Non-contact respiratory sleep syndrome monitoring and early warning device

Abstract

The invention discloses a non-contact respiratory sleep syndrome monitoring and early warning device, and belongs to the technical field of medical equipment. The non-contact respiratory sleep syndrome monitoring and early warning device comprises a monitoring sensing module, a data analysis module and an abnormity early warning module. The problem that certain limitation exists in the prior art is solved, breathing data, blood oxygen data and heart rate data of a patient are accurately collected through the infrared sensing function, the data collection quality can be judged, the accuracy and reliability of the collected data are ensured, and the accuracy and reliability of the patient are improved. The collected breathing data, blood oxygen data and heart rate data are compared with preset warning values, whether breathing, blood oxygen and heart rate of a patient are abnormal or not is judged, and the preset warning values can be dynamically adjusted based on historical monitoring data of the patient, so that the monitoring flexibility and accuracy of the device are improved, and the monitoring efficiency is improved. Warning sound can be individually set according to abnormal grading and patient characteristics to wake up the patient, and adverse events are prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and specifically to a non-contact monitoring and warning device for sleep apnea syndrome. Background Art

[0002] Sleep apnea syndrome usually manifests as apnea or hypopnea during sleep, resulting in a decrease in blood oxygen saturation, seriously affecting the sleep quality and physical health of patients. In the long run, it will cause a series of complications such as cardiovascular diseases and cognitive dysfunction. Currently, for the monitoring of sleep apnea syndrome, traditional contact monitoring methods have certain limitations. It is necessary to attach many sensors to the patient's body, which not only brings discomfort to the patient, but also increases the difficulty and error of monitoring due to complex wire connections. Therefore, it does not meet the existing needs, and for this reason, we have proposed a non-contact monitoring and warning device for sleep apnea syndrome. Summary of the Invention

[0003] The purpose of the present invention is to provide a non-contact monitoring and warning device for sleep apnea syndrome. By placing this device above the patient's pillow and using the infrared sensing function to monitor the breathing rate, blood oxygen, and heart rate of an individual during sleep, when the breathing, blood oxygen, or heart rate exceeds the warning value, a specific warning sound is emitted in a timely manner to wake up the patient to prevent adverse events, thus solving the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: A non-contact monitoring and warning device for sleep apnea syndrome, the device is arranged above the pillow at the head of the bed, and includes a monitoring and sensing module, a data analysis module, and an abnormal warning module; The monitoring and sensing module is configured to use the infrared sensing function of an infrared sensor to monitor the breathing rate, blood oxygen, and heart rate of an individual during sleep in real time, collect the patient's breathing data, blood oxygen data, and heart rate data, and determine the acquisition quality of the data. At the same time, it locates the position of the patient in the pillow area and automatically adjusts the focusing area of the infrared sensing, specifically: Retrieve the historical breathing data and blood oxygen data of the patient to obtain the standard deviation and average value respectively, calculate the corresponding ratio coefficient and compare it with the preset difference threshold, and determine whether to retrieve the initial position offset threshold as a trigger condition or adjust the initial position offset threshold according to the comparison result; The data analysis module is configured to compare and analyze the collected breathing data, blood oxygen data, and heart rate data with the preset warning values respectively to determine whether the patient's breathing and blood oxygen are abnormal. At the same time, it dynamically adjusts the warning values based on the patient's historical monitoring data; The abnormal warning module is configured to, once any abnormality occurs in the respiratory rate, blood oxygen level, or heart rate, classify the abnormal situation, combine the abnormal classification result with the patient's sleep habits and physiological characteristics, set the type, volume, frequency, and duration of the warning sound, and then emit a specific warning sound to wake up the patient. At the same time, synchronize the abnormal classification result, abnormal data, and monitoring data to the remote medical platform and the patient's family member account associated with the device.

[0005] Furthermore, the monitoring and sensing module includes: An infrared sensing module configured to emit an infrared beam towards the sleeping individual through an infrared sensor, capture the breathing condition, blood oxygen information, and heart rate information of the individual during sleep, and collect the patient's breathing data, blood oxygen data, and heart rate data; A data determination module configured to identify the error data in the breathing data, blood oxygen data, and heart rate data, and according to the identification result, respectively extract the data volume information corresponding to the error data in the breathing data, blood oxygen data, and heart rate data, so as to evaluate the acquisition quality of the breathing data, blood oxygen data, and heart rate data, and give an abnormal alarm when the acquisition quality of the breathing data, blood oxygen data, and heart rate data is abnormal; An intelligent positioning module configured to use millimeter-wave radar technology to real-time locate the position of the patient in the pillow area, and automatically adjust the focusing area of the infrared sensing module when the patient's position deviates.

[0006] Furthermore, the data determination module includes: A data identification module configured to pre-analyze the characteristics of the existing normal breathing data, blood oxygen data, and heart rate data in terms of numerical range, fluctuation law, and change trend, and establish a data identification model, input the collected breathing data, blood oxygen data, and heart rate data into the data identification model for comparison, and regard the data that does not conform to the data identification model as error data; A data extraction module configured to extract the first data volume information corresponding to the error data in the breathing data, the second data volume information corresponding to the error data in the blood oxygen data, and the third data volume information corresponding to the error data in the heart rate data; An abnormal determination module configured to compare the first data volume information, the second data volume information, and the third data volume information with the preset parameter thresholds respectively, specifically: If the first data volume information is lower than the preset parameter threshold, it is determined that the acquisition quality of the breathing data is abnormal, and an abnormal alarm is given; If the second data volume information is lower than the preset parameter threshold, it is determined that the acquisition quality of the blood oxygen data is abnormal, and an abnormal alarm is given; If the third data volume information is lower than a preset parameter threshold, it is determined that there is an abnormality in the acquisition quality of the heart rate data, and an abnormal alarm is given.

[0007] Further, the position offset threshold corresponding to the patient position offset for triggering the automatic adjustment of the focusing area is set, including: Retrieve the historical respiration data and historical blood oxygen data corresponding to the patient; Obtain the respiration data standard deviation and respiration data average value corresponding to the patient according to the historical respiration data corresponding to the patient; Obtain the blood oxygen data standard deviation and blood oxygen data average value corresponding to the patient according to the historical blood oxygen data corresponding to the patient; Perform a ratio process on the respiration data standard deviation and the respiration data average value corresponding to the patient to obtain a respiration ratio coefficient; Perform a ratio process on the blood oxygen data standard deviation and the blood oxygen data average value corresponding to the patient to obtain a blood oxygen ratio coefficient; Compare the respiration ratio coefficient and the blood oxygen ratio coefficient to obtain the coefficient difference between the respiration ratio coefficient and the blood oxygen ratio coefficient; Compare the coefficient difference between the respiration ratio coefficient and the blood oxygen ratio coefficient with a preset difference threshold; When the coefficient difference between the respiration ratio coefficient and the blood oxygen ratio coefficient does not exceed the preset difference threshold, retrieve the preset initial position offset threshold as the trigger condition for triggering the automatic adjustment of the focusing area; When the coefficient difference between the respiration ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, adjust the initial position offset threshold.

[0008] Further, when the coefficient difference between the respiration ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, adjust the initial position offset threshold, including: When the coefficient difference between the respiration ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, retrieve the respiration ratio coefficient and the blood oxygen ratio coefficient; Retrieve the historical patient position offset data corresponding to the patient; Use the historical patient position offset data corresponding to the patient to obtain the position offset standard deviation and the time interval standard deviation for triggering the automatic adjustment of the focusing area; Perform a normalization process on the position offset standard deviation and the time interval standard deviation for triggering the automatic adjustment of the focusing area to obtain the normalized position offset standard deviation and the time interval standard deviation for triggering the automatic adjustment of the focusing area; Adjust the initial position offset threshold by combining the standard deviation of the position offset after normalization processing and the standard deviation of the time interval for triggering automatic adjustment of the focus area with the respiration ratio coefficient and the blood oxygen ratio coefficient, and obtain the adjusted position offset threshold.

[0009] Further, the data analysis module includes: A data processing module configured to receive respiration data, blood oxygen data, and heart rate data from the monitoring and sensing module and perform preprocessing, including removing outliers and filling in missing values. At the same time, convert the respiration data, blood oxygen data, and heart rate data into a unified data format, and temporarily store the preprocessed respiration data, blood oxygen data, and heart rate data in a buffer area; A comparison and analysis module configured to store preset respiration warning values, blood oxygen warning values, and heart rate warning values, compare and analyze the collected respiration data with the respiration warning values, compare and analyze the collected blood oxygen data with the blood oxygen warning values, and compare and analyze the collected heart rate data with the heart rate warning values; An abnormality judgment module configured to judge whether the patient's respiration, blood oxygen, and heart rate are abnormal according to the comparison and analysis results of the comparison and analysis module; When the respiration data is higher than or lower than the respiration warning value range, the patient's respiration is abnormal; When the blood oxygen data is lower than the blood oxygen warning value range, the patient's blood oxygen is abnormal; When the heart rate data is lower than or higher than the heart rate warning range, the patient's heart rate is abnormal; A dynamic adjustment module configured to collect and store the patient's long-term historical monitoring data, and dynamically adjust the warning values according to the analysis results by analyzing the change trends and rules of the historical monitoring data.

[0010] Further, the dynamic adjustment module includes: A data collection module configured to collect the patient's historical monitoring data for a long time, including time series records of respiration rate, blood oxygen saturation data, and heart rate, as well as corresponding abnormal event records, and store the collected historical monitoring data in time series; A historical data analysis module configured to determine the normal fluctuation range and change rules of the patient's respiration, blood oxygen, and heart rate data by calculating statistical indicators such as the mean, standard deviation, and variance of the historical monitoring data. On the other hand, use time series analysis techniques to identify periodic, trend, and abnormal fluctuation patterns in the historical monitoring data, and determine the dynamic change trend of the patient's health status; A warning value adjustment module configured to dynamically adjust the respiration warning value, blood oxygen warning value, and heart rate warning value according to the patient's health change trends and rules obtained by the patient characteristic analysis module. At the same time, feedback the adjusted warning values to the comparison and analysis module and the abnormality judgment module to update the judgment basis.

[0011] Further, the abnormal warning module includes: An abnormal grading module configured to grade the abnormal respiratory data, blood oxygen data, and heart rate data according to the comparative analysis results of the data analysis module with the respiratory warning value, blood oxygen warning value, and heart rate warning value respectively, and determine different levels according to the severity of the abnormality, namely mild, moderate, and severe levels; A patient characteristic analysis module configured to collect and analyze the sleep habits and physiological characteristics of the patient, and extract the characteristics of the patient's sleep habits and physiological characteristics through statistical analysis methods. Among them, the sleep habits are the patient's sleep time, sleep posture, sleep cycle characteristics, and easily disturbed time periods, and the physiological characteristics are the patient's age, gender, hearing status, and past medical history; A warning sound setting module configured to personalize the warning sound parameters according to the abnormal grading result and the information provided by the patient characteristic analysis module, including the type, volume, frequency, and duration of the sound; A warning playback module configured to play the corresponding warning sound through the speaker on the device to wake up the patient according to the parameter configuration of the warning sound setting module after the data analysis module determines that the patient has an abnormal situation; A synchronous notification module configured to synchronize the monitored respiratory data, blood oxygen data, heart rate data, and abnormal data to the remote medical platform in real time to ensure that medical staff can obtain the patient's data in a timely manner. At the same time, through the mobile application or SMS platform associated with the patient's family member's account, a notification message is sent to the family member to inform them of the patient's current situation.

[0012] Further, the abnormal grading module is divided into three levels: mild, moderate, and severe according to the severity of the abnormality, specifically as follows: Mild: The respiratory data is higher or lower than the respiratory warning value range by no more than 5% and the duration does not exceed 1 minute; The blood oxygen data is lower than the blood oxygen warning value range by no more than 5% and the duration does not exceed 2 minutes; The heart rate data is higher or lower than the heart rate warning value range by no more than 20% and the duration does not exceed 30 seconds; Moderate: The respiratory data is higher or lower than the respiratory warning value range by no more than 10% and the duration is between 1 and 3 minutes; The blood oxygen data is lower than the blood oxygen warning value range by no more than 10% and the duration is between 2 and 5 minutes; The heart rate data is higher or lower than the heart rate warning value range by no more than 30% and the duration is between 30 seconds and 2 minutes; Severe: The respiratory data is higher or lower than the respiratory warning value range by more than 10% and the duration exceeds 3 minutes; The blood oxygen data is below the blood oxygen warning value range by more than 30% and lasts for more than 3 minutes; The heart rate data is higher or lower than the heart rate warning value range by more than 20% and lasts for no more than 30 seconds.

[0013] Furthermore, the warning sound setting module, according to the abnormal grading result and the information provided by the patient characteristic analysis module, personalizes the setting of warning sound parameters as follows: Mild: The sound type is a soft reminder tone, the volume is 40 - 50 decibels, the frequency is to remind once every 2 - 3 seconds, and the duration is 1 - 2 seconds for each reminder; Moderate: The sound type is a clear alarm tone, the volume is 60 - 70 decibels, the frequency is to remind once every 1 - 2 seconds, and the duration is 3 - 5 seconds for each reminder; Severe: The sound type is a strong alarm tone, the volume is 80 - 90 decibels, the frequency is to remind once per second, and the duration is 5 - 10 seconds for each reminder.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. Through the infrared sensing function, the present invention accurately captures the breathing, blood oxygen and heart rate information during the human body's sleep process, thereby collecting the patient's breathing data, blood oxygen data and heart rate data. By identifying incorrect data, it can judge the quality of data collection, thus ensuring the accuracy and reliability of the collected breathing data, blood oxygen data and heart rate data, and effectively avoiding misjudgment caused by data quality problems.

[0015] 2. By comparing and analyzing the collected breathing data, blood oxygen data and heart rate data with the preset warning values respectively, the present invention judges whether the patient's breathing, blood oxygen and heart rate are abnormal. The preset warning values can be dynamically adjusted based on the patient's historical monitoring data, thereby improving the flexibility and accuracy of the device monitoring. Once any one of the breathing frequency, blood oxygen or heart rate shows an abnormal situation, the abnormal situation is graded, and the grading result is combined with the patient's sleep habits and physiological characteristics to be able to personalize the setting of warning sounds, so as to emit specific warning sounds to wake up the patient and prevent adverse events. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the overall structural schematic diagram of the non-contact breathing sleep syndrome monitoring and warning device of the present invention; Figure 2 is the external view of the non-contact breathing sleep syndrome monitoring and warning device of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] To solve the existing technical problem that for the monitoring of sleep apnea syndrome, the traditional contact monitoring method has certain limitations. It is necessary to attach many sensors to the patient's body, which not only brings discomfort to the patient, but also increases the difficulty and error of monitoring due to complex circuit connections. Please refer to Figure 1 - Figure 2 This embodiment provides the following technical solutions: A non-contact sleep apnea syndrome monitoring and warning device is provided. The device is arranged above the pillow at the head of the bed and includes a monitoring and sensing module, a data analysis module, and an abnormal warning module. The monitoring and sensing module is configured to use the infrared sensing function of an infrared sensor to monitor the breathing frequency, blood oxygen, and heart rate of an individual during sleep in real time, collect the patient's breathing data, blood oxygen data, and heart rate data, and determine the quality of the data collection. At the same time, it locates the position of the patient in the pillow area and automatically adjusts the focusing area of the infrared sensing. Specifically: Retrieve the historical breathing data and blood oxygen data of the patient to obtain the standard deviation and average value respectively, calculate the corresponding ratio coefficient and compare it with the preset difference threshold, and determine whether to retrieve the initial position offset threshold as a trigger condition or adjust the initial position offset threshold according to the comparison result. The data analysis module is configured to compare and analyze the collected breathing data, blood oxygen data, and heart rate data with the preset warning values respectively to determine whether the patient's breathing and blood oxygen are abnormal. At the same time, it dynamically adjusts the warning values based on the patient's historical monitoring data. The abnormal warning module is configured to, once any one of the breathing frequency, blood oxygen, or heart rate shows an abnormal situation, classify the abnormal situation, combine the abnormal classification result with the patient's sleep habits and physiological characteristics, set the type, volume, frequency, and duration of the warning sound, and then emit a specific warning sound to wake up the patient. At the same time, it synchronizes the abnormal classification result, abnormal data, and monitoring data to the remote medical platform and the patient's family member account associated with the device.

[0019] The technical effects of the above technical solution are as follows: By placing this device above the pillow at the head of the bed, contactless monitoring of sleep apnea syndrome can be achieved, avoiding discomfort to patients, reducing the difficulty and error of monitoring, and being easy to operate without complex professional operation skills. Patients can easily use and maintain it by themselves, improving the compliance of patients with the use of the device. The monitoring and sensing module can use an infrared sensor to non-contactedly monitor the breathing rate, blood oxygen, and heart rate of a sleeping individual in real time. This method will not interfere with the normal sleep state of the patient, ensuring the naturalness and reliability of data collection, and can judge the quality of the collected data to ensure that subsequent analysis is based on accurate and effective data, providing a solid data basis for subsequent diagnosis and early warning. The data analysis module can timely judge whether there are abnormalities in breathing, blood oxygen, and heart rate by comparing and analyzing the collected data with preset warning values. At the same time, the warning values are dynamically adjusted based on the patient's historical monitoring data, fully considering individual differences and changes in the patient's own situation, making the early warning more accurate and personalized, reducing the possibility of false alarms and missed alarms. Once an abnormal situation occurs, the abnormal warning module can classify the abnormality and set specific warning sounds in combination with the patient's sleep habits and physiological characteristics. The personalized warning can more effectively wake up the patient and timely intervene in dangerous situations related to sleep apnea syndrome, thereby reducing the probability of health risks caused by apnea or abnormal blood oxygen or heart rate in patients. At the same time, the monitoring data and abnormal situations are synchronously transmitted to the remote medical platform and the account associated with the patient's family members in real time, enabling the patient's family members to timely understand the patient's condition, enhancing the sense of participation and trust of the patient and their family members in the monitoring process, and promoting the patient to more actively cooperate with the monitoring.

[0020] It should be noted that the contactless sleep apnea syndrome monitoring and warning device of the present invention is not only applicable to professional medical institutions such as hospitals, but also can be used in environments such as homes, expanding the scope of application of the device and enabling patients to receive timely and effective monitoring and warning of sleep apnea syndrome at home.

[0021] The infrared sensing module is configured to emit an infrared beam to a sleeping individual through an infrared sensor, capture the breathing condition, blood oxygen information, and heart rate information of the individual during sleep, and collect the breathing data, blood oxygen data, and heart rate data of the patient. The data determination module is configured to identify the error data in the breathing data, blood oxygen data, and heart rate data, extract the data volume information corresponding to the error data in the breathing data, blood oxygen data, and heart rate data respectively according to the identification result, evaluate the acquisition quality of the breathing data, blood oxygen data, and heart rate data, and give an abnormal alarm when the acquisition quality of the breathing data, blood oxygen data, and heart rate data is abnormal. The intelligent positioning module is configured to use millimeter-wave radar technology to real-time locate the position of the patient in the pillow area. When the patient's position deviates, it automatically adjusts the focusing area of the infrared sensing module.

[0022] The technical effects of the above technical solution are as follows: The infrared sensing module realizes non-contact monitoring. By emitting infrared beams, it accurately captures the respiration, blood oxygen information, and heart rate information of the sleeping individual, continuously and stably collects data, providing a real-time and accurate data basis for subsequent analysis, avoiding the inconvenience and interference that may be brought by contact monitoring. The data determination module can identify and extract the error data in the respiration data, blood oxygen data, and heart rate data, evaluate the data collection quality accordingly, ensure the reliability and accuracy of the monitoring data, and give an alarm in time when the data quality is abnormal, facilitating the quick discovery and solution of the collection problems, ensuring the continuous stability of the monitoring process, improving the effectiveness of the monitoring results. And through the data determination module to determine the quality of the collected data and make automatic adjustments when the data quality is abnormal, ensuring the accuracy and reliability of the monitoring data, reducing false alarms and frequent monitoring interruptions caused by data quality problems, improving the patient's trust in the monitoring results, and thus enhancing the patient's compliance. The intelligent positioning module uses millimeter-wave radar technology to real-time locate the position of the patient in the pillow area. When the patient's position deviates, it automatically adjusts the focusing area of the infrared sensing module to always accurately monitor the patient, avoiding monitoring errors caused by position changes, enhancing the flexibility and adaptability of the monitoring, improving the accuracy and reliability of the monitoring, and reducing false alarms and missed alarms caused by monitoring area deviation.

[0023] The data determination module includes: The data identification module is configured to pre-analyze the characteristics of the existing normal respiration data, blood oxygen data, and heart rate data in terms of numerical range, fluctuation law, and change trend, and establish a data identification model. Input the collected respiration data, blood oxygen data, and heart rate data into the data identification model for comparison, and regard the data that does not conform to the data identification model as error data; The data extraction module is configured to extract the first data volume information corresponding to the error data in the respiration data, the second data volume information corresponding to the error data in the blood oxygen data, and the third data volume information corresponding to the error data in the heart rate data; The abnormality determination module is configured to compare the first data volume information, the second data volume information, and the third data volume information with the preset parameter thresholds respectively. Specifically: If the first data volume information is lower than the preset parameter threshold, it is determined that the collection quality of the respiration data is abnormal and an abnormality alarm is given; If the second data volume information is lower than the preset parameter threshold, it is determined that the collection quality of the blood oxygen data is abnormal and an abnormality alarm is given; If the third data volume information is lower than the preset parameter threshold, it is determined that there is an abnormality in the acquisition quality of the heart rate data, and an abnormality alarm is issued.

[0024] The technical effects of the above technical solution are as follows: The data recognition module recognizes incorrect data through the established data recognition model, providing a scientific basis and standard for accurately screening out incorrect data in the subsequent process. Based on the recognition results of the incorrect data, the data extraction module extracts the data volume information corresponding to the incorrect data in the respiratory data, blood oxygen data, and heart rate data respectively, providing a quantitative basis for data quality assessment. By accurately extracting this information, it is possible to more accurately judge the abnormal conditions occurring in the data acquisition process. The abnormality determination module compares the extracted data volume information with the preset parameter threshold. When it is found that the data acquisition quality is abnormal, it can promptly issue an abnormality alarm, which helps to quickly discover and solve problems in data acquisition, ensuring the continuity and reliability of the monitoring process. Through the above functions, the data determination module can effectively identify and process incorrect data, ensuring the accuracy and integrity of the monitoring data, and improving the reliability and effectiveness of the entire device monitoring.

[0025] Specifically, the position offset threshold corresponding to the patient position offset for triggering the automatic adjustment of the focus area is set, including: Retrieve the historical respiratory data and historical blood oxygen data corresponding to the patient; Obtain the standard deviation and average value of the respiratory data corresponding to the patient according to the historical respiratory data corresponding to the patient; Obtain the standard deviation and average value of the blood oxygen data corresponding to the patient according to the historical blood oxygen data corresponding to the patient; Perform a ratio process on the standard deviation of the respiratory data corresponding to the patient and the average value of the respiratory data to obtain a respiratory ratio coefficient; Perform a ratio process on the standard deviation of the blood oxygen data corresponding to the patient and the average value of the blood oxygen data to obtain a blood oxygen ratio coefficient; Compare the respiratory ratio coefficient and the blood oxygen ratio coefficient to obtain the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient; Compare the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient with a preset difference threshold; When the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient does not exceed the preset difference threshold, retrieve the preset initial position offset threshold as the trigger condition for triggering the automatic adjustment of the focus area; When the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, adjust the initial position offset threshold.

[0026] The technical effects of the above technical solution are as follows: Retrieve the historical respiration data and blood oxygen data of the patient, calculate the standard deviation and average value of the respiration data, as well as the standard deviation and average value of the blood oxygen data respectively. These statistical values reflect the range and reference state of the fluctuations of the patient's respiration and blood oxygen. Process the ratio of the standard deviation to the average value of the respiration data to obtain the respiration ratio coefficient; process the ratio of the standard deviation to the average value of the blood oxygen data to obtain the blood oxygen ratio coefficient. The two ratio coefficients eliminate the influence of the absolute value differences of the data of different patients and present the fluctuation degrees of respiration and blood oxygen in a normalized manner. Compare the respiration ratio coefficient and the blood oxygen ratio coefficient, and calculate the coefficient difference between the two. This difference is used to judge the coordination of the fluctuations of respiration and blood oxygen. Compare the coefficient difference with the preset difference threshold. If it does not exceed the difference threshold, it indicates that the patient's physiological state is stable, and retrieve the preset initial position offset threshold as the trigger condition; if it exceeds the difference threshold, it indicates that the patient may have abnormal respiration or blood oxygen. It is necessary to dynamically adjust the initial position offset threshold according to the size of the difference and the relative sizes of the two ratio coefficients to adapt to the abnormal fluctuations.

[0027] By analyzing the patient's historical respiration and blood oxygen data, calculating the ratio coefficients and comparing the differences, it is possible to adjust the position offset threshold according to the individual physiological characteristics of the patient, making the trigger condition for automatically adjusting the focus area more in line with the actual situation, reducing the focus deviation, and improving the accuracy of the adjustment. Combining the multi-dimensional physiological data of respiration and blood oxygen for comprehensive judgment, reducing the influence of single-data noise on the threshold setting, filtering out accidental physiological fluctuations, avoiding frequent adjustment of the threshold, adapting to complex physiological scenarios at the same time, and enhancing the system's ability to handle abnormal situations. Dynamically generating the threshold based on historical data, reducing manual intervention, shortening the threshold setting time, improving the efficiency of the diagnosis and treatment process; reducing the subjective error of the artificially set threshold, ensuring that the trigger condition for adjusting the focus area conforms to the patient's physiological laws, and enhancing the reliability of treatment or examination.

[0028] Specifically, when the coefficient difference between the respiration ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, the initial position offset threshold is adjusted, including: When the coefficient difference between the respiration ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, retrieve the respiration ratio coefficient and the blood oxygen ratio coefficient; Retrieve the historical patient position offset data corresponding to the patient; Use the historical patient position offset data corresponding to the patient to obtain the position offset standard deviation and the standard deviation of the time interval for triggering the automatic adjustment of the focus area; Perform normalization processing on the position offset standard deviation and the standard deviation of the time interval for triggering the automatic adjustment of the focus area to obtain the normalized position offset standard deviation and the standard deviation of the time interval for triggering the automatic adjustment of the focus area; Adjust the initial position offset threshold by combining the standard deviation of the position offset after normalization and the standard deviation of the time interval for triggering automatic adjustment of the focus area with the respiration ratio coefficient and the blood oxygen ratio coefficient to obtain the adjusted position offset threshold.

[0029] Among them, the adjusted position offset threshold is obtained through the following formula: Among them, S represents the adjusted position offset threshold; S0 represents the position offset threshold before adjustment; B x represents the blood oxygen ratio coefficient; B h represents the respiration ratio coefficient; f represents a preset adjustment coefficient, and the value range of the adjustment coefficient is 0.24 to 0.58; L b represents the standard deviation of the position offset after normalization; t b represents the standard deviation of the time interval for triggering automatic adjustment of the focus area after normalization. Specifically, the blood oxygen ratio coefficient B x and the respiration ratio coefficient B h respectively reflect the degree of fluctuation of blood oxygen and respiration data relative to their average values. Taking the maximum value of the two is to determine the dominant factor in the physiological data fluctuation. If B x is larger, it indicates that the blood oxygen data fluctuation is more significant; if B h is larger, it indicates that the respiration data fluctuation is more prominent. In this way, the physiological fluctuation factor that has a greater impact on the position offset threshold can be captured. The standard deviation of the position offset L after normalization b reflects the degree of dispersion of the patient's historical position offset amplitude, and the standard deviation of the time interval t for triggering automatic adjustment of the focus area after normalization b reflects the degree of dispersion of the position offset adjustment time interval. Multiplying the two and taking the square root comprehensively considers the amplitude and frequency characteristics of the position offset. When the position offset amplitude is large and the adjustment is frequent, this value will be large, indicating that the patient's position change situation is complex; otherwise, it indicates that the position change is relatively stable.

[0030] The preset adjustment coefficient f is used to control the influence degree of the physiological fluctuation factor (reflected by max(B x , B h )) on the adjustment of the position offset threshold. The denominator plays an inhibitory role. When the position offset amplitude and frequency characteristics (reflected by ) are small, the value of this fraction is mainly determined by The decision is to place more emphasis on the influence of physiological fluctuation factors on the threshold; when the position offset amplitude and frequency characteristics are large, the denominator increases, weakening the influence of physiological fluctuation factors and causing the threshold adjustment to consider more the situation of the position offset itself. Then, the direction and degree of adjustment are determined. When the value of the fraction is small, subtracting this value from 1 gives a result close to 1, indicating that the adjusted position offset threshold S is close to the threshold S0 before adjustment, that is, the threshold changes little; when the value of the fraction is large, subtracting this value from 1 gives a small result, meaning that the adjusted threshold S will be significantly smaller than the threshold S0 before adjustment, that is, the threshold will be adjusted significantly according to the physiological fluctuations and position offset situation.

[0031] The technical effects corresponding to the above technical solutions are as follows: By introducing historical position offset data (such as the standard deviation of position offset and the standard deviation of adjustment time interval), combined with the ratio coefficient difference between respiration and blood oxygen, the threshold adjustment is more in line with the real-time physiological fluctuation pattern of individual patients. For example, if the recent position offset amplitude of a patient is large and the adjustment is frequent (i.e., the standard deviation of position offset and the standard deviation of time interval are large), the system can dynamically amplify or reduce the threshold according to the degree of incoordination (coefficient difference) between respiration / blood oxygen fluctuations, avoiding the lag or over-sensitivity of the focus area adjustment caused by a fixed threshold. Improve the system's response ability to the dynamic changes of the patient's physiological state, especially suitable for complex scenarios where respiration and blood oxygen fluctuations are asynchronous (such as patients with lung diseases having rapid breathing but relatively stable blood oxygen saturation), reducing false triggers or missed triggers caused by the fluctuation of a single physiological index. Normalize the standard deviation of position offset and the adjustment time interval standard deviation to eliminate the interference of data with different dimensions (such as the position offset unit is millimeter and the time interval unit is second), enabling multi-dimensional data to directly participate in the threshold calculation. Combine the respiration / blood oxygen ratio coefficient to form a dynamic threshold model with multi-parameter fusion. By quantifying the correlation between physiological fluctuations (respiration / blood oxygen) and position offset, avoid the roughness of adjusting the threshold solely based on experience or fixed rules, making the threshold adjustment step size and direction more accurate. For example, if the respiration ratio coefficient is significantly greater than the blood oxygen ratio coefficient (respiration fluctuation dominates) and the historical position offset standard deviation is large, the system can specifically reduce the threshold to enhance the sensitivity to respiration-related displacement. Adjust the threshold based on the statistical characteristics of historical data (such as standard deviation), rather than relying on single physiological fluctuation data, which can filter out accidental noise (such as instantaneous position offset caused by a patient's sudden cough), avoiding frequent threshold oscillations. When the patient's physiological indicators are abnormally high for a long time (such as chronic respiratory insufficiency), optimize the threshold through the long-term statistical rules of historical data, reduce system misjudgment caused by short-term data fluctuations, improve the stability of the focus area during treatment or examination, and reduce medical risks (such as radiotherapy target area offset) caused by unreasonable thresholds. Automatically complete threshold iteration based on individual patient data (respiration, blood oxygen, historical position offset) without manual intervention, and at the same time, through multi-dimensional data cross-validation (physiological fluctuation + position offset behavior), avoid misjudgment of a single indicator. While improving the automation efficiency, achieve a personalized solution of "one threshold for one person", especially suitable for groups with large individual differences in physiological indicators such as the elderly and critically ill patients, reducing the problem of insufficient adaptability caused by a general threshold.

[0032] The data analysis module includes: The data processing module is configured to receive respiration data, blood oxygen data, and heart rate data from the monitoring and sensing module and perform preprocessing, including removing outliers and filling in missing values, while converting the respiration data, blood oxygen data, and heart rate data into a unified data format, and temporarily storing the preprocessed respiration data, blood oxygen data, and heart rate data in the buffer; A comparison and analysis module, configured to store preset respiratory warning values, blood oxygen warning values, and heart rate warning values, compare and analyze the collected respiratory data with the respiratory warning values, compare and analyze the collected blood oxygen data with the blood oxygen warning values, and compare and analyze the collected heart rate data with the heart rate warning values; An abnormality judgment module, configured to judge whether the patient's respiration, blood oxygen, and heart rate are abnormal according to the comparison and analysis results of the comparison and analysis module; When the respiratory data is higher than or lower than the respiratory warning value range, the patient's respiration is abnormal; When the blood oxygen data is lower than the blood oxygen warning value range, the patient's blood oxygen is abnormal; When the heart rate data is lower than or higher than the heart rate warning range, the patient's heart rate is abnormal; A dynamic adjustment module, configured to collect and store the patient's long-term historical monitoring data, dynamically adjust the warning values according to the analysis results by analyzing the change trends and rules of the historical monitoring data.

[0033] The technical effects of the above technical solutions are as follows: After receiving the monitoring data, the data processing module removes outliers, fills in missing values, and temporarily stores the data in the buffer after unifying the data format, thus ensuring the integrity, accuracy, and consistency of the data, laying a solid foundation for subsequent analysis and processing. The comparison and analysis module can timely detect abnormal data situations by comparing the preprocessed data with the preset warning values, enabling the abnormality judgment module to judge whether the patient's respiration, blood oxygen, and heart rate are abnormal accordingly. Through timely comparison and judgment, potential health risks can be quickly identified, achieving early detection and early intervention. The dynamic adjustment module collects the patient's long-term historical monitoring data, analyzes its change trends and rules, and dynamically adjusts the warning values accordingly, fully considering individual differences and changes in the patient's own situation, making the monitoring more targeted and adaptable, and improving the accuracy and effectiveness of early warning.

[0034] The dynamic adjustment module includes: A data collection module, configured to collect the patient's historical monitoring data for a long time, including time series records of respiratory rate, blood oxygen saturation data, and heart rate, as well as corresponding abnormal event records, and store the collected historical monitoring data according to the time series; A historical data analysis module, configured to determine the normal fluctuation range and change rules of the patient's respiratory, blood oxygen, and heart rate data by calculating statistical indicators such as the mean, standard deviation, and variance of the historical monitoring data. On the other hand, time series analysis techniques are used to identify periodic, trend, and abnormal fluctuation patterns in the historical monitoring data, and determine the dynamic change trend of the patient's health status; The warning value adjustment module is configured to dynamically adjust the respiration warning value, blood oxygen warning value, and heart rate warning value according to the patient health change trend and pattern obtained by the patient characteristic analysis module. At the same time, the adjusted warning value is fed back to the comparison and analysis module and the abnormality judgment module to update the judgment basis.

[0035] The technical effects of the above technical solution are as follows: The data collection module long-term collects the historical monitoring data of the patient, providing a detailed data basis for comprehensively understanding the patient's health status, which helps to further analyze the patient health change trend. The historical data analysis module determines the normal fluctuation range and change pattern of the patient's respiration, blood oxygen, and heart rate data by calculating statistical indicators such as the mean, standard deviation, and variance, providing a scientific basis for accurately judging abnormalities. Then, by using time series analysis technology to identify the periodic, trend, and abnormal fluctuation patterns in the historical data, the dynamic change trend of the patient's health status can be grasped more accurately, thus providing a reliable basis for the dynamic adjustment of the warning value, effectively improving the matching degree between the monitoring data and the patient's actual health status, and improving the overall monitoring accuracy. The warning value adjustment module dynamically adjusts the respiration warning value, blood oxygen warning value, and heart rate warning value according to the patient health change trend and pattern, enabling the warning value to change flexibly with the change of the patient's health status, better adapting to the patient's individual situation and the health needs at different stages, avoiding the limitations that may be brought by fixed warning values, improving the timeliness and effectiveness of the warning, providing more accurate early warning for the patient's health, facilitating the adoption of corresponding measures in advance to cope with potential health risks. At the same time, the adjusted warning value is fed back to the relevant modules to update the judgment basis, realizing the dynamic optimization of the monitoring and early warning, improving the accuracy and timeliness of the early warning, and reducing the possibility of false alarms and missed alarms.

[0036] The abnormal early warning module includes: The abnormality grading module is configured to grade the abnormal respiration data, blood oxygen data, and heart rate data according to the comparison and analysis results of the data analysis module with the respiration warning value, blood oxygen warning value, and heart rate warning value respectively, and determine different levels according to the severity of the abnormality, which are three levels: mild, moderate, and severe; The patient characteristic analysis module is configured to collect and analyze the patient's sleep habits and physiological characteristics, and extract the characteristics of the patient's sleep habits and physiological characteristics through statistical analysis methods. Among them, the sleep habits are the patient's sleep time, sleep posture, sleep cycle characteristics, and easily disturbed time periods, and the physiological characteristics are the patient's age, gender, hearing status, and past medical history; The warning sound setting module is configured to customize the warning sound parameters according to the abnormality grading result and the information provided by the patient characteristic analysis module, including the type, volume, frequency, and duration of the sound; The warning playback module is configured to, after the data analysis module determines that a situation has occurred to the patient, play a corresponding warning sound through the speaker on the device to wake up the patient according to the parameter configuration of the warning sound setting module; The synchronization notification module is configured to synchronize the monitored respiratory data, blood oxygen data, heart rate data, and abnormal data to the remote medical platform in real time, ensuring that medical staff can obtain the patient's data in a timely manner. At the same time, through a mobile application or SMS platform associated with the patient's family member's account, a notification message is sent to the family member to inform them of the patient's current situation.

[0037] The technical effects of the above technical solutions are as follows: The abnormal grading module grades the abnormal respiratory data, blood oxygen data, and heart rate data as mild, moderate, and severe according to the comparison and analysis results, and can accurately identify the severity of the patient's abnormal situation, making subsequent warning measures more targeted, avoiding inaccurate judgments of the abnormal situation by the patient and medical staff due to ambiguous warning information, improving the timeliness and effectiveness of dealing with abnormalities. The patient characteristics analysis module collects information on the patient's sleep habits and physiological characteristics, providing a personalized basis for the warning sound setting module, enabling it to set parameters such as appropriate sound types, volumes, frequencies, and durations according to the patient's situation. For example, considering the patient's sleep depth and hearing status, making the warning sound more easily detectable by the patient and reducing excessive disturbance to the patient, enhancing the patient's acceptance and cooperation with the warning. The synchronization notification module synchronizes the monitored data and abnormal data to the remote medical platform in real time, ensuring that medical staff can follow up on the patient's situation in a timely manner. At the same time, a notification is also sent to the family member through a mobile application or SMS platform associated with the patient's family member's account, enabling the family member to understand the patient's condition in a timely manner, facilitating the family member to assist in care or send the patient to the hospital in a timely manner, realizing the tripartite cooperation of the patient, medical staff, and family member in dealing with the patient's health problems, and improving the efficiency and effect of the overall response to sudden health conditions.

[0038] The abnormal grading module is divided into three levels: mild, moderate, and severe according to the severity of the abnormality, specifically as follows: Mild: The respiratory data is higher or lower than the respiratory warning value range by no more than 5% and the duration does not exceed 1 minute; The blood oxygen data is lower than the blood oxygen warning value range by no more than 5% and the duration does not exceed 2 minutes; The heart rate data is higher or lower than the heart rate warning value range by no more than 20% and the duration does not exceed 30 seconds; Moderate: The respiratory data is higher or lower than the respiratory warning value range by no more than 10% and the duration is between 1 and 3 minutes; The blood oxygen data is lower than the blood oxygen warning value range by no more than 10% and the duration is between 2 and 5 minutes; The heart rate data is higher or lower than the heart rate warning value range by no more than 30% and the duration is between 30 seconds and 2 minutes; Severe: The respiratory data is higher or lower than the respiratory warning value range by more than 10% and lasts for more than 3 minutes; The blood oxygen data is lower than the blood oxygen warning value range by more than 30% and lasts for more than 3 minutes; The heart rate data is higher or lower than the heart rate warning value range by more than 20% and the duration does not exceed 30 seconds.

[0039] The technical effect of the above technical scheme is: by refining the abnormal classification standards, the abnormal conditions of respiratory, blood oxygen and heart rate data are accurately classified according to the percentage of deviation from the warning value and the duration, which can more accurately reflect the severity of the patient's abnormal condition, avoid the misjudgment or missed judgment that may have occurred in the past single threshold judgment, and make the monitoring results closer to the patient's actual health status. The abnormalities are divided into three levels: mild, moderate and severe, so that medical staff or the patients themselves can quickly and intuitively understand the urgency of the abnormal situation, and facilitate targeted measures. For example: mild abnormalities can be observed first, moderate abnormalities need timely attention, and severe abnormalities must be dealt with immediately, thereby improving the effectiveness of responding to abnormalities and better ensuring patient safety.

[0040] The warning sound setting module sets the warning sound parameters in a personalized manner according to the abnormal classification results and the information provided by the patient characteristics analysis module, as follows: Mild: The sound type is a soft prompt tone, the volume is 40-50 decibels, the frequency is once every 2-3 seconds, and the duration is 1-2 seconds each time; Moderate: The sound type is a clear alarm sound, the volume is 60-70 decibels, the frequency is once every 1-2 seconds, and the duration is 3-5 seconds per prompt; Severe: The sound type is a strong alarm sound, the volume is 80~90 decibels, the frequency is once per second, and the duration is 5~10 seconds per prompt.

[0041] The technical effect of the above technical solution is: by combining the abnormal classification results with the information provided by the patient characteristics analysis module, the parameters of the warning sound can be personalized so that the warning sound can effectively wake up the patient without excessively affecting their sleep quality. Especially for patients with poor hearing or deep sleep, more appropriate parameters can be set to ensure that they can perceive the warning.

[0042] Working principle: Utilize the infrared sensing function to non-contactedly monitor the breathing frequency, blood oxygen, and heart rate of the sleeping individual in real time, thereby collecting the patient's breathing data, blood oxygen data, and heart rate data, and determining the quality of the data collection to ensure the reliability and accuracy of the monitoring data. By comparing and analyzing the collected data with the preset warning values, it is possible to promptly determine whether there are abnormalities in breathing, blood oxygen, and heart rate. At the same time, the warning values are dynamically adjusted based on the patient's historical monitoring data, fully considering individual differences and changes in the patient's own situation, making the early warning more accurate and personalized, reducing the possibility of false alarms and missed alarms. Once an abnormal situation occurs, the abnormal warning module can classify the abnormality and set specific warning sounds in combination with the patient's sleep habits and physiological characteristics. The personalized warning can more effectively wake up the patient, making the warning sound more easily detectable by the patient and reducing excessive disturbance to the patient.

[0043] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0044] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A non-contact breathing sleep syndrome monitoring and warning device, characterized in that, The device is arranged above the pillow at the head of the bed and includes a monitoring and sensing module, a data analysis module, and an abnormal warning module; The monitoring and sensing module is configured to use the infrared sensing function of an infrared sensor to monitor the breathing rate, blood oxygen, and heart rate of an individual during sleep in real time, collect the patient's breathing data, blood oxygen data, and heart rate data, and determine the quality of the data collection. At the same time, it locates the position of the patient in the pillow area and automatically adjusts the focusing area of the infrared sensing, specifically: Retrieve the historical breathing data and blood oxygen data of the patient to obtain the standard deviation and average value respectively, calculate the corresponding ratio coefficient and compare it with the preset difference threshold, and decide whether to retrieve the initial position offset threshold as a trigger condition or adjust the initial position offset threshold according to the comparison result; The data analysis module is configured to compare and analyze the collected breathing data, blood oxygen data, and heart rate data with the preset warning values respectively to determine whether the patient's breathing and blood oxygen are abnormal. At the same time, it dynamically adjusts the warning values based on the patient's historical monitoring data; The abnormal warning module is configured to, once any one of the breathing rate, blood oxygen, or heart rate shows an abnormal situation, classify the abnormal situation, combine the abnormal classification result with the patient's sleep habits and physiological characteristics, set the type, volume, frequency, and duration of the warning sound, and then emit a specific warning sound to wake up the patient. At the same time, it synchronizes the abnormal classification result, abnormal data, and monitoring data to the remote medical platform and the patient's family member account associated with the device.

2. The non-contact breathing sleep syndrome monitoring and warning device according to claim 1, characterized in that: The monitoring and sensing module includes: An infrared sensing module configured to emit an infrared beam to the sleeping individual through an infrared sensor, capture the breathing condition, blood oxygen information, and heart rate information of the individual during sleep, and collect the patient's breathing data, blood oxygen data, and heart rate data; A data determination module configured to identify the error data in the breathing data, blood oxygen data, and heart rate data, extract the corresponding data volume information of the error data in the breathing data, blood oxygen data, and heart rate data respectively according to the identification result, evaluate the quality of the collection of the breathing data, blood oxygen data, and heart rate data, and give an abnormal alarm when the collection quality of the breathing data, blood oxygen data, and heart rate data is abnormal; An intelligent positioning module configured to use millimeter-wave radar technology to locate the position of the patient in the pillow area in real time, and automatically adjust the focusing area of the infrared sensing module when the patient's position deviates.

3. The non-contact breathing sleep syndrome monitoring and warning device according to claim 2, characterized in that: The data determination module includes: A data identification module configured to pre-analyze the characteristics of the existing normal breathing data, blood oxygen data, and heart rate data in the numerical range, fluctuation law, and change trend, establish a data identification model, input the collected breathing data, blood oxygen data, and heart rate data into the data identification model for comparison, and regard the data that does not conform to the data identification model as error data; A data extraction module configured to extract the first data volume information corresponding to the error data in the breathing data, the second data volume information corresponding to the error data in the blood oxygen data, and the third data volume information corresponding to the error data in the heart rate data; Anomaly determination module, configured to compare the first data volume information, the second data volume information, and the third data volume information with preset parameter thresholds respectively, specifically: If the first data volume information is lower than the preset parameter threshold, it is determined that there is an anomaly in the acquisition quality of the respiratory data, and an anomaly alarm is issued; If the second data volume information is lower than the preset parameter threshold, it is determined that there is an anomaly in the acquisition quality of the blood oxygen data, and an anomaly alarm is issued; If the third data volume information is lower than the preset parameter threshold, it is determined that there is an anomaly in the acquisition quality of the heart rate data, and an anomaly alarm is issued.

4. The non-contact breathing sleep syndrome monitoring and warning device according to claim 2, characterized in that: Setting the position offset threshold corresponding to the patient position offset for triggering the automatic adjustment of the focus area, including: Retrieving the historical respiratory data and historical blood oxygen data corresponding to the patient; Obtaining the respiratory data standard deviation and respiratory data average value corresponding to the patient according to the historical respiratory data corresponding to the patient; Obtaining the blood oxygen data standard deviation and blood oxygen data average value corresponding to the patient according to the historical blood oxygen data corresponding to the patient; Performing a ratio process on the respiratory data standard deviation corresponding to the patient and the respiratory data average value to obtain a respiratory ratio coefficient; Performing a ratio process on the blood oxygen data standard deviation corresponding to the patient and the blood oxygen data average value to obtain a blood oxygen ratio coefficient; Comparing the respiratory ratio coefficient and the blood oxygen ratio coefficient to obtain the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient; Comparing the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient with a preset difference threshold; When the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient does not exceed the preset difference threshold, retrieving the preset initial position offset threshold as the trigger condition for triggering the automatic adjustment of the focus area; When the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, adjusting the initial position offset threshold.

5. The non-contact breathing sleep syndrome monitoring and warning device according to claim 4, characterized in that: When the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, adjusting the initial position offset threshold, including: When the coefficient difference between the respiratory ratio coefficient and the blood oxygen ratio coefficient exceeds the preset difference threshold, retrieving the respiratory ratio coefficient and the blood oxygen ratio coefficient; Retrieving the historical patient position offset data corresponding to the patient; Using the historical patient position offset data corresponding to the patient to obtain the position offset standard deviation and the time interval standard deviation for triggering the automatic adjustment of the focus area; Performing a normalization process on the position offset standard deviation and the time interval standard deviation for triggering the automatic adjustment of the focus area to obtain the normalized position offset standard deviation and the time interval standard deviation for triggering the automatic adjustment of the focus area; Adjusting the initial position offset threshold by using the normalized position offset standard deviation and the time interval standard deviation for triggering the automatic adjustment of the focus area in combination with the respiratory ratio coefficient and the blood oxygen ratio coefficient to obtain the adjusted position offset threshold.

6. The non-contact breathing sleep syndrome monitoring and warning device according to claim 1, characterized in that: The data analysis module includes: A data processing module, configured to receive respiratory data, blood oxygen data, and heart rate data from a monitoring and sensing module and perform preprocessing, including removing outliers and filling in missing values. At the same time, convert the respiratory data, blood oxygen data, and heart rate data into a unified data format, and temporarily store the preprocessed respiratory data, blood oxygen data, and heart rate data in a buffer; A comparison and analysis module, configured to store preset respiratory warning values, blood oxygen warning values, and heart rate warning values, compare and analyze the collected respiratory data with the respiratory warning values, compare and analyze the collected blood oxygen data with the blood oxygen warning values, and compare and analyze the collected heart rate data with the heart rate warning values; An abnormality judgment module, configured to judge whether the patient's respiration, blood oxygen, and heart rate are abnormal according to the comparison and analysis results of the comparison and analysis module; When the respiratory data is higher than or lower than the respiratory warning value range, the patient's respiration is abnormal; When the blood oxygen data is lower than the blood oxygen warning value range, the patient's blood oxygen is abnormal; When the heart rate data is lower than or higher than the heart rate warning range, the patient's heart rate is abnormal; A dynamic adjustment module, configured to collect and store the patient's long-term historical monitoring data, analyze the change trends and rules of the historical monitoring data, and dynamically adjust the warning values according to the analysis results.

7. The non-contact breathing sleep syndrome monitoring and warning device according to claim 6, characterized in that: The dynamic adjustment module includes: A data collection module, configured to collect the patient's historical monitoring data in the long term, including time series records of respiratory rate, blood oxygen saturation data, and heart rate, as well as corresponding abnormal event records, and store the collected historical monitoring data according to the time series; A historical data analysis module, configured to determine the normal fluctuation range and change rules of the patient's respiratory, blood oxygen, and heart rate data by calculating statistical indicators such as the mean, standard deviation, and variance of the historical monitoring data. On the other hand, use time series analysis techniques to identify periodic, trend, and abnormal fluctuation patterns in the historical monitoring data, and determine the dynamic change trend of the patient's health status; A warning value adjustment module, configured to dynamically adjust the respiratory warning value, blood oxygen warning value, and heart rate warning value according to the patient's health change trends and rules obtained by the patient characteristic analysis module. At the same time, feedback the adjusted warning values to the comparison and analysis module and the abnormality judgment module to update the judgment basis.

8. The non-contact breathing sleep syndrome monitoring and warning device according to claim 1, characterized in that: The abnormality warning module includes: An abnormality grading module, configured to grade the abnormal respiratory data, blood oxygen data, and heart rate data according to the comparison and analysis results of the data analysis module with the respiratory warning value, blood oxygen warning value, and heart rate warning value respectively, and determine different levels according to the severity of the abnormality, namely mild, moderate, and severe levels; A patient characteristic analysis module, configured to collect and analyze the patient's sleep habits and physiological characteristics, and extract the characteristics of the patient's sleep habits and physiological characteristics through statistical analysis methods. Among them, the sleep habits are the patient's sleep time, sleep posture, sleep cycle characteristics, and easily disturbed time periods, and the physiological characteristics are the patient's age, gender, hearing status, and past medical history; The warning sound setting module is configured to personalize the warning sound parameters according to the abnormal classification result and the information provided by the patient characteristic analysis module, including the type, volume, frequency, and duration of the sound; The warning playback module is configured to play the corresponding warning sound through the speaker on the device to wake up the patient according to the parameter configuration of the warning sound setting module after the data analysis module determines that the patient has a situation; The synchronization notification module is configured to synchronize the monitored respiratory data, blood oxygen data, heart rate data, and abnormal data to the remote medical platform in real time to ensure that medical staff can obtain the patient's data in a timely manner. At the same time, through the mobile application or SMS platform associated with the patient's family member's account, a notification message is sent to the family member to inform them of the patient's current situation.

9. The non-contact breathing sleep syndrome monitoring and warning device according to claim 8, characterized in that: The abnormal classification module is divided into three levels: mild, moderate, and severe according to the severity of the abnormality, specifically as follows: Mild: The respiratory data is higher or lower than the respiratory warning value range by no more than 5%, and the duration does not exceed 1 minute; The blood oxygen data is lower than the blood oxygen warning value range by no more than 5%, and the duration does not exceed 2 minutes; The heart rate data is higher or lower than the heart rate warning value range by no more than 20%, and the duration does not exceed 30 seconds; Moderate: The respiratory data is higher or lower than the respiratory warning value range by no more than 10%, and the duration is between 1 and 3 minutes; The blood oxygen data is lower than the blood oxygen warning value range by no more than 10%, and the duration is between 2 and 5 minutes; The heart rate data is higher or lower than the heart rate warning value range by no more than 30%, and the duration is between 30 seconds and 2 minutes; Severe: The respiratory data is higher or lower than the respiratory warning value range by more than 10%, and the duration exceeds 3 minutes; The blood oxygen data is lower than the blood oxygen warning value range by more than 30%, and the duration exceeds 3 minutes; The heart rate data is higher or lower than the heart rate warning value range by more than 20%, and the duration does not exceed 30 seconds.

10. The non-contact breathing sleep syndrome monitoring and warning device according to claim 8, characterized in that: The warning sound setting module personalizes the warning sound parameters according to the abnormal classification result and the information provided by the patient characteristic analysis module, specifically as follows: Mild: The sound type is a soft reminder tone, the volume is 40-50 decibels, the frequency is to remind once every 2-3 seconds, and the duration of each reminder is 1-2 seconds; Moderate: The sound type is a clear alarm tone, the volume is 60-70 decibels, the frequency is to remind once every 1-2 seconds, and the duration of each reminder is 3-5 seconds; Severe: The sound type is a strong alarm tone, the volume is 80-90 decibels, the frequency is to remind once per second, and the duration of each reminder is 5-10 seconds.

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