1465 results about "Sleep quality" patented technology

The invention relates to the field of sleep quality monitoring and evaluation, in particular to a sleep quality monitoring and evaluation method and system. Firstly, acquisition and quality enhancement preprocessing are carried out on polysomnogram (PSG) data, and then an adaptive spatial-temporal feature learning network KAN-BiLSTM-Transform is designed and is used for automatically extracting discriminative features of sleep signals. The network is composed of a Kolmogorov-Arnold network (KAN), a bidirectional long short-term memory (BiLSTM) and a Transform, and the space-time dependency relationship of a multi-mode signal can be fully mined. In the training stage, weighted Dice loss and time sequence smooth loss are introduced, and a self-supervised pre-training normal form is adopted for parameter optimization. During application, sleep signals are input into the trained network, and the sleep stages corresponding to the time window can be automatically predicted. The method is expected to become an auxiliary tool for detecting and evaluating the sleep quality of people.

The invention relates to the technical field of health monitoring, in particular to a deep sleep analysis and early warning system integrated with heart rate monitoring, which comprises a physiological parameter acquisition module, an environmental parameter acquisition module, a deep sleep analysis module, an anomaly detection and early warning module, a dynamic feedback and optimization module, a personalized suggestion module and a data storage and analysis module. The physiological parameter acquisition module acquires physiological parameters of a user, the environmental parameter acquisition module acquires environmental parameters, the deep sleep analysis module identifies deep sleep features based on a multivariable data analysis algorithm and evaluates sleep quality, and the anomaly detection and early warning module detects physiological anomaly and triggers early warning. The dynamic feedback and optimization module optimizes model parameters according to user feedback, the personalized suggestion module generates sleep improvement suggestions, and the data storage and analysis module stores data. According to the invention, the deep sleep state of the user can be comprehensively analyzed, a personalized health management and sleep optimization scheme is provided, and the sleep quality of the user is improved.

The invention discloses a sleep monitoring analysis method and system based on HRV, and relates to the technical field of sleep monitoring, and the method comprises the following steps: at the beginning of monitoring, continuously recording a continuous heartbeat time interval of a sleeper at a preset initial heartbeat interval acquisition frequency for evaluating the autonomic nervous system state of the sleeper at night; the respiration data of the sleeper is acquired in real time while the heart rate data is acquired. By combining real-time respiration data and an intelligent increment control algorithm, when irregular respiration is detected, the heartbeat interval acquisition frequency is dynamically adjusted, so that the sampling precision of heart rate change is improved, fine fluctuation of a sleeper when apnea or irregular respiration occurs can be captured, and the sleep quality of the sleeper can be improved. In addition, the detection accuracy of the apnea event can be improved under high sensitivity, potential health risks can be recognized in time, more reliable data support is provided for improvement of sleep quality and early intervention, and therefore health damage caused by missing detection is effectively avoided.

The invention discloses a patient nursing strategy optimization system for nursing in the department of hepatobiliary and pancreatic, and relates to the technical field of nursing strategy optimization. The nursing strategy optimization platform is in communication connection with a patient information management module, a knowledge base module, a nursing evaluation module, a nursing strategy plan making module, a nursing implementation module, a communication and cooperation module and a nursing effect evaluation module. According to the patient nursing strategy optimization system for nursing in the department of hepatobiliary and pancreatic, data in multiple aspects of physiology, psychology, society and the like of a patient are comprehensively collected and analyzed through the integrated nursing evaluation module, and key indexes such as vital signs, pain degree, nutritional status, sleep quality and the like are monitored and evaluated in real time; in addition, a personalized nursing plan provided by the system is combined with specific requirements and illness state changes of the patient, so that the nursing service better fits the actual condition of the patient.

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention relates to the technical field of intelligent sleep monitoring blankets, in particular to an intelligent sleep monitoring blanket based on multi-modal physiological signal fusion and an optimization method of the intelligent sleep monitoring blanket. The signal acquisition end is used for acquiring multi-modal physiological signal data; the cloud server is used for processing and analyzing the physiological signal data, optimizing a signal acquisition process, performing sleep staging, performing sleep quality evaluation according to a sleep staging result, and providing a sleep quality report and suggestion for a user; the signal acquisition end transmits data to the cloud server end by using a Bluetooth protocol, and optimizes the acquisition process of each sensor according to a specific hierarchical architecture; the cloud server receives various multi-modal physiological signals and performs sleep data fusion, a deep learning algorithm is utilized to establish a sleep staging model to optimize a sleep environment, and an innovative multi-scale time sequence attention network and a multi-model integration framework are combined, so that the sleep staging accuracy is greatly improved, and a reliable basis is provided for sleep quality evaluation.

The invention provides a sleep stage detection method based on a multi-modal and visual transformation network, and the method comprises the steps: extracting EEG channel signals and EOG channel signals in PSG, carrying out the feature extraction of different frequency bands through a multi-scale convolutional network, and fusing the multi-modal features to enhance the classification accuracy. Thirdly, the time-space relevance of network modeling signals is transformed through vision, and then different stages of sleep are effectively recognized; the method also provides confidence evaluation of the classification result, and the reliability of the detection result is enhanced. The automatic detection method can be used for clinical sleep staging to assist doctors in evaluating sleep quality and diagnosing sleep disorders.

The invention discloses a vital sign and sleep monitoring method and system based on a millimeter-wave radar, the method utilizes the millimeter-wave radar to realize non-contact sleep monitoring, and the millimeter-wave radar is firstly used to collect radar echo signals obtained by vibration of a chest wall in a sleep state; determining the position of a monitored object and extracting a vital sign signal of the target; then accurately detecting a first amplitude body movement and a second amplitude body movement of the target; whether an apnea event happens to the target is detected in real time, and alarm information is prompted under the condition that the event happens for a long time; three sleeping postures of supine, side-lying and prostrate are detected; and finally, sleep state monitoring is formed, a user sleep quality analysis report is formed in combination with the monitored information, and health suggestions are given. The method is simple, convenient, accurate, economical and efficient, is suitable for various application scenes such as families, hospitals and nursing homes, and provides all-weather health monitoring services for users.

The invention discloses a self-adaptive music synthesis sleep aiding method and system based on physiological feedback. The method comprises the following steps: acquiring physiological data of a user; preprocessing the physiological data; evaluating the sleep state of the user based on the preprocessed physiological data; defining a music parameter space according to the sleep state; optimizing music parameters based on the sleep state and the music parameter space; generating and playing music according to the optimized music parameters; while music is played, physiological reactions of the user are continuously monitored, music parameters are adjusted in real time based on the physiological reactions, and feedback circulation is formed; analyzing a long-term sleep pattern of the user based on the data obtained in the feedback loop; according to the long-term sleep mode, updating a music parameter space and an optimization strategy; security monitoring is executed, and intervention is carried out when an abnormal condition is detected; according to the invention, a plurality of key indexes such as sleep time, deep sleep proportion, sleep quality and the like are obviously improved.

The invention discloses a sleep quality analysis system based on human body HRV measurement, and relates to the technical field of sleep quality, a preliminary analysis module can quickly judge the sleep quality fluctuation condition of a user through calculation of a heart rate variability factor HRV, when the HRV value exceeds a preset threshold value, the system automatically sends a sleep quality fluctuation command to a cloud platform, and the sleep quality fluctuation command is sent to the cloud platform. Therefore, the change of the sleep state is timely responded. Besides, the neural activity module analyzes the balance state of parasympathetic nerves and sympathetic nerves and constructs a neural activity proportion Jhb, the activity deviation of the nervous system of the user in the sleep state can be determined, and a reliable basis is provided for deep analysis of the sleep state. The sleep state comprehensive analysis module is further combined with the heart rate recovery ratio Xhb and the neural activity ratio Jhb, a sleep quality evaluation index Sgzs is calculated through the trained sleep quality prediction model, and quantitative evaluation of the sleep quality of the user is further achieved.

The invention belongs to the technical field of intelligent control equipment, and particularly relates to an intelligent room system for optimizing sleep and a working method thereof. The system comprises a data acquisition and monitoring module used for acquiring various physiological data and environmental parameters when a user moves; the machine learning prediction and evaluation module is used for analyzing, predicting and evaluating the collected data through a DNN technology; the environment control module is used for automatically adjusting environment parameters according to the current demand and the sleep stage of the user; the sleep feedback and adjustment module is used for matching the environment information with user perception and controlling the output of the terminal equipment; the central control and suggestion module is used for providing a sleep environment configuration suggestion for the user; and the data transmission module is used for performing wireless data transmission and communication with other modules. The intelligent sleep monitoring system has the advantages that the sleep condition of the user can be comprehensively monitored and analyzed, personalized sleep environments and suggestions are provided, and therefore the sleep quality and life quality of middle-aged and elderly people are improved.

The invention particularly relates to a quartered sleep state monitoring method and a quartered sleep state monitoring system based on a non-contact vibration sensor. The quartered sleep state monitoring method and the quartered sleep state monitoring system are particularly suitable for realizing non-sensitive sleep quality evaluation through an intelligent bed in a family environment. Comprising the following steps that 1, vibration signals of a human body in the sleep process are continuously collected through a non-contact vibration sensor; step 2, preprocessing the vibration signal, including denoising and segmentation processing, to obtain time domain vibration data; step 3, performing frequency domain transformation on the time domain vibration data, and extracting frequency domain features including respiratory rate, heart rate and heart variability parameters; 4, time alignment is carried out on the frequency domain features and sleep staging labels synchronously obtained through a polysomnography, and a training data set is constructed; an XGBoost classification model based on time-frequency domain feature fusion is provided, fundamental frequency and harmonic energy proportions of respiratory signals are extracted through short-time Fourier transform, and quartering classification is achieved in combination with the LF / HF ratio of heart rate variability.

The invention belongs to the field of intelligent environment comprehensive regulation and control, and particularly discloses and provides an intelligent environment comprehensive regulation and control method based on sleep aiding, which comprises the following steps: firstly, according to the heart rate and skin resistance of a user, evaluating the stability of a sleep state, automatically adjusting the brightness and color temperature of light, and creating a suitable sleep atmosphere; secondly, determining the temperature and humidity of the preference environment by analyzing the longest sleep time, the sleep time and the number of times of waking up at night in the historical sleep data of the user, and intelligently regulating and controlling the environment to match the temperature and humidity of the preference environment of the user. And finally, the system recommends a suitable music type according to the environmental noise type, and accurately recommends sleep music based on preference feedback of the user for each piece of music. Meanwhile, environmental noise decibel is monitored in real time, music volume is dynamically adjusted, harmonious coexistence of music and environmental noise is ensured, a more comfortable and quiet sleep environment is created for the user, and the sleep quality of the user is improved in multiple dimensions.

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system, computer equipment and a storage medium, and the sleep monitoring method comprises the following steps: monitoring the pressure change of the head and neck of a user through a miniature differential pressure sensor, collecting pressure data in real time, recording a respiratory cycle according to pressure fluctuation, and determining the sleep state of the user; and identifying high and low pressure peak values, and generating head pressure monitoring data. According to the invention, by monitoring the pressure change of the head and the neck in real time, the capturing capability of tiny breathing changes is improved, refined breathing cycle recording and high-precision pressure peak value recognition are realized, potential breathing abnormity can be found at the initial stage, timely response is ensured, and the breathing frequency and amplitude are comprehensively analyzed; the breathing modes are accurately classified, so that the recognition of the abnormal breathing state is quicker, more accurate and more detailed dynamic monitoring and data analysis are realized, the evaluation of the sleep depth and stage is enhanced, and the comprehensive evaluation of the sleep quality is more scientific.

The invention provides an intelligent pillow and a control method thereof, and belongs to the technical field of intelligent home furnishing.The intelligent pillow comprises a pillow body, the pillow body is provided with a snore stopping air bag, a pressure sensor array, a microphone line array and a control box, the snore stopping air bag is connected with an air pump through an air pipe, and the control box is in signal connection with the air pump; the pressure sensor array is in signal connection with the control box, and the pressure sensor array is used for collecting head pressure signals of a user and transmitting the head pressure signals to the control box; the microphone line array is in signal connection with the control box, and the microphone line array is used for collecting sound signals of a user and transmitting the sound signals to the control box so as to judge the snoring state of the user; the snore stopping air bag corresponding to the head position of the user is adjusted to change the external form of the pillow body, so that the head position of the user is changed. The snore problem of the user can be effectively relieved, and the sleep quality of the user is improved.

The invention relates to the field of smart home, and provides a smart bed sleep quality analysis method, which comprises the following steps: firstly, determining a sleep monitoring area of a smart bed, obtaining environment information, querying sleeper characteristics according to the environment information, determining target sleep data, performing segmentation processing, calculating a sleep fluctuation value and a characteristic deviation degree, and correcting to obtain a sleep correction parameter; querying key correction indexes, performing trend marking, dividing continuous association time periods, updating to obtain trend updating indexes, mining index association characteristics, constructing a sleep quality analysis strategy in combination with life data of a sleeper, performing simulation analysis to obtain strategy simulation scores, and finally generating a sleep quality analysis report corresponding to the intelligent bed according to the strategy simulation scores. The analysis effect of the sleep quality of the intelligent bed can be improved.

The embodiment of the invention discloses a waist muscle fatigue detection method and device, equipment and a medium. The method comprises the following steps: acquiring electromyographic signals and temperature data of waist muscles of a target user based on waist detection equipment, and acquiring a first fatigue degree grade of subjective evaluation of the target user; determining a second fatigue degree grade of the waist muscles according to the electromyographic signals and the temperature data; determining a target fatigue degree grade of a target user according to the first fatigue degree grade and the second fatigue degree grade; adjusting the working mode of the intelligent mattress according to the target fatigue degree grade; wherein the waist detection device is associated with the intelligent mattress. According to the technical scheme, the working mode of the mattress can be intelligently adjusted by monitoring the fatigue grade state of the waist muscles in real time, so that a personalized waist muscle relaxation scheme is provided, and the sleep quality of a target user is improved.

The invention relates to the technical field of physiological signal processing and sleep monitoring, in particular to a sleep staging method and a sleep staging system for collecting multiple physiological signals, and the sleep staging method and the sleep staging system for collecting the multiple physiological signals synchronously collect auditory meatus photoelectric volume pulse waves, temperature and head micro-motion signals through an in-ear sensor array. According to the method, the signal quality index is calculated, time domain, frequency domain and nonlinear features are extracted, a dynamic weighted fusion mechanism is adopted, feature weights are adjusted according to the signal quality index, a hierarchical depth time sequence learning model is input for sleep staging, and the sleep staging accuracy is improved to 89% or above and is improved by 15-20% compared with a single brain wave method. A sleep state evaluation report and a personalized feedback intervention strategy generated by the system are beneficial for improving sleep quality, a dynamic weighted fusion mechanism enhances system robustness, adapts to different signal qualities and ensures stable performance, and the invention provides an efficient and accurate new method for the field of sleep monitoring.

The utility model relates to the field of inflatable products, and provides a multi-layer and multi-chamber inflatable bed which comprises a mattress body defined by a top layer, a shroud ring and a bottom layer, the top layer comprises a first surface layer and a second surface layer arranged below the first surface layer, and the first surface layer and / or the second surface layer are / is connected with the shroud ring; the first surface layer and the second surface layer are connected to form a plurality of surface side cavities. The inflatable bed has the advantages that the top layer is provided with a plurality of cavities in direct contact with a human body, so that the comfort of the inflatable bed is improved, the sleep quality and the use effect of a user are improved, and the experience feeling of the user on the inflatable bed is optimized. And through the arrangement of the natural vent holes, natural adjustment is formed, the comfort of the inflatable bed is further improved, the inflatable bed can be folded and stored conveniently, and the storage occupied volume is reduced.

Provided is a health monitoring system and a health monitoring method that enable constant daily health monitoring with relatively little burden on a subject, that helps prevent and improve lifestyle-related diseases while maintaining the quality of sleep, and that leads to a long and healthy life. [Solution] A health monitoring system using a non-invasive biometric device to measure the biometrics of a subject, comprising a measurement means which is remotely operated from the outside to grasp the internal condition of the body, such as within the organs and the digestive system, and measures spatiotemporal internal environmental information using an in-vivo cybernetic avatar capable of interacting within the body, and a visualization means which visualizes the spatiotemporal internal environmental information measured by the measurement means, wherein the visualization means synthesizes and displays the measurement results from the biometric device and the measurement results of the spatiotemporal internal environmental information from the in-vivo cybernetic avatar.

The invention relates to the technical field of sleep stage classification, and particularly provides a sleep quality monitoring method fusing multi-scale features. The method comprises the following steps: preprocessing input signals, and inputting the input signals into a multi-mode sleep signal analysis network after ensuring that the signal amplitudes are consistent; capturing features of a plurality of time scales through a multi-granularity feature learning module, and carrying out feature fusion; integrating spatial information by using a spatial-temporal feature enhancement module, and mapping the three-dimensional features into a two-dimensional time sequence feature sequence; on the basis of a time context module of Mama, long-distance time dependence is modeled with linear complexity, features are averagely aggregated along the time dimension through a classification module, a classification result is output through a full connection layer and an activation function Softmax, and the method improves the accuracy of sleep stage classification while ensuring the high efficiency of sleep stage classification.

The invention discloses a health trend analysis early warning method based on big data, and relates to the technical field of data processing, and the method comprises the steps: analyzing female physiological cycle data based on big data, and building a health trend analysis model; the method comprises the steps of obtaining a physiological cycle type of a target user, collecting a sleep state, a physiological cycle and physical sign data of the target user, generating standard health monitoring data, initializing an analysis model according to basic information of the user, performing sleep quality change analysis, evaluating a health trend of the user according to an analysis result in combination with physiological cycle characteristics, and providing personalized sleep improvement suggestions. The technical problem that traditional health management in the prior art neglects the influence of the female physiological cycle on the health state and the sleep quality and lacks pertinence and individuation is solved, and individualized health management is provided by combining the female physiological cycle data and the sleep state data. And the sleep quality of women in different physiological cycle stages is improved.

The invention discloses an intelligent monitoring system for climacteric symptoms, and relates to the technical field of intelligent medical monitoring management, and the system comprises the following components: a monitoring device which is used for collecting physiological data of a user in real time, including body temperature, heart rate, blood pressure, sweat secretion, emotional fluctuation and sleep quality, the monitoring device is composed of a plurality of sensors and is worn on the body of a user. According to the intelligent monitoring system for the climacteric symptoms, the logic judgment steps of similarity comparison and repeated correction are added before health management suggestions are generated, the consistency and accuracy of a prediction report and a monitoring report on the numerical level can be effectively improved, and therefore interference of accidental noise or abnormal events on a final decision is reduced; and the scientificity, the accuracy and the reliability of the intelligent climacteric symptom monitoring system are improved.

The invention relates to the technical field of integrated sleep management, in particular to a sleep data analysis management system based on artificial intelligence. The method comprises the following steps: segmenting a sleep cycle of a user based on brain wave data to obtain N sleep stages of the user; based on the body movement monitoring data, the pulse monitoring data and the respiration monitoring data, calculating to obtain a first sleep quality characteristic value of the sleep stage of the user, and based on the sleep posture category and a sleep quality weight parameter corresponding to the sleep posture category, determining a second sleep quality characteristic value of the sleep stage of the user; the sleep quality analysis index of the sleep cycle of the user is generated based on the sleep quality parameters of the sleep stage of the user, the sleep early warning signal is generated based on the sleep quality analysis index of the sleep cycle of the user, multi-dimensional data such as brain waves, body movement, pulse, breathing and sleeping postures can be combined, the sleep stage can be accurately divided, and the sleep early warning effect is improved. And sleep quality parameters are calculated through multi-modal data fusion, so that the accuracy and practicability of sleep monitoring are improved.

The invention discloses a forehead electroencephalogram rhythm-driven sleep state staging method based on multi-dimensional features, and belongs to the field of electroencephalogram signal processing in the field of digital signal processing, and the method comprises the following steps: step 1, preparing a sleep physiological signal data set; 2, preprocessing the forehead electroencephalogram signals; 3, rhythm wave extraction is conducted on the forehead electroencephalogram signals; 4, time domain feature values, feature values based on power spectral density and feature values based on multi-scale entropy of the forehead electroencephalogram signals and the electrooculogram signals are calculated respectively; step 5, sending the characteristic values calculated in the step 4 into an SVM (Support Vector Machine) for automatic staging, and finally optimizing a characteristic value group to obtain an automatically predicted sleep staging result; the method has high accuracy, can provide visual feedback for the user, and helps the user to know the sleep quality and state of the user so as to improve the sleep habit of the user.

The invention relates to the technical field of intelligent bed early warning, and discloses an intelligent bed integrated AI monitoring and early warning method and system, and the method comprises the steps: collecting sleep-related data, and carrying out formatting and quantity stiffening processing to obtain target sleep data; constructing a sleep stage thermodynamic diagram, analyzing the sleep cycle characteristics of the intelligent bed user, analyzing the body movement frequency condition of the intelligent bed user in the sleep process by using the target sleep data, and analyzing the overall sleep characteristics of the intelligent bed user; analyzing the sleep quality of the intelligent bed user, and when the sleep quality is poor, analyzing cardiovascular health characteristics and respiratory system health characteristics of the intelligent bed user so as to analyze preliminary health state characteristics of the intelligent bed user; and collecting sleep adjustment related data after the intelligent bed user uses the adjustment equipment, analyzing the health state adjustment characteristics of the intelligent bed user, and performing sleep health early warning on the intelligent bed user in combination with the preliminary health state characteristics. According to the invention, the early warning reliability of the intelligent bed integrated AI can be improved when the number of influence factors is large.

The invention relates to the technical field of intelligent monitoring and control, and discloses a sleep environment monitoring device and a monitoring system.The sleep environment monitoring device comprises a multifunctional integrated sensor used for monitoring environment parameters in real time; the intelligent interaction and display unit is used for displaying environment parameters and sleep quality analysis reports and providing personalized settings; the environment adjusting equipment linkage unit comprises an air conditioning system, a fresh air system, a light module capable of adjusting color temperature and a driving device of an electric curtain; and the communication module is used for data transmission. Through the multifunctional integrated sensor module, accurate monitoring of environmental parameters is realized. The intelligent interaction and display module simplifies the operation process through a touch screen and a voice recognition technology. The personalized sleep environment optimization algorithm module calculates optimal environment parameters according to personalized information and real-time data, and the multi-mode environment interference early warning and processing module monitors and processes interference factors in the environment and cooperates with the environment adjustment equipment linkage module to achieve automatic adjustment of the environment parameters.

The invention discloses a polymorphic sleep data fusion self-attention sleep system and method. The system comprises a data acquisition module which is responsible for acquiring environmental data and physiological data of a user; the signal preprocessing module is used for performing segmentation, normalization and de-noising processing on the collected EEG and EOG signals; the frequency division multiplexing module is used for decomposing the preprocessed signal frame into a plurality of narrowband signals by using a frequency division multiplexing technology; the feature extraction module is used for extracting features from the decomposed narrowband signals; the model training module is used for training a model by utilizing the feature set; the sleep staging module is used for performing real-time or offline sleep staging through the trained model; the sleep quality evaluation module is used for calculating a sleep quality index based on a sleep staging result; the correlation analysis module is used for performing correlation analysis on the relationship between different sleep stages and the sleep quality indexes to generate a quality evaluation model; and the early warning system module is used for setting an early warning threshold value according to the sleep quality evaluation result and the quality evaluation model.

The invention discloses a personalized sleep intervention and first aid method and system based on multi-modal data fusion. The method comprises the steps that physiological index data and motion data of a user are collected in real time through wearable equipment; generating a sleep quality score based on the physiological index data in combination with a preset sleep quality evaluation model; generating a personalized sleep intervention scheme according to the sleep quality score and the motion data; the sleep intervention module is started by manually triggering or automatically detecting the insomnia state of the user; according to the physiological index data monitored in the sleeping process, the indexes exceeding the standard are recorded, and a prompt is given out after waking up the next day, or a first-aid mode is triggered. The personalized dynamic sleep intervention scheme is generated by quantifying the physiological indexes of the user, and the limitation of a single sensor is broken through; sleep intervention can be triggered in multiple modes, the intervention intensity can be dynamically adjusted according to the real-time physiological state, the first-aid mode can be automatically started, and first-aid delay is avoided.

The invention relates to an automatic mattress temperature adjusting method and system based on physiological data. According to the method, heart rate, body movement, respiratory rate and body temperature data of a user are collected in real time through a physiological parameter sensor in the intelligent mattress, and sleep stages of the user are identified on the basis of the data and comprise a falling asleep stage, a light sleep stage, a deep sleep stage and an awakening stage. And establishing an optimal sleep temperature database of the user, recording a corresponding relationship between physiological data of the user in different sleep stages and the mattress temperature, calculating a sleep quality score based on the body movement frequency and the deep sleep duration, and recording a compensation coefficient of the environment temperature and the mattress temperature at the same time. And determining a target adjusting temperature according to the current sleep stage of the user and the database information, and correcting the target adjusting temperature in combination with the environment temperature and the compensation coefficient to obtain a final adjusting temperature. The temperature of the mattress is dynamically adjusted to the corrected target adjusting temperature through a water circulation temperature control system arranged in the mattress, so that personalized sleep temperature control is provided.