CN116092641A - Method, system and device for dynamically adjusting sleep sensory stress level - Google Patents
Method, system and device for dynamically adjusting sleep sensory stress level Download PDFInfo
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Abstract
The invention provides a method for dynamically regulating sleep sensory stress level, which comprises the following steps: collecting and processing sensory cortex and related advanced cortex central physiological state signals of a user in a sleep process in real time to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data; identifying a sleep time phase state in real time, generating a sleep time phase curve, and generating sensory event time distribution real-time characteristics, sensory independent stress event real-time characteristics and sensory combined stress event real-time characteristics; performing real-time foundation line change analysis and variation reconciliation analysis, extracting sensory stress level real-time indexes and generating a sleep sensory stress level real-time curve and a trend curve; generating a sleep sensory stress level dynamic regulation strategy, carrying out real-time dynamic regulation, and evaluating the dynamic regulation effect. The invention realizes scientific detection, quantification and accurate dynamic adjustment of the sensory stress level in the sleeping process.
Description
Technical Field
The invention relates to the field of dynamic regulation of sleep sensory stress level, in particular to a method, a system and a device for dynamic regulation of sleep sensory stress level.
Background
Abnormal opening of sensory functions, excessive activation of sensory stress and the like exist in the sleeping process of people of different ages, different physical and psychological states and different sleeping phases, so that sleep arousal interruption, high sleep alertness, shallow sleeping depth and poor sleeping experience of different degrees are caused. In general, the stress threshold of multiple sensory functions such as good sleep, vision, hearing, smell sense, taste sense and somatic sense is improved, and the sensory experience content memory is continuously replayed and consolidated in the sleep process.
The prior art scheme CN113995939A discloses a sleep music playing method, device and terminal based on brain electrical signals, wherein the method comprises the following steps: acquiring sleep brain electrical signals of a target user; determining a target brain activity level corresponding to the sleep electroencephalogram signal; and determining target music and target volume according to the target brain activity level, and playing the target music according to the target volume. And prior art approach CN111372639a discloses a system for delivering sensory stimuli to a user to enhance cognitive domains in the user, the system comprising: a system configured to deliver sensory stimuli to a user during a sleep period to enhance a target cognitive domain; example target cognitive domains include memory consolidation, alertness, speech fluency, drowsiness, and/or other target cognitive domains; modulating the stimulus based on the target cognitive domain to be enhanced; in response to the one or more brain activity parameters indicating that the user is in sufficiently deep sleep, the system is configured to cause the one or more sensory stimulators to provide sensory stimulation to the user according to a stimulation strategy associated with the target cognitive domain; the system is configured to determine an effect of sensory stimulation provided to the user using a quantification method associated with the stimulation strategy and the target cognitive domain; the system includes one or more sensory stimulators, one or more sensors, one or more hardware processors, and/or other components.
From the above, the prior art scheme stays on the surface layer characteristic analysis and the general induction treatment of neurophysiologic signals, brain states and sleep quality, and lacks of definite quantification, real-time evaluation and dynamic regulation of sensory stress level in the sleep process; meanwhile, in the prior art, the adjustment is finished immediately, each adjustment is re-analyzed and re-adjusted, continuous and inheritable quantization and adjustment are not performed, and a long-term influence model of quantization-adjustment is not established, so that the intervention process is not scientific and personalized.
How to build a scientific and efficient dynamic evaluation method and an adjusting framework for sleep sensory stress level according to individual demands of users, and timely and reasonably dynamically evaluate, dynamically train or adjust the sleep sensory stress level of the users in the sleep process to realize normalization of the sleep sensory stress level of people of different ages, different physical and psychological states and different sleep states is a problem which is difficult to solve in current domestic and foreign sleep health management and neuroscience research.
Disclosure of Invention
Aiming at the defects and improvement demands of the existing method, the invention aims to provide a method for dynamically regulating the sleep sensory stress level, which acquires sensory center physiological event data and sleep time phase states by carrying out real-time acquisition and processing, sensory event identification, event signal separation and sleep time phase analysis on sensory cortex and related advanced cortex center physiological state signals in the sleeping process of a user, completes the real-time extraction of sensory event time distribution real-time characteristics, sensory independent stress event real-time characteristics and sensory combined stress event real-time characteristics, extracts sensory stress level real-time indexes through baseline change analysis and variation and regulation analysis, predicts, analyzes and calculates to obtain sensory stress level prediction indexes, generates a sleep sensory stress level dynamic regulation strategy in real time and carries out real-time dynamic regulation, finally generates a sleep sensory stress level regulation report and establishes a personalized sensory regulation long-term database, thereby realizing the dynamic training and regulation of the sensory stress levels in different age groups, different physical and psychological states and different sleeping states. The invention also provides a system for dynamically adjusting the sleep sensory stress level, which is used for realizing the method. The invention also provides a device for dynamically adjusting the sleep sensory stress level, which is used for realizing the system.
According to the object of the present invention, the present invention proposes a method for dynamic regulation of sleep sensory stress levels, comprising the steps of:
collecting and recording sensory cortex and related advanced cortex central physiological state signals of a user in a sleep process in real time, processing the signals, identifying sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data;
identifying a sleep time phase state in real time and generating a sleep time phase curve according to the sensory central physiological state data;
carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and the sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features;
combining a sensory stress level characteristic baseline library, carrying out real-time basal variation analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristic, the sensory independent stress event real-time characteristic and the sensory combined stress event real-time characteristic, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve;
Performing time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve;
generating a sleep sensory stress level dynamic regulation strategy in real time according to a preset regulation time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and carrying out real-time dynamic regulation on the sensory stress level of a user in the sleep process;
repeating the steps to complete the circulation dynamic regulation of all the sleep sensory stress levels, evaluating the dynamic regulation effect, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database.
More preferably, the specific steps of collecting and recording sensory cortex and related advanced cortex central physiological state signals in real time during sleep of the user, processing signals, identifying sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data further comprise:
The sensory cortex and related advanced cortex central physiological state signals in the sleeping process of the user are collected, recorded and processed in real time to obtain sensory central physiological state data;
carrying out sensory event identification on the sensory center physiological state data in real time, extracting sensory event time information, and generating or updating the sensory event time process identification set;
and carrying out real-time event signal separation on the sensory center physiological state data according to the sensory event time process identification set to generate or update the sensory center physiological event data.
More preferably, the sensory cortex refers to the primary sensory cortex and the secondary sensory cortex corresponding to the sensory organ, including at least the primary sensory cortex and the secondary sensory cortex of somatosensory, the primary sensory cortex and the secondary sensory cortex of visual sense, the primary sensory cortex and the secondary sensory cortex of auditory sense, the primary sensory cortex and the secondary sensory cortex of olfactory sense, the primary sensory cortex and the secondary sensory cortex of taste sense; the associated advanced cortex refers to advanced cortex and limbic systems associated with sensory event chronology, at least including parietal cortex, temporal cortex, prefrontal cortex, limbic systems.
More preferably, the central physiological state signal at least comprises an electroencephalogram signal, a magnetoencephalography signal, a functional near infrared spectrum imaging signal, and a functional nuclear magnetic resonance imaging signal.
More preferably, the signal processing at least comprises a/D digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-stop filtering, band-pass filtering, correction processing and band extraction; the correction processing specifically includes signal correction and predictive smoothing processing on signal data segments including artifacts or distortions in the physiological state signal, and the band extraction specifically includes extracting a band signal in a specific band range from the target signal.
More preferably, the sensory event identification means that the occurrence and characterization of a plurality of continuous different independent sensory events are distinguished from physiological status signals of the central nervous system of each sensory cortex and associated advanced cortex of the brain, and the characterization time sequence process, the event center time, the event start time and the event end time of each independent event in different cortex areas are identified, so that the sensory event time sequence identification set is generated.
More preferably, the sensory event time course identification set includes at least an event center time, an event start time, an event end time, and a negative peak time of the central physiological state signal of each cortical region during the event.
More preferably, the specific steps of identifying the sleep phase state and generating the sleep phase curve in real time according to the sensory central physiological state data further comprise:
performing learning training and data modeling on the sensory central physiological state data and the corresponding sleep stage data of the scale sleep user sample through a deep learning algorithm to obtain a sleep time phase automatic stage model;
inputting the sensory central physiological state data of the current user into the sleep time phase automatic stage model in real time, and extracting sleep time phase stage values;
and splicing the sleep time phase stage values according to a time sequence to generate the sleep time phase curve.
More preferably, the specific steps of performing real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time course identification set and the sensory central physiological event data to generate a sensory event time distribution real-time feature, a sensory independent stress event real-time feature and a sensory combined stress event real-time feature respectively further include:
carrying out real-time event time distribution feature analysis on the sensory event time process identification set to generate sensory event time distribution real-time features;
Performing real-time event space-time process feature analysis on the sensory center physiological event data, and evaluating the time sequence activity process and the independent characterization level of the sensory event in each sensory cortex and the associated advanced cortex to obtain the real-time feature of the sensory independent stress event;
and carrying out real-time event time-space association characteristic analysis on the sensory center physiological event data, and evaluating association coordination modes and association characterization levels of sensory events among different sensory cortex and associated advanced cortex to obtain the real-time characteristics of sensory association stress events.
More preferably, the sensory event time distribution real-time characteristics at least comprise a sensory event duration characteristic and a sensory event characterization time center distribution characteristic; the sensory event duration characteristics at least comprise average value, root mean square, maximum value, minimum value, variance, standard deviation, variation coefficient, kurtosis and skewness, and the sensory event representation time center distribution characteristics at least comprise the sensory cortical activity duration, multi-sensory synchronous integration time center point, associated advanced cortical activity duration, and the ratio relation of the sensory cortical activity duration and the associated advanced cortical activity duration of each event.
More preferably, the sensory independent stress event real-time characteristics at least comprise time-frequency characteristics and nonlinear characteristics; the time-frequency characteristic at least comprises different cortical areas, the total power of different channels, characteristic frequency band power duty ratio, characteristic frequency band central frequency and envelope characteristic, and the nonlinear characteristic at least comprises entropy characteristic, fractal characteristic and complexity characteristic of different cortical areas and different channels.
More preferably, the sensory combined stress event real-time features include at least sensory coupled stress event features and sensory connected stress event features; the sensory coupling stress event characteristics at least comprise phase-phase coupling characteristics, phase-amplitude coupling characteristics and amplitude-amplitude coupling characteristics between every two signals of different channels in different cortical areas, and the sensory coupling stress event characteristics at least comprise time-frequency cross characteristics, signal correlation characteristics and signal distance characteristics between every two signals of different channels in different cortical areas.
More preferably, the specific steps of combining the sensory stress level characteristic baseline library to perform real-time baseline variation analysis and mutation and blend analysis on the sensory event time distribution real-time characteristic, the sensory independent stress event real-time characteristic and the sensory combined stress event real-time characteristic, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve further include:
Establishing a baseline library of sensory stress level characteristics for a large number of healthy user populations of different age groups;
according to the sensory stress level characteristic baseline library, carrying out baseline variation analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics, and extracting the sensory stress level real-time index;
and splicing the sensory stress level real-time indexes according to the time sequence to generate or update the sleep sensory stress level real-time curve.
More preferably, the method for constructing the sensory stress level characteristic baseline library comprises the following steps:
1) Collecting and recording the rest state, sensory cortex and related advanced cortex central physiological state signals under sensory task state of healthy user groups with different age groups, performing signal processing, sensory event identification and event signal separation to obtain a baseline sensory event time process identification set and baseline sensory central physiological event data;
2) Performing sensory event time distribution feature analysis on the baseline sensory event time process identification set to generate a baseline sensory event time distribution feature set;
3) Performing sensory event space-time process feature analysis and sensory event space-time correlation feature analysis on the baseline sensory central physiological event data to respectively generate baseline sensory independent stress event features and baseline sensory combined stress event features;
4) Integrating the baseline sensory event time distribution feature set, the baseline sensory independent stress event features and the baseline sensory combined stress event features to generate a baseline sensory physiological stress feature set;
5) And carrying out mean value calculation on each feature in the baseline sensory physiological stress feature set of all user samples to obtain a rest baseline value and a task baseline value of each feature, and establishing the sensory stress level feature baseline library.
More preferably, the method for calculating the sensory stress level real-time index comprises the following steps:
1) Acquiring a resting state and a sensory stress level characteristic baseline library of a healthy user group with the same age group as the current user and the large scale number under a sensory task state, and acquiring a sensory stress level characteristic baseline comparison library;
2) Acquiring current sensory event time distribution real-time characteristics, sensory independent stress event real-time characteristics and sensory combined stress event real-time characteristics, and calculating baseline variation values of a resting baseline value and a task baseline value in a baseline comparison library with the sensory stress level characteristics, namely baseline variation analysis, so as to obtain a sleep sensory stress level characteristic variation set;
3) And carrying out variation harmonic analysis on all indexes in the sleep sensory stress level characteristic variation set to obtain variation harmonic values, namely the current sensory stress level real-time index.
More preferably, the specific calculation modes of the baseline variation analysis and the baseline variation value are as follows:
for real-valued variables
And its non-zero base line sequence +.>
For the baseline variation value of
wherein ,
respectively real value variable +.>
Baseline variation value of (i), i-th baseline value and corresponding weight,/and (ii)>
Is a positive integer.
More preferably, the variance harmonic analysis is a data analysis method which uses the variance coefficient and the absolute value variance coefficient of the numerical array as the observation base point basis, uses the mean value, the median, the quantile, the maximum value, the minimum value, the variance, the kurtosis, the skewness, the absolute value mean value, the absolute value median, the absolute value quantile, the absolute value maximum value, the absolute value minimum value, the absolute value variance, the absolute value kurtosis and the absolute value skewness of the numerical array as the main analysis harmonic items to observe and analyze the variance coefficient fluctuation state and the general trend fluctuation change of the numerical array.
More preferably, a specific calculation mode of the mutation harmonic analysis is as follows:
For numerical value arrays
For the variation and the value are
wherein ,
respectively is a numerical value array->
Variation of (a) and (b) coefficient of variation and coefficient of variation of absolute value>
The absolute value, the maximum value and the minimum value are taken as operators respectively, and the +.>
Is a positive integer.
More preferably, the specific steps of performing time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generating a sleep sensory stress level trend curve further include:
performing time sequence trend analysis on the sleep sensory stress level real-time curve, and obtaining the sensory stress level prediction index through prediction calculation;
and splicing the sensory stress level prediction indexes according to a time sequence to generate or update the sleep sensory stress level trend curve.
More preferably, the time series trend analysis at least comprises AR, MR, ARMA, ARIMA, SARIMA, VAR classical time series prediction method and deep learning prediction model.
More preferably, the specific steps of generating a sleep sensory stress level dynamic adjustment strategy in real time according to a preset adjustment time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and performing real-time dynamic adjustment on the sensory stress level of the sleeping process of the user further include:
Generating the sleep sensory stress level dynamic regulation strategy in real time according to a preset regulation time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sensory stress level real-time index and the sleep sensory stress level trend curve and combining the sleep sensory stress level dynamic regulation purpose;
and dynamically regulating the sensory stress level of the sleeping process of the user in real time according to the sleep sensory stress level dynamic regulation strategy.
More preferably, the dynamic sleep sensory stress level regulation strategy at least comprises a regulation mode, an execution part, a regulation method and a regulation intensity; the adjusting mode at least comprises sound, light, taste, electricity, magnetism, ultrasound and sleeping environment, the executing part comprises a head, a neck, a trunk part, left and right upper limbs, left and right lower limbs and various large sensory organs, the adjusting method at least comprises a constant, an increasing curve, a decreasing curve, an exponential curve, a sinusoidal curve, a periodic square wave and a random curve, and the adjusting intensity is determined by the current sensory stress level real-time index and the sleeping sensory stress level real-time prediction index.
More preferably, the steps are repeated to complete the cyclic dynamic adjustment of all the sleep sensory stress levels, evaluate the dynamic adjustment effect, extract the corresponding sensory stress related coefficient and the stress level dynamic adjustment effect coefficient, generate the sleep sensory stress level adjustment report and establish the personalized sensory adjustment long-term database, and the specific steps further include:
completing the cyclic dynamic regulation of all sleep sensory stress levels to obtain the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve of all regulation processes;
analyzing and calculating the relation characteristics of the sleep time phase curve and the sleep sensory stress level real-time curve, and extracting the corresponding sensory stress related coefficient;
analyzing and calculating relation characteristics of the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and extracting the stress level dynamic regulation effect coefficient;
according to the sleep time phase curve, the sleep sensory stress level real-time curve, the sleep sensory stress level trend curve, the corresponding sensory stress correlation coefficient and the stress level dynamic regulation effect coefficient, analyzing, calculating and generating the sleep sensory stress level regulation report;
And establishing or updating the personalized sensory adjustment long-term database according to the sleep sensory stress level adjustment report and the current state information of the user, and providing a data analysis inheritance model for the continuous sleep sensory stress level dynamic adjustment of the subsequent user.
More preferably, the calculation method of the corresponding sensory stress correlation coefficient specifically comprises the following steps:
1) Acquiring the sleep time phase curve and the sleep sensory stress level real-time curve;
2) Analyzing and calculating the relation characteristic of the sleep time phase curve and the sleep sensory stress level real-time curve to obtain a corresponding sensory stress level relation characteristic index set;
3) And carrying out weighted fusion calculation on the corresponding sensory stress level relation characteristic index set to obtain the corresponding sensory stress related coefficient.
More preferably, the calculation method of the stress level dynamic adjustment effect coefficient specifically comprises the following steps:
1) Acquiring the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve;
2) Analyzing and calculating the relation characteristics of the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve to obtain a stress level dynamic adjustment effect characteristic index set;
3) And carrying out weighted fusion calculation on the stress level dynamic adjustment effect characteristic index set to obtain the stress level dynamic adjustment effect coefficient.
More preferably, the relationship features include at least an association feature and a distance feature; the correlation features at least comprise a coherence coefficient, a pearson correlation coefficient, a Jacquard similarity coefficient, a linear mutual information coefficient and a linear correlation coefficient, and the distance features at least comprise an Euclidean distance, a Manhattan distance, a Chebyshev distance, a Minkowski distance, a normalized Euclidean distance, a Mahalanobis distance, a Papanic distance, a Hamming distance and an included angle cosine.
More preferably, the sleep sensory stress level adjustment report at least comprises the sleep phase curve, the sleep sensory stress level real-time curve, the sleep sensory stress level trend curve, the time phase sensory stress correlation coefficient, the stress level dynamic adjustment effect coefficient, all the sleep sensory stress level dynamic adjustment strategies, sensory stress level time phase distribution statistics, peak activity period summary, low peak activity period summary, abnormal activity period summary and sleep sensory stress level adjustment report summary.
More preferably, the sensory stress level phase distribution statistics are specifically the average sensory stress level, the maximum sensory stress level and the minimum sensory stress level of different sleep phases; the peak activity time summary is specifically peak time distribution corresponding to a segment exceeding a preset peak threshold value in the sleep sensory stress level real-time curve, and time numerical sum and duty ratio of the peak time distribution; the low peak activity period summary is specifically low peak period distribution corresponding to a segment exceeding a preset low peak threshold value in the sleep sensory stress level real-time curve, and time numerical sum and duty ratio of the low peak period distribution; the abnormal activity period summary is specifically abnormal period distribution corresponding to an abnormal segment deviating from a curve baseline trend in the sleep sensory stress level real-time curve, and time numerical sum and duty ratio of the abnormal period distribution.
According to the object of the invention, the invention proposes a system for dynamic regulation of sleep sensory stress levels, comprising the following modules:
the sensory event detection module is used for collecting and recording sensory cortex and related advanced cortex central physiological state signals in real time during sleeping of a user, processing the signals, identifying the sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data;
The time phase state analysis module is used for identifying the sleep time phase state in real time and generating a sleep time phase curve according to the sensory central physiological state data;
the event feature analysis module is used for carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and the sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features;
the stress level quantification module is used for carrying out real-time basal line change analysis and variation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics by combining with a sensory stress level characteristic basal line library, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve;
the stress trend prediction module is used for carrying out time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve;
The dynamic strategy adjustment module is used for generating a sleep sensory stress level dynamic adjustment strategy in real time according to a preset adjustment time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and dynamically adjusting the sensory stress level of the sleeping process of a user in real time;
the circulation regulation report module is used for completing circulation dynamic regulation of all sleep sensory stress levels, evaluating dynamic regulation effects, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database;
and the data management center module is used for visual display and data operation management of all process data in the system.
More preferably, the sensory event detection module further comprises the following specific functional units:
the sensory physiological monitoring unit is used for collecting, recording and processing sensory cortex and related advanced cortex central physiological state signals of a user in a sleep process in real time to obtain sensory central physiological state data;
The sensory event identification unit is used for carrying out sensory event identification on the sensory center physiological state data in real time, extracting sensory event time information and generating or updating the sensory event time process identification set;
and the event signal separation unit is used for carrying out real-time event signal separation on the sensory central physiological state data according to the sensory event time process identification set to generate or update the sensory central physiological event data.
More preferably, the phase state analysis module further comprises the following specific functional units:
the stage model construction unit is used for carrying out learning training and data modeling on the sensory center physiological state data of the scale sleep user sample and the sleep stage data corresponding to the sensory center physiological state data through a deep learning algorithm to obtain a sleep time phase automatic stage model;
the real-time phase identification unit is used for inputting the sensory central physiological state data of the current user into the sleep phase automatic stage model in real time and extracting sleep phase stage values;
and the time phase curve generating unit is used for splicing the sleep time phase stage values according to a time sequence to generate the sleep time phase curve.
More preferably, the event feature analysis module further comprises the following specific functional units:
The time distribution characteristic analysis unit is used for carrying out real-time event time distribution characteristic analysis on the sensory event time process identification set and generating sensory event time distribution real-time characteristics;
the time-space process characteristic analysis unit is used for carrying out real-time event time-space process characteristic analysis on the sensory central physiological event data, evaluating the time sequence activity process and the independent characterization level of the sensory event in each sensory cortex and the associated advanced cortex, and obtaining the real-time characteristics of the sensory independent stress event;
and the space-time correlation feature analysis unit is used for carrying out real-time event space-time correlation feature analysis on the sensory central physiological event data, and evaluating the correlation coordination mode and the joint characterization level of the sensory event between different sensory cortex and the correlation advanced cortex to obtain the real-time feature of the sensory joint stress event.
More preferably, the stress level quantification module further comprises the following specific functional units:
a baseline characteristic construction unit for establishing the sensory stress level characteristic baseline library of healthy user groups of different age groups and of large scale number;
the emergency index extraction unit is used for carrying out baseline variation analysis and variation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics according to the sensory stress level characteristic baseline library, and extracting the sensory stress level real-time index;
The stress curve generating unit is used for splicing the sensory stress level real-time indexes according to the time sequence to generate or update the sleep sensory stress level real-time curve.
More preferably, the stress trend prediction module further comprises the following specific functional units:
the stress index prediction unit is used for carrying out time sequence trend analysis on the sleep sensory stress level real-time curve, and predicting and calculating to obtain the sensory stress level prediction index;
and the trend curve generating unit is used for splicing the sensory stress level prediction indexes according to the time sequence to generate or update the sleep sensory stress level trend curve.
More preferably, the dynamic policy adjustment module further comprises the following specific functional units:
the regulation strategy generation unit is used for generating the sleep sensory stress level dynamic regulation strategy in real time according to a sleep stress level optimization knowledge base, the sleep time phase curve, the sensory stress level real-time index and the sleep sensory stress level trend curve and combining the sleep sensory stress level dynamic regulation purpose;
and the dynamic regulation execution unit is used for dynamically regulating the sensory stress level of the sleeping process of the user in real time according to the sleep sensory stress level dynamic regulation strategy.
More preferably, the cyclic adjustment reporting module further comprises the following specific functional units:
the circulation dynamic regulation unit is used for completing circulation dynamic regulation of all sleep sensory stress levels and obtaining the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve in all regulation processes;
the stress correlation analysis unit is used for analyzing and calculating the relation characteristics of the sleep time phase curve and the sleep sensory stress level real-time curve and extracting the corresponding sensory stress correlation coefficient;
the adjusting effect analysis unit is used for analyzing and calculating the relation characteristics of the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve and extracting the stress level dynamic adjusting effect coefficient;
the regulation report generation unit is used for analyzing, calculating and generating the sleep sensory stress level regulation report according to the sleep time phase curve, the sleep sensory stress level real-time curve, the sleep sensory stress level trend curve, the corresponding sensory stress related coefficient and the stress level dynamic regulation effect coefficient;
the sensory regulation inheritance unit is used for establishing or updating the personalized sensory regulation long-term database according to the sleep sensory stress level regulation report and the current state information of the user, and providing a data analysis inheritance model for the continuous sleep sensory stress level dynamic regulation of the subsequent user.
More preferably, the data management center module further comprises the following specific 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;
and the data operation management unit is used for storing, backing up, migrating and exporting all data in the system.
According to the purpose of the invention, the invention provides a device for dynamically adjusting sleep sensory stress level, which comprises the following modules:
the sensory event detection module is used for collecting and recording sensory cortex and related advanced cortex central physiological state signals in real time during sleeping of a user, processing the signals, identifying the sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data;
the time phase state analysis module is used for identifying the sleep time phase state in real time and generating a sleep time phase curve according to the sensory central physiological state data;
the event feature analysis module is used for carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and the sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features;
The stress level quantification module is used for combining a sensory stress level characteristic baseline library, carrying out real-time baseline variation analysis and variation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve;
the stress trend prediction module is used for carrying out time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve;
the dynamic strategy adjustment module is used for generating a sleep sensory stress level dynamic adjustment strategy in real time according to a preset adjustment time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and dynamically adjusting the sensory stress level of a user in real time in the sleep process;
the circulation regulation report module is used for completing circulation dynamic regulation of all sleep sensory stress levels, evaluating dynamic regulation effects, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database;
The data visual management module is used for visual display management and user information editing management of all data in the device;
and the data operation management module is used for storing, backing up, migrating and exporting all data in the device.
According to the method, the system and the device for dynamically adjusting the sleep sensory stress level, sensory cortex and related advanced cortex central physiological state signals in the sleeping process of a user are collected and processed in real time, sensory event identification, event signal separation and sleep time phase analysis are carried out, sensory central physiological event data and sleep time phase states are obtained, real-time extraction of sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features is completed, sensory stress level real-time indexes are extracted through baseline change analysis and variation reconciliation analysis, sensory stress level prediction indexes are obtained through prediction analysis calculation, a sleep sensory stress level dynamic adjustment strategy is generated in real time, real-time dynamic adjustment is carried out, finally, a sleep sensory stress level adjustment report is generated, a personalized sensory adjustment long-term database is established, and scientific detection, quantification and accurate dynamic training or adjustment of the sensory stress levels in different age groups, different physical and mental states are realized, so that objective and accurate basis is provided for sleep measurement and adjustment.
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 invention and are incorporated in and constitute a part of this specification, illustrate and do not limit the invention.
FIG. 1 is a schematic flow chart of a method for dynamically adjusting sleep sensory stress level according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a system for dynamic regulation of sleep sensory stress level according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a module structure of a device for dynamically adjusting sleep sensory stress level according to an embodiment of the invention.
Detailed Description
In order to more clearly illustrate the objects and technical solutions of the present invention, the present invention will be further described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the embodiments described below are only some, but not all, embodiments of the invention. Other embodiments, which are derived from the embodiments of the invention by a person skilled in the art without creative efforts, shall fall within the protection scope of the invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be arbitrarily combined with each other.
The method, the system and the device for dynamically adjusting the sleep sensory stress level realize the method and the framework for dynamically analyzing and adjusting the sensory stress level in the sleeping process, such as real-time detection, real-time analysis, real-time evaluation, real-time adjustment and the like, can be combined, energized or embedded into sleep related products and services, and provide a dynamic adjusting scheme of the sleep sensory stress level for different crowd scenes.
As shown in fig. 1, the method for dynamically adjusting sleep sensory stress level provided by the embodiment of the invention comprises the following steps:
p100: the sensory cortex and related advanced cortex central physiological state signals in the sleeping process of the user are collected and recorded in real time, subjected to signal processing, sensory event identification and event signal separation, and sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data are obtained.
The method comprises the first step of collecting, recording and processing signals of sensory cortex and related advanced cortex central physiological state signals of a user in a sleep process in real time to obtain sensory central physiological state data.
In this embodiment, the sensory cortex refers to the primary sensory cortex and the secondary sensory cortex corresponding to the sensory organ, including at least the primary sensory cortex and the secondary sensory cortex of somatosensory, the primary sensory cortex and the secondary sensory cortex of visual sense, the primary sensory cortex and the secondary sensory cortex of auditory sense, the primary sensory cortex and the secondary sensory cortex of olfactory sense, the primary sensory cortex and the secondary sensory cortex of taste sense; associated advanced cortex refers to advanced cortex and limbic systems associated with chronologically processing of sensory events, including at least parietal cortex, temporal cortex, prefrontal cortex, limbic systems.
In this embodiment, the central physiological status signal at least includes an electroencephalogram signal, a magnetoencephalography signal, a functional near infrared spectrum imaging signal, and a functional nuclear magnetic resonance imaging signal.
In this embodiment, the signal processing at least includes a/D digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-reject filtering, band-pass filtering, correction processing, and band extraction; the correction processing is specifically performing signal correction and predictive smoothing processing on a signal data segment containing artifacts or distortion in the physiological state signal, and the band extraction is specifically extracting a band signal in a specific frequency band range from the target signal.
In this embodiment, the brain electrical signals are selected to collect and record sensory cortex and associated advanced cortex central physiological status signals. The electroencephalogram signals of the user in the night sleep process are acquired and recorded through the polysomnography recorder, the sampling rate is 2048Hz, the recording electrodes are Fz, pz, oz, fp, fp2, F3, F4, T7, T8, P3, P4, P7, P8, O1 and O2 based on the international 10-20 system electroencephalogram electrode placement standard, and the reference electrodes are left and right earlobes A1 and A2.
In the embodiment, the signal processing of the electroencephalogram signal is artifact removing processing, discrete wavelet noise reduction by db4 wavelet basis, 4-layer decomposition and SURE threshold method is adopted, power frequency 50Hz and frequency multiplication notch processing and band-pass (0.5-250 Hz) filtering are completed through a hamming window and a zero-phase FIR digital filter, and seven frequency band signals of delta rhythm (0.5-4 Hz), theta rhythm (4-8 Hz), mu-alpha rhythm (8-13 Hz), beta rhythm (13-30 Hz), gamma 1 rhythm (30-60 Hz), gamma 2 rhythm (60-140 Hz) and gamma 3 rhythm (140-250 Hz) are extracted.
In the actual use scene, according to the actual situation of a user, environmental facilities, hardware facilities, dynamic regulation purposes and the like, a polysomnography recorder, a polysomnography monitor, a magnetoencephalography monitor, functional near infrared spectrum imaging equipment or functional nuclear magnetic resonance imaging equipment can be selected, and a plurality of parts of primary sensory cortex, secondary sensory cortex, top leaf combined cortex area and frontal lobe closely related to the sensory stress level are selected for acquisition and monitoring.
Secondly, carrying out sensory event identification on sensory center physiological state data in real time, extracting sensory event time information, and generating or updating a sensory event time process identification set.
In this embodiment, sensory event identification refers to distinguishing the occurrence and characterization of a plurality of continuous different independent sensory events from physiological status signals of the central nervous system of each sensory cortex and associated advanced cortex of the brain, identifying the time sequence process, the event center time, the event start time and the event end time of each independent event in different cortex areas, and generating a sensory event time process identification set.
In this embodiment, the sensory event time course identification set at least includes an event center time, an event start time, an event end time, and a negative peak time of the central physiological state signal of each cortical region during the event.
Thirdly, carrying out real-time event signal separation on the sensory center physiological state data according to the sensory event time process identification set to generate or update sensory center physiological event data.
P200: and identifying the sleep time phase state in real time according to the sensory central physiological state data and generating a sleep time phase curve.
Firstly, carrying out learning training and data modeling on sensory central physiological state data of a scale sleep user sample and corresponding sleep stage data thereof through a deep learning algorithm to obtain a sleep time phase automatic stage model.
In this embodiment, the modeling of the sleep phase automatic staging model is accomplished using a BiLSTM deep learning algorithm.
Secondly, sensory central physiological state data of the current user are input into the sleep time phase automatic stage model in real time, and sleep time phase stage values are extracted.
In this embodiment, the correspondence between the sleep phase and the sleep phase stage value is: the awake period is 0, the rapid eye movement sleep period is 1, the non-rapid eye movement sleep period I is 2, the non-rapid eye movement sleep period II is 3, and the non-rapid eye movement sleep period III is 4.
Thirdly, splicing sleep time phase stage values according to the time sequence to generate a sleep time phase curve.
In this embodiment, the sleep phase state and sleep phase stage are identified, which mainly provides a key basis for the subsequent formulation of dynamic adjustment strategies, because there is a relatively large difference in sleep sensory stress levels from one sleep phase state to another.
P300: and carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and the sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features.
The first step, analyzing the time distribution characteristics of the sensory event in real time to the sensory event time process identification set, and generating the time distribution real-time characteristics of the sensory event.
In this embodiment, the sensory event time distribution real-time characteristics at least include a sensory event duration characteristic and a sensory event characterization time center distribution characteristic; the sensory event duration characteristics at least comprise average value, root mean square, maximum value, minimum value, variance, standard deviation, variation coefficient, kurtosis and skewness, and the sensory event representation time center distribution characteristics at least comprise the sensory cortical activity duration, multi-sensory synchronous integration time center point, associated advanced cortical activity duration and the ratio relation of the sensory cortical activity duration and the associated advanced cortical activity duration of each event.
And secondly, carrying out real-time event space-time process characteristic analysis on sensory central physiological event data, and evaluating the time sequence activity process and independent characterization level of sensory events in each sensory cortex and associated advanced cortex to obtain the real-time characteristics of sensory independent stress events.
In this embodiment, the sensory independent stress event real-time features at least include a time-frequency feature and a nonlinear feature; the time-frequency characteristic at least comprises different cortical areas, the total power of different channels, the characteristic frequency band power duty ratio, the characteristic frequency band central frequency and the envelope characteristic, and the nonlinear characteristic at least comprises entropy characteristics, fractal characteristics and complexity characteristics of different cortical areas and different channels.
Thirdly, carrying out real-time event time-space correlation characteristic analysis on sensory central physiological event data, and evaluating correlation coordination modes and joint characterization levels of sensory events among different sensory cortex and correlation advanced cortex to obtain real-time characteristics of sensory joint stress events.
In this embodiment, the sensory combined stress event real-time features include at least sensory coupled stress event features and sensory connected stress event features; the sensory coupling stress event characteristics at least comprise phase-phase coupling characteristics, phase-amplitude coupling characteristics and amplitude-amplitude coupling characteristics between every two signals of different channels in different cortical areas, and the sensory coupling stress event characteristics at least comprise time-frequency cross characteristics, signal correlation characteristics and signal distance characteristics between every two signals of different channels in different cortical areas.
In this embodiment, the organoleptic coupling stress event characteristics include a phase-phase coupling index, a phase-amplitude coupling index and an amplitude-amplitude coupling index between a plurality of frequency band signals (such as θ and γ, α and β) of a single-channel electroencephalogram signal (such as F3 and P3, and F3 and T3), and a phase-phase coupling index, a phase-amplitude coupling index and an amplitude-amplitude coupling index between a plurality of frequency band signals (such as θ and γ, α and β) of a plurality of different-channel electroencephalogram signals.
In this embodiment, the time-frequency cross characteristic at least includes cross spectral density, phase-locked value, phase slope index, transfer entropy; the signal correlation characteristics include at least a coherence coefficient, a pearson correlation coefficient, a jaccard similarity coefficient, a linear mutual information coefficient, and a linear correlation coefficient, and the signal distance characteristics include at least a euclidean distance, a manhattan distance, a chebyshev distance, a minkowski distance, a normalized euclidean distance, a mahalanobis distance, a papanicolaou distance, a hamming distance, and an angle cosine.
In an actual use scene, firstly, the rapid occurrence and development of a sensory event are influenced by a plurality of factors such as physical and psychological states, sleep phases, sleep environments, memory consolidation contents and the like of a user, and the time distribution real-time characteristic of the sensory event is a key description of the time sequence characteristic of the sensory stress state. Secondly, the real-time characteristics of the sensory independent stress event reflect the characteristics of the independent activities such as the signal intensity, the frequency, the peak amplitude, the information quantity, the chaos degree, the complexity and the like of various sensory experience contents such as vision, hearing, smell, taste, somatic sense and the like in different primary stimulus sensory cortex, high-level combined cortex and marginal systems. Finally, the visual, auditory, olfactory, gustatory and somatic sense and other sensory experience contents are finally integrated into unified sensory cognition and integrated into a specific memory consolidation process, and the sensory coupling stress event characteristics and the sensory connection stress event characteristics are just the sensory stress characteristics capable of describing the unified or integrated process. In general, the relationship among the multisensory synchronous integration time center point, the ratio relationship between the sensory cortical activity duration and the associated advanced cortical activity duration, the characteristic frequency band power duty ratio, the characteristic frequency band center frequency, the phase-phase coupling characteristic, the phase-amplitude coupling characteristic, the amplitude-amplitude coupling characteristic, the cross spectrum density, the phase locking value, the pearson correlation coefficient and the Euclidean distance are selected as the relationship characteristics, so that most scene requirements can be met.
P400: and combining a sensory stress level characteristic baseline library, carrying out real-time basal variation analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristic, the sensory independent stress event real-time characteristic and the sensory combined stress event real-time characteristic, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve.
First, establishing a sensory stress level characteristic base line library of healthy user groups with different age groups and large scale numbers.
In this embodiment, the method for constructing the sensory stress level characteristic baseline library is as follows:
1) Collecting and recording the rest state, sensory cortex and related advanced cortex central physiological state signals under sensory task state of healthy user groups with different age groups, performing signal processing, sensory event identification and event signal separation to obtain a baseline sensory event time process identification set and baseline sensory central physiological event data;
2) Performing sensory event time distribution feature analysis on the baseline sensory event time process identification set to generate a baseline sensory event time distribution feature set;
3) Carrying out sensory event time-space process feature analysis and sensory event time-space correlation feature analysis on the baseline sensory central physiological event data to respectively generate baseline sensory independent stress event features and baseline sensory combined stress event features;
4) Integrating the baseline sensory event time distribution feature set, the baseline sensory independent stress event feature and the baseline sensory combined stress event feature to generate a baseline sensory physiological stress feature set;
5) And carrying out average value calculation on each characteristic in the baseline sensory physiological stress characteristic set of all the user samples to obtain a rest baseline value and a task baseline value of each characteristic, and establishing a sensory stress level characteristic baseline library.
And secondly, carrying out baseline change analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics according to the sensory stress level characteristic baseline library, and extracting a sensory stress level real-time index.
In this embodiment, the method for calculating the sensory stress level real-time index is as follows:
1) Acquiring a sense stress level characteristic baseline library of a healthy user group with the same age group and the same scale number of the current user in a resting state and a sense task state, and acquiring a sense stress level characteristic baseline comparison library;
2) Acquiring current sensory event time distribution real-time characteristics, sensory independent stress event real-time characteristics and sensory combined stress event real-time characteristics, and calculating a baseline variation value of a resting baseline value and a task baseline value in a baseline comparison library with sensory stress level characteristics, namely baseline variation analysis, so as to obtain a sleep sensory stress level characteristic variation set;
3) And carrying out mutation harmonic analysis on all indexes in the sleep sensory stress level characteristic variation set to obtain a mutation harmonic value, namely the current sensory stress level real-time index.
In this embodiment, the specific calculation modes of the baseline variation analysis and the baseline variation value are as follows:
for real-valued variables
And its non-zero base line sequence +.>
For the baseline variation value of
wherein ,
respectively real value variable +.>
Baseline variation value of (i), i-th baseline value and corresponding weight,/and (ii)>
Is a positive integer.
In this embodiment, the variance harmonic analysis is a data analysis method that uses the variance coefficient and the absolute value variance coefficient of the numerical array as the observation base point, and uses the mean, the median, the quantile, the maximum value, the minimum value, the variance, the kurtosis, the skewness, the absolute value mean, the absolute value median, the absolute value quantile, the absolute value maximum value, the absolute value minimum value, the absolute value variance, the absolute value kurtosis and the absolute value skewness of the numerical array as the main analysis harmonic items to observe and analyze the variance coefficient fluctuation state and the general trend fluctuation change of the numerical array.
In this embodiment, a specific calculation method of variance blending analysis is as follows:
for numerical value arrays 
For the variation and the value are
wherein ,
respectively is a numerical value array->
Variation of (a) and (b) coefficient of variation and coefficient of variation of absolute value>
The absolute value, the maximum value and the minimum value are taken as operators respectively, and the +.>
Is a positive integer.
Thirdly, splicing the sensory stress level real-time indexes according to the time sequence to generate or update a sleep sensory stress level real-time curve.
P500: and carrying out time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve.
The first step, carrying out time sequence trend analysis on a sleep sensory stress level real-time curve, and obtaining a sensory stress level prediction index through prediction calculation.
In this embodiment, the time series trend analysis at least includes AR, MR, ARMA, ARIMA, SARIMA, VAR classical time series prediction method and deep learning prediction model.
In an actual use scenario, the classical time-sequential prediction method can meet most of the scenario requirements.
And secondly, splicing sensory stress level prediction indexes according to a time sequence to generate or update a sleep sensory stress level trend curve.
P600: generating a sleep sensory stress level dynamic regulation strategy in real time according to a preset regulation time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and carrying out real-time dynamic regulation on the sensory stress level of the sleeping process of a user.
The method comprises the steps of firstly, generating a sleep sensory stress level dynamic regulation strategy in real time according to a preset regulation time step, a sleep stress level optimization knowledge base, a sleep time phase curve, a sensory stress level real-time index and a sleep sensory stress level trend curve and combining a sleep sensory stress level dynamic regulation purpose.
In this embodiment, the dynamic sleep sensory stress level adjustment strategy at least includes an adjustment mode, an execution part, an adjustment method, and an adjustment strength; the adjusting mode at least comprises sound, light, taste, electricity, magnetism, ultrasound and sleeping environment, the executing part comprises a head, a neck, a trunk part, left and right upper limbs, left and right lower limbs and various large sense organs, the adjusting method at least comprises a constant, an increasing curve, a decreasing curve, an index curve, a sine curve, a periodic square wave and a random curve, and the adjusting intensity is determined by the current sense stress level real-time index and the sleeping sense stress level real-time prediction index.
In this embodiment, the sleep behavior level optimization knowledge base includes not only the professional knowledge, technical means, operation parameters, safety guidance, and other information of sleep stress level adjustment, but also historical information of sleep sensory stress level dynamic adjustment of the user, namely, historical sleep time phase curves, sleep sensory stress level real-time curves, sleep sensory stress level trend curves, sleep sensory stress level dynamic adjustment strategies, sleep sensory stress level dynamic adjustment strategy effects, and the like.
In an actual usage scenario, an adjustment mode, an execution part, an adjustment method and an adjustment strength need to be selected according to specific scenario requirements.
And secondly, dynamically adjusting the sensory stress level of the sleeping process of the user in real time according to a sleep sensory stress level dynamic adjustment strategy.
In the embodiment, according to the sleep sensory stress level dynamic regulation strategy, corresponding hardware equipment is connected, regulation parameters are sent, real-time dynamic regulation of the sensory stress level of the user in the sleep process is realized, and personal safety and other unexpected factors in the regulation process are monitored.
P700: repeating the steps to complete the circulation dynamic regulation of all the sleep sensory stress levels, evaluating the dynamic regulation effect, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database.
The first step, the circulation dynamic regulation of all sleep sensory stress levels is completed, and a sleep time phase curve, a sleep sensory stress level real-time curve and a sleep sensory stress level trend curve of all regulation processes are obtained.
In the whole sleeping process of the user, the sensory cortex and the related advanced cortex central physiological state of the user are continuously collected and analyzed, the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics of the user are extracted in real time, the sleeping sensory stress level of the user is evaluated and quantified in real time, and the dynamic regulation strategy is further formulated or optimized according to the dynamic regulation purpose of the sleeping sensory stress level and the last regulation result effect, so that the continuous dynamic training and regulation of the sleeping sensory stress level of the user are realized.
And secondly, analyzing and calculating the relation characteristics of the sleep time phase curve and the sleep sensory stress level real-time curve, and extracting the corresponding sensory stress related coefficient.
In this embodiment, the time phase sensory stress related coefficient is mainly used for measuring the sleep sensory stress comprehensive level of the user in different sleep time phases. The calculation method of the corresponding sensory stress correlation coefficient specifically comprises the following steps:
1) Acquiring a sleep time phase curve and a sleep sensory stress level real-time curve;
2) Analyzing and calculating the relation characteristic of the sleep time phase curve and the sleep sensory stress level real-time curve to obtain a time phase sensory stress level relation characteristic index set;
3) And carrying out weighted fusion calculation on the characteristic index sets of the corresponding sensory stress level relationship to obtain corresponding sensory stress related coefficients.
In this embodiment, the relationship features include at least an association feature and a distance feature; the correlation features at least comprise a coherence coefficient, a pearson correlation coefficient, a Jacquard similarity coefficient, a linear mutual information coefficient and a linear correlation coefficient, and the distance features at least comprise an Euclidean distance, a Manhattan distance, a Chebyshev distance, a Minkowski distance, a standardized Euclidean distance, a Mahalanobis distance, a Papanic distance, a Hamming distance and an included angle cosine.
Thirdly, analyzing and calculating the relation characteristics of the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and extracting the stress level dynamic regulation effect coefficient.
In this embodiment, the stress level dynamic adjustment effect coefficient reflection is the difference between the actual performance and the planned performance, and is the comprehensive evaluation of the implementation result and the effect of the dynamic adjustment strategy. The calculation method of the dynamic regulation effect coefficient of the time stress level specifically comprises the following steps:
1) Acquiring a sleep sensory stress level real-time curve and a sleep sensory stress level trend curve;
2) Analyzing and calculating the relation characteristics of a sleep sensory stress level real-time curve and a sleep sensory stress level trend curve to obtain a stress level dynamic regulation effect characteristic index set;
3) And carrying out weighted fusion calculation on the stress level dynamic adjustment effect characteristic index set to obtain a stress level dynamic adjustment effect coefficient.
Fourthly, according to the sleep time phase curve, the sleep sensory stress level real-time curve, the sleep sensory stress level trend curve, the corresponding sensory stress related coefficient and the stress level dynamic regulation effect coefficient, analyzing and calculating to generate a sleep sensory stress level regulation report.
In this embodiment, the sleep sensory stress level adjustment report at least includes a sleep time phase curve, a sleep sensory stress level real-time curve, a sleep sensory stress level trend curve, a corresponding sensory stress correlation coefficient, a stress level dynamic adjustment effect coefficient, an overall sleep sensory stress level dynamic adjustment strategy, a sensory stress level time phase distribution statistic, a peak activity period summary, a low peak activity period summary, an abnormal activity period summary, and a sleep sensory stress level adjustment report summary.
In this embodiment, the sensory stress level phase distribution statistics are specifically the average sensory stress level, the maximum sensory stress level, and the minimum sensory stress level of different sleep phases; the peak activity time summary is specifically peak time distribution, time numerical sum and duty ratio of peak time distribution corresponding to a segment exceeding a preset peak threshold value in the sleep sensory stress level real-time curve; the low peak activity period summary is specifically a low peak period distribution corresponding to a segment exceeding a preset low peak threshold value in a sleep sensory stress level real-time curve, and a time numerical sum and a duty ratio of the low peak period distribution; the abnormal active period summary is specifically an abnormal period distribution corresponding to an abnormal segment deviating from the curve baseline trend in the sleep sensory stress level real-time curve, a time numerical sum of the abnormal period distribution and a duty ratio.
In an actual use scenario, sleep sensory stress level regulation reports can provide a basis and a basic material for health management and neuroscience research.
And fifthly, establishing or updating a personalized sensory adjustment long-term database according to the sleep sensory stress level adjustment report and the current state information of the user, and providing a data analysis inheritance model for the continuous sleep sensory stress level dynamic adjustment of the subsequent user.
After the whole cycle dynamic adjustment is finished, the current age, physical and psychological state, sleeping environment and other information of the user and the sleeping sensory stress level adjustment report are combined, a personalized behavior adjustment long-term database is built and continuously updated, so that the subsequent user individual sleeping sensory stress level dynamic adjustment strategy is continuously optimized and adjusted, a quantized-adjusted long-term influence model is built, complete individuation and intelligence are realized, and a better dynamic adjustment effect is achieved.
The database at least comprises corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients of individuals, and the two coefficients are reserved in the database to help to complete dynamic regulation more quickly and pertinently due to different sensory stress degrees and regulation influence factors of different individuals.
As shown in fig. 2, a system for dynamically adjusting sleep sensory stress level according to an embodiment of the present invention is configured to perform the above method, and includes the following modules:
the sensory event detection module S100 is used for collecting and recording sensory cortex and related advanced cortex central physiological state signals in a sleeping process of a user in real time, processing signals, identifying sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data;
The time phase state analysis module S200 is used for identifying the sleep time phase state in real time according to the sensory central physiological state data and generating a sleep time phase curve;
the event feature analysis module S300 is used for carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features;
the stress level quantification module S400 is used for carrying out real-time basal line change analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics by combining with the sensory stress level characteristic basal line library, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve;
the stress trend prediction module S500 is used for performing time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve;
the dynamic strategy adjustment module S600 is used for generating a sleep sensory stress level dynamic adjustment strategy in real time according to a preset adjustment time step, a sleep stress level optimization knowledge base, a sleep time phase curve, a sleep sensory stress level real-time curve and a sleep sensory stress level trend curve, and dynamically adjusting the sensory stress level of the sleeping process of a user in real time;
The circulation regulation report module S700 is used for completing circulation dynamic regulation of all sleep sensory stress levels, evaluating dynamic regulation effects, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database;
the data management center module S800 is configured to visually display and manage all process data in the system.
In this embodiment, the sensory event detection module S100 further includes the following specific functional units:
the sensory physiological monitoring unit is used for collecting, recording and processing sensory cortex and related advanced cortex central physiological state signals of a user in a sleep process in real time to obtain sensory central physiological state data;
the sensory event identification unit is used for carrying out sensory event identification on sensory center physiological state data in real time, extracting sensory event time information and generating or updating a sensory event time process identification set;
the event signal separation unit is used for carrying out real-time event signal separation on the sensory center physiological state data according to the sensory event time process identification set to generate or update the sensory center physiological event data.
In this embodiment, the phase state analysis module S200 further includes the following specific functional units:
the stage model construction unit is used for carrying out learning training and data modeling on sensory center physiological state data of the scale sleep user sample and corresponding sleep stage data thereof through a deep learning algorithm to obtain a sleep time phase automatic stage model;
the real-time phase identification unit is used for inputting sensory central physiological state data of the current user into the sleep phase automatic stage model in real time and extracting sleep phase stage values;
and the time phase curve generating unit is used for splicing the sleep time phase stage values according to the time sequence to generate a sleep time phase curve.
In this embodiment, the event feature analysis module S300 further includes the following specific functional units:
the time distribution characteristic analysis unit is used for carrying out real-time event time distribution characteristic analysis on the sensory event time process identification set and generating sensory event time distribution real-time characteristics;
the time-space process feature analysis unit is used for carrying out real-time event time-space process feature analysis on sensory central physiological event data, evaluating the time sequence activity process and the independent characterization level of sensory events in each sensory cortex and associated advanced cortex, and obtaining the real-time feature of sensory independent stress events;
The time-space correlation feature analysis unit is used for carrying out real-time event time-space correlation feature analysis on sensory central physiological event data, and evaluating correlation coordination modes and joint characterization levels of sensory events between different sensory cortex and associated advanced cortex to obtain real-time features of sensory joint stress events.
In this embodiment, the stress level quantization module S400 further includes the following specific functional units:
the system comprises a baseline characteristic construction unit, a control unit and a control unit, wherein the baseline characteristic construction unit is used for establishing a sensory stress level characteristic baseline library of healthy user groups with different age groups and large scale numbers;
the emergency index extraction unit is used for carrying out baseline variation analysis and variation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics according to the sensory stress level characteristic baseline library, and extracting a sensory stress level real-time index;
the stress curve generating unit is used for splicing the sensory stress level real-time indexes according to the time sequence to generate or update a sleep sensory stress level real-time curve.
In this embodiment, the stress trend prediction module S500 further includes the following specific functional units:
the stress index prediction unit is used for carrying out time sequence trend analysis on the sleep sensory stress level real-time curve, and obtaining a sensory stress level prediction index through prediction calculation;
The trend curve generation unit is used for splicing the sensory stress level prediction indexes according to the time sequence to generate or update a sleep sensory stress level trend curve.
In this embodiment, the dynamic policy adjustment module S600 further includes the following specific functional units:
the regulation strategy generation unit is used for generating a sleep sensory stress level dynamic regulation strategy in real time according to the sleep stress level optimization knowledge base, the sleep time phase curve, the sensory stress level real-time index and the sleep sensory stress level trend curve and combining the sleep sensory stress level dynamic regulation purpose;
the dynamic regulation execution unit is used for dynamically regulating the sensory stress level of the sleeping process of the user in real time according to the sleep sensory stress level dynamic regulation strategy.
In this embodiment, the cyclic adjustment report module S700 further includes the following specific functional units:
the circulation dynamic regulation unit is used for completing circulation dynamic regulation of all sleep sensory stress levels and obtaining a sleep time phase curve, a sleep sensory stress level real-time curve and a sleep sensory stress level trend curve in all regulation processes;
the stress correlation analysis unit is used for analyzing and calculating the relation characteristics of the sleep time phase curve and the sleep sensory stress level real-time curve and extracting corresponding sensory stress correlation coefficients;
The adjusting effect analysis unit is used for analyzing and calculating the relation characteristics of the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve and extracting the stress level dynamic adjusting effect coefficient;
the regulation report generation unit is used for generating a sleep sensory stress level regulation report according to a sleep time phase curve, a sleep sensory stress level real-time curve, a sleep sensory stress level trend curve, a corresponding sensory stress related coefficient and a stress level dynamic regulation effect coefficient by analysis and calculation;
the sensory regulation inheritance unit is used for establishing or updating a personalized sensory regulation long-term database according to the sleep sensory stress level regulation report and the current state information of the user, and providing a data analysis inheritance model for the continuous sleep sensory stress level dynamic regulation of the subsequent user.
In this embodiment, the data management center module S800 further includes the following specific 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;
and the data operation management unit is used for storing, backing up, migrating and exporting all data in the system.
As shown in fig. 3, the device for dynamically adjusting sleep sensory stress level provided by the embodiment of the invention comprises the following modules:
the sensory event detection module M100 is used for collecting and recording sensory cortex and related advanced cortex central physiological state signals in a sleeping process of a user in real time, processing signals, identifying sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data;
the time phase state analysis module M200 is used for identifying the sleep time phase state in real time according to the sensory central physiological state data and generating a sleep time phase curve;
the event feature analysis module M300 is used for carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features;
the stress level quantification module M400 is used for carrying out real-time basal line change analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics by combining with the sensory stress level characteristic basal line library, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve;
The stress trend prediction module M500 is used for performing time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve;
the dynamic strategy adjustment module M600 is used for generating a sleep sensory stress level dynamic adjustment strategy in real time according to a preset adjustment time step length, a sleep stress level optimization knowledge base, a sleep time phase curve, a sleep sensory stress level real-time curve and a sleep sensory stress level trend curve, and dynamically adjusting the sensory stress level of a user in real time in the sleep process;
the circulation regulation report module M700 is used for completing circulation dynamic regulation of all sleep sensory stress levels, evaluating dynamic regulation effects, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database;
the data visual management module M800 is used for visual display management and user information editing management of all data in the device;
the data operation management module M900 is configured to store, backup, migrate and export all data in the device.
The above-described system and apparatus of the present invention are configured to correspondingly perform the steps in the method of fig. 1, and will not be described 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. A method for dynamically adjusting sleep sensory stress levels, comprising the steps of:
Collecting and recording sensory cortex and related advanced cortex central physiological state signals of a user in a sleep process in real time, processing the signals, identifying sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data;
identifying a sleep time phase state in real time and generating a sleep time phase curve according to the sensory central physiological state data;
carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and the sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features;
combining a sensory stress level characteristic baseline library, carrying out real-time basal variation analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristic, the sensory independent stress event real-time characteristic and the sensory combined stress event real-time characteristic, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve;
Performing time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve;
generating a sleep sensory stress level dynamic regulation strategy in real time according to a preset regulation time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and carrying out real-time dynamic regulation on the sensory stress level of a user in the sleep process;
repeating the steps to complete the circulation dynamic regulation of all the sleep sensory stress levels, evaluating the dynamic regulation effect, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database.
2. The method of claim 1, wherein the steps of collecting and recording sensory cortex and associated advanced cortex central physiological state signals during sleep of the user, processing the signals, identifying sensory events, and separating event signals in real time to obtain sensory central physiological state data, a set of sensory event time course identifiers, and sensory central physiological event data further comprise:
The sensory cortex and related advanced cortex central physiological state signals in the sleeping process of the user are collected, recorded and processed in real time to obtain sensory central physiological state data;
carrying out sensory event identification on the sensory center physiological state data in real time, extracting sensory event time information, and generating or updating the sensory event time process identification set;
and carrying out real-time event signal separation on the sensory center physiological state data according to the sensory event time process identification set to generate or update the sensory center physiological event data.
3. The method of claim 2, wherein: the sensory cortex refers to a sensory organ corresponding primary sensory cortex and secondary sensory cortex, including at least one of a somatosensory primary sensory cortex and secondary sensory cortex, a visual primary sensory cortex and secondary sensory cortex, an auditory primary sensory cortex and secondary sensory cortex, an olfactory primary sensory cortex and secondary sensory cortex, and a gustatory primary sensory cortex and secondary sensory cortex; the associated advanced cortex refers to advanced cortex and limbic systems associated with sensory event chronology, including at least one of parietal cortex, temporal cortex, prefrontal cortex, limbic systems.
4. A method according to claim 2 or 3, wherein: the central physiological state signal comprises at least one of an electroencephalogram signal, a magnetoencephalography signal, a functional near infrared spectrum imaging signal and a functional nuclear magnetic resonance imaging signal.
5. A method according to claim 1 or 2, characterized in that: the signal processing at least comprises A/D digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-stop filtering, band-pass filtering, correction processing and band extraction; the correction processing specifically includes signal correction and predictive smoothing processing on signal data segments including artifacts or distortions in the physiological state signal, and the band extraction specifically includes extracting a band signal in a specific band range from the target signal.
6. A method according to claim 1 or 2, characterized in that: the sensory event identification means that the occurrence and characterization of a plurality of continuous different independent sensory events are distinguished from physiological status signals of the central nervous system of each sensory cortex and related advanced cortex of the brain, the characterization time sequence process, the event center time, the event starting time and the event ending time of each independent event in different cortex areas are identified, and the sensory event time process identification set is generated.
7. A method according to claim 1 or 2, characterized in that: the sensory event time course identification set comprises at least one of an event center time, an event start time, an event end time, and a negative peak time of a central physiological state signal of each cortical region in the event course.
8. The method of claim 1, wherein: the specific steps of identifying the sleep time phase state and generating the sleep time phase curve in real time according to the sensory central physiological state data further comprise the following steps:
performing learning training and data modeling on the sensory central physiological state data and the corresponding sleep stage data of the scale sleep user sample through a deep learning algorithm to obtain a sleep time phase automatic stage model;
inputting the sensory central physiological state data of the current user into the sleep time phase automatic stage model in real time, and extracting sleep time phase stage values;
and splicing the sleep time phase stage values according to a time sequence to generate the sleep time phase curve.
9. The method of claim 1, wherein: the specific steps of performing real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and the sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features further comprise:
Carrying out real-time event time distribution feature analysis on the sensory event time process identification set to generate sensory event time distribution real-time features;
performing real-time event space-time process feature analysis on the sensory center physiological event data, and evaluating the time sequence activity process and the independent characterization level of the sensory event in each sensory cortex and the associated advanced cortex to obtain the real-time feature of the sensory independent stress event;
and carrying out real-time event time-space association characteristic analysis on the sensory center physiological event data, and evaluating association coordination modes and association characterization levels of sensory events among different sensory cortex and associated advanced cortex to obtain the real-time characteristics of sensory association stress events.
10. The method of claim 9, wherein: the sensory event time distribution real-time characteristics at least comprise sensory event duration characteristics and sensory event characterization time center distribution characteristics; the sensory event duration characteristic comprises at least one of average value, root mean square, maximum value, minimum value, variance, standard deviation, variation coefficient, kurtosis and skewness, and the sensory event characterization time center distribution characteristic comprises at least one of sensory cortical activity duration, multi-sensory synchronous integration time center point, associated advanced cortical activity duration and ratio relation of sensory cortical activity duration to associated advanced cortical activity duration of each event.
11. The method of claim 9 or 10, wherein: the sensory independent stress event real-time characteristics at least comprise time-frequency characteristics and nonlinear characteristics; the time-frequency characteristic comprises at least one of different cortical areas, total power of different channels, characteristic frequency band power duty ratio, characteristic frequency band central frequency and envelope characteristic, and the nonlinear characteristic comprises at least one of entropy characteristic, fractal characteristic and complexity characteristic of different channels.
12. The method of claim 11, wherein: the sensory combined stress event real-time characteristics at least comprise sensory coupled stress event characteristics and sensory connected stress event characteristics; the sensory coupling stress event characteristics comprise at least one of phase-phase coupling characteristics, phase-amplitude coupling characteristics and amplitude-amplitude coupling characteristics between every two signals of different channels of different leather areas, and the sensory coupling stress event characteristics comprise at least one of time-frequency cross characteristics, signal correlation characteristics and signal distance characteristics between every two signals of different channels of different leather areas.
13. The method of claim 1, wherein: the specific steps of combining the sensory stress level characteristic baseline library, performing real-time baseline variation analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristic, the sensory independent stress event real-time characteristic and the sensory combined stress event real-time characteristic, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve further comprise:
Establishing a baseline library of sensory stress level characteristics for a large number of healthy user populations of different age groups;
according to the sensory stress level characteristic baseline library, carrying out baseline variation analysis and mutation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics, and extracting the sensory stress level real-time index;
and splicing the sensory stress level real-time indexes according to the time sequence to generate or update the sleep sensory stress level real-time curve.
14. The method of claim 13, wherein the sensory stress level profile library is constructed by the following method:
1) Collecting and recording the rest state, sensory cortex and related advanced cortex central physiological state signals under sensory task state of healthy user groups with different age groups, performing signal processing, sensory event identification and event signal separation to obtain a baseline sensory event time process identification set and baseline sensory central physiological event data;
2) Performing sensory event time distribution feature analysis on the baseline sensory event time process identification set to generate a baseline sensory event time distribution feature set;
3) Performing sensory event space-time process feature analysis and sensory event space-time correlation feature analysis on the baseline sensory central physiological event data to respectively generate baseline sensory independent stress event features and baseline sensory combined stress event features;
4) Integrating the baseline sensory event time distribution feature set, the baseline sensory independent stress event features and the baseline sensory combined stress event features to generate a baseline sensory physiological stress feature set;
5) And carrying out mean value calculation on each feature in the baseline sensory physiological stress feature set of all user samples to obtain a rest baseline value and a task baseline value of each feature, and establishing the sensory stress level feature baseline library.
15. A method according to claim 13 or 14, wherein the sensory stress level real-time index is calculated as follows:
1) Acquiring a resting state and a sensory stress level characteristic baseline library of a healthy user group with the same age group as the current user and the large scale number under a sensory task state, and acquiring a sensory stress level characteristic baseline comparison library;
2) Acquiring current sensory event time distribution real-time characteristics, sensory independent stress event real-time characteristics and sensory combined stress event real-time characteristics, and calculating baseline variation values of a resting baseline value and a task baseline value in a baseline comparison library with the sensory stress level characteristics, namely baseline variation analysis, so as to obtain a sleep sensory stress level characteristic variation set;
3) And carrying out variation harmonic analysis on all indexes in the sleep sensory stress level characteristic variation set to obtain variation harmonic values, namely the current sensory stress level real-time index.
16. The method of claim 15, wherein the baseline variation analysis and the baseline variation value are calculated in the following manner:
for real-valued variables
And its non-zero base line sequence +.>
For the baseline variation value of
wherein ,
respectively real value variable +.>
Baseline variation value of (i), i-th baseline value and corresponding weight,/and (ii)>
Is a positive integer.
17. The method of claim 16, wherein the variance harmonic analysis is a data analysis method based on at least one of a coefficient of variation and an absolute value coefficient of variation of the array of values as a basis for observation, and based on at least one of a mean, a median, a quantile, a maximum, a minimum, a variance, a kurtosis, a skewness, an absolute value mean, an absolute value median, an absolute value quantile, an absolute value maximum, an absolute value minimum, an absolute value variance, an absolute value kurtosis, and an absolute value skewness of the array of values as a basis for observation of a variance coefficient fluctuation state and a general trend fluctuation change of the array of values.
18. The method of claim 16, wherein one specific calculation of the variance blending analysis is:
for numerical value arrays
For the variation and the value are
wherein ,
respectively is a numerical value array->
Variation and coefficient of variation and absolute value variation coefficient,
the absolute value, the maximum value and the minimum value are taken as operators respectively, and the +.>
Is a positive integer.
19. The method of claim 1, wherein the specific steps of performing a time-series prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generating a sleep sensory stress level trend curve further comprise:
performing time sequence trend analysis on the sleep sensory stress level real-time curve, and obtaining the sensory stress level prediction index through prediction calculation;
and splicing the sensory stress level prediction indexes according to a time sequence to generate or update the sleep sensory stress level trend curve.
20. The method of claim 19, wherein the time series trend analysis comprises at least one of a AR, MR, ARMA, ARIMA, SARIMA, VAR classical time-series prediction method, and a deep learning prediction model.
21. The method of claim 1, wherein the specific steps of generating a sleep sensory stress level dynamic adjustment strategy in real time according to a preset adjustment time step, a sleep stress level optimization knowledge base, the sleep phase curve, the sleep sensory stress level real time curve, and the sleep sensory stress level trend curve, and dynamically adjusting the sensory stress level of the user's sleep process in real time further comprise:
generating the sleep sensory stress level dynamic regulation strategy in real time according to a preset regulation time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve and combining the sleep sensory stress level dynamic regulation purpose;
and dynamically regulating the sensory stress level of the sleeping process of the user in real time according to the sleep sensory stress level dynamic regulation strategy.
22. The method of claim 21, wherein the sleep sensory stress level dynamic adjustment strategy comprises at least an adjustment mode, an execution site, an adjustment method, and an adjustment intensity; the adjusting mode comprises at least one of sound, light, smell, electricity, magnetism, ultrasound and sleeping environment, the executing part comprises at least one of a head, a neck, a trunk, a left upper limb, a right upper limb, a left lower limb and a right lower limb and each large sensory organ, the adjusting method at least comprises at least one of a constant, an increasing curve, a decreasing curve, an exponential curve, a sinusoidal curve, a periodic square wave and a random curve, and the adjusting intensity is determined by the current sensory stress level real-time index and the sleeping sensory stress level real-time prediction index.
23. The method of claim 1, wherein the steps are repeated to complete the cyclic dynamic adjustment of all the sleep sensory stress levels, evaluate the dynamic adjustment effect, extract the corresponding sensory stress related coefficients and the stress level dynamic adjustment effect coefficients, generate sleep sensory stress level adjustment reports, and build a personalized sensory adjustment long-term database, further comprising:
completing the cyclic dynamic regulation of all sleep sensory stress levels to obtain the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve of all regulation processes;
analyzing and calculating the relation characteristics of the sleep time phase curve and the sleep sensory stress level real-time curve, and extracting the corresponding sensory stress related coefficient;
analyzing and calculating relation characteristics of the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and extracting the stress level dynamic regulation effect coefficient;
according to the sleep time phase curve, the sleep sensory stress level real-time curve, the sleep sensory stress level trend curve, the corresponding sensory stress correlation coefficient and the stress level dynamic regulation effect coefficient, analyzing, calculating and generating the sleep sensory stress level regulation report;
And establishing or updating the personalized sensory adjustment long-term database according to the sleep sensory stress level adjustment report and the current state information of the user, and providing a data analysis inheritance model for the continuous sleep sensory stress level dynamic adjustment of the subsequent user.
24. The method of claim 23, wherein the method for calculating the corresponding sensory stress correlation coefficient specifically comprises:
1) Acquiring the sleep time phase curve and the sleep sensory stress level real-time curve;
2) Analyzing and calculating the relation characteristic of the sleep time phase curve and the sleep sensory stress level real-time curve to obtain a corresponding sensory stress level relation characteristic index set;
3) And carrying out weighted fusion calculation on the corresponding sensory stress level relation characteristic index set to obtain the corresponding sensory stress related coefficient.
25. The method according to claim 23 or 24, wherein the stress level dynamic adjustment effect coefficient is calculated by the following method:
1) Acquiring the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve;
2) Analyzing and calculating the relation characteristics of the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve to obtain a stress level dynamic adjustment effect characteristic index set;
3) And carrying out weighted fusion calculation on the stress level dynamic adjustment effect characteristic index set to obtain the stress level dynamic adjustment effect coefficient.
26. The method of claim 23, wherein: the relationship features include at least one of an association feature and a distance feature; wherein the correlation characteristic comprises at least one of a coherence coefficient, a pearson correlation coefficient, a jaccard similarity coefficient, a linear mutual information coefficient, and a linear correlation coefficient, and the distance characteristic comprises at least one of a euclidean distance, a manhattan distance, a chebyshev distance, a minkowski distance, a normalized euclidean distance, a mahalanobis distance, a barbita distance, a hamming distance, and an angle cosine.
27. The method of claim 23, wherein: the sleep sensory stress level adjustment report at least comprises a sleep time phase curve, a sleep sensory stress level real-time curve, a sleep sensory stress level trend curve, a corresponding sensory stress correlation coefficient, a stress level dynamic adjustment effect coefficient, all sleep sensory stress level dynamic adjustment strategies, sensory stress level time phase distribution statistics, peak activity time period summary, low peak activity time period summary, abnormal activity time period summary and sleep sensory stress level adjustment report summary.
28. The method of claim 27, wherein: the sensory stress level time phase distribution statistics specifically comprise average sensory stress levels, maximum sensory stress levels and minimum sensory stress levels of different sleep time phases; the peak activity time summary is specifically peak time distribution corresponding to a segment exceeding a preset peak threshold value in the sleep sensory stress level real-time curve, and time numerical sum and duty ratio of the peak time distribution; the low peak activity period summary is specifically low peak period distribution corresponding to a segment exceeding a preset low peak threshold value in the sleep sensory stress level real-time curve, and time numerical sum and duty ratio of the low peak period distribution; the abnormal activity period summary is specifically abnormal period distribution corresponding to an abnormal segment deviating from a curve baseline trend in the sleep sensory stress level real-time curve, and time numerical sum and duty ratio of the abnormal period distribution.
29. A system for dynamic regulation of sleep sensory stress levels, comprising the following modules:
the sensory event detection module is used for collecting and recording sensory cortex and related advanced cortex central physiological state signals in real time during sleeping of a user, processing the signals, identifying the sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data;
The time phase state analysis module is used for identifying the sleep time phase state in real time and generating a sleep time phase curve according to the sensory central physiological state data;
the event feature analysis module is used for carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and the sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features;
the stress level quantification module is used for carrying out real-time basal line change analysis and variation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics by combining with a sensory stress level characteristic basal line library, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve;
the stress trend prediction module is used for carrying out time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve;
The dynamic strategy adjustment module is used for generating a sleep sensory stress level dynamic adjustment strategy in real time according to a preset adjustment time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and dynamically adjusting the sensory stress level of the sleeping process of a user in real time;
the circulation regulation report module is used for completing circulation dynamic regulation of all sleep sensory stress levels, evaluating dynamic regulation effects, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database;
and the data management center module is used for visual display and data operation management of all process data in the system.
30. The system of claim 29, wherein the sensory event detection module further comprises the following specific functional units:
the sensory physiological monitoring unit is used for collecting, recording and processing sensory cortex and related advanced cortex central physiological state signals of a user in a sleep process in real time to obtain sensory central physiological state data;
The sensory event identification unit is used for carrying out sensory event identification on the sensory center physiological state data in real time, extracting sensory event time information and generating or updating the sensory event time process identification set;
and the event signal separation unit is used for carrying out real-time event signal separation on the sensory central physiological state data according to the sensory event time process identification set to generate or update the sensory central physiological event data.
31. The system of claim 29 or 30, wherein the phase state analysis module further comprises the following specific functional units:
the stage model construction unit is used for carrying out learning training and data modeling on the sensory center physiological state data of the scale sleep user sample and the sleep stage data corresponding to the sensory center physiological state data through a deep learning algorithm to obtain a sleep time phase automatic stage model;
the real-time phase identification unit is used for inputting the sensory central physiological state data of the current user into the sleep phase automatic stage model in real time and extracting sleep phase stage values;
and the time phase curve generating unit is used for splicing the sleep time phase stage values according to a time sequence to generate the sleep time phase curve.
32. The system of claim 29, wherein the event signature analysis module further comprises the following specific functional units:
the time distribution characteristic analysis unit is used for carrying out real-time event time distribution characteristic analysis on the sensory event time process identification set and generating sensory event time distribution real-time characteristics;
the time-space process characteristic analysis unit is used for carrying out real-time event time-space process characteristic analysis on the sensory central physiological event data, evaluating the time sequence activity process and the independent characterization level of the sensory event in each sensory cortex and the associated advanced cortex, and obtaining the real-time characteristics of the sensory independent stress event;
and the space-time correlation feature analysis unit is used for carrying out real-time event space-time correlation feature analysis on the sensory central physiological event data, and evaluating the correlation coordination mode and the joint characterization level of the sensory event between different sensory cortex and the correlation advanced cortex to obtain the real-time feature of the sensory joint stress event.
33. The system of claim 29 or 32, wherein the stress level quantification module further comprises the following specific functional units:
a baseline characteristic construction unit for establishing the sensory stress level characteristic baseline library of healthy user groups of different age groups and of large scale number;
The emergency index extraction unit is used for carrying out baseline variation analysis and variation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics according to the sensory stress level characteristic baseline library, and extracting the sensory stress level real-time index;
the stress curve generating unit is used for splicing the sensory stress level real-time indexes according to the time sequence to generate or update the sleep sensory stress level real-time curve.
34. The system of claim 33, wherein the stress trend prediction module further comprises the following specific functional units:
the stress index prediction unit is used for carrying out time sequence trend analysis on the sleep sensory stress level real-time curve, and predicting and calculating to obtain the sensory stress level prediction index;
and the trend curve generating unit is used for splicing the sensory stress level prediction indexes according to the time sequence to generate or update the sleep sensory stress level trend curve.
35. The system of claim 34, wherein the dynamic policy adjustment module further comprises the following specific functional units:
The regulation strategy generation unit is used for generating the sleep sensory stress level dynamic regulation strategy in real time according to a sleep stress level optimization knowledge base, the sleep time phase curve, the sensory stress level real-time index and the sleep sensory stress level trend curve and combining the sleep sensory stress level dynamic regulation purpose;
and the dynamic regulation execution unit is used for dynamically regulating the sensory stress level of the sleeping process of the user in real time according to the sleep sensory stress level dynamic regulation strategy.
36. The system of claim 29 or 35, wherein the loop adjustment reporting module further comprises the following specific functional units:
the circulation dynamic regulation unit is used for completing circulation dynamic regulation of all sleep sensory stress levels and obtaining the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve in all regulation processes;
the stress correlation analysis unit is used for analyzing and calculating the relation characteristics of the sleep time phase curve and the sleep sensory stress level real-time curve and extracting the corresponding sensory stress correlation coefficient;
the adjusting effect analysis unit is used for analyzing and calculating the relation characteristics of the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve and extracting the stress level dynamic adjusting effect coefficient;
The regulation report generation unit is used for analyzing, calculating and generating the sleep sensory stress level regulation report according to the sleep time phase curve, the sleep sensory stress level real-time curve, the sleep sensory stress level trend curve, the corresponding sensory stress related coefficient and the stress level dynamic regulation effect coefficient;
the sensory regulation inheritance unit is used for establishing or updating the personalized sensory regulation long-term database according to the sleep sensory stress level regulation report and the current state information of the user, and providing a data analysis inheritance model for the continuous sleep sensory stress level dynamic regulation of the subsequent user.
37. The system of claim 29, wherein the data management center module further comprises the following specific 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;
and the data operation management unit is used for storing, backing up, migrating and exporting all data in the system.
38. A device for dynamically adjusting sleep sensory stress level, which is characterized by comprising the following modules:
The sensory event detection module is used for collecting and recording sensory cortex and related advanced cortex central physiological state signals in real time during sleeping of a user, processing the signals, identifying the sensory events and separating event signals to obtain sensory central physiological state data, a sensory event time process identification set and sensory central physiological event data;
the time phase state analysis module is used for identifying the sleep time phase state in real time and generating a sleep time phase curve according to the sensory central physiological state data;
the event feature analysis module is used for carrying out real-time event time distribution feature analysis, event independent characterization feature analysis and event combined characterization feature analysis on the sensory event time process identification set and the sensory central physiological event data to respectively generate sensory event time distribution real-time features, sensory independent stress event real-time features and sensory combined stress event real-time features;
the stress level quantification module is used for combining a sensory stress level characteristic baseline library, carrying out real-time baseline variation analysis and variation reconciliation analysis on the sensory event time distribution real-time characteristics, the sensory independent stress event real-time characteristics and the sensory combined stress event real-time characteristics, extracting a sensory stress level real-time index and generating a sleep sensory stress level real-time curve;
The stress trend prediction module is used for carrying out time sequence prediction calculation on the sleep sensory stress level real-time curve to obtain a sensory stress level prediction index and generate a sleep sensory stress level trend curve;
the dynamic strategy adjustment module is used for generating a sleep sensory stress level dynamic adjustment strategy in real time according to a preset adjustment time step, a sleep stress level optimization knowledge base, the sleep time phase curve, the sleep sensory stress level real-time curve and the sleep sensory stress level trend curve, and dynamically adjusting the sensory stress level of a user in real time in the sleep process;
the circulation regulation report module is used for completing circulation dynamic regulation of all sleep sensory stress levels, evaluating dynamic regulation effects, extracting corresponding sensory stress related coefficients and stress level dynamic regulation effect coefficients, generating a sleep sensory stress level regulation report and establishing a personalized sensory regulation long-term database;
the data visual management module is used for visual display management and user information editing management of all data in the device;
and the data operation management module is used for storing, backing up, migrating and exporting all data in the device.
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