Deep sleep analysis and early warning system integrated with heart rate monitoring

Abstract

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.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring, and in particular to a deep sleep analysis and early warning system integrated with heart rate monitoring. Background Art

[0002] With the acceleration of the pace of modern life and the improvement of people's health awareness, sleep quality has become an important factor affecting the physical and mental health of individuals, especially the deep sleep stage is of great significance to physical recovery, memory consolidation and emotional regulation. Studies have shown that stable physiological parameters such as heart rate, blood oxygen saturation and respiratory rate, as well as comfortable ambient temperature, humidity, light and noise levels are the key to ensuring high-quality sleep. However, due to life pressure and environmental interference, many people find it difficult to obtain high-quality deep sleep, so deep sleep analysis and improvement based on physiological and environmental monitoring has become a research hotspot.

[0003] Although existing sleep monitoring technologies can achieve basic collection and simple analysis of physiological data, they have many limitations in terms of accuracy, personalization, and data security. Most technologies rely only on a single physiological parameter and fail to combine environmental data for multi-dimensional analysis, making it difficult to comprehensively assess sleep quality. Secondly, existing systems usually lack personalized feedback and dynamic optimization functions, and are unable to adjust monitoring models based on users' historical data and individual differences, resulting in low accuracy of warning and analysis results. In addition, the lack of user data security and long-term trend analysis also limits the practicality of the system. Summary of the invention

[0004] The present invention provides a deep sleep analysis and early warning system with integrated heart rate monitoring.

[0005] A deep sleep analysis and early warning system with integrated heart rate monitoring, comprising a physiological parameter acquisition module, an environmental parameter acquisition module, a deep sleep analysis module, an abnormality detection and early warning module, a dynamic feedback and optimization module, a personalized suggestion module and a data storage and analysis module, wherein;

[0006] The physiological parameter acquisition module is used to monitor the user's physiological parameters in real time, including heart rate, blood oxygen saturation and respiratory rate;

[0007] The environmental parameter acquisition module is used to monitor sleep environment parameters, including room temperature, humidity, light intensity and noise level;

[0008] The deep sleep analysis module constructs a deep sleep model based on the user's physiological parameters and environmental parameters using a multivariate data analysis algorithm, identifies the characteristics of the deep sleep stage by analyzing the heart rate variability and respiratory rhythm, and evaluates the time and quality of deep sleep;

[0009] The abnormality detection and early warning module monitors the user's physiological abnormalities in real time based on the analysis results of the deep sleep analysis module, and triggers early warning prompts through the data abnormality detection algorithm to notify the user or family members to take corresponding measures;

[0010] The dynamic feedback and optimization module is used to optimize the deep sleep model according to user feedback, historical physiological parameters and historical environmental parameters to improve the accuracy of analysis and early warning;

[0011] The personalized suggestion module generates personalized sleep improvement suggestions based on the user's deep sleep analysis results and abnormal detection results, including work and rest adjustment, environment optimization suggestions and diet guidance;

[0012] The data storage and analysis module is used to store the user's historical physiological data and environmental data, and regularly generate trend analysis reports to help the user understand the trend of changes in sleep status.

[0013] Optionally, the physiological parameter acquisition module includes:

[0014] Heart rate monitoring: Use photoelectric plethysmography sensors to collect the user's pulse signal and monitor heart rate in real time;

[0015] Blood oxygen saturation monitoring: using dual-wavelength light sources (such as red light and infrared light) to obtain the user's pulse blood oxygen saturation through a photoelectric sensor;

[0016] Respiratory rate monitoring: Use chest or abdominal volume change sensors to monitor the user's respiratory rate in real time.

[0017] Optionally, the environmental parameter acquisition module includes:

[0018] Room temperature monitoring: Use temperature sensors to collect indoor temperature data of the user's sleeping environment;

[0019] Humidity monitoring: Use humidity sensors to collect humidity data of the user's sleeping environment;

[0020] Light intensity monitoring: Use light sensors to collect light intensity data in the user's sleeping environment;

[0021] Noise level monitoring steps: Use a noise sensor to collect noise intensity data in the user's sleeping environment.

[0022] Optionally, the deep sleep analysis module includes:

[0023] Data preprocessing: data cleaning and normalization of the collected physiological parameters and environmental parameters;

[0024] Heart rate variability acquisition: Use a photoplethysmography sensor to detect the time interval between adjacent heartbeats and calculate heart rate variability;

[0025] Respiratory rhythm acquisition: Use a chest strap sensor to monitor the expansion and contraction of the chest or abdomen, identify each respiratory cycle, and calculate the respiratory rate;

[0026] Feature extraction: Extract characteristic variables related to deep sleep based on preprocessed physiological parameters and environmental parameters;

[0027] Deep sleep model construction: The deep sleep model is constructed using the extracted feature variables, and the model outputs the user's deep sleep evaluation value.

[0028] Optionally, the evaluation of the time and quality of deep sleep includes:

[0029] Deep sleep stage recognition: Input the real-time monitored physiological parameters and environmental parameters into the constructed deep sleep model, and judge whether the user has entered the deep sleep stage according to the deep sleep evaluation value output by the model. If the deep sleep evaluation value exceeds the preset threshold, the user is judged to have entered the deep sleep stage and the timing starts;

[0030] Deep sleep time calculation: The time of the identified deep sleep stage is accumulated and calculated. By monitoring the duration of the deep sleep state, the total deep sleep time of the user in one sleep cycle is calculated.

[0031] Deep sleep quality assessment: Comprehensively assess the user's deep sleep quality based on the total deep sleep duration, heart rate variability and stability of breathing rhythm.

[0032] Optionally, the anomaly detection and early warning module includes:

[0033] Data acquisition: Real-time reception of physiological parameters from the deep sleep analysis module, including heart rate, blood oxygen saturation and respiratory rate;

[0034] Threshold setting: Set the upper and lower thresholds of heart rate, blood oxygen saturation and respiratory rate according to the user's normal physiological range and personalized parameters;

[0035] Abnormal identification: Identify abnormal physiological conditions by comparing real-time physiological parameters with set thresholds.

[0036] Optionally, the anomaly detection and early warning module further includes:

[0037] Abnormal confirmation: When an abnormal condition is detected, the abnormality is further confirmed through the abnormal detection algorithm to reduce false alarms and ensure the accuracy of the warning;

[0038] Warning trigger: After confirming the abnormal situation, the warning prompt is triggered and the warning information is sent to the user end or related equipment;

[0039] Notification sending: Send warning information to users or family members through wireless communication;

[0040] Record storage: record the warning information in the data storage and analysis module, including the abnormality type, occurrence time, duration and processing status.

[0041] Optionally, the dynamic feedback and optimization module includes:

[0042] User feedback collection: Provide a user feedback interface to allow users to provide feedback on the accuracy and effectiveness of warning information;

[0043] Historical data analysis: Regularly analyze the user's historical physiological parameters (such as heart rate, blood oxygen saturation and respiratory rate) and historical environmental parameters (such as room temperature, humidity, light intensity and noise level) to identify the changing trends of the user's sleep patterns and individual physiological characteristics;

[0044] Model parameter adjustment: Dynamically adjust the parameters of the deep sleep model based on user feedback, historical physiological parameters, and historical environmental parameter analysis results;

[0045] Model Adaptation: After the deep sleep model is adjusted, an adaptive test is performed to verify whether the dynamically adjusted deep sleep model accurately reflects the user's sleep pattern and physiological characteristic changes;

[0046] Application of optimization results: Apply the dynamically adjusted deep sleep model to subsequent sleep analysis and early warning judgment.

[0047] Optionally, the personalized suggestion module specifically includes:

[0048] Individual difference analysis: collect and analyze the user's historical physiological parameters and environmental parameters to identify the user's sleeping habits, environmental preferences and physiological characteristics;

[0049] Interpretation of sleep analysis results: Based on the deep sleep analysis results and abnormal detection results, interpret the user's deep sleep state, evaluate the time and quality of deep sleep and the frequency and severity of abnormal events, and provide users with targeted sleep quality improvement plans;

[0050] Generate work and rest adjustment suggestions: Provide personalized work and rest adjustment suggestions based on the user's deep sleep status;

[0051] Environmental optimization suggestion generation: Generate sleep environment optimization suggestions based on the user's environmental parameters and sleep preferences;

[0052] Diet guidance generation: Combine the user's physiological parameters and sleep analysis results to provide sleep-related diet suggestions;

[0053] Personalized health report generation: Generate a personalized health report for the user, presenting the user's deep sleep analysis results, abnormal detection records, and multi-dimensional improvement suggestions in a visual way.

[0054] Optionally, the data storage and analysis module includes:

[0055] Data storage: structured storage of the user's physiological parameters and environmental parameters;

[0056] Data encryption: Encryption technology is used to encrypt the user's physiological parameters and environmental parameters during data storage;

[0057] Historical data archiving: Archiving and storing historical physiological parameters and environmental parameters that exceed a certain time period to reduce the occupation of main storage space;

[0058] Data retrieval: Provides data retrieval functions for users, allowing users to query based on date or data type (such as physiological data or environmental data);

[0059] Trend analysis: Regularly analyze the user's historical physiological parameters and environmental parameters to generate trend reports on changes in sleep status;

[0060] Report generation: Based on the trend analysis results, visual trend analysis reports are generated regularly and the report contents are displayed through the user-side application.

[0061] Beneficial effects of the present invention:

[0062] The present invention, through the physiological parameter acquisition module and the environmental parameter acquisition module, the system can monitor the user's heart rate, blood oxygen saturation, respiratory rate, ambient temperature, humidity, light intensity and noise level in real time, and fully grasp the internal and external factors that affect sleep. The deep sleep analysis module is based on a multivariate data analysis algorithm, and constructs a deep sleep model by extracting key features such as heart rate variability and respiratory rhythm. With the support of the dynamic feedback and optimization module, it can continuously optimize the model parameters according to individual differences of users and historical data, realize the dynamic adaptation of the system to different users, and make sleep analysis and abnormality detection more accurate and reliable.

[0063] The present invention realizes real-time detection of physiological abnormalities (such as sudden increase in heart rate and decrease in blood oxygen) during sleep of users through an abnormality detection and early warning module, and combines abnormality persistence analysis, frequency analysis, trend analysis and multivariate association analysis with an abnormality detection algorithm to significantly reduce the false alarm rate and ensure the accuracy of early warning. When an abnormal event is confirmed, the system can trigger an early warning prompt in time and notify the user or family members, providing real-time health risk feedback. At the same time, the personalized suggestion module generates personalized health management plans such as work and rest adjustment, environmental optimization and diet guidance based on the user's deep sleep analysis and abnormality detection results, helping users to effectively improve their sleep quality.

[0064] In the present invention, the data storage and analysis module uses encryption technology and database archiving strategies to ensure the safe storage of user physiological data and environmental data, and supports long-term data retention and efficient retrieval. The system also generates a visual sleep trend report by performing trend analysis on the user's historical data, helping the user to gain an in-depth understanding of the long-term changes in their sleep status and supporting personalized health management and improvement strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 A schematic diagram of a system flow of an embodiment of the present invention;

[0067] Figure 2 Schematic diagram of the deep sleep analysis module process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0069] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0070] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0071] like Figure 1-Figure 2 As shown, a deep sleep analysis and early warning system with integrated heart rate monitoring includes a physiological parameter acquisition module, an environmental parameter acquisition module, a deep sleep analysis module, an abnormality detection and early warning module, a dynamic feedback and optimization module, a personalized suggestion module and a data storage and analysis module, wherein;

[0072] The physiological parameter acquisition module is used to monitor the user's physiological parameters in real time, including heart rate, blood oxygen saturation and respiratory rate;

[0073] The environmental parameter acquisition module is used to monitor sleep environment parameters, including room temperature, humidity, light intensity and noise level;

[0074] The deep sleep analysis module builds a deep sleep model based on the user's physiological parameters and environmental parameters using a multivariate data analysis algorithm. It identifies the characteristics of the deep sleep stage and evaluates the duration and quality of deep sleep by analyzing heart rate variability and respiratory rhythm.

[0075] The abnormality detection and warning module monitors the user's physiological abnormalities in real time based on the analysis results of the deep sleep analysis module, such as sudden increase in heart rate and decrease in blood oxygen saturation, and triggers early warning prompts through data abnormality detection algorithms to notify users or family members to take corresponding measures;

[0076] The dynamic feedback and optimization module is used to optimize the deep sleep model based on user feedback, historical physiological parameters, and historical environmental parameters to improve the accuracy of analysis and early warning;

[0077] The personalized suggestion module generates personalized sleep improvement suggestions based on the user's deep sleep analysis results and abnormal detection results, including work and rest adjustment, environment optimization suggestions and diet guidance;

[0078] The data storage and analysis module is used to store the user's historical physiological data and environmental data, and regularly generate trend analysis reports to help users understand the changing trends of their sleep conditions.

[0079] The physiological parameter acquisition module includes:

[0080] Heart rate monitoring: Use a photoelectric plethysmography sensor to collect the user's pulse signal and monitor the heart rate in real time. The sensor on the user's wrist or finger detects the change in light absorption of blood volume and calculates the heart rate value. The heart rate calculation formula is as follows:

[0081]

[0082] Where HR is the number of heart beats per minute (BPM), and RR is the time interval between adjacent heart beats in seconds. This step obtains stable heart rate data by continuously monitoring the RR interval to ensure the real-time and accuracy of the heart rate information;

[0083] Blood oxygen saturation monitoring: Use a dual-wavelength light source (such as red light and infrared light) to obtain the user's pulse blood oxygen saturation through a photoelectric sensor. The blood oxygen saturation calculation formula is as follows:

[0084]

[0085] Among them, R AC is the AC component of the arterial blood absorption signal, R DC It is a DC component. By analyzing the absorption changes of light of different wavelengths, it can accurately calculate the blood oxygen level and ensure the real-time blood oxygen data of the user.

[0086] Respiratory rate monitoring: Use the volume change sensor on the chest or abdomen to monitor the user's respiratory rate in real time. The sensor calculates the respiratory rate by detecting the volume change caused by breathing. The respiratory rate calculation formula is:

[0087]

[0088] Wherein, BR is the number of respirations per minute, n is the number of respirations during the monitoring period, and t is the duration of the monitoring period in minutes. This step ensures the real-time update of the respiratory rate data by continuously collecting changes in respiratory rhythm;

[0089] The physiological parameter acquisition module works together through the above steps to obtain the user's physiological parameter data in real time and accurately, providing comprehensive and high-precision data support for deep sleep analysis.

[0090] The environmental parameter acquisition module includes:

[0091] Room temperature monitoring: Use temperature sensors to collect indoor temperature data of the user's sleeping environment. The temperature sensors are installed in the sleeping area. By regularly collecting and recording temperature data, the accuracy and real-time nature of the temperature information are ensured. The temperature data is in degrees Celsius, which can detect temperature changes and identify ambient temperature fluctuations in a timely manner, providing a basis for improving the user's sleeping environment.

[0092] Humidity monitoring: Use humidity sensors to collect humidity data of the user's sleeping environment. The humidity sensors are installed in the sleeping area and collect environmental humidity data regularly to ensure the accuracy of humidity data and help identify humidity anomalies that may affect deep sleep.

[0093] Light intensity monitoring: Use light sensors to collect light intensity data in the user's sleeping environment. The light sensors are placed in appropriate locations to avoid interference from direct light sources. By detecting changes in light intensity in the environment and recording light intensity data, light intensity monitoring helps identify situations where light is too strong or too dark, thereby avoiding interference from poor lighting conditions on sleep stages.

[0094] Noise level monitoring steps: Use noise sensors to collect noise intensity data in the user's sleeping environment. The noise sensors are installed around the sleeping area and collect noise intensity data regularly. By monitoring the noise changes in the environment, the noise sources that disturb sleep are identified to help maintain a low-noise sleeping environment.

[0095] The environmental parameter acquisition module can comprehensively and accurately monitor the user's sleeping environment parameters in real time, providing precise environmental data support for deep sleep analysis.

[0096] The deep sleep analysis module includes:

[0097] Data preprocessing: Data cleaning and normalization of collected physiological parameters (including heart rate, blood oxygen saturation and respiratory rate) and environmental parameters (including room temperature, humidity, light intensity and noise level) to ensure data integrity and consistency, and improve the accuracy of data analysis by eliminating abnormal data and filling missing values;

[0098] Heart rate variability acquisition: Use the photoplethysmography sensor to detect the time interval between adjacent heartbeats (RR interval) and calculate the heart rate variability (HRV), including:

[0099] (1) Time domain analysis: Calculate the standard deviation of the RR interval and the root mean square index of the difference between adjacent RR intervals:

[0100]

[0101] Where RMSSD is the root mean square, SDNN is the standard deviation, and N is the total number of RR intervals. is the average value of the RR interval;

[0102] (2) Frequency domain analysis: Fast Fourier transform was performed on the RR interval sequence to extract low-frequency (LF, 0.04-0.15 Hz) and high-frequency (HF, 0.15-0.4 Hz) components. The low-frequency component reflects sympathetic nerve activity, while the high-frequency component is related to parasympathetic nerves and respiration.

[0103] Respiratory rhythm acquisition: Use a chest strap sensor to monitor the expansion and contraction of the chest or abdomen, identify each respiratory cycle, and calculate the respiratory rate;

[0104] Feature extraction: Based on the preprocessed physiological and environmental parameters, feature variables related to deep sleep are extracted, including low-frequency and high-frequency components in heart rate variability, the periodic stability of respiratory rhythm, and the influence of environmental noise and light on physiological rhythm;

[0105] Deep sleep model construction: The deep sleep model is constructed using the extracted feature variables. The model outputs the user's deep sleep evaluation value. The mathematical expression of the model is:

[0106] S deep =f(HRV,BR,Temp,Humidity,Light,Noise);

[0107] Among them, S deep is the deep sleep assessment value, HRV represents heart rate variability, BR represents breathing rate, Temp, Humidity.Light and Noise represent ambient temperature, humidity, light intensity and noise level respectively;

[0108] Through data preprocessing, heart rate variability and respiratory rhythm acquisition, feature extraction and model building steps, an analysis basis for deep sleep state is formed, providing comprehensive support for subsequent time and quality assessment.

[0109] Assessment of deep sleep duration and quality includes:

[0110] Deep sleep stage recognition: Input the real-time monitored physiological parameters and environmental parameters into the constructed deep sleep model, and judge whether the user has entered the deep sleep stage according to the deep sleep evaluation value output by the model. If the deep sleep evaluation value exceeds the preset threshold, the user is judged to have entered the deep sleep stage and the timing starts;

[0111] Deep sleep time calculation: The time of the identified deep sleep stage is accumulated and calculated. By monitoring the duration of the deep sleep state, the total duration of the user's deep sleep in one sleep cycle is calculated. This duration data is used to measure the time characteristics of deep sleep.

[0112] Deep sleep quality assessment: Based on the total deep sleep duration, heart rate variability and respiratory rhythm stability, the user's deep sleep quality is comprehensively assessed. The sleep quality calculation formula is:

[0113] Q deep =w 1 ×HRV stability +w 2 ×BR stability +w 3 ×T deep ;

[0114] Among them, Q deep Score deep sleep quality, HRV stability and BR stability Respectively represent the stability of heart rate variability and respiratory rhythm, T deep is the deep sleep time, w 1 、w 2 and w 3 is the weight coefficient of each indicator;

[0115] Through deep sleep stage identification, time calculation and quality assessment, a multi-dimensional analysis of the user's deep sleep is achieved, providing users with scientific sleep quality feedback and data support.

[0116] The anomaly detection and warning module includes:

[0117] Data acquisition: Real-time reception of physiological parameters from the deep sleep analysis module, including heart rate, blood oxygen saturation, and respiratory rate, ensuring the latest data for abnormality detection;

[0118] Threshold setting: Set the upper and lower thresholds of heart rate, blood oxygen saturation and respiratory rate according to the user's normal physiological range and personalized parameters (personalized parameters refer to parameters set based on the user's individual characteristics and historical physiological parameters). The thresholds can be automatically adjusted based on the user's historical data to adapt to individual differences;

[0119] Abnormality discrimination: By comparing the real-time physiological parameters with the set thresholds, physiological abnormalities are identified. For example, when the heart rate exceeds the preset range (heart rate surge) or the blood oxygen saturation is lower than the preset threshold, it is judged as an abnormal state. The specific discrimination logic is expressed as follows:

[0120]

[0121] Among them, HR is the current heart rate, HR上限 is the upper heart rate threshold, SpO 2 is the current blood oxygen saturation, SpO 2下限 is the lower blood oxygen threshold;

[0122] Through data acquisition, threshold setting and abnormality identification steps, real-time monitoring of the user's physiological state is achieved to ensure timely response when abnormalities occur.

[0123] The anomaly detection and warning module also includes:

[0124] Anomaly confirmation: When an abnormal condition is detected, the anomaly is further confirmed through an anomaly detection algorithm to reduce false positives and ensure the accuracy of the warning. The anomaly detection algorithm includes:

[0125] (1) Abnormal persistence analysis: Calculate the duration of the abnormal state T 持续 , and judge whether it exceeds the set time threshold T 阈值 , expressed as:

[0126]

[0127] If T 持续 >T 阈值 , it is judged as a valid exception;

[0128] (2) Abnormal frequency analysis: In the specified time period T 段 The frequency of internal calculation anomalies F 异常 , if the frequency of abnormal occurrence is higher than the preset frequency threshold, it is determined to be a valid abnormality, expressed as:

[0129]

[0130] Among them, abnormal event i is a single abnormal event mark;

[0131] (3) Trend analysis: by calculating blood oxygen saturation SpO 2 The rate of change of ΔSpO 2 Determine whether there is a continuous downward trend, expressed as:

[0132]

[0133] When ΔSpO 2 When it is continuously negative and exceeds the preset rate threshold, it is considered a valid anomaly;

[0134] (4) Multivariate abnormal association: Combined with multiple physiological parameters such as heart rate and blood oxygen saturation, a joint judgment is made. When the heart rate exceeds the upper threshold and the blood oxygen saturation is lower than the lower threshold, it is judged as a high-risk abnormal state, expressed as:

[0135]

[0136] Warning trigger: After confirming an abnormal condition, a warning prompt is triggered and the warning information is sent to the user end or associated devices. The warning information includes the abnormality type (such as a sudden increase in heart rate or a decrease in blood oxygen), current physiological data, and related suggestions to ensure that users are aware of their physiological status in a timely manner;

[0137] Notification sending: The warning information is sent to the user or family members through wireless communication (such as SMS, application notification, etc.). If the user is unable to respond in time, his emergency contact will receive the warning information and be able to take necessary measures;

[0138] Record storage: record the warning information in the data storage and analysis module, including the abnormality type, occurrence time, duration and processing status, for subsequent analysis and personalized model optimization;

[0139] Through abnormal confirmation, warning triggering, notification sending and record storage steps, it provides users with comprehensive health monitoring and abnormal warning functions, which helps to take effective measures in time when the user's health status becomes abnormal.

[0140] Dynamic feedback and optimization modules include:

[0141] User feedback collection: Provide a user feedback interface to allow users to provide feedback on the accuracy and effectiveness of warning information. Users mark whether the warning information is accurate or provide subjective evaluation of sleep status. Feedback data is recorded and stored in the data storage and analysis module as a basis for model optimization;

[0142] Historical data analysis: Regularly analyze the user's historical physiological parameters (such as heart rate, blood oxygen saturation, and respiratory rate) and historical environmental parameters (such as room temperature, humidity, light intensity, and noise level) to identify the changing trends of the user's sleep patterns and individual physiological characteristics. This step extracts key features through data mining and statistical analysis techniques to provide basic data for model optimization.

[0143] Model parameter adjustment: Based on user feedback, historical physiological parameters, and historical environmental parameter analysis results, the parameters of the deep sleep model are dynamically adjusted to adapt them to the physiological and environmental characteristics of individual users. The parameter adjustment uses a recursive optimization algorithm to minimize the model prediction error. The adjustment formula is as follows:

[0144]

[0145] Among them, θ new is the updated model parameter, θ old is the previous parameter value, α is the learning rate, is the gradient of the model’s loss function, used to indicate the adjustment direction;

[0146] Model Adaptation: After the deep sleep model is adjusted, an adaptive test is performed to verify whether the dynamically adjusted deep sleep model accurately reflects the user's sleep pattern and physiological characteristics. The adaptive test is based on recent real-time data to ensure that the model has good predictive ability for new data;

[0147] Application of optimization results: The dynamically adjusted deep sleep model is applied to subsequent sleep analysis and early warning judgment. The system automatically uses the adjusted model parameters for data analysis and abnormality detection to improve the accuracy of analysis and early warning;

[0148] Through user feedback collection, historical data analysis, model parameter adjustment, model adaptation and optimization result application steps, the deep sleep model is dynamically optimized, so that the system can better adapt to individual differences, improve the accuracy of deep sleep analysis and early warning, and thus more effectively improve the user's sleep quality management.

[0149] The personalized suggestion module specifically includes:

[0150] Individual difference analysis: Collect and analyze the user's historical physiological parameters and environmental parameters to identify the user's sleeping habits, environmental preferences and physiological characteristics. These individualized data provide a basis for the personalization of subsequent recommendations;

[0151] Interpretation of sleep analysis results: Based on the deep sleep analysis results and abnormal detection results, interpret the user's deep sleep state, evaluate the time and quality of deep sleep and the frequency and severity of abnormal events, and provide users with targeted sleep quality improvement plans;

[0152] Generate work and rest adjustment suggestions: Based on the user's deep sleep status, provide personalized work and rest adjustment suggestions, including adjustment of bedtime and waking time, and suggestions for relaxing activities before bedtime (such as meditation or deep breathing exercises) to prolong deep sleep time and reduce the occurrence of abnormal events;

[0153] Environmental optimization suggestion generation: Generates sleep environment optimization suggestions based on the user's environmental parameters and sleep preferences, including suggestions for adjusting room temperature, humidity, light intensity and noise level. For example, if the user has better deep sleep in a lower noise environment, the system will recommend using a white noise machine or earplugs to improve the sleep environment;

[0154] Diet guidance generation: Combined with the user's physiological parameters and sleep analysis results, it provides sleep-related dietary recommendations, including consuming sleep-promoting foods (such as foods rich in tryptophan) during a specific time period, or reducing the intake of stimulating beverages (such as coffee and strong tea) before bedtime to optimize sleep quality;

[0155] Personalized health report generation: Generate a personalized health report for the user, presenting the user's deep sleep analysis results, abnormal detection records, and multi-dimensional improvement suggestions in a visual way. The report can be viewed through the user-side application and can be set to be pushed periodically to help users continuously improve their sleep quality;

[0156] Through individual difference analysis, interpretation of sleep analysis results, generation of work and rest adjustment suggestions, generation of environment optimization suggestions, generation of dietary guidance and generation of personalized health reports, we provide users with multi-dimensional personalized health management plans, which help improve their overall sleep quality and health level.

[0157] Data storage and analysis modules include:

[0158] Data storage: The user's physiological parameters and environmental parameters are stored in a structured manner, and the data is classified and indexed using a database for quick access and analysis;

[0159] Data encryption: Encryption technology is used to encrypt the user's physiological parameters and environmental parameters during data storage to ensure data security. AES (Advanced Encryption Standard) or RSA (Public Key Encryption Algorithm) is used to encrypt sensitive data to prevent unauthorized access and ensure that user privacy is not leaked;

[0160] Historical data archiving: Archiving and storing historical physiological parameters and environmental parameters that exceed a certain time period, reducing the space occupied by the main storage and ensuring that the data can be stored for a long time. The archived data can still be retrieved as needed, supporting users' analysis of long-term trends;

[0161] Data retrieval: Provides data retrieval functions for users, allowing users to query based on date or data type (such as physiological data or environmental data);

[0162] Trend analysis: Regularly analyze the user's historical physiological parameters and environmental parameters to generate trend reports on sleep status changes. Trend analysis includes changes in deep sleep duration, changes in the frequency of abnormal events, and the impact of the environment on sleep. Trend analysis uses time series analysis and statistical methods to help users understand the long-term changes in their sleep status.

[0163] Report generation: Based on the trend analysis results, visual trend analysis reports are generated regularly and displayed through the user-side application. The report content includes the user's sleep quality score change curve, trend chart of abnormal events, and fluctuations in deep sleep time, helping users to intuitively understand their sleep health status.

[0164] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0165] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A deep sleep analysis and early warning system with integrated heart rate monitoring, characterized in that: It includes physiological parameter acquisition module, environmental parameter acquisition module, deep sleep analysis module, abnormality detection and warning module, dynamic feedback and optimization module, personalized suggestion module and data storage and analysis module, among which; The physiological parameter acquisition module is used to monitor the user's physiological parameters in real time, including heart rate, blood oxygen saturation and respiratory rate; The environmental parameter acquisition module is used to monitor sleep environment parameters, including room temperature, humidity, light intensity and noise level; The deep sleep analysis module constructs a deep sleep model based on the user's physiological parameters and environmental parameters using a multivariate data analysis algorithm, identifies the characteristics of the deep sleep stage by analyzing the heart rate variability and respiratory rhythm, and evaluates the time and quality of deep sleep; The abnormality detection and early warning module monitors the user's physiological abnormalities in real time based on the analysis results of the deep sleep analysis module, and triggers early warning prompts through the data abnormality detection algorithm to notify the user or family members to take corresponding measures; The dynamic feedback and optimization module is used to optimize the deep sleep model according to user feedback, historical physiological parameters and historical environmental parameters to improve the accuracy of analysis and early warning; The personalized suggestion module generates personalized sleep improvement suggestions based on the user's deep sleep analysis results and abnormal detection results, including work and rest adjustment, environment optimization suggestions and diet guidance; The data storage and analysis module is used to store the user's historical physiological data and environmental data, and regularly generate trend analysis reports to help the user understand the trend of changes in sleep status.

2. A deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 1, characterized in that: The physiological parameter acquisition module comprises: Heart rate monitoring: Use photoelectric plethysmography sensors to collect the user's pulse signal and monitor heart rate in real time; Blood oxygen saturation monitoring: using dual-wavelength light source, photoelectric sensor to obtain the user's pulse blood oxygen saturation; Respiratory rate monitoring: Use chest or abdominal volume change sensors to monitor the user's respiratory rate in real time.

3. A deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 2, characterized in that: The environmental parameter acquisition module includes: Room temperature monitoring: Use temperature sensors to collect indoor temperature data of the user's sleeping environment; Humidity monitoring: Use humidity sensors to collect humidity data of the user's sleeping environment; Light intensity monitoring: Use light sensors to collect light intensity data in the user's sleeping environment; Noise level monitoring steps: Use a noise sensor to collect noise intensity data in the user's sleeping environment.

4. A deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 3, characterized in that: The deep sleep analysis module includes: Data preprocessing: data cleaning and normalization of the collected physiological parameters and environmental parameters; Heart rate variability acquisition: Use a photoplethysmography sensor to detect the time interval between adjacent heartbeats and calculate heart rate variability; Respiratory rhythm acquisition: Use a chest strap sensor to monitor the expansion and contraction of the chest or abdomen, identify each respiratory cycle, and calculate the respiratory rate; Feature extraction: Extract characteristic variables related to deep sleep based on preprocessed physiological parameters and environmental parameters; Deep sleep model construction: The deep sleep model is constructed using the extracted feature variables, and the model outputs the user's deep sleep evaluation value.

5. A deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 4, characterized in that: The evaluation of deep sleep duration and quality includes: Deep sleep stage recognition: Input the real-time monitored physiological parameters and environmental parameters into the constructed deep sleep model, and judge whether the user has entered the deep sleep stage according to the deep sleep evaluation value output by the model. If the deep sleep evaluation value exceeds the preset threshold, it is determined that the user has entered the deep sleep stage and the timing starts; Deep sleep time calculation: The time of the identified deep sleep stage is accumulated and calculated. By monitoring the duration of the deep sleep state, the total duration of the user's deep sleep in one sleep cycle is calculated. Deep sleep quality assessment: Comprehensively assess the user's deep sleep quality based on the total deep sleep duration, heart rate variability and stability of breathing rhythm.

6. A deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 5, characterized in that: The anomaly detection and early warning module includes: Data acquisition: Real-time reception of physiological parameters from the deep sleep analysis module, including heart rate, blood oxygen saturation and respiratory rate; Threshold setting: Set the upper and lower thresholds of heart rate, blood oxygen saturation and respiratory rate according to the user's normal physiological range and personalized parameters; Abnormal identification: Identify abnormal physiological conditions by comparing real-time physiological parameters with set thresholds.

7. A deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 6, characterized in that: The anomaly detection and early warning module also includes: Abnormal confirmation: When an abnormal condition is detected, the abnormality is further confirmed through the abnormal detection algorithm to reduce false alarms and ensure the accuracy of the warning; Warning trigger: After confirming the abnormal situation, the warning prompt is triggered and the warning information is sent to the user end or related equipment; Notification sending: Send warning information to users or family members through wireless communication; Record storage: record the warning information in the data storage and analysis module, including the abnormality type, occurrence time, duration and processing status.

8. The deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 7, characterized in that: The dynamic feedback and optimization module includes: User feedback collection: Provide a user feedback interface to allow users to provide feedback on the accuracy and effectiveness of warning information; Historical data analysis: Regularly analyze the user's historical physiological parameters and historical environmental parameters to identify the changing trends of the user's sleep patterns and individual physiological characteristics; Model parameter adjustment: Dynamically adjust the parameters of the deep sleep model based on user feedback, historical physiological parameters, and historical environmental parameter analysis results; Model Adaptation: After the deep sleep model is adjusted, an adaptive test is performed to verify whether the dynamically adjusted deep sleep model accurately reflects the user's sleep pattern and physiological characteristic changes; Application of optimization results: Apply the dynamically adjusted deep sleep model to subsequent sleep analysis and early warning judgment.

9. The deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 8, characterized in that: The personalized suggestion module specifically includes: Individual difference analysis: collect and analyze the user's historical physiological parameters and environmental parameters to identify the user's sleeping habits, environmental preferences and physiological characteristics; Interpretation of sleep analysis results: Based on the deep sleep analysis results and abnormal detection results, interpret the user's deep sleep state, evaluate the time and quality of deep sleep and the frequency and severity of abnormal events, and provide users with targeted sleep quality improvement plans; Generate work and rest adjustment suggestions: Provide personalized work and rest adjustment suggestions based on the user's deep sleep status; Environmental optimization suggestion generation: Generate sleep environment optimization suggestions based on the user's environmental parameters and sleep preferences; Diet guidance generation: Combine the user's physiological parameters and sleep analysis results to provide sleep-related diet suggestions; Personalized health report generation: Generate a personalized health report for the user, presenting the user's deep sleep analysis results, abnormal detection records, and multi-dimensional improvement suggestions in a visual way.

10. The deep sleep analysis and early warning system with integrated heart rate monitoring according to claim 9, characterized in that: The data storage and analysis module includes: Data storage: structured storage of the user's physiological parameters and environmental parameters; Data encryption: Encryption technology is used to encrypt the user's physiological parameters and environmental parameters during data storage; Historical data archiving: Archiving and storing historical physiological parameters and environmental parameters that exceed a certain time period to reduce the occupation of main storage space; Data retrieval: Provides data retrieval functions for users, allowing users to query based on date or data type; Trend analysis: Regularly analyze the user's historical physiological parameters and environmental parameters to generate trend reports on changes in sleep status; Report generation: Based on the trend analysis results, visual trend analysis reports are generated regularly and the report contents are displayed through the user-side application.

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