CN113180615B - Organ sleep detection method and system for physical sign data analysis of wearable equipment - Google Patents

Organ sleep detection method and system for physical sign data analysis of wearable equipment Download PDF

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CN113180615B
CN113180615B CN202110379348.6A CN202110379348A CN113180615B CN 113180615 B CN113180615 B CN 113180615B CN 202110379348 A CN202110379348 A CN 202110379348A CN 113180615 B CN113180615 B CN 113180615B
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CN113180615A (en
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白雪扬
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Beijing Xueyang Technology Co ltd
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Abstract

The invention provides an organ sleep detection method and system for analyzing physical sign data of wearable equipment, wherein the method comprises the following steps: collecting pulse wave original signal waveforms of a sleeping period of a user in real time through wearable equipment; carrying out data analysis on the pulse wave original signal waveform to obtain a sleep model of a user; and combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to acquire a conditioning strategy corresponding to the physical condition of the user. The system comprises modules corresponding to the method steps.

Description

Organ sleep detection method and system for physical sign data analysis of wearable equipment
Technical Field
The invention provides an organ sleep detection method and system for analyzing physical sign data of wearable equipment, and belongs to the technical field of human body physical sign monitoring.
Background
Nowadays, with the continuous development of portable human body feature detection technology, human body feature acquisition devices are arranged on intelligent wearable equipment, such as a smart watch and a smart bracelet, so that human body feature data acquisition is commonly applied, but part of the wearable equipment has the defects that the whole health condition of a user is only provided by carrying out feature data monitoring, but the data is not utilized to analyze the health state of each organ of the user and carry out specific healthy diet nursing guidance; meanwhile, the general dietary nutrition guidance method only carries out dietary guidance through physical conditions on the surface of a user or detection data on the same day, and long-term detection and analysis of the physical conditions are not carried out, so that the daily dietary nutrition guidance of the user is not systematic. Meanwhile, the sleeping condition of each organ of the human body cannot be detected by the common wearable equipment, and specific organs cannot be nursed, so that the sleeping quality is improved; each person knows only how long he/she sleeps every day and does not know the problem of the sleeping situation of the individual organs in detail.
Disclosure of Invention
The invention provides an organ sleep detection method for analyzing physical sign data of wearable equipment, which is used for solving the problems that the existing sleep monitoring equipment only detects sleep time and cannot acquire the sleep state of an organ and a conditioning strategy corresponding to the physical health condition:
a method of organ sleep detection for wearable device sign data analysis, the method comprising:
collecting pulse wave original signal waveforms of a sleeping period of a user in real time through wearable equipment;
carrying out data analysis on the pulse wave original signal waveform to obtain a sleep model of a user;
and combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to acquire a conditioning strategy corresponding to the physical condition of the user.
Further, the wearable device is used for collecting pulse wave original signal waveforms of a sleep period of a user in real time, and the method comprises the following steps:
the method comprises the steps that a wearable device with a pulse sensor is worn, and pulse wave original signal waveforms of a user are collected in real time;
carrying out noise reduction, baseline removal and wavelet decomposition treatment on the pulse wave original signal waveform to obtain reconstructed pulse wave signal characteristic point data;
and sending the reconstructed pulse wave signal characteristic point data to a big data analysis platform.
Further, the method for sending the reconstructed pulse wave signal characteristic point data to a big data analysis platform comprises the following steps:
before sleeping, the user inputs the height and weight information into the wearable device, and the wearable device sets a sending time interval for sending pulse wave signal characteristic point data to the big data analysis platform according to the height and weight information of the user;
after the reconstructed pulse wave signal characteristic point data are obtained in real time, caching the reconstructed pulse wave signal characteristic point data obtained in real time in wearable equipment to form a cache data packet;
and sending the buffer data packet to the big data analysis platform at regular time according to the set sending time interval.
Further, setting a transmission time interval for transmitting the pulse wave signal characteristic point data to the big data analysis platform, including:
determining a transmission time interval reference value according to the height and weight information of the user, wherein the transmission time interval reference value is obtained through the following formula:
wherein ,T0 Representing a transmission time interval reference value; h represents the height of the user; m represents the current weight of the user; m is M 0 Representing an international standard weight corresponding to the height of the user; t (T) c Representing unit time, T 0 =60 min; λ represents a time adjustment coefficient;
acquiring the number of beats per minute of the first n minutes of wearing the wearable device by a user, and acquiring the transmission time interval of the pulse wave signal characteristic point data transmitted to the big data analysis platform according to the number of beats of the user and the transmission time interval reference value, wherein n is a preset time length value, and the transmission time interval is acquired through the following formula:
wherein ,Tg Representing a transmission time interval; c (C) i Indicating the number of user heartbeats corresponding to the ith minute within n minutes; c (C) max Representing the maximum value of the heartbeat of a single-minute user within n minutes; c (C) min Representing the minimum value of the heartbeat of a single-minute user within n minutes; alpha represents a transmission time adjustment coefficient; min (C) i+1 -C i ) Representing the minimum difference between the number of heartbeats in two adjacent minutes within n minutes; min (C) i+1 -C i ) The maximum value of the difference between the number of heartbeats in two adjacent minutes within n minutes is shown.
Further, the data analysis is performed on the pulse wave original signal waveform to obtain a sleep model of the user, including:
the big data analysis platform analyzes the pulse wave original signal waveform and determines the human body sign parameters of the user; wherein the human body physical sign parameters include: basic data of human body physical signs such as heart rate data, blood pressure data, blood oxygen data and the like;
and the big data analysis platform analyzes the human body sign parameters of the user to obtain a sleep model of the user.
Further, combining the sleep model of the user with the theoretical scheme of traditional Chinese medicine to obtain a conditioning strategy corresponding to the physical condition of the user, wherein the conditioning strategy comprises the following steps:
the big data platform determines the sleep state of each organ of the user in the sleep process according to the sleep model of the user,
determining the physical health condition of the user during sleep according to the sleep state of each organ;
searching a traditional Chinese medicine theoretical scheme stored in a database of the big data analysis platform, and acquiring a conditioning strategy corresponding to the physical health condition of a user;
and the big data platform feeds the conditioning strategy back to the user, and simultaneously, the sleep state of each organ of the user, the human body sign parameters and the acquired conditioning strategy are uploaded to a cloud server for storage.
An organ sleep detection system for wearable device sign data analysis, the system comprising:
the real-time acquisition module is used for acquiring pulse wave original signal waveforms of a sleeping period of a user in real time through the wearable equipment;
the data analysis module is used for carrying out data analysis on the pulse wave original signal waveform to obtain a sleep model of the user;
the acquisition module is used for combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to acquire a conditioning strategy corresponding to the physical condition of the user;
the data analysis module comprises:
the waveform analysis module is used for analyzing the pulse wave original signal waveform by the big data analysis platform and determining the human body sign parameters of the user; wherein the human body physical sign parameters include: basic data of human body physical signs such as heart rate data, blood pressure data, blood oxygen data and the like;
the model acquisition module is used for analyzing the human body sign parameters of the user by the big data analysis platform to acquire a sleep model of the user;
the acquisition module comprises:
a sleep state module for determining the sleep state of each organ of the user in the sleep process according to the sleep model of the user by the big data platform,
the health acquisition module is used for determining the physical health condition of the user in the sleeping process according to the sleeping state of each organ;
the strategy acquisition module is used for searching the traditional Chinese medicine theoretical schemes stored in the database of the big data analysis platform and acquiring a conditioning strategy corresponding to the physical health condition of the user;
and the feedback storage module is used for feeding the conditioning strategy back to the user by the big data platform, and uploading the sleep state of each organ of the user, the human body sign parameters and the acquired conditioning strategy to the cloud server for storage.
Further, the real-time acquisition module includes:
the pulse signal acquisition module is used for acquiring the pulse wave original signal waveform of the user in real time by wearing the wearable device with the pulse sensor;
the signal preprocessing module is used for carrying out noise reduction, baseline removal and wavelet decomposition processing on the pulse wave original signal waveform to obtain reconstructed pulse wave signal characteristic point data;
and the signal sending module is used for sending the reconstructed pulse wave signal characteristic point data to the big data analysis platform.
Further, the signal transmitting module includes:
the sending time setting module is used for inputting height and weight information into the wearable equipment before sleeping of a user, and the wearable equipment sets a sending time interval for sending pulse wave signal characteristic point data to the big data analysis platform according to the height and weight information of the user;
the buffer module is used for buffering the reconstructed pulse wave signal characteristic point data acquired in real time in the wearable equipment after the reconstructed pulse wave signal characteristic point data are acquired in real time, so as to form a buffer data packet;
and the buffer data sending module is used for sending the buffer data packet to the big data analysis platform at regular time according to the set sending time interval.
Further, the transmission time setting module includes:
the time reference value acquisition module is used for determining a transmission time interval reference value according to the height and weight information of the user, and the transmission time interval reference value is acquired through the following formula:
wherein ,T0 Representing a transmission time interval reference value; h represents the height of the user; m represents the current weight of the user; m is M 0 Representing an international standard weight corresponding to the height of the user; t (T) c Representing unit time, T 0 =60 min; λ represents a time adjustment coefficient;
the time interval acquisition module is used for acquiring the number of beats per minute of the first n minutes of wearing the wearable device by the user, and acquiring a transmission time interval of the pulse wave signal characteristic point data transmitted to the big data analysis platform according to the number of beats of the user and a transmission time interval reference value, wherein n is a preset time length value, and the transmission time interval is acquired through the following formula:
wherein ,Tg Representing a transmission time interval; c (C) i Indicating the number of user heartbeats corresponding to the ith minute within n minutes; c (C) max Representing the maximum value of the heartbeat of a single-minute user within n minutes; c (C) min Representing the minimum value of the heartbeat of a single-minute user within n minutes; alpha represents a transmission time adjustment coefficient; min (C) i+1 -C i ) Representing the minimum difference between the number of heartbeats in two adjacent minutes within n minutes; min (C) i+1 -C i ) Representing the number of beats in two adjacent minutes within n minutesDifference maximum.
The invention has the beneficial effects that:
the organ sleep detection method and system for analyzing the physical sign data of the wearable equipment provided by the invention acquire and analyze the human pulse wave data of the user in the sleeping process to obtain the sleep rest state and the health state of each organ in the sleeping stage of the user, and the sleep state and the health state of each organ are compared with the traditional Chinese medicine nursing scheme to provide a specific healthy diet nursing guiding method, so that the method has an obvious effect on body health management, the health management of the user is fundamentally carried out, the health management efficiency of the user is effectively improved, and the user can obtain the nursing scheme correspondingly matched with the body state of the user without going to the hospital.
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FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a system block diagram of the system of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The embodiment of the invention provides an organ sleep detection method for analyzing physical sign data of wearable equipment, as shown in fig. 1, the method comprises the following steps:
s1, acquiring pulse wave original signal waveforms of a sleeping period of a user in real time through wearable equipment;
s2, carrying out data analysis on the pulse wave original signal waveform to obtain a sleep model of the user;
and S3, combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to acquire a conditioning strategy corresponding to the physical condition of the user.
The wearable device is used for collecting pulse wave original signal waveforms of a sleep period of a user in real time, and the method comprises the following steps:
s101, acquiring pulse wave original signal waveforms of a user in real time by wearing wearable equipment with a pulse sensor;
s102, carrying out noise reduction, baseline removal and wavelet decomposition processing on the pulse wave original signal waveform to obtain reconstructed pulse wave signal characteristic point data;
and S103, sending the reconstructed pulse wave signal characteristic point data to a big data analysis platform.
The data analysis is performed on the pulse wave original signal waveform to obtain a sleep model of the user, and the method comprises the following steps:
s201, analyzing the pulse wave original signal waveform by the big data analysis platform, and determining human body sign parameters of a user; wherein the human body physical sign parameters include: basic data of human body physical signs such as heart rate data, blood pressure data, blood oxygen data and the like;
s202, the big data analysis platform analyzes the human body sign parameters of the user to obtain a sleep model of the user.
Combining a sleep model of a user with a traditional Chinese medicine theoretical scheme to obtain a conditioning strategy corresponding to the physical condition of the user, wherein the conditioning strategy comprises the following steps:
s301, the big data platform determines the sleep state of each organ of the user in the sleep process according to the sleep model of the user,
s302, determining the physical health condition of a user in the sleeping process according to the sleeping state of each organ;
s303, searching a traditional Chinese medicine theoretical scheme stored in a database of the big data analysis platform, and acquiring a conditioning strategy corresponding to the physical health condition of a user;
s304, the big data platform feeds the conditioning strategy back to the user, and meanwhile, the sleep state of each organ of the user, the human body sign parameters and the acquired conditioning strategy are uploaded to a cloud server for storage.
The principle of the technical scheme is as follows: and detecting the sleep of the organs of the user according to the big data and the algorithm by using the heart rate, the blood pressure, the blood oxygen and other user sign data acquired by the settling early warning watch, and finally obtaining the sleep health condition of the user. The method comprises the following steps: collecting human body sign data: by wearing the intelligent wearable device, analyzing data according to the original signal waveform obtained by the sensor and then uploading the data to the cloud service through big data; data analysis of pulse wave: analyzing the uploaded original signal waveform according to a big data analysis platform, and analyzing and calculating according to a special algorithm to obtain basic human body sign data such as heart rate, blood pressure, blood oxygen and the like; analyzing to obtain a sleep model: analyzing and obtaining a daily sleep model of a user through basic data of human body physical signs, heart rate, blood pressure, blood oxygen and the like; combining with the theory of traditional Chinese medicine: according to the traditional Chinese medicine theory, the sleep state of each organ of the user is obtained through analysis according to the sleep model calculated by the artificial intelligence algorithm.
The technical scheme has the effects that: the method has the advantages that the human pulse wave data in the sleeping process of the user are collected and analyzed to obtain the sleeping rest state and the health state of each organ in the sleeping stage of the user, the sleeping state and the health state of each organ are compared with the traditional Chinese medicine nursing scheme, a specific health diet nursing guiding method is provided, the health management is obviously affected, the health management is fundamentally carried out on the user, the health management efficiency of the user is effectively improved, and the nursing scheme which is matched with the body condition of the user can be obtained under the condition that the user does not need to go to a hospital.
In one embodiment of the present invention, the method for sending the reconstructed pulse wave signal feature point data to the big data analysis platform includes:
step 1, inputting height and weight information into wearable equipment by a user before sleeping, wherein the wearable equipment sets a transmission time interval for transmitting pulse wave signal characteristic point data to a big data analysis platform according to the height and weight information of the user;
step 2, after the reconstructed pulse wave signal characteristic point data are obtained in real time, caching the reconstructed pulse wave signal characteristic point data obtained in real time in wearable equipment to form a cache data packet;
and step 3, sending the buffer data packet to the big data analysis platform at regular time according to the set sending time interval.
The method for setting the sending time interval of the pulse wave signal characteristic point data to the big data analysis platform comprises the following steps:
step 101, determining a transmission time interval reference value according to the height and weight information of a user, wherein the transmission time interval reference value is obtained through the following formula:
wherein ,T0 Representing a transmission time interval reference value; h represents the height of the user; m represents the current weight of the user; m is M 0 Representing an international standard weight corresponding to the height of the user; t (T) c Representing unit time, T 0 =60 min; λ represents a time adjustment coefficient;
step 102, acquiring the number of beats per minute of the first n minutes of wearing the wearable device by the user, and acquiring a transmission time interval of the pulse wave signal characteristic point data transmitted to the big data analysis platform according to the number of beats of the user and a transmission time interval reference value, wherein n is a preset time length value, and the transmission time interval is acquired through the following formula:
wherein ,Tg Representing a transmission time interval; c (C) i Indicating the number of user heartbeats corresponding to the ith minute within n minutes; c (C) max Representing the maximum value of the heartbeat of a single-minute user within n minutes; c (C) min Representing the minimum value of the heartbeat of a single-minute user within n minutes; alpha represents a transmission time adjustment coefficient; min (C) i+1 -C i ) Representing the minimum difference between the number of heartbeats in two adjacent minutes within n minutes; min (C) i+1 -C i ) The maximum value of the difference between the number of heartbeats in two adjacent minutes within n minutes is shown.
The working principle of the technical scheme is as follows: firstly, inputting height and weight information into wearable equipment by a user before sleeping, wherein the wearable equipment sets a sending time interval for sending pulse wave signal characteristic point data to a big data analysis platform according to the height and weight information of the user; after the reconstructed pulse wave signal characteristic point data are obtained in real time, caching the reconstructed pulse wave signal characteristic point data obtained in real time in wearable equipment to form a cache data packet; and finally, sending the buffer data packet to the big data analysis platform at regular time according to the set sending time interval.
The process of setting the sending time interval of the pulse wave signal characteristic point data to the big data analysis platform comprises the following steps:
firstly, determining a transmission time interval reference value according to height and weight information of a user; and then, acquiring the heartbeat times per minute of the first n minutes of wearing the wearable device by the user, and acquiring the transmission time interval of the pulse wave signal characteristic point data transmitted to the big data analysis platform according to the heartbeat times of the user and the transmission time interval reference value, wherein n is a preset time length value.
The technical scheme has the effects that: by means of the data transmission time and the data caching mode set according to the physical characteristics of the user, data can be transmitted to the big data analysis platform at regular time in the sleeping process of the user. The big data analysis platform can receive data in a time-staggered manner, so that the problem of data receiving errors or failures caused by data congestion caused by sending excessive large amount of data to the big data analysis platform at the same time is prevented. Meanwhile, the data transmission time interval obtained through the formula can be set according to the physical practical situation of the user, so that the data transmission time interval is matched with the physical practical state of the user, the timeliness of the big data analysis platform for receiving the user data is effectively improved, and meanwhile, the data transmission frequency is set according to different states of the user body, so that the traceability of the big data analysis platform to the real-time state of the sleep stage of the user is effectively improved. The situation that pulse data cannot be timely sent to a big data analysis platform in a caching stage under the condition that the physical state of a user is abnormal due to the fact that the same fixed data sending frequency and time interval are effectively avoided. On the other hand, the time interval obtained by the formula can effectively improve the data transmission efficiency and the data transmission success rate while ensuring that the data transmission timeliness and the data transmission time interval are matched with the physical condition of the user.
An embodiment of the present invention proposes an organ sleep detection system for analyzing physical sign data of a wearable device, as shown in fig. 2, the system includes:
the real-time acquisition module is used for acquiring pulse wave original signal waveforms of a sleeping period of a user in real time through the wearable equipment;
the data analysis module is used for carrying out data analysis on the pulse wave original signal waveform to obtain a sleep model of the user;
the acquisition module is used for combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to acquire a conditioning strategy corresponding to the physical condition of the user;
the data analysis module comprises:
the waveform analysis module is used for analyzing the pulse wave original signal waveform by the big data analysis platform and determining the human body sign parameters of the user; wherein the human body physical sign parameters include: basic data of human body physical signs such as heart rate data, blood pressure data, blood oxygen data and the like;
the model acquisition module is used for analyzing the human body sign parameters of the user by the big data analysis platform to acquire a sleep model of the user;
the acquisition module comprises:
a sleep state module for determining the sleep state of each organ of the user in the sleep process according to the sleep model of the user by the big data platform,
the health acquisition module is used for determining the physical health condition of the user in the sleeping process according to the sleeping state of each organ;
the strategy acquisition module is used for searching the traditional Chinese medicine theoretical schemes stored in the database of the big data analysis platform and acquiring a conditioning strategy corresponding to the physical health condition of the user;
and the feedback storage module is used for feeding the conditioning strategy back to the user by the big data platform, and uploading the sleep state of each organ of the user, the human body sign parameters and the acquired conditioning strategy to the cloud server for storage.
Wherein, the real-time acquisition module includes:
the pulse signal acquisition module is used for acquiring the pulse wave original signal waveform of the user in real time by wearing the wearable device with the pulse sensor;
the signal preprocessing module is used for carrying out noise reduction, baseline removal and wavelet decomposition processing on the pulse wave original signal waveform to obtain reconstructed pulse wave signal characteristic point data;
and the signal sending module is used for sending the reconstructed pulse wave signal characteristic point data to the big data analysis platform.
The principle of the technical scheme is as follows: and detecting the sleep of the organs of the user according to the big data and the algorithm by using the heart rate, the blood pressure, the blood oxygen and other user sign data acquired by the settling early warning watch, and finally obtaining the sleep health condition of the user. The method comprises the following steps: collecting human body sign data: by wearing the intelligent wearable device, analyzing data according to the original signal waveform obtained by the sensor and then uploading the data to the cloud service through big data; data analysis of pulse wave: analyzing the uploaded original signal waveform according to a big data analysis platform, and analyzing and calculating according to a special algorithm to obtain basic human body sign data such as heart rate, blood pressure, blood oxygen and the like; analyzing to obtain a sleep model: analyzing and obtaining a daily sleep model of a user through basic data of human body physical signs, heart rate, blood pressure, blood oxygen and the like; combining with the theory of traditional Chinese medicine: according to the traditional Chinese medicine theory, the sleep state of each organ of the user is obtained through analysis according to the sleep model calculated by the artificial intelligence algorithm.
The technical scheme has the effects that: the method has the advantages that the human pulse wave data in the sleeping process of the user are collected and analyzed to obtain the sleeping rest state and the health state of each organ in the sleeping stage of the user, the sleeping state and the health state of each organ are compared with the traditional Chinese medicine nursing scheme, a specific health diet nursing guiding method is provided, the health management is obviously affected, the health management is fundamentally carried out on the user, the health management efficiency of the user is effectively improved, and the nursing scheme which is matched with the body condition of the user can be obtained under the condition that the user does not need to go to a hospital.
In one embodiment of the present invention, the signal transmitting module includes:
the sending time setting module is used for inputting height and weight information into the wearable equipment before sleeping of a user, and the wearable equipment sets a sending time interval for sending pulse wave signal characteristic point data to the big data analysis platform according to the height and weight information of the user;
the buffer module is used for buffering the reconstructed pulse wave signal characteristic point data acquired in real time in the wearable equipment after the reconstructed pulse wave signal characteristic point data are acquired in real time, so as to form a buffer data packet;
and the buffer data sending module is used for sending the buffer data packet to the big data analysis platform at regular time according to the set sending time interval.
Wherein, the sending time setting module includes:
the time reference value acquisition module is used for determining a transmission time interval reference value according to the height and weight information of the user, and the transmission time interval reference value is acquired through the following formula:
wherein ,T0 Representing a transmission time interval reference value; h represents the height of the user; m represents the current weight of the user; m is M 0 Representing an international standard weight corresponding to the height of the user; t (T) c Representing unit time, T 0 =60 min; λ represents a time adjustment coefficient;
the time interval acquisition module is used for acquiring the number of beats per minute of the first n minutes of wearing the wearable device by the user, and acquiring a transmission time interval of the pulse wave signal characteristic point data transmitted to the big data analysis platform according to the number of beats of the user and a transmission time interval reference value, wherein n is a preset time length value, and the transmission time interval is acquired through the following formula:
wherein ,Tg Representing a transmission time interval; c (C) i Indicating the number of user heartbeats corresponding to the ith minute within n minutes; c (C) max Representing the maximum value of the heartbeat of a single-minute user within n minutes; c (C) min Representing the minimum value of the heartbeat of a single-minute user within n minutes; alpha represents a transmission time adjustment coefficient; min (C) i+1 -C i ) Representing the minimum difference between the number of heartbeats in two adjacent minutes within n minutes; min (C) i+1 -C i ) The maximum value of the difference between the number of heartbeats in two adjacent minutes within n minutes is shown.
The working principle of the technical scheme is as follows: the operation process of the signal sending module comprises the following steps:
firstly, a user inputs height and weight information into wearable equipment by using a sending time setting module before sleeping, and the wearable equipment sets a sending time interval for sending pulse wave signal characteristic point data to a big data analysis platform according to the height and weight information of the user; after the reconstructed pulse wave signal characteristic point data are obtained in real time through the caching module, caching the reconstructed pulse wave signal characteristic point data obtained in real time in wearable equipment to form a cache data packet; and finally, adopting a buffer data sending module to send buffer data packets to the big data analysis platform at regular time according to the set sending time interval.
The operation process of the sending time setting module comprises the following steps:
firstly, determining a transmission time interval reference value according to height and weight information of a user through a time reference value acquisition module, wherein the transmission time interval reference value is acquired through the following formula:
wherein ,T0 Representing a transmission time interval reference value; h represents the height of the user; m represents a userCurrent body weight; m is M 0 Representing an international standard weight corresponding to the height of the user; t (T) c Representing unit time, T 0 =60 min; λ represents a time adjustment coefficient;
then, acquiring the number of beats per minute of the first n minutes of wearing the wearable device by the user by using a time interval acquisition module, and acquiring a transmission time interval for transmitting the pulse wave signal characteristic point data to a big data analysis platform according to the number of beats of the user and a transmission time interval reference value, wherein n is a preset time length value, and the transmission time interval is acquired by the following formula:
wherein ,Tg Representing a transmission time interval; c (C) i Indicating the number of user heartbeats corresponding to the ith minute within n minutes; c (C) max Representing the maximum value of the heartbeat of a single-minute user within n minutes; c (C) min Representing the minimum value of the heartbeat of a single-minute user within n minutes; alpha represents a transmission time adjustment coefficient; min (C) i+1 -C i ) Representing the minimum difference between the number of heartbeats in two adjacent minutes within n minutes; min (C) i+1 -C i ) The maximum value of the difference between the number of heartbeats in two adjacent minutes within n minutes is shown.
The technical scheme has the effects that: by means of the data transmission time and the data caching mode set according to the physical characteristics of the user, data can be transmitted to the big data analysis platform at regular time in the sleeping process of the user. The big data analysis platform can receive data in a time-staggered manner, so that the problem of data receiving errors or failures caused by data congestion caused by sending excessive large amount of data to the big data analysis platform at the same time is prevented. Meanwhile, the data transmission time interval obtained through the formula can be set according to the physical practical situation of the user, so that the data transmission time interval is matched with the physical practical state of the user, the timeliness of the big data analysis platform for receiving the user data is effectively improved, and meanwhile, the data transmission frequency is set according to different states of the user body, so that the traceability of the big data analysis platform to the real-time state of the sleep stage of the user is effectively improved. The situation that pulse data cannot be timely sent to a big data analysis platform in a caching stage under the condition that the physical state of a user is abnormal due to the fact that the same fixed data sending frequency and time interval are effectively avoided. On the other hand, the time interval obtained by the formula can effectively improve the data transmission efficiency and the data transmission success rate while ensuring that the data transmission timeliness and the data transmission time interval are matched with the physical condition of the user.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (1)

1. An organ sleep detection system for wearable device sign data analysis, the system comprising:
the real-time acquisition module is used for acquiring pulse wave original signal waveforms of a sleeping period of a user in real time through the wearable equipment;
the data analysis module is used for carrying out data analysis on the pulse wave original signal waveform to obtain a sleep model of the user;
the acquisition module is used for combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to acquire a conditioning strategy corresponding to the physical condition of the user;
the data analysis module comprises:
the waveform analysis module is used for analyzing the waveform of the pulse wave original signal by the big data analysis platform and determining the human body sign parameters of the user; wherein the human body physical sign parameters include: heart rate data, blood pressure data, and blood oxygen data;
the model acquisition module is used for analyzing the human body sign parameters of the user by the big data analysis platform to acquire a sleep model of the user;
the acquisition module comprises:
a sleep state module for determining the sleep state of each organ of the user in the sleep process according to the sleep model of the user by the big data analysis platform,
the health acquisition module is used for determining the physical health condition of the user in the sleeping process according to the sleeping state of each organ;
the strategy acquisition module is used for searching the traditional Chinese medicine theoretical schemes stored in the database of the big data analysis platform and acquiring a conditioning strategy corresponding to the physical health condition of the user;
the feedback storage module is used for feeding the conditioning strategy back to the user by the big data analysis platform, and uploading the sleep state of each organ of the user, the human body sign parameters and the acquired conditioning strategy to the cloud server for storage;
wherein, the real-time acquisition module includes:
the pulse signal acquisition module is used for acquiring the pulse wave original signal waveform of the user in real time by wearing the wearable device with the pulse sensor;
the signal preprocessing module is used for carrying out noise reduction, baseline removal and wavelet decomposition processing on the pulse wave original signal waveform to obtain reconstructed pulse wave signal characteristic point data;
the signal sending module is used for sending the reconstructed pulse wave signal characteristic point data to the big data analysis platform;
wherein, the signal sending module includes:
the sending time setting module is used for inputting height and weight information into the wearable equipment before sleeping of a user, and the wearable equipment sets a sending time interval for sending pulse wave signal characteristic point data to the big data analysis platform according to the height and weight information of the user;
the buffer module is used for buffering the reconstructed pulse wave signal characteristic point data acquired in real time in the wearable equipment after the reconstructed pulse wave signal characteristic point data are acquired in real time, so as to form a buffer data packet;
the buffer data sending module is used for sending buffer data packets to the big data analysis platform at regular time according to the set sending time interval;
the transmission time setting module includes:
the time reference value acquisition module is used for determining a transmission time interval reference value according to the height and weight information of the user, and the transmission time interval reference value is acquired through the following formula:
；
wherein ,T 0 representing a transmission time interval reference value;Hrepresenting the height of a user;Mrepresenting the current weight of the user;M 0 representing an international standard weight corresponding to the height of the user;T c the unit time is represented by a unit time,T 0 =60 min; λ represents a time adjustment coefficient;
a time interval acquisition module for acquiring the front of wearing the wearable device by the usernThe number of beats per minute, according to the number of beats of the user and the reference value of the sending time interval, the sending time interval of the pulse wave signal characteristic point data to the big data analysis platform is obtained, wherein,nfor a preset time length value, the transmission time interval is obtained by the following formula:
；
wherein ,T g representing a transmission time interval;C i representation ofnWithin minutes, the firstiThe number of user heartbeats corresponding to the minutes;C max representation ofnWithin a minute, a single minute user's heartbeat maximum;C min representation ofnWithin minutes, a single minute user heartbeat minimum;αrepresenting a transmission time adjustment coefficient;min（C i+1 -C i ) Representation ofnWithin a minute, the difference between the number of heartbeats of two adjacent minutes is the minimum;min（C i+1 -C i ) Representation ofnWithin a minute, the difference between the number of heartbeats of two adjacent minutes is maximum.
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