CN113180615A - 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 PDFInfo
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Abstract
The invention provides an organ sleep detection method and system for physical sign data analysis of wearable equipment, wherein the method comprises the following steps: acquiring the original pulse wave signal waveform of a user in a sleep period in real time through wearable equipment; performing data analysis on the original pulse wave signal waveform to obtain a sleep model of the user; and combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to obtain a conditioning strategy corresponding to the physical condition of the user. The system comprises modules corresponding to the method steps.
Description
Technical Field
The invention provides an organ sleep detection method and system for physical sign data analysis 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 sign acquisition devices are arranged on intelligent wearable equipment, such as an intelligent watch and an intelligent bracelet to acquire human body sign data, but some wearable equipment generally has the defects that the overall health condition of a user is provided only by monitoring the sign data, but the data is not utilized, so that the health state of each organ of the user is analyzed, and specific healthy diet nursing guidance is performed; meanwhile, the general dietary nutrition guidance method only guides diet through the surface physical condition of the user or the detection data of the day, and does not detect and analyze the physical condition for a long time, so that the daily dietary nutrition guidance of the user is not systematic. Meanwhile, the common wearable equipment cannot detect the sleep condition of each organ of the human body and can not recuperate specific organs, so that the sleep quality is improved; everyone only knows how long one sleeps everyday, and cannot specifically know the sleeping conditions of each organ.
Disclosure of Invention
The invention provides an organ sleep detection method for physical sign data analysis of wearable equipment, which is used for solving the problems that the existing sleep monitoring equipment only detects sleep duration and cannot acquire the sleep state of organs and conditioning strategies corresponding to the health condition of a body:
an organ sleep detection method for wearable device sign data analysis, the method comprising:
acquiring the original pulse wave signal waveform of a user in a sleep period in real time through wearable equipment;
performing data analysis on the original pulse wave signal waveform to obtain a sleep model of the user;
and combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to obtain a conditioning strategy corresponding to the physical condition of the user.
Further, the method for acquiring the pulse wave original signal waveform of the sleep period of the user in real time through the wearable device comprises the following steps:
acquiring the original signal waveform of the pulse wave of a user in real time by wearing wearable equipment with a pulse sensor;
denoising, baseline removing and wavelet decomposition processing are carried out on the pulse wave original signal waveform to obtain reconstructed pulse wave signal characteristic point data;
and sending the reconstructed pulse wave signal feature point data to a big data analysis platform.
Further, sending the reconstructed pulse wave signal feature point data to a big data analysis platform, including:
the method comprises the steps that a user inputs height and weight information into wearable equipment 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 obtaining reconstructed pulse wave signal characteristic point data 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 cache data packet to the big data analysis platform at regular time according to the set sending time interval.
Further, setting a sending time interval for sending the pulse wave signal feature 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 by the following formula:
wherein ,T0A reference value representing a transmission time interval; h represents the height of the user; m represents the current weight of the user; m0Representing an international standard weight corresponding to the height of the user; t iscRepresents a unit time, T060 min; λ represents a time adjustment coefficient;
acquiring the number of heartbeat times per minute of the first n minutes of wearing the wearable device by the user, and acquiring a sending time interval of the pulse wave signal feature point data sent to the big data analysis platform according to the number of heartbeat times of the user and a sending time interval reference value, wherein n is a preset time length value, and the sending time interval is acquired through the following formula:
wherein ,TgPresentation deliveryA time interval; ciRepresenting the corresponding heart beat frequency of the user in the ith minute within n minutes; cmaxRepresents the maximum value of the heartbeat of the single-minute user within n minutes; cminRepresenting the minimum value of the heartbeat of the single-minute user within n minutes; α represents a transmission time adjustment coefficient; min (C)i+1-Ci) Representing the minimum difference of the heart beat times of two adjacent minutes within n minutes; min (C)i+1-Ci) Represents the maximum difference between the heart beat times of two adjacent minutes in n minutes.
Further, the analyzing the data of the original pulse wave signal waveform to obtain a sleep model of the user includes:
the big data analysis platform analyzes the original pulse wave signal waveform to determine human body sign parameters of the user; wherein the human body sign parameters comprise: basic data of human body physical signs such as heart rate data, blood pressure data and blood oxygen data;
the big data analysis platform analyzes the human body physical sign parameters of the user to obtain a sleep model of the user.
Further, the method for acquiring the conditioning strategy corresponding to the physical condition of the user by combining the sleep model of the user with the traditional Chinese medicine theoretical scheme 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 in the sleeping process according to the sleeping state of each organ;
searching a traditional Chinese medicine theory scheme stored in a database of the big data analysis platform to obtain a conditioning strategy corresponding to the physical health condition of the user;
and the big data platform feeds the conditioning strategy back to the user, and uploads the sleep state of each organ of the user, the human body physical sign parameters and the acquired conditioning strategy to the cloud server for storage.
An organ sleep detection system for wearable device vital sign data analysis, the system comprising:
the real-time acquisition module is used for acquiring the original pulse wave signal waveform of the sleep period of the user in real time through the wearable equipment;
the data analysis module is used for carrying out data analysis on the original pulse wave 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 includes:
the waveform analysis module is used for 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 sign parameters comprise: basic data of human body physical signs such as heart rate data, blood pressure data and blood oxygen data;
the model acquisition module is used for analyzing the human body physical sign parameters of the user by the big data analysis platform to obtain a sleep model of the user;
the acquisition module includes:
a sleep state module used for the big data platform to determine the sleep state of each organ of the user in the sleep process according to the sleep model of the user,
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 scheme 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 physical sign parameters and the acquired conditioning strategy to the cloud server for storage.
Further, the real-time acquisition module comprises:
the pulse signal acquisition module is used for acquiring the original pulse wave signal waveform of the user in real time by wearing wearable equipment with a pulse sensor;
the signal preprocessing module is used for carrying out denoising, baseline removing and wavelet decomposition processing on the original pulse wave 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 feature point data to the big data analysis platform.
Further, the signal transmission module includes:
the wearable device sets a sending time interval for sending the 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 wearable equipment to form a buffer data packet after the reconstructed pulse wave signal characteristic point data is acquired in real time;
and the cache data sending module is used for sending the cache 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 sending time interval reference value according to the height and weight information of the user, and the sending time interval reference value is acquired through the following formula:
wherein ,T0A reference value representing a transmission time interval; h represents the height of the user; m represents the current weight of the user; m0Representing an international standard weight corresponding to the height of the user; t iscRepresents a unit time, T060 min; λ represents a time adjustment coefficient;
the time interval acquisition module is used for acquiring the heartbeat times per minute of the previous n minutes of wearing the wearable equipment by the user, and acquiring the sending time interval of the pulse wave signal characteristic point data sent to the big data analysis platform according to the heartbeat times of the user in combination with the sending time interval reference value, wherein n is a preset time length value, and the sending time interval is acquired through the following formula:
wherein ,TgRepresents a transmission time interval; ciRepresenting the corresponding heart beat frequency of the user in the ith minute within n minutes; cmaxRepresents the maximum value of the heartbeat of the single-minute user within n minutes; cminRepresenting the minimum value of the heartbeat of the single-minute user within n minutes; α represents a transmission time adjustment coefficient; min (C)i+1-Ci) Representing the minimum difference of the heart beat times of two adjacent minutes within n minutes; min (C)i+1-Ci) Represents the maximum difference between the heart beat times of two adjacent minutes in n minutes.
The invention has the beneficial effects that:
according to the organ sleep detection method and system for analyzing physical sign data of the wearable device, the human body pulse wave data in the sleep process of the user is collected and analyzed to obtain the sleep rest state and the health state of each organ in the sleep stage of the user, the sleep state and the health state of each organ are compared with the traditional Chinese medicine nursing scheme, a specific health diet nursing guidance method is provided, the health management is obviously performed on the health management of the user, the health management efficiency of the user is effectively improved, and the nursing scheme corresponding to the body condition of the user can be obtained under the condition that the user does not need to go to a hospital.
Drawings
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 in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides an organ sleep detection method for physical sign data analysis of wearable equipment, and as shown in fig. 1, the method comprises the following steps:
s1, acquiring the original pulse wave signal waveform of the sleep period of the user in real time through the wearable device;
s2, carrying out data analysis on the original pulse wave 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 obtain a conditioning strategy corresponding to the physical condition of the user.
Wherein, gather the original signal waveform of pulse wave of user's sleep period in real time through wearable equipment, include:
s101, acquiring the pulse wave original signal waveform of a user in real time by wearing wearable equipment with a pulse sensor;
s102, denoising, baseline removing and wavelet decomposition processing are carried out on the pulse wave original signal waveform to obtain reconstructed pulse wave signal feature point data;
and S103, sending the reconstructed pulse wave signal feature point data to a big data analysis platform.
Wherein, the data analysis of the original pulse wave signal waveform to obtain the sleep model of the user comprises:
s201, the big data analysis platform analyzes the pulse wave original signal waveform to determine human body sign parameters of a user; wherein the human body sign parameters comprise: basic data of human body physical signs such as heart rate data, blood pressure data and blood oxygen data;
s202, analyzing the human body sign parameters of the user by the big data analysis platform to obtain a sleep model of the user.
The method for acquiring the conditioning strategy corresponding to the physical condition of the user by combining the sleep model of the user with the traditional Chinese medicine theoretical scheme 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 the user in the sleeping process according to the sleeping state of each organ;
s303, searching a traditional Chinese medicine theory scheme stored in a database of the big data analysis platform to obtain a conditioning strategy corresponding to the physical health condition of the 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 to be stored.
The principle of the technical scheme is as follows: the user physical sign data such as heart rate, blood pressure and blood oxygen acquired by the mortgage early warning watch is used for detecting the sleep of the user organ according to big data and an algorithm, and finally the sleep health condition of the user is obtained. The method specifically comprises the following steps: collecting human body sign data: by wearing the intelligent morton wearable equipment, data are analyzed according to the original signal waveform obtained by the sensor and the original signal, and then the data are uploaded to cloud service through big data; data analysis of pulse waves: analyzing the uploaded original signal waveform according to a big data analysis platform, and analyzing and calculating to obtain basic data of human body signs such as heart rate, blood pressure, blood oxygen and the like according to a special algorithm; analyzing to obtain a sleep model: analyzing the basic data of human body physical signs, heart rate, blood pressure, blood oxygen and the like to obtain a daily sleep model of the user; combining the theory of traditional Chinese medicine: according to the theory of traditional Chinese medicine, the sleep state of each organ of the user is obtained through analysis according to the sleep model obtained through calculation of the artificial intelligence algorithm.
The effect of the above technical scheme is as follows: the method has the advantages that the sleep rest state and the health state of each organ in the sleep stage of the body are obtained by collecting and analyzing the human body pulse wave data in the sleep process of a user, a specific health diet nursing guidance method is provided by comparing the sleep state and the health state of each organ with the traditional Chinese medicine nursing scheme, 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 nursing scheme corresponding to the body state 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, sending the reconstructed pulse wave signal feature point data to a big data analysis platform, includes:
step 1, a user inputs height and weight information into wearable equipment 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;
step 2, after obtaining reconstructed pulse wave signal feature point data in real time, caching the reconstructed pulse wave signal feature point data obtained in real time in wearable equipment to form a cache data packet;
and 3, regularly sending the cache data packet to the big data analysis platform according to the set sending time interval.
Wherein, set up the sending time interval that pulse wave signal characteristic point data sent to big data analysis platform, include:
step 101, determining a transmission time interval reference value according to height and weight information of a user, wherein the transmission time interval reference value is obtained through the following formula:
wherein ,T0A reference value representing a transmission time interval; h represents the height of the user; m represents the current weight of the user; m0Representing an international standard weight corresponding to the height of the user; t iscRepresents a unit time, T060 min; λ represents a time adjustment coefficient;
102, acquiring the number of heartbeat times per minute of the first n minutes of wearing the wearable device by the user, and acquiring a sending time interval of pulse wave signal feature point data sent to a big data analysis platform according to the number of heartbeat times of the user and a sending time interval reference value, wherein n is a preset time length value, and the sending time interval is acquired through the following formula:
wherein ,TgRepresents a transmission time interval; ciRepresenting the corresponding heart beat frequency of the user in the ith minute within n minutes; cmaxRepresents the maximum value of the heartbeat of the single-minute user within n minutes; cminRepresenting the minimum value of the heartbeat of the single-minute user within n minutes; α represents a transmission time adjustment coefficient; min (C)i+1-Ci) Representing the minimum difference of the heart beat times of two adjacent minutes within n minutes; min (C)i+1-Ci) Represents the maximum difference between the heart beat times of two adjacent minutes in n minutes.
The working principle of the technical scheme is as follows: firstly, a user inputs height and weight information into wearable equipment 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; then, after obtaining reconstructed pulse wave signal characteristic point data 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, regularly sending the cache data packet to the big data analysis platform according to the set sending time interval.
Wherein, the process of setting the sending time interval for sending the pulse wave signal feature point data to the big data analysis platform comprises the following steps:
firstly, determining a sending time interval reference value according to height and weight information of a user; then, acquiring the heartbeat times per minute of the first n minutes of wearing the wearable device by the user, and acquiring a sending time interval of the pulse wave signal feature point data to the big data analysis platform according to the heartbeat times of the user and the sending time interval reference value, wherein n is a preset time length value.
The effect of the above technical scheme is as follows: by means of the mode that the data sending time and the data caching are set according to the physical characteristics of the user, the data can be sent to the big data analysis platform regularly in the sleeping process of the user. The big data analysis platform can receive data in a wrong time, and data congestion caused by sending a large amount of data to the big data analysis platform at the same time is prevented, so that the problem of data receiving errors or failures is solved. Meanwhile, the data sending time interval obtained by the formula can be set according to the actual physical condition of the user, so that the data sending time interval is matched with the actual physical state of the user, the timeliness of the big data analysis platform for receiving the user data is effectively improved, and meanwhile, the traceability of the big data analysis platform on the real-time state of the sleep stage of the user can be effectively improved by setting the data sending frequency according to different states of the user. The situation that the pulse data cannot be timely sent to a big data analysis platform in a caching stage under the condition that the body state of a user is abnormal due to the same fixed data sending frequency and time interval is effectively avoided. On the other hand, the time interval obtained by the formula can effectively improve the data sending efficiency and the data sending success rate while ensuring the data sending timeliness and the data sending time interval to be matched with the physical condition of the user.
The embodiment of the invention provides an organ sleep detection system for physical sign data analysis of wearable equipment, and as shown in fig. 2, the system comprises:
the real-time acquisition module is used for acquiring the original pulse wave signal waveform of the sleep period of the user in real time through the wearable equipment;
the data analysis module is used for carrying out data analysis on the original pulse wave 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 includes:
the waveform analysis module is used for 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 sign parameters comprise: basic data of human body physical signs such as heart rate data, blood pressure data and blood oxygen data;
the model acquisition module is used for analyzing the human body physical sign parameters of the user by the big data analysis platform to obtain a sleep model of the user;
the acquisition module includes:
a sleep state module used for the big data platform to determine the sleep state of each organ of the user in the sleep process according to the sleep model of the user,
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 scheme 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 physical sign parameters and the acquired conditioning strategy to the cloud server for storage.
Wherein, real-time collection module includes:
the pulse signal acquisition module is used for acquiring the original pulse wave signal waveform of the user in real time by wearing wearable equipment with a pulse sensor;
the signal preprocessing module is used for carrying out denoising, baseline removing and wavelet decomposition processing on the original pulse wave 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 feature point data to the big data analysis platform.
The principle of the technical scheme is as follows: the user physical sign data such as heart rate, blood pressure and blood oxygen acquired by the mortgage early warning watch is used for detecting the sleep of the user organ according to big data and an algorithm, and finally the sleep health condition of the user is obtained. The method specifically comprises the following steps: collecting human body sign data: by wearing the intelligent morton wearable equipment, data are analyzed according to the original signal waveform obtained by the sensor and the original signal, and then the data are uploaded to cloud service through big data; data analysis of pulse waves: analyzing the uploaded original signal waveform according to a big data analysis platform, and analyzing and calculating to obtain basic data of human body signs such as heart rate, blood pressure, blood oxygen and the like according to a special algorithm; analyzing to obtain a sleep model: analyzing the basic data of human body physical signs, heart rate, blood pressure, blood oxygen and the like to obtain a daily sleep model of the user; combining the theory of traditional Chinese medicine: according to the theory of traditional Chinese medicine, the sleep state of each organ of the user is obtained through analysis according to the sleep model obtained through calculation of the artificial intelligence algorithm.
The effect of the above technical scheme is as follows: the method has the advantages that the sleep rest state and the health state of each organ in the sleep stage of the body are obtained by collecting and analyzing the human body pulse wave data in the sleep process of a user, a specific health diet nursing guidance method is provided by comparing the sleep state and the health state of each organ with the traditional Chinese medicine nursing scheme, 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 nursing scheme corresponding to the body state 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 transmission module includes:
the wearable device sets a sending time interval for sending the 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 wearable equipment to form a buffer data packet after the reconstructed pulse wave signal characteristic point data is acquired in real time;
and the cache data sending module is used for sending the cache data packet to the big data analysis platform at regular time according to the set sending time interval.
Wherein the transmission time setting module includes:
the time reference value acquisition module is used for determining a sending time interval reference value according to the height and weight information of the user, and the sending time interval reference value is acquired through the following formula:
wherein ,T0A reference value representing a transmission time interval; h represents the height of the user; m represents the current weight of the user; m0Representing an international standard weight corresponding to the height of the user; t iscRepresents a unit time, T060 min; λ represents a time adjustment coefficient;
the time interval acquisition module is used for acquiring the heartbeat times per minute of the previous n minutes of wearing the wearable equipment by the user, and acquiring the sending time interval of the pulse wave signal characteristic point data sent to the big data analysis platform according to the heartbeat times of the user in combination with the sending time interval reference value, wherein n is a preset time length value, and the sending time interval is acquired through the following formula:
wherein ,TgRepresents a transmission time interval; ciRepresenting the corresponding heart beat frequency of the user in the ith minute within n minutes; cmaxRepresents the maximum value of the heartbeat of the single-minute user within n minutes; cminRepresenting the minimum value of the heartbeat of the single-minute user within n minutes; α represents a transmission time adjustment coefficient; min (C)i+1-Ci) Representing the minimum difference of the heart beat times of two adjacent minutes within n minutes; min (C)i+1-Ci) Represents the maximum difference between the heart beat times of two adjacent minutes in n minutes.
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; then, after the reconstructed pulse wave signal feature point data is obtained in real time through a cache module, caching the reconstructed pulse wave signal feature point data obtained in real time in wearable equipment to form a cache data packet; and finally, a cache data sending module is adopted to send cache data packets to the big data analysis platform at regular time according to the set sending time interval.
Wherein, the operation process of the sending time setting module comprises the following steps:
firstly, a time reference value acquisition module determines a sending time interval reference value according to height and weight information of a user, wherein the sending time interval reference value is acquired through the following formula:
wherein ,T0A reference value representing a transmission time interval; h represents the height of the user; m represents the current weight of the user; m0Representing an international standard weight corresponding to the height of the user; t iscRepresents a unit time, T060 min; λ represents a time adjustment coefficient;
then, acquiring the number of heartbeat times 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 sending time interval of the pulse wave signal feature point data to a big data analysis platform according to the number of heartbeat times of the user and a sending time interval reference value, wherein n is a preset time length value, and the sending time interval is acquired by the following formula:
wherein ,TgRepresents a transmission time interval; ciRepresenting the corresponding heart beat frequency of the user in the ith minute within n minutes; cmaxRepresents the maximum value of the heartbeat of the single-minute user within n minutes; cminRepresenting the minimum value of the heartbeat of the single-minute user within n minutes; α represents a transmission time adjustment coefficient; min (C)i+1-Ci) Representing the number of heart beats in two adjacent minutes within n minutesA difference minimum; min (C)i+1-Ci) Represents the maximum difference between the heart beat times of two adjacent minutes in n minutes.
The effect of the above technical scheme is as follows: by means of the mode that the data sending time and the data caching are set according to the physical characteristics of the user, the data can be sent to the big data analysis platform regularly in the sleeping process of the user. The big data analysis platform can receive data in a wrong time, and data congestion caused by sending a large amount of data to the big data analysis platform at the same time is prevented, so that the problem of data receiving errors or failures is solved. Meanwhile, the data sending time interval obtained by the formula can be set according to the actual physical condition of the user, so that the data sending time interval is matched with the actual physical state of the user, the timeliness of the big data analysis platform for receiving the user data is effectively improved, and meanwhile, the traceability of the big data analysis platform on the real-time state of the sleep stage of the user can be effectively improved by setting the data sending frequency according to different states of the user. The situation that the pulse data cannot be timely sent to a big data analysis platform in a caching stage under the condition that the body state of a user is abnormal due to the same fixed data sending frequency and time interval is effectively avoided. On the other hand, the time interval obtained by the formula can effectively improve the data sending efficiency and the data sending success rate while ensuring the data sending timeliness and the data sending time interval to be matched with the physical condition of the user.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An organ sleep detection method for wearable device sign data analysis, the method comprising:
acquiring the original pulse wave signal waveform of a user in a sleep period in real time through wearable equipment;
performing data analysis on the original pulse wave signal waveform to obtain a sleep model of the user;
and combining the sleep model of the user with the traditional Chinese medicine theoretical scheme to obtain a conditioning strategy corresponding to the physical condition of the user.
2. The method for detecting organ sleep according to claim 1, wherein the collecting pulse wave original signal waveform of the sleep period of the user in real time by the wearable device comprises:
acquiring the original signal waveform of the pulse wave of a user in real time by wearing wearable equipment with a pulse sensor;
denoising, baseline removing and wavelet decomposition processing are carried out on the pulse wave original signal waveform to obtain reconstructed pulse wave signal characteristic point data;
and sending the reconstructed pulse wave signal feature point data to a big data analysis platform.
3. The method for detecting organ sleep according to claim 1, wherein the sending the reconstructed pulse wave signal feature point data to a big data analysis platform comprises:
the method comprises the steps that a user inputs height and weight information into wearable equipment 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 obtaining reconstructed pulse wave signal characteristic point data 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 cache data packet to the big data analysis platform at regular time according to the set sending time interval.
4. The method for detecting organ sleep according to claim 3, wherein the setting of the transmission time interval for the pulse wave signal feature point data to be transmitted to the big data analysis platform comprises:
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 by the following formula:
wherein ,T0A reference value representing a transmission time interval; h represents the height of the user; m represents the current weight of the user; m0Representing an international standard weight corresponding to the height of the user; t iscRepresents a unit time, T060 min; λ represents a time adjustment coefficient;
acquiring the number of heartbeat times per minute of the first n minutes of wearing the wearable device by the user, and acquiring a sending time interval of the pulse wave signal feature point data sent to the big data analysis platform according to the number of heartbeat times of the user and a sending time interval reference value, wherein n is a preset time length value, and the sending time interval is acquired through the following formula:
wherein ,TgRepresents a transmission time interval; ciRepresenting the corresponding heart beat frequency of the user in the ith minute within n minutes; cmaxRepresents the maximum value of the heartbeat of the single-minute user within n minutes; cminRepresenting the minimum value of the heartbeat of the single-minute user within n minutes; α represents a transmission time adjustment coefficient; min (C)i+1-Ci) Representing the minimum difference of the heart beat times of two adjacent minutes within n minutes; min (C)i+1-Ci) Represents the maximum difference between the heart beat times of two adjacent minutes in n minutes.
5. The method for detecting organ sleep according to claim 1, wherein the analyzing the data of the pulse wave original signal waveform to obtain the sleep model of the user comprises:
the big data analysis platform analyzes the original pulse wave signal waveform to determine human body sign parameters of the user; wherein the human body sign parameters comprise: heart rate data, blood pressure data, and blood oxygen data;
the big data analysis platform analyzes the human body physical sign parameters of the user to obtain a sleep model of the user.
6. The method for detecting organ sleep according to claim 1, wherein the step of combining the sleep model of the user with the theoretical scheme of traditional Chinese medicine to obtain the conditioning strategy corresponding to the physical condition of the user comprises:
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 in the sleeping process according to the sleeping state of each organ;
searching a traditional Chinese medicine theory scheme stored in a database of the big data analysis platform to obtain a conditioning strategy corresponding to the physical health condition of the user;
and the big data platform feeds the conditioning strategy back to the user, and uploads the sleep state of each organ of the user, the human body physical sign parameters and the acquired conditioning strategy to the cloud server for storage.
7. An organ sleep detection system for wearable device vital sign data analysis, the system comprising:
the real-time acquisition module is used for acquiring the original pulse wave signal waveform of the sleep period of the user in real time through the wearable equipment;
the data analysis module is used for carrying out data analysis on the original pulse wave 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 includes:
the waveform analysis module is used for 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 sign parameters comprise: heart rate data, blood pressure data, and blood oxygen data;
the model acquisition module is used for analyzing the human body physical sign parameters of the user by the big data analysis platform to obtain a sleep model of the user;
the acquisition module includes:
a sleep state module used for the big data platform to determine the sleep state of each organ of the user in the sleep process according to the sleep model of the user,
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 scheme 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 physical sign parameters and the acquired conditioning strategy to the cloud server for storage.
8. The organ sleep detection system of claim 7, wherein the real-time acquisition module comprises:
the pulse signal acquisition module is used for acquiring the original pulse wave signal waveform of the user in real time by wearing wearable equipment with a pulse sensor;
the signal preprocessing module is used for carrying out denoising, baseline removing and wavelet decomposition processing on the original pulse wave 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 feature point data to the big data analysis platform.
9. The organ sleep detection system of claim 7, wherein the signal transmission module comprises:
the wearable device sets a sending time interval for sending the 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 wearable equipment to form a buffer data packet after the reconstructed pulse wave signal characteristic point data is acquired in real time;
and the cache data sending module is used for sending the cache data packet to the big data analysis platform at regular time according to the set sending time interval.
10. The organ sleep detection system of claim 9, wherein the transmission time setting module comprises:
the time reference value acquisition module is used for determining a sending time interval reference value according to the height and weight information of the user, and the sending time interval reference value is acquired through the following formula:
wherein ,T0A reference value representing a transmission time interval; h represents the height of the user; m represents the current weight of the user; m0Representing an international standard weight corresponding to the height of the user; t iscRepresents a unit time, T060 min; λ represents a time adjustment coefficient;
the time interval acquisition module is used for acquiring the heartbeat times per minute of the previous n minutes of wearing the wearable equipment by the user, and acquiring the sending time interval of the pulse wave signal characteristic point data sent to the big data analysis platform according to the heartbeat times of the user in combination with the sending time interval reference value, wherein n is a preset time length value, and the sending time interval is acquired through the following formula:
wherein ,TgRepresents a transmission time interval; ciRepresenting the corresponding heart beat frequency of the user in the ith minute within n minutes; cmaxRepresents the maximum value of the heartbeat of the single-minute user within n minutes; cminRepresenting the minimum value of the heartbeat of the single-minute user within n minutes; α represents a transmission time adjustment coefficient; min (C)i+1-Ci) Representing the minimum difference of the heart beat times of two adjacent minutes within n minutes; min (C)i+1-Ci) Represents the maximum difference between the heart beat times of two adjacent minutes in n minutes.
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