CN116682535B - Sleep sustainability detection and adjustment method, system and device based on numerical fitting - Google Patents
Sleep sustainability detection and adjustment method, system and device based on numerical fitting Download PDFInfo
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Abstract
The invention provides a sleep sustainability detection and adjustment method based on numerical fitting, which comprises the following steps: initializing a sleep sustainability dynamic detection and adjustment basic scheme, and collecting and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve; performing trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic pure curve; determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve and calculating a sleep sustainability index and a sleep sustainability curve; trend prediction analysis is carried out, a sleep sustainability dynamic regulation strategy is generated, and dynamic regulation and effect evaluation of a user sleep process are completed; a user sleep sustainability detection adjustment report is generated. The invention can realize high-efficiency intervention and adjustment of the sleep sustainability of the user.
Description
Technical Field
The invention relates to the field of sleep sustainability detection quantification and auxiliary regulation, in particular to a sleep sustainability detection regulation method, system and device based on numerical fitting.
Background
Sleep is one of the most important life physiological processes of human beings, and stable and continuous sleep is an important guarantee for people to obtain energy and physical and mental health. In real life, due to many factors such as aging, sleeping environment, physiological diseases, wounds, mental stress and the like, a series of events with poor sleep continuity or sustainability such as multiple awakenings, long-time awakening, rapid oscillation of sleep states and the like occur in the whole night sleeping process, so that the sleeping quality and physical and psychological experience of people are greatly influenced. The long-term low-quality sleep continuity or sustainability brings great adverse effects to the physical and mental health, study, work and life of people.
The applicant's proposed prior solution chinese application CN2023101940795 provides a method, system and apparatus for sleep sustainability detection quantification and assisted intervention, comprising the steps of: the physiological state signal and the environmental state signal of the sleeping process of the user are collected, monitored, processed and analyzed to generate physiological state characteristics and environmental state characteristics; performing sleep state analysis, time sequence component analysis and sustainability quantitative analysis on the physiological state characteristics, extracting a sleep sustainability index, and generating a sleep sustainability quantitative daily report; repeating the steps, continuously monitoring and tracking and analyzing the sleeping process of the user, evaluating the influence of the sleeping environment on the sleeping sustainability, extracting the optimal sleeping sustainability environment scheme, dynamically optimizing and adjusting the sleeping environment, and generating a sleeping sustainability quantitative report. According to the technical scheme, based on time sequence decomposition of the sleep duration state characteristic curve, the sleep sustainability index is extracted, and the sleep sustainability index is used as an innovative evaluation index for quantifying the sleep continuity or sustainability. But in the face of scene demands such as sustainable efficient extraction of sleep, dynamic detection and dynamic adjustment, there is a further lifting space, mainly comprising: firstly, the time sequence decomposition method has adaptability limitation in multi-scene sustainable feature extraction to cause instability of accuracy; secondly, the sleep duration state characteristic curve is obtained based on the step-type sleep time phase stage value smoothness, so that the sleep state of the user cannot be finely described and precisely quantified, and the accuracy and the sensitivity of sleep sustainability evaluation are further limited; finally, besides the sleep environment adjustment intervention, how to realize the detection quantification and dynamic adjustment of the sleep sustainability index which is faster and more efficient, stable for a long time and has high individuation for the user, and can dynamically and continuously improve the detection accuracy and the adjustment efficiency.
How to extract finer continuous change characteristics of the sleep state of the user, and more accurately quantitatively evaluate the sleep sustainability; how to realize the high-efficiency coordination of the sustainable detection quantification of sleep and the dynamic regulation, and can realize the dynamic optimization of the detection regulation process, continuously improve the detection quantification efficiency and the intervention regulation effect, and is a problem that the technical proposal of the products at home and abroad and the actual application scene need to be further optimized or solved at present.
Disclosure of Invention
Aiming at the defects and improvement demands of the existing method, the invention aims to provide a sleep sustainability detection and adjustment method based on numerical fitting, which extracts a sleep sustainability characteristic characterization curve and calculates to obtain a sleep sustainability index and a sleep sustainability curve by carrying out trend removal processing, noise removal processing and numerical fitting on a sleep state characteristic curve; secondly, generating a sleep sustainability dynamic regulation strategy and completing dynamic regulation and effect evaluation through predictive analysis of the sleep sustainability state; and finally, establishing and updating a user individual sleep sustainability database, dynamically iterating a dynamic detection and adjustment process strategy for optimizing the sleep sustainability, generating a user sleep sustainability detection and adjustment report, realizing the improvement of detection quantification efficiency and intervention and adjustment effects, and assisting a user to obtain higher sleep quality and sleep continuity. The invention also provides a sleep sustainability detection and adjustment system based on numerical fitting, which is used for realizing the method. The invention also provides a sleep sustainability detection and adjustment device based on numerical fitting, which is used for realizing the system.
According to the purpose of the invention, the invention provides a sleep sustainability detection and adjustment method based on numerical fitting, which comprises the following steps of:
Initializing a sleep sustainability dynamic detection and adjustment basic scheme, and collecting and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve;
Performing trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic pure curve;
determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve, and calculating a sleep sustainability index and a sleep sustainability index curve;
Trend prediction analysis is carried out on the sleep state characteristic curve and the sleep sustainability index curve, a sleep sustainability dynamic regulation strategy is generated, and dynamic regulation and effect evaluation of a user sleep process are completed;
generating a user sleep sustainability detection adjustment report;
And initializing and establishing or continuously updating a user personalized sleep sustainability database, dynamically iterating a dynamic detection adjustment process strategy for optimizing the sleep sustainability, and updating a sleep sustainability general database.
More preferably, the step of initializing the basic sleep sustainability dynamic detection and adjustment scheme, collecting and processing the sleep physiological signals of the user to obtain the sleep state characteristic curve and the sleep time phase curve further comprises the following specific steps:
acquiring key physiological information of a user, screening and initializing a sleep sustainability dynamic detection and adjustment basic scheme from a sleep sustainability general database or a user personality sleep sustainability database;
Dynamically monitoring, collecting and processing the sleep physiological signals of the user to obtain sleep physiological state data;
dynamically performing feature analysis and feature selection on the sleep physiological state data to generate the sleep state feature curve;
And dynamically analyzing the sleep physiological state data in a sleep phase to generate the sleep phase curve.
More preferably, the user key physiological information includes at least gender, age, physiological health status, and mental state.
More preferably, the sleep sustainability general database is a sleep sustainability detection quantification and dynamic adjustment database of the population with different health states, and at least comprises the key physiological information of the user, a sleep sustainability detection adjustment period, a sleep sustainability detection quantification process method parameter, a sleep sustainability dynamic adjustment process method parameter or strategy, a sleep sustainability index curve, a sleep sustainability adjustment effect curve and a sleep time phase curve.
More preferably, the user personalized sleep sustainability database at least comprises the user key physiological information, a sleep state characteristic curve, a sleep time phase curve, a sleep sustainability index curve, a dynamic adjustment effect curve, a time phase index distribution characteristic, a time phase effect distribution characteristic, a dynamic adjustment comprehensive effect coefficient, a numerical fitting method, a trend prediction analysis method, a sleep sustainability dynamic adjustment strategy and the sleep sustainability dynamic detection adjustment basic scheme.
More preferably, the basic sleep sustainability dynamic detection and adjustment scheme at least comprises a sleep sustainability detection and quantization process scheme and a sleep sustainability dynamic adjustment process scheme; and if the user is a new user, the sleep sustainability dynamic detection and adjustment basic scheme is obtained from the sleep sustainability general database, otherwise, the sleep sustainability dynamic detection and adjustment basic scheme is obtained from the user personality sleep sustainability database.
More preferably, the sleep physiological signal at least comprises any one of an electroencephalogram signal, an electrocardiographic signal and a respiratory signal.
More preferably, the signal processing at least comprises resampling, re-referencing, de-artifact, signal correction, noise reduction, power frequency notch, band-pass filtering, smoothing and time frame segmentation; the time frame segmentation refers to continuous sliding segmentation of a preset framing duration window for signal data according to a sampling rate of a signal and a preset framing step length.
More preferably, the feature analysis includes at least any one of numerical analysis, envelope analysis, time-frequency analysis, entropy analysis, fractal analysis, and complexity analysis.
More preferably, the sleep state characteristic curve is specifically obtained by carrying out linear combination fusion on different sleep physiological state characteristics obtained by carrying out the characteristic analysis on different sleep physiological state data, and is used for describing continuous change curves of the physiological state characteristics of a user in different sleep periods, different sleep phases and different sleep states; the sleep phase at least comprises a wake phase, a light sleep phase, a deep sleep phase and a rapid eye movement sleep phase.
More preferably, the method for generating the sleep phase curve specifically comprises the following steps:
1) Learning training and data modeling are carried out on the sleep physiological state data of the scale sleep user sample and the sleep time phase stage data corresponding to the sleep physiological state data through a machine learning algorithm, so that a sleep time phase identification model is obtained;
2) And inputting the sleep physiological state data of the current user into the sleep time phase identification model to obtain the corresponding sleep time phase stage and generating the sleep time phase curve according to a time sequence.
More preferably, the specific step of performing trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic purity curve further includes:
dynamically trending the sleep state characteristic curve to obtain a sleep state characteristic trending curve;
And dynamically denoising the sleep state characteristic trend removal curve to obtain the sleep state characteristic purity curve.
More preferably, the trending process specifically removes linear trend components and very low frequency trend components in the target signal, and at least includes any one of a mean removing process, a low-pass filtering process, a trending analysis FDA, a multi-fractal trending analysis MFDFA, and an asymmetric removal trend fluctuation analysis ADFA.
More preferably, the denoising process at least comprises any one of gaussian filtering, mean filtering, fourier transform filtering, wavelet transform filtering and wavelet packet transform filtering, and is used for eliminating gaussian noise or white noise interference introduced in the sleep state characteristic curve due to discontinuity of signal characteristic analysis of physiological acquisition and framing of the body surface on the sleep state.
More preferably, the method and the parameters for determining the numerical fitting, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic characterization curve, and calculating a sleep sustainability index and a sleep sustainability index curve further include the specific steps of:
Determining a numerical fitting method and parameters according to curve generation attributes of the sleep state characteristic curve;
Performing numerical fitting on the sleep state characteristic purity curve, and separating to obtain a sleep sustainability characteristic fitting curve and the sleep sustainability characteristic characterization curve;
And dynamically calculating to obtain the sleep sustainability index and the sleep sustainability index curve according to the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve.
More preferably, the numerical fitting method at least comprises any one of least square method, linear fitting, polynomial linear fitting, polynomial fitting, nonlinear fitting, gamma adjustment fitting and autoregressive fitting.
More preferably, the sleep sustainability characteristic characterization curve is specifically a difference curve between the sleep state characteristic purity curve and the sleep sustainability characteristic fitting curve.
More preferably, the method for calculating the sleep sustainability index specifically includes:
1) Acquiring sleep phase stage, the sleep state characteristic purity curve and the sleep sustainability characteristic representation curve corresponding to a current time frame;
2) Respectively carrying out root mean square calculation on the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve in sequence to obtain a sleep state characteristic root mean square value and a sleep sustainability characteristic root mean square value;
3) Calculating to obtain a sleep sustainability node characteristic coefficient according to the sleep state characteristic root mean square value and the relative variation of the sleep sustainability characteristic root mean square value;
4) And calculating the sleep sustainability index according to the root mean square sum product of the sleep sustainability node characteristic coefficient, the numerical fitting method correction coefficient, the user individual correction coefficient and the sleep time phase stage correction coefficient.
More preferably, a calculation formula of the sleep sustainability node characteristic coefficient is specifically:
；
Wherein, For the sleep sustainability node characteristic coefficient,/>And the sleep state characteristic root mean square value and the sleep sustainability characteristic root mean square value are respectively.
More preferably, a calculation formula of the sleep sustainability index specifically includes:
；
Wherein, For the sleep sustainability index,/>The characteristic coefficient of the sleep sustainability node, the correction coefficient of the numerical fitting method, the individual correction coefficient of the user and the sleep time phase stage correction coefficient are respectively, and/>，/>2。
More preferably, the sleep sustainability index curve is generated by concatenating the sleep sustainability indices in time sequence.
More preferably, the specific steps of performing trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic adjustment strategy, and completing dynamic adjustment and effect evaluation of the sleep process of the user further include:
dynamically trend predictive analysis is carried out on the sleep state characteristic curve to obtain a sleep state characteristic predictive value;
Dynamically trend predictive analysis is carried out on the sleep sustainability index curve to obtain a sleep sustainability index predictive value;
generating the sleep sustainability dynamic regulation strategy according to the sleep sustainability detection regulation period and a preset sleep regulation knowledge base according to the sleep state characteristic predicted value and the sleep sustainability index predicted value;
According to the sleep sustainability dynamic regulation strategy, connecting and controlling sleep regulation peripheral equipment to dynamically regulate the sleep process of a user;
And carrying out dynamic tracking evaluation on the adjusting effect, calculating a dynamic adjusting effect coefficient and generating a dynamic adjusting effect curve.
More preferably, the trend prediction analysis method at least comprises any one of AR, MR, ARMA, ARIMA, SARIMA, VAR and deep learning.
More preferably, the sleep sustainability dynamic adjustment strategy at least comprises a sleep scene, a sleep time phase, an adjustment mode, an execution mode, an adjustment method, an adjustment intensity, an adjustment time point, a duration, a target adjustment value and a device control parameter; wherein the modulation means comprises at least vocal stimulation, ultrasound stimulation, light stimulation, electrical stimulation, magnetic stimulation, temperature stimulation, humidity stimulation, tactile stimulation, and/or the likeThe implementation mode at least comprises any mode of separation mode and contact mode.
More preferably, the sleep conditioning peripheral device comprises at least a vocal stimulation device, an ultrasonic stimulation device, a light stimulation device, an electrical stimulation device, a magnetic stimulation device, a temperature stimulation device, a humidity stimulation device, a tactile stimulation device, and a light stimulation deviceAny one of the concentration control devices, and is determined by the specific manner of the control.
More preferably, the calculation mode of the dynamic adjustment effect coefficient specifically includes:
；
Wherein, For the dynamic adjustment of the effect coefficient,/>Correction coefficients for trend predictive analysis methods and，/>For the dynamically adjusted sleep phase staging correction factor,/>The sleep sustainability index before dynamic adjustment and after dynamic adjustment respectively.
More preferably, the dynamic adjustment effect curve is generated by splicing the dynamic adjustment effect coefficients according to time sequence.
More preferably, the specific step of generating the report of the detection and adjustment of the sleep sustainability of the user further comprises:
Calculating distribution characteristics of the sleep sustainability indexes under different sleep phases according to the sleep phase curve and the sleep sustainability index curve to obtain phase index distribution characteristics;
Calculating the distribution characteristics of the dynamic adjustment effect coefficients under different sleep phases according to the sleep phase curve and the dynamic adjustment effect curve to obtain phase effect distribution characteristics;
Calculating the correlation between the sleep sustainability index curve and the dynamic adjustment effect curve to obtain a dynamic adjustment comprehensive effect coefficient;
And generating the sleep sustainability detection regulation report of the user according to a preset report generation period.
More preferably, the distribution characteristics include at least any one of average, root mean square, maximum, minimum, variance, standard deviation, coefficient of variation, kurtosis, and skewness.
More preferably, the correlation calculation method at least comprises any one of coherence analysis, pearson correlation analysis, jacquard similarity analysis, linear mutual information analysis, linear correlation analysis, euclidean distance analysis, manhattan distance analysis and chebyshev distance analysis.
More preferably, the user sleep sustainability detection adjustment report at least comprises the user key physiological information, the sleep state characteristic curve, the sleep time phase curve, the sleep sustainability index curve, the dynamic adjustment effect curve, the time phase index distribution characteristic, the time phase effect distribution characteristic, the dynamic adjustment comprehensive effect coefficient and a sleep sustainability detection adjustment summary.
More preferably, the initializing establishes or continuously updates a user individual sleep sustainability database, dynamically iterates a dynamic detection adjustment process strategy for optimizing sleep sustainability, generates a user sleep sustainability detection adjustment report, and updates a sleep sustainability general database, and the specific steps further include:
Dynamically collecting detection and adjustment process data and analysis results, and initializing and establishing or continuously updating the user personality sleep sustainability database;
According to a preset detection adjustment optimization period, according to the user individual sleep sustainability database, dynamically iterating and optimizing the dynamic detection adjustment process strategy of the sleep sustainability, and continuously improving the efficiency effect of detection adjustment;
And according to a preset general database updating period, updating user key data information of the sleep sustainability general database according to the user individual sleep sustainability database and the user sleep sustainability detection adjustment report.
More preferably, the dynamic detection adjustment process method comprises at least a degussa noise processing method parameter, a numerical fitting method parameter, the characteristic curve high-frequency threshold value, a trend prediction analysis method parameter, the sleep sustainability dynamic adjustment strategy and a dynamic adjustment effect evaluation method parameter.
According to the purpose of the invention, the invention provides a sleep sustainability detection and adjustment system based on numerical fitting, which comprises the following modules:
the state acquisition analysis module is used for initializing a sleep sustainability dynamic detection and adjustment basic scheme, and acquiring and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve;
the characteristic curve extraction module is used for carrying out trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic purity curve;
The index curve calculation module is used for determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve and calculating a sleep sustainability index and a sleep sustainability index curve;
the index dynamic adjustment module is used for carrying out trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic adjustment strategy and completing dynamic adjustment and effect evaluation of a user sleep process;
the report statistical analysis module is used for generating a user sleep sustainability detection adjustment report;
The data optimization application module is used for initializing and establishing or continuously updating a user individual sleep sustainability database, dynamically iterating and optimizing a dynamic detection adjustment process strategy of the sleep sustainability, generating a user sleep sustainability detection adjustment report, and updating a sleep sustainability general database;
And the data operation management module is used for carrying out visual management, unified storage and operation management on all data of the system.
More preferably, the state acquisition and analysis module further comprises the following functional units:
the scheme initializing unit is used for acquiring key physiological information of the user, screening and initializing the sleep sustainability dynamic detection and adjustment basic scheme from a sleep sustainability general database or a user personality sleep sustainability database;
The signal monitoring processing unit is used for dynamically monitoring, collecting and processing the sleep physiological signals of the user to obtain sleep physiological state data;
The sleep characteristic extraction unit is used for dynamically carrying out characteristic analysis and characteristic selection on the sleep physiological state data to generate the sleep state characteristic curve;
And the sleep phase analysis unit is used for dynamically analyzing the sleep physiological state data in sleep phase and generating the sleep phase curve.
More preferably, the characteristic curve extraction module further comprises the following functional units:
the trend removal processing unit is used for dynamically removing trend from the sleep state characteristic curve to obtain a sleep state characteristic trend removal curve;
And the Gaussian noise processing unit is used for dynamically denoising the sleep state characteristic trend removal curve to obtain the sleep state characteristic purity curve.
More preferably, the exponential curve calculation module further comprises the following functional units:
The method parameter selection unit is used for determining a numerical fitting method and parameters according to curve generation attributes of the sleep state characteristic curve;
the characteristic curve extraction unit is used for carrying out numerical fitting on the sleep state characteristic purity curve, and separating to obtain a sleep sustainability characteristic fitting curve and the sleep sustainability characteristic curve;
and the index curve extraction unit is used for dynamically calculating the sleep sustainability index and the sleep sustainability index curve according to the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve.
More preferably, the index dynamic adjustment module further comprises the following functional units:
the sleep state prediction unit is used for carrying out dynamic trend prediction analysis on the sleep state characteristic curve to obtain a sleep state characteristic prediction value;
The sleep index prediction unit is used for dynamically predicting and analyzing the trend of the sleep sustainability index curve to obtain a sleep sustainability index predicted value;
The regulation strategy generation unit is used for generating the sleep sustainability dynamic regulation strategy according to the sleep sustainability detection regulation period and a preset sleep regulation knowledge base according to the sleep state characteristic predicted value and the sleep sustainability index predicted value;
the sleep dynamic regulation unit is used for connecting and controlling sleep regulation peripheral equipment according to the sleep sustainability dynamic regulation strategy to dynamically regulate the sleep process of the user;
and the effect tracking evaluation unit is used for dynamically tracking and evaluating the adjusting effect, calculating a dynamic adjusting effect coefficient and generating a dynamic adjusting effect curve.
More preferably, the report statistics analysis module further comprises the following functional units:
The time phase index analysis unit is used for calculating the distribution characteristics of the sleep sustainability indexes under different sleep time phases according to the sleep time phase curve and the sleep sustainability index curve to obtain the time phase index distribution characteristics;
The time phase effect analysis unit is used for calculating the distribution characteristics of the dynamic adjustment effect coefficients under different sleep time phases according to the sleep time phase curve and the dynamic adjustment effect curve to obtain the time phase effect distribution characteristics;
the comprehensive effect evaluation unit is used for calculating the correlation between the sleep sustainability index curve and the dynamic adjustment effect curve to obtain the dynamic adjustment comprehensive effect coefficient;
The user report generation unit is used for generating the user sleep sustainability detection adjustment report according to a preset report generation period;
And the user report management unit is used for uniformly managing the format output and the presentation form of the user sleep sustainability detection regulation report.
More preferably, the data optimization application module further comprises the following functional units:
The personality database updating unit is used for dynamically collecting data of the detection and adjustment process and analysis results, and initializing and establishing or continuously updating the personality sleep sustainability database of the user;
The detection adjustment optimization unit is used for dynamically iterating and optimizing the dynamic detection adjustment process strategy of the sleep sustainability according to the user individual sleep sustainability database according to a preset detection adjustment optimization period, so as to continuously improve the efficiency effect of detection adjustment;
And the general database updating unit is used for updating the user key data information of the sleep sustainability general database according to the user personal sleep sustainability database and the user sleep sustainability detection adjustment report according to a preset general database updating period.
More preferably, the data operation management module further comprises the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
the data visual management unit is used for visual display management of all data in the system;
the data storage management unit is used for uniformly storing and managing all data in the system;
and the data operation management unit is used for backing up, migrating and exporting all data in the system.
According to the purpose of the invention, the invention provides a sleep sustainability detection and adjustment device based on numerical fitting, which comprises the following modules:
the state acquisition analysis module is used for initializing a sleep sustainability dynamic detection and adjustment basic scheme, and acquiring and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve;
the characteristic curve extraction module is used for carrying out trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic purity curve;
The index curve calculation module is used for determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve and calculating a sleep sustainability index and a sleep sustainability index curve;
The index dynamic adjustment module is used for carrying out trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic adjustment strategy and completing dynamic adjustment and effect evaluation of a user sleep process;
the report statistical analysis module is used for generating a user sleep sustainability detection adjustment report;
The data optimization application module is used for initializing and establishing or continuously updating a user personalized sleep sustainability database, dynamically iterating and optimizing a dynamic detection adjustment process strategy of the sleep sustainability, and updating a sleep sustainability general database;
the data visualization module is used for carrying out unified visual display management on all process data and result data in the device;
And the data management center module is used for uniformly storing all process data and result data in the device and managing data operation.
The invention further optimizes the specific quantitative design and calculation mode of the sleep sustainability index based on the prior research of the applicant, further optimizes the extraction mode of the sleep state characteristic curve, and further applies the trending treatment, the denoising treatment and the numerical fitting method to the extraction of the sleep sustainability characteristic, so that the detection and quantification of the sleep sustainability of the user are more comprehensive and fine, the adaptability is wide, the accuracy is high and the sensitivity is high; the method further provides a calculation scheme of the dynamic adjustment effect coefficient, further provides establishment, updating and application mechanisms of a user individual sleep sustainability database and a sleep sustainability universal database, and further provides an inverse feedback application framework from detection quantization to dynamic adjustment, so that a powerful basis is provided for cooperative control of the detection quantization and the dynamic adjustment process, dynamic optimization of the detection adjustment process is realized, and detection quantization efficiency and intervention adjustment effect are continuously improved. The invention can provide a more scientific and efficient implementation method and a landing scheme for detecting, quantifying and dynamically adjusting sleep sustainability, can enable products and services related to sleep quantification or adjustment, meets the scene requirements of different users, and assists the sleep of the users.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application.
Fig. 1 is a schematic flow chart of a sleep sustainability detection and adjustment method based on numerical fitting according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the module composition of a sleep sustainability detection adjustment system based on a numerical fit according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a module structure of a sleep sustainability detection and adjustment device based on numerical fitting according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the objects and technical solutions of the present application, the present application will be further described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the embodiments described below are only some, but not all, embodiments of the application. Other embodiments, which are derived from the embodiments of the application by a person skilled in the art without creative efforts, shall fall within the protection scope of the application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be arbitrarily combined with each other.
Applicants have found that human sleep is a non-stationary time series process, and that multiple wake sleep state abrupt oscillations are an important factor or cause of non-stationary during sleep, how to track polytropic, unexpected and abrupt states is a fundamental challenge in sleep sustainability or continuity detection regulation. Meanwhile, due to the principle limitation of the physiological acquisition technology of scalp electroencephalogram EEG, electrocardiographic ECG and the like and the corresponding traditional signal characteristic analysis and extraction process, a great amount of calculation errors and introduced noise exist in the quantification of the sleep state, and particularly, the evaluation of the sleep sustainability is faced. According to the technical scheme, through the methods of sensitive characteristic selection, trend removal processing, noise removal processing, numerical fitting and the like, a fine and accurate sleep state characteristic curve can be extracted, and the high-robustness quantitative design of sleep sustainability is completed; and further, the dynamic adjustment and effect evaluation of sleep sustainability are completed, and the reverse dynamic optimization of detection adjustment is further realized, so that the overall detection adjustment scheme has better scene adaptability and technical application effect.
Referring to fig. 1, the sleep sustainability detection and adjustment method based on numerical fitting provided by the embodiment of the invention includes the following steps:
p100: initializing a sleep sustainability dynamic detection and adjustment basic scheme, and collecting and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve.
Firstly, acquiring key physiological information of a user, screening and initializing a sleep sustainability dynamic detection and adjustment basic scheme from a sleep sustainability general database or a user personalized sleep sustainability database.
In this embodiment, the user key physiological information includes at least gender, age, physiological health status, and mental state.
In this embodiment, the sleep sustainability general database is specifically a database for detecting, quantifying and dynamically adjusting sleep sustainability of people in different health states according to scale, and at least includes user key physiological information, sleep sustainability detection and adjustment period, sleep sustainability detection and quantification process method parameters, sleep sustainability dynamic adjustment process method parameters or strategies, sleep sustainability index curve, sleep sustainability adjustment effect curve and sleep phase curve.
In this embodiment, the user personalized sleep sustainability database at least includes user key physiological information, a sleep state characteristic curve, a sleep time phase curve, a sleep sustainability index curve, a dynamic adjustment effect curve, a time phase index distribution characteristic, a time phase effect distribution characteristic, a dynamic adjustment comprehensive effect coefficient, a numerical fitting method, a trend prediction analysis method, a sleep sustainability dynamic adjustment strategy and a sleep sustainability dynamic detection adjustment basic scheme.
In this embodiment, the basic sleep sustainability dynamic detection and adjustment scheme at least includes a sleep sustainability detection and quantization process scheme and a sleep sustainability dynamic adjustment process scheme; if the user is a new user, the sleep sustainability dynamic detection and adjustment basic scheme is obtained from a sleep sustainability general database, otherwise, the sleep sustainability dynamic detection and adjustment basic scheme is obtained from a user personality sleep sustainability database.
In an actual application scene, firstly determining whether a current user is a new user or an old user, and selecting an acquisition source of a sleep sustainability dynamic detection and adjustment basic scheme; the current state of the current user, including the physiological health state and the mental state, is an important consideration for the dynamic detection of sleep sustainability and adjustment of basic regimen selection.
And secondly, dynamically monitoring, collecting and processing the sleep physiological signals of the user to obtain sleep physiological state data.
In this embodiment, the sleep physiological signal at least includes any one of an electroencephalogram signal, an electrocardiographic signal, and a respiratory signal. The signal processing at least comprises resampling, re-referencing, artifact removal, signal correction, noise reduction, power frequency notch, band-pass filtering, smoothing and time frame segmentation; the time frame segmentation refers to continuous sliding segmentation of a preset framing duration window for signal data according to the sampling rate of the signal with a preset framing step length.
In this embodiment, an electroencephalogram signal is used as a sleep physiological signal to state a specific implementation process of the technical scheme. Firstly, the sleep electroencephalogram of the user is acquired and recorded through an electroencephalograph, the sampling rate is 1024Hz, the recording electrodes are F3, F4, C3 and C4, and the reference electrodes M1 and M2. And secondly, performing unified signal processing on the electroencephalogram signals, including left and right cross re-referencing, artifact removal, signal correction, wavelet noise reduction, 50Hz frequency doubling power frequency notch and 0.8-90Hz band-pass filtering by using M1 or M2 to obtain pure electroencephalogram signals. And finally, continuously sliding and dividing the pure brain electrical signals and the frequency band brain electrical signals by using a preset time window length 20s and a preset time shift step length 10s to obtain sleep physiological state data.
In this embodiment, the preset time window length 20s is also a sleep sustainability detection adjustment period.
And thirdly, dynamically performing feature analysis and feature selection on the sleep physiological state data to generate a sleep state feature curve.
In this embodiment, the feature analysis includes at least any one of numerical analysis, envelope analysis, time-frequency analysis, entropy analysis, fractal analysis, and complexity analysis. The sleep state characteristic curve is specifically obtained by carrying out linear combination fusion on different sleep physiological state characteristics obtained by characteristic analysis on different sleep physiological state data, and is used for describing continuous change curves of the physiological state characteristics of a user in different sleep periods, different sleep phases and different sleep states; the sleeping period at least comprises a pre-sleep period, a sleep period and a post-sleep period, and the sleeping time phase at least comprises a wakefulness period, a light sleeping period, a deep sleeping period and a rapid eye movement sleeping period.
In this embodiment, first, electroencephalogram data of sleep physiological state time frame data is subjected to time-frequency analysis (band power, band power duty ratio), entropy analysis (approximate entropy) and fractal analysis (hurst index) on a frame-by-frame basis. Then, through feature selection, the delta theta alpha (0.8-11 Hz) combined band power duty ratio of the F3-M2 channel, the approximate entropy normalized after taking the negative value and the Herst index normalized again after taking the negative value are directly added to obtain a sleep state feature curve.
And fourthly, dynamically analyzing the sleep physiological state data in a sleep time phase to generate a sleep time phase curve.
In this embodiment, the method for generating the sleep phase curve specifically includes:
1) Learning training and data modeling are carried out on sleep physiological state data of a scale sleep user sample and sleep time phase stage data corresponding to the sleep physiological state data through a machine learning algorithm, so that a sleep time phase identification model is obtained;
2) And inputting the sleep physiological state data of the current user into a sleep phase identification model to obtain corresponding sleep phase stages and generating a sleep phase curve according to a time sequence.
P200: and carrying out trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic pure curve.
And the first step, dynamically trending the sleep state characteristic curve to obtain the sleep state characteristic trending curve.
In this embodiment, the trending process specifically removes linear trend components and very low frequency trend components in the target signal, and at least includes any one of a mean removing process, a low-pass filtering process, a trending analysis FDA, a multi-fractal trending analysis MFDFA, and an asymmetric removal trend fluctuation analysis ADFA.
In this embodiment, the detrending analysis FDA is selected to complete the detrending process of the sleep state characteristic curve.
And step two, dynamically denoising the sleep state characteristic trend removal curve to obtain a sleep state characteristic purity curve.
In this embodiment, the denoising process at least includes any one method of gaussian filtering, mean filtering, fourier transform filtering, wavelet transform filtering and wavelet packet transform filtering, which is used to eliminate gaussian noise or white noise interference introduced in the sleep state characteristic curve due to discontinuity of signal characteristic analysis of physiological acquisition and framing of the body surface on the sleep state.
In this embodiment, the noise signal and the characteristic signal are separated from the sleep state characteristic trend-removing curve through gaussian filtering, so as to obtain a sleep state characteristic purity curve (i.e., a characteristic signal).
P300: and determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve, and calculating a sleep sustainability index and a sleep sustainability index curve.
Firstly, determining a numerical fitting method and parameters according to curve generation attributes of the sleep state characteristic curve.
In this embodiment, the numerical fitting method at least includes any one of least square method, linear fitting, polynomial linear fitting, polynomial fitting, nonlinear fitting, gamma adjustment fitting, and autoregressive fitting.
In this embodiment, taking into consideration δθα (0.8-11 Hz) combined band power ratio, approximate entropy normalized after taking negative, and hurst index normalized after taking negative into consideration, direct addition is performed to obtain a sleep state characteristic curve, and a least square method is used as a data fitting method.
And secondly, carrying out numerical fitting on the sleep state characteristic purity curve, and separating to obtain a sleep sustainability characteristic fitting curve and a sleep sustainability characteristic characterization curve.
In this embodiment, the sleep sustainability characteristic characterization curve is specifically a difference curve between the sleep state characteristic purity curve and the sleep sustainability characteristic fitting curve.
In the embodiment, numerical fitting is performed on the sleep state characteristic purity curve through a least square method, and a sleep sustainability characteristic fitting curve is obtained first; and then subtracting the sleep sustainability characteristic fitting curve from the sleep state characteristic purity curve to obtain a sleep sustainability characteristic characterization curve.
And thirdly, dynamically calculating to obtain a sleep sustainability index and a sleep sustainability index curve according to the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve.
In this embodiment, the method for calculating the sleep sustainability index specifically includes:
1) Acquiring a sleep phase stage, a sleep state characteristic purity curve and a sleep sustainability characteristic representation curve corresponding to a current time frame;
2) Respectively carrying out root mean square calculation on the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve in sequence to obtain a sleep state characteristic root mean square value and a sleep sustainability characteristic root mean square value;
3) According to the relative variation of the sleep state characteristic root mean square value and the sleep sustainability characteristic root mean square value, calculating to obtain a sleep sustainability node characteristic coefficient;
4) And calculating to obtain the sleep sustainability index according to the root mean square sum product of the sleep sustainability node characteristic coefficient, the numerical fitting method correction coefficient, the user individual correction coefficient and the sleep time phase stage correction coefficient.
In this embodiment, a calculation formula of the feature coefficient of the sleep sustainability node specifically includes:
；
Wherein, For sleep sustainability node characteristic coefficients,/>The sleep state characteristic root mean square value and the sleep sustainability characteristic root mean square value are respectively.
In this embodiment, a calculation formula of the sleep sustainability index specifically includes:
；
Wherein, Is sleep sustainability index,/>The characteristic coefficient of the sleep sustainability node, the correction coefficient of the numerical fitting method, the user individual correction coefficient and the sleep time phase stage correction coefficient are respectively, and/>，/>2。
In this embodiment, the sleep sustainability index curve is generated by concatenating sleep sustainability indexes according to time sequence.
In practical application, the people usually wake, sleep light, sleep deep and fast eye movementThe correction coefficients are 1.0, 1.6, 2.0 and 1.4 in sequence; user personality correction factor/>Mainly related to the age and sex of the user, and females are usually smaller than males, and the larger the age, the smaller the coefficient is; numerical fitting method correction coefficient/>The coefficients of different methods can be set according to the actual situations such as the generation mode of the sleep state characteristic curve, the method parameters of the trending treatment and the denoising treatment, and the like. More importantly, the dynamic optimization adjustment can be performed in a dynamic detection adjustment process method strategy for optimizing sleep sustainability in subsequent dynamic iteration.
P400: and carrying out trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic regulation strategy and completing dynamic regulation and effect evaluation of a user sleep process.
And the first step, dynamically trend prediction analysis is carried out on the sleep state characteristic curve to obtain a sleep state characteristic predicted value.
In this embodiment, the trend prediction analysis method at least includes any one of AR, MR, ARMA, ARIMA, SARIMA, VAR and deep learning.
In this embodiment, the prediction of the sleep state characteristic curve is performed by the SARIMA method.
And secondly, dynamically predicting and analyzing the trend of the sleep sustainability index curve to obtain a sleep sustainability index predicted value.
In this example, prediction of sleep sustainability index curves was accomplished using the SARIMA method.
Thirdly, according to the sleep state characteristic predicted value and the sleep sustainability index predicted value, detecting the regulation period and presetting a sleep regulation knowledge base according to the sleep sustainability to generate a sleep sustainability dynamic regulation strategy.
In this embodiment, the sleep sustainability dynamic adjustment policy at least includes a sleep scene, a sleep phase, an adjustment mode, an execution mode, an adjustment method, an adjustment intensity, an adjustment time point, a duration, a target adjustment value, and a device control parameter; wherein the modulation means comprises at least vocal stimulation, ultrasound stimulation, optical stimulation, electrical stimulation, magnetic stimulation, temperature stimulation, humidity stimulation, tactile stimulation and/orThe concentration control mode at least comprises any mode of separation mode and contact mode.
In an actual application scene, different sleep sustainability dynamic adjustment strategies need to be formulated according to specific situations of users. Such as selecting a separated vocal stimulation, a light stimulation, a temperature stimulation, a humidity stimulation,Concentration regulation and control and other modes can reduce interference to the sleeping process of the user; however, in general, the regulation efficiency of the modes of contact ultrasonic stimulation, electric stimulation, magnetic stimulation, tactile stimulation and the like is higher, and the effect is better.
And fourthly, connecting and controlling sleep regulating peripheral equipment according to a sleep sustainability dynamic regulating strategy to dynamically regulate the sleep process of the user.
In this embodiment, the sleep conditioning peripheral devices include at least a vocal stimulation device, an ultrasound stimulation device, a light stimulation device, an electrical stimulation device, a magnetic stimulation device, a temperature stimulation device, a humidity stimulation device, a tactile stimulation device, and a light stimulation deviceAny of the concentration control devices, and is determined by the specific manner of adjustment.
And fifthly, dynamically tracking and evaluating the adjusting effect, calculating a dynamic adjusting effect coefficient and generating a dynamic adjusting effect curve.
In this embodiment, a calculation manner of the dynamic adjustment effect coefficient specifically includes:
；
Wherein, For dynamically adjusting the effect coefficient,/>Correction coefficients for trend predictive analysis methods and，/>For the sleep phase stage correction coefficient after dynamic adjustment,/>Sleep sustainability index before dynamic adjustment and after dynamic adjustment respectively.
In this embodiment, the dynamic adjustment effect curve is generated by splicing the dynamic adjustment effect coefficients according to the time sequence.
In the actual application scenario, the trend prediction analysis method AR, MR, ARMA, ARIMA, SARIMA, VAR and the deep learning method are usually adoptedThe method correction coefficients are 0.70, 0.75, 0.80, 0.85, 0.9 and 1.0 in sequence. More importantly, the dynamic optimization adjustment can be performed in a dynamic detection adjustment process method strategy for optimizing sleep sustainability in subsequent dynamic iteration.
P500: a user sleep sustainability detection adjustment report is generated.
The first step, according to the sleep time phase curve and the sleep sustainability index curve, calculating the distribution characteristics of sleep sustainability indexes under different sleep time phases to obtain the time phase index distribution characteristics.
In this embodiment, the distribution characteristics include at least any one of average, root mean square, maximum, minimum, variance, standard deviation, coefficient of variation, kurtosis, and skewness.
In this embodiment, the phase index distribution features mainly include statistical distribution of average, maximum and minimum values of sleep sustainability indexes during awake period, light sleep period, deep sleep period and fast eye movement sleep period, so as to observe and analyze main features of sleep sustainability indexes under different sleep phases.
Secondly, calculating the distribution characteristics of the dynamic adjustment effect coefficients under different sleep phases according to the sleep phase curve and the dynamic adjustment effect curve to obtain phase effect distribution characteristics;
in this embodiment, the time phase effect distribution characteristics mainly include statistical distribution conditions of average, maximum and minimum values of dynamic adjustment effects in the awake period, the light sleep period, the deep sleep period and the rapid eye movement sleep period, so as to observe and analyze the dynamic adjustment effects in different sleep phases and judge the difficulty of intervention.
And thirdly, calculating the correlation between the sleep sustainability index curve and the dynamic adjustment effect curve to obtain the dynamic adjustment comprehensive effect coefficient.
In this embodiment, the correlation calculation method at least includes any one of coherence analysis, pearson correlation analysis, jaccard similarity analysis, linear mutual information analysis, linear correlation analysis, euclidean distance analysis, manhattan distance analysis, and chebyshev distance analysis.
In an actual application scene, the pearson correlation analysis can rapidly and accurately evaluate the correlation of the sleep sustainability index curve and the dynamic regulation effect curve, so as to quantify the adaptability and the stress degree of the sleep physiology of a user to the dynamic regulation, and provide a basis for the formulation of a dynamic regulation strategy.
And fourthly, generating a sleep sustainability detection adjustment report of the user according to a preset report generation period.
In this embodiment, the user sleep sustainability detection adjustment report at least includes user key physiological information, a sleep state characteristic curve, a sleep time phase curve, a sleep sustainability index curve, a dynamic adjustment effect curve, a time phase index distribution characteristic, a time phase effect distribution characteristic, a dynamic adjustment comprehensive effect coefficient, and a sleep sustainability detection adjustment summary.
In an actual application scene, different report generation periods can be set according to the user condition to meet the requirements of observing, analyzing and summarizing the sleep sustainability of the user, and the sleep sustainability detection and adjustment report of the user can be output in various formats and displayed in various forms to adapt to the requirements of different scenes.
P600: initializing and establishing or continuously updating a user individual sleep sustainability database, dynamically iterating a dynamic detection adjustment process strategy for optimizing the sleep sustainability, generating a user sleep sustainability detection adjustment report, and updating a sleep sustainability general database.
The first step, dynamically collecting data of detection and adjustment processes and analysis results, and initially establishing or continuously updating a user personality sleep sustainability database.
In this embodiment, if the user is the first detection and adjustment of the new user, the user's personalized sleep sustainability database needs to be initialized and established when the first detection and adjustment period ends, so as to provide a basis for the subsequent detection, quantization and dynamic process optimization of dynamic adjustment.
And secondly, adjusting the optimization period according to preset detection, and dynamically iterating and optimizing a dynamic detection adjustment process strategy of sleep sustainability according to a user individual sleep sustainability database to continuously improve the efficiency effect of detection adjustment.
In this embodiment, the dynamic detection adjustment process method includes at least a gaussian noise removal processing method parameter, a numerical fitting method parameter, a characteristic curve high-frequency threshold, a trend prediction analysis method parameter, a sleep sustainability dynamic adjustment policy, and a dynamic adjustment effect evaluation method parameter.
In an actual application scene, different detection and adjustment optimization periods can be set according to the user condition, so that the optimal response speed and the process sensitivity of the detection and adjustment of the sleep sustainability of the user are changed, and the efficiency effects of detection quantification and dynamic adjustment are further improved continuously.
And thirdly, updating the user key data information of the sleep sustainability general database according to the user personalized sleep sustainability database and the user sleep sustainability detection adjustment report according to a preset general database updating period.
In the embodiment, the sleep sustainability general database is continuously updated, so that the detection quantification of the sleep sustainability of people in different health states and the accumulation of key data of dynamic regulation can be ensured, the efficient matching of the sleep sustainability dynamic detection regulation basic scheme and a new user is facilitated, and the accuracy and the effectiveness of the first detection regulation of the new user are improved.
Referring now to fig. 2, a system for detecting and adjusting sleep sustainability based on a numerical fit is provided, which is configured to perform the above-described method steps. The system comprises the following modules:
The state acquisition and analysis module S100 is used for initializing a sleep sustainability dynamic detection and adjustment basic scheme, and acquiring and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve;
the characteristic curve extraction module S200 is used for carrying out trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic pure curve;
The exponential curve calculation module S300 is used for determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve and calculating a sleep sustainability index and a sleep sustainability index curve;
The index dynamic adjustment module S400 is used for carrying out trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic adjustment strategy and completing dynamic adjustment and effect evaluation of the sleep process of the user;
A report statistics analysis module S500, configured to generate a user sleep sustainability detection adjustment report;
the data optimization application module S600 is used for initializing and establishing or continuously updating a user personality sleep sustainability database, dynamically iterating and optimizing a dynamic detection adjustment process strategy of the sleep sustainability, and updating a sleep sustainability general database;
and the data operation management module S700 is used for carrying out visual management, unified storage and operation management on all data of the system.
In this embodiment, the state acquisition and analysis module S100 further includes the following functional units:
The scheme initializing unit is used for acquiring key physiological information of the user, screening and initializing a sleep sustainability dynamic detection and adjustment basic scheme from a sleep sustainability general database or a user personality sleep sustainability database;
The signal monitoring processing unit is used for dynamically monitoring, collecting and processing the sleep physiological signals of the user to obtain sleep physiological state data;
The sleep characteristic extraction unit is used for dynamically carrying out characteristic analysis and characteristic selection on the sleep physiological state data to generate a sleep state characteristic curve;
and the sleep phase analysis unit is used for dynamically analyzing the sleep phase of the sleep physiological state data and generating a sleep phase curve.
In this embodiment, the feature curve extraction module S200 further includes the following functional units:
the trend removal processing unit is used for dynamically removing trend from the sleep state characteristic curve to obtain a sleep state characteristic trend removal curve;
and the Gaussian noise processing unit is used for dynamically denoising the sleep state characteristic trend removal curve to obtain a sleep state characteristic purity curve.
In this embodiment, the exponential curve calculation module S300 further includes the following functional units:
The method parameter selection unit is used for determining a numerical fitting method and parameters according to curve generation attributes of the sleep state characteristic curve;
the characteristic curve extraction unit is used for carrying out numerical fitting on the sleep state characteristic purity curve, and separating to obtain a sleep sustainability characteristic fitting curve and a sleep sustainability characteristic characterization curve;
the index curve extraction unit is used for dynamically calculating to obtain a sleep sustainability index and a sleep sustainability index curve according to the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve.
In this embodiment, the index dynamic adjustment module S400 further includes the following functional units:
The sleep state prediction unit is used for dynamically predicting and analyzing the trend of the sleep state characteristic curve to obtain a sleep state characteristic predicted value;
The sleep index prediction unit is used for dynamically predicting and analyzing the trend of the sleep sustainability index curve to obtain a sleep sustainability index predicted value;
The regulation strategy generation unit is used for generating a sleep sustainability dynamic regulation strategy according to the sleep sustainability detection regulation period and a preset sleep regulation knowledge base according to the sleep state characteristic predicted value and the sleep sustainability index predicted value;
the sleep dynamic regulation unit is used for connecting and controlling sleep regulation peripheral equipment according to a sleep sustainability dynamic regulation strategy to dynamically regulate the sleep process of a user;
and the effect tracking evaluation unit is used for dynamically tracking and evaluating the adjusting effect, calculating a dynamic adjusting effect coefficient and generating a dynamic adjusting effect curve.
In this embodiment, the report statistics analysis module S500 further includes the following functional units:
The time phase index analysis unit is used for calculating the distribution characteristics of sleep sustainability indexes under different sleep time phases according to the sleep time phase curve and the sleep sustainability index curve to obtain time phase index distribution characteristics;
The time phase effect analysis unit is used for calculating the distribution characteristics of the dynamic adjustment effect coefficients under different sleep time phases according to the sleep time phase curve and the dynamic adjustment effect curve to obtain time phase effect distribution characteristics;
The comprehensive effect evaluation unit is used for calculating the correlation between the sleep sustainability index curve and the dynamic adjustment effect curve to obtain a dynamic adjustment comprehensive effect coefficient;
the user report generation unit is used for generating a user sleep sustainability detection adjustment report according to a preset report generation period;
And the user report management unit is used for uniformly managing the format output and the presentation form of the user sleep sustainability detection and adjustment report.
In this embodiment, the data optimization application module S600 further includes the following functional units:
The personality database updating unit is used for dynamically collecting data of the detection and adjustment process and analysis results, and initializing and establishing or continuously updating the personality sleep sustainability database of the user;
the detection adjustment optimization unit is used for adjusting the optimization period according to preset detection, and continuously improving the efficiency effect of detection adjustment according to a dynamic detection adjustment process strategy of dynamic iteration optimization sleep sustainability of the user personalized sleep sustainability database;
The general database updating unit is used for updating the user key data information of the sleep sustainability general database according to the user personal sleep sustainability database and the user sleep sustainability detection adjustment report according to a preset general database updating period.
In this embodiment, the data operation management module S700 further includes the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
The data visual management unit is used for visual display management of all data in the system;
The data storage management unit is used for uniformly storing and managing all data in the system;
and the data operation management unit is used for backing up, migrating and exporting all data in the system.
Referring to fig. 3, the sleep sustainability detection and adjustment device based on numerical fitting provided by the embodiment of the invention includes the following modules:
the state acquisition and analysis module M100 is used for initializing a sleep sustainability dynamic detection and adjustment basic scheme, and acquiring and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve;
The characteristic curve extraction module M200 is used for carrying out trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic pure curve;
The index curve calculation module M300 is used for determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve and calculating a sleep sustainability index and a sleep sustainability index curve;
The index dynamic adjustment module M400 is used for carrying out trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic adjustment strategy and completing dynamic adjustment and effect evaluation of the sleep process of the user;
The report statistical analysis module M500 is used for generating a user sleep sustainability detection adjustment report;
The data optimization application module M600 is used for initializing and establishing or continuously updating a user personality sleep sustainability database, dynamically iterating and optimizing a dynamic detection adjustment process method strategy of the sleep sustainability, and updating a sleep sustainability general database;
The data visualization module M700 is used for carrying out unified visual display management on all process data and result data in the device;
The data management center module M800 is used for unified storage and data operation management of all process data and result data in the device.
The apparatus is configured to correspondingly perform the steps of the method of fig. 1, and will not be described in detail herein.
The present invention also provides various types of programmable processors (FPGA, ASIC or other integrated circuit) for running a program, wherein the program when run performs the steps of the embodiments described above.
The invention also provides corresponding computer equipment, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the memory realizes the steps in the embodiment when the program is executed.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art to which the present invention pertains may make any modifications, changes, equivalents, etc. in form and detail of the implementation without departing from the spirit and principles of the present invention disclosed herein, which are within the scope of the present invention. Accordingly, the scope of the invention should be determined from the following claims.
Claims (38)
1. The sleep sustainability detection and adjustment method based on numerical fitting is characterized by comprising the following steps of:
Initializing a sleep sustainability dynamic detection and adjustment basic scheme, and collecting and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve;
Performing trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic pure curve; determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve, and calculating a sleep sustainability index and a sleep sustainability index curve;
Trend prediction analysis is carried out on the sleep state characteristic curve and the sleep sustainability index curve, a sleep sustainability dynamic regulation strategy is generated, and dynamic regulation and effect evaluation of a user sleep process are completed;
generating a user sleep sustainability detection adjustment report;
The method and the parameters for determining the numerical fitting are used for performing the numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve, and the specific steps of calculating the sleep sustainability index and the sleep sustainability index curve further comprise the following steps:
Determining a numerical fitting method and parameters according to curve generation attributes of the sleep state characteristic curve;
Performing numerical fitting on the sleep state characteristic purity curve, and separating to obtain a sleep sustainability characteristic fitting curve and the sleep sustainability characteristic characterization curve;
dynamically calculating to obtain the sleep sustainability index and the sleep sustainability index curve according to the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve;
The sleep sustainability characteristic characterization curve is specifically a difference curve between the sleep state characteristic purity curve and the sleep sustainability characteristic fitting curve;
the method for calculating the sleep sustainability index specifically comprises the following steps:
1) Acquiring sleep phase stage, the sleep state characteristic purity curve and the sleep sustainability characteristic representation curve corresponding to a current time frame;
2) Respectively carrying out root mean square calculation on the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve in sequence to obtain a sleep state characteristic root mean square value and a sleep sustainability characteristic root mean square value;
3) Calculating to obtain a sleep sustainability node characteristic coefficient according to the sleep state characteristic root mean square value and the relative variation of the sleep sustainability characteristic root mean square value;
4) Calculating to obtain the sleep sustainability index according to the sleep sustainability node characteristic coefficient, the numerical fitting method correction coefficient, the user individual correction coefficient and the sleep time phase stage correction coefficient; the numerical fitting method comprises the steps that a sleep sustainability node characteristic coefficient, a sleep time phase stage correction coefficient and a sleep sustainability index are positively correlated, and root mean square of the numerical fitting method correction coefficient and a user individual correction coefficient is positively correlated with the sleep sustainability index;
The calculation formula of the sleep sustainability node characteristic coefficient specifically comprises the following steps:
Wherein scpi is the sleep sustainability node characteristic coefficient, rmsF feas、rmsFfit is the sleep state characteristic root mean square value and the sleep sustainability characteristic root mean square value, respectively;
A calculation formula of the sleep sustainability index specifically comprises the following steps:
SCI is the sleep sustainability index, scpi and K fit_method、Kuser、Know_stage are the sleep sustainability node characteristic coefficient, the numerical fitting method correction coefficient, the user individual correction coefficient and the sleep time phase stage correction coefficient respectively, and 0<K fit_method、Kuser≤1,1≤Know_stage is less than or equal to 2.
2. The method of claim 1, wherein initializing a sleep sustainability dynamic detection adjustment base scheme, collecting and processing a user sleep physiological signal, and obtaining a sleep state characteristic curve and a sleep phase curve further comprises:
acquiring key physiological information of a user, screening and initializing a sleep sustainability dynamic detection and adjustment basic scheme from a sleep sustainability general database or a user personality sleep sustainability database;
Dynamically monitoring, collecting and processing the sleep physiological signals of the user to obtain sleep physiological state data;
dynamically performing feature analysis and feature selection on the sleep physiological state data to generate the sleep state feature curve;
And dynamically analyzing the sleep physiological state data in a sleep phase to generate the sleep phase curve.
3. The method of claim 2, wherein: the user key physiological information includes at least gender, age, physiological health status, and mental state.
4. A method according to claim 2 or 3, wherein: the general sleep sustainability database is a database for detecting, quantifying and dynamically adjusting sleep sustainability of people in different health states according to scale, and at least comprises key physiological information of a user, sleep sustainability detection and adjustment period, sleep sustainability detection and quantification process method parameters, sleep sustainability dynamic adjustment process method parameters or strategies, sleep sustainability index curve, sleep sustainability adjustment effect curve and sleep time phase curve.
5. A method according to claim 2 or 3, wherein: the user individual sleep sustainability database at least comprises the user key physiological information, a sleep state characteristic curve, a sleep time phase curve, a sleep sustainability index curve, a dynamic adjustment effect curve, a time phase index distribution characteristic, a time phase effect distribution characteristic, a dynamic adjustment comprehensive effect coefficient, a numerical fitting method, a trend prediction analysis method, a sleep sustainability dynamic adjustment strategy and the sleep sustainability dynamic detection adjustment basic scheme.
6. A method according to claim 2 or 3, wherein: the basic sleep sustainability dynamic detection and adjustment scheme at least comprises a sleep sustainability detection and quantization process scheme and a sleep sustainability dynamic adjustment process scheme; and if the user is a new user, the sleep sustainability dynamic detection and adjustment basic scheme is obtained from the sleep sustainability general database, otherwise, the sleep sustainability dynamic detection and adjustment basic scheme is obtained from the user personality sleep sustainability database.
7. The method of claim 2, wherein the sleep physiological signal comprises at least any one of an electroencephalogram signal, an electrocardiographic signal, and a respiratory signal.
8. The method of claim 7, wherein the signal processing comprises at least resampling, re-referencing, de-artifacting, signal correction, noise reduction, power frequency notch, band pass filtering, smoothing, and time frame segmentation; the time frame segmentation refers to continuous sliding segmentation of a preset framing duration window for signal data according to a sampling rate of a signal and a preset framing step length.
9. The method of claim 7, wherein the feature analysis includes at least any one of numerical analysis, envelope analysis, time-frequency analysis, entropy analysis, fractal analysis, and complexity analysis.
10. The method of claim 7, wherein: the sleep state characteristic curve is specifically obtained by carrying out linear combination fusion on different sleep physiological state characteristics obtained by carrying out characteristic analysis on different sleep physiological state data, and is used for describing continuous change curves of the physiological state characteristics of a user in different sleep periods, different sleep phases and different sleep states; the sleep phase at least comprises a wake phase, a light sleep phase, a deep sleep phase and a rapid eye movement sleep phase.
11. The method of any one of claims 7-10, wherein: the generation method of the sleep time phase curve specifically comprises the following steps:
1) Learning training and data modeling are carried out on the sleep physiological state data of the scale sleep user sample and the sleep time phase stage data corresponding to the sleep physiological state data through a machine learning algorithm, so that a sleep time phase identification model is obtained;
2) And inputting the sleep physiological state data of the current user into the sleep time phase identification model to obtain the corresponding sleep time phase stage and generating the sleep time phase curve according to a time sequence.
12. The method of any one of claims 1 or 2 or 7, wherein: the specific step of performing trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic purity curve further comprises the following steps:
dynamically trending the sleep state characteristic curve to obtain a sleep state characteristic trending curve;
And dynamically denoising the sleep state characteristic trend removal curve to obtain the sleep state characteristic purity curve.
13. The method of claim 12, wherein the trending process is specifically removing linear trend components and very low frequency trend components from the target signal, and includes at least any one of a mean removing process, a low pass filtering process, a trending analysis FDA, a multi-fractal trending analysis MFDFA, and an asymmetric cancellation trend fluctuation analysis ADFA.
14. The method of claim 12, wherein the denoising process includes at least any one of gaussian filtering, mean filtering, fourier transform filtering, wavelet transform filtering, and wavelet packet transform filtering to eliminate gaussian noise or white noise interference introduced in the sleep state profile due to discontinuities in signal characterization from body surface physiological acquisition and framing.
15. The method of claim 1, wherein the method of numerical fitting comprises at least any one of least square method, linear fitting, polynomial linear fitting, polynomial fitting, nonlinear fitting, gamma adjustment fitting, auto-regression fitting.
16. The method of any one of claims 15, wherein the sleep sustainability index curve is generated from stitching the sleep sustainability indices in time sequence.
17. The method according to claim 1 or 2, wherein the specific steps of performing trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic adjustment strategy, and performing dynamic adjustment and effect evaluation of a sleep process of a user further comprise:
dynamically trend predictive analysis is carried out on the sleep state characteristic curve to obtain a sleep state characteristic predictive value;
Dynamically trend predictive analysis is carried out on the sleep sustainability index curve to obtain a sleep sustainability index predictive value;
generating the sleep sustainability dynamic regulation strategy according to the sleep sustainability detection regulation period and a preset sleep regulation knowledge base according to the sleep state characteristic predicted value and the sleep sustainability index predicted value;
According to the sleep sustainability dynamic regulation strategy, connecting and controlling sleep regulation peripheral equipment to dynamically regulate the sleep process of a user;
And carrying out dynamic tracking evaluation on the adjusting effect, calculating a dynamic adjusting effect coefficient and generating a dynamic adjusting effect curve.
18. The method of claim 17, wherein the method of trend predictive analysis comprises at least any one of AR, MR, ARMA, ARIMA, SARIMA, VAR, deep learning.
19. The method of claim 17, wherein the sleep sustainability dynamic adjustment strategy includes at least sleep scenarios, sleep phases, adjustment modes, execution modes, adjustment intensities, adjustment points, durations, target adjustment values, and device control parameters; the regulation mode at least comprises any mode of vocal music stimulation, ultrasonic stimulation, optical stimulation, electric stimulation, magnetic stimulation, temperature stimulation, humidity stimulation, touch stimulation and CO 2 concentration regulation, and the execution mode at least comprises any mode of separation mode and contact mode.
20. The method of claim 17, wherein: the sleep regulation peripheral equipment at least comprises any one of vocal music stimulation equipment, ultrasonic stimulation equipment, optical stimulation equipment, electric stimulation equipment, magnetic stimulation equipment, temperature stimulation equipment, humidity stimulation equipment, touch stimulation equipment and CO 2 concentration regulation equipment, and is determined by a specific regulation mode.
21. A method according to any one of claims 18-20, wherein said dynamic adjustment effect coefficient is calculated in a manner such that:
wherein OEI is the dynamic adjustment effect coefficient, K trd_method is the trend predictive analysis method correction coefficient, 0<K trd_method≤1,Kaft_stage is the sleep phase stage correction coefficient after dynamic adjustment, and SCI pre、SCIaft is the sleep sustainability index before dynamic adjustment and after dynamic adjustment, respectively.
22. The method of any one of claims 18-20, wherein: the dynamic adjustment effect curve is generated by splicing the dynamic adjustment effect coefficients according to time sequence.
23. The method of claim 17, wherein the specific step of generating a user sleep sustainability detection adjustment report further comprises:
Calculating distribution characteristics of the sleep sustainability indexes under different sleep phases according to the sleep phase curve and the sleep sustainability index curve to obtain phase index distribution characteristics;
Calculating the distribution characteristics of the dynamic adjustment effect coefficients under different sleep phases according to the sleep phase curve and the dynamic adjustment effect curve to obtain phase effect distribution characteristics;
Calculating the correlation between the sleep sustainability index curve and the dynamic adjustment effect curve to obtain a dynamic adjustment comprehensive effect coefficient;
And generating the sleep sustainability detection regulation report of the user according to a preset report generation period.
24. The method of claim 23, wherein the distribution characteristics include at least any one of mean, root mean square, maximum, minimum, variance, standard deviation, coefficient of variation, kurtosis, and skewness.
25. The method of claim 23, wherein the correlation calculation method includes at least any one of a coherence analysis, a pearson correlation analysis, a jaccard similarity analysis, a linear mutual information analysis, a linear correlation analysis, a euclidean distance analysis, a manhattan distance analysis, and a chebyshev distance analysis.
26. The method of claim 23, wherein the user sleep sustainability detection adjustment report includes at least user key physiological information, the sleep state profile, the sleep phase profile, the sleep sustainability index profile, the dynamic adjustment effect profile, the phase index profile, the phase effect profile, the dynamic adjustment composite effect coefficient, a sleep sustainability detection adjustment summary.
27. The method of claim 1 or 2, further comprising: and initializing and establishing or continuously updating a user personalized sleep sustainability database, dynamically iterating a dynamic detection adjustment process strategy for optimizing the sleep sustainability, and updating a sleep sustainability general database.
28. The method of claim 27, wherein the initializing establishes or continuously updates a user-specific sleep sustainability database, dynamically iterates a dynamic detection adjustment process method strategy for optimizing sleep sustainability, generates a user sleep sustainability detection adjustment report, and updates a sleep sustainability generic database further comprises:
Dynamically collecting detection and adjustment process data and analysis results, and initializing and establishing or continuously updating the user personality sleep sustainability database;
according to a preset detection adjustment optimization period, according to the user individual sleep sustainability database, dynamically iterating and optimizing the dynamic detection adjustment process strategy of the sleep sustainability, and continuously improving the efficiency effect of detection adjustment; and according to a preset general database updating period, updating user key data information of the sleep sustainability general database according to the user individual sleep sustainability database and the user sleep sustainability detection adjustment report.
29. The method of claim 28, wherein the dynamic detection adjustment process method strategy comprises at least a degussa noise processing method parameter, a numerical fitting method parameter, the characteristic curve high frequency threshold, a trend predictive analysis method parameter, the sleep sustainability dynamic adjustment strategy, and a dynamic adjustment effect evaluation method parameter.
30. A sleep sustainability detection and adjustment system based on numerical fitting, comprising the following modules: the state acquisition analysis module is used for initializing a sleep sustainability dynamic detection and adjustment basic scheme, and acquiring and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve;
the characteristic curve extraction module is used for carrying out trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic purity curve;
The index curve calculation module is used for determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve and calculating a sleep sustainability index and a sleep sustainability index curve;
the index dynamic adjustment module is used for carrying out trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic adjustment strategy and completing dynamic adjustment and effect evaluation of a user sleep process;
the report statistical analysis module is used for generating a user sleep sustainability detection adjustment report;
the data operation management module is used for carrying out visual management, unified storage and operation management on all data of the system; the exponential curve calculation module further comprises the following functional units:
The method parameter selection unit is used for determining a numerical fitting method and parameters according to curve generation attributes of the sleep state characteristic curve;
the characteristic curve extraction unit is used for carrying out numerical fitting on the sleep state characteristic purity curve, and separating to obtain a sleep sustainability characteristic fitting curve and the sleep sustainability characteristic curve;
the index curve extraction unit is used for dynamically calculating the sleep sustainability index and the sleep sustainability index curve according to the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve;
the sleep sustainability characteristic characterization curve is specifically a difference curve between the sleep state characteristic purity curve and the sleep sustainability characteristic fitting curve; the method for calculating the sleep sustainability index specifically comprises the following steps:
1) Acquiring sleep phase stage, the sleep state characteristic purity curve and the sleep sustainability characteristic representation curve corresponding to a current time frame;
2) Respectively carrying out root mean square calculation on the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve in sequence to obtain a sleep state characteristic root mean square value and a sleep sustainability characteristic root mean square value;
3) Calculating to obtain a sleep sustainability node characteristic coefficient according to the sleep state characteristic root mean square value and the relative variation of the sleep sustainability characteristic root mean square value;
4) Calculating to obtain the sleep sustainability index according to the sleep sustainability node characteristic coefficient, the numerical fitting method correction coefficient, the user individual correction coefficient and the sleep time phase stage correction coefficient; the numerical fitting method comprises the steps that a sleep sustainability node characteristic coefficient, a sleep time phase stage correction coefficient and a sleep sustainability index are positively correlated, and root mean square of the numerical fitting method correction coefficient and a user individual correction coefficient is positively correlated with the sleep sustainability index;
The calculation formula of the sleep sustainability node characteristic coefficient specifically comprises the following steps:
Wherein scpi is the sleep sustainability node characteristic coefficient, rmsF feas、rmsFfit is the sleep state characteristic root mean square value and the sleep sustainability characteristic root mean square value, respectively;
A calculation formula of the sleep sustainability index specifically comprises the following steps:
SCI is the sleep sustainability index, scpi and K fit_method、Kuser、Know_stage are the sleep sustainability node characteristic coefficient, the numerical fitting method correction coefficient, the user individual correction coefficient and the sleep time phase stage correction coefficient respectively, and 0<K fit_method、Kuser≤1,1≤Know_stage is less than or equal to 2.
31. The system as recited in claim 30, further comprising: the data optimization application module is used for initializing and establishing or continuously updating a user individual sleep sustainability database, dynamically iterating and optimizing a dynamic detection adjustment process strategy of the sleep sustainability, generating a user sleep sustainability detection adjustment report, and updating a sleep sustainability general database.
32. The system of claim 31, wherein the state acquisition analysis module further comprises the following functional units:
the scheme initializing unit is used for acquiring key physiological information of the user, screening and initializing the sleep sustainability dynamic detection and adjustment basic scheme from a sleep sustainability general database or a user personality sleep sustainability database;
The signal monitoring processing unit is used for dynamically monitoring, collecting and processing the sleep physiological signals of the user to obtain sleep physiological state data;
The sleep characteristic extraction unit is used for dynamically carrying out characteristic analysis and characteristic selection on the sleep physiological state data to generate the sleep state characteristic curve;
And the sleep phase analysis unit is used for dynamically analyzing the sleep physiological state data in sleep phase and generating the sleep phase curve.
33. The system of claim 30, wherein the feature curve extraction module further comprises the following functional units:
the trend removal processing unit is used for dynamically removing trend from the sleep state characteristic curve to obtain a sleep state characteristic trend removal curve;
And the Gaussian noise processing unit is used for dynamically denoising the sleep state characteristic trend removal curve to obtain the sleep state characteristic purity curve.
34. The system of any one of claims 30-33, wherein the index dynamic adjustment module further comprises the following functional units:
the sleep state prediction unit is used for carrying out dynamic trend prediction analysis on the sleep state characteristic curve to obtain a sleep state characteristic prediction value;
The sleep index prediction unit is used for dynamically predicting and analyzing the trend of the sleep sustainability index curve to obtain a sleep sustainability index predicted value;
The regulation strategy generation unit is used for generating the sleep sustainability dynamic regulation strategy according to the sleep sustainability detection regulation period and a preset sleep regulation knowledge base according to the sleep state characteristic predicted value and the sleep sustainability index predicted value;
the sleep dynamic regulation unit is used for connecting and controlling sleep regulation peripheral equipment according to the sleep sustainability dynamic regulation strategy to dynamically regulate the sleep process of the user;
and the effect tracking evaluation unit is used for dynamically tracking and evaluating the adjusting effect, calculating a dynamic adjusting effect coefficient and generating a dynamic adjusting effect curve.
35. The system of claim 34, wherein the report statistics analysis module further comprises the following functional units:
The time phase index analysis unit is used for calculating the distribution characteristics of the sleep sustainability indexes under different sleep time phases according to the sleep time phase curve and the sleep sustainability index curve to obtain time phase index distribution characteristics;
the time phase effect analysis unit is used for calculating the distribution characteristics of the dynamic adjustment effect coefficients under different sleep time phases according to the sleep time phase curve and the dynamic adjustment effect curve to obtain time phase effect distribution characteristics;
the comprehensive effect evaluation unit is used for calculating the correlation between the sleep sustainability index curve and the dynamic adjustment effect curve to obtain a dynamic adjustment comprehensive effect coefficient;
The user report generation unit is used for generating the user sleep sustainability detection adjustment report according to a preset report generation period;
And the user report management unit is used for uniformly managing the format output and the presentation form of the user sleep sustainability detection regulation report.
36. The system of claim 32, wherein the data optimization application module further comprises the following functional units:
The personality database updating unit is used for dynamically collecting data of the detection and adjustment process and analysis results, and initializing and establishing or continuously updating the personality sleep sustainability database of the user;
The detection adjustment optimization unit is used for dynamically iterating and optimizing the dynamic detection adjustment process strategy of the sleep sustainability according to the user individual sleep sustainability database according to a preset detection adjustment optimization period, so as to continuously improve the efficiency effect of detection adjustment;
And the general database updating unit is used for updating the user key data information of the sleep sustainability general database according to the user personal sleep sustainability database and the user sleep sustainability detection adjustment report according to a preset general database updating period.
37. The system of any one of claims 30-33, further comprising: the data operation management module further comprises the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
the data visual management unit is used for visual display management of all data in the system;
the data storage management unit is used for uniformly storing and managing all data in the system;
and the data operation management unit is used for backing up, migrating and exporting all data in the system.
38. Sleep sustainability detects adjusting device based on numerical fit, its characterized in that includes following module: the state acquisition analysis module is used for initializing a sleep sustainability dynamic detection and adjustment basic scheme, and acquiring and processing a user sleep physiological signal to obtain a sleep state characteristic curve and a sleep time phase curve;
the characteristic curve extraction module is used for carrying out trend removal processing and noise removal processing on the sleep state characteristic curve to obtain a sleep state characteristic purity curve;
The index curve calculation module is used for determining a numerical fitting method and parameters, performing numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve and calculating a sleep sustainability index and a sleep sustainability index curve;
The index dynamic adjustment module is used for carrying out trend prediction analysis on the sleep state characteristic curve and the sleep sustainability index curve, generating a sleep sustainability dynamic adjustment strategy and completing dynamic adjustment and effect evaluation of a user sleep process;
the report statistical analysis module is used for generating a user sleep sustainability detection adjustment report;
The data optimization application module is used for initializing and establishing or continuously updating a user personalized sleep sustainability database, dynamically iterating and optimizing a dynamic detection adjustment process strategy of the sleep sustainability, and updating a sleep sustainability general database;
The data visualization module is used for carrying out unified visual display management on all process data and result data in the device; the data management center module is used for uniformly storing all process data and result data in the device and managing data operation;
The method and the parameters for determining the numerical fitting are used for performing the numerical fitting on the sleep state characteristic purity curve to obtain a sleep sustainability characteristic representation curve, and the specific steps of calculating the sleep sustainability index and the sleep sustainability index curve further comprise the following steps:
Determining a numerical fitting method and parameters according to curve generation attributes of the sleep state characteristic curve;
Performing numerical fitting on the sleep state characteristic purity curve, and separating to obtain a sleep sustainability characteristic fitting curve and the sleep sustainability characteristic characterization curve;
dynamically calculating to obtain the sleep sustainability index and the sleep sustainability index curve according to the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve;
The sleep sustainability characteristic characterization curve is specifically a difference curve between the sleep state characteristic purity curve and the sleep sustainability characteristic fitting curve;
the method for calculating the sleep sustainability index specifically comprises the following steps:
1) Acquiring sleep phase stage, the sleep state characteristic purity curve and the sleep sustainability characteristic representation curve corresponding to a current time frame;
2) Respectively carrying out root mean square calculation on the sleep state characteristic purity curve and the sleep sustainability characteristic characterization curve in sequence to obtain a sleep state characteristic root mean square value and a sleep sustainability characteristic root mean square value;
3) Calculating to obtain a sleep sustainability node characteristic coefficient according to the sleep state characteristic root mean square value and the relative variation of the sleep sustainability characteristic root mean square value;
4) Calculating to obtain the sleep sustainability index according to the sleep sustainability node characteristic coefficient, the numerical fitting method correction coefficient, the user individual correction coefficient and the sleep time phase stage correction coefficient; the numerical fitting method comprises the steps that a sleep sustainability node characteristic coefficient, a sleep time phase stage correction coefficient and a sleep sustainability index are positively correlated, and root mean square of the numerical fitting method correction coefficient and a user individual correction coefficient is positively correlated with the sleep sustainability index;
The calculation formula of the sleep sustainability node characteristic coefficient specifically comprises the following steps:
Wherein scpi is the sleep sustainability node characteristic coefficient, rmsF feas、rmsFfit is the sleep state characteristic root mean square value and the sleep sustainability characteristic root mean square value, respectively;
A calculation formula of the sleep sustainability index specifically comprises the following steps:
SCI is the sleep sustainability index, scpi and K fit_method、Kuser、Know_stage are the sleep sustainability node characteristic coefficient, the numerical fitting method correction coefficient, the user individual correction coefficient and the sleep time phase stage correction coefficient respectively, and 0<K fit_method、Kuser≤1,1≤Know_stage is less than or equal to 2.
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