraction, analysis of the regularity of body motion mode and breathing regularity, and judgment of the sleep stage distribution of the user; According to the sleep stage distribution, the sleep quality of the user is graded and evaluated in combination with the pre-set threshold range, the duration of each stage and the conversion frequency are obtained, and the sleep quality evaluation result is obtained; According to the sleep quality evaluation result, the sleep mode of the user is tracked in the long term through the time series analysis method, the periodic change of the sleep stage distribution is identified, and the sleep habit characteristics of the user are determined; According to the sleep habit characteristics, an adaptive adjustment mechanism is used to dynamically optimize the signal acquisition parameters of the non-contact sensor, and a monitoring configuration scheme conforming to the user's daily habits is obtained; According to the monitoring configuration scheme, the user's sleep data is uploaded to a remote server for storage and backup through cloud data synchronization technology, and a real-time updated sleep archive record is obtained; According to the real-time updated sleep archive record, a sleep quality evaluation report of the user is generated by using data visualization technology, and the sleep stage distribution and the sleep quality evaluation result are graphically displayed to determine the sleep improvement direction that the user can refer to; The step of dynamically optimizing the signal acquisition parameters of the non-contact sensor according to the sleep habit characteristics and obtaining a monitoring configuration scheme conforming to the user's daily habits comprises: According to the sleep habit characteristics, a data recording tool is used to continuously track the user's daily work and rest time period, obtain sleep-related behavior data therefrom, and preliminarily set the acquisition frequency in combination with the environmental adaptation attribute, thereby obtaining an initial monitoring scheme matching the daily habits; According to the initial monitoring scheme, a data comparison tool is used to match the acquisition frequency with the habit tracking database established in advance, and if the deviation of the acquisition frequency from the habit data in the habit tracking database exceeds the pre-set threshold range, the adaptive adjustment mechanism is used to optimize the parameter adjustment of signal acquisition, and the acquisition rule conforming to the user characteristics is determined; According to the acquisition rule, a parameter configuration tool is used to dynamically adjust the signal acquisition mode of the non-contact sensor in combination with the environmental adaptation attribute, thereby obtaining monitoring data consistent with the daily habits, and determining a monitoring frequency range suitable for the current state of the user; According to the monitoring frequency range, a data integration tool is used to associate the signal acquisition result with the sleep habit data, and the monitoring configuration is finally calibrated through the data matching attribute, thereby obtaining a personalized monitoring configuration scheme consistent with the user's daily habits.

2. The method for sleep quality analysis without a guardian according to claim 1, wherein, The step of filtering and processing the body movement frequency signal and the breathing rhythm signal according to the preliminary sleep behavior data record by using a signal denoising processing method, eliminating environmental noise interference, and determining an optimized sleep signal data set comprises: According to the preliminary sleep behavior data record, the original sleep signal containing body movement frequency and breathing rhythm is obtained, the preliminary cleaning sleep signal set is obtained by using a preset threshold to preliminarily screen the environmental noise interference in the original sleep signal; For the preliminary cleaning sleep signal set, the body movement frequency signal and the breathing rhythm signal are denoised by using a signal filtering tool, and if the environmental noise interference is detected to exceed the preset threshold, the noise component is separated by using a frequency domain conversion tool to obtain a denoised sleep signal combination; According to the denoised sleep signal combination, the data accuracy is adjusted by using a time domain analysis tool for the body movement frequency feature and the breathing rhythm feature, whether there is abnormal fluctuation is judged by comparing the normal range value established in advance, and the fine-tuned sleep signal data set is determined; For the fine-tuned sleep signal data set, the body movement frequency and the breathing rhythm are classified and stored by using a data integration tool, the data features related to behavior analysis are obtained according to the real-time recording requirements during storage, and the final optimized sleep signal data set is obtained. 3.The method for sleep quality analysis without a guardian according to claim 1, wherein, In the step of obtaining the original sleep signal containing body movement frequency and breathing rhythm from the preliminary sleep behavior data record, and preliminarily screening the environmental noise interference in the original sleep signal by using a preset threshold to obtain a preliminary cleaning sleep signal set, the original sleep signal is: wherein, denotes the raw sleep signal at the time instant , denotes the body movement frequency component, denotes the respiration rhythm component, denotes the ambient noise disturbance component; The judgment standard for environmental noise screening is: wherein, represents a preset noise screening threshold value, represents a mean value of the sleep signal, represents a standard deviation of the sleep signal, represents a threshold coefficient; The judgment logic of signal cleaning is: wherein, denotes the sleep signal after preliminary cleaning, denotes the original sleep signal, denotes the filter threshold. 4.The method for sleep quality analysis without a guardian according to claim 1, wherein, The step of using a pre-established sleep stage classification model to extract features for the optimized sleep signal data set, analyzing the regularity change of body movement mode and breathing rule, and judging the sleep stage distribution of the user comprises: According to the optimized sleep signal data set, the data is decomposed by using a preset feature extraction tool for the body movement mode and the breathing rule, a plurality of feature values related to sleep stage classification are obtained from the preliminary extraction feature combination, and the preliminary extraction feature combination is obtained; For the preliminary extraction feature combination, the data integration tool is used to classify and process a plurality of feature values, and the feature values are classified into corresponding categories according to the pre-established stage division standard, and the classified feature set is determined; According to the classified feature set, the time series comparison tool is used to dynamically match the mode change and the behavior association, the sleep stage distribution judgment basis corresponding to the sleep signal is obtained from the preliminary mapping result of the sleep stage distribution, and the preliminary mapping result of the sleep stage distribution is obtained; For the preliminary mapping result of the sleep stage distribution, the data verification tool is used to confirm the matching degree of the body movement mode and the breathing rule, and if the matching degree exceeds the preset threshold, the corresponding relationship with the sleep stage classification is confirmed, and the final sleep stage distribution data is determined. 5.The method for sleep quality analysis without a guardian according to claim 1, wherein, According to the sleep stage distribution, the sleep quality of the user is evaluated by combining a preset threshold range, the duration of each stage and the conversion frequency are obtained, and the sleep quality evaluation result is obtained by the following steps: According to the sleep stage distribution, the duration of each stage and the conversion frequency, the time series recording tool is used to record the duration of each stage and the conversion times in detail, and the dynamic change data related to the sleep quality is obtained from the preliminary time distribution record table; According to the preliminary time distribution record table, the duration of each stage and the conversion times are classified by combining the preset threshold range, if the duration or the conversion times exceeds the corresponding threshold range, it is classified into an abnormal category, and the classified sleep stage distribution data is determined; According to the classified sleep stage distribution data, the sleep quality level distribution of each stage is determined by combining the abnormal category and the behavior mode, and the data comparison tool is used to match the sleep quality standard established in advance, and the basis related to the classification evaluation is obtained, and the sleep quality level distribution of each stage is determined. According to the sleep quality evaluation result, the sleep mode of the user is tracked for a long time by the time series analysis method, the periodic change of the sleep stage distribution is identified, and the sleep habit characteristics of the user are determined by the following steps: 6.The method for sleep quality analysis without a guardian according to claim 1, wherein, According to the sleep quality evaluation result, the sleep stage distribution data of the user is collected for a long time by the time series recording tool, the starting and ending time points of each stage are labeled, and the continuous data related to the periodic change is obtained from the complete sleep stage time distribution record table; According to the sleep stage time distribution record table, the data comparison tool is used to match the periodic mode library established in advance, if the sleep stage distribution data matches the time interval of a mode in the periodic mode library, it is classified as the corresponding periodic characteristic, and the sleep stage periodic distribution characteristic of the user is determined; According to the sleep stage periodic distribution characteristic, the periodic change of each stage and the duration are associated by the data integration tool, the conversion law between different stages is obtained, whether there is regular fluctuation in the sleep mode is determined, and the long-term change trend is obtained; According to the long-term change trend, the fluctuation amplitude is classified by the data classification tool combined with the preset threshold range, if the fluctuation amplitude exceeds the threshold range, it is marked as an abnormal mode, and the sleep habit characteristics of the user are determined. According to the monitoring configuration scheme, the sleep data of the user is uploaded to the remote server for storage and backup by the cloud data synchronization technology, and the real-time updated sleep archive record is obtained by the following steps: 7.The method for sleep quality analysis without care according to claim 1, wherein, According to the sleep data, the data integration tool is used to classify the collected content, the upload priority is set for different categories of data, if the priority exceeds the preset threshold, it is transmitted to the remote server by the upload mechanism, and the classified data set is obtained; ​ For the classified data set, the data synchronization tool is combined with the synchronization frequency to detect the transmission process, and if the transmission is interrupted, the complete transmission log is obtained through the backup path reconnection; According to the complete transmission log, the data verification tool is used to compare the data uploaded to the remote server, and the consistency is verified by combining the data integrity attribute. The inconsistent part is adjusted by the repair tool to determine the data content after verification; For the data content after verification, the access range is limited by the permission management tool combined with the user permission, and the backup tool is used to generate a copy in the storage space to obtain the real-time updated sleep archive record. 8.The method for sleep quality analysis without care according to claim 1, wherein, According to the real-time updated sleep archive record, the sleep quality evaluation report of the user is generated by using the data visualization technology, and the sleep stage distribution and sleep quality evaluation result are graphically displayed to determine the sleep improvement direction that the user can refer to, comprising: According to the sleep archive, the data sorting tool is used to classify and process the updated content, the sleep stage distribution data set is obtained by hierarchical extraction of the stage distribution data, and the preliminary sorted sleep stage information is obtained. For the preliminary sorted sleep stage information, the evaluation standard tool is used to calculate the sleep quality evaluation score, and if the score is lower than the preset threshold, the score is recalculated by adjusting the parameters to determine the sleep quality evaluation result that meets the standard. According to the sleep quality evaluation result, the graphical display tool is used to visually process the stage distribution and evaluation data by combining the display form to generate an intuitive sleep quality evaluation report, and the graphical content for presentation is obtained. For the graphical content, the direction guide tool is used to match the improvement direction, and if the matching result is consistent with the low score item in the sleep quality evaluation report, the related suggestions are extracted from the pre-established suggestion library to determine the sleep improvement direction that the user can refer to.

9. An unattended sleep quality analysis system for performing the unattended sleep quality analysis method according to any one of claims 1 to 8, characterized by It includes: Sleep behavior data record acquisition module (10) for acquiring sleep-related signals from the user's surrounding environment through non-contact sensor technology, capturing body motion frequency characteristics and breathing rhythm characteristics in real time, and obtaining preliminary sleep behavior data records; Sleep signal data set determination module (20) for filtering body motion frequency signals and breathing rhythm signals by using signal denoising processing method according to preliminary sleep behavior data records, eliminating environmental noise interference, and determining optimized sleep signal data set; Sleep stage distribution condition judgment module (30) for extracting features by using pre-established sleep stage classification model for optimized sleep signal data set, analyzing the regularity of body motion mode and breathing law, and judging the sleep stage distribution condition of the user; Sleep quality evaluation result acquisition module (40) for grading the sleep quality of the user according to the sleep stage distribution condition and the preset threshold range, obtaining the duration and conversion frequency of each stage, and obtaining the sleep quality evaluation result; The sleep habit feature determination module (50) is configured to track the sleep pattern of the user for a long time by using a time series analysis method for the sleep quality assessment result, identify periodic changes in the sleep stage distribution, and determine the sleep habit feature of the user. The monitoring configuration scheme acquisition module (60) is configured to dynamically optimize the signal acquisition parameters of the non-contact sensor by using an adaptive adjustment mechanism according to the sleep habit feature, and acquire a monitoring configuration scheme that conforms to the daily habits of the user. The sleep record acquisition module (70) is configured to upload the sleep data of the user to a remote server for storage and backup by using a cloud data synchronization technology for the monitoring configuration scheme, and obtain real-time updated sleep record. The sleep improvement direction determination module (80) is configured to generate a sleep quality assessment report of the user by using a data visualization technology according to the real-time updated sleep record, perform graphical display for the sleep stage distribution and the sleep quality assessment result, and determine the sleep improvement direction that can be referred to by the user.

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