Drink prediction system based on long-term sleep resting heart rate and breathing rate data

Abstract

The invention discloses a drunkenness prediction system based on long-term sleep resting heart rate and respiration rate data, and relates to the technical field of drunkenness prediction, the drunkenness prediction system comprises a sleep monitoring subsystem, a heart rate data analysis subsystem and a regulation and control intervention subsystem; the sleep monitoring subsystem comprises a sleep respiration monitoring module and a sleep heart rate monitoring module; the sleep monitoring system is used for collecting data of a human body in the sleep process, electronic equipment is used for continuously collecting resting heart rate and breathing rate data in sleep for a long time, and the electronic equipment comprises an intelligent bracelet, a sports bracelet and a cardiotachometer and needs to have the advantages of being high in precision and good in stability so as to ensure the accuracy and reliability of the data; and meanwhile, the continuity of data acquisition is ensured, and the influence of data missing on subsequent analysis is avoided. According to the drunkenness prediction system, the influence of drunkenness on the body can be found in time by monitoring the sleep heart rate and the respiration rate for a long time, people are helped to know the physical condition of themselves, corresponding prevention measures are taken, and harm of drunkenness to health is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of drunkenness prediction, and particularly to a drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data. Background Technique

[0002] Resting heart rate refers to the heart rate when a person is awake and at rest, usually the heart rate after waking up in the morning or when sitting without activity; the resting heart rate of normal adults is usually between 60 beats / min and 100 beats / min, but this range can vary depending on age, health status, etc.; resting heart rate is usually used to evaluate a person's basic cardiovascular health; in a drunken state, due to the effect of alcohol on the nervous system, the heart rate may increase; however, a heart rate of 86 beats per minute is still within the normal range; the normal resting heart rate of adults is 60-100 beats per minute; the heart rate may vary slightly due to age, gender or other factors.

[0003] The effect of alcohol on resting heart rate varies from person to person, depending on the individual's body's adaptation to alcohol and the amount of alcohol consumed; generally speaking, alcohol can cause the heart rate to increase because alcohol affects the balance of the autonomic nervous system, especially the activity of the vagus nerve; the vagus nerve is part of the parasympathetic nervous system, which has an inhibitory effect on the heart, making the heart rate slow down; the intake of alcohol weakens this inhibitory effect, resulting in an increase in heart rate.

[0004] The effect of alcohol on sleep heart rate also exists; sleep heart rate refers to the lowest heart rate value when a person is in deep sleep; after alcohol intake, although it may help people fall asleep faster in the initial stage, it will interfere with the normal sleep cycle, especially in the second half of the night, when the effect of alcohol gradually fades, it may cause insomnia and dreaminess, thus affecting sleep quality; in addition, alcohol will also increase the number of night awakenings, further affecting the continuity and depth of sleep.

[0005] Although alcohol may make people feel sleepy and fall asleep quickly, it will disrupt the entire night's sleep pattern; alcohol will reduce the time of rapid eye movement (REM) sleep, which is a crucial stage for physical and mental recovery in the sleep cycle; in the long run, alcohol-dependent sleep disorders may lead to serious sleep problems, including difficulty falling asleep and frequent night awakenings.

[0006] To improve the sleep quality and heart rate after getting drunk, some measures can be taken. For example, moderate exercise can help accelerate blood circulation and promote alcohol excretion. Taking a walk or other mild exercise before going to bed may help improve sleep quality. If there are headache or other discomfort symptoms, painkillers or sleep-promoting drugs can be considered, but it should be carried out under the guidance of a doctor. In addition, avoiding sleeping in a noisy environment and ensuring appropriate indoor air circulation also helps improve sleep quality.

[0007] In summary, alcohol can affect the resting heart rate and sleep heart rate, leading to an increase in heart rate and a decline in sleep quality. To maintain good cardiovascular health and sleep quality, it is recommended to limit alcohol intake and take appropriate measures to improve the physical condition after intoxication. Summary of the Invention

[0008] In view of the deficiencies of the prior art, the present invention provides a drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data, which solves the problems raised in the above-mentioned background art.

[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: A drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data, including a sleep monitoring subsystem, a heart rate data analysis subsystem, and a regulation and intervention subsystem;

[0010] The sleep monitoring subsystem includes a sleep respiration monitoring module and a sleep heart rate monitoring module; it is used for data collection operations on the human body during sleep, and uses electronic devices to continuously collect the resting heart rate and respiratory rate data during sleep for a long time. The electronic devices include smart bracelets, sports bracelets, and heart rate monitors, and the devices need to have the characteristics of high precision and good stability to ensure the accuracy and reliability of the data; at the same time, ensure the continuity of data collection to avoid data loss affecting subsequent analysis;

[0011] The heart rate data analysis subsystem uses data analysis algorithms and machine learning techniques to analyze the long-term collected heart rate and respiratory rate data; establishes a heart rate and respiratory rate model under normal sleep conditions, and identifies drunken sleep characteristics by comparing real-time data with the normal data model, and determines the user's sleep state according to the drunken sleep characteristic values;

[0012] Among them,

[0013] The heart rate data analysis subsystem includes a drunkenness prediction module and a heart rate data analysis module;

[0014] The drunkenness prediction module, according to the data analysis results, combines historical drunkenness data and individual physical characteristics to establish a drunken sleep prediction model; uses the data model to predict the possibility of an individual having a drunken sleep within a fixed future time period and issues an early warning in advance; when the heart rate and respiratory rate data of an individual show a change trend similar to that of previous drunken sleep, the system predicts that the individual will enter a drunken sleep state and issues a reminder notice in time;

[0015] The heart rate data analysis module is used for the analysis operation of the heart rate data during sleep to evaluate the sleep quality; if the heart rate is stable and at a low level during sleep, it will indicate that the sleep quality is good; if the heart rate shows obvious fluctuations, it is determined that there is a sleep disorder;

[0016] The heart rate data analysis module includes basic heart rate statistical analysis and heart rate variability analysis (HRV).

[0017] The basic heart rate statistical analysis is used to measure and record the basic heart rate at a fixed time every day (such as when waking up in the early morning without getting out of bed), and at the same time record the heart rate data during sleep for long-term trend analysis, so as to understand whether the user's sleep heart rate is within the normal range and whether there are abnormal fluctuations.

[0018] At the same time, time series analysis is used to establish a time series model, and the heart rate data is statistically analyzed in equal time units to help understand the trend and periodic changes of the sleep heart rate data and predict future heart rate changes.

[0019] The heart rate variability analysis (HRV) is used to reflect the activity of the cardiac autonomic nervous system; time domain analysis, frequency domain analysis and non-linear analysis are carried out on the sleep heart rate data; the heart rate time domain analysis indicators include average heart rate, maximum heart rate, minimum heart rate, and heart rate variability; the heart rate frequency domain indicators include low frequency component (LF), high frequency component (HF), and low frequency component / high frequency component ratio; thus, the health status, stress level and recovery ability of the heart are evaluated.

[0020] The regulation and intervention subsystem starts corresponding intervention measures when the system predicts the situation of drunk sleep; it emits vibration and sound reminders through intelligent devices to remind the drunk person to adjust the sleep posture, so as to avoid the trachea being blocked by vomit caused by lying on the back; and the intelligent device is also linked with the smart home system to synchronously adjust the indoor temperature and humidity to improve the sleep quality of the drunk person; at the same time, the system also has the ability to send notifications to emergency contacts so that they can take further measures in time.

[0021] Optionally, the sleep breathing monitoring module uses a smart bracelet to perform real-time monitoring operations on the breathing state of the user during sleep, thereby observing the changes in the central nervous system, breathing, and cardiovascular system of the user in the sleep state to meet the basic data requirements for drunk prediction; the smart bracelet is used to collect and analyze the sleep data of the user.

[0022] Optionally, the sleep breathing monitoring module also includes electrode patch sensors. The respiratory motion sensors are respectively tied to the breathing-sensitive parts of the chest and abdomen as required; the airflow sensors are pasted on the left and right front nostrils and in front of the lips with double-sided tape; the snoring sensors are fixed to the patient's ear holes or the lower jaw; finally, the electrocardiogram and blood oxygen saturation sensors are connected; by monitoring eye movement, airflow in the mouth and nose, and chest and abdomen movement, and at the same time monitoring blood oxygen and heart rate conditions and then analyzing, it is evaluated whether the diagnostic criteria for sleep apnea syndrome are met.

[0023] Optionally, the sleep heart rate monitoring module is used to collect the user's resting heart rate data and the real-time heart rate fluctuations during the user's sleep. The monitoring device includes a heart rate sensor and a motion sensor; the heart rate sensor is used to detect the user's heart rate and is worn as a wrist device, a forearm device or other devices in contact with the body skin; the motion sensor is used to measure the user's motion conditions to determine the user's inactive periods.

[0024] The heart rate sensor detects the user's heart rate optically and converts the detected heart rate signal into data.

[0025] The motion sensor measures or determines the user's motion during free-living states over time to obtain motion data.

[0026] Optionally, there are two heart rate sensors in the monitoring device, which are respectively set as the first sensor and the second sensor. The optoelectronic detection data collected by the first sensor is processed by the first processor to obtain heart rate data and saved. The first heart rate data corresponding to the resting time period determined according to the state detection data collected by the second sensor is extracted from the saved heart rate data, and then this data is processed by the second processor to obtain the resting heart rate. Two processors are set in this module. The low-power first processor is connected to the first sensor, and the high-power second processor executes the resting heart rate algorithm to process the heart rate data corresponding to the resting time period. Subsequently, the data collected by the heart rate sensor and the motion sensor are arranged according to the same time line to form a data comparison.

[0027] Optionally, the acquisition of the resting heart rate data by the heart rate sensor needs to be repeated multiple times, and the interval time between each time needs to be greater than 60 - 90 minutes, and the average value is taken as the final result.

[0028] Optionally, the regulation and intervention subsystem also includes regular work and rest advice intervention and diet advice intervention.

[0029] Optionally, for the regular work and rest advice intervention, using the monitoring results of the user's sleep state by the heart rate data analysis subsystem, intervention prompts for the user's work and rest time are given to adjust the user's sleeping and waking-up times, thereby improving the rate of the user's body recovery after drinking.

[0030] Optionally, for the diet advice intervention, according to the analysis results of the heart rate data, a balanced diet model is adopted, including foods rich in amino acids, minerals and B vitamins such as whole grains, vegetables, fruits, nuts, beans, and seafood, and vitamin D is supplemented correspondingly.

[0031] The present invention provides a drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data, which has the following beneficial effects:

[0032] The drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data can detect the impact of drunkenness on the body in a timely manner by monitoring the sleep heart rate and respiratory rate for a long time. It helps people understand their physical conditions, take corresponding preventive measures, and reduce the harm of drunkenness to health. When the system detects that the drunken sleep frequency of an individual is too high, it can remind them to reduce the amount of alcohol consumed or change their drinking habits. During the drunken sleep process, dangerous situations such as vomit blocking the trachea and apnea may occur. When the system detects abnormal breathing, it will issue an alarm in a timely manner and take intervention measures to avoid accidents such as suffocation. The system can provide personalized health advice and intervention plans based on the individual's heart rate and respiratory rate data. Detailed implementation manner

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0034] The present invention provides a technical solution: a drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data, including a sleep monitoring subsystem, a heart rate data analysis subsystem, and a regulation and intervention subsystem;

[0035] The sleep monitoring subsystem includes a sleep respiration monitoring module and a sleep heart rate monitoring module; it is used for collecting human data during sleep. Using electronic devices, it continuously collects the resting heart rate and respiratory rate data during sleep for a long time. The electronic devices include smart bracelets, sports bracelets, and heart rate monitors, and these devices need to have the characteristics of high precision and good stability to ensure the accuracy and reliability of the data; at the same time, ensure the continuity of data collection to avoid data loss affecting subsequent analysis;

[0036] The sleep respiration monitoring module uses a smart bracelet to monitor the respiratory state of the user during sleep in real time, thereby observing the changes in the central nervous system, respiration, and cardiovascular system of the user in the sleep state to meet the basic data needs for drunkenness prediction; use the smart bracelet to collect and analyze the user's sleep data.

[0037] The sleep respiration monitoring module also includes electrode sheet sensors. The respiratory movement sensors are respectively strapped to the respiratory sensitive parts of the chest and abdomen as required; the airflow sensors are pasted on the left and right front nostrils and in front of the lips with double-sided tape; the snoring sensors are fixed to the patient's ear holes or mandible; finally, connect the electrocardiogram and blood oxygen saturation sensors; monitor eye movement, oral and nasal airflow, and monitor the movement of the chest and abdomen, and at the same time monitor the blood oxygen and heart rate conditions and then analyze to evaluate whether the diagnostic criteria for sleep apnea syndrome are met.

[0038] The sleep heart rate monitoring module is used to collect the user's resting heart rate data and the real-time heart rate fluctuations during the user's sleep. The monitoring device includes a heart rate sensor and a motion sensor; the heart rate sensor is used to detect the user's heart rate and is worn as a wrist device, a forearm device or other devices in contact with the body skin; the motion sensor is used to measure the user's motion situation in order to determine the user's inactive period;

[0039] The heart rate sensor detects the user's heart rate optically and converts the detected heart rate signal into data;

[0040] The motion sensor measures or determines the user's motion during the free-living state over time to obtain motion data;

[0041] There are two heart rate sensors in the monitoring device, which are respectively set as the first sensor and the second sensor. The optoelectronic detection data collected by the first sensor is processed by the first processor to obtain heart rate data and saved. The first heart rate data corresponding to the resting period determined according to the state detection data collected by the second sensor is extracted from the saved heart rate data, and then this data is processed by the second processor to obtain the resting heart rate; two processors are set in this module. The low-power first processor is connected to the first sensor, and the high-power second processor executes the resting heart rate algorithm to process the heart rate data corresponding to the resting period. Subsequently, the data collected by the heart rate sensor and the motion sensor are arranged according to the same time line, so as to form a data comparison.

[0042] The acquisition of the resting heart rate data by the heart rate sensor needs to be repeated multiple times, and the interval time between each time needs to be greater than 60 - 90 minutes, and the average value is taken as the final result;

[0043] The heart rate data analysis subsystem uses data analysis algorithms and machine learning technologies to analyze the long-term collected heart rate and respiratory rate data; establish a heart rate and respiratory rate model under normal sleep conditions, and identify the drunken sleep characteristics by comparing the real-time data with the normal data model, and determine the user's sleep state according to the drunken sleep characteristic values;

[0044] Among them,

[0045] The heart rate data analysis subsystem includes a drunkenness prediction module and a heart rate data analysis module;

[0046] The drunkenness prediction module, according to the data analysis results, combines the historical drunkenness data and the individual's physical characteristics to establish a drunken sleep prediction model; uses the data model to predict the possibility of an individual having a drunken sleep within a fixed future time period and issues an early warning in advance; when the heart rate and respiratory rate data of an individual show a change trend similar to that of previous drunken sleep, the system predicts that the drunken sleep state will be entered and a reminder notice will be issued in time;

[0047] The heart rate data analysis module is used to analyze the heart rate data during sleep to evaluate the sleep quality. If the heart rate is stable and at a low level during sleep, it indicates good sleep quality. If the heart rate fluctuates significantly, it is judged as a sleep disorder.

[0048] The heart rate data analysis module includes basic heart rate statistics analysis and heart rate variability analysis (HRV);

[0049] Basic heart rate statistical analysis is used to measure and record the basic heart rate at a fixed time every day (such as when you wake up in the morning but don't get up yet), and also record the heart rate data during sleep for long-term trend analysis to understand whether the user's sleeping heart rate is within the normal range and whether there are any abnormal fluctuations;

[0050] At the same time, time series analysis is used to establish a time series model, and the heart rate data is counted in equal time units to help understand the trend and periodic changes of sleep heart rate data and predict future heart rate changes;

[0051] Heart rate variability analysis (HRV) is used to reflect the activity of the cardiac autonomic nervous system; time domain analysis, frequency domain analysis and nonlinear analysis are performed on the sleeping heart rate data; heart rate time domain analysis indicators include average heart rate, maximum heart rate, minimum heart rate and heart rate variability; heart rate frequency domain indicators include low frequency component (LF), high frequency component (HF) and low frequency component / high frequency component ratio; this is used to assess the health status, stress level and recovery ability of the heart;

[0052] The control and intervention subsystem starts corresponding intervention measures when the system predicts drunken sleep. It sends vibration and sound reminders through smart devices to remind the drunk to adjust their sleeping posture to avoid lying on their backs and causing vomiting to block the trachea. The smart devices are also linked with the smart home system to synchronously adjust the indoor temperature and humidity to improve the sleep quality of the drunk. At the same time, the system also has the ability to send notifications to emergency contacts so that they can take further measures in time.

[0053] The regulatory intervention subsystem also includes regular work and rest suggestion intervention and diet suggestion intervention;

[0054] Regular work and rest suggestion intervention, using the heart rate data analysis subsystem to monitor the user's sleep status, giving the user intervention reminders on the work and rest time, adjusting the user's bedtime and waking time, so as to improve the user's body recovery rate after drinking;

[0055] Dietary intervention is recommended, based on the analysis results of heart rate data, to adopt a balanced diet pattern, including the intake of whole grains, vegetables, fruits, nuts, beans, seafood and foods rich in amino acids, minerals and B vitamins, and corresponding vitamin D supplementation.

[0056] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

Claims

1. A drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data, characterized in that, It includes a sleep monitoring subsystem, a heart rate data analysis subsystem, and a regulation and intervention subsystem; The sleep monitoring subsystem includes a sleep respiration monitoring module and a sleep heart rate monitoring module; it is used for data collection operations on the human body during sleep. Using electronic devices, it continuously collects resting heart rate and respiration rate data during sleep for a long time. The electronic devices include smart bracelets, sports bracelets, and heart rate monitors, and these devices need to have the characteristics of high precision and good stability to ensure the accuracy and reliability of the data; at the same time, ensure the continuity of data collection to avoid data loss affecting subsequent analysis; The heart rate data analysis subsystem uses data analysis algorithms and machine learning techniques to analyze the long-term collected heart rate and respiration rate data; establish a heart rate and respiration rate model under normal sleep conditions, and identify the characteristics of drunken sleep by comparing real-time data with the normal data model, and determine the user's sleep state according to the drunken sleep characteristic values; Among them, The heart rate data analysis subsystem includes a drunkenness prediction module and a heart rate data analysis module; The drunkenness prediction module establishes a drunken sleep prediction model based on the data analysis results, combined with historical drunken data and the individual's physical characteristics; uses the data model to predict the possibility of an individual having drunken sleep within a fixed future time period and issues an early warning in advance; when the heart rate and respiration rate data of an individual show a similar change trend to previous drunken sleep, the system predicts that the individual will enter the drunken sleep state and issues a reminder notice in time; The heart rate data analysis module is used for the analysis operation of heart rate data during sleep to evaluate the sleep quality; if the heart rate is stable and at a low level during sleep, it will prompt that the sleep quality is good; if the heart rate shows obvious fluctuations, it is determined that there is a sleep disorder; The heart rate data analysis module includes basic heart rate statistical analysis and heart rate variability analysis (HRV); The basic heart rate statistical analysis is used to measure and record the basic heart rate at a fixed time every day (such as when waking up in the early morning and not getting out of bed), and at the same time record the heart rate data during sleep, and conduct a long-term trend analysis to understand whether the user's sleep heart rate is within the normal range and whether there are abnormal fluctuations; At the same time, a time series model is established using time series analysis to statistically analyze the heart rate data in equal time units to help understand the trend and periodic changes of the sleep heart rate data and predict future heart rate changes; The heart rate variability analysis is used to reflect the activity of the cardiac autonomic nervous system; conduct time domain analysis, frequency domain analysis, and non-linear analysis on the sleep heart rate data; the heart rate time domain analysis indicators include average heart rate, maximum heart rate, minimum heart rate, and heart rate variability; the heart rate frequency domain indicators include low-frequency component, high-frequency component, and low-frequency component / high-frequency component ratio; thus evaluate the heart's health status, stress level, and recovery ability; The regulation and intervention subsystem, when the system predicts the situation of drunk sleep, activates corresponding intervention measures; sends vibration and sound reminders through intelligent devices to remind the drunk person to adjust the sleeping position, so as to avoid the trachea being blocked by vomit caused by lying on the back; and the intelligent device is also linked with the smart home system to synchronously adjust the indoor temperature and humidity to improve the sleep quality of the drunk person; at the same time, the system also has the ability to send notifications to emergency contacts so that they can take further measures in a timely manner.

2. The drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data according to claim 1, wherein: The sleep respiration monitoring module uses a smart bracelet to perform real-time monitoring operations on the respiration state of the user during sleep, thereby observing the changes in the central nervous system, respiration, and cardiovascular system of the user in the sleep state to meet the basic data requirements for drunk prediction; uses the smart bracelet to collect and analyze the sleep data of the user.

3. The drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data according to claim 2, characterized in that: The sleep respiration monitoring module also includes electrode patch sensors. The respiratory movement sensors are respectively strapped to the respiration-sensitive parts of the chest and abdomen as required; the airflow sensors are pasted on the left and right front nostrils and in front of the lips with double-sided tape; the snoring sensor is fixed to the patient's ear hole or mandible; finally, the electrocardiogram and blood oxygen saturation sensors are connected; monitor eye movement, airflow in the mouth and nose, and chest and abdomen movement, and at the same time monitor blood oxygen and heart rate conditions and then analyze to evaluate whether the diagnostic criteria for sleep apnea syndrome are met.

4. A drunkenness prediction system based on long-term sleep resting heart rate and respiration rate data according to claim 1, characterized in that: The sleep heart rate monitoring module is used to collect the user's resting heart rate data and the real-time heart rate fluctuations during the user's sleep. The monitoring device includes a heart rate sensor and a motion sensor; the heart rate sensor is used to detect the user's heart rate and is worn as a wrist device, forearm device or other device in contact with the body skin; the motion sensor is used to measure the user's motion situation to determine the user's inactive period; The heart rate sensor detects the user's heart rate optically and converts the detected heart rate signal into data; The motion sensor measures or determines the user's motion during the free-living state over time to obtain motion data.

5. A drunkenness prediction system based on long-term sleep resting heart rate and respiration rate data according to claim 4, characterized in that: There are two heart rate sensors in the monitoring device, which are respectively set as the first sensor and the second sensor. The photoelectric detection data collected by the first sensor is processed by the first processor to obtain heart rate data and saved. The first heart rate data corresponding to the resting time period determined according to the state detection data collected by the second sensor is extracted from the saved heart rate data, and then this data is processed by the second processor to obtain the resting heart rate; Two processors are set in this module. The low-power first processor is connected to the first sensor, and the high-power second processor executes the resting heart rate algorithm to process the heart rate data corresponding to the resting time period. Subsequently, the data collected by the heart rate sensor and the motion sensor are arranged according to the same time line to form a data comparison.

6. The drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data according to claim 5, wherein: The acquisition of the resting heart rate data by the heart rate sensor needs to be repeated multiple times, and the interval time between each time needs to be greater than 60 - 90 minutes, and the average value is taken as the final result.

7. A drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data according to claim 1, characterized in that: The regulation and intervention subsystem also includes regular work and rest advice intervention and diet advice intervention.

8. A drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data according to claim 7, characterized in that: The above-mentioned regular schedule advice intervention uses the monitoring results of the user's sleep state by the heart rate data analysis subsystem to give the user intervention prompts for the schedule, adjust the user's sleep and wake-up times, so as to improve the body recovery rate of the user after drinking.

9. The drunkenness prediction system based on long-term sleep resting heart rate and respiratory rate data according to claim 7, characterized in that: The above-mentioned diet advice intervention adopts a balanced diet model according to the analysis results of the heart rate data, including foods rich in amino acids, minerals, and B vitamins such as whole grains, vegetables, fruits, nuts, beans, and seafood, and correspondingly supplements vitamin D.